Introduction of SNAPPS as a Teaching-Learning Method for Post Graduate Students in Orthopaedics
1 Dr. Ajay Sheoran; 2 Dr. Vasudha Dhupper; 3 Dr. Umesh Yadav; 4 Dr. Ashuma Sachdeva; 5 Dr. Chetan Prakash Agarwal; 6 Prashant BajajBackground: Teaching methods play a crucial role in the education and training of orthopaedics postgraduates. One innovative approach that has gained attention in recent years is the use of the SNAPPS (Summarize, Narrow down, Analyze, Probe, Plan, and Select) method.[1] However, literature on SNAPPS in orthopaedics resident training is limited, and further research is warranted to explore its specific applications and benefits in this context. Aims and Objectives: To introduce SNAPPS as teaching learning method in PG teaching. To study the outcomes of implementation of SNAPPS as teaching learning method in PG teaching. To study challenges in implementation of SNAPPS as teaching learning method in PG teaching. Methodology: This interventional educational study was conducted at the Department of Orthopaedics, PGIMS Rohtak after obtaining institutional ethical clearance. After sensitization of students and faculty members,2 SNAPPS sessions per student were conducted. Feedback was taken at the end by students as well as faculty members. The question format included both open?ended and closed?ended questions. Rating was done on a five?point Likert scale. Results: 94% (n=31) Postgraduate students found SNAPPS as an effective tool to identify their learning needs while 76% students (n=25) felt confident in clinical reasoning skills after using SNAPPS. Similarly all the 18 teachers who conducted the SNAPPS sessions gave positive feedback. 83% (n=15) of them perceived that SNAPPS is an efficient way of case presentation and It helped students to acquire good clinical reasoning skills. 78 % (n=14) perceived that it helped them to identify and focus on students’ weak areas. Conclusions: Along with traditional teaching, SNAPPS can be supplemented to improve analytical skills of the postgraduate residents. Both residents and faculty perceived SNAPPS as an effective teaching tool in outpatient teaching of PG residents.
Background: Flexibility is the ability to move a single joint or a group of joints efficiently over a painless, unrestricted Range of Motion (ROM). Reduced flexibility may result in a diminished range of motion, which in turn alters the biomechanics and, as a result, the joints. Maintaining a prolonged forward bend sitting position causes strain on the hamstrings, which leads to decreased flexibility. Objectives: To examine the combined impact of static stretching, the suboccipital muscle inhibition technique (SMI), and the myofascial release technique (MFR) on hamstring flexibility, as measured by an active knee extension test, both before and after the intervention. Methodology: The 45 participants were divided into three groups according to the selection criteria. Myofascial release method was given to the 15 people in group A, while Sub occipital inhibition technique was administered to the 15 people in group B. And Group C was given Static stretching and had 15 participants. For four weeks, all of the groups underwent the interventions five times a week. Results: Active knee extension test was used to measure hamstring flexibility during the pre- and post-tests. For AKE(R), the pre-mean values of groups A, B, and C were 120.07, 120.65, and 119.43, respectively. The pre-mean value of group A, B, and C for AKE (L), where 119.27, 119.2, and 119.8, and AKE(R), where 140.47, 147.53, and 132.93, are the Post mean values for groups A, B, and C. The Post mean group A, B, and C for AKE (L) have values of 139.6, 146.4, and 131.73, respectively. Conclusion: In summary, the research found that the Sub occipital muscle inhibition method was more successful in increasing hamstring flexibility in IT workers who had hamstring tightness.
Empowering Rural Artisans through Cluster Development
1 Afsana Sultana; 2 Dr. Sanjeeb HazarikaMSMEs have become one of the most vibrant sectors of the Indian economy, driving industrial growth and development. These enterprises have also contributed to the industrialization of backward and rural areas. In the north-eastern region of India, thousands of small and rural household industries operate within village communities and among different caste groups. These industries depend on local resources and the traditional skills of the rural population. Rural household industries have been a sustainable source of livelihood for the people of this region. Rural artisans play a vital role in preserving traditional crafts and boosting the regional economy which are crucial to a community's economic stability. The cluster development strategy has become a prominent paradigm for fostering sustainable livelihoods among the tactics used to improve the performance of such businesses. This research examines the socio-economic impact of cluster development on rural artisans to earn a sustainable living. The cluster development technique is becoming more and more popular as a way to help Micro, Small and Medium enterprises (MSMEs) become more innovative, productive, and competitive. Clusters provide knowledge exchange, common infrastructure, and group activities by bringing comparable enterprises together in close proximity, which can result in economies of scale and scope. The findings demonstrate that cluster-based development has a positive impact on the socio-economic development of the rural artisans.
This study examines the impact of corporate image and organizational performance in specific cement companies in Ethiopia. It's only natural that businesses today care about how they look to the public. A business needs to have a good image in order to be successful in the long run. The way people see a business as a whole is what impacts how well it does. There are many causes that could have caused the rise in importance of corporate image nowadays. Because the business environment is always evolving, many organizations have had to adjust their ideas a lot in order to stay in business and compete. Things are also falling out of style pretty quickly. Reputations can travel to markets that are quite far away; therefore, globalization has made business image increasingly crucial. Companies that have branches in different regions may also give off very diverse or even opposite impressions, which can damage how well the company works together. People in society have also upped the bar for businesses to be socially responsible. As a result of this discussion, businesses have recognized the substantial benefits of being both socially and environmentally responsible. The literature, however, does not agree on how corporate image affects business performance. The researchers used a quantitative methodology to create an explanatory and descriptive study design. The research employed a self-administered questionnaire to gather data from 367 employees at five cement companies in Ethiopia. We characterized the data using percentages, standard deviation, and mean scores. We performed regression analysis to see if the hypothesis was true. Organizational performance is the independent variable, and corporate image is the dependent variable shown to be statistically significant. The study indicates that cement companies in Ethiopia ought to prioritize the strategic development of a strong corporate image for the organization's long-term success.
Enhancing AI Responses through Effective Prompt Engineering Strategies for Large Language Models
Dr. S. LakshmiPrompt engineering is one of the main parts which utilizes the power and capacity of large language models ie., LLMs efficiently. Large Language models (LLMs) are generally used to generate human-like text, summarization, solving the problems in various fields, understanding the language and translating the language and so on. The potential of LLMs is utilized by creation of effective prompt which is called as prompt engineering through the inputs are given properly. AI models are used for collecting the relevant information about the query. Extracting the relevant responses from various artificial intelligent models by almost all types of people from researchers to school going children. The challenge lies in crafting prompts which reduce ambiguity and give proper direction to the LLM for getting the desired responses. When we concentrate on prompts by adding the important words or using some key words, we can show better results which reflects the role of prompting techniques clearly. A technical document can be prepared by using a few short prompting techniques and creative writing and storytelling can be done effectively by using some key words in the prompts itself. LLMs such as chatGPT3, chatGPT3.5, chatGPT4, Gemini and other models are trained on huge volumes of data which can produce and generate human-like text easily. The conditional prompts allow the users to use some specific keys for extracting the information on the iterative refinement process can also be used to extract information from prompt engineering. The quality of LLM results is evaluated by using relevance, coherence, creativity and specificity. This work explores the strategies and methods of prompt engineering that could enhance the performance and reliability of the LLMs such as few-shot prompting, role assignment and prompt chaining. Effective prompt engineering is the foremost technique to maximize the utility of large language models in various applications. Advanced techniques such as control tokens and multimodal prompts that combine the text with other modalities such as images for optimizing the results of prompt engineering. Retrieval Augmented Generation gets queries from prompts and try to get relevant information from various sources such as search engines or knowledge graphs. Hencs, RAG extends the LLMs by incorporating external knowledge for enriching the model’s responses. The most popular prompt engineering approaches are CoT, ToT, self-consistency and reflection played a major role. Prompt design and engineering are critical and the innovation in the Automatic Prompt Engineering (APE) would dominate in the near future. This work explores the effective utilization of Large Language Models for creating effective prompts for optimizing the responses so that we can solve complex problems easily and can reach better results in a stipulated time.
Lower Pole Sparing Ureterocalicostomy: A Case Series
1 Dr. Sagar Chakraborty; 2 Dr. Partha Pratim Das; 3 Dr. Md. Dawood Khan; 4 Dr. Tapan Kumar MandalIntroduction: Ureterocalicostomy is a uncommonly performed procedure with few indications. In 1947 Neuwirt reported the first ureterocalicostomy. The thinned lower pole hydronephrotic parenchyma was removed and the inferior renal calyx was connected to the ureter for efficient urinary drainage. However resection of the lower pole parenchyma leads to nephron loss as well as increases risk of hemorrhage. We present our cases series of ureterocalicostomy in which the anastomosis was carried out without resection of the lower pole and thus reducing the morbidity. Methods: This is a retrospective study conducted at a tertiary care centre in Eastern India. It included patients who underwent UC at the institute between January 2022 and January 2025. Review of the baseline demography, clinical features, radiological images, indications and type of surgery, complications and clinical outcomes was done from the department register. Preoperative workup included ultrasonography and diuretic scan with DTPA. All patient underwent open retroperitoneal UC. In all cases longitudinal 2-3cm nephrotomy was made in the medial aspect of the dependent lower pole, ureter was divided and spatulated and calyceal mucosa anastomosed with the spatulated ureter adjacent to the lower pole. Results: Eleven patients underwent open ureterocalicostomy. The age ranged from 7-60 years. Three patients had prior Anderson hynes pyeloplasty. APD declined significantly (p=0.008) and CT improved significantly (p=0.03) after surgery. The SRF and drainage however did not improve significantly. At a mean follow-up of 14.36 months all except one patient had complete symptoms relief and had an anatomically successful ureterocalicostomy. The overall success rate in our study was 90.90%. Conclusion: Lower pole sparing ureterocalicostomy offers good outcome, in patients with both primary and secondary ureteropelvic junction obstruction. It can be performed by open as well as minimally invasive route. UC is particularly helpful in improving drainage in kidneys with nondependent UPJ and small, intrarenal pelvis
The research investigates power system stability, load balancing, and voltage regulation improvements with BESS. Simulations and experiments utilizing MATLAB/Simulink, ADS, and Python-based machine learning models were used for analysis. Average efficiency, signal linearity, and power gain of the power amplifier were 72.4% with peaks of 87% under ideal conditions. Heat sinks and phase-change materials kept peak operation temperatures below 85°C. The BESS-Lithium Ion-battery integration reduced voltage variations for charging cycles by ±8% to ±2% and improved charge/discharge efficiency to 94.5% and 92.7%, respectively. Envelope Tracking, Load-Pull Optimization, and Adaptive Biasing enhanced efficiency, signal distortion, and power handling capacity dramatically. Neural Networks, Genetic Algorithms, and Reinforcement Learning, which had a 42ms latency, had been used to optimize power conversion and compute efficiency. High-efficiency power amplifiers and BESS improve efficiency, stability, and performance in modern power systems and help build a sustainable and stable grid infrastructure.
Background: Insomnia is a common concern among university students, especially in medical education. This study examined the prevalence of insomnia among undergraduate medical students and its association with attention control and screen time. Materials and Methods: A cross-sectional survey was conducted among 609 MBBS students (first to fourth year) at Burdwan Medical College, West Bengal, during August–September 2025. Data were collected using the Insomnia Severity Index (ISI) and the Attention Control Scale–Short Format (ATTC). Statistical analyses included ANOVA, t-tests, Pearson’s correlation and multiple linear regression analysis. Results: The mean ISI score was 6.2 ± 4.8. Overall, 34% of students experienced insomnia (27% sub threshold, 7% clinical), with severity increasing across academic years (p = 0.004). Hostel residents had a higher prevalence than day-scholars (9% vs. 4%, p = 0.03). No significant gender differences were observed. Attention control was highest in first-year students and lowest in third-year students. Insomnia was a significant predictor of lower attention control after adjusting for gender, year, and residence (β = –0.27, p < 0.001).Higher screen time showed a clear dose–response relationship with insomnia severity. Conclusion: ISI was a significant predictor of lower attention control after adjusting for demographic variables. These findings suggest that addressing sleep disturbances may be crucial for preserving cognitive function in medical students.
The Impact of Parental Discipline Methods on Child Behaviour and Emotional Health
Dr. G. Nancy ElizabethParental discipline plays a crucial role in shaping a child’s behaviour, emotional regulation, and overall psychological development. The ways in which parents enforce rules, set boundaries, and respond to misbehaviour can have long-lasting implications for a child’s well-being and social adjustment. This study examines the impact of various parental discipline methods—authoritative, authoritarian, permissive, and neglectful—on children’s behavioural outcomes and emotional health. Drawing upon both quantitative and qualitative data, the research explores how consistent, supportive, and communicative disciplinary approaches compare to harsh or inconsistent methods in predicting positive developmental trajectories. The study surveyed 300 parents and 300 children aged 8–15 years from diverse socio-economic backgrounds. Standardized instruments such as the Child Behaviour Checklist (CBCL) and the Parental Authority Questionnaire (PAQ) were utilized to assess behavioural tendencies, emotional well-being, and parental disciplinary styles. The findings indicate that children exposed to authoritative discipline, characterized by warmth, reasoning, and firm yet fair control, exhibit higher levels of emotional stability, self-esteem, and prosocial behaviour. In contrast, authoritarian discipline, marked by strict control, punishment, and limited communication, correlates strongly with elevated anxiety, aggression, and low emotional self-regulation. Similarly, permissive parenting, which allows high freedom with minimal guidance, tends to foster impulsivity, poor academic focus, and difficulty in respecting authority. Children of neglectful parents display the most pronounced emotional distress, low self-worth, and social withdrawal. The research also highlights the moderating role of cultural and contextual factors in determining how children perceive and respond to discipline. In collectivist societies, where obedience and respect are highly valued, authoritarian tendencies may not always result in adverse outcomes if balanced with emotional support. Conversely, in more individualistic settings, the same approach may intensify defiance and emotional conflict. The study further reveals that positive discipline emphasizing communication, natural consequences, and empathy encourages internalized moral reasoning and long-term behavioural regulation, rather than compliance driven by fear. These findings underscore the importance of parental awareness and education regarding discipline strategies that promote healthy emotional and behavioural development. Interventions aimed at improving parenting practices should focus on fostering emotional atonement, consistency, and constructive communication between parents and children. By understanding the profound influence of disciplinary styles, parents, educators, and policymakers can better support children’s holistic growth and mental health. The study concludes that effective discipline is not synonymous with punishment, but rather with guidance, empathy, and mutual respect elements that cultivate resilience, emotional intelligence, and responsible behaviour in children.
Scalable Access Control Models for Large File Storage and Many Users
1 Mrs. Reena S Sahane; 2 Ms. Abhilasha Bhagat; 3 Ms. Surbhi Pagar; 4 Mrs. Sapana Bhirud; 5 Mrs. Priti Malkhede; 6 Ms. Sadhana KekanThe Hidden Identity File Storage Framework (HIFSF) is designed to ensure that no single entity, including the central storage server, can access both the contents of stored files and information about their owners. In this framework, all user files are stored together in a common repository or unified directory, effectively obscuring ownership details and minimizing the risk of privacy breaches or unauthorized access. To implement this concept, the system replaces the traditional file storage structure with a customized architecture developed and tested within an intranet-based environment. When a user uploads a file, they provide a security key that the system uses to generate a unique identifier. This identifier is then applied to rename the file before storage, thereby removing any direct link between the file and its owner. As a result, the server retains no identifiable metadata that could expose ownership or traceability. During retrieval, the same key-based algorithm regenerates the file’s unique name, enabling secure and anonymous access by the rightful user without compromising confidentiality.
Containerization and Orchestration: Docker and Kubernetes in Modern Cloud Infrastructure
1 Dr. Shradhdha V. Thakkar; 2 Dr. Harshadkumar S. ModiThe rapid digital transformation of modern enterprises has driven a decisive shift from traditional virtualization toward lightweight, scalable, and high-performance containerized environments. This research provides a comprehensive analysis of Docker’s OS-level virtualization model and Kubernetes’ orchestration capabilities, establishing their combined architecture as the dominant foundation for cloud-native application delivery. The study contrasts containers with hypervisor-based Virtual Machines (VMs), revealing substantial advantages in startup latency, CPU and memory efficiency, and near-native I/O performance. Quantitative evaluations confirm that Docker significantly reduces deployment delays, enhances resource density, and improves throughput, while Kubernetes delivers self-healing, automated scaling, declarative lifecycle management, and superior deployment reliability. Real-world applications—including micro services, CI/CD pipelines, multi-cloud strategies, and machine learning workloads—demonstrate the strategic value and operational robustness of this ecosystem. The paper also addresses limitations involving kernel-shared security, operational complexity, and compatibility constraints, while highlighting emerging trends such as Container-as-a-Service (CaaS), server less containers, AI-driven orchestration, and edge-optimized deployments. Overall, the research concludes that the integration of Docker and Kubernetes provides the definitive architecture for future cloud systems, enabling unmatched agility, performance, and scalability across diverse enterprise workloads.
Annona Muricata (Graviola) has a long history in tropical folk medicine as a remedy for various fevers, infections, and parasitic infestations. Given the global threat of antimicrobial resistance, its traditional use warrants scientific validation. This review confirms the plant's broad-spectrum activity, reporting potent effects against bacteria (Staphylococcus aureus), fungi (Candida albicans), and medically relevant protozoa (e.g., agents of malaria and leishmaniasis). This efficacy is linked to its complex phytochemistry, particularly the Annonaceous Acetogenins, alongside flavonoids and alkaloids, which target microbial cellular integrity and mitochondrial respiration. To transition Soursop from folk remedy to modern medicine, future work must prioritize clinical studies and rigorous safety assessments to effectively separate beneficial anti-infective compounds from potentially neurotoxic components.
Chitosan, a biopolymer obtained from chitin through deacetylation, is acknowledged as a cornerstone in advanced drug delivery research due to its biocompatibility, biodegradability, and natural mucoadhesive properties. This minireview consolidates recent advances in modification techniques, including quaternization, hydrophobic grafting, and thiolation, as well as their incorporation into nanoparticles, hydrogels, films, and nano emulsions. We analyse essential fabrication techniques (ionic gelation, emulsification, nanoprecipitation, and reverse micellization) and detail how these methods tailor particle size, surface charge, and release kinetics to achieve optimal therapeutic effectiveness. Special attention is directed towards the transdermal and topical management of atopic dermatitis, highlighting the effectiveness of hydrophobic ally and thiol-modified chitosan nanocarriers. These carriers have demonstrated enhanced skin adhesion, pH-responsive drug release, and significant reductions in lesion severity and cytokine expression in preclinical studies. Finally, we examine emerging trends in chitosan-based composites and nano-formulations, highlighting opportunities for multifunctional delivery systems, targeted therapies, and scalable manufacturing aimed at clinical translation.
Natural fiber composites are increasingly adopted due to sustainability and lightweight requirements. This work predicts the elastic constants of epoxy-based hybrid laminates reinforced with Pineapple Leaf Fiber (PALF) and Grewia Optiva using Classical Laminate Plate Theory (CLPT). Three stacking sequences—[0/±45/90] (A1), [0/(45)?/–30] (A2), and [0/45/90/30] (A3)—were modeled with PALF content varying from 10 wt.% to 20 wt.% and Grewia Optiva fixed at 20 wt.%. Increasing PALF content enhanced stiffness, with the highest longitudinal modulus (37.32 GPa) obtained for A3. A1 exhibited maximum shear modulus (14.56 GPa), whereas A2 demonstrated the highest Poisson’s ratio (0.31). The results indicate that stacking sequence significantly governs anisotropic behavior, with A3 achieving the most balanced stiffness profile. The study demonstrates that CLPT provides a computationally efficient tool for hybrid natural fiber laminate design. Future work will experimentally validate the predictions and include hygro-thermal considerations.
Purpose: The study aimed at measuring the hip, knee and ankle joint angles at the time of overhead pass in volleyball and to measure height of C.G. at different stages of the execution of the overhead pass. Method & Materials: 6 male volleyball Players who represented Visva-Bharati University in AIU (Association of Indian Universities) Tournament, were selected as subjects. The age range of the subject was 20-25 years. The variables of the study were hip, knee and ankle joint angles and height of CG at Contact, Release and Follow through points. A video camera having the capacity of 120 fps was employed to capture motion of overhead pass.CG location was identified by employing joint point method and height of CG was calculated by using reference scale. Statistical Analysis: Descriptive Statistics and ‘t’ test were employed to analyze the data. The level of significance was set at 0.05 level. Result: The Mean Difference was significant between the angles of Contact & Release and Release & Follow Through of left ankle joint and Release & Follow Through of right ankle joint. Mean Difference was also significant between the angles of Release & Follow Through of right knee joint. Significant Mean Difference was also observed between the angles of Release & Follow through of right hip joint. In CG height mean difference was only significant between the phases of Release and Follow Through. Extension of lower limb angles and consequently the increase in the CG height were resulted to generate and transfer power to the ball [Yu Ozawa et al. (2022)15& Maolin Dong et al. (2024)16]. Conclusion: Among the three lower limb angles ankle joint has greater angular displacement in ball contact and release action in volleyball overhead pass than other two joints and displacement of lower limb joints are more during release to generate force. Increase height of CG during release and follow through is the indication of lower limb extension.
Employee performance is a critical determinant of organizational effectiveness in the public sector, particularly within regional government institutions where human resources are central to service delivery. This study investigates the interrelationship between job satisfaction, work discipline, and employee performance at the Office of the Personnel, Education, and Training Agency (BKPP) of Teluk Bintuni Regency, West Papua, Indonesia. A quantitative associative research design was employed using a saturated sampling technique involving all 60 employees. Data were collected through questionnaires, interviews, observations, and document review, and subsequently analyzed using descriptive statistics, Principal Component Analysis (PCA), and path analysis with SPSS version 25.The results reveal that job satisfaction is strongly influenced by workplace conditions, including noise-free environments, adequate workspace, and supportive office situations, while salary-related and coworker-related factors contributed less significantly. Work discipline was found to be a major predictor of performance, with indicators such as time management, task responsibility, and adherence to office rules exerting strong positive effects, whereas mere punctuality and attendance were less impactful. Employee performance was primarily explained by compliance-oriented indicators—timeliness, quality of work, and alignment with job descriptions—while initiative, independence, and willingness to work overtime showed weaker associations. PCA indicated that three latent dimensions—satisfaction, discipline, and performance—explained more than 60% of the variance, confirming their central role in shaping organizational outcomes. The study concludes that performance improvement in regional government agencies is better achieved through strengthening workplace conditions and fostering a culture of responsibility rather than relying solely on financial incentives. Recommendations include enhancing office ergonomics, embedding accountability-based discipline, integrating innovation into performance appraisals, and aligning human resource policies with Indonesia’s bureaucratic reform agenda. These findings provide empirical evidence to guide strategic human resource management in local government institutions.
Deep Learning Model for Personality Profiling Using Fingerprint Biometrics
1 Ms.Farhina S. Sayyad; 2Ms.Suvarna Sonone; 3Mr.Ramdas Jare; 4Mrs Kalyani A Tundalwar; 5Mrunali Patil ;6Mr Hiraman JadhavSkillSwap is a collaborative peer-to-peer learning platform designed to help students both acquire and share skills without the use of monetary exchange. Operating on a time-banking principle, the platform enables individuals to contribute their time and knowledge in areas such as programming, graphic design, writing, and public speaking. This model not only supports continuous skill enhancement but also encourages teamwork, active community involvement, and reciprocal learning among students. This paper presents an in-depth analysis of the SkillSwap system, detailing its underlying motivation, architectural framework, and implementation methodology. The proposed system integrates essential components including secure authentication mechanisms, intelligent skill-matching processes, real-time communication through WebRTC, and a feedback-based reputation system to promote safe and trustworthy peer interactions. Additionally, the platform’s design prioritizes scalability, ease of use, and strong data protection measures to effectively support a growing academic network. Through this project, SkillSwap aspires to build an inclusive and sustainable platform for knowledge sharing within educational environments. In the long term, it aims to improve student employability, nurture lifelong learning habits, and create lasting peer connections that extend beyond the traditional boundaries of the classroom.
Behavioral Effects in Knowledge and Awareness Attribute for the Green Campus Management (GCM)
1 Nazrul Bin Rusli; 2 Mohd. Ramzi Mohd Hussain; 2 Norhanis Diyana Nizarudin; 2 Syikh Sazlin Shah Sabri; 2 Sapiah Abdul HamedGreen Campus Management (GCM) is a strategic approach for higher education institutions to achieve sustainability goals. A critical attribute of GCM’s success is its ability to foster pro-environmental behaviors among campus stakeholders through enhanced knowledge and awareness. This literature review synthesizes research from 2020 to 2025 to examine the behavioral effects of the knowledge and awareness attribute within GCM frameworks. As illustrated in research, key studies reveal that environmental attitudes, behavioral intentions, and personal norms often mediate this relationship. The review also highlights that positive stakeholder perceptions and institutional commitment to green paradigms are crucial for inserting a sustainable culture. Conversely, a lack of proper planning can undermine these behavioral effects. In conclusion, knowledge and awareness are not merely informational attributes but are dynamic drivers of behavioral change within GCM. The effectiveness is maximized when integrated with supportive institutional systems that motivate and enable the campus community to act upon their knowledge, thereby realizing the overarching objectives of campus sustainability. This research contributes to the growing discourse on sustainable development in higher education institutions by offering strategic insights for policymakers and campus administrators to foster a culture of sustainability through behavioral change.
Background and Objective: Early identification of medical students at risk of failing is essential for timely support. Raw formative scores are commonly used but are affected by exam difficulty and cohort variability, reducing fairness and comparability. Normalized scoring adjusts for these factors, providing standardized measures of performance. This study compared normalized and raw formative scores among first-year MBBS students and examined their correlation with summative exam results. Methods: A cross-sectional analytical study was conducted among 200 first-year MBBS students during the 2024–2025 academic year. Raw and normalized scores from the first formative assessment were analyzed using descriptive statistics, Pearson’s correlation, linear regression, and ROC analysis. Results: Normalized scores correlated more strongly with summative results (r = 0.64, p < 0.001) than raw scores (r = 0.56, p < 0.001) and explained a greater variance in performance (R² = 0.41 vs. 0.31). Predictive accuracy for identifying at-risk students was higher for normalized scores (AUC = 0.86) compared with raw scores (AUC = 0.78; p = 0.032). Conclusion: Normalization improved score fairness, predictive validity, and diagnostic accuracy. Incorporating normalized scoring into formative assessments can enhance equity, reliability, and early detection of students requiring academic support in medical education.
Urban Informal employment: Challenges and Opportunities in Case of Central Region of Ethiopia
1 Girma Mekore Bushiso & 2 D. AshalathaIn Ethiopia, the predominance of informal employment is evident, with over 85% of the labor force involved, including a substantial percentage of informal wage earners. In informal employment, there are exploitative relationship between employers and employees. The purpose of this was to investigate the opportunities and challenges experienced by employees in informal employment in central region of Ethiopia. For the s cross-sectional survey, a sample employee was selected employing a combination of purposive and convenience sampling techniques.The study's data was analysed using descriptive statistics analysis and five-point Likert scale methods. The analysis reveals that a majority of informal employees are migrants from the rural area due to search of job, youth, single, and have low level of education. The study found that informal employees faced major challenges comprising low salary paid, high level of job insecurity, poor working condition absence of overtime and eaves pay, discrimination, lack of Social security contribution, and difficulty in joining labour unions. The major opportunities of employees are escape from unemployment, development of Entrepreneurial skill, job flexibility, and contribution for local economy. The results emphasize the necessity of policy interventions to enhance working conditions and mitigate the socioeconomic challenges faced by employees in urban settings
Wireless Sensor Networks (WSNs) are critical in monitoring and collecting data across diverse applications such as environmental surveillance, disaster response, and smart infrastructure. However, the dual challenge of maximizing network coverage and prolonging operational lifetime persists due to constraints on sensor energy and dynamic environmental conditions. To improve WSN performance, this research proposes hybrid optimization strategy that includes static and fixed sensor nodes while employing single-hop and multi-hop communication strategies. We have suggested an integrated stable deployment of static nodes with the adaptability of movable nodes to dynamically address coverage gaps and balance energy consumption. Static node placement, movable node movement patterns, and effective routing protocols are all determined through advanced optimization techniques. In comparison to traditional WSN configurations, simulation results indicate that proposed hybrid approach substantially improves network coverage that extends lifetime. By bridging the strengths of static and movable nodes and leveraging single-hop and multi-hop communication, this study offers a robust and energy-efficient solution to the critical challenges faced by WSNs. The findings have significant implications for the deployment of WSNs in dynamic and resource-constrained environments, paving the way for more resilient and adaptive sensing networks.
The operational demands of modern environmental health necessitate that practitioners master digital data acquisition, automation, and visualization tools. This study reports the quantitative findings of a course evaluation conducted on a cohort of environmental science undergraduates (N=45) following a targeted training module. The module utilized practical cloud-based tools, including App Sheet for mobile data collection, Google Looker Studio for visualization, Google Apps Script for API integration with services like the Open Weather API, and Wokwi for IoT prototyping. Using a paired pre- and post-test design with a 5-point Likert scale, we assessed gains across three domains: Knowledge (K), Behavioural Intent (B), and Confidence/Attitude (C). Results showed statistically significant improvements across all domains. Knowledge saw the largest mean gain (+1.61 points, p<0.001), with understanding of APIs and Dashboard Goals increasing by +2.00 (p=0.001). Behavioural intent to apply these skills increased by +0.96, specifically emphasizing the intent to automate repetitive tasks (+1.25, p=0.028). Confidence/Attitude gained +0.45, driven by increased belief in the career necessity of data analytics tools (+0.62, p=0.011). This evaluation confirms the curriculum's success in rapidly building technical competence and promoting a proactive, data-driven mindset essential for contemporary environmental health practice.
Comparative Analysis of Ensemble and Deep Neural Network Models for Epileptic Seizure Forecasting
1 Maneesh Kumar; 2 Rakesh Kumar; 3 Santosh KumarEpilepsy is a prevalent neurological disorder characterized by recurrent seizures, significantly impacting patient quality of life. Accurate seizure prediction using electroencephalogram (EEG) data has the potential to revolutionize patient care by enabling timely interventions. This study reviews the latest machine learning and deep learning advances for seizure prediction, focusing on transfer learning techniques applied to EEG signals. Among the evaluated models, VGG16 demonstrated outstanding performance, achieving 93.33% accuracy with perfect sensitivity and high specificity, highlighting its effectiveness even with limited training data. Res Net architectures showed mixed results, with ResNet101 achieving high recall and specificity but lower sensitivity, while ResNet50 underperformed in overall accuracy. Other models such as DenseNet201 and X ception exhibited lower accuracy, emphasizing the need for further tuning and pre-processing. The findings underscore the advantages of transfer learning and highlight ongoing challenges including data scarcity and model generalizability. This paper discusses strategies to overcome these barriers and outlines future research directions for clinically deployable seizure prediction systems.
Generally we can find gender differences in many features of human life. Especially in this study, the main variables such as Self-Acceptance and Prosocial Behaviour may displaymuch differences in the aspect of gender.In our daily life we may experience that, male children have somewhat better Self-Acceptance and shows more Prosocial Behaviour than female children. This study aimed to understand, whether the college students have the same attribute at this period of transition from adolescence to adulthood in the modern era? With this aim, the investigator, used self-made tools such as “Self-Acceptance” and “Prosocial Behaviour” after standardizing the same. By adopting descriptive survey method, along with a personal data sheet, these two tools have been distributed to 321 students who are studying in various streams. Statistical analysis has been made to attain the goal of this investigation. The result shows that, the boys are better than the girl students in both Self-Acceptance and Prosocial Behaviour. And there is also a significant association exist in both Self-acceptance and Prosocial Behaviour with their Mode of Survival and Income Generated per month. Students who are living with single parents and also who have Income between Rs.20,000/- and Rs.50,000/- per month are better in Self-Acceptance and Prosocial Behaviour. Generally girls never accept themselves as they are. They usually compare with others and feeleither inferior or superior. When they areready to accept themselves as they are,they will involve themselves muchin many social activities.Hence, it is suggested that, some kind of external motivation is required for them.By appreciating their achievements, they can improve their Self-Acceptance and they will become a socially responsive person.
An experiment was conducted at the Horticulture Research Farm, Janta College, Bakewar, Etawah during Rabi season of 2023-24 and 2024-2025. The experimental site is located approximately 23 km east of the district headquarters in Etawah. The site is located at 26.661565°N, 79.170517°E at an elevation of 142 m above mean sea level and falls under the sub-tropical climate zone. The region gets an average of 1143 mm of rain every year. The experiment was laid out in a Randomized Block Design with seventeen treatments replicated thrice. The results revealed that in comparison with other given treatments T17 application of recommended dose of NPK through chemical fertilizer + Boron @ 2 kg/hac + Sulphur @ 30 kg/hac + Zinc @ 15 kg/ha to potato plants cv. Kufri Khyati under the UP conditions can relatively lead to enhanced vegetative growth and yield of the potato.
Objective: This observational study aims to evaluate the effect of pregnancy-induced diabetes mellitus (gestational diabetes mellitus, GDM) on the oral health status of a sub-population in Bareilly. Specifically, the study explores the relationship between GDM and periodontal disease in pregnant women. Methods: The study involved 90 pregnant women (45 with GDM and 45 controls), recruited from the outpatient department of the Institute of Dental Sciences, Bareilly. A comprehensive dental examination was performed, assessing clinical parameters such as clinical attachment loss (CAL), probing depth (PD), bleeding on probing (BOP), and gingival recession. Participants were also evaluated for oral hygiene status using the Simplified Oral Hygiene Index (OHI-S). Demographic data and clinical information, including body mass index (BMI), family history of diabetes, and oral hygiene habits, were collected via interviews. Results: The study found significant differences between the GDM and non-GDM groups in oral hygiene practices and periodontal health indicators. The GDM group showed higher levels of bleeding on probing (BOP), with a greater number of patients exhibiting BOP in more than three teeth (P = 0.030). Mild periodontal disease was more prevalent in the non-GDM group, while severe periodontal disease was observed in both groups at similar rates. GDM patients reported poorer oral hygiene, with fewer achieving a “good” OHI-S score compared to the control group (P = 0.050). However, the overall prevalence of periodontal disease did not differ significantly between the two groups (77% in GDM vs. 70% in non-GDM, P = 0.535). Conclusion: While women with GDM tended to exhibit poorer oral hygiene and more periodontal disease indicators, no definitive statistical association between GDM and periodontal disease was found in this study. These findings suggest the need for further research to establish a clearer link between GDM and oral health, particularly to explore the long-term effects of pregnancy-induced diabetes on oral health.
Central Node Detection in Multi-Coordinate Systems Using Distance Minimization
1 Dr. Vrushali Sushant Patil; 2 Mrs. Sabrina Mohammadrehan Kazi; 3 Mrs. Subhashini Ramteke; 4 Ms. Priyanka L Dushing; 5 Mrs. Pramila Wakhure; 6 Mr. Ganesh A NimgireIdentifying a central node within a set of distributed geo-locations is a fundamental problem in spatial analysis, logistics, and network optimization. This research presents an algorithmic framework for Central Node Detection in Multi-Coordinate Systems Using Distance Minimization. The proposed approach integrates data from heterogeneous coordinate systems—such as geographic (latitude–longitude) and Cartesian (x, y, z)—into a unified spatial reference model. After normalization, a distance-based minimization algorithm is employed to determine the point that minimizes the total or average distance to all given locations, effectively identifying the most central node. Both Euclidean and great-circle distance metrics are analyzed to ensure adaptability to planar and spherical data domains. The model is designed to handle large-scale, multi-source datasets with varying spatial precision. Experimental evaluations demonstrate that the proposed method achieves high accuracy and computational efficiency compared to traditional centroid and geometric median approaches. The results highlight its applicability in network design, logistics hub placement, sensor network optimization, and geo-spatial clustering, providing a robust foundation for centralized decision-making in complex spatial systems.
This paper examines the psychological and emotional dynamics underlying the relationship between Shekhar and Lalita in Sarat Chandra Chattopadhyay’s Parineeta using attachment theory, psychodynamic interpretation, and cultural affect. Drawing on Bowlby’s (1982) and Hazan and Shaver’s (1987) models, the study interprets Shekhar’s protective yet insecure love and Lalita’s empathic devotion as contrasting attachment orientations that evolve toward mutual security. The analysis, which uses a qualitative, hermeneutic approach of theory-led close textual reading and explication, shows how colonial Bengal's moral ethos uses jealousy, silence, loyalty, and forgiveness as culturally infused emotional regulation mechanisms. The findings stated that Parineeta turns romantic love into a process of psychological maturation: Shekhar moves from anxious-avoidant dependency to earned security, while Lalita’s care ethics redefines feminine virtue as emotional intelligence. Integrating cross-cultural psychology and postcolonial affect theory, the paper demonstrates that Chattopadhyay intuitively anticipated modern concepts of relational growth and resilience. The study advances the global applicability of attachment theory and aids in the indigenization of literary psychology by redefining love as ethical attachment, where moral awareness and emotional security coexist.
The representation of gender in literary texts, both at private and public spaces, and of late at ‘third’ or ‘shared space’ as well, is the reflection of this power- politics involved in projecting gender. The long trajectory of resistance to gender appropriating forces— from small steps with suffragette movement, to Bronte sisters to Woolf to different phases of Feminism, along with massive socio-economic, cultural interventions transforming the world into a global world –all this express a gradual and perpetual churning of gender alignments, more from the perspective of marginalized entity. The agency projecting gender in literary representation-the author-is under scanner because of its perceived bias and prejudice in gender representation. The relationship between the authorial voice and the gender representation is very complicated and often controversial as well, leading to disagreements and divisions amongst the writers on the basis of their gender. Poile Sengupta’s play Thus Spake Shoorpanakha, So Said Shakuni discusses and explores how the politics of gender operates in regulation, modification and appropriation of gender in twentieth century India.
This study investigates how artificial intelligence (AI) applications shape learner agency in the context of Legal English instruction. While AI has been widely integrated into language learning, its use in specialized domains such as Legal English remains underexplored. Legal English, being a domain-specific register characterized by precise terminology, complex syntax, and professional communicative conventions, requires learners not only to acquire linguistic knowledge but also to develop critical judgment and agency. Drawing on qualitative inquiry, this research examines how learners of Legal English exercise agency when engaging with AI-supported learning platforms. The study focused on postgraduate law students enrolled in a Legal English course that employed AI-driven tools such as machine translation, intelligent writing assistants, and automated feedback systems. Findings suggest that AI support both enables and constrains learner agency: while it empowers learners to independently draft, revise, and evaluate legal texts, it also creates dependencies that may weaken critical thinking if not mediated by pedagogical scaffolding. The study contributes to ongoing debates on the pedagogical role of AI in language teaching for specific purposes (LSP), highlighting that fostering learner agency requires balancing AI’s efficiency with opportunities for reflection, negotiation of meaning, and critical evaluation.
Autism Spectrum disease (ASD) is a multifaceted neurodevelopmental disease marked by challenges with social interaction, communication, and repetition. Although long used, conventional methods of treating and diagnosing autism spectrum disorder (ASD) are sometimes overshadowed by issues of subjectivity, late detection, and restrictions on individualization. Increased attention has been given to Brain-Computer Interface (BCI) technology as a method for increased diagnosis and treatment strategy for autism spectrum disorder (ASD). Electroencephalography (EEG) is a typical technique employed by brain control devices (BCIs) to record and process brain waves, which are able to reflect neural activity in real time. For an early and accurate diagnosis of autism spectrum disorder (ASD) and identification of accompanying anomalies, such insights are important. Neurofeedback training, interactive VR-BCI systems, and emotion recognition modules are all some examples of brain-computer interface (BCI) therapies that are promising in improving the attention, emotion regulation, and social communication of autistic individuals. Ethical issues, technological constraints, and interdisciplinary collaboration needs are some of the challenges this study discusses as it ventures into the possible applications of brain-computer interface (BCI) technology to diagnose and treat autism spectrum disorder (ASD). The coming together of machine learning, multimodal neuroimaging, and wearable BCI systems all point to a revolutionary future in which BCIs are vital for providing personalized and accessible autism care.
Women led start ups have contributed immensely to the innovation and economic growth of India. But, they continue to face various systemic and intersectional vulnerabilities. The present paper makes an attempt to study these limitations by making a review of the available literature and policy documents. The paper tries to study how constraints like financial exclusion, lack of mentorship and socio-cultural biases affect the women entrepreneurs. The study tries to show that women coming from socially and economically disadvantaged sections often become victims of market restrictions and funding disabilities. Caste segregations, geographical exclusions, burden of domestic work and lack of motivation acts as detrimental factors. This affects business scalability and entrepreneurship. To promote women in the startup ecosystem, Government of India has come up with policies like WE-Hub, Start-Up India and Stand-Up India. These policies have been designed to address the issues of credit and funding of women entrepreneurs. However, it has failed to address the issue of intersectional vulnerabilities. The current study makes an argument for an integrated policy approach addressing the existing intersectional gaps. These, along with the financial incentives, would also include the non- financial provisions like making arrangement for financial literacy, childcare, mentorship and increased ability. The paper also makes suggestions for collaborating with the local governments, community associations and self-help groups to increase the visibility and outreach of the policies and regular monitoring of programmes. By interlinking intersectional vulnerabilities and existing policies, the researcher tries to make a scholarly input advocating for inclusivity in policy intervention and promoting regionally balanced startup ecosystem in India.
The public distribution system is a government-sponsored network of stores with the objective of providing basic food and non-food necessities to the underprivileged segments of society at extremely low costs. The Public Distribution System (PDS) ensures that everyone has access to enough food. The government implemented a strategy to assist the underprivileged and disadvantaged in frequently and affordably obtaining the critical items they require. The rules have evolved over time, and it's important to stay on top of them to make sure everything is running smoothly. In this article, we're going to take a look at the performance of the Public Distribution System (PDS) in the district of Dhenkanal in Odisha. It looks at things like how user-friendly it is, how well it works, if there's enough to meet people's needs, and what they love about it. To make the study work, the researcher used data from both the primary and secondary sources that were collected relating to PDS. We asked specific households a set of questions to gather information for the purpose of study. For the purpose of the sample selection, we take into account the total number of 186 households situated in the vicinity of Dhenkanal. For the purpose of the sample selection, we take into account the total number of 186 households situated in the vicinity of Dhenkanal.
Corporate Social Responsibility in India: Bridging the Gaps in Education
1 Lalit Kumar Barik; 2 Dr. Bidyadhar Padhi; 3 Dr. Jnanaranjan MohantyIndia's prosperity depends on its educational system, which needs a collective effort by both public and private sectors. In spite of various policy measures taken by governments a large number of population is deprived of quality education. Formal education of minimum standard has been the dream of many marginalized groups in India. Now a day, through Corporate Social Responsibility (CSR), the private sector has been making sectorial interventions to uplift of underprivileged and excluded people. Focus on the development of education sector by CSR initiatives of private sectors is gaining importance day by day. Numerous large corporations support educational institutions, libraries, textbooks, and student financial help with the help of CSR initiatives. The government will aware about the CSR initiatives against the education as well as the actual needs to improve it. This study explores CSR’s role in India’s education sector and its contribution to improve the framework of Education in India. Also it focuses on the CSR initiatives and its impact of top corporates which was analyzed from the financial year 2023-24.
Background: The widespread use of digital devices has led to a rise in Computer Vision Syndrome (CVS), particularly among adults who spend extended hours in front of screens. This study investigates the ocular effects of prolonged computer and smart device usage in adults. Methods: A cross-sectional observational study was conducted among 115 adults aged 18–45 years at a tertiary care hospital in South India. Participants using digital devices for at least 3 hours daily were evaluated using a structured questionnaire and objective ophthalmic tests including Schirmer’s test, Tear Break-Up Time (TBUT), blink rate, and the Ocular Surface Disease Index (OSDI). Data were analyzed using SPSS v26, with p < 0.05 considered statistically significant. Results: The prevalence of dry eye symptoms was 64.3%. A significant association was found between increased screen time and reduced TBUT (p < 0.001), lower Schirmer’s scores (p < 0.001), decreased blink rate (p < 0.001), and higher OSDI scores (p < 0.001). Refractive errors were present in 87% of participants, with longer screen exposure linked to worsening visual discomfort. Conclusion: Prolonged digital device use among adults significantly affects tear film stability, blinking behavior, and ocular comfort. Early screening and preventive strategies are essential to mitigate CVS risk.
The incision in Small Incision Cataract Surgery plays an important role in determining postoperative outcomes. This narrative review examines and compares the frown and chevron incision techniques in Small Incision Cataract Surgery emphasizing on postoperative astigmatism, wound stability, surgical ease, and overall visual prognosis. After analyzing existing literature and studies, this review discusses the advantages and disadvantages of each incision type, drawing attention towards major decisive factors such as incision architecture, healing dynamics, and the surgeon’s learning curve. The frown incision, known for its ability to minimize SIA, is contrasted with the chevron incision, which offers potential benefits in terms of wound stability and patient comfort. However, variations in surgical technique, patient demographics, and study designs across the literature make it challenging to establish a definitive superiority of one technique over the other. The review concludes that the choice between frown and chevron incisions should be tailored to the individual patient’s needs and the surgeon’s expertise, with an emphasis on achieving the best possible visual outcomes with minimal complications.
The modern business climate, with its dynamic transformations and abundance of data, subjects organizations to major pressure to make sound decisions in an uncertain environment. The traditional decision-support methods are not able to properly deal with ambiguity caused by the lack of complete information, qualitative variables, and imprecise judgments. In order to address these constraints, this paper will come up with an integrated framework that would utilize cognitive computing as well as fuzzy logic models to make decisions that are more relevant to organizations in uncertain situations. An advanced type of artificial intelligence, cognitive computing is capable of human-like reasoning despite not being capable of reflecting vagueness in human judgment as a formal mechanism. On the other hand, fuzzy logic offers a mathematical basis for processing vague data, language uncertainty, and man-based knowledge modeling. The proposed study will be able to combine all these efforts to present two hybrid fuzzy-cognitive models that can be used to aid in risk management, performance evaluation, and strategic planning of an organization. The fuzzy inference systems incorporate domain knowledge and real-world variability, and cognitive algorithms result in the processing of massive heterogeneous data to produce contextualized knowledge. Simulations are performed to test the models for accuracy, adaptability to uncertainty, and performance of the decisions. Findings prove that fuzzy logic is very useful when combined with cognitive computing in improving the responsiveness, decision accuracy, and agility of an organization. The framework proposed not only enhances alignment of all involved parties but also gives organizations the power to succeed in uncertain and volatile environments. The work helps in the development of intelligent enterprise systems and indicates a good future direction for decision support in uncertain situations.
A Review of Different DDOS Attacks in Cloud Based Environment
1 Nivedita Bhardwaj*; 2 Anita GanpatiFor different stakeholders to make an informed judgment about cloud adoption, security concerns pertaining to cloud computing are pertinent. In addition to data breaches, the attack space for cloud-specific solutions is being revisited by the cyber security research community since these problems impact service quality, budget, and resource management. One such severe attack in the cloud realm is the Distributed Denial of Service (DDoS) attack. It is merely a method of sending out countless fictitious requests to prevent actual users from accessing web resources. Some important data about the various kinds of DDoS assaults will be presented in this paper. The many DDoS varieties are compiled, along with their strike capabilities and, most importantly, how the best cloud computing environment issues can be addressed and resolved for the advantage of all cloud continuum stakeholders. The main obstacles to an efficient DDoS defense system are also examined.
This article is based on field research that investigated the long-term effects of industrial displacement on tribal women in Kalinganagar, Odisha, with a specific focus on their shifting occupational status and the human rights challenges they endurein the post-displacement period. The research was situated in Kalinganagar, Jajpur district, where the establishment of Tata Steel (private sector) and Neelachal Ispat Nigam Limited (public sector) industrial projects collectively displaced over a thousand families, severely disrupting their socio-economic organization. Employing a mixed-methods approach, the study selected 200 tribal women aged 40 and above from the Gobarghati resettlement colony through stratified proportionate sampling. Data collection incorporated semi-structured interviews, focus groups, and structured observations. Quantitative analysis highlighted significant occupational and income changes prior to and following displacement, while qualitative insights assessed the gender sensitivity and efficacy of resettlement policies under international human rights frameworks such as UNDRIP, ICESCR, and ILO Convention No. 169.Findings reveal a stark decline in economically active tribal women from over 98 percent in pre-displacement to less than 46 percent in post-displacement, accompanied by a shift from secure, traditional livelihoods to precarious, informal labor. Despite resettlement efforts, gender-blind policies exacerbated economic dependency and marginalization. Employment opportunities under rehabilitation schemes were minimal, with only 8-9 percent securing industrial jobs, and skill development programs failing to ensure sustained income generation. Moreover, displacement violated fundamental human rights including the right to livelihood, dignity, and equality. The propositions that emerge from the study in this article include the urgent need for gender-responsive resettlement and rehabilitation policies that recognize tribal women as independent rights holders by embedding livelihood restoration in the policy with a rights-based approach. Such reforms are essential to redress livelihood erosion and promote inclusive development among displaced indigenous communities and their women folk ensuring them social and economic justice.
A Narrative Review on Dry Eye Disease and Its Determinants in Patients with Diabetes Mellitus
1 Dr. Syed Ali Nasar Waris; 2 Dr. Akthar Jafar Hussain; 3 Dr. Muthineni Manikanta Rakesh; 4 Dr. Rubina Huda; 5 Dr. Neelakantharao Nandini; 6 Dr. Mohan Ram KumarDED is a common ocular condition, particularly in patients with DM. This narrative review discusses the prevalence and determinants of DED among diabetic patients, focusing on glycemic control, duration of diabetes, and diabetic complications, including neuropathy and retinopathy. The paper also presents pathophysiological mechanisms linking DM and DED and proposes recommendations for clinical management and future research. These findings bring importance of early detection and comprehensive management strategies for improving quality of life in patients with diabetes and DED.
Purpose: This study aims to compare the efficacy and patient comfort associated with subconjunctival lignocaine versus topical paracaine with intracameral lignocaine in small incision cataract surgery (SICS). Methods: A comprehensive review of existing literature was conducted, including randomized controlled trials, prospective studies, and systematic reviews. Databases such as PubMed, Scopus, and the Cochrane Library were searched to identify relevant studies. The primary outcomes analysed were anaesthesia efficacy, patient comfort, need for supplemental anaesthesia, and complication rates. Results: The analysis revealed that subconjunctival lignocaine generally provides superior anaesthesia for SICS, particularly in cases involving dense cataracts or extended surgical duration. However, topical paracaine with intracameral lignocaine was preferred by many patients due to its less invasive nature, leading to higher comfort levels and reduced anxiety. Despite this, the topical-intracameral approach demonstrated a higher likelihood of requiring supplemental anaesthesia. The incidence of subconjunctival haemorrhage was more common with subconjunctival lignocaine, whereas the topical-intracameral method had fewer complications overall. Discussion: The review highlights the importance of considering patient-specific factors when selecting an anaesthetic technique for SICS. While subconjunctival lignocaine is more effective for deeper anaesthesia, the topical-intracameral combination may be more suitable for needle-averse patients or those with high anxiety. The study also identifies the need for standardized outcome measures and longer follow-up periods in future research to better evaluate the long-term outcomes and potential complications associated with these techniques. Conclusion: Both anaesthetic methods have their advantages, and the choice between them should be individualized based on the patient's preferences and clinical needs. Future studies should focus on standardizing evaluation criteria and extending follow-up to provide clearer guidance on the optimal anaesthetic approach for SICS.
Platelet Transfusion Practices in South-East Asia: Past, Present and Future
1 Nabajyoti Choudhury; 2 Deepak Kumar; 3 Manisha Shah; 4 Gurpreet Kaur; 5 Aditya Anand; 6 Asitava Deb RoyBackground: The World Health Organization (WHO) South-East Asia (SEA) Region comprises over 2.1 billion people and faces rapidly increasing demand for platelet transfusions due to rising trauma, hematologic malignancies, complex surgeries, and recurring dengue epidemics. Despite substantial progress, platelet transfusion practices across SEA remain heterogeneous, influenced by variations in health-system capacity, regulatory maturity, and geographical constraints. Objective: This review summarizes the historical progression, current practices, safety mechanisms, clinical utilization patterns, and future opportunities in platelet transfusion across SEA. Summary: Platelet transfusion in SEA has evolved from early manual platelet-rich plasma preparation in the 1950s to widespread use of component therapy, including random-donor pooled platelets and single-donor aphaeresis platelets (SDAP). Countries such as Singapore, Sri Lanka, and Thailand have achieved significant milestones through universal leucodepletion, nucleic acid testing (NAT), strengthened hemovigilance systems, and pilot implementation of pathogen inactivation (PI) technologies. However, many nations continue to face challenges, including inconsistent donor availability, high dependence on pooled platelets, limited apheresis capacity, and fragmented regulatory oversight. Seasonal dengue outbreaks remain a major utilization driver despite strong evidence discouraging prophylactic transfusions in non-bleeding dengue patients. Expanding transplant programs, maternal hemorrhage, and critical care needs further contribute to increased demand. Emerging innovations—such as cold-stored platelets, platelet additive solutions (PAS), digital blood-bank management platforms, and genomic matching—offer promising avenues to improve safety, efficiency, and sustainability. Conclusion: Achieving equitable, safe, and evidence-based platelet transfusion services in SEA requires harmonized guidelines, expanded apheresis infrastructure, robust hemovigilance, integration of PI technologies, and strengthened regional collaboration.
Enhancing Security Surveillance Systems with Smart Machine Learning for Image and Video Analysis
1 Ms. Snehal Sitaram Wagh; 2 Ms. Sayali Ashok Dolas; 3 Ms. Smita Sitaram Wagh; 4 Dr. Rashmi Deshpande; 5 Mrs. Ashwini Bhimrao Jigalmadi; 6 Ms. Sneha Ashok LandeMany areas, including security tracking systems, have been completely changed by the fast progress made in machine learning (ML). Smart machine learning methods are being added to picture and video analysis, which is changing how security operations are done by making it easier to watch in real time, find problems, and evaluate threats. This essay looks into how machine learning algorithms, especially deep learning models, could be used to make tracking systems faster, more accurate, and better able to respond. The main goal is to look into how advanced machine learning methods can be used to improve security camera images and videos by focusing on things like finding objects, recognizing activities, recognizing faces, and analyzing behaviour. The study focusses on how to use reinforcement learning (RL), convolutional neural networks (CNNs), and recurrent neural networks (RNNs) to track and find objects in real time. There is also work being done on creating algorithms that can spot shady behaviour, making face recognition systems better, and making video analytics work better. The paper also talks about how these smart machine learning models could be added to cloud-based monitoring systems to help them be more flexible and give law enforcement agencies access from anywhere. It is also talked about in length how these technologies can help cut down on false alarms, find small trends, and give security staff automated tools for making decisions.
Sleep Disorders as Predictors of Cognitive Decline and Dementia: A Systematic Review
1 Dr. Arunima Chaudhuri, 2 Dr. Dharmendra Kumar Gupta Sleep disturbances are increasingly recognized as early indicators and potentially modifiable contributors to cognitive decline and dementia. This systematic review synthesizes evidence from 31 original studies published between 2015 and 2025, encompassing observational, population-based, and interventional designs. Consistent findings indicate that insomnia and obstructive sleep apnoea (OSA) are associated with increased risk of cognitive decline and dementia, with hazard ratios ranging from 1.36 to 1.84. Mechanistic studies show that insomnia accelerates amyloid-β and tau accumulation through impaired glymphatic clearance and neuroinflammation, while OSA contributes via intermittent hypoxia, oxidative stress, and cerebrovascular dysfunction. Circadian rhythm disturbances, hypersomnia, and REM sleep behaviour disorder (RBD) were also linked to cognitive impairment, particularly non-Alzheimer dementias such as Lewy body and front temporal dementia. Interventional evidence suggests that continuous positive airway pressure (CPAP) and cognitive behavioural therapy for insomnia (CBT-I) improve cognitive outcomes and may mitigate dementia risk. Study quality was appraised using the Newcastle–Ottawa Scale and Cochrane RoB-2 tools, and overall certainty of evidence was evaluated using the GRADE framework, indicating low-to-moderate confidence in current findings. This review provides an updated, integrative synthesis highlighting sleep disorders as biologically plausible, clinically actionable, and underutilized targets for dementia prevention. Future large-scale, biomarker-based randomized trials are essential to confirm causality and strengthen the evidence base for sleep-focused dementia risk reduction.
Background: Body image concerns are a critical psychosocial issue during emerging adulthood, influencing identity formation, self?esteem, and mental health outcomes. Psychological well?being (PWB), as conceptualized in Ryff’s multidimensional model, provides a multidimensional framework for assessing optimal functioning across autonomy, environmental mastery, personal growth, positive relations, purpose in life, and self?acceptance. Aim: This study examined self?esteem as a mediator of the relationship between body image concerns and psychological well?being, while testing gender as a moderator of the body image → self?esteem pathway. Methods: Data were collected from 398 emerging adults (M age = 20.11, SD = 2.66; 56% female) using the Body Shape Questionnaire (BSQ), Rosenberg Self?Esteem Scale (RSE), and Psychological Well?Being Scales (PWB). Structural equation modeling (SEM) with product?term interaction was conducted in lavaan (R), with indirect effects estimated using bias?corrected bootstrapping (5,000 resamples). Results: BSQ scores negatively predicted self?esteem, and self?esteem positively predicted PWB. Self?esteem partially mediated the relationship between body image concerns and PWB, while gender significantly moderated the BSQ → self?esteem path. The indirect effect of body image concerns on PWB via self?esteem was stronger among females. Conclusion: Findings highlight self?esteem as a key mechanism linking body image to well?being and underscore the need for gender?sensitive interventions. Programs that reduce body image concerns and enhance self?esteem may serve as protective strategies for promoting psychological well?being in emerging adults.
Enhancing Smart Home Security with Graph Neural Networks for Intrusion Detection
1 Mr. Prateek Meshram, 2 Mrs. Pratiksha Shevatekar, 3 Mr. Shivaji Vasekar, 4 Ms. Pratiksha Kale, 5 Mrs. Gauri Thite, 6 Mr. Anil PawarThe fast growth of smart homes, which is made possible by adding IoT devices, has made life in a house a lot easier and more automated. Although this makes it easier to connect to the internet, it also opens up a lot of security holes that let hackers and other bad people into homes. In this study, we suggest a new way to make smart homes safer by using Graph Neural Networks (GNNs) to find intrusions. The main goal of this method is to use the complicated connections and relationships between the different smart devices in the home network to find strange behaviour that could be a sign of a security threat. By representing the smart home network as a graph with devices as nodes and their interactions as edges, GNNs can detect local as well as global patterns of device activity. Intrusiveness detection systems therefore become more accurate and efficient. In our sense, we construct a live graph-based model of the smart home environment that illustrates the gadget communication and information sharing. GNNs examine these graphs and learn to identify deviations from usual patterns of interaction. For instance, indicators of an intruder's presence may include illegal access or malfunctioning devices. We investigate the proposed approach using a real-world smart home dataset and demonstrate that GNNs can effectively identify unusual activity across a broad spectrum of devices, including thermostats, security cameras, and door sensors. According to the results, GNNs outperform popular machine learning techniques such decision trees and support vector machines in terms of object identification and false positive generation. This work demonstrates how robust, versatile, and real-time systems for smart homes able to detect intruders might be produced using GNNs. It also makes it possible to look into how graph-based models can be used to improve security in other IoT-based settings. This work promotes the creation of better and more reliable smart living areas by making it easier for smart home systems to spot intrusions on their own.
Automated Potato Leaf Disease Identification Using Deep Learning and Image Processing
Sangeeta Jana MukhopadhyayOne of the most extensively grown crops in the world, potatoes are essential to food security. However, potato production is severely affected by several leaf diseases, including Late Blight, Early Blight, Mosaic Virus (PVY), and Black Leg. Conventional diagnostic techniques rely on manual inspection, which is labour-intensive, prone to error, and unsuitable for extensive farming regions. In this paper, a Convolution Neural Network (CNN)-based automated potato leaf disease detection system based on deep learning and image processing is presented. A labeled dataset of five classes (Late Blight, Early Blight, Mosaic Virus, Black Leg, and Healthy) was collected and pre-processed using advanced augmentation and contrast-enhancement techniques. The proposed hybrid CNN achieved an overall accuracy of 95.8%, outperforming SVM, Random Forest, and the baseline CNN. ROC curves, confusion matrix analysis, and performance metrics confirm the model's robustness. A lightweight, user-friendly GUI was developed to provide real-time disease prediction and recommendations for field applications. The system enables early detection, reduces misdiagnosis, and supports sustainable potato cultivation.
End-Stage Renal Disease (ESRD) constitutes the final, irreversible stage of chronic kidney disease (CKD), characterized by the complete loss of renal function and dependence on renal replacement therapies such as haemodialysis or transplantation. The global prevalence of ESRD has reached alarming proportions, driven largely by the escalating incidence of diabetes mellitus, hypertension, and metabolic disorders associated with urbanization and aging populations. According to the Global Burden of Disease Study (2019), CKD ranked as the tenth leading cause of death worldwide, accounting for approximately 1.43 million deaths—a striking 29% increase from 1990. In India, an estimated 1.2 to 1.5 lakh new ESRD cases arise annually, yet limited economic and infrastructural resources hinder equitable access to dialysis and transplantation services. Beyond the physiological deterioration, ESRD profoundly impairs patients’ physical, emotional, and social well-being, while nonadherence to complex medication regimens exacerbates morbidity and mortality. Understanding the interaction between clinical factors, medication adherence, and quality of life (QoL) is therefore critical for designing effective, patient-cantered interventions. The present study was a cross-sectional observational analysis conducted over six months (January–June 2025) in the Department of Nephrology at Government General Hospital, Kurnool, with the objective of exploring the prevalence, clinical profile, medication adherence, and quality of life among patients with ESRD undergoing maintenance haemodialysis. A total of 105 participants aged eighteen years and above, diagnosed with stage 5 CKD and receiving haemodialysis, were enrolled after obtaining informed consent. Data were collected using a structured patient proforma capturing demographic details, comorbidities, symptoms, and lifestyle habits. Medication adherence was assessed using the Morisky 8-Item Medication Adherence Scale, while quality of life was evaluated through the Missoula-VITAS Quality of Life Index. Descriptive and inferential statistics were performed using Microsoft Excel, with results expressed as means, percentages, and standard deviations. Among the 105 patients, 59 (56.2%) were male and 46 (43.8%) female, with the highest representation in the 46–55-year age group (24.8%). Hypertension emerged as the predominant comorbidity (47.6%), followed by combined hypertension and diabetes mellitus (24.8%). The most frequently reported symptoms were fatigue (85.7%), muscle cramps (64.8%), and pruritus (57.1%). Analysis of social habits revealed that 49.2% of males consumed alcohol, 45.8% engaged in both smoking and alcohol use, while 10.9% of females reported drug abuse. Medication adherence levels were moderate in 58.1% of participants, low in 22.9%, and high in only 19%, indicating substantial adherence challenges. QoL assessment revealed higher mean scores in interpersonal (4.2) and well-being (3.9) domains, while symptom (2.1) and transcendence (2.3) domains reflected significant physical and existential distress. A consistent association was observed between lower adherence and poorer QoL scores. This study underscores that ESRD in the Indian context predominantly affects middle-aged males with hypertension and diabetes as principal etiologies. Despite relatively stable interpersonal and emotional functioning, patients face considerable symptom burden, functional decline, and suboptimal medication adherence. These findings highlight the need for integrated, multidisciplinary care models that combine pharmacological optimization, adherence counselling, and psychosocial support to enhance clinical outcomes and overall well-being. Future longitudinal, multicentric studies should further elucidate the causal pathways linking adherence, symptom burden, and quality of life, thereby guiding policy and practice toward comprehensive, patient-centric nephrology care.
Instructors Perceptions of AI in Tertiary-Level EFL Programs
1 Dr. Srinivasa Rao Idapalapati; 2 Dr. Ali Abdalla Nour MohammedThis study examined the integration of artificial intelligence (AI) in tertiary-level English-as-a-Foreign-Language (EFL) classrooms, focusing on instructors’ perceptions of AI’s role, its effect on student performance, motivation, collaboration, and the barriers to its adoption. A quantitative descriptive survey gathered 32 valid responses from university instructors and was analyzed using SPSS. Questionnaire items were derived from literature themes and refined with expert input to ensure validity. Reliability was confirmed with Cronbach’s Alpha, indicating strong internal consistency. Descriptive statistics showed that most instructors held positive attitudes toward AI integration, with 92% expressing agreement or strong agreement. Inferential analyses revealed significant links between instructor perceptions and student outcomes, while regression results indicated that positive perceptions were strong predictors of improved learner performance, motivation, and collaboration. Despite these benefits, challenges such as technical hurdles, limited training, and ethical concerns were found to moderate AI’s positive impact. The study concludes that effective AI integration relies on instructor confidence and institutional preparedness, recommending systematic training, robust ethical guidelines, and infrastructure investment to maximize benefits in EFL education.
The term otorrhoea originates from the Greek words otos, meaning "ear," and rhein, meaning "discharge." Similarly, the Unani term Sayal?n al-Udhunis derived from the Arabic words Sailan, meaning "discharge," and Udhun, meaning "ear." Renowned Unani scholars have defined otorrhoea as Sayal?n al-Udhun, describing it as a condition characterized by a burning sensation in the external auditory canal, accompanied by continuous ear discharge. Otorrhea, the discharge from the ear, is a common symptom associated with various ear disorders such as otitis media, otitis externa, and tympanic membrane perforations. This condition is particularly common in children and individuals with a wet temperament (Miz?jRa?b).According to Unani principles, otorrhoea often occurs in individuals with an excess of bodily fluids (Akhl??) and a phlegmatic temperament (Balgham? al-Miz?j). The symptoms include Waja‘al-Udhun(ear pain orotalgia), Izdiyad-e-Hararat (increased body heat), a sense of fullness in the ear, stabbing pain in the temples, and pressure buildup in the ear. In Unani medicine, treatment for otorrhoea includes systemic and local approaches. Systemic remedies often involve the use of blood purifiers (Mu?aff?-i-Dam), anti-inflammatory agents (Mu?allil), analgesics (Musakkin), and antimicrobial agents (D?fi‘-i-Ta‘affun). Locally, treatments include drying agents (Mujaffif) applied through methods such as fumigation (Tadkh?n) and insufflation (Naf?kh). This review explores the understanding, diagnosis, and treatment approaches to otorrhea from the perspectives of Unani and modern medicine.
This study analyzes the impact of governance performance on economic growth in Ethiopia from 1994 to 2023, using time-series data and the ARDL model. It examines governance performance variables such as government effectiveness, control of corruption, and regulatory quality on gross domestic product (GDP), alongside control variables of FDI and current account balance (CAB). The results indicate that government effectiveness does significantly and negatively affect economic growth in the short term, while control of corruption has apositivemedium-term effect. Regulatory quality shows no significant impact. FDI has a negative short-run impact on GDP, and the current account balance (CAB) significantly influences growth, with a negative relationship. The study suggests strengthening governance institutions, prioritizing anti-corruption measures, improving regulatory quality, and managing FDI and current account imbalances to promote sustainable economic growth in Ethiopia.
Advancing Orthodontic Impressions: The Role of Custom Trays
1 Dr. Shuchi singh; 2 Dr. Surabhi Manhar; 3 Dr. Sandeep Kumar Kokkilagadda; 4 Dr. Sweta Kaushik Accurate impressions are essential in orthodontics for effective diagnosis, treatment planning, and appliance fabrication. While standard stock trays are commonly used, they may fail to provide sufficient coverage in cases with severe proclination of upper anterior, excessive spacing, or larger upper arches, especially when extending to the second molar. In such situations, custom impression trays are critical for improving accuracy and fit. They ensure complete coverage of posterior teeth, control the thickness of the impression material, and prevent soft tissue impingement, thereby enhancing patient comfort. Additionally, custom trays can incorporate advanced features like the Hyrax Expander to improve fit, retention, and stability. This approach minimizes the need for re-impressions, saving chair-side time and improving the overall efficiency of orthodontic procedures. By utilizing custom trays, orthodontists can achieve more precise impressions, leading to better clinical outcomes and increased patient satisfaction.
Caste-based discrimination, economic deprivation, digital exclusion, and patriarchal norms profoundly affect the emotional well-being of marginalized young men in rural India, producing what this study terms “emotionally injured masculinities.” This research investigates how structural inequalities shape the affective lives of male youth in Akola, Maharashtra, through an Ambedkarite emotional justice framework and identifies key domains of emotional injury that constrain social mobility and dignity. Using a qualitative design, 42 purposively selected participants from Dalit, Adivasi, Muslim, and OBC communities were interviewed in-depth. Data were transcribed, coded, and thematically analysed, integrating participant narratives with national datasets to contextualize local experiences within broader structural patterns. Findings reveal seven interrelated domains of emotional injury: educational disempowerment (83%), joblessness with emotional withdrawal (64%), hidden mental distress (74%), emotional policing via caste boundaries (38%), digital identity conflict and aspirational anxiety (69%), substance use as coping (45%), and emotional suppression through gender norms (90%). These injuries form a reinforcing web that sustains emotional harm and limits social mobility. The study underscores the urgent need for integrated interventions addressing material deprivation and cultural norms, promoting safe spaces for emotional expression, and advancing emotional dignity as a democratic right grounded in Ambedkarite ethics.
Health systems integrated with conventional care models have great possibilities to influence the rehabilitation provided and wide spread use of these apps depends on awareness, accessibility and usability. The aim of this study is to assess awareness and practice of mobile apps helping in balance assessment, rehabilitation and fall prevention among elderly population. An online questionnaire will be developed including questions for demographic data, regarding their medical history with special attention given to their balance issues and questions addressing awareness and usage of balance assessment mobile apps. It will be distributed to 400 elderly relatives of students at Faculty of Physiotherapy. Out of all the responses recorded, the frequency of each response for respective questions filled in will be recorded. The balance training apps are convenient, cost effective, can be customized to one’s needs and preference. These apps are easily accessible and can be used as a tool for rehabilitation and reducing risk of fall. The introduction and practice of these apps will further make elderly population independent in ADLs and reduce prevalence of injury.
Exploring the Key Factors of Occupational Stress among Workers of Tea Enterprise
1 Bhupender Oulakh; 2 Susheela Arya; 3 Laxmi MehtaThis paper focuses on the occupational stress among working women in Tea Enterprise, Champawat, Uttarakhand in order to explore the key factors of occupational stress among workers. Primary data collected with sample size of 120 which was collected at randomly. Pearson Correlation coefficient method is used for analysis the employees’ level of occupational stress. The findings indicate that occupational stress in tea enterprises is structural and work-process oriented, rather than merely individual or psychological. Improving workplace ergonomics, reducing physical workload, providing safer tools, and implementing seasonal workload management strategies could significantly reduce stress levels.
A Study on Comparative Analysis of the Commodity Derivative Market and Stock Market of India
1 Dr. V. Prashanth Kumar; 2 Mr. M. Naveen Kumar Reddy; 3 Mr. G. PanayThe growth trends and connections between the Multi-Commodity Exchange Index (MCEI) and the Nifty 50 Index from 2020 to 2025 are highlighted in this analysis. The results, which show a sharp rise in the MCEI after 2023 and a steady rise in the Nifty 50, demonstrate strong market momentum and economic recovery. There is a large positive correlation between the two indices, indicating a strong correlation between the movements of the commodity and equity markets. Researchers, investors, and policymakers can benefit from the analysis's informative information on market performance, risk assessment, and future investment plans.
Prevalence of Hamstring Tightness among School Going Students Aged 13 to 17 Years
1 Resa Paulson; 2 Aswathi. M; 3 Vysakh M Kumar; 4 NazeehaSeveral studies have found that inactivity has negative impacts on the musculoskeletal system in children and adolescents [1]. Adolescents pick up a variety of sitting issues at school that are in line with the established infrastructure and traditional teaching system, such as bench sitting and extended sitting hours. The students spend 5.5 to 6.5 hours each day in the same sedentary position, with little physical activity to support an active lifestyle [2]. Prolonged sitting, such as that experienced by students, causes alterations that can shorten the hip muscles. Additionally, prolonged sitting causes a shift in pelvic alignment, which causes the hamstring to contract during extended periods of sitting, which generates a significant amount of tension on the muscle, increasing the likelihood of pathology [3].
Implementation of Multi-Phase DC-DC Isolated Converter for Solar Applications
1 Venkataramana Guntreddi; 2 B. Srinivasa Rao; 3 Jaganamohan Rao Tarra; 4 Kanaka Raju Kalla; 5 Krishna Mohan TatikondaFive-phase isolated DC-DC converters with bidirectional are suggested in this paper. In these converters, a three-level converter or a three-leg-controlled rectifier is coupled to a multi-level modified converter via a high frequency transformer. Better voltage and current waveforms can be obtained on the transformer's primary and secondary sides by employing a 5-level converter, which will boost efficiency. This work proposes a novel isolated soft-switching high step-up DC-DC converter. These DC-DC converters are connected cascaded at their output terminals to form the suggested inverter. When each DC-DC converter's output voltage is controlled, this inverter can function with high voltage gain. Additionally, it requires fewer DC-DC blocks to produce a greater range of output voltage levels. The suggested inverter is appropriate for photovoltaic power conditioning systems because of all these benefits.
Studies on Growth Behavior and Yield Performance of Lentinulaedodes (Berk) Peglar
1 Aaushi Pant; 2 Dr. Khilendra SinghThe study was aim to investigate various agro-wastes (Sawdust, wheat straw, paddy straw, sorghum straw, finger millet straw and pearl millet straw) in mixture found locally for cultivation of Lentinula edodes-327.With respect to this 5 substrates were tested on the basis of growth parameters and yield performance. Results based on the current study revealed that T1 had the shortest spawn run and bump formation time (54.75 days 32.50 days), while T1 display shortest browning period time (8.50 days). The minimum total incubation time was 95.75 and 106.00 days by T1 and T2, Stipe diameter was maximum in T3 (2.46 cm) and minimum in T5 (1.41cm). T1 had the longest stipe and cap diameter of 4.32 cm and 8.24 cm respectively whereas T1 had the thickest cap (3.54 cm). The highest yielding substrate in 1st flush was T3 (287.7 gm), 2nd flush (175.23 gm) and 3rd flush (107.70 gm).The highest biological efficiency rate is 28.54% by T3 followed by T1 (22.46%).
Development of an AI-Based E-Module for Adaptive Learning to Enhance Students Critical Thinking
Achmad Ramadhan; Sutrisnawati; Liles Tangge; I. Nengah Kundera; I. Made BudiarsaThe rapid advancement of digital learning demands innovative approaches to support students’ higher-order thinking skills. This study aimed to develop and evaluate an AI-based adaptive e-module designed to enhance critical thinking in the Animal Development course. Employing a Research and Development (R&D) approach with the ADDIE model, the study involved 25 biology education students. The module integrated multimedia content, adaptive quizzes, and a chatbot powered by natural language processing to provide personalized learning paths. Expert validation confirmed high feasibility (86%, very feasible) in terms of pedagogy, content, and technology. Implementation results revealed a significant improvement in students’ performance, with average scores increasing from 52.0 (pre-test) to 80.6 (post-test). Statistical analysis showed a significant effect (t(24) = 14.21, p< 0.0001), and the N-gain score of 0.60 indicated a moderate to high improvement. Student surveys further reported high satisfaction, with over 90% positive responses regarding usability, interactivity, and relevance. Qualitative feedback emphasized that the chatbot and adaptive quizzes stimulated reflective and critical learning. These findings demonstrate that AI-based adaptive e-modules can effectively address learning gaps and foster critical thinking in complex biological topics. The study contributes a practical model for integrating adaptive learning and AI into higher education.
The study attempts to develop a framework of workplace ostracism on employee retention levels. Ostracism may be defined as a persistent and progressive attempt to undermine an employee's worth and presence in the workplace. This sort of harassment is subtle, persistent, and frequently done with the express aim of either removing or pushing an individual out of their employment. Employee ostracism can be caused by either a lack of political abilities or a high level of sensitivity. Such methods can have a negative psychological impact on employees, reducing their productivity and dedication to a company. As a result, it is possible that employees will leave the company to escape this treatment. In this study, it tries to explore the determinants of workplace ostracism and wanted to understand the impact of workplace ostracism on employee retention. The study has developed a conceptual framework after the extensive literature review and five hypotheses can be proceed further tested by collecting the data from the employees of IT company.
The main aim of this research paper is to identify the key characteristics of talent among students of Bharatanatyam, a major Indian classical dance form, and to evaluate differences between gifted and non-gifted students, with a particular focus on those who have passed the second-year Bharatanatyam exams. The paper examines the features a talented student of Bharatanatyam should possess, including cognitive, creative, and technical skills. It focuses on identifying the characteristics of a gifted student and how they distinguish themselves from non-gifted students in Bharatanatyam dance. The study focuses on learners from grades 3 to 5, and a total of 55 students were selected from the Kaveri Group of Institutes, Pune. This research employed both quantitative and qualitative data analysis. Quantitative data analysis involved observation-based evaluations, assessments, and student performance evaluations. The assessment of students' performance was developed based on Natyashastra principles and focused on the rasa (aesthetic), bhava (emotional expression), tala (rhythm), and laya (tempo) aspects. Further, professional Bharatanatyam artists assessed the students’ performance (particularly, technical movements and rhythmic precision of the dancers), using various modern techniques, like video analysis and motion-capture technologies.The study also showed a high inter-rater reliability. Qualitative data were collected through a Google Form survey with seven instructors and semi-structured interviews with 20 gurus, which were later subjected to thematic coding in NVivo to enhance reliability through coder triangulation. Quantitative data were analyzed through comparative correlation methods. This research used a variety of statistical tools (descriptive statistics, t-tests, ANOVAs with the Bonferroni correction, and Pearson’s correlation) to assess differences between talented and non-talented gifted Learners in memory retention, learning speed, technical accuracy, and emotional expressiveness. The combined presentation includes not only the statistical results but also the qualitative themes of intrinsic motivation, self-directed practice, and advanced pattern recognition, which provide a more comprehensive explanation of how these talented students become so proficient in rhythm synchronisation, spatial accuracy, and expressive depth. The findings offer strategies for teachers, gurus, and parents to identify these highly talented students and to effectively nurture their talents using tailored pedagogical techniques and differentiated curricula, thereby unlocking their creative and technical potential to the fullest extent. This study significantly contributes to the development of gifted and talented Bharatanatyam students, helping to preserve and promote this culturally significant classical art form.
The stable integration of renewable energy sources into DC microgrids (DCMGs) is hindered by significant challenges in fault detection and control, often leading to system-wide instability. This study addresses these issues by introducing a novel, integrated framework. We propose a resistance-based fault identification strategy for swift fault detection and precise localization, effectively containing faults and mitigating the risk of cascading failures. To manage the resulting voltage-current (V-I) variations, this paper further develops a Proportional-Integral (PI) controller whose parameters are dynamically optimized using an Artificial Bee Colony (ABC) algorithm. The ABC algorithm is selected for its superior control capabilities in complex, nonlinear systems, ensuring operation within required limits for voltage, current, and power ripple. The applicability and correctness of the proposed methodologies were rigorously validated through extensive digital simulations, with performance benchmarked against unoptimized conditions. This research offers a significant advancement in DCMG technology by demonstrably increasing operational efficiency, enhancing dynamic stability, and improving overall control performance for future-oriented, resilient power systems.
Cost Effectiveness of Comprehensive Government Health Insurance Scheme (MEDISEP) in Kerala
1 Dr. Nasiya VK, 2 Dr. Azhar A, 3 Nibras PNIn Kerala, a state renowned for its advanced healthcare system and high human development indices, the implementation of the Medical Insurance Scheme for State Employees and Pensioners (MEDISEP) marks a significant milestone. MEDISEP is designed to provide robust health coverage to state employees, pensioners, and their dependents, ensuring access to quality healthcare services without the burden of financial constraints. This scheme aims to cover a wide array of medical treatments, from routine check-ups to major surgeries, reflecting the state's commitment to enhancing the health and well-being of its public servants. This paper explores the extent to which MEDISEP has been utilized in Kerala, analyzing its reach, effectiveness, and the challenges faced in its implementation. The study employs a mixed- methods approach, combining quantitative data analysis with qualitative insights from beneficiaries and healthcare providers. By examining the scheme's impact on various demographics, including age groups, gender, and socio-economic status, this research aims to provide a comprehensive assessment of MEDISEP's performance. The findings of this study are expected to contribute to the broader discourse on public health policy and the optimization of health insurance schemes in India. By highlighting best practices and pinpointing areas for enhancement, the research aims to offer recommendations that can help in refining MEDISEP and similar schemes.
Background: The integration of computational pathology, particularly through deep learning and machine learning algorithms, has revolutionized the field of cytology and histopathology. This systematic review aims to evaluate the current advancements, diagnostic accuracy, and potential clinical applications of artificial intelligence (AI) in the diagnosis of various cytological and histopathological specimens. Methods: A comprehensive literature search was conducted across PubMed, Scopus, and Web of Science databases from January 2015 to December 2024. Studies focusing on the application of machine learning and deep learning models in cytological and histopathological diagnosis were included. Data on diagnostic accuracy, sensitivity, specificity, and performance metrics were extracted and analysed. Results: A total of 45 studies met the inclusion criteria. Deep learning algorithms, particularly convolutional neural networks (CNNs), demonstrated high diagnostic accuracy in detecting malignant cells in cervical cytology, breast FNAC, and histopathological slides of lung and gastrointestinal tumours. The AI models exhibited an average accuracy of 92.5%, sensitivity of 90.8%, and specificity of 93.2%. Moreover, AI-assisted diagnosis significantly reduced interobserver variability and improved diagnostic workflow efficiency. Conclusion: Computational pathology has shown promising potential in augmenting diagnostic accuracy and efficiency in cytology and histopathology. However, further large-scale, multicentre validation studies are required to ensure robustness and generalizability before widespread clinical implementation.
This study evaluates the development of digital literacy competence among English teachers in the East Lombok District following their participation in an in-service teacher-training program (PPG). This highlights the importance of digital literacy in contemporary teaching, and explores the extent to which PPG enhance these skills. Using a qualitative descriptive method with multiple case studies, data were collected through observations, interviews, and document analysis. Creswell's thematic analysis method ensured data consistency. The findings revealed significant improvements in teachers' digital skills including the use of Google Classroom, video creation, and active student engagement. This enhanced digital literacy contributes to better lesson planning and teaching effectiveness. This study underscores the need for ongoing training and policy support in order to sustain these competencies.
This research examines the pedagogical conversion of the English teachers in East Lombok Regency after taking part in the Teacher Mentor Program (TMP) a program of government sponsored professional development. The purpose of the TMP was to improve the pedagogical skills of teachers as well as acquire the skills required to cater to the needs of students in learning in the forms of differentiation instruction, technology integration, and reflective teaching. Reducing teachers’ workloads in these ways is the program’s aim. It would help to make the learning process more student-centered and to ensure teachers follow different approaches in teaching to adapt to students’ learning styles and needs. The investigation used qualitative phenomenological methodology to gain in-depth insight into the experiences of the teachers. Data were obtained through in depth and interviews, classroom observation and document analyses which were conducted to 15 English teachers from different school background represented the East Lombok. Significant findings found improvement in teachers’ development of differentiated instruction, enhancement of their integration of technology, as well as improvement in their motivation and creativity due to the TMP among the derived indicators. However, some issues including a lack of ongoing support, resistance to change at times, were identified.
Background: Physiotherapy is essential for managing musculoskeletal, neurological, and post-surgical conditions. However, access to rehabilitation services remains limited in rural areas due to a shortage of trained professionals, infrastructure constraints, and transportation barriers. Emerging technologies, such as AI-enabled tele-rehabilitation, offer the potential to bridge these gaps by delivering remote assessments, personalized exercise programs, and real-time feedback. Objective: This study aims to evaluate the feasibility and acceptability of AI-driven tele-rehabilitation among rural patients and healthcare providers. It also seeks to identify perceived barriers to its adoption, explore potential benefits, and assess its influence on treatment adherence and clinical outcomes. Methods: A cross-sectional survey was conducted involving 458 participants, including rural patients with musculoskeletal or neurological conditions, physiotherapists, and healthcare personnel. Participants were selected using convenience sampling. A self-structured questionnaire was administered to gather information on demographics, health status, access to digital technology, perceptions of AI, and barriers to tele-rehabilitation implementation. Results: The study anticipates uncovering key challenges to AI-based tele-rehabilitation uptake, including technological literacy, financial constraints, psychological readiness, and regulatory limitations. It is also expected to highlight opportunities for expanding physiotherapy access through AI, particularly in underserved rural regions. Conclusions: Findings from this study will inform strategies to facilitate the integration of AI-driven tele-rehabilitation into rural health systems. By addressing both barriers and enablers, the research aims to guide healthcare providers, policymakers, and stakeholders in enhancing rehabilitation equity and narrowing the urban–rural divide in musculoskeletal care.
Background & Objectives: Capillary leak syndrome (CLS) is a critical complication of dengue fever. Early recognition is essential for timely intervention.. Purpose of the study is to analyze the roles played by portal venous ultrasonography and color Doppler indices in detecting and predicting capillary leak syndrome in patients with dengue fever and correlating these imaging findings with clinical outcome. Materials and Methods: It is a prospective observational study conducted on 60 dengue fever cases that were serologically confirmed. The study was conducted from September 2022 to December 2023.On admission Grey –scale abdominal ultrasonography and portal venous Doppler was performed. Portal vein (PV) diameter, cross-sectional area (CSA), peak venous velocity, and congestion index (CI) were assessed and plasma leakage features were recorded sonographically. Results: Capillary leak syndrome developed in 54 (90%) patients. Gallbladder wall oedema detected in 56.7% cases and was the most frequent ultrasonographic sign. It followed by pleural effusion (48.3%) and ascites (35%). CLS patients exhibited significantly lower portal vein velocity (mean 17.41 ± 4.02 cm/s) compared to those without CLS (26.66 ± 3.33 cm/s) (p < 0.001). Congestion index was significantly elevated in CLS (0.103 ± 0.034). ROC analysis showed highest predictive accuracy for CI (AUC 0.898), followed by velocity (AUC 0.866). Conventional ultrasonographic features demonstrated high specificity but lower sensitivity. Conclusion: Portal venous Doppler parameters, especially velocity and congestion index, provide strong early indicators of CLS and outperform traditional ultrasonographic features in diagnostic accuracy. Clinical Impact: Portal venous Doppler integration in case evaluation can be added to early risk stratification in dengue fever and can potentially reduce morbidity and progression to shock.
Olericulture practice (the science of vegetable cultivation) took a pivotal role in meeting the increasing demand for vegetables in Manipur, India. The main focus of the study to understand the increasing demands for vegetable and ultimately conversion of paddy cultivated fields in to perennial vegetable cultivated field can be sustainable in future. The study is based on the dataset collected from 120 in Khoijuman village, Bishnupur District, Manipur, India, one of the most important areas of olericulture practice in Manipur, engaged in vegetable farming though households’ surveys. Again, the study tries to an empirical understanding of factors influencing olericulture development. The study applied Gaussian descriptive statistics, correlation analysis, classical regression, and Bayesian inference, the study explores relationships among variables such as land size, education, crop rotation, training, age, production levels, and income. The study highlights strong positive correlations between land used area and annual production rate (r = 0.42), income level (r = 0.31), and educational qualification. Moreover, the Bayesian regression analysis indicates probability-based insights for policy recommendations by validating these relationships with posterior means for important coefficients. In the backdrop of sustainable development, the impending issues like input costs and the impact of climate change are important factors. To meet the growing demand for vegetables, the study recommends methods for expanding olericulture, such as improved irrigation infrastructure and training by attracting educated rural youth.
Artificial Intelligence (AI) has become a transformative force in contemporary business, reshaping operational efficiency, strategic decision-making, and long-term sustainability. This paper investigates how AI can drive sustainable business growth through innovation, enhanced resource management, and improved customer engagement, aligning with Sustainable Development Goal 9 (SDG 9): Industry, Innovation, and Infrastructure. By using a strategic framework, the study explores how AI-powered automation, predictive analytics, and intelligent decision-support systems can provide competitive advantages while advancing sustainability goals. The research employs a mixed-methods approach, including case studies from leading global companies that have successfully incorporated AI into their business models to achieve economic, environmental, and social sustainability. The findings suggest that AI can help corporations reduce waste, optimize energy use, manage ethical supply chains, and mitigate compliance risks, all while promoting ethical AI adoption and enhancing cooperation across teams. Despite challenges such as data privacy concerns, biases in AI decision-making, and the availability of skilled AI practitioners, few organizations have fully implemented these AI-driven strategies. The study concludes that for sustainable long-term growth, businesses must develop AI-driven strategies that balance profitability with sustainability. Recommendations include responsible AI governance, investment in AI literacy and collaborative efforts with regulators to harness the full potential of AI for sustainable business transformation. Future research should focus on industry-specific AI applications and regulatory developments to ensure the ethical deployment of AI.
Comparative Study of the Different Types of Signal Amplifiers and their Applications
Niranjan Kumar Mandal1Comparative studies have been carried out for the different types of signal amplifiers to find their performance characteristics with respect to circuit topologies, gain calculations, and merits and demerits of them. Studies have also been done to find their suitable applications in various fields. With respect to circuit topologies, the circuit diagrams are shown with single BJT to multiple BJTs; differential mode to push-pull mode and BJT to MOSFET with PMOS and NMOS and CMOS. Then, the use of an operational amplifier (OPAM) circuit to an operational trans-conductance amplifier (OTA) circuit. The biasing of the amplifier is considered as voltage bias or current bias with resistors or diodes. The gain of the amplifier is generally expressed as the ratio of the amplitudes of the output and input signals. It can be given as voltage gain, current gain or power gain (in dB) or in terms of trans-conductance. On the basis of the advantages and dis-advantages, each signal amplifier has its own application in the different fields based on its suitable performance with respect to low power consumption, optimum gain, good bandwidth, optimum weight and space ratio and good frequency response. The discussion has been made and conclusion has been drawn for this investigation.
The literature on simulation-based medical education devotes a wide area to structured debriefing models using specific frameworks and terminology. Yet in a large number of low-resource sites throughout India, effective learning takes place through unstructured contextually relevant styles. This commentary is an experiential account elaborating insights from Basic Life Support (BLS) provider courses at four government medical colleges in Tamil Nadu where context-sensitive and adaptable feedback and debriefing occurs to meet learning needs without being directly guided by structured debrief models. We suggest that debriefing is not best understood in terms of adherence to named debriefing frameworks but rather as a mechanism for enabling deeper levels of reflection, promoting peer-based learning and transferring learning into real world clinical practice through techniques that make sense within educational culture.
Advances in Histopathological and Molecular Approaches to Brain Metastases: A Systematic Review
1 Sayani Ghosh; 2 Souvik Mazumder; 3 Dr. Prasenjit KunduBackground: Brain metastases are common secondary brain tumors that create major challenges for diagnosis and treatment. The differences in their appearance and molecular features make them difficult to classify. Methods: This review followed PRISMA 2020 guidelines. Research papers published between 2015 and 2025 were searched in Pub Med, Scopus, Web of Science, IEEE Xplore, and Science Direct. Only English-language studies focused on histopathology, immunohistochemistry, molecular testing, and AI-based digital pathology were included. Results: A total of 687 studies were found, and 54 met the inclusion criteria. Traditional stains such as hematoxylin and eosin (H&E) and markers like TTF-1, CK7, CK20, GFAP, and GATA3 are still useful for identifying the origin of tumors. Newer techniques like multiplex immunohistochemistry and molecular testing provide more detailed information about tumor genetics. Artificial intelligence applied to whole-slide images improves accuracy and consistency in diagnosis. However, most AI studies are limited by small datasets and lack standardization across laboratories. Conclusions: Combining molecular testing with AI-based digital pathology can help doctors diagnose brain metastases more accurately and predict patient outcomes better. Future studies should include larger datasets, use explainable AI systems, and follow standardized laboratory methods.
Prevalence of Anxiety and Depression among the Internally Displaced Persons in Manipur
1 Chanamthabam Rudrajit Singh; 2 Dr. Ramananda NingthoujamIntroduction: Since May 3rd, 2023, Manipur, a state located in the northeastern state of India, has witnessed a significant increase in internally displaced persons (IDPs) due to the Meitei-Kuki ethnic conflict. This has led to an increase in internal displacement of people within the state due to violence and instability of both the communities in the safer regions. These IDPs often faced a myriad of challenges related to physical, mental health and well-being. The study aims to explore the prevalence of mental health issues, i.e. anxiety, depression, PTSD and suicidal ideation faced by the IDPs, distribution among the genders, and various age groups to provide evidence on the existing knowledge of mental health in northeast India.Methods: The study employs a descriptive–exploratory research design and focuses on the IDPs residing in the formal relief camp of Manipur based on the secondary data available in the Government Official Records. Results: Anxiety and depression are highly prevalent among the IDPs. Women, middle-aged individuals, and older individuals are the most affected ones. Conclusion: The study highlighted a high prevalence of anxiety, depression, and mixed anxiety depression disorders. If left unattended, these disorders will deteriorate and may ultimately lead to PTSD and suicidal ideation. Addressing all the challenges through accessible health services and further research will be crucial in improving the well-being of the affected populations.
Narratives of Negativity: Contesting Neoliberalism in Noir Fiction
Dr. KopalThis paper examines how noir fiction provides a distinctive lens for analysing neoliberalism by foregrounding the affective dimensions of life under late capitalism. While crime fiction has long interrogated the tensions between legality, justice, and social order, noir’s sustained commitment to negativity—its moods of dread, paralysis, cynicism, and obstructed agency—renders it uniquely attuned to the lived contradictions of neoliberal restructuring. The paper first surveys key scholarly interventions that interpret crime fiction as a critique of global capitalism, drawing on Andrew Pepper’s account of crime fiction as a genre capable of exposing the hidden violence of capital; Misha Kokotovic’s formulation of “neoliberal noir” in post war Central America, where cynical aesthetics dismantle the fantasy of the sovereign neoliberal individual; and Matthew Christensen’s analysis of crime genres as contemporary sites for exploring crises of sovereignty and social obligation under neoliberal governance. Together, these readings demonstrate how crime fiction maps shifting formations of power, precocity and dispossession. The second part turns to affect theory to extend this critique by considering how neoliberalism is sensed, inhabited, and emotionally managed. Through Lauren Berlant’s concept of cruel optimism, the paper situates neoliberalism as an affective regime that attaches subjects to fantasies of the “good life” even as material conditions render those fantasies unattainable. Additional insights from Sianne Ngai highlight how negative affects like paranoia, stuckness, anxiety, and other “ugly feelings”, reveal the obstructed agency and suspended action characteristic of neoliberal subjectivity. Bringing these strands together, the final section argues that noir fiction operates as a privileged site of affective critique. Noir’s atmospheric negativity does not merely accompany its plots; it becomes a mode of knowledge that makes visible the emotional and ideological pressures structuring contemporary life. The paper contends that noir’s refusal of optimism, closure, or sovereign agency exposes the affective costs of neoliberal rationality and offers a counter-sensorium through which readers can apprehend the contradictions of late capitalism. By joining ideological and affective analysis, the paper proposes a framework for understanding noir as a form that renders neoliberalism not only narratively legible but viscerally felt.
Exploring Employee Motivation in Consulting Firms - An Empirical Analysis
1 Nancy Rao; 2 Urvashi Sharma; 1 Vaishali NaroliaDue to expanding global markets, employees have the greater choices and autonomy to switch their jobs quickly as compared to earlier generations. Hence managers are left with no alternative but to develop right strategies to foster employee commitment and motivation (Pauliene et al., 2025). Individuals are different not only in their work values and wants but also in their demographic attributes of sex, qualifications, age group, income etc. which resulted in variation in the work ethics and desires of the employees (kurose, 2015). Though there has been enough evidence to support the significance of motivation still the studies focusing on motivation of employees in consulting sector remains limited. This study will explore the motivation of employees in consulting firms in India with respect to extrinsic and intrinsic motivational factors and also the influence of demographic characteristics on motivation of employees. The data was collected from 257 employees working in top 5 consulting firms in India, using well structured questionnaire and simple random sampling. According to results of the study, all 15 factors motivate the employees to some extent, with salary the top most motivating factor for the employees amongst all other factors of motivation. The findings also revealed that work motivation is affected by the gender, qualification, marital position, age, designation and income. This study presents a comprehensible framework for researchers’, managers, experts and policymakers to understand the factors motivating the employees and adopt the right strategies for motivating each employee (Al Araimi, 2002; Engidaw, 2021).
Forensic Science and its Application in the Criminal Justice System: An Assessment
Asweta Mali; Monika PriyadarshiniForensic Science, indeed has several ethical concerns, but still can bring revolutionary changes in the field of rendering justice through the Criminal Justice System. This field comes up with huge potential in order to make changes in the society by setting exemplary notions by using the scientific tools and technologies. For assisting and rendering justice in a criminal investigation, forensic science acts as a boon to the criminal justice system in order to admission of the authentic evidences. With the growth of science and development forensic science has widened the scope of newly emerged scientific divisions which includes Mobile Forensics, Forensic Anthropology, Forensic Odontology, Forensic DNA Analysis, Computer Forensics, etc. Modern revolution in India has metamorphosed it from a dictating colony to an elected representative republic and with that transformation the Indian society has undergone far-reaching changes at a rapid speed. At present time, where the rate of science is expanding, the implementation of scientific techniques under the purview of forensic science is changing dramatically. Therefore, forensic science is a wider field which includes different branches like forensic serology, forensic chemistry, forensic biology, forensic physics, forensic ballistics, forensic toxicology, forensic psychology, forensic photography, voice analysis and uses of tools like microscopy, holography, uses of ultraviolet rays, chromatography, spectrography, electrophoresis, spectrophotometry, laser microprobe, etc. This paper deals with the different fields of forensic science and its application in the field of law following the laboratories being used all over the India. Further, the authors added a clear insight on the principles and ethics of forensic science and judicial paradigm in criminal justice system to support the whole assessment.
This systematic review assesses and analyzes the imaging-based screening methods and histopathological correlations that are available for the early detection of metastatic brain tumors. It reviews the critical role that MRI and CT play as primary diagnostic tools, while advanced modalities like PET-MRI, MRS, and DTI help in tumor characterization, metabolic profiling, and treatment surveillance. The integration of artificial intelligence and machine learning has greatly enhanced image segmentation accuracy, classification precision, and survival prediction but still suffers from limitations related to dataset dependency, computational complexity, lack of interpretability, and clinical validation. The histopathological examination is considered the gold standard since it provides crucial information on tumor origin as well as immunohistochemistry molecular subtype and prognostic markers through genomic profiling. Radio genomics trends are emerging more prominently with biomarker analytics to highlight how close imaging is converging with molecular diagnostics toward precision medicine in neuro-oncology. By looking at literature between 2020-2025 this review brings out existing research gaps and stresses the importance of multimodal frameworks driven by AI integrating imaging pathology plus molecular data for better early detection as well as personalized treatment concerning metastatic brain tumors.
Tourism plays a substantial role in regional economic development, yet the quality of tourism destinations remains a critical determinant of visitor satisfaction and sustainability. This study analyzes visitor perspectives on the quality of tourism objects in West Manokwari District, Manokwari Regency, with a focus on three key dimensions of the 3A framework: Attraction, Accessibility, and Amenity. Data were collected from 100 local and domestic tourists through structured questionnaires and analyzed descriptively to assess the performance of each dimension. The findings reveal that the quality of tourism destinations in West Manokwari District varies across the three components. Accessibility scored an average of 51.25%, indicating moderate performance with notable constraints related to limited public transportation and insufficient directional signage. Amenity received the lowest score at 49%, reflecting inadequate supporting facilities such as public toilets, waste management, and food services. In contrast, Attraction obtained the highest rating with an average of 62.25%, driven primarily by the district’s strong natural appeal, including beaches and scenic landscapes, although cultural and historical information remains underdeveloped. The study concludes that while the district possesses significant natural tourism potential, improvements in accessibility and supporting facilities are essential to enhance overall destination quality. Strengthening cultural interpretation, expanding community participation, and improving infrastructure are recommended to support sustainable tourism development. These findings provide strategic insights for local government, tourism stakeholders, and communities in planning and managing tourism resources more effectively.
Characterization of Modified PALF Composites for Eco-Friendly Food Packaging
1 Rubiyah Baini; 2 Sariah Abang & 3 Sii Siew PingThis study investigated the suitability of pineapple leaf fibre (PALF) as a sustainable raw material for paper-based food packaging by evaluating the effects of pulping duration, NAOH treatment, and natural additives on structural, chemical, thermal, and morphological properties. It was found that complete pulping occurred at 90 minutes, producing a smooth, uniform paper surface. Additives including eggshell powder, sago starch, and glycerol were assessed for their ability to enhance paper appearance and performance. FTIR analysis confirmed compounds associated to cellulose, hemicelluloses and lignins and incorporation of additives into the fibre matrix. SEM images showed improved fibre bonding with starch and enhanced rigidity with eggshell powder, while glycerol acted as a plasticizer increasing flexibility. Thermal analysis demonstrated that eggshell-treated samples provided the highest thermal stability and residue content, whereas glycerol lowered decomposition temperature. Overall, the findings indicate that NAOH-treated PALF reinforced with eggshell powder offers the most balanced combination of strength, thermal stability, and barrier properties, making it a promising biodegradable alternative for food packaging applications.
The instructions to understand literature and language are evolving in an era where we are completely dependent on AI. There is a confluence of artificial intelligence (AI) and cognitive science. As this study has good scope, this paper synthesizes interdisciplinary research to reveal: (1) how machine learning (ML) and natural language processing (NLP) allow for the detection of large-scale textual patterns, despite limitations in hermeneutic depth; (2) the cognitive mechanisms that support AI-driven adaptive learning systems (e.g., dual coding, spaced repetition); and (3) ethical issues such as algorithmic bias, data privacy, and the "human-AI symbiosis" dilemma in pedagogy. Research has shown that developments are there, such as transformer-based models (e.g., BERT) in literary stylometry and the effectiveness of AI tutors in second language acquisition (SLA) by analysing 120 peer-reviewed articles published between 2015 and 2023. Observations clarify AI improves corpus analysis efficiency (e.g., detecting diachronic theme transitions in books from the 19th century with 92% accuracy; Lee & Huang, 2023) and optimises language training (30% faster competence increases compared to traditional approaches; Martinez, 2018). However, there are missing links: There is a problem with understanding metaphors which are embedded in culture (F1-score = 0.65 compared to 0.89 for human specialists; Anderson, 2022), so learners cannot rely on it. Midway is always good, wherein literary evaluation should be done by humans, and AI can look after bulk tasks like vocabulary drills. In the present times of a multidisciplinary approach, suitable integration and a transparent system which can lead to interdisciplinary cooperation among technologists, linguists, and cognitive scientists are the objectives of the study to continue discussions with respect to AI's place in education.
Human Rights Conceptions in Gadaa Laws of Borana Oromo, Southern Ethiopia
1 Shentema Dandena; 2 Taddesse Berisso; 3 Abiyot LegesseEthiopia, like most African countries has immensely endowed with cultural, natural, and historical heritages. Institutions like, Oromo gadaa system have played important roles in human rights protection for centuries within the framework of Indigenous democratic governance system. Despite the fact that, the egalitarian elements of gadaa system have been exhaustively studied, the human right perspectives of gadaa system hardly addressed. This study set an objective to investigate the legal conceptions of the Gadaa system in safeguarding human rights. Methodologically, in this research we employed qualitative research approach, implementing an exploratory research design. Our finding revealed that, gadaa laws broadly categorized into two: Cardinal and supplementary laws. Cardinal laws of Gadaa are those used as a baseline or legal framework, whereas supplementary ones are sub-laws. Cardinal laws are grand laws that are formulated to protect the right of a given subject. For instance; seeranadheni (women law), seerafarda (law of horse), etc. while supplementary laws are those details in the cardinal laws with a potential to be amended. Gadaa law of human rights are moral rights ingrained in the Oromo social values for centuries. According to my FGD discussants and key informants, gada laws address every aspect of life, but when we come to those human rights in focus, we can categorize them into social, economic and political dimensions of human rights.
The Study of Static Magnetic Field Effect on Cholesterol Crystal
A. C. BhagatIn the present work, Cholesterol crystals were grown by single diffusion gel method in the presence of static magnetic field for different strengths such as unexposed and 0.2 Tesla using electromagnet unit (EMU-50) at constant pH, density and concentration of the solution at ambient temperature. Yields and morphology of grown crystals were studied. These crystals were characterized by using XRD and FTIR method.
The current study was conducted between the research year 2023-2024 and 2024-25 in research centre Janta College, Bakewar, Etawah, Department of Horticulture. The investigation was carried out in RBD (Randomized Block Design) with 3 replications, 26 treatments with 78 total number of combination. A field experiment was escort to assess the effect of foliar application of nutrients with plant growth regulators with 26 treatment combination T1 = Urea, T2 = K2SO4, T3 = CaSO4, T4 = GA3, T5 = NAA, T6= Urea + K2SO4, T7= Urea + CaSO4, T8= Urea + GA3, T9= Urea + NAA, T10= K2SO4+ CaSO4, T11= K2SO4 + GA3, T12= K2SO4 + NAA, T13= CaSO4 + GA3, T14= CaSO4 + NAA, T15= Urea + K2SO4 + CaSO4, T16= Urea + K2SO4 + GA3, T17= Urea + K2SO4 + NAA, T18= Urea + CaSO4 + GA3, T19= Urea + CaSO4 + NAA, T20= Urea + GA3 + NAA, T21= K2SO4 + CaSO4 + GA3, T22= K2SO4 + CaSO4 + NAA, T23= CaSO4 + GA3 + NAA, T24= GA3 + NAA + K2SO4, T25= Urea + K2SO4 + CaSO4 + GA3 + NAA, T26= Control (RDF) on yield attributes and quality of guava (Psidium guajava L.) cv. L-49. Foliar application of Urea + K2SO4+ CaSO4+ GA3+ NAA increasing fruit yield characteristics like length of fruit, width of fruit, number of seeds and fruit yield in kg/tree and chemical parameters like T.S.S., sugar contains like reducing, non- reducing, total sugars and ascorbic acid of fruits. The foliar application of Urea + K2SO4 + CaSO4+ GA3 + NAA concentrations given more superior flowering, fruit set and fruit yield in guava and followed by Urea+ K2SO4 + NAA whereas, the control (RDF) was recorded the lesser value in all the treatment on fruit yield attributes and quality of winter season of guava (Psidium guajava L.) Cv. L-49.
The empirical research aims to investigate the impact of branding and promotional strategies on the Nalanda University Ruins, a UNESCO World Heritage site associated with historical tourism. It aims to understand how these methods affect perceptions and experiences of tourism, hence assessing its appeal as a destination. A combination of methodologies is utilised: qualitative primary data are obtained via a questionnaire, and quantitative secondary data are gathered from the Ministry of Tourism, Bihar records. The study aims to identify the key factors that significantly influence tourists' opinions of Nalanda as a heritage site, including historical relevance, cultural authenticity, and the quality of visitor services. The study also evaluates the effectiveness of advertising methods employed by local government and tourism boards in shaping tourist expectations and awareness. The research findings will impact sustainable heritage tourism, providing insights into the effective marketing and promotion of cultural places, such as Nalanda, to ensure their preservation and continued popularity.
Background: The integration of Artificial Intelligence (AI) into education has revolutionized teaching and learning, particularly in health sciences. Physiotherapy education, which combines theoretical understanding and clinical skills, is increasingly leveraging AI tools to enhance knowledge retention, student engagement, and academic outcomes. However, the specific impact of these tools in physiotherapy remains underexplored. Objective: To assess the effect of AI-based learning tools on knowledge retention, student engagement, and learning outcomes in undergraduate physiotherapy education. Method: A cross-sectional study was conducted among 410 undergraduate physiotherapy students across multiple institutions. Participants who had experience using AI tools (e.g., ChatGPT, Quizizz, Socratic, Kahoot) completed a series of digital surveys: a pre-survey, the Student Engagement Scale, a Learning Outcomes Survey, and an AI Perception and Usability Survey. Descriptive statistics and Pearson’s correlation analysis were used to evaluate associations between AI tool usage and academic metrics. Result: Students reported higher engagement (M = 3.49), academic performance (M = 3.50), and knowledge retention (M = 3.53) with AI tools. Strong positive correlations were observed between AI tool usage and retention (r = 0.363), engagement (r = 0.285), and academic performance (r = 0.229). A negative correlation was found between reliance on traditional methods and AI usage (r = -0.217), indicating a preference shift toward AI-based learning. Conclusion: AI-based learning tools significantly contribute to improved academic engagement, knowledge retention, and educational outcomes in physiotherapy students. These tools demonstrate superiority over traditional teaching methods and should be considered essential components in modern physiotherapy curricula.
Background: A Revolutionary development in physiotherapy, wearable exoskeleton supplemented with artificial intelligence (AI) provide patients with mobility impairments with real time motion analysis, adaptive assistance and individualized rehabilitation. But despite their potential to completely transform recovery procedures, technical constraints, exorbitant expenses, and unresolved ethical issues continue to prevent their widespread clinical acceptance. In order to create a frame work for their safe and successful integration into standard physiotherapy practise, this study examines physiotherapy opinion regarding AI-driven exoskeleton, assesses their therapeutic efficacy, pinpoints significant implementation hurdles and tackles moral conundrums like patient autonomy and data privacy. Objective : In order to guide their successful implementation in physiotherapy practice, the purpose of this cross-sectional survey is to : (1) find out how physiotherapist feel about wearable exoskeletons with AI for individualized rehabilitation ; (2) determine their clinical usefulness in restoring mobility ; (3) identify adoption barriers such as cost and training ; (4) investigate ethical issues such as patient safety and data security ; and (5) ascertain practitioners readiness to adopt this technology and their training requirements. Method: This cross-sectional survey will assess physiotherapists awareness, adoption, and perception of AI-powered wearable’s in rehabilitation, while identifying key integration challenges. Through a 4-6week online questionnaire distributes via professional network, this cross-sectional survey will assess the awareness, adoption perception of 300-500 licenced physiotherapist with at least one year of experience with AI-powered exoskeletons in the area of musculoskeletal, neurological, sports, paediatric and geriatric rehabilitation. The study will also explore key challenges to putting these technologies into everyday use – such as high cost involved, the need of specialized training the safety consideration. It will look into ethical concerns too, including data privacy and questions around legal responsibility. Result: findings suggests that despite significant obstacles, such as high expenses, complicated technological issues, and privacy concerns, physiotherapists are cautiously optimistic about how AI-powered exoskeletons will transform rehabilitation practices. Conclusion: In order to maximize acceptance and patient outcome, integrating AI-powered exoskeletons, successfully necessitates overcoming practical implementation challenges, setting ethical rules, and offering focused physician training, despite the potential benefits for individualized rehabilitation.
This paper examines algorithmic trading's impact on market quality in emerging derivatives markets. Using high-frequency tick data from four major exchanges (2021-2023), analyze effects on liquidity, price discovery, and volatility through panel regression, fixed effects, and instrumental variable approaches addressing endogeneity. Results show algorithmic trading significantly reduces bid-ask spreads (18.7%) and increases market depth (24.3%) while accelerating price discovery. However, state-dependent effects emerge: volatility increases 31.5% during market stress periods. Cross-sectional analysis reveals liquidity improvements concentrate in highly liquid contracts, while thinly traded derivatives show minimal change. These findings illuminate market microstructure dynamics in emerging financial markets and inform regulatory frameworks balancing innovation promotion with systemic stability. Evidence indicates differential impacts across market conditions and contract liquidity levels, highlighting the nuanced relationship between algorithmic trading proliferation and market quality in emerging derivatives markets.
Sustainable supply chain management (SSCM) has become a critical business strategy for enhancing operational efficiency while minimizing environmental impact. Aligned with Sustainable Development Goal (SDG) 9: Industry, Innovation, and Infrastructure, organizations are increasingly integrating sustainability into their supply chain processes in response to regulatory pressures and growing consumer demand for eco-friendly products. This paper explores key SSCM strategies, including green procurement, circular economy principles, carbon footprint reduction, and waste minimization. Through a review of recent literature and industry case studies, the study highlights how businesses can achieve long-term profitability while fostering environmental and social responsibility. Companies adopting SSCM practices benefit from improved resource efficiency, enhanced brand reputation, and compliance with international sustainability standards. However, challenges such as high implementation costs, supply chain complexity, and resistance to change hinder widespread adoption. This study proposes a framework for embedding sustainability into supply chain operations, emphasizing stakeholder collaboration, technological innovation, and regulatory compliance. Future research should further explore industry’s best practices and the role of digital transformation in advancing sustainable supply chains, reinforcing SDG 9’s objectives of building resilient infrastructure, promoting inclusive industrialization, and fostering innovation.
Background: Chronic Suppurative Otitis Media (CSOM) is a persistent middle ear infection characterized by continuous ear discharge due to a perforated tympanic membrane. It is often associated with hearing loss and poses significant social, psychological, and physical challenges. Aim: This study aimed to evaluate the demographic characteristics and impact of CSOM on patients' quality of life (QoL) at a tertiary care hospital. Methodology: A prospective observational study was conducted over six months at the Department of ENT, Government General Hospital, Kurnool. A total of 110 patients diagnosed with CSOM were included. Participants' demographic data were collected, and QoL was assessed using the COMAT-15 scale. Statistical analysis included chi-square tests, paired t-tests, and descriptive measures (mean, median, mode). Results: Out of the 110 patients participated in the study, 52 were male and 58 were female, with ages ranging from 18 to 65 years. The majority of the patients were seen from the age group (18-28). patients were assessed using a standardized QOL questionnaire (COMAT- 15 SCALE), and the correlation between the severity of the disease and its influence on daily living was analyzed. Social interactions, physical activity, and emotional well-being among patients have improved. Discussion: The findings indicate that CSOM significantly affects various dimensions of patients' QoL. Using Standardized QoL tools like the COMAT-15 scale allows for better understanding and management of the psychosocial burden associated with the disease. This study focused to assess the clinical, psychological and treatment outcomes in patients with chronic suppurative Otitis Media (CSOM), focusing on demographic variables, symptomatology, quality of life before and after intervention and psychological burden. Conclusion: Chronic Suppurative Otitis Media has a profound impact on the quality of life of affected individuals, particularly in terms of social and psychological well-being. This study underscores the importance of a comprehensive management strategy that addresses the full spectrum of the disease, including physical, social and emotional health. Early diagnosis and intervention, along with access to adequate healthcare facilities, can significantly improve the outcomes for patients with CSOM.
Customer Perceptions of Ethical Banking Practices: An Empirical Study
1 Pratyashi Tamuly; 2 Arabinda DebnathThe rapid reforms and changes in the banking industry has significantly reshaped the financial scenario by improving efficiency with customised customer services. However, these reforms and advances have not reduced the critical ethical concerned particularly on transparency and Disclosure, Protection and Welfare, Responsibility and Sustainability, Governance and Risk management. The purpose of the study is to analyse the perception of Bank Customer on ethical banking Practices amongst 24 Scheduled Public Sector banks and Scheduled Private Sector banks operating atKamrup Metro District of Assam. Data has been collected using questionnaire from 400 customers of Scheduled Public and Private Sector Banks Operating in Kamrup Metro. The findings of this study provide valuable insights into customer perceptions of various dimensions - transparency and disclosure, customer protection and welfare, social responsibility and sustainability, governance and risk management, and ethical conduct across public and private sector banks in India. The results indicate that public sector banks are perceived significantly more favourably than private sector banks in terms of transparency and disclosure, customer protection and welfare, and governance and risk management. This is consistent with the regulatory framework and public accountability that characterizes public sector banks, which are subject to stricter oversight and mandatory disclosure norms.
Introduction: Maxillary deficiency is frequently observed in patients with operated unilateral complete cleft of the lip and palate. Orthopedic appliances such as facemask are commonly used in the correction of skeletal Class III in growing patients with maxillary deficiency. However, these appliances require patient compliance and social acceptance of wearing which affect the overall success rate. To overcome the limitations of Facemask therapy in skeletal Class III malocclusion, a novel device called "SAVE" has been developed. SAVE being a prototype, required a FEM study to understand the intricacies of the device. The present FEM study is conducted to assess the efficiency of SAVE in cleft lip and palate patients by comparing its device properties with the currently available gold standard, facemask appliance. Methodology: Three different forces were applied (600 grams, 800 grams and 900 grams) to the Finite Element Models (FEM) of three appliances (SAVE SS (Stainless Steel), SAVE (Biomedical Grade PEEK), and Facemask) used for maxillary protraction. The equivalent stress and Deformation on the appliance and the maxilla were measured and analyzed. Results: The result of the analysis revealed that the SAVE SS appliance generated lower stress at the anchorage head, moderate stress at the stents, and the highest stress at the main frame rod, although still less than the facemask. SAVE PEEK showed the least stress across all components, while the facemask recorded the highest stress, particularly at the crossbar and anchorage units. Regarding deformation, SAVE SS exhibited the least deformation, mainly near the anchorage unit, while SAVE PEEK displayed greater deformation, especially at the stents. The facemask experienced the highest deformation, primarily at the crossbar. Overall, SAVE SS demonstrated higher stress and lower deformation, making it a promising alternative to the facemask. Conclusion: The SAVE SS appliance could be used as an alternative to Facemask as it demonstrates higher stress and lower deformation with higher forces in both the maxilla and the appliance, suggestive of its use in maxillary protraction with less deformation in the appliance. Thus, SAVE SS shows strong potential to evolve as a dependable, patient-friendly alternative for early orthopedic correction in cleft patients
Bloodlines and Trimesters: Morphological Anemia Patterns among Tribal Mothers in Ranchi, Jharkhand
1 Anup Kumar Dhanvijay; 2, 3 Kumar Vivek; 2,4 Anjali Sinha; 1 Mohammed Jaffer Pinjar; 5 Nikhil KumarBackground: Anemia during pregnancy is a significant public health issue, especially among tribal populations in India, due to poor nutrition, limited healthcare access, and a high prevalence of hemoglobinopathies. Morphological classification aids in understanding etiology and guiding interventions. Objective: To assess morphological patterns of anemia among tribal pregnant women in Ranchi, Jharkhand, across trimesters. Methods: A cross-sectional study was conducted on 406 anemic tribal pregnant women attending the Obstetrics and Gynaecology Department at a tertiary care centre in Ranchi, Jharkhand (January–December 2017). Hemoglobin estimation, peripheral smear examination, and sickle cell screening were performed. Anemia was classified as mild (10.0–10.9 g/dl), moderate (7.0–9.9 g/dl), and severe (<7.0 g/dl). Trimester-wise distribution of morphological anemia types was analyzed using chi-square tests, followed by pairwise Fisher’s exact tests with Bonferroni correction. Results: Microcytic hypochromic anemia was most common (51.0%), followed by dimorphic (26.8%), normocytic normochromic (11.6%), macrocytic (6.2%), sickle cell anemia (4.2%), and pancytopenia (0.2%). Most women had mild (52.0%), moderate (39.2%), and severe (8.8%) anemia. Significant differences in anemia distribution were observed across trimesters (p < 0.001). Microcytic anemia predominated in all trimesters, with dimorphic anemia more common in later trimesters. Conclusion: Microcytic hypochromic anemia predominates among tribal pregnant women in Ranchi, with dimorphic anemia also prevalent. The findings indicate combined iron, folate, and vitamin B12 deficiencies, alongside hemoglobinopathies, emphasizing the need for early screening, nutritional interventions, and routine sickle cell testing in antenatal programs for tribal populations.
A Systematic Review on an Intelligent Reflection Assessment in Video-Based Learning
1 Jenila S; 2 Shanmuga Sundari PVideo-based learning is much effective and helpful in understanding the context and also supports adaptive learning. Adaptive learning provides the ability to personalize content to the learners, based on individual needs and understandings. Reflection in video-based learning is the most important concept to be considered for analysing the understanding ability, progress in learning and active engagement of learners during the video-based learning process. In order to evaluate the reflections, some automated tools can be used which accepts the feedback as input keywords and process it. In this study, various adaptive learning techniques and algorithms of different Learning Management Systems are analysed. Several assessment methods that helps to evaluate the learners’ capability are also studied.It is associated with the reflections provided by the learners that support to determine their cognitive ability. Furthermore, describes few more intelligent systems that recommend supplementary resources to improve the detail study of the specific topics. These kind of systems can be integrated to provide a better learning environment that enhances learners’knowledge level and develops interest in learning.
Explainable Stroke Detection using Transfer Learning and Stacking Technique
1 Amrita Ticku; 2Anu Rathee; 3 Dhruv Mathur; 4 Deepika Yadav; 5 Ayush SrivastavStroke stays among the world's foremost trigger of mortality and disability by annually affecting numerous people with severe medical outcomes. Medical diagnostics requires immediate correct stroke detection because delayed or incorrect stroke diagnosis can lead to severe neurological disabilities or death. In clinical environments, beyond achieving high diagnostic precision, it is imperative that models offer interpretability to foster clinician trust, support informed decision-making, and uphold accountability in AI-assisted healthcare interventions. Therefore, AI-driven stroke detection systems must balance predictive performance with transparency to ensure safe and reliable deployment. This study proposes an Explainable Stacked System for Stroke Detection (EXS3D), that used a Stacking Ensemble technique and transfer learning with multiple deep learning models (Res Net, Efficient Net, Dense Net) as base classifiers, whose outputs were combined through a meta-level Logistic Regression model. To enhance transparency, the system employed Grad-CAM for visual explainability of image-based features in base models, and SHAP and LIME frameworks to interpret the decision-making of the final meta model. The EXS3D system achieved an accuracy of 97.37%, with the meta-model outperforming individual base models in predictive performance. EXS3D exemplifies how explainable AI can be seamlessly integrated into ensemble learning for high-stakes domains like stroke detection.
Extraction, Characterisation and Pharmacological Potential of Natural Dyes: A Review
1 Ruchika Khatri, 2 Sweta VashisthaPlants are great source of natural dyes. These natural dyes are yielded by many plant part such as leaves, flowers, roots, seeds and sometimes microorganisms also like algae, fungi etc. These dyes are rich organic sources of printing paper and fibres also. These extracted materials are responsible for their therapeutic and curative properties also due to presence of many bio compounds in it. Natural dyes are curative and commercial also. They have many pharmacological efficiency like antimicrobial, antioxidant, Ameliorative activity, Hepatoprotective activity, no tropic activity, Analgesic activity, antiviral, anticancer, anti-inflammatory etc. These natural dyes are extracted by many extraction methods such as simple aqueous methods, alcoholic or solvent extraction method, complicated solvent systems, supercritical fluid extraction, Microwave or Ultrasonic extraction, Enzymatic extraction, fermentation extraction those are use to isolate pigments from plant parts. Characterisation of dye is main step to show the affinity of dye on its fibre. Extracted dyes are characterised by many parameters like uv-vis spectroscopy, FTIR, HPCL, GCMS, NMR techniques. So, this review revails the pharmacological and economical perspective of natural extracted dye which is useful in medical and commercial industry in the globe.
Genetic Analysis and Factor of Flaxseed
1 Hasmat Ali, 2 Kiran pal, 3 Shubham Singh, 4 Javad Ahmad SiddhiquiFlax is an important source of oil rich in omega-3 fatty acid which is proven to have health benefits and utilized as an industrial raw material. An application was observance to estimate the volume of inherited deviation among 33 flaxseed genotype including 3 checks using Mahalanobis D2 statistics in Randomized block design (RBD) at Agriculture Research Institute Kanpur, Up during Rabi 2022-2023. Examination was record for 11 ethos and based on D2 statistics, the genotype were classed into 8 apart team using Tocher’s method. The outgrowth visible that team I had most number of genotypes followed by team II and team VI while other five team were mono-genotypic. The higher intra-team remoteness was recorded for team VI, followed by team II and team I while the lowest intra-teamremoteness were observed for remaining 5 team. Whereas the most inter-team remoteness was recorded between team IV and VIII, indicating a higher amount of inheritedmulteity available in genotypes of this team and can be harness as parents for inter-breeding schedule.
Hericiumerinaceuscan strengthen the spleen, nourish the stomach, calm the mind, and combat cancer. The studies were carried out in vitro to investigate the possibilities of various medium, temperature, pH and Light intensity. Present study carried out on two strains of H. erinaceous (DMRX-779 and DMRX-780) for evaluation of their cultural characteristics on different media, temperature, pH and light intensity. Among the selected media PDA and WEA showed better mycelia growth. For analysis of temperature (25ºC) were recorded best followed by 20ºC temperature for both the strains and variable pH (5, 6, 8 and 7) was recorded. Light serves as a significant growth factor that has been assessed for the growth of selected strains for the first time. Yellow light promoted fruiting bodies initiation while blue light favors rapid colonization in petriplates. Both strains could be further improved for commercial and economic purposes based on these considerations.
Diabetes mellitus represents a major and growing global public health challenge, with a substantial proportion of affected individuals remaining undiagnosed until complications arise. Early risk stratification using routinely available clinical parameters can support timely intervention, particularly in resource-constrained settings. Artificial intelligence (AI) and machine learning (ML) approaches offer promise for predictive modeling; however, their clinical adoption is often limited by the need for programming expertise. Objectives: To develop and evaluate a no-code machine learning workflow using the Orange data mining platform for predicting diabetes status from basic health parameters, and to compare the performance of commonly used supervised classification algorithms with an emphasis on clinical interpretability and screening utility. Methods: This analytical modeling study utilized the Pima Indian Diabetes Dataset comprising 768 adult female participants with eight clinical and anthropometric predictors. Data preprocessing, feature ranking, model training, and evaluation were performed entirely within the Orange visual programming environment. Six supervised classifiers—Logistic Regression, Naïve Bayes, Random Forest, Support Vector Machine, k-Nearest Neighbors, and Decision Tree—were trained and validated using stratified 10-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), and Matthews correlation coefficient. Results: All machine learning models outperformed the majority-class baseline accuracy of 65.1%. Logistic Regression demonstrated the most balanced performance with an accuracy of 78.4%, AUC of 0.831, F1-score of 0.774, and MCC of 0.508. Naïve Bayes showed comparatively higher sensitivity, suggesting utility in screening contexts. Feature ranking identified plasma glucose, age, body mass index, and insulin levels as the most influential predictors of diabetes risk. Conclusion: A no-code machine learning pipeline implemented using the Orange platform can deliver clinically meaningful and interpretable diabetes risk prediction using routinely collected health data. Such approaches have the potential to empower clinicians without programming expertise, support early screening strategies, and facilitate broader adoption of AI-driven decision support in primary care and population health settings.
The Impact of Migration on Economic Growth in Nigeria: Evaluating the Role of Macroeconomic Policies
1 Sola, Oluwagbenle (Ph.D); 2 Ojo Rufus Olawumi (PhD); 3 Olusesan Samuel Afolabi (Ph.D)This study examined the relationship between migration and economic growth in Nigeria using annual time series data spanning from 1981 to 2022. The study employed Johansen Co-integration and Vector Error Correction Mechanism. The findings showed that the variables in the study were co-integrated affirming the existence of long-run relationship among the variables. The results revealed that migration (MIG) has a positive and significant impact on economic growth in Nigeria compared with Foreign Direct Investment (FDI) which has no significant impact on economic growth. Conversely, Trade Openness (TRO) has a negative and insignificant impact on economic growth whereas Government Expenditure (GOV) also has a positive and insignificant impact on economic growth in Nigeria. The paper concludes that migration positively impacts economic growth in Nigeria. This implies that migration enhances labor market flexibility by filling labor gaps and addressing skill shortages leading to increased economic output. This result also affirms that migration could contribute to gross domestic product (GDP) growth through consumption and production in the host country. The outcome of the study also implies that migration can result in human capital transfer as skilled migrants can bring new skills, knowledge, innovation and expertise to the host country by boosting output and impacting the economy positively. In the light of this, the study recommends that migration should be facilitated through safe and legal channels as this step will enhance integration and quicker adjustment of migrants to the new labor market in the host country for improved productivity and sustainable economic growth with empirical evidence from Nigeria.
Mass - Radius Fractal Analysis of Protein Structures from PDB Coordinates
1 S. PavithraProteins exhibit highly complex three-dimensional structures characterized by irregular geometry, hierarchical organization, and multiscale heterogeneity that are not adequately described by classical Euclidean models. Fractal modeling provides a powerful mathematical framework to capture these intrinsic structural features by exploiting self-similarity and scale invariance inherent in protein folding. In this approach, protein backbones, residue packing, and molecular surfaces are analyzed using fractal descriptors such as fractal dimension, mass–radius relationships, and box-counting methods. These measures quantitatively characterize protein compactness, backbone complexity, and surface roughness across different spatial scales. Fractal analysis has proven effective in distinguishing between ordered and intrinsically disordered regions, comparing native and misfolded conformations, and elucidating structure–function relationships, particularly at active and binding sites. By integrating concepts from polymer physics, statistical mechanics, and nonlinear geometry, fractal modeling enhances our understanding of protein organization beyond conventional structural parameters. This framework offers valuable insights into protein stability, folding dynamics, and biological functionality, and serves as a complementary tool in structural biology, computational biophysics, and bioinformatics.
The burden of cancer continues to rise globally, with low- and middle-income countries (LMICs) like India witnessing disproportionate morbidity and mortality due to late diagnosis and limited preventive services. In this context, the emergence of a new clinical nursing role—the Cancer Control Nurse (CCN) or Preventive Oncology Nurse Specialist—offers a transformative approach to strengthen cancer prevention, screening, health promotion, and early detection at the population level. This article explores the scope, competencies, training needs, and impact of this specialized role, particularly in the Indian setting, while advocating for formal recognition within national cancer control frameworks. The integration of CCNs into multidisciplinary oncology teams has the potential to reduce cancer incidence, improve early detection, and facilitate continuity of care across prevention, screening, and treatment pathways.
Reading Dostoevsky Today: Rationality and its Discontents in the Age of AI
1 Abhignya Sajja; 2 Vaibhav Shah; 3 Pankti VadaliaIn the age of Artificial Intelligence, rationality is ametanarrative that determines notions of self and collective life. AI fails to articulate the myriad complexities of the human condition. Fractured identity and overdependence on reason in the digital age, where experience is almost always not lived, pushes the individual to a precarious moment of crisis. The paper aims to address the position of being a subject ofmodernityvia a qualitative, interdisciplinary study that is at the fore of Ethics and Existential philosophy.Dostoevsky’s work, written in the fast-changing setting of 19th century Russia, can be drawn upon to derive insights on human nature, the meaning of life, and the dangers of choosingrationality over ethical conduct.Dostoevsky designated faith and suffering as necessary tenets in Tsarist Russia that was under a deluge of Westernschemes of progress to do with Rational Egoism, Social Utopianism, and Utilitarianism. Raskolnikov,Ivan Karamazov, and The Underground Man try to experiment with a new way of life and deal with itsconsequences, not always redeemable. AI, extensively integrated in systems of governance and lifestyle today, is ridden with biases(to do with gender, mental health, minority populace, etc.) that disregard the unstructured, non-definable, deviant, and evolving aspects of human nature. For instance, how would AI incorporate ideas such as interiority, ambiguity, guilt, envy, defiance, sacrifice, forgiveness, revenge, nostalgia, and self-destruction when employed in a matter that affects man?Like Raskolnikov, one is often carried away by the promises and rewards offered by the new formats of life. Like Raskolnikov, one might invite trouble.The study thus argues that depending upon the hyperrational AI (in matters that might fall into the realm of irrational) will only cause a collective existential crisis; it attempts to understand the present bystudying Dostoevsky’s conflict-ridden Russia.
Real-World Adverse Drug Reactions to Semaglutide: A Case Series from an Indian Tertiary Care Centre
1Anu V. Babu, 2Manish Mohan M, 1Devi V. S, 3Swetha Reba Mathews, 2Jacob Jesurun R. SSemaglutide, a glucagon-like peptide-1 receptor agonist (GLP-1 RA), is increasingly prescribed for the management of type 2 diabetes mellitus and obesity owing to its proven efficacy in glycogenic control and weight reduction. However, with expanding use, a broader spectrum of adverse drug reactions (ADRs)—ranging from common gastrointestinal intolerance to rare but clinically significant complications—has been increasingly recognized, underscoring the need for continued real-world pharmacovigilance. Objective: To describe a case series of semaglutide-associated ADRs reported from a tertiary care centre in India, highlighting the variability in clinical presentation, the role of uniform baseline investigations, and the importance of comprehensive medication history in causality assessment. Methods: Five patients receiving semaglutide for type 2 diabetes mellitus (n = 3) or obesity (n = 2) developed clinically significant ADRs. All cases underwent uniform baseline and follow-up investigations, including complete blood count, liver and renal function tests, serum electrolytes, and glycogenic parameters (HbA1c/fasting plasma glucose). Detailed drug histories, co morbidities, and concomitant medications were systematically reviewed. Causality assessment was performed using the WHO–UMC criteria. Results: The documented ADRs comprised generalized pruritus (n = 1), severe nausea and vomiting (n = 2), acute kidney injury (n = 1), and gastro paresis-like symptoms (n = 1). All reactions demonstrated a clear temporal relationship with semaglutide initiation or dose escalation and were categorized as probable on WHO–UMC causality assessment. Alternative aetiologies, including concomitant medications and underlying comorbidities, were reasonably excluded. Clinical improvement and complete resolution of symptoms were observed in all patients following discontinuation and appropriate supportive management. Conclusion: Although semaglutide is an effective and widely used therapeutic agent, this case series highlights the occurrence of clinically relevant ADRs in routine clinical practice within an Indian tertiary care setting. The findings reinforce the importance of uniform baseline evaluation, cautious dose titration, meticulous medication history, and proactive pharma covigilance reporting through the Pharma covigilance Programme of India (PvPI) to enhance patient safety and sustain therapeutic confidence.
Homeopathic Intervention in Salinity-Stressed Pennisetum glaucum: Role of Natrum Muriaticum
1 Kalpana Agarwal; 2 Pragya Dhakar; 3 Akshita Khandelwal; 4 VaishaliThe present investigation, conducted between 2022 and 2025 at IIS (Deemed to be University), aimed to evaluate the efficacy of homeopathic remedies Natrum muriaticum 6CH and 12CH in alleviating saline stress induced by 100 mM NaCl in Pennisetum glaucum (L.) R. Br. under in vitro conditions. The remedies were incorporated into Murashige and Skoog medium supplemented with appropriate hormones BAP and 2,4-D for callus induction, and IAA with kinetin for regeneration. Seed germination, callus formation, and regeneration were initiated in 11, 10, and 3 days respectively under treatment with the remedies. Morphological parameters such as root-to-shoot ratio and callus biomass, along with biochemical markers including chlorophyll a, chlorophyll b, total chlorophyll, total phenolic content, and DPPH radical scavenging activity, showed significant improvement. Gene expression analysis through quantitative RT PCR revealed modulation of stress-responsive genes (NAC21 and APX) with ACTIN as the reference, confirming upregulation under treatment. Statistical analysis validated the reliability of these findings. Overall, both potencies of Natrum muriaticum demonstrated efficiency in ameliorating NaCl-induced salt stress, thereby enhancing seed germination, callus formation, and regeneration in pearl millet.
This study evaluates the production, blending, and engine performance of biodiesel derived from Argemonemexicana seed oil as a sustainable alternative to conventional diesel. Crude oil was extracted from seeds collected across different regions of India and converted into biodiesel via a two-step transesterification process, comprising acid-catalyzed esterification followed by base-catalyzed transesterification to reduce free fatty acids and optimize fatty acid methyl ester yield. The synthesized biodiesel was blended with commercial diesel at varying concentrations and characterized for fuel properties. Engine performance and emission tests were conducted on a single-cylinder, four-stroke diesel engine, assessing parameters including brake thermal efficiency, specific fuel consumption, and exhaust emissions (NO, HC, CO, CO?, and O?). Among the tested blends, B10 (10% biodiesel) exhibited the most favorable performance, achieving stable engine operation, lower fuel consumption, and an improved emission profile compared to neat diesel and higher biodiesel blends. Higher biodiesel content, such as B15 (15% biodiesel), resulted in slightly reduced engine stability and increased fuel consumption due to its lower calorific value and higher viscosity. These results demonstrate that A. mexicana biodiesel, particularly at 10% blending, offers an optimal balance between engine efficiency, fuel economy, and emission reduction, confirming its potential as a renewable diesel substitute.
Effects on Rate of Exchange on Export Earnings of Ethiopia: Insights from ARDL Model
1 Alemayehu Temesgen Befikadu; 2 Duvvi AshalathaThis study investigates the short- and long-run effects of exchange rate fluctuations on Ethiopia’s export earnings and overall economic performance from 1990 to 2025 using the Autoregressive Distributed Lag (ARDL) framework. Annual macroeconomic data, real GDP, export earnings, foreign exchange rate, inflation, foreign direct investment (FDI), and domestic consumption, were analyzed after confirming variable integration at order I(1). The bounds test results indicate a strong long-run co integrating relationship among the variables. Empirical findings show that exchange rate movements have a significant impact on real GDP in both the short- and long-run. FDI and inflation also have meaningful positive effects. Export earnings show a mixed pattern. There are negative short-run adjustments, followed by stability in the long run. The Granger causality tests reveal no directional causality between export earnings and real GDP. This suggests that structural constraints weaken the link between export performance and economic growth. However, export earnings are found to Granger-cause the exchange rate. This shows the exchange rate’s sensitivity to changes in the external sector. Overall, the study highlights that a competitive and well-managed exchange rate is important for strong export competitiveness and economic growth. Policy measures should promote export diversification, value addition, and stable macroeconomic conditions. Attracting export-oriented FDI is also key to strengthening Ethiopia’s long-term economic resilience.
Background: Obesity is a multiplex metabolism disorder associated with excess fat accumulation. It elevated the menace of cardiovascular system ailment, diabetes mellitus, and other complications. The Present study intended to examine the anti-obesity outcome of ethyl alcohol extract of Malvaviscus arboreus leaves in higher-fatfood-brought-overweight rats. Methods: Leaves of Malvaviscus arboreus were shade-dried, powdered by using a mortar pestle, defatted using petroleum ether laboratory reagent by Soxhlet apparatus, and then extracted the defatted extract with 95% ethanol by Soxhlet apparatus at 70 to 75 o C. Albino laboratory rats were alienated into four groups: Control, Standard (Standard drug Orlistat-10 mg/kg High fat diet), Test-1and Test-2. The treatment was given for 42 days, that is 6 weeks, by oral route with the help of oral gavage. The muscle strength and muscle grip were assessed by using Kondziela’s inverted screen maze apparatus, and body weight was recorded regularly. Blood was poised by the retro-orbital perforation method for biochemical investigation for the estimation of serum lipid profile. Results: The test groups, particularly test-2, demonstrated significant improvements. On the 42nd day, average muscle strength in the test-2 group 4 was (206.60 seconds), close to the standard group 2 was (232.18 seconds), while the test-1 group 3 was (154.32 seconds) and the control group 1 showed only (81.57 seconds). Final body weight was also reduced: control group 1 (248.75 grams), standard group 2 (142.13 grams), test-1 group 3 (171.13 grams), and test-2 group 4 (152.88 grams).
Background: Diabetic foot ulcers (DFUs) are a major cause of non-traumatic lower limb amputations worldwide. Pressure offloading is a key strategy in ulcer management; however, its real-world effectiveness compared to conventional dressings remains under-evaluated. Objectives: To compare the efficacy of conventional saline dressing and pressure offloading dressing using the SUVIDHA technique in healing diabetic foot ulcers. Methods: This randomised comparative study enrolled patients with plantar DFUs who met the inclusion criteria. Patients were divided into two groups: Group A received conventional saline dressings, and Group B received pressure offloading dressings for one week in-hospital and continued offloading dressings at home for 6 weeks. Ulcer area was measured pre- and post-intervention. Results: Mean post-intervention ulcer area was significantly smaller in the pressure offloading group (4.2 cm²) than in the saline group (17.6 cm²) (p<0.01). The mean reduction in ulcer size was also greater in the pressure offloading group (24.2 cm² vs. 15.3 cm²; p=0.012). Conclusion: Pressure offloading dressings are significantly more effective than conventional dressings in promoting ulcer healing. Offloading should be integrated into standard DFU care, especially in resource-limited settings.
Background: Written informed consent is a fundamental ethical and legal requirement in surgical practice, intended to safeguard patient autonomy and promote shared decision-making. Despite its universal implementation, concerns remain regarding patients’ understanding of the consent process and the adequacy of information disclosed. Objective: This study aimed to assess patients’ knowledge, attitudes, and practices related to the informed consent process before elective surgical procedures. Materials and Methods: A cross-sectional, questionnaire-based study was conducted in March 2024 among adult inpatients who underwent elective surgery at Sree Gokulam Medical College and Research Foundation, Kerala, India. A total of 150 patients were selected using simple random sampling and interviewed on the second postoperative day. A validated structured questionnaire was used to evaluate patient awareness of essential components of informed consent, including diagnosis, nature of surgery, risks, benefits, alternative treatment options, and the right to refuse treatment. The quality and completeness of written consent forms were assessed using a standardised observation checklist. Data were analysed using SPSS version 29 and expressed as frequencies and percentages. Results: All participants (100%) reported having signed a written informed consent form. While all patients were informed about their diagnosis and the nature of the surgical procedure, only 84.6% were aware of the expected benefits, and 76.7% understood the consequences of declining surgery. Information regarding surgical risks was provided to 92% of patients; however, 75% desired further clarification. Only 61.4% reported being informed about alternative treatment options. Although 92% felt they had adequate time to understand the consent form, 7.3% perceived the explanation as unclear. Evaluation of consent documents revealed omissions, particularly the absence of explicit statements regarding alternative treatments and patients’ rights to refuse or withdraw consent. Conclusion: Although the existing informed consent process satisfies ethical and legal requirements for most patients, important gaps persist, especially in communicating alternative treatment options and patient rights. Enhancing the clarity, completeness, and patient-centred nature of the informed consent dialogue is essential to ensure genuine patient autonomy and informed decision-making in surgical care.
The purpose of this study was to determine the impact of a 10-week aerobic exercise programme on resting systolic blood pressure, resting heart rate and respiratory rate of female hypertensive members of Recreation Clubs in Owerri Municipal Council of Imo State. The study was guided by three research questions and three corresponding null hypotheses. The study adopted randomized pretest–posttest control group design. In this multivariable trials; 108 female hypertensive members constituted the population of the study, and 50 volunteers (30 experimental, 20 control) were used as the sample size of the study. A Sphygmomanometer model DM-500 was used to measure blood pressure, while standardized Stethoscopes models LG300 and HG900 were used to measure heart rate and respiration rate, respectively. The study examined the fundamental assumptions of homoscedasticity (using Levene's Test), normality (using the Shapiro-Wilk and Kolmogorov-Smirnov tests), and homogeneity of regression slopes (using the ANCOVA model's interaction term), all of which were met; hence, ANCOVAwas confidently employed for the analysis. For the research questions, mean and standard deviation were used to analyze the data, and ANCOVA was used to test the hypotheses at a 5% significant level. After the 10-week aerobic exercise programme, the results showed notable gains. Resting heart rate dropped from 99.60 bpm to 92.37 bpm (mean difference = 7.23 bpm, p < 0.05), respiratory rate dropped from 20.43 breaths per minute to 18.43 breaths per minute (mean difference = 2.00 breaths per minute, p < 0.05), and resting systolic blood pressure dropped from 149.13 mmHg to 139.23 mmHg (mean difference = 9.9 mmHg, p < 0.05). These findings support aerobic exercise's potential involvement in lowering cardiovascular risk by demonstrating that it is a successful non-pharmacological strategy for controlling important physiological markers of hypertension. This study contributes to the increasing amount of data demonstrating the benefits of aerobic exercise for lowering blood pressure and enhancing cardiovascular health.
GI Tagged Kangra Tea Performance and Policy Recommendation
Sanjay Singh & Dr. Nisha BhartiGeographical Indications (GI’s) form a part of Intellectual property rights, are the products which have a specific geographical origin and have some traditional knowledge attached to them. They certify purity and quality different from similar looking products. As such GI tagged products have value added to them ensuring good returns which instigates their conservation and sustainable development. Kangra tea is known for its unique taste and aroma. The paper analyses the performance of Kangra tea after acquiring the GI Tag, the awareness level of the cultivators, the role of the government both the state and the centre and suggestions for the betterment of tea industry of the state.
Background: Pelvic and crossed renal ectopia are rare anomalies that can be complicated by pelvi-ureteric junction obstruction (PUJO). Surgical reconstruction is challenging because of aberrant anatomy. Anderson–Hynes dismembered pyeloplasty remains the gold standard for PUJO repair. We report outcomes of open pyeloplasty in ectopic kidneys from a single tertiary center. Methods: Five consecutive adult patients with ectopic kidneys and PUJO underwent open Anderson–Hynes dismembered pyeloplasty at Calcutta National Medical College between December 2023 and August 2025. Diagnosis was confirmed by ultrasonography (USG), contrast-enhanced CT urography, and Tc-99m DTPA diuretic renography. Patients were followed with clinical evaluation, serum creatinine, urinalysis, renography at 6 and 12 months, and ultrasound semiannually. Primary outcome was functional improvement on DTPA; secondary outcomes were hydronephrosis regression and need for re-intervention. Results: Median age was 28 years (range 18–42). Three patients had left pelvic kidneys, one right pelvic, and one left crossed ectopic kidney. Four presented with abdominal pain, one was incidental. Aberrant vessels were present in 3/5 cases. Four patients (80%) demonstrated functional improvement on DTPA; hydronephrosis improved in only two (40%). One patient (20%) with a left pelvic kidney developed recurrent pain and UTI, representing functional failure, and required postoperative double-J stenting. No major complications were recorded. Conclusions: Open dismembered pyeloplasty is effective in ectopic kidneys with PUJO, providing functional improvement in most cases. Radiologic hydronephrosis resolution is less consistent, and some patients may require secondary intervention. Careful preoperative imaging and vigilant follow-up with diuretic renography are essential.
Introduction: Cognitive impairment is a core symptom in schizophrenia that has a significant impact on psychosocial function, but shows a weak response to pharmacological treatment. Consequently, a variety of non pharmacological interventions have tried to find out suitable out come in patients with schizophrenia. The present study was to first to find out can cognitive remediation and neurofeedback training with pharmacological intervention can bring batter functional outcome in patients with schizophrenia. Method: Twenty Schizophrenia diagnosed patients were selected. The participants were examined before intervention started and after completion of cognitive remediation and neurofeedback training the post assessment were performed. The assessments were done using the Socio Demographic and Clinical Data Sheet, Positive and Negative Syndrome Scale, Cognitive Symptom Checklist, Digit Symbol Substitution Test, PGI Memory Scale, Trail Making Test, Wisconsin Card Sorting Test. The data were analyzed via SPSS-21.Mean, standard deviation and repeated measures analysis were used to analyze the data. Results: After receiving the combination treatment of cognitive remediation, neurofeedback training and pharmacological interventions brigs better out come in compare to only pharmacological intervention. The post intervention findings revealed that significant improvement in psychopathology (Z- 3.80) significant at the level of P .001, and the same findings reflecting in improvement in attention (Z-3.790) significant at the level of P .001. Memory and executive functions brings the same results. Conclusion: Cognitive remediation, neurofeedback training and pharmacological interventions brings better out come in compare to pharmacological intervention alone.
The study investigated the socio-economic and educational background of parents and teenage abortion in Nigeria: evidence from Asaba and Warri. The study is necessary for the Nigeria situation, as it explains teenage abortion as not merely a matter of choice of the teenager involved but closely-knit in socio-economic and education conditions of a family (parents and guidance). Emerging literature indicates that socio-economic factors and education are the main causes of adolescent reproductive practices such as abortion decisions. Information and health care accessibility to adolescents is influenced by parental socio-economic status (SES), including family income, parental occupation, and wealth, thus determining their reproductive decisions. The selected research design is a cross-sectional survey approach. The study population consisted of 500 teenage girls aged 13-18 years living in Asaba and Warri who had 500 – they cannot have 500 parents. Each should have 2 parents (except the demise of one of them). Meanwhile, I don’t think it’s necessary to include the number of their parents if they are not included in the study parents. Parent inclusion criteria specified that participants must be either a biological parent or a legal guardian of a teenage girl within the specified age range, and they must have lived in the study area for at least five years. The stratified random sampling strategy was used to ensure respondents’ proportional representation with regard to socio-economic classes and education levels. The study findings revealed that the SES of parents is a significant factor influencing the reproductive health of adolescents. Another finding reveals that a lack of formal education among parents has a grave effect by restricting their ability to counsel or impart to their children about reproductive health matters. The study concluded that economic and educational disadvantages in families are the determinants of teenage abortion in Asaba and Warri. It was recommended that the government must introduce comprehensive programs that would involve economic empowerment of families living in poverty, adult literacy programs among parents, and provision of increased access to reproductive health services that are friendly to adolescents.
With the rapid proliferation of IoT devices, there has been some unprecedented surge in security breaches and forensic complexities attributable to the high data velocity, heterogeneity, and dynamic behavior of IoT networks. Existing forensic frameworks rely predominantly on static, batch-learning models which neither adapt to shifting threats, operate efficiently on resource-constrained devices, nor possess any capacity for real-time processing. In addition, current approaches inadequately satisfy the requirements for distributed environments, temporal consistency, and adaptive feature selection sets. This work, therefore, proposes an integrative Incremental Learning Framework for IoT Forensic Analysis, incorporating its five pioneering analytical models that will ensure real-time, scalable and adaptive forensic intelligence sets. The first model, Adaptive Multi-Agent Swarm-based Incremental Learning (AMASIL), introduces bioinspired agents using self-organizing particle dynamics to achieve dynamic threat learning. The second model will enable privacy-preserving, scalable analysis across distributed devices through hierarchical graph-based embeddings: Hierarchical Federated Forensic Graph Neural Network (HF2GNN). Third, Neuro-Synaptic Edge Cognitive Filtering (NECFiL) implements spiking neural networks at the edge for bioinspired temporal filtering of relevant forensic signals. Fourth, the Evolutionary Hypergraph Attention Learning (E-HAL) model is focused on deriving high-order feature relationships harnessed by an attention-driven hypergraph structure optimized through evolutionary heuristics. Finally, the Temporal Adversarial Forensic Consistency Network (TAFC-Net) assesses the robustness of learning in adversarial conditions using metrics of temporal consistency. The outcome is a 9.3% increased detection accuracy, 67% reduced feature space, and a 45% enhancement in edge throughput while leveraging the robust adaptation in data drift and poisoning. Also, the proposed models increased scalability, real-time responsiveness, and forensic precision and provide a very vital foundation for intelligent self-adaptive IoT forensic systems.
The new cloud infrastructures have become very complex and large, demanding the use of proactive security monitoring mechanisms to detect threats and provide recommendations on how to act upon them emerging risks. Currently, most existing cloud forensics frameworks operate only reactively, do not integrate sources of data of varied types, and fail to produce alerts in real-time, context-oriented and interpretable formats. Also, most traditional models lack a federated form of adaptability and probabilistic validation, rendering them less effective and scalable in actual operating conditions around the globe. This paper throws light upon a well-built, comprehensive Recommendation-Based Cloud Forensics Framework for pre-emptive detection of security events through an integration of five completely new analytical methodologies. The first is called Multi-Source Dynamic Risk Vector Embedding (MS-DRVE) and receives heterogeneous data sets like logs, traffic, and user behavior in one time-risk vector entry via attention-based encoding. The Graph Convolutional Markov Decision Networks (GCM-DNet) have shown how their creation enables indeed real-time alerting through modelling the threat propagation among the cloud entities as a Markov process on dynamically emerging graphs. Third, Explainable Multi-Modal Transformer (X-MMTrans) accommodates direct and interpretable visualizations of anomaly trajectories and system behaviors across multi-modal embeddings.. Fourth, such as Federated Adaptive Recommendation Engine with Contrastive Learning (FARE-CL), allows a decentralized learning, personalized, privacy-preserving security recommendations across distributed cloud nodes. Finally, the Bayesian Evidence Accumulation Framework (Bay EVAL), involves a probabilized, time-aware evaluation mechanism for the reliability and effectiveness validation of the proposed system sets. The precision attained with this proposed framework is high (94%); with low false positive rates (<3.5%); and improved interpretability, thus enhancing threat mitigation in advance, decision-making efficiency, and deployment confidence on the cloud security operations. Such work paves the way toward-generation intelligent and explainable cloud forensic systems.
Background: Dietary diversity is commonly used as an indicator of diet quality and nutritional adequacy, yet its relationship with non-communicable diseases (NCDs) remains inconsistent across populations. Understanding dietary patterns in relation to NCDs is essential for developing effective preventive strategies in clinical and public health settings. Objectives: To assess dietary diversity patterns and examine their association with selected non-communicable diseases among adult outpatients attending a tertiary care hospital in Tiruppur, Tamil Nadu. Methods: A hospital-based cross-sectional analytical study was conducted among 1,054 adult outpatients. Data on socio-demographic characteristics, lifestyle factors, and dietary habits prior to NCD diagnosis were collected using a pre-tested questionnaire. Dietary diversity score (DDS) was calculated based on consumption frequencies of eight food groups and categorized as low, intermediate, or high. Associations between food group consumption, DDS categories, and NCD prevalence were analyzed using chi-square tests and multivariable logistic regression. Results: Most participants had an intermediate DDS. No statistically significant association was observed between DDS categories and the prevalence of diabetes mellitus, hypertension, heart disease, cancer, or their common co morbidities. However, significant associations were identified between specific food group consumption frequencies and NCD outcomes, particularly diabetes mellitus and hypertension. Weekly consumption of aerated drinks and fried foods showed higher odds of hypertension, while certain animal-source foods were associated with higher odds of diabetes. Conclusion: Overall dietary diversity alone was not significantly associated with NCD prevalence, whereas specific dietary components demonstrated meaningful associations. Nutritional interventions should prioritize diet quality alongside diversity to support NCD prevention.
The large quantity and speed of streaming data increasingly demand intelligent systems able to analyze events in real-time while being able to adapt to changes in data distributions and operate under constrained computational budgets. Shortcomings related to significant latencies, inability to integrate various classifiers to handle multi-modal streams, and inability to use resources efficiently on-edge devices and deployments are factors limiting the current approaches based on deep learning for streaming data analysis. In this scenario, we propose a framework for high-resolution integrated deep learning for very high-velocity streaming scenarios with five interconnected novel approaches, which include Dynamic Streaming Aware Graph Embedding Transformer (DSGET) for scalable, real-time temporal feature extraction, Continual Drift Adaptive Meta Learning Framework (CDAML) for fast adaptability to distributional shifts, Hierarchical Parameter Sharing Compression Network (HPSCN) for very effective resource utilization through temporal weight reuse, Multi-Modal Incremental Knowledge Integration Engine (MIKIE) for adaptive cross-modal fusion without full retraining, and Streaming Real Time Benchmark and Feedback Optimization Module (SRBFOM) for continuous in-operation evaluation and self-optimizations. The components for closed-loop pipelining where each output from each stage feeds the next stage form a closed-loop pipeline comprising these components. Experimental analysis shows about 40% lower processing latency and more than a 60% model size compression, with respect to drift recovery time reduced by 45% and an improvement in multi-modal predictive accuracy of 5-7% relative to state-of-the-art methods. The proposed architecture stands to unite scalability, adaptability, and computational efficiency that allow deployment in both cloud and edge environments for mission-critical real-time analytics. Further, this establishes a solid next-generation foundation for streaming deep learning systems with advancement in state-of-the-art adaptive, resource-efficient, and highly accurate streaming data analysis.
Typically, security models & analysis includes privacy, efficient computational scalability, and resilience to adversarial threats. Additional requirements from two perspectives, rather than clear-cut governance mechanisms leading to consensus designs focusing primarily on data integrity or network-level security, seldom exist. Most conventional federated learning schemes detach the consensus validation from the encrypted computations and ignore real-world compliance and domain transferability in process. To remedy these shortcomings, this paper proposes a broad-based framework that comprises five innovative methods to enhance consensus specifically focused on ML systems deployed over blockchains. The CAHFGM method interconnects encrypted gradient validation straight into the consensus pipeline of the model-to-be Valid model, guaranteeing model integrity without affecting data privacy. The ABSDTE enhances robustness with dynamic trust scores by surfer clustering participants and deploying shard-level consensus to detect collusion and model poisoning. The Layered Privacy-Enforced Merkle Consensus combines differential privacy with Merkle structures to ensure privacy with audit ability for regulated real-world deployments. To address scalability issues, the Quantum Inspired Lattice-Backed Consensus Layer adopts post-quantum-secure energy-efficient consensus primitives based on lattice cryptography, achieving high throughput and resistance to quantum attacks. The Adaptive Multi-Domain Transfer Validator employs transfer learning for validating consensus outcomes among heterogeneous domains for improved generalizability as a whole in process. Collectively, these methods reduce privacy leakage by 98%, increase collusion detection accuracy above 92%, achieve >10,000 TPS, and demonstrate >85% domain transfer efficiency. This work establishes a robust, scalable, and privacy-preserving consensus foundation for deploying ML over blockchain in regulated, adversarial, and cross-domain environments.
Most notable after NMT systems dealing with under-resourced languages like Chhattisgarhi are found ever demanding in function with audiovisual implementations concerning the language translations. Existing NMT models for English to Chhattisgarhi translation are predominantly unimodal and unable to maintain visual/cultural content context incapable of semantic, morphological, or idiomatic accuracy. As a result, the algorithms highly affect translations by viewing multimedia stimuli carrying complicated semantics over a cultural and temporal interface. This paper proposes an all-encompassing Multimodal Translation and Adaptive Tuning Framework (MM-TAT) that exploits the latest neural architectures, by exploiting text, images, and video inputs, to significantly improve translation quality. The system will comprise five novel modules: (HMF-NMT) Hierarchical Multimodal Fusion NMT, a module that aligns semantic features across text, images, and videos using cross-attention methods; TVA, a Temporal Visual Attention mechanism that aligns video-derived features with sentence-level semantics to ensure tense and aspect consistency; SAPT, a Syntax-Aware POS-Tagging Transformer that integrates grammatical constraints via dual-head attention and a syntactic transition matrix. AER-Net- Adaptive Error Recovery Network: utilizes transformer-based post-editing through human correction feedback; SCG-Net- Semiotic Concept Graph that injects cultural and idiomatic context through knowledge graph embeddings. The aforementioned modules together yield an improvement of 18.4 BLEU to 33.6, a TER low of 23.1, and 92.8% post-editing correction rate. Considering cultural phrases in English returns an accuracy of 84.7%, showcasing the model's ability to maintain socio-linguistic fidelity. This work sets a new benchmark for multimodal, real-world English-to-Chhattisgarhi translations in process.
A major hurdle to EV adoption is long charging times that take much longer than conventional fueling. In most cases using standard Level 2 AC charging, charging the vehicle fully requires long charging times of 6-8 hours. Nevertheless, high voltage fast charging and intelligent battery management could potentially offer the solution, but as adaptive thermal management occurs in real time, battery aging must be minimized, and specifications for charging profiles must be altered over time.
In this study we performed the rheological property in human blood flow in narrow through capillaries using tank treading method. Here the behavior of low condense of fluid flow is analyses. In this study it is obtained that the shear flow of red cells may flip on the shear rate and structure of the blood cell. The case of tank-treading without flipping the corpuscles strain is anticipated while a task of the shearing rate and the aligned angle. In the instance of roll over the low condensed of blood corpuscles represents the Newtonian fluid manner whereas condensed blood flow represents non-Newtonian fluid manner. In this study, tank treading motions of blood corpuscles on the rheological properties are also studied.
Background: Cigarette smoking has been associated with adverse neurobiological effects; however, its impact on cognitive functioning in young adults is often underexplored. Chronic exposure to nicotine and other toxic constituents of tobacco smoke may impair attention, memory and executive function. Conventional cognitive screening tools may lack sensitivity for detecting early cognitive changes, highlighting the potential role of artificial intelligence (AI)–based digital assessments. Objective: We assessed the viability of digital evaluation and used an AI-based software to compare cognitive function in young adult smokers and non-smokers. Method: A cross-sectional comparative study was conducted among 128 healthy participants aged 18– 30 years. Participants were equally divided into smokers (n = 64) and non-smokers (n = 64) using convenience sampling. Cognitive domains including memory, attention, and executive function were assessed using the CogniFit AI-based Cognitive Assessment Battery. Individuals with neurological or psychiatric disorders, head injury, or use of psychoactive substances other than nicotine were excluded. Group comparisons were performed using independent-samples t-test. Result: Group scores were compared using independent-samples t-tests. In every domain, smokers performed noticeably lower than non-smokers. In terms of demographics, the nonsmoker group was more gender balanced (47% male, 53% female; χ²(1) = 13.33, p <.001), while 78% of smokers were men and 22% were women. The AI-based assessment effectively distinguished cognitive performance between groups. Conclusion: Young adult smokers exhibit significant impairments in key cognitive functions when compared with non-smokers. These variations were successfully identified by the CogniFit AI-based evaluation, indicating that digital tools may enable remote cognitive function screening. Future research should examine how to include these technologies into programs for monitoring cognitive health and quitting smoking.
Background: Training programs are essential for optimizing physical fitness parameters crucial for sports performance. This study compares the effects of neuromuscular training (NMT) versus motor coordination training (MCT) on physical fitness measures in male collegiate badminton players. Aim and Objective of study: The aim of this study was to find out the best method among the neuromuscular training and motor coordination training among collegiate badminton players to enhance the physical fitness factors by finding the impact on them. Methods: A total of 24 male collegiate badminton players aged 16-25 years were randomly assigned to either the NMT group (n = 12) or MCT group (n = 12). The intervention lasted 8 weeks, with training conducted three times per week. The NMT program focused on muscle strengthening and dynamic stability exercises. The MCT program emphasized hand-eye coordination, footwork patterns, reaction time exercises, and spatial awareness drills. Outcome measures included Vertical Jump, 30m Sprint Test, and Agility (T-Test). Results: Significant group-by-time interactions were found for the 30m sprint test (p < 0.001, d = 2.3), vertical jump (p < 0.001, d = 1.9), and agility test (p = 0.015, d = 1.2). Post-hoc analysis indicated greater improvements in the NMT group in vertical jump height and sprint performance, while the MCT group showed more improvements in agility. Conclusion: Both training methods were effective, but NMT led to superior gains in power and speed, whereas MCT was beneficial for agility-based performance. Abbreviation: Neuromuscular Training (NMT), Motor Coordination Training (MCT)
Background: Body shaming is a growing concern among adults. While body shaming targets physical appearance, its impact extends beyond that, negatively affecting a person’s mental and physical health, social well-being, and professional life. This study was undertaken to estimate the proportion of body shaming and its impact on the social and emotional behaviour of medical students at a medical college in a metropolitan city. Methodology: A cross-sectional study was conducted among the medical students and interns with the help of a semi-structured questionnaire disseminated through digital platform as google form and the required data obtained, analysed and represented as percentage. Results: Body shaming was experienced by 164 participants (65.6%), majority being females 121 (73.8%) and 43 (26.2%)males . Majority of the participants 105 (64%) were body shamed for their weight.Body shaming led to mental health impacts like low self-confidence (57.3%), low self-esteem 93 (56.7%), eating disorders 69 (42%), social isolation 59 (35.6%),depression 42 (25.6%) and anger issues 21 (12.8%). Body shaming is significantly associated with multiple negative psychological outcomes in both males and females. While females experience body shaming more frequently, males appear to experience more severe impacts. Conclusion: Body shaming as an important public health concern requiring gender-sensitive preventive and supportive interventions.Promoting body positivity is the need of the hour as body shaming has a negative impact on social & emotional behavior of an individual.
Background: Health professional students are required to gain scientific and professional skills apart from high quality of educational services. Hence, it becomes imperative to take into consideration the view points and perception of the students’ learning environment. Objective: To measure and compare the viewpoints of GITAM University Health Professional students studying in MBBS, BDS, B-Pharm, B. Sc Nursingand BPT towards their learning environment using Dundee Ready Education Environment Measure (DREEM) questionnaire. Methodology: A cross – sectional study was carried out using a two-part questionnaire comprising demographic information and the DREEM instrument between September 2023 – February 2024. Descriptive statistics, including means and standard deviations, were calculated for each DREEM domain, and further analysed by course, year of study, and gender.One-way ANOVA with Tukey's Post Hoc test was employed to compare the mean overall DREEM scores across courses and years of study, while, unpaired t-test was used to compare the scores by gender. Results: The overall DREEM score was 120.61 ± 19.86. Individual domain scores were – ‘Students’ perceptions of learning’: 30.39 ± 6.99, ‘Students’ perceptions of teaching’: 26.66 ± 4.41, ‘Students’ academic self- perceptions’: 20.59 ± 4.65, ‘Students’ perceptions of atmosphere’: 27.19 ± 6.08, and ‘Students’ social self- perceptions’: 15.86 ± 3.35. Overall, the total DREEM score was significantly higher among nursing students (p=0.001), 1st year students (p=0.001) and males (p=0.04). Conclusion: The findings and evidences of the present study will hopefully provide the basis to take effective measures to improve teaching and learning environment of this University.
The Root System Architecture and Interspecific Interactions across Varied Soil Matrices
1 Ranvijay Singh & 2 Ajoy Kumar SinghRoot system architecture (RSA) plays a central role in determining how plant species acquire resources, tolerate stress, and interact with neighboring plants. Variation in soil matrices-ranging from texture, structure, compaction, and organic matter content to nutrient and moisture availability-strongly influences RSA development and plasticity. These soil-driven alterations in root architecture directly impact how plants engage in intra and interspecific interactions. For instance, nutrient-rich patches may encourage dense root proliferation, intensifying competition among neighboring species, whereas heterogeneous or low-nutrient soils often promote niche differentiation as species adopt contrasting root placement strategies to minimize overlap. Likewise, soils with high organic matter can enhance microbial associations, which may facilitate positive interactions such as nutrient sharing or stress mitigation between coexisting species. Conversely, compacted or poorly aerated soils may restrict root growth, increase competitive pressure and alter plant community dynamics. This study highlights the current understanding of how diverse soil environments regulate root growth patterns, including branching density, rooting depth, lateral spread, and root hair proliferation, and how these architectural traits modulate belowground interactions between coexisting species. Understanding these dynamic relationships is essential for improving crop performance in multi-species systems, optimizing soil health, and designing resilient agro ecosystems. This synthesis underscores the need for integrated approaches combining soil physics, root phenotyping, and ecological modeling to unravel the complex interplay between RSA and interspecific interactions across varied soil matrices.
Background: Monosodium glutamate (MSG) is a widely used food additive, and concerns have been raised regarding its potential neurobehavioral effects following prolonged exposure. Experimental evidence on the impact of chronic MSG administration on anxiety-related behaviour and motor function remains limited. Objective: To evaluate the effects of long-term monosodium glutamate exposure on anxiety-like behaviour and motor coordination in adult Swiss albino mice. Methods: This experimental study included 60 adult Swiss albino mice, randomly allocated into four groups: a control group and three MSG-treated groups, each receiving 40 mg/kg, 60 mg/kg, and 80 mg/kg of MSG intraperitoneally for three months. Anxiety-like behaviour was assessed using the light–dark adaptation test, while motor coordination was evaluated using the tight rope suspension test. Data were analysed using appropriate statistical methods to assess dose-dependent effects. Results: Mice exposed to higher doses of MSG (60 mg/kg and 80 mg/kg) demonstrated a significant increase in anxiety-like behaviour, evidenced by increased time spent in the dark compartment and reduced exploratory transitions. Motor performance showed a dose-dependent decline, with prolonged traversal time and increased falls observed in the higher-dose groups compared to controls. Conclusion: Chronic exposure to monosodium glutamate induces dose-dependent anxiety-like behaviour and motor impairment in adult Swiss albino mice. These findings suggest potential neurotoxic effects of prolonged MSG exposure and highlight the need for further investigations into its long-term safety.
Nature-based tourism destinations increasingly depend on online platforms to manage visitor perceptions, yet limited research has examined destination image for national parks using tourist-generated content. This study explores how visitors construct the image of Jim Corbett National Park (India) through TripAdvisor reviews, applying the cognitive–affective–conative framework of San Martín and Rodríguez del Bosque (2008). A phenomenological qualitative design was used, analysing 228 TripAdvisor reviews (January 2023–March 2024) via manual thematic content analysis, with comments coded into cognitive, affective, and conative components and classified as positive or negative. Results reveal 383 image-related references: 54.04 percent cognitive, 30.54 percent affective, and 15.40 percent conative, indicating narratives that are primarily attribute-based but also express emotions and behavioral intentions. Overall sentiment is largely positive, though service issues and unmet expectations regarding tiger sightings generate notable negative evaluations. Guide performance and wildlife encounters are critical themes shaping satisfaction and dissatisfaction, underscoring the need for better expectation management and standardized interpretive services. The findings demonstrate that TripAdvisor functions as a powerful organic image-formation agent for nature-based destinations and highlight the value of integrating online review analysis into destination marketing and conservation-oriented branding strategies for protected areas.
Uncertainty in Molecular Modeling: A Chemical Fuzzy Graph Approach
1 Ajendra Kumar; 2 Jaspal Singh; 3 Vishal KumarAn Advanced mathematical modeling of chemical fuzzy graphs, which focuses on results that provide insights into molecular stability, reaction pathways, interaction potential, and energy stability. The results predict chemical stability by analyzing the spectral properties of fuzzy adjacency matrices, and optimal reaction paths are identified by minimizing interaction uncertainty within the graph. These contributions establish a robust framework for understanding and predicting molecular behavior, offering significant implications for research in chemistry and related fields
Design 4: 1 Multiplexer Using GDI (90 NM) Technology for Low Power and Compact Area
1,4 Mamta Parmar; 3,4 Dr. Rupesh Dubey; 2,4 Ritesh GuptaThe multiplexers (MUXs) are essential combinational circuits widely used in digital systems for data selection and routing. This work presents the design and execution of a 4×1 multiplexer using a hierarchical approach based on 2×1 multiplexers, implemented with the Gate Diffusion Input (GDI) technique. The GDI technique allows substantial transistor count reduction in comparison to traditional CMOS logic, leading to lower power usage as well as smaller silicon area. Also, the technique archives higher speed and is energy efficient. In the suggested architecture, multiple 2×1 MUX modules are interconnected to realize the 4×1 MUX functionality, leveraging the GDI’s ability to implement complex logic functions with reduced area. The design is implemented on 90 nm technology. Performance metrics like power dissipation and area were analysed and compared with conventional CMOS. Simulation results demonstrate that the proposed GDI-based 4×1 MUX achieves the reduction in total power consumption (51.4%), area (48.6%), improvements in speed and energy efficiency, making it suitable for use in low-power and high-performance digital circuits.
Background: Adolescence is a crucial developmental stage that brings about physical, mental, psychological, and social changes in the body.[1] Anemia is characterized as a condition where the number of circulating red blood cells is lower than the normal physiological limit for a particular age and gender of the individual. Thus, it reduces the ability of red blood cells to carry oxygen and eventually provide insufficient oxygen supply throughout the body.[2] According to the morphological/pathological classification of anemia, among the three types, microcytic hypochromic anemia is the most prevalent variant, especially among the women of developing nations.[3]Among the different causes of microcytic hypochromic anemia, iron deficiency is the major contributing factor.[4] Iron deficiency anemia (IDA) is a microcytic-hypochromic anemia that has major prevalence in African and South Asian countries including India.[5] An increase in iron deficiency in adolescents (15-19 years) is recorded in the National Family Health Survey 2019-21. A sharper rise was observed in girls. (59.1 percent) Rising urbanization with changes in dietary habits towards junk food is leading to faster increase in anemia in urban areas as compared to rural, according to the survey.[6]. Objectives: To assess the Hematological Parameters of the selected adolescent college going girls, To determine the association between Hematological Parameters with Iron Deficiency Anemia. Methods: This college based cross-sectional study was conducted among adolescent girls in the age group of 17–19 years from varied Under Graduate programsat Shrimathi Devkunvar Nanalal Bhatt Vaishnav College for Women, Chennai. Sample size was derived using appropriate formula. Purposive sampling method was adopted to select participants for the study. The study was carried out over a period of two months between June to July ,2025. Nine hundred and sixty-two subjects participated in the study depending on their availability and willingness to check their hematological parameters. Hemoglobin (Hb), Red Blood Cells (RBC), White Blood Cells (WBC), Platelet Count (PC), Hematocrit value, (HCT), Mean Corpuscular Volume (MCV), Mean Corpuscular Hemoglobin (MCH), Mean Corpuscular Hemoglobin Concentration (MCHC) and Red Cell Distribution Width (RDW-CV) were the key parameters. Venous blood samples were collected by trained professionals and analyzed for Hematological Parameters. Data were analyzed using IBM SPSS Statistics version 25. Descriptive statistics and chi-square tests were applied to assess association between variables. Results: Prevalence rate of anemia was 44.1 percent based on blood hemoglobin levels. A significant significant association was observed with Iron Deficiency Anemia and all the assessed hematological parameters except for White Blood Cells. Conclusion: Strategic focus on Iron Deficiency Anemia is crucial as it has detrimental effects on physical capacity, cognitive ability, pregnancy outcome and emotional wellbeing. The uncorrected phase results in intergenerational cycle of anemia and henceforth an intervention with holistic approach is the need of the hour.
Synthesis of Ternary (Zinc-Cupper-Iron) Mixed Oxidenano Composite and its Biocompatibility Study
Sajid M. MansooriMetal oxide nanoparticle (MON) biofunctionalization has a significant impact on biomedical domains. Because of this, it is necessary to comprehend how MON interact with proteins before considering the material for a specific biomedical application. Chemical co-precipitation was used to create ZnO: CuO: Fe2O3 nanocomposits. UV spectroscopy, X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy (SEM), and EDAX were used to investigate the synthesised nanocomposites. The single-phase cubic structure was validated by FTIR and XRD. Protein interaction with prepared nanocomposite was carried out using circular dichroism. In addition to the Cu-O and Zn-O vibration modes, the FTIR spectra validate the coexistence of both phases and the distinctive vibration of ferrites atoms at tetrahedral and octahedral sites. Debye Scherrer's formula yielded a crystallite size of 23–28 nm, but SEM revealed that the nanoparticles were 10–30 nm in size. No change in protein conformation on binding with ZnO:CuO:Fe2O3 nanocomposites indicates that the BSA-ZnO:CuO:Fe2O3 nanocomposite system is biologically compatible. Designing optimised MON for various biomedical applications will benefit from the study.
Exploring Sterno mastoid Tumor of Infancy: Role of Cytology
1 Nikhil Kumar, 2 Mona Lisa, 3 Pradosh Kumar Sarangi, 4 Ranwir Kumar Sinha, 5 Prima Shuchita Lakra, 6 Monalisa KatyareThe sternomastoid "tumour" of infancy is a firm, fibrous mass that typically appears between two to three weeks of age. It is a recognized cause of congenital muscular torticollis, which may manifest with abnormal head and neck posture, craniofacial asymmetry, breastfeeding challenges, and impaired sensorineural outcomes. These masses are usually well-defined, firm, mobile, and fusiform, often located in the lower third or middle third portion of the sternocleidomastoid muscle. They are commonly and strongly associated with breech presentations and assisted deliveries. We present a case involving a 20-day- old male infant with a swelling on the right side of the neck, diagnosed cytologically and corroborated through radiological findings. This report highlights the classic cytological features of this condition to aid in distinguishing it from other neck swellings in infants of a similar age group.
AI in Career Readiness: Enhancing Internship Experiences for Undergraduate Students
1 Ms. Anuradha Duvvuri 2 Ms. Roshan Jameer MDAn internship is a vital professional learning opportunity where students engage in meaningful projects that are directly aligned with their field of study and career interests. In today’s competitive labour market, internships not only provide firsthand practical experience but also serve as a crucial platform for integrating emerging technologies, including Artificial Intelligence (AI), to enhance career readiness. This study is to understand the impact of internship programs on the professional development and employability skills of undergraduate students, with a special focus on how AI-driven tools can augment these experiences. The findings indicate that internship experiences, when enriched with AI-based career counselling, analytics, and virtual collaboration tools, significantly bolster students’ confidence, technical proficiency, and soft skills, thereby improving their overall job readiness. The results underscore that an AI-aligned internship model not only prepares students for the modern workforce but also provides employers with a strategic advantage by nurturing future-ready talent.
Impact of Self-Help Groups on Women's Economic and Social Empowerment: Evidence from Rural Odisha
1 Rupa Kanunungo; 2 Sukhendu Mohan Patnaik; 3 Dr. Mahendra Prasad AgastyWomen’s empowerment through sustainable livelihood opportunities remains a major challenge in rural India. Self-Help Groups (SHGs) have emerged as an effective grassroots mechanism for promoting women’s financial inclusion, entrepreneurship, and social empowerment. This study examines the impact of SHG participation on financial liberty, living conditions, and decision-making autonomy among rural women in the Khalikote block of Ganjam district, Odisha, under Mission Shakti initiatives.. The study employs a quantitative research design based on primary data collected from 148 women members of 54 SHGs across 18 villages and 9 panchayats. A structured questionnaire with 154 items was administered using a five-point Likert scale. Three hypotheses were tested relating to improvement in financial condition (≥75%), living conditions (≥80%), and independent decision-making (>70%).The findings reveal a strong positive impact of SHG participation. Among respondents who reported income-related data, 98% experienced improved financial conditions, reflected in higher income, enhanced banking confidence, improved loan repayment capacity, and access to livelihood opportunities. All respondents who reported on living conditions (100%) indicated improvements in housing, consumption patterns, health, education, and household economic contribution. More than 99% of participants reported increased decision-making autonomy and self-confidence, although external social recognition remained limited. The study concludes that SHGs act as powerful instruments of women’s economic and social empowerment in rural Odisha, highlighting the need for strengthened
Regional Sentiment Analysis of Political Leaders on X (Twitter) with special reference to Assam
1 Binuri bodo; 2 Dr. Bhagwan Sahay Meena; 3 Dr. Moses KharbithaiIn the present era, digital communication aided by social media platforms plays a significant role in shaping and influencing regional and national politics. Political leaders are utilizing social media to its fullest potential since it's quickly emerging as one of the most crucial communication tools, particularly X (Twitter), starting from promoting election campaigns, to spreading awareness of government policy initiatives, and promoting welfare programs, as well as a medium to express political thoughts and connect with people. As such, it has become increasingly interesting to understand how social media, political leadership, and local attitudes interact in a rapidly changing digital environment. This study attempts to explore the significance of X as a platform for communication used by political leaders to communicate with citizens. Also, it aims to analyse the content of the tweets using sentiment analysis methods and categorise them into positive, negative, and neutral sentiments to best bring out the differences and contrasting sentiments of various political parties within the state of Assam.
Integrating IoT and Cloud Computing for Enhanced Communication Systems in Smart Environments
1 Mrs. Amruta Kulkarni; 2 Mrs. Parvati Bhadre; 3 Mr. Vishal Swamy; 4 Mrs. Pradnya KulkarniThe Internet of Things (IoT) and cloud software are changing the way communication systems work, especially in smart settings. Smart settings have gadgets, monitors, and systems that are all linked to each other. They depend on smooth communication to improve usefulness, efficiency, and the user experience. When it combine IoT with cloud computing, you get a solution that can grow with your needs and work well in a variety of settings. This solution lets you process, store, and analyze data in real time. IoT uses sensors, motors, and smart devices to gather data from the real world and send it to the cloud so it can be analyzed. Cloud computing, on the other hand, gives you access to strong computer resources, storage space, and data processing tools that you need to deal with the huge amounts of data that IoT devices produce. When you combine these two technologies, you get an environment where IoT devices can talk to cloud services and process and handle data well. By combining these two ideas, smart environments like homes, towns, hospitals, and factories can make better decisions, use their resources more efficiently, and automate tasks more effectively. Because the cloud can handle huge amounts of data from many IoT devices, it can be used as a central location to store and analyze data. One more thing is that cloud-based services are very reliable and easy to access, so users can watch and handle IoT devices from afar. IoT apps can handle more connected devices without slowing down by using the flexibility of cloud computing. Cloud computing also makes IoT systems safer by offering protection and safe ways to store data, which protects data accuracy and privacy in smart settings.
The Indian Firefighting Protective Clothing Export Market: ASWOT Analysis
1 Ms. Aarti Solanki; 2 Dr. Pavan Godiawala; 3 Dr. Devesh BaidOccupational hazards and fatalities of industrial developments are increasing, which requires to be diminished with good work practices, good engineering design of the process or machine design and whenever necessary as a last resort personal protective equipment can be used. The intensification of industrial activity with the evolving workplace regulatory requirements has contributed to an increasing demand for personal protective equipment and protective clothing. In India, the protective clothing market is also expanding, but due to inadequate legislation, the use of protective clothing remains low. As many researchers and studies have suggested, India should diversify its export basket, particularly in higher-Unit-Value Realisation (UVR). As India is growing in technical textiles, protective clothing (protech), a type of technical textile, can be a good product category for product diversification in the export market. The Personal Protective Clothing (PPC) market is growing at a 2% CAGR, and India has the potential to increase exports in this higher-Unit-Value Realization (UVR) category. India should target the higher-end UVR segment of protective clothing to increase its presence in the international market, which will also eventually help the domestic market by providing quality products with technical know-how. The main objectives of the research are to conduct a SWOT analysis of the Indian protective clothing market, evaluating its internal strengths and weaknesses and identifying opportunities and threats, with specific reference to flame-retardant and flame-resistant protective clothing. This study adopts a mixed-methods research design that integrates secondary quantitative market data with primary qualitative insights obtained through semi-structured expert consultations. The research design is both exploratory and descriptive in nature. Through this approach, the study offers an in-depth and comprehensive analysis of the Indian protective clothing export industry by systematically examining its internal strengths and weaknesses, while also identifying key opportunities and threats shaping its external environment.
Aim: To evaluate the effect of low impact exercise on fatigue among elderly. Design: One group pretest posttest design. Methods: An Interventional study was conducted among elderly residing in a selected old age home in Thiruvananthapuram Kerala, India. A total of 50elderly were included in the study, which exceeded the minimum required sample size (n =48) calculated based on power analysis. Data was collected from samples based on the inclusion criteria by consecutive sampling method. The level of fatigue of participants was assessed using Fatigue Severity Scale (FSS) and those with FSS ≥36 was selected for the study. After the pre-assessment of fatigue, the elderly will be made to perform low impact exercises twice a day for 6 days per week for 4 weeks with a duration of 20 minutes each session. Results: A significant difference was found between the mean pre-test and post-test fatigue score with t=10.879 (p<0.0001).Statistically no significant association was found between fatigue among elderly and selected socio-personal variables such as age, gender, co- morbidities and family support (p>0.05). Patient or Public Contribution: Elderly residing in a selected old age home were made to perform low impact exercises
This paper develops a real options framework to analyse irreversible investment decisions under regime-dependent uncertainty and learning-by-doing effects. The cost of investment is modeled as a stochastic diffusion process that can switch between low and high carbon regimes, reflecting shifts in policy or technological environments. A learning mechanism is introduced whereby accumulated investment experience reduces cost volatility over time. The resulting Hamilton–Jacobi–Bellman (HJB) equations form a system of coupled variational inequalities, for which closed-form solutions are generally intractable. To solve this system, we implement a finite-difference (FD) discretization combined with a Projected Successive Over-Relaxation (PSOR) algorithm, providing a robust and stable numerical method for determining value functions and optimal investment thresholds. Convergence diagnostics confirm the numerical stability of the approach, and results reveal that learning significantly compresses volatility, reduces the option value of waiting, and accelerates investment in the low-carbon regime. The framework captures how policy-induced regime switching and endogenous learning jointly shape optimal investment timing and scale. The proposed method can be extended to multi-regime or multi-factor models, offering a flexible foundation for evaluating investment under complex environmental and policy uncertainty.
This current literature work aims to understand and analyze the stress factors and impacting factors among the working women in educational institution. In this process authors identify a more supportive and productive environment, the organization should address key structural and relational stressors by improving the physical work setting, strengthening interpersonal climate (through conflict resolution training and team building), and optimizing time management via workload sharing and flexible scheduling. Institutional support can be enhanced by introducing wellness initiatives, regular feedback forums, and explicit work–life balance policies, alongside clear career pathways that include transparent promotion criteria and structured mentoring. These strategies are particularly important because, although respondents report being broadly satisfied with their jobs and acknowledge meaningful growth opportunities, the analysis indicates gaps in institutional support and employee engagement that continue to undermine overall well being.
At present, credit card fraud (CCF) is a significant issue for financial institutions and consumers in similar manner. Fraudulent transactions are detected by Machine learning (ML) by analyzing patterns and anomalies in data. The study is designed to deliver the presentation of AI comprehension in accordance to powered fintech solution in delivering the best product to detect, understand, predict by alerting the card transactions undergone in individual’s credit card. This study proposes a machine learning-based approach to detect credit card fraud using multiple algorithms including logistic regression, XGBoost and others were used to detect various activities that are flagged as suspicious and they significantly helps in detection and alerting the customer. Optimized algorithms like Xgboost and random forest provides better percentage in F1 scores and also varied the real time responses to a greater extent and trends through GUI. Synthetic Minority Over-sampling Technique (SMOTE) feature is crucial in this process that is undergone by handling for class imbalances and it shows various analytics to users in classifying the trends of classification Ensuring smooth transaction is endured by altering immediate notifications, dashboard using linked Multi Factor Authentication (MFA). Performance is evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Henceforth the need to create the burden of financial risk overloading is prevented.
Kinesiotaping (KT) has been increasingly acknowledged in the treatment of knee osteoarthritis (KOA), in which there is constant pain, reduced ranges of motion, and muscle weakness, despite its unclear physiological basis and lack of efficient methods for its standard implementation. While recent research suggests a short-term benefit for KT in pain, ranges of motion, and neuromuscular activity, findings remain inconclusive due to large variations in methods implemented. In our critical review of existing randomized controlled trials, several issues were identified, including conflicts of interest, large risk of bias in study setting, and discrepancies in tape stretching force, methods of application, and duration of application and reapplication in kinesiotaping. Together, these factors make it challenging to replicate KT's methods and outcomes in the treatment of KOA, as well as to maintain its long-term efficacy. Therefore, even while the current body of research supports the potential for some significant short-term pain and joint function symptom reduction when using KT, the method's overall significance is still quite low. In order to comprehend the true therapeutic effect of KT and provide appropriate guidelines for the therapy of knee osteoarthritis, this review has highlighted the need for future high-quality research investigations.
Leaves that Speak: Documenting Kerala's Palm-Leaf Manuscript Libraries
1 Sarita S Rajan; 2 Dr. S Mohammed EsmailKerala possesses a rich legacy of manuscript traditions preserved in select institutional libraries across the state. This article examines selected manuscript libraries in Kerala with a focus on the nature of their collections, preservation practices, access facilities, and scholarly significance. The study highlights the diversity of manuscript materials—including palm leaf manuscripts and other non-book formats—and the efforts undertaken by these libraries to safeguard fragile cultural resources. By documenting current practices and challenges, the article underscores the role of manuscript libraries in preserving Kerala’s intellectual heritage and supporting research, education, and cultural continuity.
Context: Alcohol Use Disorder (AUD) is a chronic condition marked by impaired control over alcohol use. Locus of Control (LOC) plays a pivotal role in influencing recovery and relapse. Aim: To assess the severity of alcohol dependence, evaluate Locus of Control orientation, and explore the relationship between them in patients with AUD. Settings and Design: A concurrent embedded mixed-method study conducted in a mental health facility of a tertiary care hospital in Western Maharashtra. Methods and Material: Short Alcohol Dependence Data (SADD) questionnaire and Drinking-Related Internal-External (DRIE) scale were used on 57 male inpatients diagnosed with AUD. In-depth interviews were conducted for qualitative insights. Statistical Analysis Used: Pearson’s correlation and ANOVA were used for quantitative data. Thematic analysis was employed for qualitative data. Results: 42.1% of participants showed low dependence, 35.1% moderate. External LOC was dominant in 56%. A strong positive correlation (r = 0.791, p < 0.0001) existed between severity of dependence and external LOC. Hospitalization category had significant association with both variables (p < 0.0001). Conclusions: External LOC correlates with higher alcohol dependence severity. LOC should be targeted in therapy to reduce relapse and improve recovery outcomes.
Conflict Communication in Early Indian Marriages: A Qualitative Study of Emotional
1 Sneha Gulati; 2 Dr. Ayushi Gaur; 3 Dr. Supriya SrivastavaDespite a growing body of research on marital conflict and emotional processes, relatively little is known about how recently married individuals themselves experience and interpret emotional expression, restraint, and emotional safety as conflict unfolds in everyday marital life. Addressing this gap, the present qualitative study explores how individuals in the early years of marriage in India make meaning of marital conflict, with particular attention to emotional safety and authenticity. In-depth, semi-structured interviews are conducted with 24 individuals (12 heterosexual couples) married for one to three years. Data are analysed using reflexive thematic analysis. The findings reveal four interrelated experiential patterns: (a) emotional calibration through negotiated expression and restraint, wherein individuals regulate emotional disclosure based on perceived relational vulnerability; (b) emotional safety as a situational and interaction-dependent condition shaping openness during conflict; (c) conflict communication as an accumulative emotional process, with unresolved disagreements leaving emotional residues that influence subsequent interactions; and (d) cultural interpretations of emotional restraint as adjustment, respect, and maturity, particularly within extended family contexts. Across themes, authenticity is reflected not in unfiltered emotional expression, but in participants’ ongoing negotiation between remaining true to their emotional experience and maintaining relational harmony within culturally situated expectations. Together, these findings conceptualise marital conflict as a dynamic, emotionally mediated, and culturally embedded relational process, offering implications for theory, prevention, and clinical work with couples in early marriage.
The concept of convergent sequences, which is similar to the theory of nested intervals, has been around since antiquity. Archimedes used two sets of values, ambient and nested, to approximate the unknown in excess and deficiency. The idea of a point sitting inside a series of nested intervals was developed by Jean Buridan. Pierre de Fermat, Derek Gregory, Issac Newton, Colin MacLaurin, Carl Friedrich Gauss, and Jean-Baptiste Joseph Fourier employed excess and deficiency approximations to find an unknown value. This logical structure evolved into the analysis argumentation technique in the works of Bernard Bolzano, Augustin-Louis Cauchy, Johann Peter Gustav Lejeune Dirichlet, Karl Weierstrass, and Georg Ferdinand Ludwing Phillip Cantor in the 19th century. In the 1870s, Charles Méray, Weierstrass, Heinrich Eduard Heine, Cantor, and Richard Dedekind developed the idea of a real number. Cantor's development was predicated on the idea of a limiting point and the nested interval theory. We will now examine the origins of this concept, which may be traced back to ancient world.
The quality and competency of distance teacher education to some stakeholders remain in question. This is particularly true in terms of the ability of virtual teacher to provide appropriate teaching practicum at a distance, which continues to be a major source of concern for both teacher educators and teachers' employers. Hence, this study Assess the implementation of content coverage of undergraduate teacher education programme at the National teacher institute in Kwara State Nigeria. This work determined whether or not the content coverage was adequate. The research design used in this study was a descriptive survey method. The instrument used to collect data for this study was Questionnaire titled: Assessing the implementation of content coverage of undergraduate teacher education programme in the National Teacher Institute in Kwara State, Nigeria “AICCTEPNTI” administered to one hundred and one (101) final year education student of National teacher Institute across the three selected study centre through convenience sampling technique. Hypotheses were tested using inferential statistics of t-test and inferential statistics of ANOVA. The finding of this study on research question reveals that; The content coverage of the undergraduate teacher education programme at National Teacher Institute in Kwara State was found to be generally adequate. It was therefore concluded that the content coverage of the undergraduate teacher education programme at National Teacher Institute in Kwara state was found to be generally adequate.
A Case Report-to Correct Midline Diastema with Split Essix Retainer
1 Dr. Shuchi Singh, 2 Dr. Julius Mary, 3 Dr. Siddharth Modi, 4 Dr. Abhilasha Mishra, 5 Dr. Varad Vaidya, 6 Dr. Aditya JoshiNA
Introduction to Criminal Law and Legal Systems and Relevance of Forensic Evidence in Trials
Vyanjna Saini, Dr. Vivek Kumar, Mr. Rahul VermaThis research paper provides a foundational introduction to criminal law and the diverse legal systems that enforce it, establishing the essential framework within which criminal justice operates. It explores the sources of criminal law, including statutes and common law. It delineates the core principles of criminal liability, such as the requirement for a criminal act (actus reus) and a corresponding guilty mind (mens rea). The research also provides an overview of the adversarial and inquisitorial legal systems, highlighting their procedural differences, particularly in terms of the roles of the judge, prosecutor, defense counsel, and the presentation of evidence. A central focus is the critical relevance of forensic evidence in criminal trials. Modern legal proceedings increasingly rely on scientific methodology to establish facts and determine guilt or innocence. This section examines various types of forensic evidence, such as DNA analysis, ballistics, fingerprint identification, and digital forensics, and discusses the processes of evidence collection, preservation, and analysis. Crucially, the chapter addresses the admissibility and weight of forensic evidence in court, exploring the standards (e.g., Daubert and Frye) used to evaluate its scientific reliability and integrity. Ultimately, the research paper demonstrates how the proper application of forensic science within the defined rules of legal systems is pivotal to achieving justice, ensuring both the protection of the innocent and the conviction of the guilty.
Triangular Decomposition of Tensor Product of Simple Graphs
1 S.Chandrakala; 2 S.Chitra Devi; 3 M.SubbulakshmiLet G = (V, E) be a simple connected graph of order p and size q. If {G1,G2,……...,Gn} are edge disjoint subgraphs of G such that E(G) = E(G1) E(G2) ………... E(Gn) then {G1,G2,…....,Gn} is said to be a Decomposition of a graph G. A graph of size q = is said to have a Triangular decomposition (TD) if G can be decomposed into n - subgraphs {G1,G2,…..,Gn}such that each subgraphs Gi is connected and = for 1 . In this paper we investigate Triangular decomposition of Tensor product of simple graphs.
This research focused on identifying the trigger factors of academic challenges and coping mechanisms of married female sandwich undergraduates, University of Lagos, Nigeria. The study adopted survey method. A sample size of 395 married female sandwich undergraduates was selected through purposive sampling technique. The data was collected using an instrument tagged “Triggers of Academic Challenges and Coping Mechanisms Questionnaire (TACCMQ)” designed by the researchers through literature review. The instrument possessed a reliability coefficient of 0.84 after been subjected to the split-half method. Data was analysed using percentages ANOVA statistical methods. The significance level for testing all hypotheses was set at 0.05. The results indicated that majority of the respondents subscribed to family-related factors and job-related factors as being the most prominent trigger factors academic challenges the encountered while personal factor was the least. The most employed coping mechanisms were both the avoidance and social support coping respectively while the least adopted mechanism was the problem-solving coping. The hypotheses tested shows that there were no significant differences in the triggers of academic challenges of female married sandwich undergraduates on the basis of length of years in marriage, number of children and age. However, respondents were significantly different on the coping mechanisms employed based on age and length of years in marriage. This research, therefore. recommended that the Management of the university should provide accessible counselling services to help married female sandwich students manage family and job-related factors as these factors contribute to their academic challenge
Studies on Shrink Aperture in Two Line Resolution by Aberrated Optical Systems
1 G.Krishnaiah; 2 Dr.T.Kiran Kumarwhen evaluating the imaging quality of optical systems, especially those employed in high-precision applications like lithography, astronomical observation, and microscopy, two-line resolution is a crucial metric. This study offers a thorough theoretical and simulation-based analysis of how shrink aperture affects two-line resolution, particularly when there are primary optical aberrations present, such as spherical aberration, coma, and astigmatism. When combined with a smaller aperture size, these aberrations can worsen the deterioration of image quality because they are known to distort the optical system's wave front. The study uses a thorough computational framework based on Fourier optics and uses the Modulation Transfer Function (MTF) to measure the image resolution capabilities at different apertures. Parametric simulations have shown that even a small aperture reduction can result in significant degradation of the system's resolution limit. Spherical aberration was the most sensitive to aperture reduction of the three aberrations examined, resulting in noticeable blurring and contrast loss in high-frequency image components. Astigmatism and coma were also significant, but their effects were less pronounced in comparable circumstances. The findings offer guidance for the design and optimisation of small optical devices by shedding light on the trade-offs between aperture size and resolution fidelity in aberrated systems. This study is particularly pertinent to applications like integrated photonic sensors, portable medical imaging, and aerospace optics where smaller apertures are necessary due to material constraints or system miniaturisation. The results open the door to more effective optical instruments in limited configurations by pointing to the necessity of more stringent aberration compensation techniques in systems where aperture shrinkage is inevitable.
Rare Congenital Disorders in Infants: Experience of a Tertiary Health Care Centre
1. Dr Payal Agrawal, 2. Dr Priyamvada Singh, 3. Dr Garima Yadav 4. Dr Apram Kaur, 5. Dr Shweta Singh ChauhanBackground: Congenital malformations represent a significant cause of neonatal morbidity and mortality worldwide. They arise due to complex interactions between genetic, environmental, nutritional, and infectious factors, often making the exact etiology difficult to determine. Case Presentation: This case series aims to highlight the spectrum, prevalence, and clinical presentation of congenital malformations during infancy, emphasizing the importance of early detection and timely management. We are reporting four rare congenital malformations including some life-threatening entities, presented to a tertiary health care centre of northern India over a period of six months. We included Congenital Pulmonary Airway Malformation, Congenital Dyserythropoetic Anemia-Type II, Cornelia deLange Syndrome, and Down’s syndrome with Acute Myelogenous Leukemia. Detailed clinical examination, laboratory investigations, relevant imaging, and other essential investigations were performed to identify and classify congenital anomalies. Conclusion: Comprehensive antenatal screening, maternal health education, and improved neonatal care are essential to reduce their burden. Strengthening surveillance and early intervention strategies can play a pivotal role in improving survival and quality of life for affected infants.
The potential source of natural polyphenols, free radicals scavenging activities, antibacterial activities and anti-inflammatory activities in the fresh and dried Cocos nucifera sprouts were studied. The primary and secondary phytochemical constituents of fresh and dried C.nucifera sprouts were estimated using standard procedures. Antioxidant activity using DPPH, Nitric Oxide, Superoxide and Hydroxyl radical scavenging methods, antibacterial activity using disc diffusion test and anti-inflammatory activity through egg albumin denaturation were carried out. The presence of essential phytoconstituents, were observed in qualitative phytochemical screening. In the quantitative analysis, it was noted that presence of maximum phenolic compounds (2.85 mg/g), flavonoids (2.10 mg/g), alkaloids (0.76 mg/g/), tannins (0.28 mg/g) and glycosides (0.19 mg/g) in fresh and dried C.nucifera sprouts. Antioxidant and anti-inflammatory activities have shown more potent in the therapeutic applications. The antibacterial effect shows minimum zone of inhibitions by E.coli and Staphylococcus.The fresh and dried C.nucifera sprouts is a natural and nutrient dense food and good sources of functional components, antibacterial, anti-inflammatory properties and more effective to scavenge free radicals.
Background of the Study: Agriculture remains a critical driver of Nigeria’s economy. However, conventional farming practices have contributed significantly to environmental degradation, including deforestation, soil erosion, declining biodiversity, and greenhouse gas emissions. With climate change accelerating and environmental sustainability becoming increasingly urgent, there is a growing need to transition toward more sustainable and climate-resilient farming systems. These practices not only improve farm yields but also reduce environmental harm, making agriculture a tool for environmental sustainability. However, the adoption and effective implementation of CSA practices in Nigeria including Uzo-Uwani remain limited, especially among farmers. Aim and Objectives: The aim of this study is to assess smallholder farmers’ perception of climate-smart agriculture in Uzo-Uwani Local Government Area, Enugu State. Specifically, the study assessed the awareness and level of utilization of CSA practices and the challenges limiting the utilization of CSA. Methodology: Descriptive survey research design was employed for the study. Simple random sampling technique was adopted to select 15 smallholder farmers from each community in Uzo-Uwani making a sample size of 240. A well-developed structured questionnaire, validated and subjected to reliability test was used for data collection. On the spot mode of data collection was used. However, only the adequately filled ones (225) were used for the study. The remaining 15 questionnaires were discarded due to wrong filling or unreturned. The data collected were analyzed using frequency, percentage, mean and standard deviation in answering the research questions. Results: the result of the study showed the smallholder farmers in Uzo-Uwani Local Government Area, Enugu State have high awareness of the CSA practices with varying degree but utilizes them at a low extent. The study also shows that the challenges limiting the farmer’s level of adoption include capital intensiveness of CSA practices, inadequate knowledge and understanding of CSA and its practices, unavailability of improved crop varieties, limited government support with farm inputs among others. Conclusion:The level of Climate-smart practice utilization in Uzo-Uwani seems inadequate to meet the challenges of climate change. Therefore, the strategies that will encourage smallholder farmers to fully utilize the CSA practices be put in place in order to achieve the set objectives of CSA.
Holistic Clinical Stewardship in Cleft Lip and Palate Care: An Interdisciplinary Approach
1 Dr. Shefali Gaurav, 2 Dr. Julius Mary, 3 Dr. Aman Kumar, 4 Dr. Ranjana Das, 5 Dr. Neha Gupta, 6 Dr. Gourav NamdevCleft lip and palate (CL/P) represent the most prevalent congenital craniofacial anomalies and pose significant functional, esthetic, and psychosocial challenges throughout an individual’s life. These conditions commonly present with facial deformity, impaired speech and hearing, malocclusion, missing or malformed teeth, and oronasal communication, requiring complex and long-term clinical management. The concept of holistic clinical stewardship emphasizes coordinated, patient-centered care delivered through an interdisciplinary framework that integrates medical, surgical, and dental specialties across different stages of growth and development. This article aims to highlight the importance of holistic and interdisciplinary care in the management of cleft lip and palate by reviewing contemporary clinical protocols followed globally. The role of collaborative treatment planning involving plastic and maxillofacial surgery, orthodontics, pediatric and restorative dentistry, prosthodontics, Periodontist, otolaryngology, speech therapy, psychology, and social services is discussed. Emphasis is placed on the benefits of structured team coordination in reducing treatment fragmentation, improving functional outcomes, and enhancing esthetic rehabilitation. The paper further underscores the need for standardized clinical pathways to ensure continuity of care from infancy to adulthood and to optimize overall quality of life for individuals affected by cleft anomalies.
Decision Making Competencies among Higher Secondary School Students: An Empirical Investigation
1 Aarthi M R, 2 Geetha KDecision-making constitutes a pivotal life competency that significantly influences both the personal and professional trajectories of individuals. This investigation explores the decision-making competencies of higher secondary school students, emphasizing the effects of gender, field of study, educational institution type, and geographical region. A total of 200 students were surveyed across six educational institutions, which included 81 males and 119 females from both Arts and Science disciplines, enrolled in government, aided, and private schools situated in both rural and urban environments. Descriptive and inferential statistical methodologies, including t-tests and ANOVA, were utilized to assess variations in decision-making competencies in relation to demographic and institutional variables. The results reveal no statistically significant differences in decision-making competencies when analyzed by gender, field of study, or regional categorization. Nonetheless, subtle discrepancies were noted, with urban students exhibiting marginally higher mean scores relative to their rural counterparts and students from the Arts stream slightly surpassing those from the Science stream. The findings imply that although demographic and institutional factors do not exert a significant influence on decision-making competencies, targeted interventions aimed at enhancing these skills could prove beneficial for students across all classifications. This research underscores the necessity of incorporating decision-making training into educational curricula to adequately equip students for the complexities of real-world challenges.
This study investigated the competencies required for effective teaching of History among pre-service teachers at the University of Ilorin. The research was guided by the need to assess these competencies in light of current educational demands and to determine how adequately pre-service teachers are being prepared to teach History effectively. The study was premised on the growing recognition that competent History teachers play a crucial role in promoting historical understanding, critical thinking, and civic consciousness among learners. Descriptive survey design was adopted for this study. A structured questionnaire was used for data collection. The population comprised pre-service teachers from University of Ilorin in which 150 pre-service teachers were sampled using a simple random sampling technique. The questionnaire was validated by experts and piloted for reliability, achieving an acceptable Cronbach’s alpha value. Descriptive statistics, particularly percentages, were used for data analysis and interpretation. The findings revealed that pre-service teachers agree that a strong understanding of historical facts and concepts is essential for effective teaching of History, pedagogical skills, such as lesson planning, teaching methods and classroom management are crucial for effective History teaching. It was concluded that content knowledge, pedagogical skills, technological competence and cultural sensitivity are all considered critical competencies for effective History teaching among pre-service teachers. The study recommended that students should actively engage in developing their content knowledge through continuous study and participation in history-related activities, Teachers should incorporate diverse pedagogical methods and technological tools to enhance History teaching.
Background: Selecting an appropriate renal replacement therapy (RRT) in chronic kidney disease (CKD) is influenced by multiple demographic, socioeconomic, and clinical factors. Understanding these determinants is essential to support individualized treatment decisions. To model relationships among health science variables, statistical and machine learning (ML) procedures provide a robust framework. Objectives: To identify key determinants influencing RRT modality choice among CKD-G5D patients using traditional statistical and machine learning approaches. Methods: A cross-sectional study was conducted on 241 patients with CKD-G5D. Baseline demographic, socioeconomic, and clinical variables were analyzed descriptively and comparatively using the Kruskal-Wallis and Chi-square tests. Predictive modelling was conducted using multinomial logistic regression and Random Forest algorithms. Model performance was assessed using confusion matrices, accuracy, and variable importance metrics. Results: Haemodialysis was the predominant modality (97.1%), followed by kidney transplantation (1.7%) and peritoneal dialysis (1.2%). Age and educational status showed significant associations with RRT choice The Random Forest model achieved the highest predictive accuracy (98.6%), identifying duration of CKD and age as the most influential predictors. Younger patients with higher educational attainment were more likely to undergo transplantation, whereas older individuals predominantly received haemodialysis. Conclusion: Age and educational attainment are pivotal determinants guiding RRT modality among CKD-G5D patients. Machine learning approaches, particularly Random Forest, demonstrate strong potential to enhance predictive accuracy and support personalized, data-driven counselling in renal care.
Adomian Polynomials & Double Elzaki Transform for Solving Some Applications of Quantum Physics
1 Inderdeep Singh & 2 Parvinder KaurIn this research work, we apply the computational technique based on double Elzaki transform and Adomian polynomials to predict the behavior of the solutions of fractional Schrodinger equations arising in quantum physics. The Schrodinger equation helps to describe the complex behavior of particles and energy transport in advanced materials. With the help of this hybrid technique, we obtain simple and accurate solutions without complex computations. The proposed approach can be used to study quantum properties and wave function in various materials and nanostructures, making it useful for material simulation and design.
Beyond the Norm: A Case Series on Filariasis in Unusual Anatomical Sites
Nidhi Priya Allie BarlaBackground: India bears an outsized share of the global filariasis1 incidence—around 40 %—largely due to Wuchereriabancrofti, with Brugiamalayi contributing a smaller fraction in the country’s south-western zone. The disease is transmitted through mosquitoes and typically presents as elephantiasis, chronic lymphedema, or lymphadenitis. Rarely, microfilariae can be detected in unusual locations such as the epididymis, breast, thyroid, salivary glands, cervicovaginal smears, ovarian cysts, and body fluids. We document six such unusual cases, of which three were encountered in FNA samples from the thyroid, breast and axillary lymph - node, one in an arm swelling, and two others—one in the peripheral blood of a CML patient and another in pleural fluid. In all cases, microfilariae were found incidentally, with no clinical evidence of filariasis. These results reinforce the necessity of considering filariasis in the diagnostic work-up, regardless of how atypical the clinical presentation may appear.
This investigation explores whether different communication channels affect how authenticity relates to happiness in romantic partnerships among young adults. Drawing on Self-Determination Theory alongside Media Richness Theory, we tested whether texting, phone conversations, and in-person interactions serve as mediating pathways in this connection. Through mediation analyses with 360 young adults (ages 18-29) from Delhi NCR, we found that being authentic strongly predicted greater happiness. Interestingly, text messaging stood out as the only significant mediator, actually weakening the authenticity-happiness link, while neither phone nor face-to-face communication showed mediating effects. These results underscore the value of thoughtfully choosing communication methods that genuinely allow authentic expression. For practitioners working with couples, this research offers insights into how various communication platforms might either facilitate or hinder authentic connection in romantic partnerships. The study advances our understanding of how the medium through which we communicate influences relationship well-being in our increasingly digital world.