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; 2 Mr. Ramdas P. Jare; 3 Suvarna Sonone; 4 Mr Hiraman Jadhav; 5 Mrunali PatilSkillSwap 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.