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.