Volume 14 | Issue 6 |

 Abstract

This study investigates the role of social media as a tool for self-learning among B.Ed. student-teachers using secondary data. Drawing upon peer-reviewed journals, policy reports, and academic literature, the study explores the pedagogical implications of platforms such as YouTube, WhatsApp, Telegram, and Instagram. The findings indicate that social media significantly enhances accessibility, flexibility, collaboration, and digital competence. However, issues such as distraction, misinformation, and lack of digital literacy hinder effective utilization. The study concludes that structured integration of social media into teacher education programmes is essential for maximizing learning outcomes.

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 Keywords

Social Media, Self-Learning, Digital Learning, E-Learning and Teacher Education.

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This study examines the extent and determinants of digital technology adoption among farmers in the Mungeli district of Chhattisgarh, a region where agriculture contributes significantly to the Gross State Domestic Product (GSDP) yet faces a productivity-employment gap. Utilizing a descriptive and analytical research design, primary data was collected from a stratified random sample of 120 farmers across the Mungeli, Lormi, and Pathariya blocks.

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 Keywords

Farmer, Digital adoption, Agriculture, Chhattisgarh, State, Economy, Digital distribution, Work

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 Abstract

Automated segmentation of cardiac structures from cine magnetic resonance imaging (cMRI) is a cornerstone of cardiovascular diagnostics, yet the field remains without a unified evaluation framework that rigorously examines the interplay between backbone architecture, adversarial supervision, and optimizer design. More critically, no systematic investigation has explored whether quantum-inspired computational paradigms -- specifically Quantum-Behaved Particle Swarm Optimization (QPSO) and quantum-classical hybrid Quantum Convolutional Neural Networks (QCNN) -- can deliver meaningful performance advantages in this domain. This paper closes both gaps through an exhaustive benchmarking study of thirty deep learning configurations on the publicly available Automated Cardiac Diagnosis Challenge (ACDC) dataset, the gold standard for cardiac Magnetic Resonance Imaging (MRI) segmentation evaluation. Seven backbone architectures are interrogated -- standard encoder-decoder network with skip connections (UNet), Residual Network with 34 layers encoder-based UNet (ResNet34-UNet), lightweight Custom UNet (CustomUNet), Shifted Window Transformer-based UNet (SwinUNet), cardiac-specialized segmentation network (CardioSeg), Quantum Convolutional Neural Network (QCNN), and Attention-gated UNet (AttentionUNet) -- each systematically paired with training strategies spanning gradient-based optimizers including Adaptive Moment Estimation (Adam), Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMSProp), Nesterov-accelerated Adaptive Moment Estimation (NAdam), and Adaptive Gradient Algorithm (Adagrad), alongside classical Particle Swarm Optimization (PSO), Quantum-Behaved Particle Swarm Optimization (QPSO), and Generative Adversarial Network (GAN)-augmented adversarial supervision. Performance is rigorously quantified using mean Dice Similarity Coefficient (DSC), Intersection over Union (IoU), and 95th-percentile Hausdorff Distance (HD95) across four segmentation targets: background, Right Ventricle (RV), Myocardium (MYO), and Left Ventricle (LV). The results are striking: AttentionUNet trained with Generative Adversarial Network (GAN) adversarial supervision and Quantum-Behaved Particle Swarm Optimization (QPSO) establishes a new performance ceiling with Dice Similarity Coefficient of 0.8625, Intersection over Union of 0.7612, and 95th-percentile Hausdorff Distance of 1.90 mm -- approaching the inter-observer agreement of expert cardiologists. Equally remarkable, the proposed Quantum Convolutional Neural Network (QCNN) delivers a competitive Dice Similarity Coefficient of 0.7596 using only 1.95 million parameters -- sixteen times fewer than the standard UNet -- redefining the parameter efficiency frontier for cardiac segmentation. Across the majority of backbone architectures evaluated, Quantum-Behaved Particle Swarm Optimization consistently outperforms both classical Particle Swarm Optimization and Adaptive Moment Estimation, establishing quantum-inspired hyperparameter optimization as a practically viable, computationally non-burdensome strategy for deep medical image segmentation. These findings provide the field with its most comprehensive cross-architecture, cross-optimizer reference to date and deliver clear, actionable guidance for designing cardiac segmentation pipelines across diverse clinical deployment scenarios.

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 Keywords

cardiac MRI segmentation; ACDC benchmark; attention UNet; GAN-augmented training; quantum-behaved PSO; quantum-classical CNN; deep learning; metaheuristic optimization. Sustainable management.

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Data mining is the process is to extract information from a data set and transform it into an understandable structure. There are several major data mining techniques have been developing and using in data mining projects recently including classification, clustering, prediction, sequential patterns and decision tree. With the huge amount of information available online, the World Wide Web is a fertile area for data mining research. Data mining refers to the process of retrieving knowledge by discovering novel and relative patterns from large datasets. Analyzing bank databases for analyzing customer behavior is difficult since Bank databases are multi-dimensional, comprised of monthly account records and daily transaction records. Clustering the datasets, assessment and the way of expressing customer's demands and the provinces of requests should be recognized for providing services to the customers, banks, financial and credit institute. It can make a group of abstract objects into classes of similar objects. In the clustering, firstly partition the set of data into groups based on data similarity and then assigns the labels to the groups. The overall goal of this research work is to evaluate the performance of HAC, K-means and density based clustering (DBSCAN) data mining algorithms by considering the different data sets. HAC is a method of cluster analysis which seeks to build a hierarchy of clusters. It has bottom-up and top-down approach. K-means clustering to partition n observations into K clusters in which each observation belongs to the cluster with the nearest mean. Density based clusters are the dense areas in the data space separated from each other by sparse areas. This research presents a comparative analysis for various clustering algorithms. In experiments the effectiveness of algorithms is evaluated by comparing the results on the datasets.

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HAC, DBSCAN, K-means, WEKA

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ABSTACT This study examines the differences in sports achievement motivation and anxiety levels between Athletes and non-athletes female in Agra, Uttar Pradesh. A total of 40 subjects (20 sports persons and 20 non-sports persons) aged 20-25 years were selected. Data were collected using Kamlesh's Sports Achievement Motivation Test (SAMT) and Spielberger's Anxiety Inventory Questionnaire. The results revealed significant differences in both variables, with non-sports persons exhibiting higher achievement motivation (mean = 24.08) and anxiety levels (mean = 34.55) compared to sports persons (mean = 20.42 for achievement motivation and 30.97 for anxiety). The findings underscore the psychological distinctions between these groups and highlight the influence of competitive sports on reducing anxiety and shaping motivational behaviour .

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Key Words: Motivation, Anxiety, Sports person and Non-Sports person.

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Palitya is a condition described in Ayurvedic classics characterized by premature whitening of hair. Hair is considered an important marker of health, youthfulness, and beauty. Although greying of hair is a physiological phenomenon associated with ageing, the occurrence of grey hair at a young age is considered pathological. Ayurveda attributes Palitya mainly to aggravated Pitta Dosha, often associated with Vata, affecting the hair follicles and disturbing normal pigmentation. Classical texts describe various dietary, lifestyle, psychological, and hereditary factors responsible for the development of Palitya. Modern biomedical science links premature greying with oxidative stress, melanocyte dysfunction, nutritional deficiencies, environmental factors, and genetic predisposition. The increasing prevalence of premature greying among younger populations highlights the need for preventive and therapeutic approaches. Ayurvedic management emphasizes Nidana Parivarjana, Shodhana, Shamana, Rasayana therapy, and lifestyle modifications. This review aims to critically analyze the Ayurvedic concept of Palitya, correlate it with contemporary scientific understanding, and explore the scope of integrative management.

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Palitya, Premature Greying, Ayurveda, Hair Disorders, Pitta Dosha, Rasayana.

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Panchayati Raj Institutions (PRIs) are local self-government bodies in rural ar- eas. In Jharkhand, the state government has given 50% reservation to women in these institutions. This paper studies how PRIs help in women's empowerment in Jharkhand. Jharkhand has many tribal communities and faces problems like poverty and low education. Using government reports, research papers, and real success stories, this study finds that many women have become leaders. Women leaders focus on water, sanitation, education, health, and jobs. However, problems like male family members controlling decisions (proxy), low literacy, and social resistance still exist. The paper suggests better training, strong rules against proxy lead- ership, and linking women groups with panchayats. This research will help understand how local governance can empower women in tribal states

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Panchayati Raj Institutions, Women's Empowerment, Jharkhand, Tribal Women, Political Reservation, Local Governance

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The rapid proliferation of Generative Artificial Intelligence (GenAI) tools -- including large language models and multimodal assistants -- has fundamentally altered the academic landscape. Despite widespread student adoption, empirical evidence on their impact on measurable learning outcomes remains limited and contested. Objectives: This study examines the effect of structured GenAI integration on GPA, critical thinking, writing quality, engagement, course completion, and academic integrity across five disciplines in higher education. Methods: A quasi-experimental mixed-methods design was employed across seven universities (N = 2,847 students; 1,423 GenAI group, 1,424 control) over two semesters (2023-2024). Data were collected via standardised assessments, engagement surveys, LMS analytics, and semi-structured interviews.

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generative AI, ChatGPT, higher education, student learning outcomes, GPA, academic integrity, engagement, mixed-methods

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Phytochemicals are multipurpose secondary metabolites of plants that have medicinal qualities. In present research work we examined the various phytoconstituents present in lemongrass (Cymbopogan flexuosus), its pharmacological potential including antioxidant potential. Lemongrass is well known for its hypoglycemic and hypolipidemic effects as well as its anti-oxidant, anti-microbial, anti-inflammatory, anti-hypertensive, anti-diabetic, anti-mutagenicity, and anxiolytic qualities. As a result, it is extensively utilized in the food, feed, cosmetics, and pharmaceutical industries. Strong bioactive components found in lemongrass include terpenoids, alkaloids, and phenolic metabolites, which include phenolic acids, flavonoids, stilbenes, and lignans. Lemongrass commonly used to enhance health by creating a balanced intestinal environment. At the industrial level, lemongrass is regarded as a fundamental food and feed ingredient because it doesn't have any toxicity or residual problems. By producing antimicrobial, anti-inflammatory, and antioxidant reactions, lemongrass powder and essential oils are utilized to modify the gut environment and improve the gut system's capacity to absorb nutrients.

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 Keywords

Silver Nano particles, Antioxidant potential, lemongrass

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The rapid integration of Artificial Intelligence (AI) tools into academic environments has fundamentally altered the manner in which students engage with learning material, complete assignments, and develop cognitive skills. This mixed-method study investigates the behavioral and cognitive dimensions of AI's impact on student learning habits, with particular emphasis on AI dependency, critical thinking erosion, ethical drift, and micro-learning behavior. A survey of 100-150 undergraduate and graduate students was conducted, complemented by qualitative interviews and thematic analysis. Quantitative findings reveal that 78% of respondents use AI tools daily, while 62% report an inability to complete tasks efficiently without AI assistance. Critically, 52% acknowledged reduced critical thinking effort, and 44% rarely verify AI-generated responses. The study identifies five novel behavioral constructs -- Cognitive Offloading, AI Dependency Behavior, Micro-Learning Habit Formation, AI Trust Bias, and Ethical Drift -- that existing literature has largely overlooked. Based on these findings, this paper proposes a practical AI Restriction and Monitoring System (ARMS) for educational institutions. The study concludes that AI should be positioned as a cognitive scaffold rather than a cognitive replacement, and provides actionable recommendations for educators, policymakers, and technology developers.

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 Keywords

Artificial Intelligence, Learning Habits, Cognitive Offloading, AI Dependency, Critical Thinking, Educational Technology, Mixed-Method Research, Behavioral Analysis

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