Volume 14 | Issue 2

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Samajik Samrasta Ka Aadarsh : Rahim Ka Drishtikon

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Samajik Samrasta Ka Aadarsh : Rahim Ka Drishtikon

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Cash management is a crucial element of financial management that determines the operational efficiency of Self Help Groups (SHGs). Adequate availability of cash and its timely utilization influence the successful implementation of developmental programmes undertaken by SHGs. The present study examines the cash management practices of Self Help Groups in Bangalore Urban District and analyses their impact on programme implementation. The study is based on primary data collected from 120 SHG members through a structured questionnaire. Statistical tools such as mean, standard deviation and correlation analysis were employed for data analysis. The findings reveal that effective cash planning, timely inflows and proper utilization of cash significantly enhance programme implementation in SHGs. The study concludes that strengthening cash management practices is essential for improving the performance and sustainability of SHGs.

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Cash Management, Programme Implementation, Financial Management, Self Help Groups, Bangalore Urban

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Abstract: Email spam remains one of the most persistent challenges in modern communication systems, with traditional machine learning approaches struggling to adapt to evolving spam tactics. This paper proposes Adaptive Constructive Learning with Feature Importance Weighting (ACLFIW), a novel approach that combines dynamic confidence thresholds, intelligent feature selection, and temporal pattern recognition to create a robust, evolving spam detection system. Our experimental results demonstrate that ACLFIW achieves 100% accuracy on test data while maintaining computational efficiency through selective feature expansion and intelligent pruning. Comparative analysis with traditional approaches (Naive Bayes, SVM, Random Forest) shows 15-22% improvement in adaptability metrics and significantly reduced model retraining overhead. The system's ability to detect emerging spam threats and maintain performance under concept drift makes it particularly suitable for production environments requiring real-time adaptation.

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Constructive Learning, Feature Importance Weighting, Spam Detection, Concept Drift, Adaptive Machine Learning, Temporal Pattern Recognition

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The sustainability of Self Help Groups (SHGs) largely depends on the effective management and circulation of funds. Flow of funds analysis helps in understanding the sources, utilization and movement of financial resources within SHGs and assists in estimating future fund requirements. The present study examines the flow of funds and its impact on the financial sustainability of Self Help Groups in Bangalore Urban District. The study is based on primary data collected from 120 SHG members using a structured questionnaire. Statistical tools such as mean, standard deviation, ANOVA and descriptive analysis were used. The findings reveal that efficient fund flow management, supported by regular savings and bank linkage, enhances financial sustainability and planning capability of SHGs. The study concludes that systematic flow of funds analysis is essential for strengthening the long-term viability of SHGs.

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Flow of Funds, Financial Sustainability, Fund Management, Self Help Groups, Bangalore Urban

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Self Help Groups (SHGs) play a vital role in promoting financial inclusion and socio-economic empowerment of women in India. Effective financial management is essential for the sustainability and performance of SHGs, particularly in urban and semi-urban regions. The present study examines the financial management practices of Self Help Groups in Bangalore Urban District with special reference to accounting practices, internal control mechanisms and financial decision-making processes. The study is based on both primary and secondary data. Primary data were collected from 120 members of selected SHGs using a structured questionnaire. Statistical tools such as mean, standard deviation, correlation analysis and ANOVA were used for data analysis. The findings reveal that proper accounting systems, effective internal controls and participative financial decision-making significantly contribute to the overall financial effectiveness of SHGs. The study concludes that strengthening financial management practices enhances sustainability and improves the economic outcomes of SHGs.

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Self Help Groups, Financial Management, Accounting Practices, Internal Control, Bangalore Urban

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Automated Teller Machines (ATMs) become essential to everyday banking. They let people handle their money quickly and safely, at least in theory. But the truth is, ATM machines face a growing list of security threats like card skimming, hackers sneaking into accounts, replay attacks, and people intercepting sensitive data as it travels between the ATM and the bank. Most ATMs still use just single-factor authentication and basic security, which leaves them wide open to new kinds of cyber-attacks. If banks want to keep up, they need a tougher communication system for ATMs--one that really protects the privacy and integrity of every transaction. There is a strong need to verify users, encrypting the data that moves back and forth, and making sure the ATM and the bank's servers are always talking to the right partner. With these upgrades, banks will cut down on fraud, keep people's data safer, and make ATM banking more dependable for everyone.

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Automated Teller Machine (ATM), Secure Communication, Banking Security, Authentication Mechanisms, Data Confidentiality, Financial Transactions, Cybersecurity

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The present study aims to formulate and evaluate a herbal hair serum using leaf extracts of Piper betel and Ixora coccinea, plants traditionally recognized for their antimicrobial, antioxidant, and hair growth-promoting properties. The leaves were collected, authenticated, shade-dried, and subjected to extraction using appropriate solvents. Preliminary phytochemical screening confirmed the presence of bioactive constituents such as flavonoids, tannins, phenolic compounds, terpenoids, and glycosides. The herbal hair serum was formulated incorporating natural excipients including aloe vera gel, vitamin E, lavender oil, rose water, Tween 80, and sodium benzoate. The prepared formulations were evaluated for physicochemical characteristics such as appearance, homogeneity, spreadability, transparency, pH, and antifungal activity. The pH of theformulation was found to be within the acceptable range for scalp application. Antifungal activity assessed by the well diffusion method demonstrated significant inhibitory effects against fungal strains. Among the formulations developed, the optimized formulation exhibited satisfactory stability, smooth texture, and effective antifungal activity. The findings of the study indicate that the formulated herbal hair serum is safe effective, and suitable for improving scalp health, reducing dandruff, and enhancing overall hair quality.

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Hairserum, formulation, safety, efficacy, daily use, Anti- fungal activity

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Maadhyamik Star Par Adhyayanarat Chhaatr Evan Chhaatraon kee Paryaavaran Ke Prati Jaagarookata Ka Adhyayan

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Maadhyamik Star Par Adhyayanarat Chhaatr Evan Chhaatraon kee Paryaavaran Ke Prati Jaagarookata Ka Adhyayan

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The Sundarbans region of West Bengal represents one of the most ecologically sensitive and socio-economically marginalized areas in India. Characterized by its deltaic landscape, dense mangrove forests, frequent cyclones, tidal flooding, and fragile livelihoods, the region poses severe challenges to human development. Gosaba, one of the largest inhabited islands of the Sundarbans in South 24 Parganas district, reflects these complexities acutely. Geographic isolation, limited transportation, inadequate educational infrastructure, poverty, and vulnerability to climate change have historically restricted access to quality education, healthcare, and sustainable livelihoods for its residents. Within this challenging context, Beacon House, Gosaba, has emerged as a vital institution dedicated to social transformation and community development. Beacon House is not merely an educational center; it represents a holistic development model that integrates education, women and child empowerment, community participation, and environmental consciousness. Its initiatives aim to address structural inequalities while responding to the everyday realities of life in a disaster-prone, remote rural setting .Education in Gosaba has long been affected by school dropouts, child labor, early marriage, gender disparity, and seasonal migration. Children from economically disadvantaged households often lack academic support, learning materials, and exposure to broader opportunities. Beacon House intervenes at this critical juncture by providing inclusive, accessible, and community-oriented educational support that prioritizes marginalized children, especially girls. Through formal schooling assistance, remedial education, and life-skill development, the institution seeks to break the intergenerational cycle of poverty and exclusion. Beyond education, Beacon House plays a significant role in women's empowerment and community resilience. In the Sundarbans, women face compounded disadvantages due to poverty, limited mobility, low literacy, and vulnerability during natural disasters. Beacon House addresses these issues through vocational training, self-help group formation, health awareness programs, and leadership development. These efforts not only enhance household income but also strengthen women's participation in decision-making processes at the family and community levels .Environmental vulnerability is another defining feature of Gosaba. Rising sea levels, salinity intrusion, and frequent cyclones threaten both livelihoods and settlements. Recognizing the interdependence between human development and ecological sustainability, Beacon House integrates environmental education into its programs. Awareness initiatives related to mangrove conservation, climate adaptation, and disaster preparedness equip communities with knowledge essential for survival in an increasingly uncertain ecological future.

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Beacon House, Gosaba, Sundarbans, rural education, women empowerment, community development, climate resilience.

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Classical dominance-based and indiscernibility relations are often too rigid to analyse real data affected by noise, perturbation, or instability. This paper introduces a strong hybrid equivalence framework that integrates exact algebraic dominance with a ?-restricted similarity induced by a sensitivity distance. The sensitivity distance measures behavioural deviation between objects, allowing both metric analysis and tolerance-based interpretation. We study the algebraic properties of sensitivity relations, derive necessary and sufficient conditions for transitivity via ultrametric inequalities, and construct ?-neighbourhoods that generate covering-based approximations. Lower and upper rough approximations, lattice ordering under ?-variation, and the interaction between hybrid equivalence and dominance are established. A healthcare risk-classification case study demonstrates how the hybrid model yields robust and interpretable groupings in the presence of uncertainty. The proposed framework provides a mathematically consistent foundation for sensitivity-aware reasoning, stability-based decision analysis and approximate classification.

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Dominance relation; Sensitivity distance; Rough sets; Tolerance relation; Hybrid equivalence; ?-neighbourhood; Approximate reasoning; Robust decision analysis.

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In the contemporary and highly competitive manufacturing environment, exemplary corporate leadership and a strong organizational culture have emerged as critical drivers of organizational development. The present study aims to examine the impact of exemplary corporate leadership and organizational culture on organizational development in selected manufacturing companies. The research focuses on identifying key leadership practices and cultural dimensions that influence organizational growth, employee engagement, operational efficiency, and long-term sustainability. A descriptive and analytical research design is adopted, using primary data collected from managerial and supervisory employees through structured questionnaires, supported by secondary data from journals, reports, and published literature. Appropriate statistical tools such as percentage analysis, correlation, and chi-square tests are employed to analyse the data. The findings of the study reveal a significant relationship between exemplary corporate leadership, organizational culture, and organizational development, indicating that effective leadership practices coupled with a supportive and adaptive culture positively influence organizational performance. The study provides valuable insights for manufacturing organizations, policymakers, and academicians by emphasizing the need to strengthen leadership practices and cultivate a positive organizational culture to achieve sustainable organizational development.

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Exemplary Leadership, Organisation Culture, Manufacturing industries etc.

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Digital banking has revolutionized financial services by offering convenience, accessibility, and efficiency. However, its rapid adoption has introduced significant cybersecurity challenges. This paper explores the key threats, vulnerabilities, and mitigation strategies associated with digital banking adoption, focusing on phishing, malware, social engineering, regulatory compliance, and emerging technologies. The study highlights the importance of robust cybersecurity frameworks to ensure trust and resilience in digital banking ecosystems.

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Digital Banking, Cybersecurity, Phishing, Malware, Social Engineering, Regulatory Compliance, Multi-Factor Authentication, AI Threat Detection, Mobile Banking Security, Financial Technology, Data Breaches, Ransomware, Insider Threats, Blockchain Security

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Pandit Deendayal Upadhyay ke Samajik Darshan ki Samkaleen Sarkari Nitiyon ke sath Tulnatmak Vishleshan.

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Pandit Deendayal Upadhyay ke Samajik Darshan ki Samkaleen Sarkari Nitiyon ke sath Tulnatmak Vishleshan.

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Abstract Plant-derived exosome-mimetic nanocarriers (PDENMs) represent a rapidly advancing class of bioinspired drug delivery systems that bridge the gap between natural extracellular vesicles and synthetic nanocarriers. These vesicles, derived from edible and medicinal plants or fabricated using plant lipid extracts, exhibit unique advantages such as excellent biocompatibility, low immunogenicity, biodegradability, scalability, and intrinsic affinity for biological membranes. Oral and vaginal routes of drug administration are particularly attractive due to their non-invasive nature; however, both routes present formidable physiological barriers that limit drug stability, absorption, and therapeutic efficacy. PDENMs have demonstrated the ability to protect encapsulated drugs from harsh biological environments, enhance mucosal penetration, and enable targeted or sustained drug release. This comprehensive review critically examines the current state of research on PDENMs, covering their sources, isolation and fabrication techniques, physicochemical characterization, drug loading and functionalization strategies, and mechanisms of cellular uptake. Special emphasis is placed on oral and vaginal drug delivery applications, supported by preclinical evidence. Safety, toxicity, regulatory considerations, and future research directions are discussed to provide a translational perspective on the clinical potential of PDENMs.

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Plant-derived nanocarriers; Exosome-mimetic vesicles; Oral drug delivery; Vaginal drug delivery; Nanomedicine; Mucosal delivery

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This study investigates the aetiology of opioid dependence among the female demographic in the Tezu Administrative Centre. Moving beyond individual pathology, the research identifies a confluence of socio-cultural, environmental, and biomedical determinants that precipitate addiction. Key findings isolate seven primary variables: (1) seasonal exposure and labour during the opium lancing and latex extraction period, (2) analgesic substitution for chronic morbidity, (3) recreational peer reinforcement, (4) ritualistic cultural sanctioning, (5) dyadic influence within domestic partnerships, (6) geographical location within a high-yield cultivation belt encompassing Lohit and Anjaw, and (7) high social acceptability. The study argues that addiction in this region is a structural outcome of these intersecting factors.

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Opioid Dependence, Women in Arunachal, Mishmi Tribe, Tezu, Agrarian Labour, Social Aetiology, Kaanang.

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: Facial Emotion Recognition (FER) is a vital part of affective computing as well as human-computer interaction since it allows intelligent systems to recognize human emotional states based on their facial expression. Although convolutional neural networks (CNNs) with deep learning have been used to successfully perform spatial models in images of faces, these still have a shortcoming in their ability to capture the temporal changes in emotions that are in the real world. On the other hand, time models enhance dynamic expression but have low spatial discrimination. In response to these shortcomings, this paper will suggest a Hybrid Spatial-Temporal Deep Learning Framework that learns to model both the appearance and emotional state of faces of both still images and video frames. The framework proposed combines the CNN-based spatial feature extraction with the Long Short-Term Memory (LSTM) based temporal modeling in a unified end-to-end implementation. The comprehensive experiments done on FER-2013 and CK+ data sets prove that the hybrid CNN-LSTM model greatly outperforms the spatial-only baselines, having a higher accuracy, stability, and generalization. Besides, statistical significance testing establishes that the performance gains realized are not deceptive and are not as a result of random variation. The findings confirm such a problem as the efficacy of hybrid spatial-temporal learning of strong facial emotion recognition in unconstrained settings.

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Facial Emotion Recognition; Hybrid Spatial-Temporal Learning; Convolutional Neural Networks; Long Short-Term Memory; Deep Learning; Affective Computing; Human-Computer Interaction.

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A study with quasi-experimental one group pre-test-post-test design was used to assess the effectiveness of structured teaching programme on knowledge regarding ill effect of plastic on health among the community people of Andharua, Jagganathprasad, Bhubaneswar. In this study community people were selected by using simple random sampling technique and data were collected by using self-structured questionnaire method. Demographic variables reveals that 23 community people were male whereas 7 community people were female. From the pre-test and post-test knowledge scores, it was found that in pre-test 20% of people had poor level of knowledge whereas none of them in post-test.70% of people had average level of knowledge in pre-test while 6.7% in post-test.10% of people had good level of knowledge in pre-test whereas 33.3% in post-test.In pre-test none of them had excellent level of knowledge whereas almost i.e. 60% in post-test. At the end, it is concluded that maximum people had poor and average knowledge in pre-test whereas maximum people had good and excellent knowledge in post-test. Therefore, it is interpreted that post-test scores were astonishingly much more than the pre-test scores after implementing the structured teaching programme. The difference in paired pre-test and post-test is (-485). This indicates an increase in knowledge scores after implementing structured teaching programme and calculated value of 't' (29): 15.88 was found highly significant at 0.05 level of significance. Association between the level of knowledge among the community people was highly significant with their age and significant with their education and not significant with gender, family income, occupation, types of family, method of waste disposal and previous source of knowledge.

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STRUCTURE TEACHING PROGRAM ,ILL EFFECT, PLASTIC, COMMUNITY

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Serverless computing (Function-as-a-Service, FaaS) is severely limited by the COLD start problem, a latency penalty incurred during container initialization. Current mitigation approaches, relying primarily on static keep-alive policies or single-source time-series prediction, fail to balance performance consistency with resource cost, often leading to a 40-60% waste in resource provisioning. We introduce COLDSTART (Cost- and Latency-Optimized Serverless Orchestration via Multi-Modal Prediction and Confidence-Based Warming), a novel, intelligent orchestration framework designed to minimize COLD start occurrences and resource overhead simultaneously. Our core contribution is a Multi-Modal Prediction (MMP) architecture that fuses six heterogeneous data streams--spanning user behavior, API metrics, and system telemetry--into a unified feature space. This space is analyzed by a tri-model Ensemble Learning Engine (XGBoost, LSTM, and Transformer) to generate highly accurate invocation forecasts. Crucially, we propose a Confidence-Based Warming (CBW) mechanism that gates resource allocation based on a calculated prediction confidence score (C), thereby minimizing expensive false positives. Through extensive experimentation on simulated production workloads, COLDSTART achieved a 68% reduction in COLD starts and improved 95th percentile latency to under 200 ms, concurrently delivering a 45% reduction in operational waste compared to single-source ML baselines. This work demonstrates a robust, context-aware solution essential for sustainable and high-performance serverless deployments. Index Terms--Serverless Computing, Function-as-a-Service (FaaS), COLD Start Mitigation, Multi-Modal Prediction, Ensemble Learning, Cost Optimization, Proactive Warming.

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Serverless Computing, Cold Start Mitigation, Function-as-a-Service (FaaS), Multi-Modal Prediction, Ensemble Learning, Confidence-Based Warming, Cost Optimization, Latency Optimization

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In this modern digital era, interface design has transformed from static to dynamic interactions, where motion plays an important role in guiding user experience. Motion and animation has no longer restricted to decorative functions; Rather than, they act as narrative medium that leads users, visualize meanings, and enhance emotional involvement. This paper observes motion as narrative in interface design, concentrating on how storytelling is crafted through animation, transitions, feedback and micro-interactions Anchored in Narrative Theory, the study examines user interactions as narrative sequence in which motion organize the initiation, development, and resolution of tasks. Guided by qualitative and descriptive case study methodology, the study focus on digital platforms such as Duolingo, Airbnb, and Google's Material Design to explain how narrative motion shapes onboaring, navigation, feedback, and brand experience. These findings highlights that well-designed motion amplifies clarity, stability, usability and emotional engagement, while overused or poorly implemented motion may lead to inclusive design and performance challenges. The study highlights that narrative motion is an effective design strategy that connects functionality and experience, with growing conceptual importance in AI-driven personalization and immersive AR and VR environments.

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Interface Design, Human-Computer Interaction (HCI), Digital Storytelling, Motion Design, Narrative Theory, Animation, User Experience, Immersive Interfaces

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The present seasonal study was conducted in the Gorja Lake near Bhadrawati. Monthly and seasonal variation in the biological parameters for phytoplanktonic study like Chlorophyceae in Gorja lake were studied during the study period of June 2024 to May 2025 in which 17 species were recorded. This study indicates that the lakes of central India exhibit substantial variation in their biotic and abiotic characteristics.

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Gorja lake, Chlorophyceae diversity, Phytoplankton.

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