Abstract

Autoimmune diseases arise from complex interactions among genetic susceptibility, environmental exposures, immune dysregulation, and tissue-specific factors. Although genetic variants contribute substantially to disease risk, they do not fully explain disease penetrance, heterogeneity, or progression. Epigenetic mechanisms provide a dynamic regulatory interface through which environmental signals can influence gene expression and immune-cell function without altering the underlying DNA sequence. This review examines epigenetic regulation of immune tolerance as a mechanistic bridge connecting inherited susceptibility with environmental exposure in autoimmunity. The review integrates evidence on DNA methylation, histone modifications, chromatin remodeling, and non-coding RNAs and their effects on T cells, regulatory T cells, B cells, macrophages, and other immune populations. Particular emphasis is placed on the Treg–FOXP3 axis, including epigenetic regulation of the FOXP3 locus, Treg stability, plasticity, and the maintenance of peripheral tolerance. Disease-specific evidence from systemic lupus erythematosus, rheumatoid arthritis, multiple sclerosis, type 1 diabetes, and Sjögren syndrome is considered alongside environmental influences including nutrition, infections, psychological stress, pollutants, microbiota, lifestyle, and hormonal factors. The review further examines the potential of epigenetic signatures as biomarkers and the opportunities and limitations of targeted epigenetic therapies. An integrative Ayurvedic perspective is presented cautiously as a hypothesis-generating framework linking constitutional variability, environmental influences, lifestyle, metabolism, and immune regulation with contemporary epigenetic research. Overall, epigenetic regulation may represent a critical mechanistic interface linking genetic susceptibility and the exposome to altered immune tolerance, chronic inflammation, and autoimmune tissue injury, while providing opportunities for biomarker development, disease stratification, prevention, and precision therapeutic strategies.

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Epigenetics; Autoimmunity; Immune tolerance; Genetic susceptibility; Environmental exposure; Exposome; Treg–FOXP3 axis; DNA methylation; Immune dysregulation; Precision medicine.

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Abstract

This study investigates the ethnobotanical knowledge associated with diabetes management in the Kalampur and Thuamul Rampur areas of Kalahandi district, Odisha, India. Traditional plant-based healthcare is particularly important in rural and tribal communities where access to modern medical facilities is limited. The research aimed to document medicinal plants used locally for managing diabetes mellitus and to assess the significance of indigenous knowledge in primary healthcare practices. Fieldwork was conducted through structured and semi-structured interviews with traditional healers, elderly people, and knowledgeable local informants. Collected plant specimens were identified using standard taxonomic methods, while information regarding their medicinal applications was systematically recorded. The study documented several medicinal plant species belonging to different families, including Fabaceae, Moraceae, and Rutaceae. Various plant parts, such as leaves, bark, fruits, and roots, were commonly used to prepare remedies in the form of decoctions, powders, and fresh extracts. The findings demonstrate the richness and continued relevance of indigenous knowledge in diabetes management. The study highlights the importance of conserving medicinal plant diversity and traditional knowledge. Further pharmacological and scientific studies are recommended to validate the therapeutic potential, safety, and effectiveness of these plants and to explore their possible integration into modern healthcare.

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Ethnobotanical, Diabetes mallitus, Herbal plants

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Abstract Background Nutrigenomics has evolved from the study of individual gene–nutrient interactions toward an integrated understanding of how genetic, epigenetic, metabolic, microbial, environmental, and behavioral factors influence individual responses to diet. This transition has gained particular relevance in the present era of increasing nutrition-related chronic diseases and rapid advances in multi-omics, artificial intelligence, and digital health. Objective This review examines recent advances in nutrigenomics, with emphasis on the progression from gene–diet interactions and nutrigenetics to epigenomics, gut microbiome research, multi-omics integration, artificial intelligence, and precision nutrition. It further evaluates emerging clinical applications, translational challenges, ethical considerations, and future directions. Main Findings Nutrigenetic studies demonstrate that genetic variation can modify responses to dietary factors, although reported gene–diet interactions remain heterogeneous and incompletely validated. Epigenomic research provides a dynamic interface between nutritional and environmental exposures and gene regulation, while the gut microbiome adds an important biological layer through microbial transformation of dietary substrates and production of bioactive metabolites. Multi-omics approaches enable integration of genomic, transcriptomic, epigenomic, proteomic, metabolomic, and microbiome information to characterize nutritional phenotypes more comprehensively. Artificial intelligence, machine learning, wearable technologies, and continuous metabolic monitoring further provide opportunities for prediction of individual dietary responses and dynamic nutritional intervention. Emerging evidence suggests potential applications of precision nutrition in obesity, type 2 diabetes, cardiovascular disease, and metabolic dysfunction-associated steatotic liver disease, although clinical evidence remains heterogeneous. Conclusion Nutrigenomics is increasingly moving from molecular discovery toward precision nutrition. However, routine implementation remains limited by methodological heterogeneity, reproducibility, population diversity, data privacy, cost, health inequities, and insufficient long-term evidence demonstrating superiority over high-quality conventional dietary care. Future progress will require diverse longitudinal cohorts, standardized multi-omics methodologies, robust external validation, well-designed intervention trials, responsible use of artificial intelligence, and equitable clinical implementation. Precision nutrition should therefore be viewed as an emerging evidence-based framework that complements established nutritional principles rather than as a basis for universally prescribed genomic diets.

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Nutrigenomics; nutrigenetics; gene–diet interaction; epigenomics; gut microbiome; multi-omics; precision nutrition; artificial intelligence; personalized nutrition; nutritional phenotyping; digital health; metabolic disease

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Abstract

Research on solar PV (SPV) water pumping systems has been conducted across several fields, including engineering, agricultural economics, consumer behavior, and policy research. This makes it possible for the same factors affecting adoptions to be described differently and investigated using various methods. This review aims to collate 119 studies published from 1990 to 2026, synthesize the factors associated with SWP adoption, review how SWP adoption has been investigated, and pinpoint existing gaps. A theoretical integrative review with thematic analysis was used. The studies were analyzed at the factor level: 595 factor observations were collapsed into 46 factors, grouped into nine themes. Fifty studies were selected that were directly related to behavioral outcomes, such as adoption, adoption intention, purchase intention, and willingness to pay for a solar pumping system. The remaining studies, which examined the adoption and behavioral outcomes of other solar photovoltaic (SPV) and renewable energy technologies, were used as supporting evidence to understand the broader technical, economic, institutional, and contextual conditions shaping solar pumping adoption. The synthesis indicates that the literature places the greatest emphasis on economic considerations. The initial cost is seen as a barrier to adoption, whereas government subsidies are viewed as an enabler. However, the evidence is weaker for other funding methods, such as credit and loans. Another key difference in the review is that awareness research generally focuses on solar technology, whereas there is much less work on awareness of specific schemes, subsidies, and application processes. Even after considering their relevance to the utilization and ongoing functionality of solar pumps, the market and service conditions, such as availability of spare parts, repair services, and after-sales services, remain comparatively unexplored. The methodological synthesis also confirms that in the majority of studies, there is no theoretical approach, and that in the relatively few studies with explicit theory, the theories vary widely. This paper introduces a more precise categorization of the economic dimension into cost burden, investment return, and financial access/affordability, based on these results. The review results will create a coherent evidence base for future empirical studies and a more structured base for the conceptual models to be developed for research into solar water pump adoption.

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solar photovoltaic water pumping; solar irrigation; PM-KUSUM; technology adoption; financial incentives; subsidy; farmer awareness; thematic synthesis

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Abstract

Large Language Models (LLMs) are becoming integral to cyber-defense processes, including threat intelligence, security event analysis, incident investigation, decision support, and automated response. As implementations incorporate retrieval-augmented generation (RAG), persistent memory, external tools, and agentic execution, the security landscape broadens beyond mere model parameters to include data, context, retrieval, state, authorization, tool interfaces, and underlying infrastructure. This systematic review compiles security literature across these dimensions and categorizes findings based on attack surface, adversarial mechanisms, security implications, defense strategies, evaluation methods, and adaptability capabilities. The review examined 1,945 records, eliminated 527 duplicates, screened 1,418 unique entries, analyzed 455 full texts, and selected 168 studies for qualitative synthesis. It highlights key threat classes such as prompt and indirect injection, jailbreaks, training and retrieval-data poisoning, information leakage, retrieval manipulation, memory contamination, tool misuse, and manipulation of agentic actions. Additionally, it reveals that security vulnerabilities can transcend layers: content controlled by attackers can impact retrieval and context, modify model reasoning, and influence downstream tool or agent responses. To address these complexities, the review puts forward the Attack Surface–Threat–Impact–Defense–Adaptation (ASTIDA) framework as a cross-layer analytical approach for linking vulnerabilities to impacts, controls, and necessary adaptations. The findings also suggest that static defenses and response-level assessments are inadequate for dynamic agentic systems; evaluations should include adaptive attack strategies, trajectory-level safety measures, provenance-aware retrieval, least-privilege access for tools, runtime monitoring, utility preservation, and operational cost considerations. Finally, the review notes a significant benchmarking gap between traditional cybersecurity datasets and LLM-security evaluations, and it recommends a cohesive research roadmap for developing reproducible, adaptive, and system-level assessments of cyber-defense security.

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Large Language Models; LLM Security; Cyber Defense; Adversarial Attacks; Prompt Injection; Data Exposure; Retrieval-Augmented Generation; LLM Agents; Adaptive Defense; Security Evaluation; Artificial Intelligence Security.

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Abstract

Delivering individualized and educationally significant learning experiences has become more difficult due to the growing number of online learning platforms. The majority of current systems rely on unchanging course designs or recommendation systems, which are unable to modify content in response to the unique behaviour of each student. In order to dynamically create individualized learning experiences, this study presents a framework for creating personalized courses that makesV use of learners' search behaviour and preferences as well as Large Language Models (LLMs). After analysing learner interaction data, interests, intent, and knowledge gaps are translated to structured prompts for LLM-based course creation. In order to ensure coherence and alignment with learner objectives, the system generates adaptive course components, such as timelines, learning materials, assessments, and curriculum. Scalability and ongoing adaptability as learner behaviour changes are key components of the suggested architecture. Performance-based tests, learner feedback, and engagement metrics are used in evaluation. The findings demonstrate the potential of LLM-driven course creation to revolutionize individualized learning in digital learning contexts by highlighting its efficacy in raising student engagement and satisfaction.

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Large Language Models; Search Behaviour; Learner’s Preferences; Content Generation

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Abstract

Modern Odia poetry has repeatedly demonstrated that the greatest poetic achievements are often realized not through verbal abundance but through extraordinary semantic concentration. Amaresh Pattanaik's "Rāmāyaṇa (Śeṣa Ichhā Rābaṇara)," originally published in Abuddha Garuda (1994) and later included in Kabita Samagra (2nd Part), is one such masterpiece of philosophical compression. Comprising only a few lines, the poem radically reimagines one of the most controversial figures of Indian epic tradition—Rāvaṇa—not as the archetypal villain of the Rāmāyaṇa but as a dying visionary whose final utterance transcends vengeance, conquest, and personal redemption. Instead, his last wish becomes an ethical testament addressed to the Earth and to humanity itself. Through this startling inversion, Pattanaik dismantles inherited moral binaries and reconstructs myth as an arena of existential inquiry, ecological responsibility, and universal humanism. The poem neither absolves nor condemns Rāvaṇa; rather, it transforms him into a symbolic consciousness confronting mortality, memory, and the fragile continuity of civilization. This article offers a comprehensive critical examination of the poem from multiple philosophical, literary, and theoretical perspectives. Drawing upon hermeneutics, archetypal criticism, myth criticism, existential philosophy, phenomenology, ecocriticism, comparative mythology, ethical criticism, and Indian philosophical traditions, it demonstrates how Pattanaik's brief lyric simultaneously engages with the Vālmīki Rāmāyaṇa, the Upanishads, Buddhist compassion, Gandhian humanism, and modern existential thought. The article argues that the poem's central images—dream, age, Earth, faith, and the lamenting Krauncha bird—constitute a coherent symbolic architecture through which personal extinction is transformed into collective hope. The poet's remarkable linguistic economy enables each image to resonate across multiple cultural and philosophical traditions while remaining deeply rooted in Odia literary modernism. The article also situates Pattanaik's poetic vision within a wider global framework through comparisons with the works of Rabindranath Tagore, T. S. Eliot, Rainer Maria Rilke, W. B. Yeats, Pablo Neruda, and other major poets whose works interrogate mortality, civilization, and spiritual continuity. Ultimately, the article contends that Pattanaik's "Rāmāyaṇa" is not merely a reinterpretation of epic mythology but a profound meditation on the ethical future of humanity. Its enduring significance lies in its ability to convert the defeated voice of myth into a universal prayer for faith in mankind, thereby revealing one of the highest achievements of contemporary Odia poetry.

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Ravana; Ramayana; Humanism; Myth Criticism; Existentialism; Ecocriticism; Symbolism; Comparative Literature; Indian Modernism; Philosophical Poetry.

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Abstract Background: Prakriti (psychosomatic constitution) is one of the fundamental concepts of Ayurveda and forms the basis of individualized healthcare. It represents the innate constitutional phenotype established at conception through the relative predominance of Vāta, Pitta, and Kapha Doṣas, influencing morphology, physiology, metabolism, psychological traits, disease susceptibility, and therapeutic response throughout life. Recent advances in genomics, systems biology, and precision medicine have renewed scientific interest in understanding the biological basis of Prakriti and its relevance to contemporary healthcare. Objective: This review aims to critically examine the classical Ayurvedic concept of Prakriti and integrate it with current evidence from physiology, homeostasis, genetics, epigenetics, systems biology, Ayurgenomics, disease susceptibility, and precision medicine to establish its scientific relevance in modern individualized healthcare. Methods: A comprehensive narrative review was undertaken using classical Ayurvedic texts, including the Charaka Samhita, Sushruta Samhita, and Ashtanga Hridaya, together with contemporary peer-reviewed literature on constitutional biology, genomics, multi-omics, systems biology, and precision medicine. Relevant evidence was critically synthesized to correlate classical constitutional principles with modern biomedical concepts. Results: Classical Ayurvedic literature consistently identifies Prakriti as a stable constitutional phenotype governing physiological regulation, metabolic characteristics, psychological behaviour, environmental adaptability, and susceptibility to disease. Contemporary studies demonstrate significant associations between Prakriti and genetic polymorphisms, differential gene expression, epigenetic regulation, metabolic signatures, inflammatory pathways, gut microbiome composition, and pharmacogenomic variability. These findings support the biological plausibility of Prakriti as a multidimensional constitutional framework and demonstrate substantial conceptual convergence with systems biology and Predictive, Preventive, Personalized, and Participatory (P4) medicine. Emerging applications of artificial intelligence and digital health technologies further enhance the objectivity and reproducibility of constitutional assessment. Conclusion: Prakriti represents a scientifically plausible constitutional model that integrates traditional Ayurvedic wisdom with contemporary biomedical science. Its convergence with physiology, systems biology, multi-omics, and precision medicine highlights its potential to advance individualized disease prevention, risk stratification, therapeutic optimization, and evidence-based integrative healthcare. Further multicentric, longitudinal, and translational research is required to strengthen its global acceptance and clinical applicability.

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Keywords

Ayurveda; Prakriti; Constitution; Personalized Medicine; Precision Medicine; Homeostasis; Ayurgenomics; Systems Biology; Epigenetics; Disease Susceptibility.

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Abstract

The abstract should highlight the thesis. The objectives and findings should be correlated and clear. Asummary of the thesis with details highlighting various chapters should be included in theabstract. The introduction and literature review are not required to incorporate in the abstract part ofthereport. The abstract should be compacted within one page of around 300 words. At the end of the abstract,keywordsshould be mentioned.Keywords aretheimportantbuzzwordsofthetopic.

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Dr. Phani Mohanty's Ahalyā represents one of the most philosophically sophisticated achievements in modern Odia poetry. Although the poem has generally been interpreted as a feminist reinterpretation of the Ramayana myth or as an exploration of gendered suffering, such readings only partially illuminate its remarkable philosophical architecture. The present study argues that Ahalyā is fundamentally an ontological poem that investigates the evolution of Being through silence, memory, waiting, suffering, nature, and spiritual awakening. Employing an interdisciplinary philosophical framework that synthesizes Martin Heidegger's ontology, Edmund Husserl's phenomenology, Paul Ricoeur's hermeneutics, Advaita Vedānta, Buddhist philosophy, and the Upanishadic conception of consciousness, this article demonstrates that Mohanty transforms the ancient myth into an inquiry into the metaphysics of existence itself. Rather than understanding the stone merely as a symbol of punishment, the study interprets it as the primordial condition of Being before self-consciousness. Silence emerges as a metaphysical language that transcends ordinary speech, while waiting becomes the existential structure of human life rather than a narrative device. Furthermore, the article argues that redemption in Ahalyā signifies not miraculous deliverance but ontological awakening, wherein fragmented consciousness gradually recovers its original completeness. Through close textual analysis supported by philosophical interpretation, the study positions Ahalyā within the broader traditions of world philosophical poetry and demonstrates its relevance to contemporary debates in comparative literature, phenomenology, myth criticism, and Indian philosophy. Ultimately, the article contends that Mohanty's poem is neither merely mythological nor exclusively feminist; it is a profound meditation on the continuous becoming of human consciousness.

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Ontology; Phenomenology; Being; Silence; Consciousness; Advaita Vedānta; Modern Odia Poetry; Mythopoesis; Existentialism; Indian Philosophy.

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Abstract

Data integrity has become a critical pillar of pharmaceutical quality systems, ensuring that all GxP records are accurate, complete, consistent, and reliable across the product lifecycle. Drawing on ALCOA and ALCOA+ principles—Attributable, Legible, Contemporaneous, Original, Accurate, Complete, Consistent, Enduring, and Available—this review article examines how data integrity requirements have evolved globally and how they directly influence patient safety, product quality, and regulatory confidence. It outlines the fundamentals of data integrity, including data lifecycle concepts, data governance frameworks, and the role of computerized systems and electronic records in modern GMP environments. Key global regulatory expectations are summarized for ICH, US FDA (21 CFR Part 11), EU GMP Annex 11, PIC/S, MHRA, WHO, and CDSCO, highlighting increasing convergence around risk based, lifecycle oriented control of paper and electronic data. Within pharmaceutical operations, the article discusses practical data integrity implementation across manufacturing, QC laboratories, QA, stability studies, analytical testing, validation activities, and batch record management, emphasizing the importance of robust documentation and Computerized System Validation (CSV) for EBR, LIMS, MES, ELN, and ERP platforms. Common data integrity deficiencies—such as missing data, backdating, unofficial records, shared accounts, disabled audit trails, and inadequate GDP—are reviewed in the context of recent warning letter trends, illustrating the regulatory impact of poor data governance. The article further explores risk based approaches, Data Integrity Risk Assessment (DIRA), root cause analysis, CAPA, and Quality Risk Management (QRM) as essential elements of a sustainable data integrity framework. Finally, emerging technologies including Artificial Intelligence, Machine Learning, blockchain, IoT, cloud based and digital Quality Management Systems, and cybersecurity are considered as enablers of next generation data monitoring and protection, provided they are validated, well governed, and integrated into a strong quality culture. Together, these elements present a comprehensive perspective on current expectations and future directions for data integrity in pharmaceutical industries.

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Data integrity; ALCOA; ALCOA+; Pharmaceutical quality systems; 21 CFR Part 11; Computerized System Validation (CSV); Electronic Batch Records (EBR); LIMS; MES; Data governance; Data Integrity Risk Assessment (DIRA); Quality Risk Management (QRM); FDA warning letters; Artificial Intelligence; Machine Learning; Blockchain; IoT; eQMS; Cybersecurity.

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Abstract

Green Analytical Chemistry has emerged as an essential paradigm in pharmaceutical analysis, addressing the environmental, economic, and occupational costs associated with conventional solvent intensive testing. This review traces the conceptual foundation of green analytical chemistry from its origin as an extension of green chemistry through the formulation of twelve guiding principles, and examines how these principles have been translated into practical laboratory strategies. Sample preparation approaches including miniaturized extraction, solvent free techniques, microextraction, and green solvent classes such as deep eutectic solvents and ionic liquids are discussed alongside instrumental advances in ultra-high-performance liquid chromatography, supercritical fluid chromatography, capillary electrophoresis, spectroscopy, and ambient ionization mass spectrometry. The review further evaluates the major greenness assessment tools, including the analytical eco scale, the green analytical procedure index, the analytical greenness metric, its sample preparation counterpart, the complex green analytical procedure index, and the red green blue twelve model, illustrating their application through case studies drawn from stability indicating assays, impurity profiling, dissolution testing, and cleaning validation. Particular attention is given to the integration of green principles with analytical quality by design, showing how risk assessment and design of experiments can simultaneously optimize analytical performance and environmental sustainability within a regulatory lifecycle framework. The growing contribution of artificial intelligence, machine learning, digital laboratories, automation, and continuous monitoring toward sustainable analytical practice is also considered. Finally, the review addresses the technical, economic, regulatory, and organizational barriers that continue to slow widespread adoption, and outlines the trajectory toward smart, largely solvent free, sustainability aware quality control laboratories integrated with continuous pharmaceutical manufacturing. Taken together, the evidence indicates that green analytical chemistry is no longer a peripheral consideration but a central design objective for the next generation of pharmaceutical analytical methods.

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Keywords: green analytical chemistry, greenness metrics, sustainable pharmaceutical analysis, analytical quality by design, artificial intelligence

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Grit and self-control are two essential non-cognitive psychological constructs that contribute to long-term goal attainment, adaptive functioning, and overall well-being. While grit reflects sustained passion and perseverance toward long-term objectives, self-control involves regulating thoughts, emotions, and behaviors to resist immediate temptations. Despite their conceptual overlap, limited empirical research has examined their relationship among Indian adults. The present study investigated the association between grit and self-control in an adult population using a quantitative, correlational research design. Vibha Dissertation.pdf A total of 100 adults aged 18–40 years participated in the study through convenience sampling. Standardized psychometric instruments were administered to assess grit and self-control. Descriptive statistics, Pearson’s product-moment correlation, and simple linear regression analyses were performed using statistical software to examine the relationship and predictive influence of grit on self-control. Vibha Dissertation.pdf The findings revealed a significant positive correlation between grit and self-control (r = 0.61, p < .001). Regression analysis further demonstrated that grit significantly predicted self-control, accounting for approximately 37% of the variance (R² = 0.37), indicating a moderate predictive effect. These findings suggest that individuals with higher perseverance and sustained commitment toward long-term goals also exhibit greater capacity for self-regulation and impulse control. The study contributes to the growing literature in positive psychology by providing empirical evidence supporting the complementary roles of grit and self-control in adult functioning. The findings have practical implications for psychological interventions, counseling, educational settings, and organizational training programs aimed at enhancing resilience, self-regulation, and goal-directed behavior. Future research should employ longitudinal designs, larger and more diverse samples, and investigate additional psychological variables that may mediate or moderate this relationship.

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Grit, Self-Control, Adults, Positive Psychology, Self-Regulation, Pearson Correlation, Regression Analysis, Psychological Well-being.

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Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by persistent difficulties in social communication, interaction, and restricted, repetitive behaviours. Parent-report measures play an important role in monitoring autism-related symptoms, but the lack of standardized assessment tools in regional languages limits their use among Malayalam-speaking families. The present study aimed to translate and culturally adapt the Autism Treatment Evaluation Checklist (ATEC) into Malayalam and to examine whether Malayalam-speaking parents of children with ASD were able to understand and complete the adapted checklist. A translation and adaptation method was employed and the study was conducted in two phases. Phase I involved the translation, back-translation, and expert validation of the ATEC by five experienced Speech-Language Pathologists (SLPs). Phase II involved administering the adapted Malayalam version to 40 Malayalam-speaking parents of children aged 2–5 years diagnosed with ASD from northern and southern districts of Kerala. The findings indicated that the translated questionnaire was clear, culturally appropriate, and easily understood by the majority of parents. The adapted Malayalam ATEC was found to be feasible for assessing speech/language, sociability, sensory/cognitive awareness, and health/physical/behaviour domains in children with ASD. The study highlights the importance of culturally adapted parent-report measures in facilitating effective assessment and monitoring of autism symptoms among Malayalam-speaking populations and provides a foundation for future psychometric validation and clinical use.

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Autism Spectrum Disorder, Autism Treatment Evaluation Checklist (ATEC), Malayalam, translation, cultural adaptation, parent-report questionnaire, Speech-Language Pathology.

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Abstract

Semantic verbal fluency is a sensitive indicator of lexical organisation and cognitive-linguistic development during middle childhood. The present study examined semantic verbal fluency performance across 12 structured semantic categories in typically developing Hindi–English bilingual children aged 8–10 years attending government primary schools in the eastern district of Delhi, India. A total of 60 children divided into two age groups, 8–9 years and 9–10 years with 30 children per group, each comprising 15 boys and 15 girls participated in the study. A self-developed vocabulary list containing 12 semantic categories was administered for one minute per category. Highly significant age-related differences were observed across 11 of the 12 categories. More familiar categories used in day-to-day life such as Body Parts, Animals, Fruits, and Food Items yielded the highest retrieval scores, while Furniture and Musical Instruments produced the lowest, indicating that category familiarity and ecological relevance significantly affect lexical retrieval efficiency. Gender differences revealed an age-dependent pattern: boys and girls performed comparably across categories at 8–9 years, whereas by 9–10 years, boys significantly outperformed girls in 10 of the 12 categories, with no gender effect seen in Stationery and Food Items. These findings suggest that semantic verbal fluency in middle childhood is experientially shaped and influenced by category familiarity, developmental maturation, and gender-differentiated environmental exposure rather than a fixed cognitive trait. The present study also lays a vital foundation for evidence-based assessment, empowering speech-language pathologists (SLPs) to design targeted interventions, accurately monitor clinical progress, and optimize therapeutic management for Hindi-medium bilingual children within the Indian context.

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Keywords: Semantic verbal fluency, middle childhood, Hindi–English bilingual, lexical retrieval

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Skin cancer is one of the most common yet potentially fatal cancers worldwide, and its outcome depends heavily on how early it is detected. In this work we present a practical, end-to-end dermoscopic image analysis pipeline aimed at reliable screening in day-to-day clinical settings. Our approach combines careful image preprocessing (hair removal, colour constancy, and lesion-centred cropping) with a compact convolutional backbone based on EfficientNet-B3, extended with a lightweight spatial attention block that helps the network focus on lesion regions rather than surrounding skin. The model is trained on a merged collection drawn from the ISIC 2019 and ISIC 2020 archives (about 58,000 dermoscopic images across seven diagnostic categories) using class-balanced sampling, mix-up augmentation and a two-stage fine-tuning schedule. On a held-out test set the proposed pipeline reaches 94.6% overall accuracy, a macro AUC of 0.962 and a recall of 0.93 on melanoma, clearly outperforming a VGG-16 baseline (87.1% / 0.891 / 0.79). We also show Grad-CAM heat-maps that make the model's decisions easier for clinicians to review. The results suggest that a well-tuned mid-sized CNN with attention and careful data curation is a strong, deployable option for early skin cancer screening.

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Skin cancer, melanoma, dermoscopy, deep learning, EfficientNet, spatial attention, ISIC, Grad-CAM, early detection

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A variety of analytical methods are available for the determination of important pharmaceutical drugs; however, many of these methods require costly instrumentation and complex analytical procedures. This review highlights simple, economical spectrophotometric methods that offer analytical accuracy comparable to that achieved by advanced instrumental techniques, including HPLC, HPTLC, and GC–MS.

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Drugs, Spectrophotometric analysis, Insecticides

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Abstract Title: Skilling Nurses for Killing Fires: a Pre experimental study on effect of Simulation Based Fire Prevention Bundle on Fire Preparedness and Decision Making Skills among Nurses Author: Mr Birdev Deokate Aim- Evaluate the effectiveness of simulation based fire prevention bundle on fire preparedness and decision making skills among Nurses of selected hospitals in Solapur city Objectives: 1. To compare pre and post simulation intervention fire preparedness and decision making skill score of nurses. Hypothesis- H0- There will be no difference in the fire preparedness and decision making skill scores of nurses before and after administration of simulation based fire prevention bundle. Methodology One group pre-test post-test experimental design, with sample size 200 nurses working in three hospitals. Simple random sampling technique used. Consent is taken before pre-test. Permission from IREC and concerned hospital administrative authority taken before study Tool- A self-reporting questionnaire of 40 items with 20 each for fire preparedness and decision making skills. Administered online and offline as per the convenience of participants. Results- 1. Fire preparedness pre-test mean score is 7.85 and post-test 19.06, difference in mean score is 11.21 and the ‘t’ value is 62.62 with significant change (p<0.05) in pre and post-test scores. 2. Decision making skills pre-test mean score 6.56 and post-test is 19.49, difference in means score is 12.93 and the ‘t’ value is 55.56 with significant change (p<0.05) in pre and post-test scores. 3. H0 is rejected. Conclusion- The simulation based fire prevention bundle was effective in enhancing fire preparedness and decision making skills of nurses working in hospital.

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Simulation Based Learning / Simulation Based Fire Prevention Bundle, Fire Preparedness, Decision Making Skills, Nurses / Nursing, Hospital Safety, Fire Prevention, Pre-test, Post-test, Design / Experimental Study, Skill Enhancement, Healthcare Workers.

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The unprecedented advancement of Artificial Intelligence (AI), Machine Learning (ML), and Natural Language Processing (NLP) has revolutionized the interaction between human beings and machines by enabling computers to comprehend, analyse, generate, and translate natural languages with remarkable precision. While substantial progress has been achieved in computational processing for high-resource languages such as English, Chinese, French, German, and Spanish, the technological development of many Indian languages remains comparatively limited owing to inadequate linguistic resources, insufficient annotated corpora, and fragmented research initiatives. Odia, one of the six Classical Languages of India and the official language of the state of Odisha, occupies a distinctive position because of its rich literary heritage, complex grammatical structure, extensive morphological system, and unique orthographic characteristics. Despite possessing a literary tradition extending over a millennium, the language continues to face considerable computational challenges in the age of digital communication and intelligent language technologies. The present study critically investigates the theoretical foundations, historical evolution, linguistic characteristics, computational methodologies, available language resources, and recent developments in Odia Language Processing (OLP). The paper further explores major components of computational linguistics, including corpus development, text preprocessing, tokenization, morphological analysis, part-of-speech tagging, syntactic parsing, semantic analysis, named entity recognition, machine translation, optical character recognition, speech technologies, sentiment analysis, transformer-based language models, and generative Artificial Intelligence. Employing a qualitative and interdisciplinary research methodology based on descriptive, analytical, and comparative approaches, the study synthesizes existing scholarly contributions and identifies significant research gaps affecting the advancement of Odia language technology. The study argues that the future of Odia Language Processing depends upon systematic corpus creation, open-source computational tools, multilingual transformer architectures, large language models, collaborative academic research, governmental policy initiatives, and interdisciplinary cooperation between linguists and computer scientists. It concludes that the digital empowerment of Odia is not merely a technological necessity but also a cultural imperative essential for preserving linguistic diversity and ensuring equitable participation in the rapidly evolving knowledge society.

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Odia Language Processing, Computational Linguistics, Natural Language Processing, Artificial Intelligence, Machine Learning, Corpus Linguistics, Transformer Models, Low-Resource Languages, Digital Humanities.

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Abstract The present study examines the progress and problems of higher education in Meghalaya, with specific reference to the North-Eastern Hill University (NEHU) and its affiliated colleges. Situated within the broader expansion of higher education in India, the study situates Meghalaya’s institutional development in terms of access, equity, quality, and governance challenges. Using a descriptive survey method, primary data were collected from university and college teachers, administrators, research scholars, and students, along with secondary sources including official reports and institutional records. The findings reveal that while higher education in Meghalaya has expanded significantly since statehood, particularly in terms of institutional growth and student enrolment, it continues to face structural constraints. Key issues include inadequate infrastructure, shortage of qualified faculty, limited research output, outdated curriculum, weak ICT integration in teaching-learning processes, financial constraints, and inefficiencies in administration and examination systems. Furthermore, disparities in library resources, co-curricular engagement, and guidance and counselling services indicate uneven quality across institutions. Despite these limitations, institutions such as NEHU have contributed substantially to regional educational development and knowledge dissemination. The study concludes that higher education in Meghalaya is characterised by a dual reality of quantitative expansion and qualitative stagnation, necessitating policy reforms, improved governance mechanisms, and enhanced investment in academic and infrastructural development.

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Creative Commons Attribution 4.0 and The Open Definition

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Keywords Higher Education; Meghalaya; North-Eastern Hill University (NEHU); Affiliated Colleges; Educational Development; Quality Assurance; Curriculum Reform; Higher Education Policy; Institutional Governance; India.

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Molten oxide electrolysis (MOE) is one of the most attractive sustainable steel production routes with a capacity to eliminate greenhouse gas emissions in iron production, as it allows direct and in situ production of liquid iron from iron oxides in carbon-neutral conditions. The prolonged degradation of inert anodes is, however, a significant technical barrier for industrial-scale adoption. A literature-based conceptual framework was constructed to facilitate an understanding of the degradation mechanisms of candidate inert anode materials employed in molten oxide electrolysis, and the influence of factors such as electrochemical oxidation, hot slag corrosion, grain boundary penetration, thermal stress, oxide spalling, and breakaway corrosion on anode longevity is systematically addressed. The generalised multi-stage degradation model was formulated by synthesising current understanding drawn from prior literature. Future experimental validations utilising cutting-edge characterisation methods, including EIS, SEM, EDS, XRD and ICP-MS, were identified. Subsequently, a general Industry 5.0-enabled monitoring framework integrated with AI, digital twin, IIoT, and predictive maintenance to facilitate real-time condition monitoring and life assessment of inert anodes was conceptualised. These results will guide future scientific experiments, smart process monitoring, and robust inert anode design for sustainable steelmaking.

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Creative Commons Attribution 4.0 and The Open Definition

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The steel industry is entering an era of profound transformation – characterised by Industry 4.0 concepts and technologies, such as AI, the Industrial Internet of Things (IIoT), cyber-physical systems (CPS), cloud computing, digital twins and big data analytics. So far, considerable advances have been made with regard to automation and digitisation of blast furnaces, continuous casting plants, rolling mills and predictive maintenance. On the other hand, a relatively uncharted area in terms of digital maturity exists in secondary steelmaking processes (1). Since secondary metallurgy, with activities such as controlling steel cleanliness and inclusions, temperature uniformity of the steel bath, yield optimisation of additives and a consistently high product quality, contributes significantly to quality steel production (2). The Ladle Furnaces (LF), RH degassers, vacuum degassing facilities, CAS-OB stations, and ladle treatment plants affect directly quality metallurgical parameters and, therefore, represent a strategic potential for digitising processes (2). However, despite the increasing penetration of Industry 4.0 concepts into manufacturing, the gap between research and practice in assessing digital maturity of secondary metallurgy plants is significant (3). Established Industry 4.0 maturity models tend to analyse manufacturing systems at a level of enterprise or entire plants and therefore do not sufficiently cover the metallurgical, process or quality aspects relevant to secondary metallurgy processes (4). Steel producers currently, therefore, lack a framework to evaluate the status of digital transformation, measure success relative to other companies, identify technology needs, and develop roadmaps to autonomous metallurgy systems (5). There is no specific methodology available that holistically evaluates on the basis of metallurgical quality figures, operating efficiency parameters, digital availability, digital integration, and data readiness for prediction analyses the status of digital maturity in secondary metallurgy (6). This lack must be closed by a comprehensive maturity assessment methodology for secondary steelmaking. The objective of the present paper is to introduce an integrated digital maturity index – the Secondary Metallurgy Digital Readiness Index (SMDRI) – that can be used for integrated steel plants. This index comprises five successively progressing levels of maturity – "basic operation”, "process control”, "digital monitoring”, "predictive metallurgy”, and "autonomous secondary metallurgy”. The methodology integrates industrial, technological and metallurgical KPIs, e.g., LF power consumption, electrode consumption, RH vacuum efficiency, inclusion index, steel cleanliness score, heat turnaround time, digital integration ratio, and predictive analytics usage and determines a digital maturity indicator that can be classified on a standard scale of 0-100 (7). Mathematical procedures have been developed to calculate this index. The presented index provides the basis for defining a relationship between process automation, operating efficiency, connectivity, data, prognostics, and autonomously operating control functions in secondary metallurgy. The research will also describe the role of artificial intelligence (AI), machine learning, digital twins and process analytics for these technologies (8). This paper introduces for the first time the novel Secondary Metallurgy Digital Readiness Index (SMDRI) framework. With the introduction of the SMDRI for secondary steelmaking processes, this paper makes a significant theoretical and practical contribution by providing steel manufacturers, process managers and policymakers with a standardised tool to determine, monitor and benchmark their level of digitalisation, to identify suitable digital transformation actions, to align technology investments with their objectives and ultimately to achieve their transformation into an autonomously operating, 5.0-ready plant (9).

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Creative Commons Attribution 4.0 and The Open Definition

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Secondary Metallurgy, Digital Transformation, Industry 4.0, Steel Industry 5.0, Ladle Furnace, RH Degasser, Digital Twin, Artificial Intelligence, Steel Cleanliness, Operational Excellence, Smart Manufacturing, SMDRI.

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Gopapura O Anyaanya Kabitaa represents a remarkable contribution to contemporary Indian poetry in English translation, integrating Odia cultural consciousness with Vedic metaphysics, Upanishadic philosophy, Puranic symbolism, ecological imagination, and existential inquiry. This paper undertakes a comprehensive critical investigation of the poetic collection through interdisciplinary perspectives, including myth criticism, archetypal criticism, eco-criticism, phenomenology, existentialism, hermeneutics, comparative literature, and Indian aesthetic theory. The study argues that the collection transcends conventional lyricism by constructing a philosophical universe in which poetry becomes an instrument of self-realization, cultural remembrance, and spiritual awakening. The poems move seamlessly between the intimate and the cosmic, the historical and the timeless, the personal and the universal. Rural Odisha, particularly the symbolic landscape of Gopapura, is transformed into a metaphysical space where rivers, forests, conches, birds, silence, memories, and mythic figures become living embodiments of consciousness. Throughout the collection, myth is not employed merely as ornamentation but is reinterpreted as a dynamic philosophical language capable of illuminating the dilemmas of the contemporary human condition. Figures such as Yājñavalkya, Bhīṣma, Rādhā, Kṛṣṇa, Brahman, and numerous Vedic sages function as archetypes through which the poet interrogates identity, mortality, ethical responsibility, memory, and liberation. A distinguishing characteristic of the collection is its profound engagement with Indian philosophical traditions. The poems repeatedly revisit concepts such as Brahman, Māyā, Ātman, Dharma, Karma, Mokṣa, and the unity of existence, while simultaneously confronting modern anxieties arising from ecological degradation, alienation, fragmentation of identity, and spiritual emptiness. The poetic voice refuses simplistic solutions, instead presenting reality as an unfolding dialogue between silence and speech, presence and absence, knowledge and mystery. The recurrent movement from external landscapes toward inner consciousness establishes the collection as a sustained meditation on the nature of existence itself. Nature occupies a central position within the poetic imagination. Rivers, forests, mountains, oceans, birds, shells, flowers, winds, and seasons emerge not merely as descriptive settings but as philosophical participants in humanity's search for truth. The ecological vision presented in the collection resonates with ancient Indian cosmology while simultaneously addressing contemporary environmental concerns. The poems propose that ecological destruction is inseparable from spiritual alienation, suggesting that the restoration of harmony between humanity and nature requires a corresponding renewal of inner consciousness. The study further demonstrates that Gopapura O Anyaanya Kabitaa embodies a distinctive synthesis of indigenous aesthetics and global literary consciousness. The collection draws upon classical Sanskrit poetics, Bhakti traditions, Odia folklore, and Vedic symbolism while simultaneously engaging literary modes associated with modernism, postmodernism, existential philosophy, and world mythology. References extending from Indian sacred traditions to figures such as Odin illustrate the poet's expansive comparative imagination, transforming the collection into an intercultural dialogue rather than a purely regional literary enterprise. Stylistically, the poems employ free verse enriched by symbolism, paradox, mythic allusion, personification, rhetorical questioning, archetypal imagery, meditative repetition, and philosophical reflection. Language functions simultaneously as aesthetic expression and epistemological inquiry. Silence itself becomes an expressive medium, often conveying meanings beyond linguistic articulation. Consequently, the poems challenge conventional distinctions between poetry, philosophy, prayer, meditation, and cultural memory. This paper concludes that Gopapura O Anyaanya Kabitaa occupies a significant position within contemporary Indian literature by redefining poetry as a holistic mode of philosophical investigation. The collection revitalizes ancient Indian intellectual traditions while addressing the ethical, ecological, and existential crises of the twenty-first century. Its fusion of regional cultural identity with universal human concerns establishes it as a major literary achievement deserving sustained scholarly attention within Indian, comparative, and world literary studies.

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Contemporary Indian Poetry; Odia Literature; Myth Criticism; Vedic Philosophy; Upanishads; Eco-criticism; Archetypal Criticism; Spiritual Humanism; Comparative Literature; Symbolism; Existentialism; Indian Aesthetics; Cultural Memory.

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Ketoprofen is a widely prescribed non-steroidal anti-inflammatory drug (NSAID) used in the management of acute and chronic inflammatory conditions, pain syndromes, and musculoskeletal disorders. Despite its well-established pharmacological efficacy, the clinical utility of ketoprofen is limited by poor aqueous solubility, gastrointestinal toxicity, short biological half-life, and systemic adverse effects associated with conventional dosage forms. These challenges have driven significant research interest toward the development of advanced drug delivery systems aimed at improving therapeutic efficacy, safety, and patient compliance. This review critically examines the evolution of ketoprofen delivery from conventional oral and topical formulations to advanced carrier-based systems, including transdermal patches, lipid-based nanocarriers, polymeric nanoparticles, nano emulsions, cyclodextrin complexes, and stimuli-responsive delivery platforms. The physicochemical and biopharmaceutical characteristics of ketoprofen are discussed in relation to formulation design, followed by an overview of its pharmacological actions and therapeutic applications across inflammatory and pain-related diseases. Particular emphasis is placed on nanocarrier-mediated and transdermal delivery strategies that enable controlled release, enhanced skin permeation, site-specific targeting, and reduced systemic toxicity. Furthermore, regulatory considerations, safety aspects, and translational challenges associated with advanced ketoprofen formulations are highlighted. The review concludes with future perspectives on smart and personalized delivery approaches, underscoring the potential of next-generation drug delivery systems to redefine ketoprofen therapy.

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Ketoprofen, Nanocarriers, Transdermal drug delivery, Stimuli-responsive systems, Targeted drug delivery, Controlled release

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Creative Commons Attribution 4.0 and The Open Definition

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Calculation of Peak Ground Acceleration (PGA) and making contouring map of it is an important stage of doing seismic hazard analysis. Preparation of PGA contouring map in reality is very difficult and time taking. So, preparation of PGA contouring map using synthetic data is a convenient way. It helps in dealing with huge numbers of earthquake events for greater accuracy. This study has tried to solve various common complexities after doing extensive studies in each step which makes the calculation of PGA very simple. Application of this technique on the study region of Andaman and Nickobar region is also very important due lack of similar studies.

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Creative Commons Attribution 4.0 and The Open Definition

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Formation of isoxazoles & fused heterocyclic isoxazoles derivatives constitute an interesting class of heterocycles which have diverse biological activities. We describe the application, reactivity and synthesis of isoxazole derivatives, more specifically, the application, reactivity, and synthesis of 3,5-disubstituted, 3,4- disubstituted, and 3,4,5-trisubstituted isoxazole using the green method. The biological activity of these compounds suggests that the synthesized compounds could be good candidates for future investigations. Isoxazole is a five-membered heterocyclic compound having various pharmacological actions.Synthesized compound was docked with Structure of the human histamine H1 receptor in complex with doxepin for anti- inflammatory activity (PDB Code- 3rze). In microorganisms Study (PDB Code- 1aj0) for antimicrobial activity. Crystal structure of Mycobacterium tuberculosis uracil-DNA glycosylase in complex with 5-nitrouracil, Form III (PDB code 4WS5) for anti-TB activity. These compound shows promising anti- inflammatory, antimicrobial activity study in comparison to standard drugs was used. These synthesized 2a-2j were confirmed through spectral characterization and 2a,2j confirmed by IR, NMR, Mass spectroscopy.

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Isoxazole derivatives, Green chemistry, Microwave-assisted synthesis, Molecular docking,Spectral characterization, Pharmacological evaluation, Heterocyclic compounds, Medicinal chemistry.

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ABSTRACT The research seeks to explore the tremendous effect of social media on the political knowledge, attitudes, and behaviour of the young in Shillong, the capital city of the state of Meghalaya In India. The research is based on the theoretical frameworks of Manuel Castell's "Network Society," Clay Shirky's "Cognitive Surplus," and the new phenomenon of "Lateral Political Socialization" in the digital age. The research is based on an exploratory research methodology using primary research material from the sample of 40 young people in the city of Shillong, linked with secondary research material based on the phenomenon of the Voice of the People Party (VPP) In the recent Lok Sabha Elections of 2024. The results reveal that social media has permanently killed conventional political gatekeeping, with "digital capital" being the new dominant electoral force. However, despite the misperception of the Shillong youth with respect to the consumption of social media, they take a highly critical approach to digital semiotics and express a considerable need for long-form policy conversations such as regional political podcasts. Additionally, although the medium of socialization has shifted laterally, the youth are strongly opposed to the political authority of social media influencers and instead prefer traditional, educated local leaders. The results further reveal a considerable disconnect between online enthusiasm and mobilization, with the youth aware of their own "slacktivism." Finally, the research reveals that this virtual world is plagued with problems, as evidenced by the fast spread of disinformation through trusted networks such as WhatsApp and the deepening of ethnic divisions as a result of emotional polarization facilitated by algorithms. In conclusion, the dissertation offers recommendations to the state, which should insist on institutionalizing digital media literacy and invest in localized long-form digital communications to safeguard the democratic process.

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Social Media, Political Awareness, Urban Youth, Shillong, Digital Democracy, Youth Political Participation, Electoral Information, Political Socialization, Digital Media, Misinformation, Affective Polarization, WhatsApp, Voice of the People Party, Digital Media Literacy

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The research study titled “A study on organizational culture on employee job satisfaction with special reference to Bimetal Bearings Limited, Chennai”. The study aims to assess leadership styles, evaluate communication practices, examine Diversity and Inclusion initiatives and investigate employee engagement practices within the organization. Through investigating these facts, the research seeks to offer significant perspectives on the ways in which organisational culture affects employee satisfaction. The real objective is to evaluate the organizational culture on employee job satisfaction. Research design adopted for this study is descriptive research. The sampling method that is chosen is non-probability sampling. The sampling technique used is area sampling and the sample size is 120 which is determined by the organization. The responses were recorded by conducting a survey with the help of a questionnaire. The questionnaire has been designed in such a way so as to bring out the most accurate data, which will enable the study to get the closet vicinity of its objectives. The data collects from the survey has been appropriately analysed and has been interpreted in a meaningful way to offer some suggestions and recommendations. The data has also been put through a series of statistical tool (percentage method, chi-square, correlation analysis) that will close down the distance on the objective of the study. It is found that there is no significant relationship between gender of the respondent and leadership style in the organization. It is suggested that supervisors need to regularly acknowledge and value employees' contributions to foster job satisfaction. It is concluded that organizations must prioritize the cultivation of a positive culture that fosters collaboration, autonomy, and a sense of belonging among employees to ensure sustained success and employee well-being. It is imperative for organizations to continuously evaluate and adapt their culture to meet the evolving needs and expectations of their workforce in order to thrive in today's dynamic business landscape.

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Creative Commons Attribution 4.0 and The Open Definition

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Conflict is necessary outcome of individual and group interaction. People with different ideologies and backgrounds try to introduce their own principles in work aspects that give rise to conflict. Conflict when well managed and directed& is immensely useful in improving an organisation functioning and productivity along with providing individuals a valuable opportunities to evolve a join philosophy of work and cooperation. However if not managed conflict weakens the organisational outcome, creates stress for the individual, introduces dissatisfaction and reduces efficiency. Conflict is resolved through leadership, better communication and good negotiation all of them bringing the positive outcome through the issues involved in the conflict

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Conflict, organisational outcome, productivity

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Creative Commons Attribution 4.0 and The Open Definition

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An employee is an individual who works for an organization or company in exchange for compensation, typically in the form of wages or salary. Employees are hired to perform specific tasks or roles within the organization and are bound by an employment agreement or contract outlining their rights, responsibilities, and terms of employment. The objective of the study is to analyse the impact of leadership styles on employee performance and job satisfaction in Hyundai Motor India Limited, Chennai. Total number of sample taken for the study is 120 respondents. Descriptive research design and Convenience sampling techniques were used for the study. Primary data and secondary data have been used in the study. Simple Percentage analysis, chi square analysis and correlation analysis have been applied in this study to reach the finding of the study. It is found that there is no significant relationship between the educational qualification of the respondents and job performance. It is suggested that the business must keep up a positive atmosphere for workers in general. The business needs to realise that when workers are happy in their jobs, they are more likely to participate in decision-making. It is concluded that the giving workers the chance to work autonomously and providing leaders with feedback and assistance enhances their performance and job satisfaction even more. In order to maximise employee performance and job satisfaction, firms must implement leadership styles that promote involvement, autonomy, and acknowledgment.

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Creative Commons Attribution 4.0 and The Open Definition

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"Manpower" can also refer to the human resources or labor force of a company, including considerations such as recruitment, training, deployment, and management of personnel to achieve organizational objectives. The objective of the study is to analyse on manpower planning for enhanced productivity in Sundaram Clayton Limited., Chennai. The sample of the study is 185. Descriptive research design and convenience sampling method has been used. Questionnaire has been used as a primary data. Simple percentage analysis, chisquare analysis and correlation statistical tools have been applied to reach the findings of the study. It is found that there is positive relationship between experience of the respondents and comfortable to discuss the performance with the supervisors/ managers in the organisation. It is suggested that the organisation must guarantee that the input provided by managers and supervisors is useful and aids staff in pinpointing areas in need of development. It is concluded that consistently providing constructive performance feedback to employees promotes their growth and engagement, which in turn enhances productivity inside the workplace. Moreover, maintaining high productivity levels requires encouraging a positive work-life balance and making use of pertinent performance measures. The results, taken as a whole, highlight the significance of a strategic approach to personnel planning, placing a focus on ongoing enhancements in hiring, workload management, performance reviews, and employee welfare in order to promote organisational success.

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Creative Commons Attribution 4.0 and The Open Definition

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The increasing demand for reliable real-time monitoring and data acquisition systems in industrial, environmental, and research applications has led to the development of intelligent embedded data logging solutions. This project presents the design, implementation, and evaluation of a microcontroller-based system capable of acquiring, monitoring, and logging temperature data along with accurate time and date information. In this proposed work, the system is built around the LPC2148 ARM7 microcontroller and utilizes an LM35 temperature sensor for real-time temperature measurement. The on-chip Real-Time Clock (RTC) of the LPC2148 is employed to generate precise timestamps, ensuring reliable historical tracking of sensor data. The acquired temperature values and corresponding timestamps are displayed on a 16×2 LCD and simultaneously transmitted to a host computer through UART communication using the MAX232 interface. A user-friendly keypad-based interface enables modification of RTC parameters and temperature threshold settings during system operation. Performance analysis of the proposed system was conducted based on sensor response, timestamp accuracy, temperature acquisition accuracy, data transmission reliability, real-time monitoring capability, alert generation efficiency, and system reliability. Experimental results confirmed accurate data logging, stable communication, and efficient long-term monitoring performance. The integration of the LPC2148 internal peripherals such as ADC and RTC reduces the dependency on additional external hardware modules, thereby minimizing circuit complexity, power consumption, and overall implementation cost. The ARM7-based architecture provides improved processing capability, enabling faster sensor data acquisition, real-time decision-making, and efficient handling of multiple peripheral operations. Compared to conventional temperature monitoring and data logging systems, the proposed he proposed ARM7 (LPC2148)-based Time-Stamped Sensor Data Logger is an efficient and reliable system designed for real-time sensor monitoring, data acquisition, and timestamped data logging. The system demonstrates high performance across all operational parameters, achieving 92% sensor response, 95% timestamp accuracy, 93% temperature acquisition accuracy, 92% data transmission reliability, 95% real-time monitoring capability, 90% alert generation efficiency, and 93% overall system reliability. By integrating real-time sensing, automatic timestamp generation through a Real-Time Clock (RTC), LCD-based visualization, configurable monitoring settings, and UART-based communication, the system ensures accurate and continuous data recording with minimal hardware complexity. Its enhanced reliability, improved user interaction, flexible monitoring capabilities, and efficient data management make it highly suitable for industrial automation, environmental monitoring, laboratory experiments, and research-oriented applications where precise and time-synchronized data logging is essential.

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Keywords: Embedded Systems, LPC2148, ARM7 Microcontroller, Sensor Data Logging, Real-Time Clock (RTC), Temperature Monitoring, LM35 Sensor, Timestamp Synchronization, Data Acquisition, UART Communication.

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HR practices refer to the strategies, policies, and activities implemented by an organization to manage its workforce effectively. These practices aim to enhance employee performance, ensure well-being, and align human resource functions with organizational goals. They cover various aspects such as recruitment, training, performance management, compensation, employee relations, and workplace well-being initiatives. Effective HR practices contribute to employee satisfaction, productivity, and overall business success. HRM practices refer to organisational activities directed at managing the pool of human resources and ensuring that the resources are employed towards the fulfilment of organisational goals. HRM practices may differ from one organisation to another and from one country to another. The objective of the study is to analyse HR practices for enhancing employee well being and resilience at Ava Cholayil Health Care Pvt. Ltd., Chennai. The sample of the study is 120. Descriptive research design and convenience sampling method has been used. Questionnaire has been used as a primary data. Simple percentage analysis, chi-square analysis and correlation statistical tools have been applied to reach the findings of the study.It is found that 22.5% of the respondents are neither agree nor disagree towards the regular sessions on mental well-being and resilience are conducted in the organisation. 23.3% of the respondents are disagree towards the managers demonstrate support for employees facing mental health challenges in the organisation. It is suggested that To enhance work-life balance, the organization must adopt flexible work arrangements including remote work choices and movable work schedules. To lessen employee burnout, the company should use workload management techniques that guarantee responsibilities are divided equally. It is concluded that a proactive approach to employee well-being is not only advantageous but also necessary for organizational success, as the results underscore the significance of HR interventions in creating a resilient workforce.

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Creative Commons Attribution 4.0 and The Open Definition

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In recent times, hypertension has emerged as a primary cause of health globally. The market is filled with multiple types of antihypertensive formulations. In this Telmisartan is the one which is used in the treatment of hypertension. Telmisartan comes under Angiotensin II receptors blocker (ARBs) class. It binds to angiotensin II type 1 (AT1) receptor with high affinity, causing inhibition of the angiotensin II on vascular smooth muscle, ultimately leading to a reduction in blood pressure. Literature review revealed that there are many methods like UV Visible spectrophotometry, HPLC, HPTLC for determination of telmisartan. The aim of this review is to summarize them. The absorption maxima of telmisartan is 296 nm by using methanol as a solvent for Telmisartan. The linearity of this drug shows in the range of concentration of 2-90 µg/ml. Beer’s law was obeyed in this concentration range with correlation coefficient of 0.999.The average percentage recovery is in the range of 98.75-103 %. All the presented developed methods were validated according to ICH guidelines including parameters such as specificity, linearity and range, precision, accuracy, LOD and LOQ.

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Analytical Method development & Validation, UV spectroscopy, ICH Guidlines

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The advancement of artificial intelligence (AI) has led to the development of intelligent virtual assistants (IVAs) that offer seamless interaction between users and their environments. This report presents the design and implementation of a real-time voice assistant, capable of understanding and processing natural language commands while providing contextual information about the surrounding environment. The system is designed to address various user needs, including accessibility for visually impaired individuals, hands-free operation in specialized work settings, and enhanced interactivity in smart environments. The IVA integrates automatic speech recognition (ASR) models, natural language processing (NLP), and machine learning techniques to recognize user commands and provide accurate responses. Advanced NLP models such as BERT and GPT are used to understand user intent, while adaptive learning mechanisms ensure the assistant continuously improves based on user interactions. The system also supports multimodal interactions, combining voice commands with visual and environmental data to enhance user experience. The architecture includes a message broker for real-time communication between voice recognition, object detection, and response generation modules, ensuring quick and reliable information processing. Performance metrics show that the system achieves an intent recognition accuracy of over 90%, with a response time of less than 2 seconds. This demonstrates significant improvement over existing virtual assistants, particularly in adaptability and user personalization. The report discusses the methodology, system architecture, and technologies used in the project, including speech recognition frameworks like Google’s Speech-to-Text and machine learning libraries such as TensorFlow. The project has successfully addressed limitations in current systems by incorporating a learning engine and scalable architecture, making it a promising solution for future applications in accessibility and smart environments.

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Artificial Intelligence (AI), Machine Learning (ML), Deep Learning, Natural Language Processing (NLP, Speech Recognition (ASR), Intent Recognition

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Artificial intelligence (AI) systems are spreading quickly across many industries, creating previously unheard-of problems with data security and privacy. Three crucial aspects of AI-related privacy problems are thoroughly examined in this research paper: hazards associated with AI-powered surveillance, bulk data gathering techniques, and vulnerability to data breaches and misuse. This study finds important gaps in current privacy regimes and suggests integrated remedies through a methodical examination of recent literature, empirical data from 2018 to 2025, and case studies from various countries. According to our research, the amount of data created worldwide has increased to 120 zettabytes per year, with AI systems using about 65% of structured data for training show that between 2019 and 2025, the use of AI-powered monitoring increased by 347%, which raises serious concerns for civil liberties. Additionally, an examination of 1,247 data breach instances shows that AI systems are particularly vulnerable, with an average breach cost of $4.88 million-73% more than that of conventional systems. By creating a novel Privacy-Security-AI (PSA) paradigm that incorporates technological protections, legal safeguards, and moral governance frameworks, this study adds to the scholarly conversation. We assess the effectiveness of privacy-preserving technologies in practical uses, such as homomorphic encryption, federated learning, and differential privacy. The study highlights important conflicts between privacy protection and AI innovation and suggests sensible solutions that respect both basic rights and technical progress. Only 23% of businesses using AI systems have thorough privacy-by-design procedures, according to our analysis, underscoring the critical need for uniform frameworks. The creation of algorithmic impact evaluations, required data protection audits, and improved transparency methods are just a few of the practical suggestions this report offers to legislators, engineers, and organizational leaders. This study provides a basis for future research and policy creation in this crucial area by advancing knowledge of the intricate interactions between AI capabilities and privacy imperatives through the synthesis of technological, legal, and ethical viewpoints.

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Artificial Intelligence Privacy, Mass Data Collection, AI Surveillance, Data Security Breaches, Privacy-Preserving Technologies, Algorithmic Governance, Data Protection Frameworks, Civil Liberties

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ABSTRACT This study aimed to examine the significant relationship between teacher career-related support self-efficacy and work productivity among public elementary school teachers in Caraga North District, Division of Davao Oriental. A descriptive-correlational research design was employed, with data gathered from 125 teachers in Caraga North District, Division of Davao Oriental using adapted standardized survey questionnaires. The data were analyzed using mean, standard deviation (SD), Pearson product-moment correlation, and regression analysis. Findings revealed that both teacher career-related support self-efficacy and work productivity were rated at a very high level. Results of the correlation analysis indicated a moderately strong and statistically significant positive relationship between the two variables. Regression analysis revealed that all domains of career-related support self-efficacy such as get ready, empower self, get curious, empower skills, emotional support, and instrumental support, significantly influenced work productivity, with emotional support showing the highest predictive power. The findings suggest that school leaders and policymakers may design programs that strengthen teacher self-belief and professional support systems in order to sustain high productivity levels in educational settings.

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career-related support self-efficacy, work productivity, public elementary school teachers, descriptive-correlational, education

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Mathru Bhashayum Samoohavum Oru Padanam

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Mathru Bhashayum Samoohavum Oru Padanam

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Human rights are the fundamental basis for human dignity, equality, and freedom; they go beyond the limits set by nationality, race, religion, gender, or culture. After the destruction of World War II, the United Nations (UN) became the primary international organization dedicated to promoting and protecting human rights at the international level. The UN has established a global framework for human rights through the application of various instruments, including its Charter, Universal Declaration of Human Rights, several international treaties, treaties’ bodies, and peacekeeping forces. Despite this progress, instances of abuse of human rights by states continue all around the world, as well as armed conflicts, humanitarian crises, discrimination, and political oppression. This paper examines how the UN serves as the leading proponent of human rights by assessing the consistency and success of the UN’s states’ goals with real achievements. The research includes examining how the concept of human rights was created over time, how the UN-developed system for protecting human rights has changed and grown, and what role different parts of the UN play in protecting human rights like the Human Rights Council, Office of the High Commissioner for Human Rights, treaty-monitoring bodies (like the Committee for the Elimination of Racial Discrimination), and peacekeeping operations. This study will also discuss the UN’s efforts in promoting the protection of women’s rights, children’s rights, and to give hope to refugees; in creating an environment for achieving racial equality; and in encouraging sustainable development. The research also investigates the many significant challenges that the organisation faces, including political influence over the organisation, selective activism within the organisation, the veto power of Security Council member states, and institutional constraints against the organisation. Numerous case studies of recent examples, such as the Rwandan Genocide, the Bosnian Conflict, the Syrian Crisis, the Russo-Ukrainian War, and the Israel-Palestine Conflict were used to support the paper's argument; each of these case studies represented an instance where the UN's actions at the time were recorded as being inadequate or ineffective. Therefore, to enhance the UN's effectiveness in protecting human rights throughout the twenty-first century, institutional reforms, enhanced accountability, and greater international cooperation with the UN will be necessary.

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UN, HRC, CASE STUDIES

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Creative Commons Attribution 4.0 and The Open Definition

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The convergence of ultra-dense 5G/6G connectivity, massive-scale Internet of Things (IoT) ecosystems, and cloud-native distributed architectures has fundamentally transformed the network security landscape. Billions of heterogeneous endpoints generate enormous traffic volumes while introducing unprecedented attack surfaces—ranging from volumetric distributed denial-of-service (DDoS) floods and sophisticated polymorphic malware to advanced persistent threats targeting programmable network elements themselves. Concurrently, mission-critical applications impose strict non-functional requirements: sub- millisecond to tens-of-millisecond response times, strict data locality for privacy compliance (e.g., GDPR, CCPA, emerging sovereign cloud regulations), minimal backhaul bandwidth consumption, and robust operation under partial failures or adversarial conditions. Conventional security paradigms are increasingly inadequate in this context. Static perimeter- based defenses and signature-matching intrusion detection/prevention systems (IDS/IPS) suffer from poor zero-day detection rates and inability to scale with dynamic traffic patterns. Centralized cloud-based machine learning solutions, while capable of high accuracy through large-scale training, incur prohibitive round-trip latencies (often hundreds of milliseconds), expose sensitive flow metadata to central aggregation risks, and create attractive single points of failure. Early SDN-based security mechanisms improve visibility and policy agility but remain vulnerable to controller overload, flow-table exhaustion, and topology discovery attacks, particularly when relying on centralized control logic. This doctoral thesis addresses these intertwined challenges by proposing, implementing, and rigorously evaluating a novel hybrid security framework that tightly integrates the strengths of Software-Defined Networking (SDN) programmability with the low-latency, privacy- preserving inference capabilities of Edge Artificial Intelligence (Edge AI). The core contribution is a hierarchical, closed-loop architecture that deliberately distributes intelligence across three logical layers: 1. Edge Intelligence Layer — Lightweight, quantized deep learning models (hybrid CNN-LSTM for supervised multi-class classification and variational autoencoders for unsupervised reconstruction-based anomaly detection) are deployed directly on resource-constrained edge nodes (smart gateways, programmable routers, cloudlets, small-cell baseband units). These perform real-time inference on local flow statistics and sampled packets, achieving sub-10 ms detection latency and enabling optional fast- path mitigation actions (immediate drop or rate-limiting) for ultra-critical bursts. 2. SDN Control Layer — A resilient, clustered SDN controller (Ryu/ONOS-based) aggregates compact anomaly alerts from distributed edge nodes, executes cross-domain correlation using a deeper ensemble model (Transformer-augmented for sequence and topology awareness), and dynamically programs mitigation policies into the data plane via OpenFlow 1.5+ extensions or P4Runtime. Controller resilience is enhanced through leader election, east-west synchronization, and load-aware failover. 3. Orchestration & Learning Layer — Northbound applications provide visualization, operator oversight, threat-intelligence integration, and coordination of privacy- preserving federated learning rounds (via Flower framework with differential privacy noise injection) to continuously refine edge models without ever transmitting raw packet payloads or sensitive flow features to a central repository. Key technical innovations include: • A hybrid detection engine that combines supervised precision on known threats with unsupervised sensitivity to zero-days, achieving macro-averaged F1-scores of 0.97– 0.99 across benchmark datasets (CIC-IDS2018, InSDN, custom 5G-IoT traces). • A closed-loop feedback mechanism that enables sub-50 ms end-to-end proactive response (from anomaly onset to policy enforcement), with edge-first inference reducing backhaul traffic by over 95% compared to cloud-centric alternatives. • Federated model updates with differential privacy (ε=1.0) that maintain model utility while preventing inference attacks on individual edge contributions. • SDN-specific resilience extensions (clustered controllers, anomaly-aware load balancing, priority-based flow rules) that mitigate controller saturation and flow-table poisoning attacks more effectively than single-controller baselines. The framework was prototyped using Mininet for realistic topology emulation (10–100 nodes), Ryu/ONOS controllers with custom security modules, TensorFlow Lite for edge inference, and Flower for federated aggregation. Over 150 simulation runs evaluated performance across diverse conditions: varying traffic loads (100 Mbps–1 Gbps), attack intensities (low to high volumetric/probing/exfiltration), topology scales, and edge resource constraints (512 MB–4 GB RAM, 1–8 cores). Comparative analysis against baselines (Snort-style signature IDS, centralized SDN-ML, cloud-offloaded AI) demonstrated: • Detection accuracy: 97–99% overall, with robust zero-day handling (F1 > 0.85 on unseen classes). • Latency reduction: 80–90% end-to-end compared to centralized approaches. • Resource efficiency: <5% average CPU/memory overhead on edge nodes during normal operation. • Resilience: Clustered controller deployments reduced overload impact by 35–50% during saturation attacks. • Scalability: Graceful degradation up to 100 nodes, with multi-link configurations accelerating accuracy convergence. The results underscore the emergence of collective intelligence in distributed networks: additional active edge links enrich feature diversity and accelerate convergence to near-perfect accuracy, while privacy-by-design principles ensure regulatory alignment without compromising detection efficacy. Despite these advances, the work acknowledges limitations inherent to simulation-based evaluation, preliminary adversarial robustness testing, and short-term experiment durations that do not fully capture long-horizon model drift. These constraints are explicitly documented, together with concrete mitigation strategies and pathways toward physical testbed validation, larger-scale deployments, and integration with emerging 6G paradigms (terahertz communication, integrated sensing and communication, quantum-safe cryptography). This thesis delivers a validated, reproducible, and extensible blueprint that meaningfully bridges the longstanding divide between SDN programmability and Edge AI intelligence. By enabling proactive, scalable, privacy-respecting, and resilient defense in hyper-connected ecosystems, the proposed framework represents a substantive contribution toward autonomous, self-healing networks capable of confronting the evolving cyber threat landscape of the 2030s and beyond.

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Creative Commons Attribution 4.0 and The Open Definition

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Surface water is a vital resource for rural communities, but it is often vulnerable to contamination due to inadequate sanitation practices and surrounding human activities. The present study was undertaken to evaluate the bacterial contamination of surface water sources in Negh and Jani villages of Uttar Pradesh. Water samples were collected from different sites, including ponds, canals, and storage sources, and analyzed using standard microbiological methods such as Total Viable Count (TVC), Most Probable Number (MPN) test, Gram staining, and biochemical characterization. The Total Viable Count indicated a high microbial load in all samples, with pond water showing the highest level of contamination. The MPN test confirmed the presence of coliform bacteria in all samples, indicating fecal pollution of the water sources. Gram staining revealed Gram-negative rod-shaped bacteria, suggesting the presence of coliform organisms. Biochemical tests, including Methyl Red (MR), Voges–Proskauer (VP), and Simmons citrate tests, confirmed the dominance of Escherichia coli in all isolates. The findings clearly indicate that the contamination is mainly due to poor sanitation, open defecation, agricultural runoff, and improper disposal of waste into water bodies. The detection of E. coli highlights the potential health risks associated with the direct use of these water sources. Consumption of such contaminated water may lead to serious waterborne diseases such as diarrhea, dysentery, typhoid, and cholera. In conclusion, the surface water sources in Negh and Jani villages are microbiologically unsafe and require proper treatment before use. Regular monitoring, improved sanitation facilities, and public awareness are essential to ensure safe water quality and protect public health in rural areas.

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Keywords: Surface water, Total Viable Count, Most Probable Number, Escherichia coli, rural sanitation. 2*Corresponding Author: Dr. Rekha Yadav, Assistant Professor, Department of Microbiology, Meerut Institute of Engineering and Technology, Meerut Uttar Pradesh, India

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Herbal cosmetics are the preparations used to enhance the human appearance. Herbal hair gel is formulated from natural ingredients and herbal extracts helps in controlling hair falling, removes dandruff. The aim of this research work was to formulate and evaluate herbal hair gel containing Aloe vera and flax seed for hair growth potential and anti dandruff activity. Flax seed (Linum usitatissimum) contains vitamin E which improves hair growth and result in stronger follicles. Flax seed and Aloe vera extracts were prepared by aqueous extraction. Five different types of gel formulations containing varying concentration of Flax seed and Aloe vera extracts were prepared and evaluated. The formulations (F1 to F5) were evaluated for various parameters like colour, odour, gel texture, clarity, pH, viscosity, spreadability, extrudability, gel strength, homogeneity, stability studies and in vitro antifungal activity. Among the five formulations, F4 was stable for long period of time without compromising the antifungal activity. The overall results of this study support that herbal hair gel prepared using Aloe vera and Flax seed could be used for hair growth and reduction of dandruff.

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Creative Commons Attribution 4.0 and The Open Definition

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This project presents the fabrication of a banana peel grinding machine that converts organic waste into eco-friendly fertilizer using a manual handle-driven spur gear mechanism. Banana peels are rich in essential nutrients and can be effectively reused for soil enrichment. The machine consists of a grinding unit, spur gear system, and a hand-operated handle for power transmission. When the handle is rotated, motion is transferred through gears to crush the banana peels into fine particles. This cost-effective and electricity-free system is suitable for rural and small-scale applications, promoting sustainable waste management and organic farming practices.Banana peels are one of the major organic wastes generated from households, hotels, fruit markets, and food processing industries. This project focuses on the fabrication of a banana peel fertilizer production system that reduces waste and promotes sustainable agricultural practices. The machine consists of a feeding hopper, grinding unit, motor arrangement, collection chamber, and supporting frame. The grinding mechanism helps in reducing banana peels into fine particles, which decompose faster and improve nutrient availability. Banana peels are naturally rich in potassium, phosphorus, calcium, and other micronutrients that enhance soil fertility and plant growth.The fabricated grinding unit machine provides a simple, low-cost, and environmentally friendly solution for organic waste management. By converting banana peel waste into fertilizer, the system helps reduce environmental pollution and supports eco-friendly farming methods. The processed fertilizer can be used in gardens, farms, and nurseries to improve soil texture and crop productivity without harmful chemicals. This project also creates awareness about recycling biodegradable waste into useful agricultural products. The overall system is compact, easy to operate, and suitable for small-scale agricultural and domestic applications.

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Keywords Banana Peel Fertilizer Organic Waste Management Eco-Friendly Fertilizer Grinding Unit Machine Sustainable Agriculture Spur Gear Mechanism Manual Handle Drive Organic Farming Biodegradable Waste Waste Recycling Soil Enrichment Nutrient-Rich Fertilizer Composting Process Agricultural Waste Utilization Low-Cost Fabrication Green Technology

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The logistics and transportation industry plays a significant role in the economic development of India by ensuring smooth movement of goods and efficient supply chain operations. Transportation cost management has become one of the most critical areas for logistics companies due to increasing fuel prices, operational inefficiencies, rising competition, and customer expectations regarding timely delivery. The present study titled “A Study on Transportation Cost Management in Logistics Companies in Nagpur” aims to analyze the major factors affecting transportation cost and evaluate the cost management practices adopted by logistics companies operating in Nagpur. The study focuses on companies such as ACTRANS GLOBAL SERVICES PVT. LTD., Gati Logistics, Om Logistics, and VRL Logistics. Both primary and secondary data sources were used for the research. Primary data was collected through structured questionnaires and interactions with logistics professionals, while secondary data was collected from journals, research papers, books, and industry reports. The study identifies fuel cost, fleet utilization, route planning, vehicle detention, maintenance expenses, and dead mileage as the major contributors to transportation cost. The findings reveal that transportation contributes nearly 70% of total logistics cost, while fuel alone contributes approximately 60% of transportation expenses. The research also highlights that technologies such as GPS tracking, route optimization systems, and fleet management tools help improve operational efficiency and reduce unnecessary costs. The study further concludes that effective transportation cost management positively impacts profitability, operational performance, customer satisfaction, and overall supply chain efficiency. However, challenges such as traffic congestion, fuel price fluctuations, inefficient driver performance, and high detention time continue to affect logistics operations. The research suggests that logistics companies should focus on better route planning, improved fleet management, reduction of dead mileage, technological integration, and driver performance management to optimize transportation costs and enhance competitiveness in the logistics industry.

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Creative Commons Attribution 4.0 and The Open Definition

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Transportation Cost Management, Logistics Industry, Supply Chain Management, Fleet Utilization, Fuel Cost, Route Optimization, Logistics Efficiency, Transportation Operations, Cost Optimization, Nagpur Logistics

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Creative Commons Attribution 4.0 and The Open Definition

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The explosive growth of generative Artificial Intelligence—particularly large language models (LLMs) and increasingly multimodal foundation models—has transformed their role from research curiosities into core infrastructure powering education, healthcare, legal systems, financial services, creative industries, and national security applications. By early 2026, models with hundreds of billions of parameters are routinely deployed in consumer products, enterprise workflows, and public services, often with direct, unfiltered user interaction. This scale and accessibility have dramatically amplified the consequences of safety failures: a single adversarial prompt can now elicit hate speech, self-harm instructions, election disinformation, fraud facilitation, biometric data leakage, or coordinated agentic misuse. Real-world incidents reported between 2024 and 2026—from jailbroken consumer chatbots generating bomb-making instructions to enterprise models leaking proprietary source code—have demonstrated that safety is no longer an academic concern but an urgent societal and regulatory imperative. Despite substantial progress in alignment techniques (reinforcement learning from human feedback, constitutional AI, representation engineering, and guardrail layering), empirical evidence continues to reveal persistent and evolving vulnerabilities. Frontier models that appear robust on standard benchmarks routinely fail under adaptive, multi-turn, or cross-modal adversarial conditions. Traditional safety evaluation paradigms have proven fundamentally inadequate: manual red teaming, while creative, is labor-intensive, non-reproducible, and limited to a few thousand prompts per cycle; static benchmarks (e.g., AdvBench, HarmBench) quickly become saturated and fail to reflect novel attack vectors; and one-off vendor-led evaluations lack transparency and independent verification. This thesis directly addresses these shortcomings by introducing a fully automated, feedback-driven red teaming framework capable of systematically discovering, categorizing, and quantifying safety vulnerabilities in generative AI systems at scale. The proposed framework transforms safety evaluation from a sporadic, human-bound exercise into a continuous, reproducible, and extensible engineering process. It comprises four tightly integrated components: 1. an adaptive adversarial prompt generation module powered by a strong attacker LLM guided by category-specific meta-prompts and historical failure memory; 2. a standardized, asynchronous interaction layer supporting black-box, semi-black-box, and (where permissible) white-box access to target models via API or local inference; 3. a hybrid judgment pipeline combining LLM-as-a-judge with fine-grained rule-based classifiers and severity rubrics for detecting explicit, implicit, and context-dependent violations; and 4. a closed-loop refinement engine that evolves failing attacks through tree-of-thought branching, reward-model guidance, or evolutionary selection, dramatically increasing discovery of subtle failure modes over successive iterations. The framework is explicitly designed for real-world constraints: it operates without requiring gradients or internal activations, supports both proprietary (OpenAI, Anthropic, Google) and open-weight models (Llama-3.1, Mistral, Gemma derivatives), and produces fully auditable traces suitable for regulatory reporting. Extensive experimental evaluation across frontier and mid-tier models (GPT-4o series, Claude 3/3.5, Llama-3.1-70B/405B, Mistral-Large-2, Gemini-1.5) demonstrates that the framework consistently achieves 60–85% attack success rates across 10–12 high-priority risk categories, including hate speech, self-harm promotion, misinformation, illegal/criminal advice, regulated substance instructions, bias amplification, PII leakage, and prompt injection leading to policy override. Crucially, feedback-driven refinement yields 1.8×–3.2× higher violation discovery after only 3–5 iterations compared to static prompt sets, exposing failure modes—such as gradual conversational grooming, non-English obfuscation, and indirect policy circumvention—that remain invisible to conventional benchmarks. Quantitative metrics (attack success rate, category coverage, query efficiency, severity-weighted harm score, and cross-run reproducibility) provide a rigorous, comparable basis for safety assessment, with reproducibility variance typically below 5% under seeded conditions. Comparative analysis with human red teaming efforts (both internal and from publicly reported exercises) reveals that automation delivers 10–50× greater breadth, near-perfect replicability, and dramatically lower per-violation cost, while human experts retain an edge in culturally nuanced or ethically borderline cases. The most effective paradigm emerges as hybrid: automated systems explore the vast prompt space and triage failures, while humans focus on high-severity anomalies and strategy innovation. Beyond immediate technical contributions, this research carries significant implications for AI governance. As regulatory frameworks mature—the EU AI Act (2026 obligations), U.S. Executive Order follow-ons, and emerging international standards—all mandate documented adversarial robustness testing for general-purpose models with systemic risk. The proposed framework directly satisfies these requirements by producing traceable, version-controlled evidence of evaluation scope, methodology, and outcomes. More fundamentally, the results reinforce that safety cannot be treated as a pre-deployment checkbox: many critical failures only manifest through prolonged, adaptive interaction, mirroring real-world misuse. Continuous automated red teaming—integrated into MLOps pipelines, runtime monitoring, and post-release regression testing—emerges as an essential practice for responsible stewardship. In summary, this thesis presents a mature, open-architecture solution for scalable safety evaluation of generative AI. By transforming red teaming into an automated, iterative, and measurable discipline, it provides researchers, developers, auditors, and policymakers with a powerful new tool to ensure that increasingly capable AI systems remain safe, trustworthy, and aligned with human values in an era of rapid deployment and evolving threats. The framework, empirical findings, and accompanying codebase lay a robust foundation for the next generation of AI safety infrastructure.

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Creative Commons Attribution 4.0 and The Open Definition

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As global temperatures rise due to climate change, ensuring the thermal resilience of staple crops like wheat (Triticum aestivum L.) is critical for future food security. This paper presents an integrative multi-omics framework—comprising genomics, transcriptomics, proteomics, and metabolomics—to unravel the complex molecular mechanisms underlying heat stress tolerance in wheat. Utilizing recent advances in next-generation sequencing, high-throughput transcriptome profiling, proteomic mass spectrometry, and metabolite fingerprinting, we identify key biomarkers, regulatory networks, and candidate genes associated with thermo-tolerance. Particular focus is placed on heat shock proteins (HSPs), transcription factors (Hsf, DREB, NAC), antioxidant enzyme pathways, and secondary metabolite accumulations. By mapping stress-responsive quantitative trait loci (QTLs) with corresponding transcript and metabolite profiles, this study provides a holistic understanding of heat adaptation. The article also explores how multi-omics data integration using machine learning pipelines (e.g., deep neural networks, random forests) can enhance breeding models. This research lays a genomic foundation for precision breeding programs, offering viable strategies to develop climate-resilient wheat cultivars.

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Wheat, Heat Stress, Genomics, Transcriptomics, Proteomics, Metabolomics, Multi-Omics, Molecular Breeding, Climate Resilience

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Creative Commons Attribution 4.0 and The Open Definition

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Digital media has had a significant impact on political communication and the involvement of citizens in democracy.Digital media has revolutionized political communication and citizen engagement in democratic processes. The social media landscape (Facebook, Twitter (X), Instagram, YouTube, and the like) is one of the significant sources of political information that shapes political consciousness, opinion building, electoral voting, and civic participation. This study aims to analyze how digital media affects political behavior by a big data approach, using social media analysis. This research is descriptive research and analytical research which is type of descriptive research. This research is secondary data which obtained from the various social media. The research uses the big data analytics, sentiment analysis, machine learning methods and engagement analysis to gauge the interactions of the users like likes, shares, comments, hashtags, political discussions, etc. The analysis is centered on the user's awareness and exposure to politics, the formation of echo chambers, as well as the influence on their voting.The analysis is based on the understanding of political awareness, exposure to misinformation, echo chambers and political influence on voting among users. The results show that digital media is a tool which is important in raising political consciousness and involvement with politics. Digital media users who have high exposure to digital media are more knowledgeable and engaged in politics than those who have low exposure to digital media. This study also reveals that the engagement level of the content that is created using the multimedia approach is higher compared to the text-based approach. The research points to some significant issues regarding misinformation, fake news, algorithm filtering and echo chambers that leads to political polarization and skewed perceptions of politics. Sentiment analysis also reveals that negative political content predominates on online platforms, and has a significant impact on users' attitudes and choices. The research shows that digital media is a strengthening and a challenging element of today's political systems. It can improve political communication, participation, and awareness but can also lead to a risk of misinformation, polarization, and a lack of exposure to alternative viewpoints. The research suggests increased and better digital literacy initiatives, better content moderation, transparency of algorithms and digital platforms, and responsible social media use to support healthier democratic engagement.

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Creative Commons Attribution 4.0 and The Open Definition

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Quinoxaline stands out as an important bicyclic nitrogen compound with broad applications in material science, catalysis, and biochemistry. In this review, I focus on environmentally friendly ways to make quinoxaline, especially methods that avoid transition metals. These greener approaches increase sustainability by using resources efficiently, cutting down on toxic byproducts, and reducing pollution and waste.Some of the latest techniques include aqueous azaannulation, metal-free cyclizations, mechanochemical reactions, and organocatalysis. Not long ago, researchers mostly looked at yield to judge a method’s value. Now, they use Life Cycle Assessment (LCA) to get a fuller picture of each method’s environmental impact.I also cover innovative catalysts and how researchers use them in drug discovery and advanced materials. The ongoing shift toward greener methods opens the door for these compounds to play a bigger role in technology and medicine, all while lessening their environmental footprint.

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quinoxaline ,efficiency , green synthesis, eco-friendly , hetrocycle

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Creative Commons Attribution 4.0 and The Open Definition

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In an unprecedented socio-economic disruption, the COVID-19 pandemic was accompanied by an unintended "Shadow Pandemic" of a surge in gender-based violence (GBV). The novelty of this paper lies in the socio-economic structure of domestic violence that is responsible for this explosion in Delhi, India, rather than the psychological strain of living within the walls. This study combines both macroeconomic indicators (food inflation and male unemployment rate) using a triangulation method, and micro-level sociological survey data of women throughout the megacity. Using Intra-household Bargaining Models, these results show that the enforced lockdowns were an exogenous event which systematically broke the women's Extra-Environmental Parameters (EEPs). The lockdowns effectively reduced the bargaining power of the women from inside the home as lockdowns reduced outside options to zero and collapsed social networks. At the same time,rising prices and lack of employment among men within households became economic catalysts that led to violent compensatory behaviors on the part of the abusers, compounded by the weaponization of the digital gender gap. This paper ultimately highlights the fact that GBV was not just a psychological consequence, but a predictable product of extreme economic hardship coming together deeply entrenched with patriarchy. The study supports the critical need to mainstream gender-responsive economic support and effective social protection systems in the next generation of models for crisis management.

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Shadow Pandemic, Domestic Violence, Socio-Economic Shock, Digital Gender Divide, Double Burden, She-cession

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Information asymmetry and market volatility pose significant challenges to the financial sustainability of small-scale farmers in developing economies. While traditional statistical models struggle with the non-linear nature of agricultural pricing, this paper proposes a robust Ensemble Learning Framework based on a Random Forest Regressor for 15-day price forecasting. A key novelty of this work is the implementation of Uncertainty Quantification; by analyzing the variance across individual decision tree estimators, the system provides a confidence interval for every prediction, offering a measure of reliability to the end-user. The model incorporates a decade of historical market data from the Agmarknet portal, augmented with customengineered Climatic Stress features—such as temperature and rainfall thresholds—to capture supply-chain disruptions. Experimental results yield an R² score of 92.2% and a Mean Absolute Error (MAE) of ₹234 per quintal, significantly outperforming baseline linear models. The framework is deployed via a high-performance Flask-based REST API, providing localized, actionable intelligence to farmers through a mobile interface. This research demonstrates that combining ensemble variance with domain-specific feature engineering provides a superior, risk-aware approach to agricultural commodity forecasting.

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 Primary Keywords 1. Ensemble Learning: The core methodology utilizing a Random Forest architecture. 2. Agricultural Price Forecasting: The primary application and objective of the research. 3. Random Forest Regressor: The specific ML model implemented for price estimation. 4. Uncertainty Quantification: The process of using estimator variance to provide confidence intervals. 5. Feature Engineering: The custom transformation of raw data into domainspecific inputs like "Climatic Stress".  Secondary & Technical Keywords 1. Climatic Stress Features: Binary indicators for temperature and rainfall thresholds (T > 40°C or R > 200mm). 2. Agmarknet Portal: The source for a decade of historical market data used in training. 3. Trigonometric Temporal Encoding: The use of Sine and Cosine functions to model cyclical monthly data. 4. Flask REST API: The highperformance framework used for realtime model deployment. 5. Coefficient of Determination (R^2): The primary metric used to validate the model's accuracy, reaching 92.2%. 6. Mean Absolute Error (MAE): The average magnitude of error, recorded at ₹234 per quintal.  Domain-Specific Context 1. Information Asymmetry: The market challenge addressed to help small-scale farmers. 2. Market Volatility: The unpredictable price fluctuations the system aims to predict. 3. Confidence Interval: The reliability metric provided alongside every price prediction.

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The quick development of artificial intelligence (AI) in marketing has changed how brands connect with consumers through highly personalized experiences. This growth has also created a significant issue called the personalization-privacy paradox. Consumers want personalized interactions but also worry about data privacy. This study looks at how privacy concerns affect consumer trust, emotional attachment, and brand loyalty in AI-driven marketing settings. The research examines whether greater personalization improves customer relationships or causes discomfort from feeling intruded upon. With a structured survey method, the study explores consumer attitudes toward AI tools like recommendation engines, targeted ads, and predictive analytics. The goal is to find out when personalization changes from being seen as helpful to intrusive. Additionally, the study looks at how trust serves as a link between privacy concerns and emotional attachment, and how this connection influences long-term brand loyalty. The research gives marketers insights to create strategies that balance personalization with responsible data use, transparency, and consumer control. By tackling this paradox, the study helps deepen our understanding of consumer behaviour in digital environments and provides practical advice for brands that want to create meaningful, trust-based relationships in a data-driven world.

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Creative Commons Attribution 4.0 and The Open Definition

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Personalization, Privacy Concerns, Consumer Trust, Emotional Attachment, Brand Loyalty

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Creative Commons Attribution 4.0 and The Open Definition

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This dissertation, titled “Reasonable Restrictions on Media Reporting of Sub Judice Matters in India,” studies the growing conflict between the freedom of the press under Article 19(1)(a) and the right to a fair trial under Article 21 of the Constitution of India. In today’s world of constant news updates and social media, the limits of responsible reporting have become unclear. This has led to the rise of what is known as “trial by media,” where the media influences public opinion and sometimes affects ongoing court cases.The study identifies an important gap in the law. Existing legislation, especially the Contempt of Courts Act, 1971, does not properly deal with modern challenges like social media, viral content, and algorithm-driven news spread. Using a doctrinal research method, this dissertation analyses important court judgments and reports, including those of the Law Commission of India. It also looks at the psychological impact of media trials, such as how they can influence witnesses and even create indirect pressure on judges. The findings of this research show that the current legal system in India is not fully prepared to handle trial by media in the digital age. Although courts have taken steps to control the situation through judicial decisions, these measures are often temporary. Self-regulatory bodies like the NBDSA have also not been very effective.

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Creative Commons Attribution 4.0 and The Open Definition

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Sub Judice, Trial by media, Reasonable Restrictions, Art 19, art 21, Contempt Of Courts

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Creative Commons Attribution 4.0 and The Open Definition

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Solapur is the third largest district of the Maharashtra State of India harboring great deals of plant wealth. In the present paper, 1438 species belonging to 695 genera and 143 families of flowering plants have been recorded for the district. Poaceae and Fabaceae are the most diverse families in the district with 157 and 130 taxa respectively, followed by Asteraceae (85), Euphorbiaceae (61), Cyperaceae (47), Mimosaceae (40), Acanthaceae (38), Convolvulaceae (37), Malvaceae (35) and Solanaceae (32 taxa). Acacia is the largest genus with 25 taxa, followed by Euphorbia (23), Cyperus (22), Crotalaria (19), Ipomoea (19), Cassia (18), Alysicarpus (14), Indigofera (13), Ficus (13) and Fimbristylis (12). During the present work, a remarkable new species Crinum solapurense Gaikwad et al. was described, and Astraea lobata (L.) Klotz. added to the list of flora of India. Thus, the district has a great potential of plant wealth.

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Flora, Solapur, Genera, Species, Biodiversity

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Creative Commons Attribution 4.0 and The Open Definition

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Agriculture remains the backbone of many developing economies, yet farmers continue to face significant challenges such as lack of technical knowledge, limited access to modern farming practices, and dependency on intermediaries for selling their produce. These intermediaries often reduce farmers’ profit margins and limit their direct access to markets. The proposed project, Agricultural E-Learning and ECommerce Platform (Seed2Skill), is designed to address these issues by integrating digital education with a direct online marketplace, thereby empowering farmers and reducing reliance on middlemen.

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AI-Based Crop Disease Prediction

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This project focuses on the development of an intelligent system that uses Artificial Intelligence (AI) to predict natural disasters and improve response strategies. Natural disasters such as floods, earthquakes, cyclones, and wildfires cause significant damage to life and property. Early prediction and efficient response are critical to minimizing their impact. The proposed system utilizes machine learning and deep learning algorithms to analyse historical and real-time data collected from weather reports, satellite sources, and environmental sensors. Based on this data, predictive models are built to identify patterns and forecast the likelihood, location, and severity of potential disasters. In addition to prediction, the system provides an effective response mechanism by generating real-time alerts, evacuation plans, and safety guidelines for users. It includes features such as live monitoring dashboards, emergency notifications, and resource management to assist both the public and disaster management authorities. The system is designed to work efficiently even in limited or offline conditions by using pre-collected government and satellite data. By integrating AI with disaster management, this project aims to enhance preparedness, reduce risks, and support timely decision-making during critical situations.

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Key words are: • Disaster Management • Real-Time Monitoring • Risk Assessment • Emergency Response • Data Analytics • Natural Disasters • Evacuation Planning • Resource Management Vendor Filtering

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FusionCart – A Dual-Mode E-Commerce Platform is an innovative digital solution designed to integrate both product-based and service-based commerce into a single unified system. The platform addresses the growing demand for a comprehensive marketplace where users can seamlessly purchase physical goods while also booking services such as event planning, vendor hiring, and on-demand assistance. Traditional e-commerce systems primarily focus on product transactions, whereas service platforms operate independently, leading to fragmentation and inefficiency. FusionCart bridges this gap by offering a hybrid ecosystem that enhances user convenience and operational efficiency.The platform leverages advanced technologies such as Artificial Intelligence (AI) and real-time data processing to deliver personalized recommendations, dynamic pricing, and intelligent vendor selection. The AI-based recommendation engine analyzes user behavior, preferences, and transaction history to provide relevant product and service suggestions. Additionally, the system includes features such as live chat communication, secure payment integration, real-time booking management, and budget optimization tools, ensuring a smooth and interactive user experience. FusionCart is designed with a modular architecture that supports scalability, flexibility, and high performance. It enables vendors to manage their profiles, update pricing dynamically, and interact directly with customers. The inclusion of location-based services, such as navigation assistance, further enhances usability by helping users identify nearby service providers. Security and data privacy are prioritized through robust authentication mechanisms and encrypted transactions. Overall, FusionCart represents a next-generation e-commerce solution that combines the strengths of traditional online shopping and service marketplaces. By providing a unified, intelligent, and user-centric platform, it aims to redefine the digital commerce landscape and improve both customer satisfaction and vendor engagement.

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chatbot which suggest, place order in our own regional language

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Core Focus and Objectives The primary aim of the study is to analyze how structured financial analysis can support accurate tax planning, ensure timely compliance, and optimize tax benefits like Input Tax Credit (ITC). It emphasizes that for diversified firms, taxation management must shift from a reactive compliance burden to a proactive strategic function that enhances overall profitability. +2 Key Research Areas Taxation Landscape: The research examines the complexities of the Goods and Services Tax (GST), Income Tax, and Tax Deducted at Source (TDS) regimes in India. It highlights specific challenges such as GST rate classifications (ranging from 5% to 18%), ITC reconciliation (GSTR-2A vs. GSTR-3B), and the management of working capital constraints caused by tax outflows. +4 Business Diversification: It investigates how expanding into interior and exterior finishing services complicates tax management due to varying GST rates and composite/mixed supply classifications. +2 Financial Literacy: The study evaluates the awareness levels and knowledge gaps among finance professionals regarding frequent regulatory changes and the use of digital tools for compliance. +2 Methodology The research utilizes a mixed-method approach: Quantitative: Structured surveys conducted with a target sample of 56 respondents, including accountants and tax consultants. +1 Qualitative: Semi-structured interviews and direct observations to understand practical implementation challenges. Case Study: A focused evaluation of the existing practices at Sona Infra Build Pvt Ltd. +1 Primary Findings and Conclusion The study concludes that integrating financial planning with taxation strategies—through tools like real-time tracking, ERP systems, and ratio analysis—is essential for sustainable growth in the construction sector. While respondents generally show functional compliance behavior, there is a significant need for enhanced tax literacy and better technological automation to reduce procedural errors.

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Creative Commons Attribution 4.0 and The Open Definition

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Introduction In today’s service-driven economy, especially in the multiplex cinema industry, customer experience and operational efficiency play a crucial role in determining business success. Multiplex theatres are no longer just places to watch movies; they have evolved into complete entertainment destinations where customers expect comfort, convenience, quality service, and memorable experiences. This combined research focuses on two major aspects of the multiplex industry: Customer Experience Management (CEM) with reference to PVR Cinemas Role of Operations Associates in theatre operations with reference to Cinepolis India Pvt. Ltd. Both studies aim to understand how internal operations and customer-facing services together influence overall customer satisfaction and business performance. Objectives of the Study The primary objectives of the combined research include: To analyze customer experience and satisfaction levels in multiplex theatres To understand the role and responsibilities of Operations Associates in daily theatre functioning To examine operational workflow and coordination between departments To evaluate service quality factors such as staff behavior, cleanliness, waiting time, and ambience To identify operational challenges during peak hours and high-demand situations To provide practical suggestions for improving service quality and operational efficiency The studies also aim to bridge the gap between theoretical concepts of service management and real-time industry practices. Research Methodology Both studies adopted a descriptive research design, which focuses on observing and analyzing existing conditions without manipulating variables. Data Collection The research is based on both primary and secondary data: Primary Data: Structured questionnaires distributed to customers Direct observation during internship Informal discussions with staff and supervisors These methods helped in understanding real-time customer behavior, operational workflow, and service challenges. Secondary Data: Company websites Research articles and journals Books related to operations and service management Sampling Method Convenience sampling was used Respondents were selected based on availability Sample size (PVR study): 50 respondents Tools Used Questionnaire (close-ended questions) Observation techniques Percentage analysis and tabular representation Scope of the Study The research is limited to specific locations: PVR Cinemas – Vega City, Bengaluru Cinepolis – Arekere, Bengaluru The study focuses mainly on: Theatre-level operations Customer service quality Employee roles and responsibilities It does not include financial or corporate-level analysis. Literature Review Insights Previous studies highlight that customer satisfaction in service industries depends on multiple factors such as: Reliability and responsiveness Staff behavior and communication Cleanliness and ambience Technology and convenience The SERVQUAL model is widely used to measure service quality. Research also shows that: Multiplexes are seen as lifestyle destinations Customer experience includes emotional and psychological aspects Technology (online booking, digital payments) improves convenience However, there is limited research focusing on: Branch-level analysis Role of frontline employees like Operations Associates Integration of all service touchpoints Operational Workflow and Role of Employees One major focus of the study is understanding the operational workflow in multiplex theatres. The workflow includes: Ticket booking and verification Customer entry and exit management Seating assistance Food and beverage services Interval management Cleaning and show turnover Operations Associates play a key role in managing these activities. Role of Operations Associates Ticket verification Guiding customers Managing crowd flow Maintaining discipline inside theatres Coordinating with departments They act as a bridge between management and customers, directly influencing customer experience. Customer Experience Factors The study identifies several key factors affecting customer satisfaction: 1. Ambience Cleanliness Comfortable seating Attractive interiors 2. Staff Behavior Politeness and helpful nature Communication skills Quick response to queries 3. Audio-Visual Quality Screen clarity Sound quality 4. Food and Beverage Services Quality of food Pricing 5. Waiting Time Ticket counters Food counters Entry procedures These factors collectively shape the overall customer experience. Findings of the Study Positive Findings Majority of customers are satisfied with overall experience Strong performance in: Cleanliness Staff behavior Theatre ambience Audio-visual quality Employees contribute significantly to service quality Good coordination between departments improves efficiency Negative Findings / Issues Long waiting times during peak hours High pricing of food and beverages Crowd management challenges Staff shortage during busy periods These issues affect customer satisfaction despite good service quality. Operational Challenges Identified The study highlights several real-world challenges: Peak-hour pressure and crowd control Delay in service delivery Coordination issues between departments Maintaining cleanliness during continuous shows Managing customer complaints Internship observations showed that: Long queues at food counters reduce satisfaction Efficient billing systems are necessary Proper staff allocation is important during rush hours. Suggestions for Improvement Based on analysis, the following improvements are suggested: Operational Improvements Increase staff during peak hours Better crowd management systems Use of data to predict footfall Improve coordination between departments Service Improvements Reduce waiting time Improve food pricing strategies Enhance customer feedback systems Technology Adoption Digital ticketing Faster billing systems QR-based feedback collection These changes can improve efficiency and customer satisfaction. Conclusion The combined study clearly shows that customer experience and operational efficiency are closely linked. Efficient operations lead to better service delivery Frontline employees (Operations Associates) play a crucial role Customer satisfaction depends on both tangible and intangible factors The research concludes that: Multiplex theatres are generally performing well Customer satisfaction levels are high However, continuous improvement is necessary to remain competitive In today’s environment, where customers have multiple entertainment options like OTT platforms, delivering a seamless and enjoyable experience is essential for customer retention and business growth. Overall Summary This combined research provides a comprehensive understanding of: Multiplex operations Customer experience management Role of employees in service delivery It successfully bridges the gap between theory and practice and offers practical insights for improving performance in the entertainment industry.

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Cinepolis, Operations Associate, PVR Cinemas, Customer Satisfaction.

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Sales Strategies, Two-Wheeler Dealership, Customer Satisfaction, Automobile Industry, Personal Selling, Promotional Techniques, Customer Relationship Management, Sales Performance

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Creative Commons Attribution 4.0 and The Open Definition

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Sales Strategies, Two-Wheeler Dealership, Customer Satisfaction, Automobile Industry, Personal Selling, Promotional Techniques, Customer Relationship Management, Sales Performance

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This study presents a comprehensive analytical examination of employee retention strategies through non-monetary rewards and long-term incentives, with specific reference to IndusViva HealthSciences Pvt. Ltd., operating within the Indian direct selling industry. The research aims to explore how structured incentive systems, beyond traditional salary-based compensation, contribute to sustained workforce engagement, productivity, and organizational growth in a network-driven business model. The direct selling industry in India has evolved into a significant economic sector, contributing substantially to employment generation and entrepreneurial opportunities. Unlike conventional business structures, direct selling relies on independent distributors who function both as consumers and marketers. This dual role necessitates innovative retention mechanisms that go beyond fixed compensation. The study identifies that non-monetary rewards—such as recognition programs, rank advancements, lifestyle incentives, and aspirational rewards—play a pivotal role in motivating individuals to remain engaged in such decentralized organizational systems. A key contextual foundation of the study is the regulatory transformation introduced by the Consumer Protection (Direct Selling) Rules, 2021. These regulations have reshaped the operational and ethical framework of direct selling companies by mandating transparency, prohibiting recruitment-based earnings models, and emphasizing genuine retail activity. The research highlights how IndusViva has aligned its compensation structure with these regulatory requirements, ensuring compliance while maintaining strong motivational incentives for its distributors. The study adopts a qualitative and analytical research methodology based on secondary data derived from the IndusViva Compensation Plan 2026 and relevant academic literature. It systematically examines the company’s compensation architecture, focusing on seven revenue streams and seven bonus categories. These components collectively form a multi-dimensional incentive system designed to reward various aspects of distributor performance, including personal sales, team development, leadership, and long-term loyalty. One of the central findings of the study is that non-monetary rewards act as powerful psychological motivators. Drawing upon established theories such as Goal-Setting Theory, Self-Determination Theory, and Tournament Theory, the research explains how recognition, achievement milestones, and aspirational rewards (such as international travel, luxury assets, and status-based titles) create a sense of purpose and belonging among distributors. These rewards not only enhance motivation but also foster emotional attachment to the organization, thereby improving retention rates. The research further explores the structural design of IndusViva’s compensation plan, which incorporates key performance metrics such as Point Value (PV), Group Volume (GV), and Global Bonus Units (GBU). These metrics provide a standardized framework for measuring productivity and calculating earnings. The study highlights that the plan’s complexity, while analytically robust, may pose comprehension challenges for new participants. This complexity can potentially lead to misinterpretation of earning opportunities and unrealistic expectations, which are identified as critical factors influencing distributor attrition. Another significant aspect analyzed in the study is the binary organizational structure used in the compensation plan. While this structure simplifies team-building by focusing on two primary organizational legs, it introduces challenges such as volume imbalance and carry-forward limitations. The study evaluates how IndusViva addresses these issues through mechanisms like volume capping, spillover benefits, and additional bonuses, thereby ensuring a balanced incentive system. The role of long-term incentives is also critically examined. Unlike short-term monetary gains, long-term incentives such as rank advancement, loyalty bonuses, and royalty income streams encourage sustained participation and consistent performance. These incentives are strategically designed to align individual goals with organizational objectives, promoting long-term commitment rather than short-term opportunistic behavior. The findings reveal that the integration of monetary and non-monetary rewards creates a holistic retention strategy. Non-monetary incentives enhance intrinsic and identified motivation, while monetary rewards provide tangible financial benefits. This combination ensures that distributors remain engaged not only for income generation but also for personal growth, social recognition, and lifestyle enhancement. The study also identifies several challenges within the current system. The complexity of the compensation plan may hinder understanding among participants with limited financial literacy. Additionally, the aspirational nature of high-level rewards may create unrealistic expectations if not supported by transparent income disclosures. The research emphasizes the importance of effective training, clear communication, and simplified explanatory tools to address these challenges. From a practical perspective, the study provides actionable suggestions for improving retention strategies. These include simplifying compensation plan communication, enhancing onboarding and training programs, leveraging digital tools for performance tracking, and strengthening recognition systems. The research also suggests that companies should focus on building a strong support ecosystem for distributors, including mentorship programs and continuous skill development initiatives. In conclusion, the study establishes that non-monetary rewards and long-term incentives are critical determinants of employee retention in the direct selling industry. IndusViva’s compensation plan demonstrates how a well-structured incentive system can drive engagement, loyalty, and sustained performance. However, the effectiveness of such systems depends on their clarity, transparency, and alignment with participant expectations. The research contributes to academic literature by providing a detailed case-based analysis of a contemporary Indian direct selling compensation plan in a regulated environment. It also offers practical insights for organizations seeking to design effective retention strategies in similar business models. Ultimately, the study underscores the importance of balancing financial rewards with psychological and social incentives to create a sustainable and motivating work environment.

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RETENTION STRATERGIES , direct selling industry , non monetary reward system , PV , GV

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Agriculture plays a vital role in the economy of many developing countries, especially in rural and remote regions. However, farmers in these areas often face significant challenges in transporting agricultural products, seeds, fertilizers, and other essential supplies due to poor transportation infrastructure and high fuel costs. Conventional transportation methods depend mainly on fuel-powered vehicles, which increase operational expenses and contribute to environmental pollution. To address these issues, this project proposes an Intelligent Solar-Powered Electric Carrier for Remote Agricultural Supply Chain, which provides a sustainable and cost-effective transportation solution for agricultural activities. The proposed system utilizes renewable solar energy to power an electric carrier designed specifically for agricultural logistics. A Solar Panel is used to convert sunlight into electrical energy, which is stored in a Rechargeable Battery. The stored energy is then used to operate an Electric Motor that drives the carrier. The entire system is controlled using a Arduino Uno, which collects and processes data from various sensors integrated into the system. Several sensors are used to enhance the intelligence and safety of the carrier. A Load Cell Sensor is used to measure the weight of the agricultural goods being transported. A Ultrasonic Sensor helps detect obstacles and ensures safe navigation of the vehicle. Environmental conditions are monitored using sensors such as the Temperature Sensor and Rain Sensor. Additionally, Current Sensor and Voltage Sensor monitor the electrical performance of the system to ensure efficient energy usage and battery safety. The system also incorporates wireless communication through the NodeMCU (ESP8266), which enables IoT-based monitoring and data transmission. Artificial Intelligence techniques can be applied to analyze the collected sensor data and optimize system performance, including load management, obstacle avoidance, and energy consumption. A LCD Display is used to show system status and sensor readings, while a Buzzer provides alerts in case of abnormal conditions. The HC-05 Bluetooth Module allows wireless communication with mobile devices for control and monitoring. The proposed system offers several advantages, including reduced dependence on fossil fuels, lower transportation costs, and environmentally friendly operation. By combining renewable energy, sensor technology, IoT connectivity, and intelligent monitoring, the system improves the efficiency and reliability of agricultural transportation in remote areas. Overall, the Intelligent Solar-Powered Electric Carrier provides a smart and sustainable solution that supports modern agriculture and enhances the agricultural supply chain in rural regions.

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Creative Commons Attribution 4.0 and The Open Definition

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Background: Financial literacy — the ability to understand and apply fundamental financial concepts including budgeting, saving, investing, risk management, and tax planning — has emerged as a critical determinant of individual economic well-being and national financial stability. In India, the intersection of a rapidly expanding digital economy, a young demographic dividend (with over 600 million citizens below the age of 25), and a proliferating fintech ecosystem has created an unprecedented inflection point in investment behaviour. Despite government-led financial inclusion initiatives such as Jan Dhan Yojana, Pradhan Mantri Suraksha Bima Yojana, and SEBI's investor awareness programmes, empirical research consistently highlights significant gaps in financial literacy — particularly among young adults aged 18 to 35 — that translate into suboptimal investment decisions, overexposure to high-risk instruments, and inadequate long-term financial planning. Research Context and Rationale: The proliferation of mobile-first investment platforms — including Zerodha, Groww, Paytm Money, Angel One, and Upstox — has dramatically lowered the barriers to market participation for young Indian investors. The COVID-19 pandemic further accelerated this democratisation: between March 2020 and March 2022, India added over 40 million new Demat accounts, with a disproportionate share among first-time investors below the age of 30. Simultaneously, social media platforms including YouTube, Instagram, and Telegram have become primary channels of financial education and investment advice for this demographic, raising important questions about the quality, reliability, and long-term consequences of media-mediated financial literacy. This confluence of digital access, demographic youth, and media influence creates a research environment of considerable complexity and practical urgency. Methodology: This study employs a mixed-methods research design combining quantitative survey analysis with qualitative secondary data review. A structured questionnaire was administered to 150 valid respondents drawn from the young investor population (ages 18–35) across Bengaluru, Mumbai, and Delhi NCR, using purposive and snowball sampling techniques. The instrument assessed five dimensions: demographic and financial profile, financial literacy knowledge, investment awareness and behaviour, risk perception, and platform engagement. Statistical analyses included descriptive statistics, Pearson correlation, one-way ANOVA, and multiple linear regression, executed using IBM SPSS Statistics version 27. Secondary data were drawn from SEBI Annual Reports, RBI publications, NSE and BSE investor reports, NCFE (National Centre for Financial Education) surveys, and peer-reviewed academic literature. Key Findings: The study reveals a substantial financial literacy gap among young Indian investors, with a mean financial literacy score of 3.21 out of 5 across the sample — indicating moderate but incomplete financial knowledge. Awareness is highest for equity instruments (shares and mutual funds) and lowest for derivatives, bonds, and alternative investments. Multiple regression analysis identifies three primary predictors of investment decision quality: financial literacy level (β = 0.43, p < 0.001), social media financial content exposure (β = 0.31, p < 0.001), and formal financial education (β = 0.24, p < 0.002). The study documents significant behavioural biases including overconfidence (reported by 61.3% of respondents), herding behaviour (54.7%), and loss aversion asymmetry in portfolio management. One-way ANOVA reveals statistically significant differences in financial literacy scores across income brackets (F = 6.82, p = 0.002) but not across gender after controlling for income. Implications and Recommendations: The findings support a multi-stakeholder strategic response encompassing college-level financial literacy curriculum integration, SEBI-mandated platform onboarding assessments, fintech UX design improvements for financially under-literate users, and structured regulation of social media financial influencers (finfluencers). The paper contributes to the growing interdisciplinary literature on financial literacy, behavioural finance, and digital financial inclusion in emerging market contexts, offering a practically actionable framework for educators, policymakers, fintech operators, and regulators.

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Financial Literacy, Investment Awareness, Young Investors, Behavioural Finance, Fintech, India, SEBI, Mutual Funds, Risk Perception, Digital Finance, Gen Z, Millennials

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Abstract This report presents a practical study conducted at Ezzi Glass and Metal LLC with a focus on examining the organization’s Human Resource Management (HRM) practices and their impact on overall operational efficiency. The main objective of the study was to understand the structure, roles, and effectiveness of the HR department and to analyze how various HR functions contribute to organizational performance and employee productivity. The study covers major HR activities including recruitment and selection, employee onboarding, attendance and leave management, payroll coordination, performance monitoring, employee relations, and compliance with company policies and labor regulations. The data for this report was gathered through direct observation, interaction with employees and HR personnel, and analysis of internal records and documentation. The findings reveal that Ezzi Glass and Metal LLC follows systematic HR procedures that support workforce discipline, maintain organizational structure, and promote a positive working environment. The HR department plays a vital role in ensuring smooth communication between management and employees while maintaining regulatory compliance. However, the study also identifies opportunities for improvement in areas such as digitalization of HR processes, structured training programs, and performance appraisal systems. In conclusion, effective human resource management significantly contributes to organizational stability and long-term growth, and strategic improvements can further enhance overall efficiency and employee satisfaction.

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Creative Commons Attribution 4.0 and The Open Definition

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This research examines how digital marketing strategies, specifically SEO, content marketing, and social media, impact online visibility and customer acquisition at Numero Uno Marketing Pvt. Ltd.. Adopting a descriptive and empirical research design, the study analyzes primary data collected from 30 participants. Findings indicate that SEO is the most effective strategy for long-term organic growth, with results typically becoming visible after one month of consistent effort. The study concludes that an integrated digital marketing approach significantly increases website traffic and customer inquiries, confirming a positive relationship between online presence and business growth.

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Digital Marketing, Search Engine Optimization (SEO), Content Marketing, Social Media Marketing, Customer Acquisition, Online Presence

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This study focuses on the formulation and evaluation of sustained release tablets of Atenolol using a Quality by Design (QbD) approach. QbD provides a systematic framework for developing pharmaceutical products by emphasizing product understanding, process control, and risk management. The Quality Target Product Profile (QTPP) was defined, and Critical Quality Attributes (CQAs), Critical Material Attributes (CMAs), and Critical Process Parameters (CPPs) were identified. Design of Experiments (DoE) was applied to optimize formulation variables and evaluate their impact on drug release and tablet properties. Analytical method development and validation were performed using RP-HPLC in accordance with ICH guidelines to ensure accuracy, precision, and robustness. The optimized formulation demonstrated controlled and consistent drug release, enhancing therapeutic effectiveness and patient compliance. The study confirms that the QbD approach facilitates robust formulation development, minimizes variability, and ensures high-quality pharmaceutical products with regulatory flexibility.

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Keywords Atenolol Sustained Release Tablets Quality by Design (QbD) Design of Experiments (DoE) Critical Quality Attributes (CQA) Quality Target Product Profile (QTPP) Analytical Method Validation RP-HPLC Pharmaceutical Development Design Space

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Abstract This study focuses on analyzing the marketing practices and customer awareness towards healthcare products at Life Care Medicals. In today’s competitive healthcare market, effective marketing strategies and informed customers play a crucial role in business growth and customer satisfaction. The main objective of this study is to examine how Life Care Medicals promotes its healthcare products and to understand the level of awareness among customers regarding these products. The research is based on both primary and secondary data. Primary data was collected through questionnaires and direct interaction with customers visiting the medical store, while secondary data was gathered from journals, websites, and company-related sources. The study evaluates various marketing practices such as product display, pricing strategies, promotional activities, customer service, and availability of products. The findings indicate that Life Care Medicals adopts basic yet effective marketing practices like proper product placement, discounts, and personalized customer service. However, there is limited use of advanced promotional tools such as digital marketing and social media engagement. Customer awareness about general healthcare products like medicines, supplements, and hygiene items is moderate, but awareness about specialized or new products is relatively low. The study concludes that while Life Care Medicals has a strong customer base and trust, there is significant scope to improve marketing strategies by adopting modern techniques and increasing awareness through educational campaigns and digital platforms. Enhancing customer knowledge will not only improve sales but also contribute to better health outcomes. Overall, the research highlights the importance of integrating effective marketing practices with customer awareness to achieve sustainable growth in the healthcare retail sector.

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Creative Commons Attribution 4.0 and The Open Definition

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The stock market plays a significant role in wealth creation and economic development by offering individuals and businesses opportunities to invest and grow their capital. However, due to market volatility, lack of awareness, and risk factors, many investors find it difficult to make informed investment decisions on their own. As a result, stock market advisory companies have gained importance by providing professional guidance, market insights, and investment strategies tailored to investor needs. Profideskk Research. is a start-up stock market advisory company that provides guidance and advisory services to clients interested in investing in the stock market. The company focuses on helping investors understand market movements, identify suitable investment opportunities, and make informed decisions with the objective of achieving better returns while managing risk. Profideskk offers advisory services based on market research, analysis, and client-specific financial goals. In such a company, the role of a Business Development Executive (BDE) is extremely important. The BDE acts as the direct link between the company and potential clients. The primary responsibility of a BDE at Profideskk is to communicate with clients, explain the company’s stock market advisory services, understand client investment requirements, and persuade them to invest in the services offered by the company. This involves regular interaction with clients through calls, meetings, and follow-ups. The role requires not only strong communication and convincing skills but also a basic understanding of stock market concepts and investment advisory services. A BDE must build trust with clients, clarify their doubts regarding stock market risks and returns, and guide them towards suitable advisory plans. Since Profideskk is a start-up, the success of the company largely depends on how effectively BDEs are able to attract new clients and maintain long-term relationships with existing ones.

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Creative Commons Attribution 4.0 and The Open Definition

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The rapid advancement of digital technology has fundamentally transformed the way businesses communicate with customers and position their brands in the marketplace. Traditional marketing practices that relied heavily on print media, television advertisements, and physical promotion have gradually been replaced by digital platforms such as social media, search engines, company websites, and online review systems. This transformation has reshaped consumer behaviour, enabling customers to gather information, compare alternatives, and evaluate service providers before making purchase decisions. Digital marketing refers to the use of internet-based platforms and electronic communication tools to promote products and services, interact with customers, and build brand awareness. Unlike conventional marketing methods, digital marketing allows real-time interaction, measurable performance tracking, targeted advertising, and personalized communication. It has become an essential strategic tool for organizations seeking sustainable growth and competitive advantage. In service-oriented industries, the role of digital marketing becomes even more significant. Services are intangible in nature and cannot be physically examined before purchase. Therefore, customers rely heavily on online portfolios, customer reviews, visual representations, and digital testimonials to assess service quality. Interior design is a high-involvement service that involves substantial financial investment and emotional decision-making

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Creative Commons Attribution 4.0 and The Open Definition

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The HR Suresh & Co, a Chartered Accountancy firm providing services in auditing, taxation, accounting, and financial consultancy. The primary objective of the study is to understand the organisational structure, operational system, employee profile, and overall performance of the firm. The study also examines employee satisfaction, technology adoption, audit quality perception, communication effectiveness, and training practices within the organisation. This is based on both primary and secondary data. Primary data were collected through a structured questionnaire administered to 50 respondents, including Chartered Accountants, article assistants, accounts executives, and administrative staff. Secondary data were gathered from company records, official documents, and relevant academic references. The collected data were analysed using percentage analysis and presented through tables, pie charts, and bar graphs to ensure clear interpretation. The findings reveal that HR Suresh & Co has a young and moderately experienced workforce, with 44% of employees having 1–3 years of experience. A majority of respondents (48%) perceive audit quality as high, and 40% rate communication within the firm as effective. The study also indicates that 44% of employees consider the work environment to be good, while 80% agree that technology improves work efficiency. Overall, the firm demonstrates strong professional practices, structured management, and positive employee perception. However, there is scope for improvement in areas such as advanced technology adoption and career development opportunities. The study provides practical exposure to organisational functioning and contributes to understanding professional service management in the context of a Chartered Accountancy firm.

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Creative Commons Attribution 4.0 and The Open Definition

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In today’s competitive business environment, customer acquisition has become a critical factor for organizational growth and sustainability. With the rapid advancement of digital technology, businesses are increasingly relying on digital marketing services to enhance their visibility and reach potential customers. The present study titled “A Study of Customer Acquisition through Effective Sales Strategies” focuses on analyzing how structured sales strategies contribute to acquiring new customers in a digital marketing company, specifically at Alpha Numero Uno Marketing Pvt. Ltd. The primary objective of the study is to examine the effectiveness of different sales strategies such as tele-calling, personalized pitching, follow-ups, objection handling, and customer education in influencing customer decisions. The research also aims to understand the relationship between sales strategies, customer response, and customer acquisition. The study is based on primary data collected through a structured questionnaire from 60 respondents. Convenience sampling technique was adopted for data collection. Various statistical tools such as percentage analysis, conversion rate analysis, and comparative analysis were used to interpret the data. The findings were presented using tables, pie charts, bar charts, and graphical representations for better understanding. The results of the study indicate that effective communication, multiple follow-ups, personalized sales approaches, and customer awareness about digital marketing significantly improve conversion rates. It was also observed that experienced sales executives achieve higher customer acquisition due to better objection handling and relationship-building skills. However, challenges such as budget constraints, lack of awareness, and delayed decision-making affect the sales process. The study concludes that well planned and customer centric sales strategies play a vital role in improving customer acquisition in the digital marketing industry. By strengthening communication quality, enhancing follow-up systems, and focusing on customer education, organizations can increase their conversion rates and achieve sustainable growth.

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Creative Commons Attribution 4.0 and The Open Definition

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seismic generally helps us to characterize the reservoir by imaging the subsurface by faults, folds and traps and identify rock properties like porosity, lithology via seismic inversion by transforming rock data into rock properties that P-impedances, S-impedance, density enabling better quantification of porosity, fluid saturation and lithology that improves well placement, reduced uncertainty , reduced drilling risks, optimize field placements. This paper explores the application the application of seismic inversion techniques to enhance reservoir characterization, ultimately improving exploration and production decisions like AVO (Amplitude vs offset) analysis that gives clues about fluid type and lithology, which helps to spot gas sands, oil-water contact. How AVO analysis gathers data has been discussed here. Pre-stack depth migration, azimuthal anisotropy, 4D seismic, machine learning for better imaging, fracture detection, monitor reservoir changes, pattern recognition in seismic data, are also discussed here, through which we can optimise well placement, predict production trends, reduce uncertainty, and enhance the recovery factor of oil. Not only that, the paper also deals with Full waveform inversion (FWI), Least-squares-Migration (LSM), Distributed acoustic sensing (DAS), Passive seismic, which helps in better imaging, prospect de-risking, monitor reservoirs, and optimise output, which helps in increased recovery and reduced costs, also discussed here. Not only do these papers deal with how seismic attributes have been calibrated with well log-derived rock physics parameters such as porosity, mineral composition, effective stress distribution and fluid saturation through rock physics models. elastic parameter cross plots and rock physics templates are used to link up seismic responses to reservoir properties in a physically consistent manner. This integrated approach improves the lithofacies discrimination and also identifies fluid-related anomalies and the prediction of reservoir properties away from the well control situation

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:- Seismic attributes , Rock -physics ,Reservoir characterisation, Seismic inversion ,AVO Analysis ,Elastic properties ,Lithofacies discrimination

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