ACTA SCIENTIAE is an international, peer reviewed, open access, scientific and scholarly journal which publishes Research papers, Review papers, Mini reviews, Case reports, Case studies, Short communications, Letters, Editorials, Books, Thesis, Dissertation works, etc., from all the aspects of Agricultural Science, Biological Science, Business, Chemical Science, Education Science, Engineering and Technology, Humanities, Life Science, Physical Science, Marine Science, Social science. After publishing, articles are freely available through online without any restrictions or any other subscriptions to researchers and readers worldwide ISSN:1517-4492 | E-ISSN:2178-7727 | Publisher: Lutheran University of Brazil | Year:1999
A COMPREHENSIVE REVIEW ON ANTIDIABETIC POTENTIAL OF ABIES WEBBIANA LINDL.
The rise in the incidence of type 2 diabetes mellitus (T2DM) around the world highlights the critical need for non-synthetic therapy options that are safe, inexpensive, and capable of targeting multiple targets. The Himalayan medicinal conifer Talisastra, or Abies webbiana, has a long tradition of usage in Ayurvedic treatment of respiratory and metabolic diseases, and currently it has the potential to be a natural anti-diabetic. The phytochemical characteristics, pharmacological processes, and translational potential of this compound are examined in this detailed review. Studies from phytochemical studies show that the plant contains bioactive terpenoids (abienol, limonene, and camphene), flavonoids (quercetin and kaempferol), and phenolics (gallic and caffeic acids) that have significant anti-inflammatory, antioxidant, and enzyme-inhibitory effects. A study conducted in both experimental environments and computational models shows that the α-amylase, α-glucosidase, and DPP-IV enzymes are inhibited, insulin sensitivity is enhanced through the PI3K/Akt and AMPK pathways, and pancreatic β-cells are protected from oxidative stress. Network pharmacology and molecular docking have verified interactions with multiple targets, including important metabolic regulators such as PPARγ, AKT1, NF-κB, and SIRT1. The effects of reducing glucose, normalising lipids, and protecting tissues are further supported by in vivo animal models. However, there is a dearth of evidence on toxicity, absorption, and pharmacokinetics, which limits its application in clinical settings. These restrictions could be solved by integrating delivery systems based on nanotechnology with autonomous screening. The transition of A. webbiana from an ethnomedical practice to a treatment based on scientific data requires standardisation, safety profiling, and clinical validation. Collectively, the data presented here indicate that A. webbiana represents a promising botanical candidate for future generations of phytopharmaceuticals used to treat diabetes.
Rupa Ganguly, Dildar Husain, Priyanka Singh
GENDER AND SEXUALITY: A STUDY OF SHYAM SELVADURAI'S FUNNY BOY
Shyam Selvadurai's Funny Boy (1994) is a significant postcolonial novel that explores the intersections of gender, sexuality, ethnicity and identity in Sri Lankan society. Through the experiences of the protagonist Arjie Chelvaratnam, the novel examines the challenges faced by individuals who do not conform to traditional gender roles and heterosexual norms. Arjie's journey from childhood to adolescence reveals the social pressures that regulate gender expression and sexual behavior. The novel presents masculinity as a socially constructed ideal that often suppresses individuality and emotional freedom. It also portrays the awakening of same-sex desire and the difficulties of negotiating sexual identity in a conservative society. Furthermore, the narrative links personal struggles with broader ethnic tensions between the Tamil and Sinhalese communities, demonstrating how different forms of marginalization intersect. Through themes of resistance, self-discovery and identity formation, Selvadurai critiques patriarchal structures and heteronormative expectations. This study analyzes the representation of gender and sexuality in Funny Boy and explores how the novel challenges dominant social norms. By examining Arjie's experiences, the paper highlights the complexities of growing up as a queer individual within a society shaped by patriarchy, cultural traditions and ethnic conflict. Ultimately, Funny Boy emerges as a powerful narrative of self-expression and resistance against oppressive social structures.
COMPARATIVE EVALUATION OF ANTIMICROBIAL EFFICACY OF NANOCHITOSAN-COATED ORTHODONTIC BRACKETS AGAINST FOUR ORAL MICROORGANISMS: AN IN VITRO STUDY
Background and Aim. Fixed orthodontic appliances create plaque-retentive microenvironments that promote colonization by cariogenic and opportunistic microorganisms, increasing the risk of enamel demineralization and white spot lesion (WSL) formation. Conventional compliance-dependent preventive strategies demonstrate limited long-term efficacy. Surface modification of orthodontic brackets with antimicrobial nanocoatings offers a promising compliance-independent approach. This study aimed to comparatively evaluate the antimicrobial efficacy of nanochitosan-coated stainless-steel orthodontic brackets against four clinically relevant oral microorganisms in an in vitro setting. Methods. A total of 160 pre-adjusted edgewise stainless-steel maxillary premolar brackets (MBT prescription, 0.022″ slot) were divided into two groups of 80 each: Group A (nanochitosan-coated) and Group B (uncoated control). Nanochitosan nanoparticles (molecular weight 3,800–20,000 Da; mean particle size ~50 nm) were deposited on bracket surfaces via a standardized hydrothermal method and confirmed by scanning electron microscopy (SEM) and field emission SEM (FESEM). Four microorganisms Streptococcus mutans, Lactobacillus acidophilus, Enterococcus faecalis, and Staphylococcus aureus were standardized to 10⁵ CFU/mL using a spectrophotometer (OD 600 nm) and inoculated into Mueller–Hinton broth containing bracket samples. Colony-forming unit (CFU) counts were recorded at T0 (1 h), T1 (6 h), T2 (12 h), T3 (24 h), and T4 (72 h). Intragroup and intergroup comparisons were performed using paired t-test and one-way ANOVA, respectively, at a 95% confidence level (p ≤ 0.05). Results. No microbial growth was detected in either group at 1 and 6 hours. From 12 hours onward, uncoated brackets demonstrated a progressive and statistically significant increase in CFU counts for all four organisms, reaching mean values of 123.75 ± 2.446, 124.45 ± 2.459, 123.80 ± 2.876, and 124.65 ± 1.954 × 10⁵ CFU/mL at 72 h for S. mutans, L. acidophilus, E. faecalis, and S. aureus, respectively. In contrast, nanochitosan-coated brackets maintained consistently low and stable CFU counts (22–24 × 10⁵ CFU/mL) across all time points. Intergroup differences were highly significant for all organisms at all post-baseline intervals (F values 1,025–27,604; p < 0.001). Conclusions. Nanochitosan surface modification significantly enhances the antimicrobial properties of stainless-steel orthodontic brackets, providing broad-spectrum, sustained inhibition of bacterial adhesion and proliferation. This compliance-independent coating strategy holds considerable potential for reducing biofilm accumulation, enamel demineralization, and white spot lesion formation during fixed orthodontic treatment. Further in vivo studies are warranted to confirm clinical durability and biocompatibility.
nanochitosan; orthodontic brackets; antimicrobial activity; white spot lesions; biofilm inhibition; Streptococcus mutans
Dr. Rahul Paul,Dr. Deepti Yadav, Dr. Ish Kumar Sharma, Dr. Mainak Halder,Dr. Ratika Sawhney,Dr. Kanika Gupta
TRANSFORMER-BASED ABSTRACTIVE TEXT SUMMARIZATION:A COMPREHENSIVE REVIEW OF MODELS, CHALLENGES, AND EVALUATION
The exponential growth of digital textual information across news portals, academic repositories, legal corpora, and social media platforms has intensified the need for efficient automated text summarization systems. Transformer-based architectures have emerged as the dominant paradigm in abstractive text summarization, offering superior capabilities in modeling long-range linguistic dependencies and generating fluent, coherent summaries. This review paper provides a comprehensive examination of the development, current state, and open challenges of transformer-based abstractive text summarization. We systematically analyze foundational encoder-decoder architectures, pre-trained language models such as BERT, GPT-2, BART, T5, and PEGASUS, and survey critical research contributions addressing hallucination mitigation, factual consistency, long-document processing, and evaluation methodology. We further review prominent benchmark datasets including CNN/Daily Mail, XSum, MultiNews, and ArXiv, and discuss evaluation metrics spanning ROUGE, BERTScore, BARTScore, and NLI-based faithfulness measures. Our analysis identifies persistent challenges in scalable inference, domain adaptation, multilingual summarization, and reliable automatic evaluation, and outlines promising directions for future research in this rapidly evolving field.
Abstractive summarization, Transformer, BERT, BART, T5, PEGASUS, ROUGE, hallucination, factual consistency, pre-trained language models, natural language processing, deep learning.
COMPARATIVE ANALYSIS OF MACHINE LEARNING ALGORITHMS FOR CLASSIFYING CHEST X-RAY IMAGES OF COVID-19
The fast spread of COVID-19 has made it essential to have quick and accurate ways to diagnose the disease using medical images. In this study, we present a comparative analysis of six machine learning algorithms for classifying chest X-ray images into three categories: COVID-19, pneumonia, and normal. The algorithms evaluated include Support Vector Machine (SVM), Random Forest, Decision Tree, K-Nearest Neighbors (KNN), Naive Bayes, and Logistic Regression. The models were trained and validated using a dataset of labeled X-ray images, with performance assessed based on key metrics such as validation accuracy, test accuracy, Matthews Correlation Coefficient (MCC), and ROC-AUC scores. Among the models, the SVM algorithm achieved the highest validation accuracy of 92.6% and test accuracy of 94.0%, with an MCC of 0.8785 and a macro-average ROC-AUC of 0.9877. Logistic Regression and Random Forest followed closely, with Logistic Regression attaining a test accuracy of 93.8% and Random Forest achieving 92.6%. K-Nearest Neighbors showed moderate performance, while Naive Bayes exhibited the lowest accuracy, likely due to its simplistic assumptions. Overall, the SVM model outperformed the others, demonstrating its robustness in distinguishing between the three categories in chest X-ray images. This comparative study highlights the potential of machine learning techniques in automating the detection of COVID-19 and pneumonia from radiographs, contributing to faster and more accurate diagnostic tools in healthcare.
Healthcare, Machine Learning, COVID-19, SVM, KNN, Regression, Random Forest
CERTAIN INVESTIGATION AND PREDICTION OF BIGMART SALES USING MACHINE LEARNING TECHNIQUES
Accurate sales forecasting is critical for retail optimization, yet many predictive models struggle with the inherent noise and categorical complexity of retail datasets. This study presents a rigorous investigation into machine learning techniques for predicting sales using the Big Mart dataset. Unlike previous narrative studies, we implement a reproducible pipeline incorporating median-imputation for missing values, one-hot encoding for categorical features, and a 5-fold cross-validation protocol to ensure model stability. We evaluate four distinct architectures: Linear Regression, Polynomial Regression, Decision Trees, and XGBoost. Our results demonstrate that XGBoost significantly outperforms traditional models, achieving a Root Mean Squared Error (RMSE) of 0.0231 and a Mean Absolute Error (MAE) of 0.018 after systematic hyperparameter tuning. Beyond predictive accuracy, we provide a feature importance analysis revealing that Item MRP and Outlet Type are the primary drivers of sales variance. This work contributes a validated framework for retail forecasting that balances predictive power with business interpretability, offering actionable insights for inventory management and strategic decision-making.
Predictive Analysis, Polynomial Regression, Linear Regression, Xgboost and Decision Tree.
GLOBALISATION AND ITS INFLUENCE ON INDIA’S EXPORT–IMPORT DYNAMICS: AN ANALYTICAL STUDY
Globalisation has changed India's economy a lot, especially its trade with other countries. India's integration with global markets has grown since the economic reforms of 1991. This has led to more exports, a wider range of imports, more competition, and faster economic growth. This analytical study investigates the impact of globalisation on India's export-import dynamics, emphasising growth trends, structural transformations, trade composition, and policy reforms. The study analyses secondary data from government reports, economic surveys, WTO publications, and trade databases to elucidate significant trends in India's trade performance. The results show that the Indian economy is becoming more open, that it is shifting from exporting raw materials to manufactured goods and services, that it is importing more technology-intensive goods, and that it is participating more in global value chains. The paper concludes that globalisation has led to more trade and economic growth, but there are still big problems like trade deficits, reliance on foreign technology, and unstable global markets.
Globalisation, India, Export–Import Dynamics, Trade Liberalisation, Economic Reforms, External Sector
TRANSFORMER-BASED ABSTRACTIVE TEXT SUMMARIZATION: MODELS, CHALLENGES, AND EVALUATION
Text summarization has undergone significant transformation with the advent of transformer-based neural architectures. This research examines the landscape of transformer models for abstractive summarization, exploring how attention mechanisms and pre-training strategies have revolutionized the field. We investigate prominent models including BERT, GPT variants, T5, and PEGASUS, analyzing their architectural innovations and summarization capabilities. The study addresses persistent challenges including factual consistency, handling long documents, computational efficiency, and evaluation reliability. Through comprehensive analysis of existing approaches and evaluation methodologies, we identify critical gaps in current summarization systems and propose directions for advancement. Our findings reveal that while transformer models achieve impressive fluency and coherence, ensuring factual accuracy remains problematic. The research contributes a structured understanding of the transformer summarization ecosystem, highlighting that future progress depends on developing better evaluation metrics, improving factual grounding, and creating more efficient architectures suitable for practical deployment.
Transformer Models, Abstractive Summarization, Natural Language Processing, Attention Mechanisms, Neural Networks, Pre-training, Evaluation Metrics
AI-BASED MULTIMODAL LEARNING ANALYTICS FOR PREDICTING BEHAVIORAL CHANGES IN STUDENTS WITH SPECIAL EDUCATION
The present project is aimed at forecasting behavioral changes in the students with Special Education Needs (SEN) based on the analysis of their learning activities and interactions. Classroom activity, accomplishment of tasks, attendance, and teacher observations are all gathered and handled in order to come up with trends in student behavior. The machine learning model predicts possible positive or negative behavior change to support the teacher and caregivers to offer prompt assistance and intervention. It also contains basic visualization, like dashboard or charts, so that it is easier to have the educators see the trends of behaviors at a glance. With a simple data-driven model, the project permits the behavior in the classroom to be managed in advance, promotes engagement, and facilitates better learning the results in a practical and readily constructible way.
Multi modal learning analytics (MMLA), special education needs (SEN)
Dr. Srinivasan Nagaraj, Dr.Y. Dasaratha Rami Reddy, Ms. Somesula Sujatha, Dr. G. Sreenivasula Reddy, Ms. Shaik Muneera
EVALUATION OF MOLAR PROTRACTION WITH DISTRACTOR AND MICRO-OSTEOPERFORATION- A COMPARATIVE STUDY”
Background: Accelerating orthodontic tooth movement is a major clinical goal to reduce treatment duration. Techniques such as micro-osteoperforation (MOP) and distraction osteogenesis (DO) have been proposed as effective adjuncts. Materials and methods This in-vivo randomized clinical study was conducted on 30 patients with Class I malocclusion requiring molar protraction. The subjects were randomly allocated into three groups of 10 each: Group A treated with conventional T-loop mechanics, Group B with distractor-assisted osteogenesis, and Group C with micro-osteoperforation (MOP) combined with T-loop mechanics. Standard MBT fixed appliances were used in all patients. Molar protraction was measured intraorally as the distance between the second molar and premolar using a digital vernier caliper at baseline (T0), 45 days (T1), and 90 days (T2). The collected data were subjected to statistical analysis using appropriate parametric and non-parametric tests, with the level of significance set at p < 0.05. Objective: To evaluate and compare the amount and rate of second molar protraction using conventional T-loop mechanics, distractor-assisted osteogenesis, and MOP combined with T-loop mechanics. Results: All groups showed significant molar protraction (p<0.05). The distractor group demonstrated the highest rate of tooth movement, followed by MOP with T-loop, while conventional T-loop showed the least movement. Conclusion: Distractor-assisted osteogenesis provides maximum acceleration but is invasive. MOP offers a minimally invasive alternative with significant acceleration, while T-loop remains a reliable conventional method.
Molar protraction, Micro-osteoperforation, Distraction osteogenesis, T-loop, Accelerated orthodontics
Rahul Paul, Deepti Yadav, Ish Kumar Sharma, Sanjeev K Shokeen,Ratika Sawhney,Kanika Gupta
DESIGN OVER HYPE: EMPIRICAL EVIDENCE ON EFFECTIVE AI INTEGRATION IN EDUCATION
Artificial Intelligence (AI) has grown rapidly from optional experiments to core infrastructure in the current learning environment while it’s evidence of real impact remains inconclusive. This study synthesizes contemporary empirical research on AI-integrated learning, teaching and assessment in the era of generative systems and large language models. A comprehensive search on popular reputed databases resulted in 612 records which was further filtered to include only 35 peer reviewed studies after performing PRISMA 2020 reporting standards. The corpus ranges from K-12, higher education, and professional learning, encompassing AI-powered personalization, smart tutoring systems, generative AI assistants, AI-enhanced assessment, chatbots and adaptive feedback environments. Across various study results, AI-powered interventions consistently show positive influence on academic performance, with an average impact from small to large (g/d ≈ 0.43–0.70), while promoting growth in metacognitive regulation, learner engagement and self- efficacy. The most effective outcomes occur when AI functions as structured, feedback-oriented and pedagogically-aligned co-instructor, whereas peripheral or unstructured usage results in modest or minimal impact. Moderator patterns reflect the necessity of instructional design, institutional capacity, educator readiness, and student AI literacy, as well as growing concerns on equity, transparency, academic integrity and over-reliance on generative outputs. The review outlines that AI-enabled learning environments can improvise both cognitive and affective results when coupled with supervised, ethically governed and context-sensitive scenarios, and identifies necessity for longitudinal, cross-disciplinary and multi-site assessments of generative and adaptive AI in education.
Metacognitive, AI literacy, self-efficacy.
Adyasha Samal, S Suriya, S. Sindhu, Dr.C.Priya
AI-BASED DECISION SUPPORT FOR CROP FARMING
Agriculture plays a crucial role in ensuring food security and economic sustainability. Farm- ers face challenges such as climate variability, crop diseases, soil nutrient imbalance, and volatile market prices. Traditional decision-making methods often lead to reduced productivity and financial losses.This paper presents AgroVision, an AI-driven intelligent decision support system designed to assist farmers throughout the crop lifecycle. The system integrates Convolutional Neural Networks for crop disease detection, supervised learning models for soil fertility estimation and crop yield prediction, and time-series forecasting models for market price prediction.Unlike IoT-based systems, AgroVision operates without hardware sensors, making it cost- effective and accessible to small and medium-scale farmers.
Artificial Intelligence, Crop Disease Detection, Soil Fertility Estimation, Yield Prediction, Market Forecasting, Sustainable Agriculture
Dr. G. Sreenivasula Reddy, Dr. Srinivasan Nagaraj, Ms. Somesula Sujatha, Dr.Y. Dasaratha Rami Reddy, Mrs. P.Mahaboob Chand
DYNAMIC PRESSURE ON A FLOATING PLATFORM DUE TO SURFACE WAVES
Floating platforms serve a wide range of strategic and functional purposes, including offshore infrastructure for industrial operations and oil exploration, as well as civic applications such as floating airstrips and recreational parks. They are also critical in defence sectors, particularly for naval bases and mobile support systems. Given their exposure to dynamic environmental forces, especially wave action, understanding their structural response is essential for safe and efficient design. In this study, the behaviour of a rectangular floating platform is examined under hydrodynamic loading. The platform is assumed to float on an infinitely extended fluid domain with finite depth, which reflects realistic ocean conditions where lateral boundaries have negligible influence but depth remains a governing factor. The problem is treated as a coupled fluid–structure interaction system, where the fluid domain is modelled in three dimensions using pressure as the primary nodal variable, while the platform is represented as a two-dimensional structural element characterized by displacement. To accurately capture the mechanics, the platform behaviour is modelled using Mindlin plate theory, which accounts for transverse shear deformation and is suitable for moderately thick plates. The fluid motion is governed by the Navier–Stokes equations, simplified under the assumptions of incompressibility and inviscid flow to focus on dominant pressure effects. The base of the fluid domain is considered perfectly rigid, ensuring complete wave reflection without energy dissipation—an assumption that helps isolate the impact of wave-structure interaction. The resulting time-dependent coupled equations are solved using Newmark-beta method, which provides stable and accurate solutions for dynamic systems. Dynamic pressure acting on the platform is evaluated, and the additional stresses induced by wave action are quantified in terms of bending moments. These are further expressed as non-dimensional coefficients, offering practical utility for engineers to estimate bending responses efficiently in design applications.
Floating Platforms, Hydrodynamic Loading, Lateral Boundaries, Mindlin Plate Theory, Navier–Stokes Equations.
Dr. Bhagirathi Tripathy, Dr. Pratap Kumar Pani, Dr. Abhijeet Das, Dr. Krishna Pada Bauri
Desire and Denial: Masculinity Crisis and the Politics of Intimacy in Red Lipstick: The Men in My Life by Laxmi Narayan Tripathi
The present research explores the issue of intimacy and masculinity crisis in Laxmi Narayan Tripathi's memoir titled Red Lipstick: The Men in My Life (2016) using the theory of hegemonic masculinity by R.W. Connell. According to the paper's main postulate, hegemonic masculinity constructed by society in India is inherently fragile in its confrontation with male lust for a transgender person. Using qualitative textual analysis, the research detects the presence of four major themes in Red Lipstick (i.e., desire and denial, secrecy, power disparity, and emotional labour). Based on these themes, it is possible to conclude that the characters of the men depicted in the book tend to pursue intimacy in private and disown it publicly in compulsive manner. By referring briefly to the ideas of Butler regarding gender performativity and Sedgwick about the closet, the paper demonstrates that Red Lipstick becomes an essential field for understanding the disruption of hegemonic masculinity due to the presence of transgender individuals.
: hegemonic masculinity, transgender studies, masculinity crisis, intimacy.
IMPACT OF EMPLOYEE EXPERIENCE ON EMPLOYEE PRODUCTIVITY WITH SPECIAL REFERENCE TO PRECOT MERIDIAN LIMITED, WALAYAR, PALAKKAD
Employee experience has emerged as a critical factor influencing productivity and overall organizational performance, especially in manufacturing industries like textiles. This study examines the impact of employee experience on employee productivity at Precot Limited (formerly Precot Meridian Limited), Walayar, Palakkad. The research focuses on key factors such as work environment, management support, communication, welfare measures, and employee engagement. A descriptive research design was adopted, and data were collected from 306 employees selected using Krejcie and Morgan’s sample size table and a simple random sampling method. Quantitative analysis, including correlation, revealed a strong positive relationship (r = 0.72) between employee experience and productivity. The findings indicate that a supportive work environment, effective communication, recognition, and welfare initiatives significantly enhance employee motivation, engagement, and efficiency. The study highlights the importance of employee-centric HR practices for achieving sustainable organizational growth and improved performance in the textile sector.
Employee Experience, Employee Productivity, Work Environment, Management Support, Employee Engagement, Textile Industry
STUDY ON WORKFORCE AGILITY AND ORGANIZATIONAL CITIZENSHIP BEHAVIOUR OF HIGHER EDUCATION PROFESSIONALS
The higher education sector is experiencing rapid transformation due to globalization, technological advancements, and increasing societal expectations, thereby demanding greater adaptability from academic professionals. In this context, the present study examines the relationship between workforce agility and organizational citizenship behaviour (OCB) among higher education professionals in Coimbatore district. The study adopts a descriptive research design and collects data from 150 respondents using a structured questionnaire comprising workforce agility and OCB scales. Statistical tools such as mean, standard deviation, t-test, ANOVA, correlation, and regression analysis were employed. The findings reveal that the majority of respondents exhibit a moderate level of workforce agility and OCB. Significant differences were observed in workforce agility and OCB based on designation, while OCB also varied across institution types. The study further identifies a positive and significant relationship between workforce agility and OCB (r = 0.239, p < 0.05). Regression analysis indicates that workforce agility significantly predicts OCB, explaining 5.7% of the variance. The results suggest that professionals who are adaptable, flexible, and proactive are more likely to engage in extra-role behaviours that support organizational effectiveness. The study concludes that enhancing workforce agility through continuous learning, skill development, and supportive institutional practices can strengthen organizational citizenship behaviour among higher education professionals, thereby contributing to institutional growth and performance
A STUDY ON IDEAL TIME AND METHODS OF COMMITTED SUICIDE IN VILLUPURAM DISTRICT, TAMIL NADU
The study examines the patterns, causes, and socio-demographic profile of suicide in India with a specific focus on Villupuram District, Tamil Nadu. Suicide is a major public health concern and one of the leading causes of death globally, particularly among youth. In India, over 1.7 lakh suicides were reported in 2023, with rising trends linked to family problems, illness, and substance abuse. The study adopts a descriptive research design using secondary data collected from Government Villupuram Medical College records (October 2023–September 2025), covering 160 cases. Findings reveal that males (65%) and married individuals (82%) constitute the majority of victims, with the highest incidence in the 21–30 age group. Family-related issues emerged as the primary cause, followed by interpersonal conflicts. Most suicides occurred during late night and early morning hours, commonly through poisoning and hanging. The study highlights the need for community-based interventions, mental health awareness, and strengthened preventive strategies to address this growing social problem.
Family Problems, Mental Health, Suicide, Villupuram District
HIGH-PERFORMANCE CR₂O₃ THIN FILMS VIA OPTIMIZED SOL-GEL DEPOSITION: STRUCTURAL AND ELECTRICAL ANALYSIS
Nanostructured Cr₂O₃ thin films were synthesized using the sol-gel method and deposited onto glass substrates by spin coating. The optimization of deposition parameters, including precursor concentration (0.5 M, 1.0 M, 1.5 M), spin speeds (1000, 2000, 3000 rpm), and deposition times (30, 60, 90 seconds), was performed using Design Expert software. The films were annealed at 500°C for 2 hours to promote crystallization. Characterization of the films was carried out using X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), atomic force microscopy (AFM), and transmission electron microscopy (TEM). The XRD patterns revealed a dominant (104) diffraction peak at 2θ = 36.5°, indicating the formation of Cr₂O₃ with a crystallite size of approximately 25 nm. FTIR spectra showed strong peaks at 570 cm⁻¹, confirming Cr-O bonding in the films. SEM images demonstrated smooth, uniform surfaces with no cracks at a spin speed of 2000 rpm, and AFM analysis revealed an average surface roughness of 3.2 nm. TEM images confirmed the nano-sized grains and homogeneity of the films. The electrical conductivity of the films was found to be 1.2 × 10⁻³ S/cm, and the thermal conductivity was measured at 0.85 W/m·K. The optimized Cr₂O₃ thin films exhibited good crystallinity, excellent morphology, and desirable electrical and thermal properties, making them suitable for applications in sensors and optoelectronics.
Crystallinity, Electrical conductivity, Energy storage, Nanostructured thin films, Spray deposition, Chromium oxide
Cu2+ and Cr6+ recovery from Simulated Waste Water using Zinc oxide Loaded Potato Peel Nanocomposites
In the present study, potato peel loaded nanoparticles were employed as adsorbent materials for the removal of Cu2+ and Cr6+ from aqueous solutions. Experimental observations indicated that different nanoparticles exhibited significant adsorption potential toward ampicillin. The maximum adsorption capacities were calculated as 128.6 mg/g for copper and 98.99 mg/g for chromium using ZnO@PP nanoparticles. Equilibrium analysis revealed that the adsorption behavior was best represented by the Freundlich isotherm model, suggesting multilayer adsorption on a heterogeneous surface. Kinetic investigations further indicated that the adsorption process is mainly controlled by a chemisorption mechanism. The removal of both metals is likely governed by several interactions, including surface complexation, electrostatic attraction, and hydrogen bonding between the adsorbent surface and antibiotic molecules. Thermodynamic evaluation demonstrated that the adsorption process is spontaneous and thermodynamically favorable. In addition, all combinations of nanoparticles showed satisfactory regeneration ability, maintaining considerable adsorption efficiency for up to three successive reuse cycles.
Antibiotics, copper oxide NPs, iron oxide NPs, synthesis and characterization, adsorption studies.
BRIDGING SCIENCE AND POLICY: THE CLEAN FRAMEWORK FOR SYSTEMS-LEVEL MICROPLASTICS MITIGATION WITHIN THE ONE HEALTH APPROACH
Microplastics are increasingly recognized as pervasive pollutants with adverse impacts on ecosystems, animal health, and human well-being. Despite growing evidence of their prevalence and toxicity, existing mitigation strategies remain fragmented, reactive, and sector-specific. This paper introduces the CLEAN framework, Cut, Lock, Extract, Amend, and Network, as an OSH-inspired One Health approach to systematically address the global microplastics crisis. The framework emphasizes source reduction through sustainable production and circular economy practices (Cut), containment and filtration measures across industrial and environmental pathways (Lock), removal of legacy contamination from soils, sediments, and aquatic systems (Extract), ecosystem restoration and remediation through habitat protection and soil–water amendments (Amend), and cross-sectoral collaboration and policy alignment that integrates community action with global governance (Network). The CLEAN framework is grounded in the principles of systems thinking, recognizing that microplastic pollution arises not from isolated sources but from a web of interrelated human, environmental, and industrial activities. Cut calls for upstream interventions, such as redesigning materials, reducing plastic dependency, and incentivizing biodegradable alternatives to prevent microplastics at the source. Lock involves midstream solutions like installing advanced filtration in wastewater treatment plants, stormwater systems, and industrial effluents to prevent microplastics from entering ecosystems. Extract targets downstream interventions by deploying technologies such as sediment dredging, bioremediation, and magnetic separation to remove accumulated microplastics from the environment. Amend focuses on rebuilding ecological resilience through techniques like phytoremediation, soil conditioning, and restoration of wetlands and riparian zones. Finally, Network underscores the importance of harmonizing regulations, aligning research agendas, mobilizing community awareness, and fostering public–private partnerships for long-term systemic change. Importantly, the CLEAN framework aligns with the One Health paradigm, which recognizes that the health of people, animals, and ecosystems is interconnected. By integrating insights from environmental monitoring, toxicology, epidemiology, and policy analysis, the framework provides a comprehensive, evidence-informed pathway to reduce the burden of microplastic pollution across multiple levels of society. It not only supports scientific advancement but also promotes practical interventions that can be scaled and adapted globally. As global plastic production continues to rise, and microplastics are detected in everything from ocean plankton to human blood, the need for a coordinated, proactive response has never been more urgent. The CLEAN framework offers a structured roadmap for stakeholders—scientists, policymakers, industries, and communities—to act cohesively, transforming fragmented efforts into a unified global strategy for mitigating microplastic pollution and safeguarding planetary health.
Circular Economy, CLEAN Framework, Environmental Monitoring, Human Well-Being, Microplastics.
Dr. Abhijeet Das, Dr. Satchidananda Mishra, Dr. Krishna Pada Bauri, Dr. Bhagirathi Tripathy
NARRATIVE ALCHEMY: TRANSFORMING CRIME THROUGH ADAPTATION IN LARS KEPLER’S THE HYPNOTIST
This research unravels and explores the intricate process of adapting Scandinavian crime narratives, delving into the transformation of Lars Kepler’s novel The Hypnotist to its cinematic counterpart, Hypnotisoren. It examines the literary works within crime genre, evolve across different media. By analysing the interplay between source texts and the adaptation of the Swedish film titled Hypnotisoren. The research mainly focuses to unveil the nuances of narrative transformations, exploring how elements such as character development, plot structure, and thematic elements are modified in the process using the Adaptation theory proposed by Linda Hutcheon in path with analysing the intertextual relationship between the original novel and its potential adaptations, specifically investigating how the crime genre is transformed during adaptation. The process and concepts undertaken towards the transformation process are bound through a comprehensive examination. This study seeks to shed light on the dynamic relationship between crime fiction and its adaptations, offering insights into the creative choices made during the transition of these narratives into a film, various other concepts residing under crime fiction narratives and adaptions are discussed to prove understating about the subject.
Crime genre, Scandinavian Crime, Fiction and film, Adaptation, Narrative Focus.
SOCIALIZATION EXPERIENCES OF STUDENT POLICE CADETS IN ERNAKULAM DISTRICT, KERALA
The present study examines the socialization experiences of Student Police Cadets (SPC) in Ernakulam district using a descriptive research design. The primary objective is to analyse how participation in the SPC programme influences students’ behavioural development, civic awareness, discipline, and social responsibility. Primary data were collected from 308 SPC students through a structured interview schedule, while secondary data were obtained from official reports and academic sources. A systematic sampling technique was employed to select 37 schools from a total of 74 SPC schools in the district, ensuring representation of rural–urban areas and school types. The findings reveal that the SPC programme plays a significant role in enhancing socialization among cadets. Most respondents reported positive experiences in terms of discipline, teamwork, leadership, and respect for social norms and authority. Statistical analysis indicates that variables such as gender, type of school, social category, family type, religion, parental education, and occupation do not significantly influence socialization experiences. However, area of residence and economic status show significant differences, suggesting that contextual factors shape the extent of socialization. Thus, the study highlights the effectiveness of the SPC programme in fostering responsible citizenship and positive behavioural outcomes among adolescents, thereby contributing to community-oriented policing and youth development.
Socialization, Student Police Cadet, Behavioural Development, Civic Awareness, Youth Development
A STUDY ON THE EFFECT OF ORGANIZATIONAL SUPPORT ON KNOWLEDGE SHARING, EMPLOYEE ENGAGEMENT, AND JOB SATISFACTION AMONG EMPLOYEES WITH REFERENCE TO ITI PALAKKAD
In today’s knowledge-driven and competitive business environment, organizational support, knowledge sharing, and employee engagement are critical determinants of job satisfaction and overall performance. This study investigates the effect of organizational support on knowledge sharing, employee engagement, and job satisfaction among employees at ITI Limited, Palakkad, a public sector organization engaged in electronic switching systems and critical missions like ISRO. A descriptive research design was adopted, and data were collected from 120 employees using structured questionnaires. The study analyzed socio-demographic characteristics, levels of organizational support, knowledge sharing, engagement, and job satisfaction, and examined relationships among these variables using correlation, ANOVA, and regression analysis. Findings reveal that organizational support is positively associated with knowledge sharing, employee engagement, and job satisfaction, with regression analysis confirming that organizational support, knowledge sharing, and engagement are significant predictors of job satisfaction. The study highlights the need for supportive policies, knowledge-sharing initiatives, and employee engagement strategies to enhance satisfaction and performance. These insights can guide organizations in designing interventions that foster collaboration, motivation, and organizational effectiveness.
knowledge sharing, employee engagement, job satisfaction, employees, performance, organisational effectiveness.
Ms. Nandhini Devi C , Dr. K. Thirumalaisamy
INVESTOR SENTIMENT TOWARDS FINTECH DURING GLOBAL CONFLICTS
Geopolitics problems and cross-border wars have been historically experienced in the financial markets that have caused change in behavior and perceptions of risk among various sectors by investors. The recent several years have witnessed the market to be facing sudden explosion of financial technology (FinTech) that presents some alternative dynamics in the investment decision making process, in particular during periods of uncertainty. This research is aimed at examining how investors feel about the tendency toward investing in FinTech during international conflicts and the assessment of how the existing crises affect investment preferences, maximum risk, and trust in the stock market. The research applies the mixed methodology whereby the quantitative method of analyzing market dynamics has been utilized in conjunction with the primary data that has been collected following the design of structured questionnaires, which were administered to retail and institutional investors. The intention to draw their impacts on investor sentiment credits four important variables, perceived risk, expected returns, technological trust and geopolitical awareness. The testing and the correlations of the statistical tools include the correlation and regression analysis to test the relations of Fintech investment behavior and the global conflict indicators. The findings will indicate that despite the global conflicts being more likely to increase market instability and raise risk aversion, the investments in FinTech will be less vulnerable to such factors due to their digital nature, innovation capabilities, and the possibility to help during times of crisis and access finances. However, the mood of investors is probable to shift based on financial literacy, past experience of investment and the news on geopolitics. The current body of work is a product of this research that forms an understanding of the behavioral and strategy component of the FinTech investment in the context of the geopolitical instability. The results will be of value to investors, policymakers and business managers in designing adaptive strategies of investment and enhancing the financial system stability to the unpredictable global environments.
Investor Sentiment, FinTech Investment, Global Conflicts, Geopolitical Risk, Market Volatility, Behavioral Finance
Dr. Sumant Wachasundar, Dr. Jaspal Gidwani, Dr. Mahek Iram Qureshi, Dr. Vaibhav Bhalerao, Dr. Mukesh Patil
A Hybrid CNN–YOLO Framework for Skin Disease Classification and Real-Time Lesion Detection
Skin diseases are a significant global health issue. They can be as mild as common rashes or as serious as deadly cancers like melanoma. Getting the right diagnosis quickly is key it helps save lives and lowers healthcare costs. Traditionally, doctors examine the skin with their eyes and rely on their experience, but this approach can vary from doctor to doctor, take a lot of time, and isn’t always available everywhere. Now, thanks to breakthroughs in Artificial Intelligence (AI) and especially Deep Learning (DL), computers can help doctors analyze medical images and make more accurate diagnoses automatically.In this work, we introduce a smart system for detecting skin diseases. It uses two powerful AI tools: Convolutional Neural Networks (CNN) to sort images into different disease types, and You Only Look Once (YOLO) to quickly find and highlight problem spots on the skin. We trained and tested this system with a large set of skin images called the HAM10000 dataset, which includes many kinds of skin conditions. The CNN learns to recognize unique patterns in the images, while YOLO helps pinpoint exactly where the skin problem is— and it does this in real time. Our results show that this combined approach is very accurate at classifying diseases and spotting the affected areas, making it a useful tool for doctors and healthcare workers in real-world settings.
Skin Disease Detection, Deep Learning, CNN, YOLO, HAM10000, Medical Image Processing, Artificial Intelligence.
“MARINE ANIMAL-DERIVED BIOACTIVE PEPTIDES: A COMPREHENSIVE REVIEW OF MECHANISMS IN OXIDATIVE STRESS AND HYPERGLYCEMIA MANAGEMENT”
Bioactive peptides derived from marine animals have emerged as promising therapeutic agents for managing metabolic disorders, particularly oxidative stress and hyperglycemia associated with diabetes. Specifically, this review synthesizes evidence from preclinical and in vitro studies on peptides extracted from sponges, mollusks, and fish. These peptides show multiple biological activities: they inhibit lipid peroxidation, enhance insulin sensitivity, and exert antioxidant effects by upregulating enzymes such as superoxide dismutase, catalase, and glutathione peroxidase. In experimental models induced by diabetogenic factors, peptide-treated groups show improvements compared to controls, including reduced fasting blood glucose, less oxidative damage, and preserved tissue architecture in the pancreas and liver. Furthermore, mechanistic insights reveal interactions with key pathways like Nrf2/Keap1 and AMPK, supporting their hypoglycemic and cytoprotective roles. Collectively, these findings position marine bioactive peptides as a natural alternative to synthetic antidiabetic drugs, highlighting the need for their clinical application in diabetes management.
Marine bioactive peptides; Oxidative stress; Hyperglycemia; Antioxidant activity; Experimental animals; Diabetes mellitus
IMPACT OF ONLINE CONSUMER REVIEW ON E-COMMERCE
This study investigates the significant role of online consumer reviews in shaping purchase intentions within the dynamic e-commerce landscape. Drawing on primary data collected through a structured survey of 100 Indian e-commerce users, the research examines various dimensions of review influence, including valence (positive/negative), quantity, type (written/star rating), and source (verified buyer). The findings reveal a high degree of reliance on reviews, with a significant majority of respondents agreeing that positive reviews enhance and negative reviews discourage purchase. Key insights include the strong correlation between the number of reviews and consumer trust, and the perceived superior trustworthiness of verified buyer reviews. The study concludes that online reviews act as a powerful form of electronic word-of-mouth (eWOM), serving as a critical heuristic for consumers navigating product uncertainty and complexity. These insights offer actionable implications for e-commerce platforms and retailers seeking to optimize their review management and marketing strategies.
Online Reviews, Consumer Purchase Intention, E-commerce, Electronic Word-of-Mouth (eWOM), Consumer Trust, Verified Buyer.
ADI SANSKRITI: A STUDY ON DIGITAL PRESERVATION, EDUCATION, AND ECONOMIC EMPOWERMENT OF TRIBAL CULTURE IN INDIA
The rapid expansion of digital technologies has created new opportunities for the preservation and promotion of indigenous cultural heritage. This study examines AdiSanskriti, a national-level digital initiative launched by the Ministry of Tribal Affairs, India, aimed at preserving tribal culture while supporting education and economic empowerment. Using a qualitative research approach based on secondary data, the study analyzes official reports and platform content to understand the structure, objectives, and functional components of AdiSanskriti. Content analysis is employed to identify key themes such as digital documentation of tribal heritage, transmission of indigenous knowledge, institutional governance, and livelihood generation through online market places. The findings reveal that AdiSanskriti functions as an integrated digital ecosystem combining a cultural repository, digital learning modules, and an e-commerce platform for tribal artisans. Institutional guidance by autonomous bodies and State Tribal Research Institutes ensures ethical oversight, cultural authenticity, and community participation. The study highlights the platform’s potential to enhance cultural visibility, support intergenerational knowledge transfer, and create economic opportunities for marginalized communities. The challenges related to digital literacy, infrastructure, and sustainability remain. The paper concludes that AdiSanskriti represents a promising model for digitally mediated cultural preservation and empowerment, offering insights for policymakers, cultural institutions, and researchers seeking to integrate technology with heritage conservation and inclusive development.
AdiSankristi, AdiVishwavidyalaya, AdiHaat, AdiSampada, Tribal Culture
Jothimani.B , Muruganada Jeyanthi R, Anandraj K.C .and Dr. S.Aravind
A USER STUDY AMONG THE AFFILIATED COLLEGES OF BHARATHIAR UNIVERSITY ON THE PERCEIVED EFFECTIVENESS OF STRATEGIC LIBRARY MANAGEMENT IN THE DIGITAL ERA
This study investigates the perceived effectiveness of strategic library management in the digital age, focusing on colleges associated with Bharathiar University. It talks about how academic libraries have gone from being places to store books to places where people can find digital information. It also stresses how important it is to use strategic management to make sure that the library's services are in line with the goals of the school. Good library management includes using technology, improving the digital infrastructure, putting the needs of users first, and giving staff ongoing training. The study employs both descriptive and inferential methodologies. It asks 250 students and faculty members how they feel about different parts of library management, like strategic planning, technology integration, digital infrastructure, e-resource availability, digital resource accessibility, staff competence, user training programs, service quality, user satisfaction, and how effective the library seems to be. The results show that people think the best part is using technology together, but the digital infrastructure needs to be better. People are very happy with how well the library's digital resources work. They know that the library helps kids do well in school. But to make service better, we need to look at things like how reliable the internet is and how to help staff learn new skills. The research shows that the library is good at making plans and giving good service. To stay competitive and make sure it stays useful in the long run in the digital academic world, it needs to keep spending money on staff training, technology, and infrastructure. Some ideas for improving library services and making users happier are regular performance audits, better digital infrastructure, and more programs to teach people how to use the library.
User Study, Bharathiar University, Digital Library, Library Management
AN AUTOBIOGRAPHICAL STUDY OF NIMADI BOLIBHASHA; PRESERVING INDIA’S REGIONAL LINGUISTIC HERITAGE
This research article constructs a comprehensive aatmakatha (self-narrative) of Nimadi, a Western Indo-Aryan dialect spoken along the Narmada River in Madhya Pradesh. Employing both diachronic and synchronic approaches, the study traces the linguistic evolution of Nimadi from its roots in Sauraseni Apabhramsa, through medieval pastoral and oral traditions, to its contemporary emergence as a developing literary medium. Despite its rich repository of Indigenous Knowledge Systems (IKS) and a notable literary resurgence shaped by contributors such as Padma Shri Jagdish Joshila and Shri Mahadev Prasad Chaturvedi, Nimadi is currently confronted with a “silent crisis” of endangerment, driven by rapid urbanization and entrenched linguistic hierarchies. The article foregrounds key literary milestones, including the pioneering Nimadi epic Ammar Bol, while also examining the dialect’s dynamic “confluence” with neighboring linguistic systems such as Malvi, Marathi, and Gujarati. Through an analysis of historical migration patterns and recent standardization initiatives—most notably the compilation of a 30,000-word Nimadi–Hindi Shabdkosh—the study underscores the urgency of preserving regional languages. It argues that safeguarding Nimadi is not merely a linguistic endeavor but a cultural imperative, as the dialect encodes ancestral knowledge, ecological wisdom, medicinal practices, and the collective identity of the Nimar region.
Nimadi Bolibhasha, Linguistic Heritage, Autobiographical Approach, Indigenous Knowledge Systems (IKS), Vernacular Identity, Language Endangerment, Nimar Region, Dialect Preservation
Dr. Prachi Sathe, Dr. Anisha Jain, Dr. Shalini Modh