Volume 13 | Issue 4

 Abstract

Drug is any chemical substance derived from plants, animals or mineral resources, be it traditional or orthodox which is used for internal application to the human body in the treatment of diseases, reduce pain or modify the functions of the various system of the body. Excessive Usage of these Drugs without the prescription from the doctors is called as Drug Addiction. The Drug Addiction has been on a steady rise over the past few years amongst students.The excessive addiction of drugs amongst students is making students dull and they tendtolosetheircharacters.Studentsaresoaddictedtodrugsnowadays.TheGovernmentand educational institutions are taking many serious measures to stop the usage of these drugs amongst students.This study shows the influence of drugs on college students in Coimbatore City.

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 Keywords

Drugs,Addiction,Student,DrugAbuse

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Creative Commons Attribution 4.0 and The Open Definition

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Rapid urbanization and increased traffic congestion make it difficult for emergency vehicles to navigate crowded areas, causing delays that can be life-threatening. Traditional traffic systems rely on static infrastructure, which lacks real-time adaptability. This project proposes an IoT and AI-driven system that uses sensors and deep learning algorithms to monitor traffic density and detect emergency vehicles. It dynamically adjusts lanes to create clear paths, activating road changes through a microcontroller-based unit like Arduino or Raspberry Pi.The system also includes LED indicators, audio alerts, and digital screens on traffic signals to guide drivers. IoT integration allows cloud-based data processing and remote monitoring, enabling real-time optimization of emergency paths. By automating traffic management, this system reduces response times and enhances overall efficiency, contributing to smart city goals with a more adaptive, intelligent approach to urban traffic.

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 Keywords

Emergency lane system,Traffic management,IoT, CNN-based vehicle detection&Smart road infrastructure.

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Agricultural waste burning represents a significant environmental challenge in rural areas, particularly during harvesting seasons, and constitutes a major source of air pollution. This study investigates the extent and implications of air pollution resulting from crop residue burning, examining its effects on human health, environmental sustainability, and agricultural productivity. The combustion of agricultural waste releases numerous pollutants into the atmosphere, including particulate matter (PM?.? and PM??), carbon monoxide (CO), nitrogen oxides (NO?), sulfur dioxide (SO?), and volatile organic compounds (VOCs), all of which significantly degrade air quality [1]. Beyond contributing to air pollution, this practice increases greenhouse gas emissions, reduces soil fertility, and disrupts local ecosystems [2]. This paper explores the underlying causes of agricultural waste burning, assesses its multifaceted consequences, and proposes evidence-based mitigation strategies, including alternative waste management approaches, improved agricultural practices, and policy interventions. Our findings emphasize the urgent need for comprehensive approaches to address this critical environmental and public health challenge facing rural communities worldwide.

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 Keywords

Crop residue burning, Air pollution, Sustainable agriculture, Ecosystem disruption, Mitigation strategies, Rural health

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Silver nanoparticles (AgNPs) have garnered considerable attention owing to their distinctive physicochemical characteristics and extensive applicability across biological, environmental, and industrial domains. Conventional techniques for AgNP production often use hazardous compounds, prompting apprehensions about toxicity and environmental consequences. Green synthesis offers a sustainable alternative by using biological sources, including plant extracts, fungus, bacteria, and biomolecules, to facilitate nanoparticle creation. This method employs an environmentally sustainable process in which natural phytochemicals and microbial enzymes serve as reducing and stabilizing agents, guaranteeing biocompatibility and improved functioning. The characterization of green-synthesized AgNPs employs techniques including UV-Visible spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier-transform infrared spectroscopy (FTIR), and dynamic light scattering (DLS) to assess structural integrity, morphology, and stability. These nanoparticles have numerous uses in medicine, catalysis, water purification, food packaging, and sensor technologies, underscoring their significance in breakthroughs in nanotechnology. Notwithstanding their promise, obstacles like scalability, repeatability, and mechanistic optimization persist, requiring more study to enhance synthesis techniques and broaden transdisciplinary applications. This study offers a thorough examination of the processes, characterisation, and applications of green-synthesized AgNPs, highlighting their importance in sustainable science and technology.

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Silver nanoparticles (AgNPs), Bioreduction, Eco-friendly nanotechnology, Nanoparticle characterization, Biomedical applications

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Text summarization continues to be a challenging task--how do we capture the essence of a long document in just a few sentences, without losing meaning or introducing errors? In this work, we present a hybrid approach that combines the strengths of both extractive and abstractive summarization, striking a balance between factual accuracy and natural language generation. Our method starts by selecting important sentences using a KL-divergence-based technique, which helps ensure the summary remains grounded in the original content. These selected sentences are then passed to a fine-tuned T5 model, known for its ability to generate fluent, coherent summaries. To further enhance performance, we apply reinforcement learning that guides the model to optimize its output based on ROUGE scores. This three-stage process leads to summaries that are not only readable but also more aligned with the core information in the source text. We evaluate our approach on several benchmark datasets and observe notable improvements over standard baselines. Overall, this work shows that a thoughtfully designed hybrid method--leveraging statistical grounding and neural fluency--can produce high-quality summaries. Future directions include exploring different ways to measure summary quality and adapting the approach to new domains and languages.

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Text Summarization, Hybrid Summarization, Extractive Summarization, Abstractive Summarization, Kullback-Leibler Divergence, BART, Reinforcement Learning, ROUGE.

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In India, sexuality, sexual movements, and queer identities are closely tied to Section 377 of the Indian Penal Code, a colonial-era law imposed by the British in 1860 to criminalize same-sex relationships. This law suppressed India's historically diverse approach to sexuality, which included acceptance of same-sex love in ancient texts and poetry. The paper explores how Section 377, rooted in Victorian morality, erased India's rich sexual traditions, with its impact still felt today. While the Delhi High Court partially decriminalized certain aspects of Section 377 in 2009, the Supreme Court reinstated it in 2013, intensifying the fight for sexual rights. The LGBTQIA+ movement in India gained momentum in the 1990s, driven by organizations like ABVA and the Naz Foundation, with international NGOs playing a key role. However, this movement often sidelined poorer and more diverse queer identities, influenced by Western perspectives. The 2018 Supreme Court ruling decriminalizing same-sex relations was a major legal victory, but the paper argues that it has not led to full societal acceptance or equality. Challenges such as workplace discrimination, lack of mental health support, and cultural stigma persist. The paper concludes that true equality in India requires more than legal reform; it demands a shift in social attitudes and structural changes to support marginalized queer identities.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Section 377, LGBTQIA+ rights, Colonialism and sexuality, Queer identities, Decriminalization, Social acceptance

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The present study examined the relationships between stoicism, social comparison, and mental well-being among North Indian college students, while also considering differences across gender, education level, and spiritual or religious beliefs. Contrary to expectations, stoicism did not significantly predict mental well-being or social comparison, and no mediation effect was observed. However, non-binary participants reported significantly higher stoicism scores than their male and female counterparts, suggesting that stoicism may function as a coping response to minority stress in this population. These findings highlight the need to critically evaluate the cultural validity of Western psychological measures and underscore the importance of intersectional and context-sensitive approaches to mental health research in non-Western populations.

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 Keywords

Stoicism, Social Comparison Theory, Mental Well-Being, Psychological Resilience, Emotional Regulation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Controlling a blast furnace in real time is exceptionally demanding: the furnace's internal temperature, pressure, and gas flows shift constantly, and even slight deviations can push the silicon content outside of acceptable limits or force emergency shutdowns. Traditional Computational Fluid Dynamics (CFD) models provide detailed spatial snapshots of these conditions, but each simulation can take hours or days--far too slow to guide minute-to-minute adjustments on the plant floor. Two algorithms are ensembled to balance accuracy and interpretability. Deployed on a cloud platform, the system processes live sensor feeds and delivers new predictions in under a minute via a web dashboard. Early pilot results indicate a 5-10% boost in throughput, a 7% cut in fuel use, and a notable reduction in CO? emissions, demonstrating that real-time, explainable machine learning can both accelerate decision-making and promote greener steelmaking.

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Blast Furnace, Machine Learning, CFD Simulation, XGBoost, Real-Time Prediction, Intelligent Ironmaking Systems

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Creative Commons Attribution 4.0 and The Open Definition

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Rural artisans in India and Karnataka have played a significant role in the economic and cultural background of the society since ancient times. Handicraft skills like weaving, carpet making, wood carving, clay and metal work have been a source of folk heritage and livelihood. However, modernization, industrialization, globalization and technology-based modern production methods have posed many challenges to their lives and businesses. Shortage of raw materials, lack of direct access to the market, over-prevalence of middlemen, financial difficulties, lack of interest of the younger generation and lack of institutional support are the major challenges. This study analyzes the available literature on the change, continuity and revival efforts of rural artisans in India and Karnataka. The diversity of these professions and the severity of the problems they face are evident through the examples from different states. The study also examines the effectiveness of projects undertaken by researchers and policymakers to preserve and revitalize these professions in the present era. The focus of this study is on the belief that the innovative power of rural artisans, combining the skills, creativity, and heritage of rural artisans with modern technology and marketing, can pave the way for building a sustainable rural economy.

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 Keywords

Rural Artisans, Traditional Professions, Modernization and Renewal, Market Challenges, Economic Situation, Entrepreneurship, Cultural Heritage, Impact Of Globalization, Cooperation and Support System, Career Continuity and Change.

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This paper explores the intricate processes of identity construction in the war memoirs - Malala Yousafzai's I Am Malala and Ishmael Beah's A Long Way Gone. By analyzing their personal narratives, the research examines how experiences of violence, displacement, and resilience shape selfhood. Drawing on theories of trauma, postcolonialism, and narrative identity, the study highlights how these memoirs function both as testimonies and as acts of self-definition. It argues that the fragmentation caused by conflict is not only a site of trauma but also a foundation upon which new identities are built. Through comparative textual analysis, this paper uncovers how memory, culture, and the global discourse on war influence the formation and articulation of identity in youth caught in conflict.

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Identity construction, war memoirs, trauma narrative, postcolonialism, youth and conflict, narrative identity, autobiography, memory and resilience

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 Abstract

Kangaroo mother care (KMC) is a technique that involves skin to skin contact between a newborn infant and a care giver.. This review standpoints the effect of KMC on post natal mothers to gain knowledge and practice, weight gain of LBW neonates,. KMC-TP also improves breastfeeding rates and reduces mortality rates during the hospital stay as well as at home. KMC-TP can be provided not only by mothers but also by fathers, Grandparents and other adults in the family. However, it is not routinely practiced in hospitals. Short-term and long-term KMC-TP is beneficial for life survival, growth and development, neurobehavioral development, organ development, breastfeeding, and mother-infant bonding. When the babies were delivered before completion of 37wks of gestation those babies are more likely to experience neonatal mortality and morbidity due to acute breathing problems, gastrointestinal problems, autoimmune disorders, and neurological defects as compared to full-term and normal-weight gain infants. A thorough literature search was conducted using key databases like PubMed and Google Scholar, as well as web of sciences and Medical Subject nursing Heading (MeSH) terms and related keywords. Clinical health experts also believed that implementing with good practices of KMC-TP would assist mothers in developing their emotional bonds with their baby. As a result, KMC managed babies had better weight gain ,earlier hospital discharge and more impressively, empowers parents : good bonding , increased confidence , and deep satisfaction due to active involvement in KMC-TP and also improves health outcome by providing early Breast feeding, temperature regulation and Strong bonding. It is crucial to remember that KMC-TP works better for babies with very low birth weight (VLBW), preterm babies, infant wellbeing and develop good bonding among mothers with their infants. The mother-child relationship enhances growth parameters like physical, behavioral and psychosocial parameters.

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Kangaroo mother care , mother, infant

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Creative Commons Attribution 4.0 and The Open Definition

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This research paper examines the health implications of night shift paintings in various industries, specifically in IT and production sectors. The study explores bodily, intellectual, and social fitness demanding situations faced by using night time shift employees and employs Orange Data Mining strategies to analyze actual-international datasets for patterns in health risks. By understanding those developments, effective answers are proposed to mitigate the poor impact of night shifts on employees' nicely-being.

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Night Shift Work, Occupational Health, Sleep Disorders, Mental Health, Circadian Rhythm, Fatigue, Orange Data Mining, Workplace Wellness, Health Solutions, Data-Driven Analysis.

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As machine learning models become more prevalent, safeguarding privacy has become a critical challenge. This paper offers a comprehensive review of privacy-preserving machine learning (PPML) approaches, highlighting key techniques, existing challenges, and potential directions for future research. It examines the integration of privacy-preserving strategies into machine learning algorithms, workflows, and system architectures. The review also underscores the dynamic regulatory landscape and emphasizes the urgent need for innovative measures to address privacy concerns effectively. To advance the field, we introduce a structured framework, termed the Phase, Guarantee, and Utility (PGU) model, for systematically evaluating PPML approaches and offering guidance for both researchers and practitioners. By encouraging collaboration across disciplines--including machine learning, distributed systems, security, and privacy--this work aims to accelerate the development of robust and privacy-focused machine learning systems

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Machine learning differential Privacy

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Creative Commons Attribution 4.0 and The Open Definition

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Business dashboards use Key Performance Indicators (KPIs) to give essential insights, but it's still challenging to efficiently extract and analyze large amounts of data. Decision-making is frequently hampered by cognitive overload, which reduces the usefulness of these tools. In order to automatically extract and summarize KPIs from dashboard screenshots, this study presents a Business Dashboard Summarizer that uses Large Language Models (LLMs), computer vision, and optical character recognition. The solution improves accessibility, reduces information overload, and improves business intelligence productivity by turning raw data into concise, natural-language summaries. This study demonstrates how AI-driven summarization can optimize data interpretation and make strategic insights easy to use and more actionable.

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Creative Commons Attribution 4.0 and The Open Definition

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Business Dashboard, KPI Extraction, LLMs, AI Summarization, Computer Vision, Business Intelligence, Optical Character Recognition.

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Creative Commons Attribution 4.0 and The Open Definition

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This study investigates the economic and social impact of digital payment adoption in rural areas, where financial inclusion and access to formal financial services have long been significant challenges. With the increasing penetration of mobile phones and internet connectivity, digital payment systems are becoming more accessible to rural populations, offering a promising solution to these challenges. The research aims to assess the level of adoption, evaluate economic benefits such as increased financial inclusion, reduced transaction costs, and improved access to financial services, and explore social implications including changes in financial behavior, empowerment, and social relationships. The study uses a mixed-methods approach, combining quantitative survey data with qualitative insights gathered from rural communities through focus groups and interviews. The findings provide valuable insights into the potential benefits and challenges of promoting digital payments in rural areas, informing policymakers, financial institutions, and digital payment service providers on how to harness the potential of digital payments for promoting financial inclusion and economic development. By shedding light on the complex interactions between digital payments, economic outcomes, and social dynamics in rural areas, this research contributes to the development of targeted strategies and interventions that can enhance the accessibility, effectiveness, and sustainability of digital payment systems in these regions. Ultimately, this study aims to foster a more inclusive and sustainable financial ecosystem in rural areas, where digital payments can play a key role in improving the economic and social well-being of rural populations.

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 Keywords

Digital payments, rural areas, financial inclusion, economic impact, social impact, financial behavior, empowerment

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

An innovative device referred to as a virtual hand gesture mouse seeks to enable individuals to operate computers using more ease than before without any conventional hardware. Advanced computer vision strategies are explored to identify movements of the human hand from a camera shot including shape analysis, object detection, and machine learning. Specific hand movements, including finger movements for moving the cursor, clicking, scrolling, and dragging objects, are commonly recognized by the system. The Hygienic, Simple and Convenient Touchless Technology Touchless technology is a hygienic, simple, and convenient way of approaching different activities that are executed on a daily basis. The touchless technology is characterized by the absence of any physical contact with the device that one is using to execute a certain application or activity. It is mostly used in places such as the hospitals where there are high chances of contracting various diseases through physical contact with the devices. According to researchers, the amount of bacteria in a single computer mouse is more than that of a human toilet. In order to reduce the chances of contracting diseases, the touchless technology is applied in places such as the hospitals to prevent any form of physical contact with the devices. The touchless technology is also used in the public places where touching is not allowed. In such places, the human beings are expected to perform various activities based on the touchless technology. The touchless technology is not only applied in public places but also in the case of virtual reality where one is expected to perform various activities in accordance to the touchless technology. The touchless technology enables the user to be able to use a device without necessarily having any physical contact with the device. The hand gesture control can also be used to aid the physically challenged users.

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Creative Commons Attribution 4.0 and The Open Definition

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Touchless technology, Virtual hand gesture mouse, Hand gesture control, Physical contact, Virtual reality, Computer vision

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: This project showcases a detailed design and implementation of a real-time physiological monitoring system that uses a low-power, secure Wireless Body Area Network (WBAN) built on the E-SECMAC algorithm. The system allows for continuous monitoring of vital signs such as electrocardiogram (ECG), heart rate, blood pressure, and body temperature, all while keeping energy consumption low and ensuring secure data transmission through advanced encryption methods. By taking advantage of the E-SECMAC cluster-based structure and adaptive clustering approach, the system significantly enhances scalability, energy efficiency, and reliability, making it ideal for remote health monitoring. Its performance has been thoroughly tested through extensive simulations and experiments, proving its capability to deliver accurate and dependable physiological data in real time. This innovative system has the potential to transform healthcare delivery by facilitating remote monitoring of patients with chronic conditions, leading to better patient outcomes and lower healthcare costs. Moreover, its flexibility and scalability make it a compelling option for a variety of applications, including sports, fitness, and elderly care. Overall, this project contributes to the advancement of next-generation healthcare technologies, setting the stage for the widespread use of WBANs in remote health monitoring. The insights gained from the system's design, implementation, and evaluation highlight the transformative potential of WBANs in enhancing healthcare delivery and improving patients' quality of life.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Wireless Body Area Network (WBAN), E-SECMAC algorithm, physiological monitoring, low-power design, secure communication, and real-time monitoring.

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Creative Commons Attribution 4.0 and The Open Definition

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This study focuses on analyzing Total Dissolved Solids (TDS), biological activity, and key physiological indicators across various regions surrounding Raipur. TDS is one of the primary impurities found in mixing water, and its concentration must adhere to specific limits as outlined in construction codes, given its potential to affect concrete properties. The research examines how variations in TDS levels influence both the fresh and hardened states of concrete. TDS, characterized by its low signal-to-noise ratio and optical inertness, is typically detected indirectly through changes in optically active water quality parameters (WQPs) within the visible, near-infrared, and infrared spectra. The findings suggest elevated TDS concentrations in contaminated sites, with marked vertical variations in TDS and bulk conductivity, indicating significant spatial and depth-related variability. Beyond its effects on construction materials, elevated TDS levels adversely impact ecosystems and human health, influencing organs such as the gastrointestinal system, heart, lungs, skin, liver, and reproductive system. Furthermore, water with TDS levels exceeding 500 ppm can cause excessive scaling in household appliances like boilers, heaters, and pipelines. To mitigate the risks associated with high TDS levels, various removal techniques have been developed, including ion exchange, electrochemical methods, precipitation, desalination, nanofiltration, distillation, ultrafiltration, forward osmosis, and reverse osmosis. However, each method has its own limitations regarding efficiency and applicability across different water matrices. This review explores the relationship between TDS and other physicochemical parameters based on an analysis of fifteen water samples collected from multiple locations within Raipur city. The primary objective of the research is to assess the quality of drinking water available to residents. The results demonstrate that many water samples exhibit TDS concentrations within the permissible limits set by the Bureau of Indian Standards (BIS), with detailed observations presented in the discussion section. The study emphasizes the importance of public awareness regarding the long-term health risks associated with consuming water from various regions.

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 Keywords

KEYWORDS: Total Dissolve Solids, Conductivity, Temperature, Raipur

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 Abstract

The potential of artificial intelligence (AI) to automate data collection, analysis, and interpretation from multiple sources is the main topic of this paper, which examines the integration of AI into pharmacovigilance (PV) systems. In addition to discussing difficulties and moral issues including algorithmic prejudice and data privacy, it offers suggestions for further study and application in this important area.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Pharmacovigilance, Artificial Intelligence, Adverse drug reactions.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study presents an explainable artificial intelligence (XAI)-driven framework for lithium-ion battery prognostics, addressing the dual challenges of prediction accuracy and interpretability in battery management systems (BMS). Using publicly available datasets from NASA and CALCE, Gradient Boosting, LSTM, and Transformer models were developed, with the Transformer model achieving the best results (MAE: 0.0275, RMSE: 0.0356). To ensure transparency, SHAP and LIME were applied for global and local feature attribution, while attention mechanisms revealed critical temporal patterns. Key degradation indicators such as internal resistance growth and voltage variation were consistently identified. The proposed framework offers a practical and trustworthy solution for accurate and interpretable RUL and SOH prediction, supporting real-world deployment in electric vehicles and energy storage applications.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

XAI, Lithium-ion Battery, SHAP, LIME, RUL/SOH

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Creative Commons Attribution 4.0 and The Open Definition