Volume 13 | Issue 4

 Abstract

Diasporic literature has always been associated with the loss of identity, the quest for self, nostalgia, and uprootedness from culture and tradition. Home away from home has always been difficult, and now in this 21st century with all the technological advances, it still feels the same. With all these developments in science and technology, it is not always easy to come back to our homeland. With all the adjustments made by us away from home, we somewhere try to create our own identity in a new land. On the verge of creating our own identity, we tend to lose ourselves more. Diasporic writers such as Kiran Desai, Rohinton Mistry, V.S. Naipaul, Jhumpa Lahiri, and Chitra Banerjee Divakaruni have spoken about identity loss, and their stories are always about identifying and creating one's own identity. The Mistress of Species (1997) by Chitra Banerjee Divakaruni is the story of Tilo and her transformation into Maya, where she identifies herself. This paper will focus on the journey of Tilo and her experiences with other characters who are all lost in a foreign land. The magical effects of the spices helped the characters deal with their problems. When Tilo stepped out of her store to assist someone, she broke free from the chains tied to her which represents the society and the role of women who are always guided by society. It is true that in today's time we have come far away from these situations, but doesn't it still feel the same when a woman suddenly does something that she was not expected to do? Globalisation has made us all come together and has helped us to reach a post-modern world, but still creating our own identities and discovering ourselves will always be a continuation for all the upcoming generations.

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Indian Diasporic Writing, Self-quest, loss of identity, Nostalgia, Homeland.

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Over the years, youth entrepreneurship has become a key driver of innovation, employment, and economic development in India. Seeing its potential, the Government of India initiated the 'Startup India' program in 2016 to develop a strong ecosystem that fosters entrepreneurial culture among the youth. This paper discusses the role of such government programs in encouraging and maintaining youth-led startups throughout the nation. Based on an in-depth analysis of policy initiatives, financial support schemes, skill development initiatives, and regulatory changes launched under the Startup India initiative, the research assesses the effectiveness of these interventions among young entrepreneurs. It also analyzes the issues confronted by youth in availing benefits and regional variations in startup development. Based on secondary data, case studies, and current trends, the paper showcases the success of Startup India in empowering future generations of entrepreneurs while providing recommendations on how to further increase the inclusivity and sustainability of such efforts.

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Start-up India, youth entrepreneurship, government initiatives, innovation ecosystem, economic development

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Indian Micro, Small, and Medium Enterprises (MSMEs) are vital to the nation's economy, significantly contributing to GDP, exports, and employment. While globalization and digital transformation present opportunities for these enterprises to expand internationally, numerous challenges hinder their progress. This paper explores these obstacles, including lack of digital infrastructure, technological illiteracy, financial constraints, regulatory complexities, logistical inefficiencies, and difficulties in building trust in foreign markets. The paper presents a multi-faceted approach to address these challenges, encompassing policy reforms, capacity building initiatives, technological advancements, and financial support. It also studies the use of blockchain for secure transactions using Escrow Payment System, Artificial Intelligence for personalized customer engagement, and fintech solutions for seamless cross-border payments. By identifying key policy recommendations, technological interventions, and best practices, this study aims to contribute to the sustainable digital globalization of Indian MSMEs, ensuring their competitiveness in the ever-evolving global economy.

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MSME's, Exports, Logistics, Blockchain, Escrow Payment System

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 Abstract

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Detection of Chronic Heart Failure (CHF) is essential for effective intervention and improved patient outcomes. Traditional diagnostic methods, including ECGs and echocardiograms, require specialized equipment and expertise, making them less accessible in resource-limited environments. Modern approach is that an automated CHF detection system leveraging machine learning (ML) techniques, specifically the Random Forest classifier, to analyze heart sound recordings (phonocardiograms - PCG) for easy and simple approach for detecting the CHF risk. This helps the patient to take the required precautions and avoid the risk of the CHF. This project reviews the detection of the CHF risk and helps accurately analyze Heartbeat of a person.

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CHF, ECG, PCG, Random Forest, Web Interface

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To detect and predict heart attack, we use the dataset from the UCI repository by the use of machine learning classifiers. Enhancing accuracy, facilitating early detection, and allocating healthcare resources as efficiently as possible are the objectives. Our main goal is to identify most suitable classifier which we can use for diagnostic applications. To predict heart disease, we use several machine learning approaches are used and compare their accuracy to get best result.This study discovered that the RF approach obtained 78% accuracy coupled with 100% sensitivity and specificity utilizing a heart attack dataset that we gathered from Kaggle three-classification based on k-nearest neighbor (KNN), decision tree (DT), and random forests (RF) algorithms. To construct a model training and testing of data evaluated. Accuracy, precision, recall, and F1-score were used to evaluate the models. With an accuracy of 80.33%, the SVM model outperformed the others.

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Concept of Rajdharma in ancient India and its study in the perspective of Indian constitution

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Concept of Rajdharma in ancient India and its study in the perspective of Indian constitution

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Blood group identification plays a pivotal role in critical medical applications such as blood transfusions, organ transplantation, and trauma care. Conventional methods for determining blood type involve invasive procedures, specialized reagents, and trained personnel--factors that often hinder immediate response in emergencies and limit access in remote or under-resourced environments. This project introduces an innovative, non-invasive method for blood group detection using fingerprint image processing. By leveraging the uniqueness of fingerprint biometrics and the capabilities of modern machine learning techniques, particularly Convolutional Neural Networks (CNNs), the system aims to classify blood groups (A+, A-, B+, B-, AB+, AB-, O+, O-) solely based on fingerprint patterns. This project aims to develop an automated blood group detection system using image processing and fingerprint analysis to provide a non-invasive and rapid approach. The proposed system integrates computer vision techniques using algorithms and AI to interpret images encompassing tasks like image classification, often leveraging deep learning models like Convolutional Neural Networks (CNNs) and biometric analysis to determine an individual's blood group.

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Blood Group Detection, Fingerprint Biometrics, Image Processing, Convolutional Neural Network (CNN),Minutiae Extraction

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The Murshidabad District Museum, located in Jiaganj in West Bengal, is an important cultural institution that presents the region's rich historical and artistic heritage, including artifacts from the Mughal and colonial periods. It is strategically located and has precious collections, but it still lags behind in attracting a substantial number of visitors. The key issues are low awareness, competition from other prominent local sites, transportation barriers, ineffective marketing, and lack of appeal to casual tourists. The museum's collections ranging from the 9th to the 13th centuries, provide a deep exploration of the region's cultural evolution. This paper explores these challenges and proposes strategies to enhance the museum's visibility and visitor engagement. Some of the suggestions include outreach in local media, better signage, transportation, online presence, interactive exhibits, and visitor facilities. All these approaches will allow the Murshidabad District Museum to reach more people, receive more recognition, and become an important cultural landmark in the area.

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Murshidabad District Museum, Cultural heritage, Tourism, Visitor experience, Marketing strategies, Public awareness.

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Creative Commons Attribution 4.0 and The Open Definition

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Artificial Intelligence (AI) is increasingly influencing high-stakes decision-making across various domains, particularly within the criminal justice system, where predictive risk assessment models inform decisions regarding parole, sentencing, and bail. The Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) tool has been widely utilized to assess recidivism risk; however, it has also faced scrutiny due to potential racial biases, especially its disproportionate false positive rates (FPR) for African-American individuals compared to Caucasian and Hispanic counterparts. This study conducts a fairness analysis of COMPAS-based predictions using machine learning models, evaluating the impact of different classifiers on racial groups and examining whether mitigation techniques can improve fairness outcomes. Our analysis employs Logistic Regression, Decision Trees, and Random Forest classifiers to evaluate the fairness of risk assessments. To address these biases, we implement fairness-aware adjustments, which progressively reduce disparities within each classifier. After mitigation, the FPR for African-Americans decreases by 11%, and the Disparate Impact Ratio improves significantly, reducing from 0.11 to 0.08 (Random Forest). These reductions indicate that fairness-aware methods can enhance equitable outcomes while maintaining model performance. We advocate for continued fairness interventions, policy regulations, and interdisciplinary efforts to ensure the responsible deployment of AI in real-world decision-making processes for high-impact applications such as criminal justice, where biased decisions can have severe consequences.

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Machine learning, AI Fairness, Bias Mitigation, Demographic Parity Difference, Sal.44889ocial Impact

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Diabetic retinopathy (DR) is a leading cause of preventable blindness, necessitating accurate and early diagnosis. This paper presents EfficientViT, a novel deep learning framework that combines EfficientNetV2's local feature extraction with a Vision Transformer (ViT) for global context modeling to improve DR grading on the APTOS 2019 dataset. Our hybrid architecture leverages cross-attention fusion to integrate CNN and transformer features, while contrastive pretraining enhances performance with limited labeled data. The model achieves 97.9% accuracy (with test-time augmentation) and a 0.990 AUC, outperforming ResNet50 (94.5%) and DenseNet121 (95.8%). To ensure clinical interpretability, we introduce attention-guided Grad-CAM++, generating heatmaps with a 63% IoU overlap against clinician annotations--a 12% improvement over standard Grad-CAM. Comprehensive evaluations reveal robust performance across all DR severity grades, with 96% sensitivity for proliferative DR (Grade 4). This research introduces several key innovations to enhance diabetic retinopathy (DR) screening, including a hybrid CNN-Transformer architecture that combines EfficientNetV2 and Vision Transformer (ViT) with attention fusion for optimized feature learning, achieving state-of-the-art 97.9% accuracy on the APTOS 2019 dataset. The framework incorporates self-supervised contrastive pretraining to reduce labeled data requirements by 40%, addressing annotation bottlenecks in medical imaging. Notably, the system generates explainable AI outputs through lesion-localizing heatmaps that align with clinician markings, bridging the critical gap between algorithmic performance and clinical trust. Together, these advances--spanning novel model design, data-efficient training, and interpretable outputs--deliver a clinically viable solution that balances high diagnostic accuracy with real-world deployability for DR screening programs.

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Diabetic Retinopathy, Deep Learning, EfficientNetV2, Vision Transformer, Explainable AI, Medical Imaging.

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The formation of Jharkhand in 2000, carved out from Bihar, marked a significant milestone rooted in decades of socio-political aspirations for autonomy, development, and recognition of indigenous identity. This study, titled "From Aspirations to Realities: Tracing the Journey of Jharkhand's Political Landscape and Governance Challenges," aims to critically examine the trajectory of the state's political evolution and governance dynamics since its inception. It explores how the early aspirations for socio-economic development, cultural preservation, and effective governance have been addressed or undermined by the interplay of political leadership, administrative policies, and grassroots mobilization. By analysing the shifts in political paradigms, governance frameworks, and the resultant socio-economic outcomes, this research seeks to highlight persistent challenges and achievements within Jharkhand's political fabric. Furthermore, it delves into issues of corruption, regional disparities, tribal rights, and policy implementation that have continuously shaped the state's governance landscape. Through a comprehensive review of policy documents, electoral trends, and qualitative interviews, the study provides nuanced insights into how Jharkhand's journey from aspirations to realities reflects broader struggles for effective governance and inclusive development.

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Tribal, Land, Development, Governance, Political, Rights

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The management of biomedical waste (BMW) has grown to be a major concern for healthcare practitioners due to the increased risk of infection and harm. Handling medical waste improperly can have detrimental effects on one's health. The hospital personnel in every healthcare setting urgently need to be made more aware of BMWM. It is not only legally required but also socially responsible to handle biological waste effectively. Assessing staff nurses' knowledge of biomedical waste management and determining the relationship between chosen demographic characteristics and staff nurses' degree of knowledge about BMW management were the goals. The nursing staff members who interact with BMW were the participants in a descriptive cross-sectional study. A semi-structured survey was used to gather the data. Findings reveal that 20% of staff nurses had inadequate knowledge, whereas 50% had high knowledge. Knowledge of qualified biomedical waste management and the length of their clinical practice were shown to be significantly correlated (p<0.05). Results show that twenty percent of staff nurses lacked expertise, whereas fifty percent had strong knowledge. Knowledge of qualified biomedical waste management and the duration of their clinical practice were shown to be significantly correlated (p < 0.05). Conclusions: Because staff nurses handle biomedical waste directly, they should receive regular training and supervision are essential for better healthcare waste management and implementation.

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Biomedical waste, Hospital, Knowledge, Staff nurses

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The paper explores the historical evolution of Bhutan as a buffer state between India and China, tracing its transformation from the colonial era to contemporary geopolitical dynamics. The analysis begins with British colonial interventions in the Eastern Himalayan region, particularly focusing on the 1772 skirmish, the Treaty of Sinchula (1865), and the Treaty of Punakha (1910), which established Bhutan's early dependence on and alignment with British India. These colonial-era policies and treaties not only redefined Bhutan's territorial and diplomatic orientation but also initiated its role as a strategic buffer zone. The post-colonial phase, including the 1949 Indo-Bhutan Treaty and the rise of Communist China, further entrenched Bhutan's buffer status. Events such as the 1959 Tibetan Uprising and the 1962 Sino-Indian War intensified India's strategic interest in Bhutan. More recently, the 2007 Indo-Bhutan treaty revision and the 2017 Doklam standoff have reinforced Bhutan's geopolitical relevance. The hypothesis guiding this study is: Colonial-era interventions laid the foundation for Bhutan's evolution as a buffer state between India and China. Using a qualitative, descriptive approach rooted in postcolonial theory, the paper uses manual content analysis to highlight how colonial legacies continue to influence Bhutan's foreign policy and regional positioning.

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British Colonialism, Postcolonial theory, Bhutan, Buffer state, Colonial legacy

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The main aspect is to introduced and study some new class of function called Nano generalized semi Continuous function (briefly gscf) and Nano generalized semi Irresolute function(briefly gsif) Also, introduce and study about Nanosemi-interior , Nanosemi-closure and Nano generalized semi Continuous and Irresolute. And also to investigate some properties of these sets and to study some properties, remarks related to these sets.

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Ngeneralized semi closed sets, Ngeneralized semi Continuous function, Ngeneralized semi Irresolute function.

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Morphometry is a statistical and mathematical study of the Earth's surface that examines the dimension, form, and composition of landscapes. For this investigation, the Venna River basin has been chosen. This river has 60 km length with 125 sq. Km. Catchment area. It's provide the water for near about 30 villages. For the present study, 30-meter resolution SRTM remote sensing data were obtained from the USGS website and processed using ERDAS Imagine and ArcGIS software. The generated DEM, map of slope, and drainage map were applied for the morphometric study of the river Venna Watershed.

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Catchment, Morphometric SRTM, USGS.

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This study examines the social well-being among college students. The aim of the study was (i) To find the level of social well-being and its dimensions, such as social acceptance, social responsibility, social coherence and social support among college students and (ii) To find out the significant difference between college students in their social well-being and its dimension with respect to gender, type of family, social group member, type of family, locality, subject and mobile usage. A survey method was used for the study. A simple random sampling technique was used for selecting 107 college students from Thoothukudi, Tamilnadu. Descriptive and inferential statistical techniques were used for analyzing data. The level of social acceptance among college students was found to be moderate. The level of social responsibility, social coherence, social support and social well-being among college students is low, and there is no significant difference between college students in their social well-being and its dimensions with respect to gender, degree, type of family, locality of residence, social group member, subject and mobile usage per day.

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Social well-being, college students, social acceptance, social responsibility, social coherence and social support

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Abstract For the purpose of designing cities that are able to effectively respond to climate change and rapid urbanization, urban-scale environmental performance evaluations are necessary. High-resolution, multi-scale, and temporal assessments of multiple interrelated environmental criteria are made possible by RS technologies. Despite its growing adoption in urban sustainability, a comprehensive review of RS's role in multi-criteria decision-making is still lacking. This review analyzes 124 research articles to explore RS applications in spatio-temporal analysis, impact evaluation, mitigation strategy assessment, and predictive modeling across five interconnected environmental criteria: urban air quality, urban heat, outdoor thermal comfort, building energy consumption, and solar potential. RS facilitates the integration of morphological, thermal, and meteorological data, enabling the evaluation of urban interdependence, such as the influence of urban form on air pollution dispersion, heat retention, and energy demand. Machine learning and AI-enhanced models improve air quality predictions, urban heat mitigation strategies, energy forecasting, and solar potential assessments. Real-time urban climate monitoring at finer spatial scales is further enhanced by UAVs, LiDAR, and nanosatellite technologies, facilitating dynamic planning interventions. Advances in AI-driven downscaling, digital twins, and nano satellite networks continue to expand RS capabilities despite difficulties with data resolution, temporal coverage, and real-time monitoring. RS gives urban planners and policymakers the tools they need to create climate-adaptive, energy-efficient, and resilient cities by making it easier to make decisions based on multiple factors.

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Keywords :Remote sensing ,Environment ,Air quality ,Urban heat ,Outdoor thermal comfort ,Building energy ,Solar photovoltaic

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Abstract: The global community is currently grappling with the challenges of environmental degradation and pollution, primarily driven by the excessive exploitation of natural resources by humanity in pursuit of unplanned development through rapid scientific and industrial advancements. It is imperative that we now implement measures for environmental restoration alongside conservation efforts, facilitated by effective environmental education to ensure a sustainable future for society. India possesses significant potential to offer the essential environmental educational framework required globally, owing to its rich natural resources. The Indian education system is undergoing transformation through the introduction of the new National Education Policy, which aims to tackle prevalent environmental issues and their solutions from early childhood education through to postgraduate and research levels. This paper reviews the current status of environmental education, highlighting its key components and challenges, while also evaluating its alignment with the newly adopted education policy.

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Key words: New Education Policy, Environmental Science, Environmental Education, Future challenges.

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This prospective analysis investigates the impact of such work schedules on the stress levels and overall work-life quality of Tamil Nadu State Transport Corporation (TNSTC) employees, focusing on the Kumbakonam region. The transport plays a vital role in the development of our country. Its main objective is to connect the urban and rural areas. Most of the students and office goers are using the buses. India's passenger transport for small and medium distances is bus oriented and many other transport industries the safety of travelers and other road users is of prime importance. The main objective of this paper is identified and analysis the factors influencing job stress among the transport employees. A multiple linear regression model demonstrates that irregular work schedules significantly decrease the quality of work life among TNSTC employees. These findings emphasize the need for organizational strategies and support systems tailored to mitigate the stressors associated with different shifts, enhance employee well-being, and improve public transportation services in the Kumbakonam region.

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Shift work, irregular schedules, stress, work-life quality, employee well-being, public service, occupational health.

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Creative Commons Attribution 4.0 and The Open Definition