International Journal of LAW, Arts and Humanities
Maternal and Reproductive Health in Ladakh, India: A Comprehensive Review of Current Status, Challenges, and Future Directions
Maternal and reproductive health is a key component of public health and an important indicator of healthcare system performance. Women living in highaltitude and geographically remote regions such as Ladakh face unique challenges in accessing quality maternal and reproductive healthcare due to difficult terrain, harsh climatic conditions, and limited healthcare infrastructure. This study examines the status of reproductive and maternal health in the Union Territory of Ladakh using secondary data from the National Family Health Survey (NFHS-5, 2019–21) and NFHS-6 (2023–24). A descriptive and comparative analysis was conducted to assess key indicators, including fertility, family planning, antenatal care, institutional delivery, postnatal care, and women's nutritional status. The findings indicate that Ladakh has made notable progress in maternal healthcare, with high coverage of antenatal care and institutional deliveries. However, challenges such as maternal anaemia, nutritional deficiencies, geographical isolation, and limited access to specialized healthcare services continue to affect maternal and reproductive health outcomes, particularly in remote communities. The study highlights the need to strengthen primary healthcare services, improve maternal nutrition, expand telemedicine and outreach programmes, and enhance emergency obstetric care. These findings provide valuable evidence for policymakers and healthcare providers to design targeted interventions aimed at improving reproductive and maternal health in Ladakh and other high-altitude regions
Copyright (c) 2026 https://creativecommons.org/licenses/by-nc-nd/4.0
A Study of Challenges Faced by Students in Rural Areas of Palghar District
Education plays an important role in individual development, social mobility and economic progress. However, students living in rural areas frequently experience barriers that affect their educational access, regular attendance, participation and academic performance. The present study, titled “A Study of Challenges Faced by Students in Rural Areas of Palghar District,” examines the technological, financial and transportation-related difficulties experienced by rural students. The study adopted a descriptive research design and collected primary data from 160 respondents through a structured questionnaire. Frequency, percentage, descriptive statistics, a one-sample ttest and the Friedman test were used to analyse and interpret the collected data. The findings reveal that transportation-related difficulties were perceived as the most prominent challenges faced by rural students, followed by technological difficulties, while financial challenges were comparatively less prominent. The statistical results confirm that the levels of the challenges experienced by students differed significantly. Poor road connectivity, inadequate public transportation, long travelling distances, limited access to digital devices, weak internet connectivity and insufficient technological facilities adversely affect students’ educational experiences. The study concludes that educational institutions and government authorities should improve rural transportation services, road and digital infrastructure, internet access and institutional support to promote regular attendance, equitable educational participation and better academic development among students in the rural areas of Palghar district.
Stability and Bifurcation Analysis of Delay Differential Equation Models in Dynamical Systems
Delay Differential Equations (DDEs) have emerged as an essential mathematical framework for modeling dynamical systems in which the current state depends not only on present conditions but also on past events. Such delays naturally arise in biological, ecological, engineering, epidemiological, and control systems, where time-lagged interactions significantly influence system behavior. This study presents a comprehensive stability and bifurcation analysis of a class of delay differential equation models to investigate the influence of time delay on the qualitative dynamics of nonlinear systems. The mathematical model is formulated by incorporating discrete time delays, and the equilibrium points are determined through analytical techniques. Local stability analysis is carried out using the characteristic equation and eigenvalue approach, while the conditions for asymptotic stability are established using linearization methods. Furthermore, Hopf bifurcation theory is employed to identify the critical delay values at which the system transitions from stable equilibrium to sustained oscillatory behavior. Numerical simulations are performed to validate the theoretical findings and to illustrate the effects of varying delay parameters on system dynamics. The results demonstrate that increasing time delay can destabilize an otherwise stable system, leading to periodic oscillations and complex dynamic patterns. The proposed framework provides valuable insights into the role of delays in nonlinear dynamical systems and offers a robust analytical approach for predicting stability boundaries and bifurcation phenomena. These findings contribute to the advancement of delay differential equation theory and provide practical guidance for the design, analysis, and control of real-world systems influenced by delayed intera
Development and Convergence Analysis of High-Order Iterative Methods for Solving Large-Scale Nonlinear Systems
Large-scale nonlinear systems frequently arise in numerous scientific and engineering applications, including computational fluid dynamics, structural optimization, electrical power systems, machine learning, robotics, inverse problems, and nonlinear finite element analysis. The efficient numerical solution of these systems remains one of the most challenging problems in computational mathematics because conventional iterative methods often suffer from slow convergence, high computational cost, and poor robustness when handling high-dimensional nonlinear equations. This research presents the development and convergence analysis of a high-order iterative framework designed to improve computational efficiency while maintaining numerical stability for solving large-scale nonlinear systems. The proposed methodology integrates higher-order Taylor approximations, adaptive correction strategies, Jacobian-based linearization, and convergence acceleration techniques to construct an efficient iterative solver with improved local convergence characteristics. Mathematical convergence properties are established using fixed-point theory and error analysis, demonstrating higher convergence order compared with classical Newton-type algorithms. Extensive numerical experiments performed on benchmark nonlinear systems verify the accuracy, robustness, and computational efficiency of the proposed algorithm. The obtained results indicate substantial reductions in iteration count, computational time, and residual error while preserving numerical stability for large-scale engineering problems. Consequently, the proposed high-order iterative framework provides an effective computational tool for solving complex nonlinear systems encountered in modern scientific computing and engineering optimization.
Copyright (c) 2025 https://creativecommons.org/licenses/by-nc-nd/4.0
SOCIAL MEDIA USAGE AND ITS IMPACT ON ACADEMIC PERFORMANCE AMONG SECONDARY SCHOOL STUDENTS OF IMPHAL WEST DISTRICT, MANIPUR
Social media is a substantial component of the students' diet. Students of secondary schools use platforms of social media like Facebook, YouTube, Instagram, WhatsApp for chats, fun, sometimes for teaching. The study looked at the society and how it affects their studies. The descriptive survey was the approach used for the study. Fifteen questions were put forward in the self-designed questionnaire and 30 secondary school students were selected randomly for the survey. Data collected was analysed through the percentage. The findings reveal that most students use social media regularly for most of them it means 'communication' and 'fun'. Some learning purposes, whereas some students noted that they did not study as well as they would like to due to too much social media use. potentials of students can be developed from quality of learning products can be enhanced.
IS ELAINE A WILLING VICTIM OF/TO CORDELIA? A PSYCHOFEMINIST STUDY OF ATWOOD’S CAT’S EYE
In Margaret Atwood’s (1939 -) Cat’s Eye (1988), the narrator protagonist, and Painter Elaine Risley, narrates her own life story through a retrospective lens, jumping between her present-day middle-aged life and childhood memories of trauma. Her narrative is characterized by significant ups and downs stemming largely from the severe bullying she suffered from her childhood ‘best friend’, Cordelia, who acts as a powerful bully, making Elaine a victim of ‘psychological torment’. Elaine’s victimization allows her or rather drives her to turn to painting to achieve a therapeutic restorative effect, using art to process, control and gain distance from the lasting influence of Cordelia and her traumatic past. Her paintings act as a therapeutic release that helps her process the past. However, the novel depicts this process of healing as incomplete. Despite achieving success as an artist and confronting her past at a retrospective show, Elaine ultimately realizes that she is not really fully free of the psychological scars or the lasting influence of Cordelia’s memory. The haunting process of Cordelia persists, indicating that the past remains fundamentally intertwined with her identity.
torture, victim, victimization, retrospective, art, memory, past, vision.
This paper examines the significance of postcolonial perspectives in the literary works of Chinua Achebe, with particular emphasis on Things Fall Apart. Achebe, a pioneering Nigerian author, is celebrated for his nuanced portrayal of African identity and culture during the colonial era. His writings foreground indigenous worldviews, challenge Eurocentric narratives, and illuminate the complexities of cultural encounter, resistance, and adaptation. Furthermore, it explores how Achebe’s characters assert their independence amid colonial oppression while balancing the demands of tradition and modernity. It also delves into themes like identity development, cultural upheaval, and the resilience of African communities facing external influences. Achebe’s works critique colonial structures and legacies, providing deep insights into the sociopolitical realities of postcolonial Africa. A modest attempt has been made to explain Achebe’s role in transforming global literature and in advocating for diverse cultural perspectives, thereby contributing to the wider discussion of postcolonial writing and its significance for decolonisation and cultural self-determination
Postcolonial, colonizer, sociopolitical, identity, cultural disruption.
AGRARIAN EXPLOITATION AND INDUSTRIAL STAGNATION IN KASHMIR UNDER EARLY DOGRA RULE (1846–1885)
The transfer of Kashmir to Maharaja Gulab Singh under the Treaty of Amritsar (1846) inaugurated an era of profound economic dislocation for the region's population. This paper examines the agrarian and industrial conditions of Kashmir during the early Dogra period (1846–1885), focusing on the structural mechanisms through which the ruling state systematically extracted resources from the predominantly Muslim peasantry and artisan class. Drawing on primary archival sources from the National Archives of India as well as contemporary travel accounts and administrative records, the study analyses the feudalisation of state machinery, exploitative land revenue systems, the oppressive begar (forced labour) institution, and the deliberate suppression of industrial enterprise. The paper further examines the catastrophic impact of the famines of 1864 and 1877–79, which exposed the structural vulnerabilities of an economy governed without regard for productive investment or social welfare. The findings demonstrate that the early Dogra administration prioritised fiscal extraction over economic development, resulting in the long-term impoverishment of Kashmir's agrarian and artisanal communities. These historical processes have left enduring socio-economic scars on the region.
Dogra rule, Kashmir agrarian history, land revenue, begar, forced labour, Kashmir economy, Gulab Singh, Ranbir Singh, feudalism, colonial India.
INDIA’S CULTURAL SOFT POWER STRATEGY IN BANGLADESH: CHALLENGES AND OPPORTUNITIES FOR VIKSIT BHARAT@2047
India's cultural diplomacy efforts in Bangladesh positively correlate with enhanced bilateral relations, increased people-to-people exchanges, and improved national branding, thereby contributing to India’s vision of becoming a hegemon in South Asia, and Bangladesh's strategic importance can become a major factor in Viksit Bharat @2047. The culture plays an important role in India's soft power strategies in Bangladesh for altering the broader regional power dynamic in South Asia and its implications for Viksit Bharat@2047. The digital cultural diplomacy has shaped the perceptions, attitudes, and alliances between India and Bangladesh. It further explores the significance of cultural exchange programs (art and culture; films and television), and societal interactions through diasporic communities between the two countries in strengthening soft power relations and fostering mutual understanding. Education and research are also an important part of cultural diplomacy and can contribute to India’s vision of a developed nation. Moreover, the study has adopted a mixed-method approach and qualitative methods to analyse cultural diplomacy between India and Bangladesh. Ethnographic research is used for systematic observation of the cultural practices of different groups in Bangladesh and India through participant observation. The case study approach has been used to analyse the cultural implications of rituals and festivals in these events and how this cultural diplomacy shaped India-Bangladesh relations. Annual reports of the “ICCR, Ministry of External Affairs, Ministry of Culture, Bangladesh Ministry of Cultural Affairs, and Embassies of India and Bangladesh” are used as authentic qualitative sources. Thus, the main aim of the study is to highlight the different roles of nonstate actors in contributing to the soft power dynamics in the region.
WOMEN’S ARE CUSTODIANS OF CULTURE: THE BANJARA EXPERTISE.
Women’s have historically played a vital role in the preservation and transmission of Banjara culture, functioning as its primary custodians across generations. The Banjara community, known for its rich nomadic heritage, vibrant traditions, and distinctive cultural expressions, relies heavily on women to sustain its social, cultural, and artistic identity. This abstract explores how Banjara women act as carriers of cultural knowledge through their everyday practices, creative expressions, and social responsibilities. Banjara women preserve cultural heritage through traditional attire, especially the iconic embroidered costumes, accessories, and textile art that symbolize community identity and history. These artistic practices are not merely decorative but serve as visual narratives that reflect migration patterns, social values, and ancestral memories. Skills related to embroidery, mirror work, and costume-making are passed from mothers to daughters, ensuring continuity of tradition within the household and the wider community. Beyond material culture, women are central to maintaining oral traditions such as folk songs, lullabies, myths, and ritual chants. These oral forms transmit moral values, historical experiences, and collective wisdom, reinforcing cultural cohesion. Women also play a key role in performing and organizing rituals associated with birth, marriage, festivals, and religious observances, thereby safeguarding ceremonial knowledge and community norms. Socialization is another crucial domain where women act as cultural custodians. Through child-rearing practices and informal education, they infuse cultural values, language, customs, and ethical standards in younger generations. Despite social and economic changes, including modernization and migration, Banjara women continue to adapt while retaining core cultural elements, demonstrating resilience and agency.
THE EXPONENTIAL GROWTH OF MSMES AND DRIVERS OF SUSTAINABLE ECONOMIC RECOVERY IN INDIA: A REVIEW OF EVIDENCE FROM 2020–2025”
Micro, Small and Medium Enterprises (MSMEs) play a pivotal role in fostering economic resilience, employment generation, and inclusive development in India. The disruptions caused by the COVID-19 pandemic significantly affected global business ecosystems, placing MSMEs under severe operational and financial pressure. Despite these challenges, emerging literature suggests that the sector demonstrated notable adaptability and recovery in the post-pandemic period. This study presents a structured review of scholarly and policy literature published between 2020 and 2025 to examine how MSMEs contributed to the sustainable growth of the Indian economy during and after the crisis. The review synthesizes findings from academic journals, policy reports, and institutional publications focusing on themes such as financial resilience, digital transformation, government support mechanisms, and employment generation. Particular attention is given to policy initiatives including Atmanirbhar Bharat Abhiyan and the expansion of credit support schemes like Pradhan Mantri Mudra Yojana, which played a significant role in stabilizing MSME operations. The analysis highlights that digital adoption, policy interventions, and entrepreneurial adaptability have been key drivers enabling MSMEs to sustain economic activity and support national recovery. The paper contributes by consolidating fragmented post-pandemic research and identifying critical gaps for future empirical investigation on MSME resilience and sustainable development in emerging economies.
Srihari Jwalapuram1,Dr.Gunnam Venkata Reddy2
MUSIC AND CULTURAL ETHOS IN THE SELECTED WORKS OF VIKRAM SETH, NAMITHA DEVIDAYAL, NEELUM GOUR, MEERA SHIVSHANKAR; A THEMATICAL STUDY
This study examines the representation of music as a cultural archive in four contemporary novels: An Equal Music by Vikram Seth, The Music Room by Namita Devidayal, Requiem in Raga Janki by Neelum Saran Gour, and Raagamalika by Meera Shivakumar. The paper argues that music in these texts functions not merely as aesthetic background but as a powerful cultural metaphor that shapes identity, memory, and emotional experience. The research talks about the various genres of music like Indian and western. Vikram Seth’s An Equal Music reflects the ethos of late twentieth-century, European urban alienation and artistic individualism. This story blends love and identity. Namitha Devidayals’s The Music Room foregrounds the hierarchical and devotional dimensions of the Indian guru–shishya tradition. Namitha herself was a disciple of Kishori Amonkar who is a famous Hindustani singer. In Requiem in Raga Janki by Neelum Gour, music becomes a repository of partition memory, mourning, and cultural rupture. Neelum Gour portrays the biography of the real life Hindustani classical singer Janki Bai Illahabadi’s tragic music journey, whereas Meera Shivashankar’s Raagamalika explores Carnatic music as a site of gendered negotiation between tradition and modernity. It’s an anthology of 18 short stories based on 18 carnatic ragas. Through a comparative cultural analysis, the study demonstrates that music operates as a transnational language that preserves collective memory, articulates trauma, and negotiates subjectivity across diverse cultural contexts. By situating these texts within frameworks of cultural materialism, memory studies, and postcolonial theory, the paper highlights how musical aesthetics intersect with power, history, and identity formation. Ultimately, the research proposes that contemporary fiction reimagines music as a living cultural ethos that bridges personal emotion and collective history
Subramani C.N , Dr. B Krishna Chandra Keerthi
STUDENTS’ PERCEPTIONS OF THE EFFECTIVENESS OF ENGLISH LANGUAGE LABORATORY IN ENHANCING LANGUAGE SKILLS: A STUDY ON GOVERNMENT PRIMARY SCHOOL LEARNERS
The integration of English Language Laboratories (ELLs) in government primary schools reflects a growing emphasis on technology-mediated language instruction within the Indian educational context. This study examines students’ perceptions of the effectiveness of English Language Laboratory sessions in enhancing core language skills among Government Primary School learners. Grounded in principles of Communicative Language Teaching (CLT) and Computer-Assisted Language Learning (CALL), the research investigates how structured laboratory exposure influences listening, speaking, reading, and writing proficiency, as well as learner motivation and communicative confidence. A quasi-experimental pre-test–post-test design was employed with a purposive sample of primary school students. Data were collected using standardized achievement tests, a structured perception questionnaire, and classroom observation schedules. Quantitative data were analysed through descriptive statistics and paired sample t-tests to determine statistical significance, while qualitative responses were thematically analysed to capture learners’ experiential insights. Findings reveal statistically significant improvement in post-test scores across all four language skills, particularly in listening comprehension and pronunciation accuracy. Students reported increased engagement, reduced speaking anxiety, and greater willingness to participate in communicative activities. The results suggest that systematic implementation of English Language Laboratories can positively influence both linguistic outcomes and affective dimensions of language learning in government school settings. The study provides empirical evidence supporting the pedagogical value of technology-enabled language environments in primary education and offers practical implications for policy makers, curriculum planners, and teacher training programs in developing contexts.
COMPARATIVE STUDY OF MULK RAJ ANAND AND CHETAN BHAGAT’S SELECT NOVELS:
A comparative study of Mulk Raj Anand and Chetan Bhagat's select novels, namely Coolie, Untouchable, The Leaves and the Bud, Half Girlfriend, One Indian Girl, Two States, and Five Point Someone, has been undertaken. This selection is based on the fact that these two authors, when compared, represent different eras of Indian society. Mulk Raj Anand, a pre-independence writer, portrayed the social conditions of that time, including the prevalent caste system, racial discrimination, and social hierarchy. On the other hand, Chetan Bhagat is a contemporary writer who depicts the current social issues and problems faced by Indian society. This article is based on the analysis of their novels, focusing on understanding Indian society, social commentary, literary analysis, comparative analysis, social realism, portrayal of marginalized communities, women's empowerment and identity, social hierarchy, and class structure. Technical terms and definitions are explained, and the importance of solving social problems is discussed. The article concludes by highlighting the key takeaways, understanding the problems, and presenting the results.
Koyalakonda Raja Shekher,Dr. Sirisha Iruvuri
భగవద్గతీ లో స్వీ య అవగాహన - మనోవైజ్ఞానిక విశ్లషే ణ
"లోక సమసాాలఖిన్నభవంతు" అంటూ లోక కళ్యయ ణం కోసం నరుడి అభ్యయ నా తికి నారాయణుడు బోధించిన జీవన సార్ం భగవద్గీత్. ఇందులోన ీకనిష్ణునవా సందేశం ఆధువాక యుగావాకి సరైన సమాధానం. నేటి క్నలంలో మనం ఎదుర్క ంటునా భౌతిక, మానసికపర్మైన వివిధ సంఘర్ణష లకు, వయకిత్ా వ లోపాలకు ధ్పాథమిక క్నర్ణం స్వవ య అవగాహన లోపమే అవా పరిగణంచి, భగవద్గీత్ ద్వవ రా స్వవ య అవగాహనను ఎల్ల పంపందంచుకోవచ్చో తెలియజెప్పే లక్షయ ంతో ఈ అంశావాా ఎంచుకోవడం జరిగంద. భగవద్గీత్లో వయకి ా అంత్ర్త్ీ సవ రూపము మరియు స్వవ య అవగాహనకు సంబంధించిన లోతైన భావనలు ఉనాా యి. ఈ పరిశోధన పధ్త్ం భగవద్గీత్లోవా స్వవ య అవగాహన భావనను మన్నవైజ్ఞావాక దష్ణష్టకోి ణంలో విశ్లష్టా ంచి, ఆధువాక మన్నవైజ్ఞావాక సిద్వధంతాలతో ద్వవా సారూపయ త్ను కూడా పరిీలిలాంద. వయకిగాత్ జీవన నైపుణ్యయ లలో స్వవ యఅవగాహన ఒక ధ్పాథమిక మరియుకీలకమైన అంశంగా పరిగణంచబడుతుంద. ఈ అధయ యనంలో అంత్ర్త్ీ అవగాహనను పంపందంచుకొనుటకు ఉపయోగపడే భగవద్గీత్ లోవా శోాక్నలను గురింాచి సుటివా విశ్లష్టా ంచి స్వవ య గురింాపుకు ఎల్ల తోడే డతాయోపరిీలించడం జరిగంద. ఈ శోాక్నల ద్వవ రా వయకి ాఅసలు సవ రూపం ఏమిటో తెలియజేయడం, మనలిా మనం గమవాంచే దష్ణష్టికోణ్యవాా అభివష్ణదధచేయడం ద్వవ రా స్వవ య అవగాహనకు తోడే డవచుో . ద్గవావలామనలో గందర్గోళం, ఆందోళన త్గుీతాయి. సేషత్ి మరియుఅంత్ర్త్ీ శాంతి పరుగుతాయి. ధ్పతి వయకి ాజీవన నైపుణ్యయ లు అభివష్ణదధ చేలకోసులంటేముందుగా త్న పటాత్నకు స్వవ యఅవగాహన ఉండాలి. ఈ జ్ఞానం ద్వవ రా వయకి ాఇత్ర్ నైపుణ్యయ లను పంపందంచుకొనుట లలభం అవుతుంద. భగవద్గీత్ యొకక ధ్పతిపాదనల ధ్పాముఖ్య త్ను గురింాచి, ఆచరించడం ద్వవ రా వయకి ాస్వవ య అవగాహనను పంపందంచుకుంటూ ఇత్ర్ జీవన నైపుణ్యయ లను కూడా అభివష్ణదధచేలకోవచో వా పరిశోధన పధ్త్ం తెలియజేలాంద. అల్లగే భగవద్గీత్ పై అనేక తాతివ క, భకి ాపర్మైన మరియు వయకిత్ా వ విక్నస సంబంధిత్ పరిశోధనలు ఉనా పే టికీ భగవద్గీత్ లోవా జీవన నైపుణ్యయ లను పరిీలించిన పరిశోధనలు పరిమిత్ంగా ఉనాా యి. ఈ కోణంలో విసష్ణాత్మైనటువంటి పరిశోధన జర్గాలి్ న అవసర్ం ఉందవా ఈ పరిశోధన సూచిలాంద.
THE URBAN PARADOX: BALANCING PROFESSIONAL EXCELLENCE AND TRADITIONAL EXPECTATIONS AMONG BENGALURU’S MEDICAL STAFF
This study examines the intersection of Person-Environment (P-E) Fit theory and role conflict among female medical professionals in Bengaluru. As India’s healthcare hub, Bengaluru presents a unique "urban paradox": while offering unparalleled professional opportunities, its infrastructure—characterized by extreme traffic congestion and high living costs—exacerbates the friction between clinical demands and traditional domestic expectations. Utilizing secondary data from labour statistics, health reports, and existing sociological literature, this paper analyses how environmental misfits contribute to chronic stress. The findings suggest that the "double burden" of professional excellence and gendered societal roles leads to significant role-identity conflict. The study concludes that systemic urban interventions and organizational flexibility are essential to sustaining the female healthcare workforce.
EMPLOYMENT OF TASK-BASED PEDAGOGY FOR ENHANCING FLUENCY AMONG PROFESSIONAL STUDENTS
This study examines the effectiveness of Task-Based Pedagogy (TBP) in enhancing fluency among professional students. TBP focuses on real-world tasks, promoting active learning and communicative competence. Professional students participated in tasks like role-plays, case studies, and projects, mirroring real-world scenarios. Results showed significant improvement in fluency, critical thinking, and collaboration skills. TBP offers a practical solution for educators, providing students with skills and confidence for professional communication. The approach has implications for curriculum design and teaching methodologies, emphasizing authentic language learning experiences. TBP is a valuable approach for enhancing fluency, preparing students for success in their careers.
1BAHUROJU NAGA BHAVANI, 2Dr. B. KRISHNA CHANDRA KEERTHI
APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN ACADEMIC LIBRARIES
The application of artificial intelligence involves the areas such as artificial intelligence, expert system, artificial neural network, fuzzy logic, image processing, natural language processing, speech recognition, robotics etc. Though these areas are not separate, at times two or more applications are contributes to enrich the library services. In this article, the authors have explored the various possible applications of artificial intelligence as mentioned above. In addition, authors explain the possible areas where few of these applications can be implemented which enhances the quality ofservices and thereby create the potential impact of AI on library services.
EFFECT OF ONLINE EDUCATION ON STUDENT ENGAGEMENT DURING AND AFTER COVID-19
The COVID-19 pandemic forced an unprecedented global shift from traditional face-to-face classroom instruction to online education, disrupting the academic experiences of over 1.6 billion students worldwide. This paper examines the multifaceted effects of online education on student engagement during the acute phase of the pandemic (2020–2021) and in its aftermath (2022–2024). Employing a mixed-methods research design, data were collected from 1,245 undergraduate and postgraduate students across twelve universities in six countries, supplemented by in-depth interviews with 84 faculty members and educational administrators. Quantitative data were analysed using descriptive statistics, multiple regression analysis, and ANOVA, while thematic analysis was applied to qualitative data. Findings reveal a statistically significant decline in behavioural and emotional engagement during the pandemic period, with a partial — though not complete — recovery in the post-pandemic era. Cognitive engagement showed greater resilience, particularly among self-regulated learners. Key factors moderating engagement included digital infrastructure quality, instructor technological proficiency, institutional support systems, socioeconomic status, and the nature of academic disciplines. Notably, the study identifies a persistent 'engagement deficit' among students from low-income backgrounds and developing nations, raising critical equity concerns. The paper recommends hybrid learning models, sustained investment in digital literacy, and targeted psychological support as sustainable strategies for enhancing post-pandemic student engagement. The findings have significant implications for educational policy, institutional planning, and pedagogical redesign in the new normal of higher education.
Online education, student engagement, COVID-19, hybrid learning, digital divide, higher education, post-pandemic pedagogy, e-learning, academic performance
Role of Digital Libraries in Enhancing Research Productivity and Information Accessibility in Higher Educational Institutions
This study examines the role of digital libraries in enhancing research productivity and improving information accessibility among faculty members, research scholars, and postgraduate students in higher educational institutions of Assam. The study was conducted across five highly populated districts of Assam, namely Kamrup Metropolitan, Nagaon, Cachar, Dibrugarh, and Sonitpur. A quantitative research approach was adopted, and data were collected using a structured questionnaire. Out of 200 distributed questionnaires, 198 valid responses were obtained and analyzed. Statistical techniques including descriptive statistics, Chi-square test, Independent Sample t-test, One-way ANOVA, Correlation Analysis, Multiple Regression Analysis, Factor Analysis, and Structural Equation Modelling (SEM) were employed. The findings reveal that digital library usage significantly enhances research productivity and information accessibility. Furthermore, technological infrastructure and user competency were found to positively influence the effectiveness of digital libraries. The study provides valuable insights for policymakers and higher educational institutions in strengthening digital library services for academic excellence.
RESEARCH OUTPUT AND GENDER EQUITY: A POST-WORKSHOP STUDY OF THE KITP PROBES OF TRANSPORT IN STARS PROGRAM
Gender equity remains a significant concern in the physical sciences despite decades of efforts to improve diversity and inclusion. Physics and astronomy continue to exhibit gender disparities in participation, leadership positions, publication productivity, and collaborative research opportunities. Scientific workshops and collaborative research programs are often designed not only to advance knowledge but also to foster professional networks, mentorship opportunities, and inclusive research environments. The Kavli Institute for Theoretical Physics (KITP) program titled Probes of Transport in Stars, conducted between October and December 2021, sought to bring together experts in stellar observations, stellar evolution, and computational astrophysics to address major questions related to transport processes within stars. The program also emphasized diversity and inclusion as part of its broader scientific objectives. A post-workshop analysis published in 2022 examined the early publication outcomes associated with the program and identified notable gender disparities in research output. Although women represented approximately 38% of workshop participants, only 19% of publications produced during the first six months following the program had female first authors, while 61% of the resulting papers contained exclusively male author lists. These findings raised important questions regarding collaboration formation, authorship opportunities, and gender equity in scientific research environments. This study investigates research output and gender equity associated with the KITP Probes of Transport in Stars program using a quantitative and bibliometric approach. The research evaluates publication productivity, authorship patterns, collaboration networks, and participant experiences to understand the relationship between workshop participation and subsequent research outcomes. Statistical analyses are employed to compare publication rates, collaboration indicators, and gender representation among participants. The study aims to contribute to the growing literature on diversity and inclusion within physics research communities. By examining the outcomes of a high-profile astrophysics workshop, the research provides insights into the effectiveness of current equity initiatives and identifies factors influencing participation in collaborative scientific activities. The findings are expected to support the development of evidence-based strategies that promote inclusive collaboration, equitable research opportunities, and increased representation of underrepresented groups within physics and astronomy.
PREDICTIVE MAINTENANCE OF ELECTRONIC SYSTEMS USING ADVANCED MACHINE LEARNING TECHNIQUES
The increasing complexity of modern electronic systems has significantly increased the importance of effective maintenance strategies for ensuring system reliability, operational efficiency, and cost reduction. Electronic systems used in industries such as telecommunications, manufacturing, aerospace, healthcare, power electronics, and consumer electronics are often subject to component degradation, thermal stress, electrical faults, and environmental influences that may lead to unexpected failures. Traditional maintenance approaches, including reactive maintenance and scheduled preventive maintenance, often result in unnecessary downtime, increased maintenance costs, and inefficient utilization of resources. Consequently, predictive maintenance has emerged as an intelligent maintenance strategy that leverages data analytics and machine learning techniques to predict equipment failures before they occur and support proactive maintenance decisions. Advancements in sensor technology, Industrial Internet of Things (IIoT), cloud computing, and artificial intelligence have enabled the continuous monitoring of electronic system health through real-time data acquisition. Machine learning algorithms can analyze large volumes of operational data, identify hidden patterns, detect anomalies, and estimate the remaining useful life of electronic components. Techniques such as Random Forest, Support Vector Machine (SVM), Artificial Neural Networks (ANN), and deep learning models have demonstrated considerable effectiveness in predictive maintenance applications. These methods facilitate early fault detection, minimize downtime, improve maintenance scheduling, and enhance system reliability. This study investigates predictive maintenance of electronic systems using advanced machine learning techniques. The research focuses on the collection and analysis of sensor-based operational data, feature extraction, model development, and performance evaluation. Multiple machine learning algorithms are employed to predict potential failures and assess system health conditions. Statistical and machine learning techniques are utilized to compare model performance and identify the most suitable predictive approach for electronic maintenance applications. The findings are expected to demonstrate that machine learning-based predictive maintenance significantly improves fault detection accuracy and maintenance efficiency compared to traditional maintenance practices. The study contributes to the growing field of intelligent maintenance systems by providing a framework for integrating machine learning into electronic system health management. Furthermore, the research highlights the potential of predictive analytics in supporting Industry 4.0 initiatives and advancing the development of autonomous maintenance solutions for next-generation electronic systems.
MULTIPLE REGRESSION ANALYSIS AS A TOOL FOR ACADEMIC ACHIEVEMENT PREDICTION
Academic achievement is one of the most important indicators used to evaluate student learning outcomes, educational effectiveness, and institutional performance. Educational institutions increasingly rely on statistical methods and data analytics to understand the factors influencing student performance and to develop strategies that enhance academic success. Predicting academic achievement has become a major area of interest in educational research because accurate prediction models can assist educators, administrators, and policymakers in identifying at-risk students, allocating resources effectively, and improving educational planning. Various demographic, psychological, socioeconomic, and academic factors contribute to student performance, making academic achievement prediction a complex analytical problem. Among the available statistical techniques, multiple regression analysis has emerged as one of the most widely used methods for predicting academic performance. Multiple regression enables researchers to examine the simultaneous effects of several independent variables on a dependent variable and quantify the relative contribution of each predictor. In educational contexts, variables such as attendance, study habits, parental education, socioeconomic status, prior academic performance, motivation, and classroom engagement are frequently analyzed to predict academic achievement. The ability of multiple regression models to identify significant predictors and generate reliable forecasts makes them valuable tools for educational assessment and decision-making. This study investigates the application of multiple regression analysis as a tool for academic achievement prediction. The research aims to identify key factors influencing student performance and develop a predictive model capable of estimating academic achievement based on selected explanatory variables. The study employs quantitative research methods and utilizes statistical techniques including descriptive statistics, correlation analysis, multiple regression analysis, ANOVA, and hypothesis testing to examine relationships among variables and assess model effectiveness. The findings are expected to demonstrate that multiple regression analysis provides a reliable framework for understanding and predicting academic achievement. Variables such as attendance, study hours, previous academic performance, and parental support are anticipated to exhibit significant relationships with student outcomes. The study contributes to the field of educational statistics by highlighting the practical value of predictive modeling in academic environments. Furthermore, the research offers insights that can assist educators in implementing evidence-based interventions designed to improve student achievement and educational quality. As educational institutions increasingly adopt data-driven decision-making approaches, statistical prediction models will continue to play a crucial role in supporting academic success and institutional effectiveness.
CHANGING CONSUMER EXPECTATIONS IN THE ERA OF QUICK COMMERCE
The rapid evolution of digital commerce has significantly transformed consumer purchasing behaviour and retail business models. Among the most notable developments in recent years is the emergence of quick commerce (Qcommerce), a specialized form of e-commerce that emphasizes ultra-fast delivery of products, often within minutes of order placement. Driven by advancements in mobile technology, digital payment systems, logistics networks, and changing consumer lifestyles, quick commerce has become increasingly popular among urban consumers seeking convenience, speed, and seamless shopping experiences. The growth of quick commerce platforms has fundamentally altered consumer expectations regarding product availability, delivery speed, service quality, and personalization. Consumers now demand not only rapid delivery but also accurate order fulfillment, flexible payment options, real-time tracking, and highly responsive customer support. Research indicates that convenience, speed, ease of use, and service quality are among the primary factors influencing consumer adoption of quick commerce platforms. This study examines changing consumer expectations in the era of quick commerce and analyzes the factors influencing customer satisfaction and purchasing behaviour. The research focuses on key variables such as delivery speed, convenience, personalization, digital platform usability, promotional offers, and customer experience. A quantitative research approach is employed to investigate the relationship between quick commerce services and consumer satisfaction levels. The study further explores how technological innovations, mobile commerce applications, and data-driven personalization strategies influence consumer decision-making. As quick commerce platforms compete to provide increasingly efficient services, consumer expectations continue to evolve beyond traditional retail standards. Recent studies indicate that consumers increasingly view rapid delivery as a basic service expectation rather than a premium feature. The findings are expected to demonstrate that delivery speed, convenience, and personalized experiences significantly influence customer satisfaction and loyalty. The study also highlights emerging challenges related to operational sustainability, rising service expectations, and competitive pressures within the quick commerce ecosystem. The research contributes to the understanding of modern consumer behaviour and provides valuable insights for businesses seeking to develop effective customer-centric strategies in the rapidly evolving digital retail environment.
Timetable Optimizer: Automated University Schedule Generation Using CP-SAT Constraint Solving and Large Language Model Intelligence
This paper presents Timetable Optimizer, a production-grade automated scheduling system that addresses both the generation and operational maintenance of university course timetables through a hybrid architecture combining con- straint satisfaction solving and large language model intelligence. University timetable scheduling is a well-established NP-hard combinatorial optimization problem requiring simultaneous sat- isfaction of hundreds of hard constraints—faculty availability, room capacity, laboratory batch splitting, and room-type match- ing—alongside soft preferences for workload fairness and session continuity. In practice, manual scheduling through spreadsheets consumes days of administrative effort and produces schedules with latent conflicts discovered only after the semester begins. The proposed system is built on a three-service architecture: a Next.js frontend providing role-differentiated interfaces for four user types; an Express.js backend serving as API gate- way, data orchestrator, and job dispatcher; and a Python CP- SAT solver worker using Google OR-Tools, connected via a Redis job queue. The scheduling model is formulated as a 5- dimensional binary decision variable problem over a space of 6 days and 8 time slots per day, enforcing seven categories of hard constraints with pre-pruning eliminating 70–80% of decision variables before solving. Natural language constraints entered by faculty are parsed to structured JSON using the Google Gemini API, eliminating the formal specification barrier. Asynchronous job processing with Server-Sent Events delivers real-time progress feedback throughout solver execution. Daily operational overrides are managed through an append-only log applying greedy substitute matching without requiring solver re- execution. The system additionally supports PDF and iCal export, academic year rollover, comprehensive audit logging, and role- based access control. Evaluation demonstrates solver execution within 10–120 seconds for realistic institutional instances, sub- 50ms authorization latency, and correct constraint enforcement across all hard constraint categories.
Smart AI Caller: A Survey and Implementation Report on Real-Time Multilingual Voice Agents
Smart AI Caller is an advanced open-source AI Voice Agent platform designed to transform the way organizations interact with customers through telephone and web-based voice channels. The platform leverages modern artificial intelligence technologies to create natural, real-time conversational experiences that closely resemble human interaction. Built using a scalable microservices architecture, the system integrates powerful AI components including Large Language Models (LLMs), Speech-to-Text (STT), and Text-to-Speech (TTS) technologies into a unified real-time voice processing pipeline. This architecture allows the system to accurately interpret spoken language, generate intelligent responses based on context, and deliver them through natural-sounding synthesized speech. By combining these technologies with a modular infrastructure, Smart AI Caller enables flexible deployment, high scalability, and efficient performance for businesses seeking automated voice-based customer support and engagement solutions. A key capability of the platform is its multilingual support, designed to address the linguistic diversity of users. Smart AI Caller natively supports more than ten Indian languages, including Hindi, Tamil, Telugu, and Bengali, enabling organizations to communicate with users in their preferred language and improve accessibility and user satisfaction. The system is optimized to deliver sub-second response latency, ensuring smooth and natural conversations without noticeable delays. In addition, the platform integrates seamlessly with enterprise telephony providers such as Twilio, Vonage, and SIP-based systems, allowing organizations to incorporate AI voice agents directly into existing communication infrastructures. The system also supports web-based voice interactions, enabling businesses to deploy AI-powered assistants on websites and digital platforms. To simplify the design and management of conversational agents, the platform provides a visual drag-and-drop workflow builder that allows users to create complex conversation flows without requiring extensive programming knowledge. Furthermore, Smart AI Caller incorporates a Retrieval-Augmented Generation (RAG) knowledge base that enables document-based question answering by retrieving relevant information from organizational documents and knowledge repositories. This ensures that responses remain accurate, contextual, and informative during conversations. The project report presents the overall system architecture, component design, data flow mechanisms, database schema, infrastructure technologies, and performance benchmarks, demonstrating that production-grade AI voice capabilities can be achieved within an open, extensible, and scalable platform suitable for modern enterprise applications.
The increasing adoption of online grocery shopping has created a need for secure, efficient, and user-friendly e-commerce platforms. While many existing systems support product browsing and online payments, they often lack secure delivery verification and delivery partner management. This paper presents ShopNest, a MERN stack-based e-commerce platform for grocery delivery that integrates product ordering, payment processing, OTP-based delivery confirmation, and automated earnings tracking. The proposed system enables customers to browse products, manage carts, apply discounts, and place orders using Razorpay or Cash on Delivery (COD). Administrators can manage products, inventory, and orders, while delivery partners can select shifts, verify OTPs, and monitor earnings. The application is developed using React.js, Node.js, Express.js, MongoDB Atlas, and Razorpay APIs. Experimental testing confirms that the system performs reliably in user authentication, payment integration, order processing, and delivery verification. ShopNest provides a scalable and practical solution for modern grocery e-commerce operations.
SCIENTIFIC WORK – RESEARCH & DESIGN CONSULTANCY WEBSITE
The rapid growth of modern web technologies has transformed the way organizations establish digital presence and interact with users. Consultancy organizations require responsive, scalable, and professional web applications to effectively communicate services, showcase projects, and maintain online visibility. Traditional consultancy websites often fail to satisfy modern standards due to poor responsiveness, outdated interfaces, and limited scalability. This paper presents the design and implementation of “Scientific Work – Research & Design Consultancy Website”, a frontend-based consultancy platform developed using React, TypeScript, Tailwind CSS, Vite, and EmailJS. The system focuses on responsive design, modular architecture, reusable components, optimized rendering, and efficient communication. The platform includes Home, About, Services, Projects, Internship, and Contact modules to improve accessibility and usability. The implementation demonstrates how modern frontend technologies can be effectively utilized for creating scalable and maintainable web applications.
The rapid proliferation of social media platforms has transformed global communication, enabling billions of users to share content, form communities, and engage in real-time conversations. However, these platforms are also increasingly plagued by issues such as fake profiles, toxic content, misinformation, privacy violations, and algorithmic biases that harm user experience and safety. Existing platforms often lack transparency, ethical AI integration, and personalization mechanisms that truly benefit the user. This report presents the design and development of SOCIO-SPHERE AI, an AI-powered full-stack social media web application built using Python, Django, Django REST Framework, React, and Tailwind CSS. The platform integrates multiple artificial intelligence and machine learning modules to create a safe, intelligent, and personalized social media experience.
The Dairy Mart Website is a smart and user-friendly e-commerce platform developed to simplify and modernize the process of purchasing dairy products through an online system. With the increasing demand for fresh and easily accessible dairy products such as milk, curd, butter, cheese, paneer, yogurt, and other dairy items, there is a growing need for a reliable digital platform that can efficiently connect customers with dairy suppliers. Traditional dairy purchasing methods mainly depend on physical stores and manual order management, which often result in limited accessibility, inefficient inventory handling, and time-consuming transactions. To overcome these challenges, the proposed Dairy Mart Website provides an online platform that allows customers to browse products, place orders, and manage purchases conveniently from anywhere at any time. The system provides secure user registration and authentication functionalities that allow customers and administrators to access the platform safely. Customers can browse different dairy products, view product details, search products by category, and add products to their shopping cart before placing orders. The system processes customer requests efficiently and stores all transaction details securely in the database. The application is developed using HTML, CSS, and JavaScript for the frontend interface, Python and Django for backend development, and SQLite/MySQL for database management. The admin module enables administrators to manage products, update inventory, monitor customer orders, and maintain business operations effectively. The website also supports features such as order history management, responsive web design, inventory monitoring, and secure database handling. Experimental testing confirms that the system performs efficiently, provides accurate order processing, and improves customer convenience. The Dairy Mart Website transforms traditional dairy product distribution into a modern digital platform by integrating web technologies, database management, and automated business operations. The proposed system provides a practical, scalable, and efficient solution for online dairy product management and distribution.