Abstract: The relationship between women's paid work and their authority over money has rarely been examined as a single, connected process in the Indian urban context. This study treats the transformation of gender roles and the growth of financial independence as two faces of the same shift, and investigates how the former feeds the latter among 540 urban working women across four cities of Rajasthan — Jaipur, Jodhpur, Kota, and Udaipur — using a structured questionnaire supplemented by twenty-four in-depth interviews. Six dimensions of gender-role change were measured — shared household responsibility, professional identity, participation in decision-making, career aspiration, work-life negotiation, and the loosening of traditional norms — alongside financial independence expressed through income control, savings behaviour, investment participation, financial planning, and contribution to household financial decisions. Findings show that role change is well advanced in the symbolic and aspirational register — 79.6% endorse the legitimacy of working women and 74.2% treat their career as central to identity — yet thins out in the domestic and authority register, where shared housework (68.5%) and joint financial decision-making (61.3%) lag, and where 38.4% report active resistance to changing norms within the family. Financial independence is correspondingly partial: 47.1% of respondents fall in the high or very-high band of a constructed Financial Independence Index, but a large moderate-to-low majority retains constrained authority over investment and long-horizon planning. A chi-square test confirmed a significant association between the degree of gender-role transformation and the level of financial independence (chi-square = 64.82, p < 0.001). Pearson correlation showed decision-making autonomy positively associated with financial independence (r = 0.62, p < 0.01), while a traditional household structure was negatively associated with it (r = -0.47, p < 0.01). Multiple regression established that, controlling for age, income, and education, role-transformation variables explain a substantial share of the variance in financial independence, with decision-making autonomy the strongest single predictor (beta = 0.39). One-way ANOVA confirmed significant differences in financial independence across education, occupation, and family-structure groups. The paper consolidates these results into the Gender Role Transformation and Financial Empowerment Framework for Urban Working Women (GRTFEF-UWW) and offers recommendations for educators, employers, policymakers, and families.
Keywords: Gender Roles,Working Women,Financial Independence,Women's Empowerment, Decision-Making Autonomy, Work-Life Balance, Economic Participation, GRTFEF-UWW
Abstract: The convergence of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) is reshaping the maintenance paradigm in modern manufacturing, shifting it from reactive and time-based scheduling toward condition-based, predictive intervention. Unplanned equipment downtime remains one of the largest sources of avoidable cost in process and discrete manufacturing, and conventional preventive maintenance addresses it only crudely, replacing components on fixed calendars regardless of their actual condition. This paper develops an integrated, AI-enabled predictive-maintenance (PdM) framework for smart manufacturing systems built on an IIoT data backbone. Adopting a comparative and analytical secondary-research methodology, the study synthesises peer-reviewed literature, industry white papers, and technology reports published between 2018 and 2026 to construct a layered reference architecture spanning smart sensing, edge computing, cloud infrastructure, a machine-learning engine, predictive analytics, decision support, and maintenance execution. The relative performance of the principal AI techniques — artificial neural networks, support vector machines, random forests, deep learning architectures, reinforcement learning, and explainable AI — is examined against the core PdM tasks of fault detection, diagnosis, and remaining-useful-life (RUL) estimation. Comparative analysis indicates that ensemble and deep-learning models consistently achieve higher diagnostic accuracy, while explainability and edge-deployability emerge as decisive criteria for industrial adoption rather than raw accuracy alone. A cost-benefit synthesis drawn from reported industrial deployments suggests that mature AI-PdM programmes can reduce unplanned downtime by 30–50% and maintenance cost by 20–30% while extending asset life. On the strength of this analysis, the paper proposes the AI-Driven Predictive Maintenance Framework for Smart Manufacturing Systems (AIPMF-SMS), a seven-component architecture comprising a data-collection layer, an edge-intelligence layer, an AI analytics engine, a predictive-decision layer, a maintenance-management layer, a continuous-learning and feedback mechanism, and a cross-cutting cybersecurity and governance layer. The framework explicitly addresses the data-quality, scalability, security, and workforce-skill barriers that have constrained real-world deployment. The paper closes with future research directions — digital twins, generative AI, federated learning, and sustainable manufacturing — and with policy and industrial recommendations for accelerating adoption in emerging-economy manufacturing contexts.
Keywords: Predictive Maintenance,Industrial Internet of Things (IIoT),Artificial Intelligence,Machine Learning, Smart Manufacturing, Industry 4.0, Remaining Useful Life, Edge Computing, AIPMF-SMS
Abstract: India's youth population now constitutes one of the largest concentrations of social media users anywhere in the world, with active internet users crossing 886 million in 2024 and a substantial share of that growth concentrated among adolescents and young adults. This expansion has delivered genuine benefits in education, civic participation, and social connection, but it has also been accompanied by a parallel rise in problematic patterns of use and exposure to digitally mediated harm. Drawing on peer-reviewed Indian clinical and public-health research, government data from the National Crime Records Bureau (NCRB) and the Indian Cyber Crime Coordination Centre (I4C), international comparative evidence from the World Health Organization's Health Behaviour in School-aged Children (HBSC) survey, and civil-society documentation from UNICEF and Child Rights and You (CRY), this paper examines the scale and character of social media addiction among Indian youth and maps the principal cyber risks that accompany it: cyberbullying, online harassment, identity theft, phishing and financial fraud, privacy breaches, sextortion and image-based abuse, and misinformation and deepfakes. The analysis indicates that Indian studies report social media or internet addiction-related behaviour in roughly one-fifth to two-fifths of college-going samples depending on instrument and setting, a range broadly consistent with international meta-analytic estimates, while financial cybercrime complaints routed through the I4C's 1930 helpline have grown from under a hundred lakh in 2023 to more than three crore in 2025. The paper situates these findings within established psychological frameworks, including the Fear of Missing Out (FOMO) construct grounded in self-determination theory and the Interaction of Person-Affect-Cognition-Execution (I-PACE) model of problematic technology use, to explain why adolescents and young adults are disproportionately vulnerable to both compulsive engagement and exploitation. It then proposes the Youth Cyber Safety and Digital Well-Being Framework (YCSDWF), a seven-pillar preventive architecture spanning digital literacy, responsible usage norms, cyber hygiene, privacy protection, mental health support, institutional and policy interventions, and community awareness, designed for phased implementation by schools, families, platforms, and government bodies. The paper closes with policy recommendations directed at the Ministry of Electronics and Information Technology (MeitY), the Ministry of Education, state governments, and digital platforms, and identifies the principal limitations of the secondary-data approach used here, including the continuing scarcity of large-scale, nationally representative Indian data on adolescent digital well-being.
Keywords: Social Media Addiction,Cyber Risks,Youth,India,Cyberbullying, Online Safety, Digital Well-Being, FOMO, Problematic Internet Use, YCSDWF, Deepfakes, Sextortion
Abstract: The proliferation of digital platforms has produced a twin effect: unprecedented access to information and connectivity for women, and an equally unprecedented expansion of online gender-based violence (OGBV). In India, reported cybercrimes against women grew by more than 93% between 2020 and 2022 — a trajectory that official estimates suggest has continued sharply upward through 2024. This paper conducts a systematic, multi-dimensional analysis of cybercrime targeting women in India, examining six primary categories: online harassment and cyberstalking, non-consensual intimate image (NCII) abuse, online financial fraud, identity theft and impersonation, cyber blackmail and sextortion, and online grooming. Employing a mixed-methods framework combining secondary data analysis of NCRB records (2018-2024), doctrinal legal analysis of applicable legislation, and comparative benchmarking against international best-practice jurisdictions (United States, United Kingdom, European Union), the study identifies systemic failures across three interacting domains: legislative inadequacy, institutional capacity deficits, and structural gaps in victim support ecosystems. The investigation reveals that India's current legal architecture — distributed across the Information Technology Act 2000, the Indian Penal Code (now Bharatiya Nyaya Sanhita 2023), and the DPDPA 2023 — is fragmented, technologically outdated, and procedurally hostile to victim-centered justice. Conviction rates remain below 5%, reporting rates are estimated at under 15% of actual incidence, and platform accountability mechanisms are largely absent. In response, this paper proposes the SHERIELD Model — a six-pillar, gender-responsive cybercrime policy framework calibrated to India's institutional, financial, and socio-cultural context. Policy recommendations are directed at the Ministry of Home Affairs (MHA), Ministry of Electronics and Information Technology (MeitY), the National Crime Records Bureau (NCRB), state police departments, and digital platforms operating in Indian jurisdiction. This research contributes an original, actionable, and evidence-grounded framework to the critically underserved intersection of gender justice, digital rights, and cybersecurity policy in developing nation contexts.
Keywords: Cybercrime, Women, Online Gender-Based Violence,, OGBV, India, NCII,,Cyberstalking, Sextortion, Digital Safety, Legal Framework, SHERIELD Model, NCRB, Platform Accountability, Digital Rights
Abstract: Digital twin technology has emerged as one of the most consequential enablers of the Industry 4.0 transition, offering a living, synchronised virtual replica of a physical asset that mirrors its state, behaviour, and degradation in near real time. When coupled with predictive maintenance, the digital twin promises to transform maintenance from a scheduled or reactive activity into a continuously informed, anticipatory discipline. This paper examines the opportunities and challenges of digital twin-based predictive maintenance in smart manufacturing systems and develops an original conceptual framework to operationalise it. Adopting a comparative, analytical secondary-research methodology, the study synthesises peer-reviewed literature, conference proceedings, and industry reports published between 2018 and 2026 to construct an eight-layer reference architecture spanning the physical asset, sensing and data acquisition, industrial IoT connectivity, digital twin modelling, cloud computing and storage, artificial-intelligence analytics, predictive decision-making, and maintenance execution. The relationship between the digital twin and the maintenance decision is analysed across its principal applications — equipment health monitoring, real-time process optimisation, predictive scheduling, production planning, quality management, energy optimisation, supply-chain integration, and autonomous operation. A comparative assessment of existing digital twin frameworks reveals that most address modelling fidelity or connectivity in isolation, while few integrate continuous learning and security as structural concerns. A cost-benefit synthesis drawn from reported deployments indicates that mature digital twin-based predictive-maintenance programmes can reduce unplanned downtime by 30–50%, lower maintenance cost by 20–35%, and extend asset life materially, while improving overall equipment effectiveness. On the strength of this analysis, the paper proposes the Digital Twin-Driven Predictive Maintenance Framework for Smart Manufacturing Systems (DTPMF-SMS) — an eight-component architecture comprising a physical asset layer, a sensor and data-acquisition layer, a digital twin modelling layer, an AI and analytics engine, a predictive-maintenance decision layer, a maintenance-management layer, a continuous feedback and learning mechanism, and a cross-cutting cybersecurity and governance layer. The framework explicitly confronts the cost, data-quality, interoperability, scalability, standardisation, and skills barriers that have constrained adoption. The paper concludes with future research directions — generative AI, explainable AI, federated learning, self-healing systems, sustainable manufacturing, and Industry 5.0 — and with policy and industrial recommendations for emerging-economy manufacturing contexts.
Keywords: Digital Twin, Predictive Maintenance,Smart Manufacturing, Industry 4.0,Industrial Internet of Things,Artificial Intelligence, Cyber-Physical Systems, DTPMF-SMS
Abstract: Financial literacy has emerged as a decisive determinant of economic agency, yet its influence on the investment behaviour of working women in semi-urban India remains under-examined. This study investigates the relationship between financial literacy and investment decision-making among 540 working women in Ujjain District, Madhya Pradesh, combining a structured questionnaire survey with focused interviews. Financial literacy was measured across six dimensions: financial knowledge, financial awareness, budgeting and savings practices, knowledge of financial products, investment awareness, and digital financial literacy. Investment decision-making was assessed through avenue selection, risk tolerance, portfolio diversification, planning horizon, and the structure of the decision process itself. Findings reveal that only 31.5% of respondents fall in the high financial-literacy band, with literacy declining sharply among lower-income and lower-education groups. A chi-square test established a statistically significant association between literacy level and the breadth of investment instruments held (χ² = 78.42, p < 0.001), while Pearson correlation indicated a strong positive relationship between the composite literacy score and an investment-quality index (r = 0.71, p < 0.01). Multiple regression confirmed that financial literacy is the single strongest predictor of sound investment decisions (β = 0.48), ahead of income and education. Women with higher literacy diversified more widely, tolerated calibrated risk, planned over longer horizons, and engaged more actively with retirement and digital instruments, whereas low-literacy respondents concentrated savings in low-yield, traditional instruments and deferred decisions to family members. On the strength of these results, the paper proposes the Financial Literacy–Investment Decision Framework for Working Women (FLIDF-WW), an integrated model linking financial education, awareness, risk assessment, investment planning, digital competence, decision capability, and financial well-being. The study contributes empirical evidence from a hitherto under-researched Tier-II Indian setting and offers targeted recommendations for educators, employers, financial institutions, and policymakers seeking to translate financial capability into measurable economic security for women.
Keywords: Financial Literacy, Investment Decision-Making, Working Women,, Risk Tolerance, Portfolio Diversification,,Digital Financial Literacy, Financial Well-Being, FLIDF-WW