SNDT Women's University, also called by its full name Shreemati Nathibai Damodar Thackersey Women's University, is a women's university in the city of Mumbai, India. The university headquarters are at Churchgate in South Mumbai, while the main campus is at Churchgate there are two other campuses one in Santacruz–Juhu area of Mumbai and another at Pune. The university has affiliated colleges in Maharashtra, Assam, Uttar Pradesh, Bihar, Madhya Pradesh, Surat and Goa, as well.
PurposeThis study aims to examine how financial exclusion, kinship networks, trust and resilience strategies shape informal borrowing practices among urban informal workers in India. It aims to highlight how socially embedded credit systems function as structural alternatives to formal banking.Design/methodology/approachA qualitative, case-based methodology was used. Semi-structured interviews were conducted with 15 informal workers in Mumbai, of which four diverse cases were selected for vignette construction. Data were thematically coded, combining inductive insights with theory-informed categories.FindingsThe findings reveal four interconnected dynamics: financial exclusion by banks reinforces dependence on shadow lending; kinship and friendship networks operate as embedded credit systems, ensuring liquidity through reciprocity; repayment is enforced through social collateral, trust and reputation rather than contracts; and diversified borrowing across multiple lenders functions as a resilience strategy akin to portfolio risk management. Together, these themes demonstrate that informal finance is a relational architecture sustaining livelihoods and enterprise in the absence of inclusive formal credit.Research limitations/implicationsThe study focuses on a small sample in Mumbai, limiting generalizability but offering depth. Future research can extend to cross-city comparisons.Practical implicationsPolicies that integrate trust-based mechanisms and multi-source flexibility from informal systems into microfinance and banking products could enhance financial inclusion.Social implicationsThe findings underscore the role of kinship and community ties in sustaining resilience, suggesting that financial inclusion strategies must account for embedded social practices.Originality/valueThis study contributes to financial inclusion literature by showing that informal borrowing is not merely residual but structurally embedded within urban social networks. By using narrative vignettes, it humanizes financial practices and highlights how resilience emerges from collective social mechanisms rather than individual financial behaviors.
The fast proliferation of renewable energy sources (RES) in modern power grids has increased the complexity of the Optimal Power Flow (OPF) problem, which is nonlinear, nonconvex, and uncertainty-driven. This study proposes a Renewable-integrated Multi-Objective Optimal Power Flow (MOOPF–RE) architecture that improves both techno-economic and environmental performance under wind-solar variability. We propose a hybrid algorithm gbestABC–NSGA-II that combines global-best guided Artificial Bee Colony exploration with NSGA-II’s elitist non-dominated sorting to achieve superior convergence, diversity preservation, and feasibility restoration. Stochastic wind and solar models based on Weibull and lognormal distributions, together with adaptive repair-based constraint handling and an adaptive grid-crowding archive, ensure realistic uncertainty representation and well-distributed Pareto fronts. An integrated Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution (AHP-TOPSIS) module identifies the best compromise solution in accordance with operator preferences. Extensive trials on IEEE 30-, IEEE 57-, and Indian 62-bus systems show considerable improvements, including reductions of up to 6.75
Renewable energy sources like wind, solar PV, and tidal energy are increasingly impacting power system operations, creating uncertainty and intermittency that make traditional deterministic Optimal Power Flow (OPF) formulations insufficient. This paper introduces a stochastic Multi-Objective Optimal Power Flow (MOOPF–PET) framework that minimizes four conflicting objectives: (i) total generation cost, (ii) emissions, (iii) active power loss, and (iv) voltage deviation. Wind speed, solar irradiance, and tidal flow uncertainties are modeled with Weibull, lognormal, and Gumbel distributions, respectively, allowing for a realistic representation of renewable variability in the MOOPF formulation. A hybrid gbestABC–NSGA-II algorithm is proposed to effectively tackle high-dimensional, nonlinear, and constrained optimization problems by combining the global exploration strength of the Artificial Bee Colony algorithm with the elitist non-dominated sorting and diversity preservation features of NSGA-II. A Diversity-Enhanced Tri-Stage Constraint-Handling (DEST) strategy is used to improve feasibility and robustness under complex constraints, while Pareto archive management with crowding-distance sorting enhances convergence and distributes Pareto-optimal solutions effectively. The framework is validated on standard ZDT and DTLZ benchmarks and the modified IEEE 30-bus, Standard IEEE 57-bus and IEEE 118-bus test system across various renewable penetration scenarios. Comparative studies with top multi-objective algorithms like MOGWO, MOPFA, NSGA-II, MOPSO, MOMVO, MOAHA, and MOSSA show consistent performance gains. The proposed approach reduces generation costs by about 5–12
Abstract Integrating renewable energy sources adds uncertainty, nonlinearity, and complexity to multi-objective optimal power flow (MOOPF) problems, requiring strong and efficient optimization frameworks. This paper presents an improved hybrid Gbest-guided Artificial Bee Colony and NSGA-II (GbestABC–NSGA-II) algorithm with a dual-state epsilon-based constraint handling strategy to enhance feasibility, convergence, and solution diversity. The framework combines global-best guided exploitation with elitist non-dominated sorting for a balanced exploration-exploitation trade-off. The dual-state epsilon mechanism manages constraint violations, while epsilon-dominance archiving improves Pareto front convergence and diversity. Realistic thermal generator modeling is enhanced by using multi-fuel cost functions, valve-point loading effects, and prohibited operating zones for better practical applicability. Renewable uncertainty is modeled with Weibull-distributed wind speed and lognormal solar irradiance for precise stochastic representation. A BWM–TOPSIS decision-making approach is used to find the best compromise solution. The proposed method’s effectiveness is validated on modified IEEE 30-bus and 57-bus systems across various operational scenarios, including renewable integration and security constraints. The results show notable performance gains, with up to a 7% decrease in generation costs, around 24–26% lower emissions, and a significant drop in transmission losses compared to traditional methods. The statistical evaluation using IGD, HV, PDI, and Wilcoxon rank-sum test shows the proposed algorithm’s superior convergence, diversity preservation, and robustness. The findings confirm that the hybrid framework efficiently and reliably solves complex renewable-integrated MOOPF problems, improving techno-economic and environmental performance.
The rapid increase in cardiovascular diseases has necessitated the development of intelligent, scalable, and realtime healthcare solutions capable of early diagnosis and prevention. Smart healthcare systems integrating the Internet of Things (IoT) and Machine Learning (ML) have emerged as transformative technologies that enable continuous monitoring, data-driven decision-making, and predictive analytics in clinical environments. This study presents a comprehensive framework for heart disease prediction using IoT-enabled sensing devices and advanced machine learning algorithms, emphasizing real-world applicability and algorithmic development. The proposed system leverages wearable and embedded sensors to collect physiological parameters such as heart rate, blood pressure, and electrocardiogram signals, which are transmitted through cloud-based architectures for preprocessing and analysis. Machine learning models, including supervised and ensemble approaches, are developed to identify patterns and predict cardiovascular risk with high accuracy. The study further explores optimization strategies, feature selection techniques, and model interpretability to enhance predictive performance and clinical reliability. Real-world implementation scenarios are analyzed to demonstrate the feasibility of integrating such systems into modern healthcare infrastructures, including remote patient monitoring and telemedicine platforms. Additionally, the research highlights the challenges associated with data quality, privacy, interoperability, and scalability while proposing solutions for robust deployment. The findings indicate that IoT and ML-based healthcare systems significantly improve early diagnosis, reduce mortality rates, and support personalized treatment strategies. This research contributes to the advancement of intelligent healthcare by bridging the gap between theoretical models and practical applications in heart disease prediction.
