Historic structures, representing rich cultural and architectural legacies, face gradual deterioration due to aging, environmental factors, and neglect. This study emphasizes the importance of retrofitting as a means to preserve these heritage buildings without compromising their structural safety or historical authenticity. It particularly focuses on the challenges faced in Indian states like Haryana, Rajasthan, and Jharkhand, where traditional materials are scarce and documentation is limited. A quantitative research methodology was adopted, utilizing Multi-Criteria Decision Analysis (MCDA) to assess the performance, compatibility, and sustainability of various retrofitting materials. Evaluation was based on key factors such as strength, durability, cost, environmental impact, and aesthetic compatibility. .

The increasing integration of artificial intelligence (AI) in critical sectors such as healthcare, finance, and criminal justice has intensified the call for explainable AI (XAI) systems. This study addresses the ethical and operational necessity of transparency in AI decision-making by employing interpretable machine learning (ML) models. Using publicly available datasets (Iris and Breast Cancer), Random Forest and Logistic Regression models were trained and evaluated with standard performance metrics. .

The evolution of Very-Large-Scale Integration (VLSI) design is pivotal to advancing modern digital systems, driven by demands for enhanced performance, energy efficiency, and scalability. This study investigates state-of-the-art VLSI design techniques addressing challenges posed by nanometer-scale technologies, including power density, thermal management, and process variability. By integrating architectural innovations such as pipelining, parallelism, and multi-core designs with logic optimization and advanced physical layout strategies, the research aims to balance speed, area, power, and cost. .

The increasing complexity and decentralization of modern power systems have made power quality improvement a vital concern. The evolution from traditional unidirectional grids to distributed, intelligent networks—featuring renewable integration and digital loads—has introduced challenges in maintaining voltage, frequency stability, and harmonic control. This study explores both conventional and advanced technologies such as capacitor banks, active filters, dynamic voltage restorers, and smart inverters, alongside the role of decentralized energy resources and microgrids in enhancing power quality. .

Urban transportation systems are central to promoting economic opportunity and social inclusion. However, rapid urban growth and socioeconomic diversity have exacerbated inequalities in access, particularly among marginalized communities. This study investigates equity and accessibility across three urban zones Central Business District, Inner Suburb, and Outer Periphery—by integrating spatial and financial metrics of mobility. Using municipal GTFS feeds, census data, and GIS-based field verifications, the study quantifies disparities in employment access, transit frequency, fare burden, and pedestrian infrastructure..

This study investigates the aeroelastic behavior of long-span bridges under wind loads, focusing on the phenomenon of flutter—a potentially catastrophic aerodynamic instability. Wind-induced vibrations interact with the bridge’s natural modes, creating feedback loops that may lead to excessive oscillations and structural failure. To understand and mitigate this risk, a comprehensive methodology was employed, integrating classical analytical models with high-fidelity Computational Fluid Dynamics (CFD) and Finite Element Modeling (FEM)..

The rapid evolution of mechanical systems in aerospace, automotive, manufacturing, and robotics sectors demands advanced lubrication technologies that ensure enhanced reliability, efficiency, and durability under increasingly extreme operating conditions. This study investigates cutting-edge lubrication solutions—including synthetic, nanostructured, ionic liquids, magnetorheological fluids, and bio-based lubricants—evaluating their performance in terms of friction reduction, thermal stability, load capacity, and sustainability. .

The advancement of superconducting materials holds significant promise for revolutionizing energy transmission, storage, and high-performance computing by enabling near-zero electrical resistance at practical temperatures. This study evaluates various superconductors, focusing on critical parameters such as critical temperature (Tc), critical current density (Jc), magnetic field tolerance (Hc), and thermal stability to assess their applicability in next-generation electrical components. Using a combination of theoretical modeling, literature review, and controlled cryogenic experiments, the research analyzes both conventional low-temperature and emerging high-temperature superconductors, including iron-based and hydrogen-rich compounds..

The increasing demand for sustainable energy solutions has driven a transition from centralized to decentralized Distributed Generation (DG) systems, which utilize renewable sources near consumption points to enhance efficiency and reliability. However, integrating DG into power grids introduces challenges in optimal power dispatch due to their intermittent and dispersed nature. Concurrently, predicting dengue outbreaks through environmental data analysis is critical for public health management. This study applies advanced machine learning techniques to environmental time-series data from a dengue-prone tropical region, utilizing six classifiers to forecast outbreaks..

The development of the Smart-Efficient Transformer (SET) model represents a transformative leap in power transformer technology. Through integrating advanced structural optimizations with machine learning (ML) intelligence, the SET enhances energy efficiency, thermal stability, and operational reliability. ML algorithms such as Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN) were employed to ensure accurate fault prediction, anomaly detection, and real-time adaptive control. .

This study presents a comparative analysis of traditional versus modern intelligent substation architectures in the context of fault tolerance and grid resilience. Results reveal that advanced technologies such as AI-based fault prediction, digital twins, IoT sensors, and wide-area coordination significantly outperform conventional systems in fault detection, rapid recovery, and minimizing system downtime. Cyber Intrusion Detection, AI-Based Fault Prediction, and Wide Area Protection emerged as the most effective methods..

The present study investigates the seismic behavior of structural building models with and without mass abnormalities using Response Spectrum Method (RSM) in ETABS software. The models were analyzed under RS-X and RS-Y load cases to evaluate key structural parameters such as story displacement, drift, shear, stiffness, and acceleration. The analysis revealed that regular buildings exhibited higher story displacement along the X-axis compared to mass abnormal models, attributed to increased stiffness in the latter..