The rapid proliferation of online buying applications has significantly transformed consumer purchasing behaviour, particularly among Generation Z (Gen Z), a digitally native cohort with high reliance on mobile technologies. This study examines the behavioural determinants influencing Gen Z consumers’ adoption and usage of online buying applications in Trichy. Drawing on technology adoption and consumer behaviour theories, the research investigates the impact of app usability, social media exposure, influencer marketing, promotional strategies, trust, and convenience on purchase intention, impulse buying behaviour, consumer satisfaction, and continued usage. Primary data were collected from 300 Gen Z consumers using a structured questionnaire, and Structural Equation Modelling (SEM) was employed to test the proposed relationships. The findings indicate that app usability, social influence, and promotional stimuli significantly influence purchase intention and impulse buying behaviour, while trust and convenience are critical drivers of consumer satisfaction and sustained usage. The study extends existing technology adoption models by incorporating social and promotional dimensions specific to Gen Z consumers and provides practical insights for online retailers and digital marketers seeking to effectively engage this emerging consumer segment.

This paper explores the integration of Artificial Intelligence (AI) technologies into next-generation electric vehicles (EVs) to enhance performance, efficiency, and user experience. Through a detailed case study, we analyze the application of AI-driven systems such as advanced battery management, predictive maintenance, autonomous driving capabilities, and intelligent energy optimization. The study highlights the potential of AI to address key challenges in EV technology, including range anxiety, charging infrastructure optimization, and real-time adaptive control. Results demonstrate significant improvements in vehicle reliability, energy efficiency, and safety, paving the way for smarter, more sustainable electric mobility solutions. This research provides valuable insights into the future of AI-enabled EVs and offers a roadmap for industry adoption.

Harmonics in transformers have become a critical concern due to the increasing penetration of nonlinear loads, power electronic converters, and renewable energy systems in modern power networks. Excessive harmonic distortion leads to additional copper and core losses, overheating, derating of transformers, reduced efficiency, and compromised power quality. To address these challenges, researchers have proposed a wide range of harmonic mitigation techniques over the past decades. This review paper presents a comprehensive study of various harmonic reduction methods applied in transformers, including design-based approaches such as winding configurations, phase-shifting techniques, and use of K-factor rated transformers, as well as external solutions like passive filters, active filters, hybrid filters, and advanced modulation strategies. The strengths, limitations, and application suitability of each technique are discussed to provide a comparative understanding. Furthermore, the review highlights recent advancements that integrate artificial intelligence and optimization algorithms for adaptive harmonic mitigation. This study aims to guide engineers and researchers in selecting appropriate harmonic reduction strategies for enhancing transformer reliability, efficiency, and service life in power systems.

Solar energy is one of the most promising renewable energy sources, yet the efficiency of solar panels can decline by up to 30% due to dust and debris accumulation. Conventional cleaning practices, such as manual wiping or water-based methods, are often labor-intensive, water-dependent, and unsuitable for large-scale installations. To overcome these limitations, this study proposes an autonomous robotic cleaning system that operates without human intervention. The system combines a sensor-based efficiency monitoring unit, a brush and water-spray mechanism, and a programmed movement algorithm to ensure effective cleaning. It is further equipped with intelligent navigation to prevent falls from panel edges and wireless communication for real-time voltage tracking. Experimental validation shows notable improvements in power output, reduced maintenance effort, and enhanced operational reliability. The findings highlight the potential of autonomous cleaning technology as a sustainable solution for long-term solar panel performance and efficiency.