Darshan Patel Research Profile

Entrepreneurial Orientation Among Diploma Students in Tiruchirappalli: Exploring Motivations, Institutional Support, and Employment Impact

Entrepreneurship as a source of employment in Tiruchirappalli District, Tamil Nadu: When small businesses act as a source of employment growth it would result in a decrease in the unemployment rate in the country especially for the youth population. Despite a succession of studies on entrepreneurship education and its policies and challenges in the region, the factors influencing entrepreneurial orientation outside formal education are unclear. The current study aims to determine the major motivations for entrepreneurship among Diploma students at Tiruchirappalli. And the continuing discussion of whether entrepreneurs are born or made highlights the importance of understanding these forces. Through a review of existing literature, this study aims to identify the factors that influence students' ability to create their own business ventures or continue with their family businesses. Because these students can launch and grow their own businesses, Diploma students are a significant target for this study.

Blockchain and Hybrid Encryption: A Dual Layer Approach to Secure Data Communication

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Industrial Relations at Enterprise Level: A Case Study

Industrial relations in India have witnessed a long journey, from the phase of industrialisation to independence era to the age of economic reforms, but a lot needs to be addressed given the role of cordial industrial relations in desired economic growth of a country. The key lies in recognition of workforce as an essential and integral part of the organisation and not merely a tool for the production. The second National commission on labour recommended consolidation of labour laws, given that there are numerous labour laws both at the state level and the central level. Most of the labour laws are applicable to organisations employing a given number of workers, mostly ten or more workers. In order to escape the laws, organisations have been using contract labour more, so as to avoid the constraints of hiring and firing in adjusting to production demands. This study concentrates upon how the broad structure of specific patterns of industrial relations operates in a given context, and thereby provide an insight for solving a number of emerging problems. It helps to locate where the problem is, which may require modifications in the structural patterns. This provides an objective analysis to which those responsible for managing industrial relations in industry can relate their own experiences and can help them to seek avenues of change in realising their industrial relations objectives.

Demystifying Business Intelligence (BI): A Comprehensive Guide

Business Intelligence (BI) has emerged as a fundamental component of modern business strategy, revolutionizing how organizations harness data for strategic decision-making. This comprehensive article explores the evolution of BI from traditional reporting to dynamic analytics, examining its architectural framework, technological components, implementation methodologies, and real-world applications. The article documents how organizations across diverse sectors increasingly integrate BI capabilities into core business functions, recognizing the competitive advantages of data-driven approaches. It analyzes the critical success factors for effective implementation, highlighting the importance of governance frameworks, user experience design, and iterative deployment strategies. The article further examines the specialized tools supporting various aspects of the analytical lifecycle, from data storage and processing solutions to visualization platforms and programming frameworks. Through detailed examination of functional applications spanning sales, marketing, operations, and finance, the article demonstrates how BI translates theoretical concepts into tangible business outcomes. Finally, it explores emerging trends including augmented analytics, natural language processing, edge computing, embedded BI, and data democratization, that continue to reshape the analytical landscape.

Generative AI for Product Development: From Ideation to Design and Testing

The integration of generative artificial intelligence into product development processes represents a fundamental paradigm shift from traditional human-driven methodologies to collaborative human-AI frameworks that transform every stage of the development lifecycle. Contemporary organizations are leveraging AI technologies across ideation, strategic planning, design prototyping, testing validation, and organizational workflow integration to achieve unprecedented levels of efficiency and innovation capability. AI-powered systems demonstrate remarkable proficiency in automated requirement generation, multi-modal data synthesis for strategic intelligence, rapid sketch-to-prototype translation, comprehensive test case creation, and agile workflow optimization. The evolution encompasses sophisticated natural language processing for documentation automation, computer vision algorithms for design interpretation, machine learning models for user behavior prediction, and intelligent decision support systems for feature prioritization. Cross-functional team dynamics have been fundamentally altered as AI systems facilitate seamless information translation between specialized domains while enabling continuous validation through real-time monitoring and automated feedback mechanisms. Professional skill requirements continue evolving as organizations adapt to human-machine collaboration models that emphasize higher-order cognitive capabilities, including critical thinking, creative problem-solving, and strategic planning. Implementation challenges span technical infrastructure requirements, change management complexities, and cultural adaptation barriers that demand comprehensive strategic approaches for successful AI adoption across product development environments.

ACOUSTIC SIMULATION AND AI IN PRESERVING ANCIENT MUSICAL SPACES

Palatial, amphitheatrically, and temple musical sites must be preserved to conserve intangible cultural treasures, notably historical soundscapes. Ancient architects and musicians understood sound and designed structures around specific acoustic principles to enhance ceremonial and musical experiences. Unfortunately, environmental changes, structural deterioration, and urbanisation threaten these acoustic environments. We investigate how acoustic modelling and AI may reproduce and preserve the sound of long-lost music performance venues. 3D modelling, room acoustic simulations, and AI-driven sound analysis recreate the original audio experiences. The research analyses case studies of heritage places and historical architectural data, material attributes, and spatial arrangements to anticipate acoustic responses. Artificial intelligence systems use machine learning and deep learning to analyse and improve models of sound propagation, reverberation, and resonance patterns for predictive modelling. Acoustic simulations augmented using artificial intelligence may help us comprehend building acoustics, how people listened to music in the past, and how to conserve cultural treasures for future generations. These methodologies may also guide restoration, VR recreations, and educational platforms, ensuring that historic performance spaces will inspire future generations. The project highlights the interdisciplinary intersection of heritage science, computational acoustics, and AI to preserve human civilisations' auditory history via technology-driven preservation.

Scholar9 Profile ID

S9-052023-0900005