Abstract:
Agricultural marketing reforms in India have been pivotal in reshaping the economic landscape of farming communities. This study evaluates the impact of key reforms APMC amendments, e-NAM integration, MSP-based procurement, and the Paddy Procurement Automation System (P-PAS) on farmers in Khurda district of Odisha. A mixed-method approach employing primary data from 120 farmers surveyed across three blocks of Khurda and secondary data from OSAMB, OSCSC, and GoI databases was adopted for the period 2018–2024. The hypothesis tested was that post-reform mechanisms have significantly improved farmers' price realisation and market access. Results indicate that Odisha's paddy procurement grew from 49 lakh MT in 2017–18 to 74.92 lakh MT in 2024–25, while MSP rose from ₹1,750 to ₹2,300 per quintal. However, in Khurda district, only 42.5% of farmers were aware of e-NAM, and intermediary dominance remained a significant constraint. The study concludes that reforms have produced partial but uneven outcomes, with structural gaps in e-governance adoption, infrastructure, and awareness requiring targeted policy intervention.
Area: Department of Business Management
Author: Sabyasachi Ray1, Dr. Puja Kumari2
DOI: MJAP/05/0599
Abstract:
The agricultural sector of India, which provides about 46.1 percent of the national working population and almost 16 percent of the GDP, is still characterized by small and marginal farmers which occupy 86.1 percent of the total operated holdings. These farmers experience chronic threats of disjointed farmlands, low access to the market, increasing costs of inputs, as well as information asymmetry, which reduced their income and bargaining power. This paper will be seeking to explore how agri-startups and Farmer Producer Organizations (FPOs) can synergize to promote better market access and value realization by the smallholder farmers in India. The study aims at determining the growth pattern and performance of FPOs and agri-startups and also determining the effect of these on the market integration and income improvement of farmers. Descriptive-analytical research design was followed and secondary data were used, such as NABARD, SFAC, Ministry of Agriculture, and published research. The hypothesis states that joint working of FPOs and agri-startups would greatly enhance the market participation and price realization of farmers. Findings show that the membership of FPOs has a positive effect on the net returns, and electronic platforms such as e-NAM have incorporated more than 1.78 crore farmers into the transparent market system. The paper finds that, institutional empowerment of FPOs through technology-based interventions of agri-startups can form a sound ecosystem in empowering smallholders and in marketing sustainable agricultural produce.
Area: Department of Management
Author: Manoj Kumar Agarwal1, Dr. Reema Singh2
DOI: MJAP/05/0560
Abstract:
The rapid integration of renewable energy sources and the increasing complexity of modern power systems necessitate advanced intelligent energy management solutions. This research investigates machine learning-driven approaches for intelligent energy management and demand prediction in smart grids, with specific focus on the Indian energy sector context. The study employs a comprehensive analysis of deep learning models including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), Support Vector Regression (SVR), and hybrid architectures for accurate load forecasting and demand prediction. The hypothesis posits that hybrid machine learning models demonstrate superior performance compared to traditional forecasting methods in smart grid applications. Results indicate that hybrid CNN-LSTM models achieve prediction accuracy of 93.38% with RMSE values ranging from 0.56 to 3.99% MAPE across different datasets. The study analyzes data from Indian smart grid implementations showing electricity demand growth of 6.3% annually, with renewable capacity projected to reach 500 GW by 2030. Findings demonstrate that ML-driven energy management systems reduce energy wastage by 12.96% while improving grid stability to 96.25%, thereby validating the hypothesis that advanced machine learning techniques significantly enhance smart grid operational efficiency and sustainability.
Area: Department of Electrical Engineering
Author: Ritik Raj1, Rishikant Garg2
DOI: MJAP/05/0558
Abstract:
Power transmission systems face critical voltage stability challenges due to increasing load demands and integration of renewable energy sources. This research investigates the application of Flexible AC Transmission Systems (FACTS) devices for enhancing voltage stability in transmission networks. The study examines Static VAR Compensator (SVC), Static Synchronous Compensator (STATCOM), Thyristor Controlled Series Capacitor (TCSC), and Unified Power Flow Controller (UPFC) on IEEE 14-bus test system. Continuation Power Flow (CPF) methodology with Newton-Raphson technique was employed to analyze voltage stability margins. Results demonstrate that UPFC provides maximum loading margin improvement of 48.3%, followed by STATCOM (42.7%), SVC (38.5%), and TCSC (35.2%). Voltage profile improvements ranged from 12.4% to 18.6% across critical buses. Statistical analysis reveals significant correlation (r=0.94) between reactive power compensation and voltage stability enhancement. Power loss reduction of 32.8% was achieved with optimal FACTS placement. The research confirms that strategic deployment of FACTS devices substantially improves transmission system voltage stability, reduces losses, and enhances overall power system security.
Area: Department of Electrical Engineering
Author: Mohammad Irfan1, Nikita Khobragade2
DOI: MJAP/05/0557
Abstract:
India's digital banking sector has undergone a rapid and unprecedented transformation, driven by technological innovation, policy reforms, and evolving customer expectations. As of 2024, India hosts approximately 295.5 million digital banking users, placing it ahead of the United States. The Reserve Bank of India's Digital Payments Index (RBI-DPI) reached 493.22 in March 2025, underscoring the deepening penetration of digital payments nationwide. This paper examines how customer-centric leadership strategies shape customer experience within India's digital banking ecosystem. The study objectives are to identify the key leadership strategies enabling digital transformation and to assess their measurable impact on customer experience outcomes. A secondary data-driven, descriptive research design was adopted, utilizing data sourced from RBI reports, Deloitte's Digital Banking Maturity (DBM) study, and peer-reviewed literature published between 2018 and 2025. The hypothesis posits that customer-centric leadership strategies significantly and positively influence customer satisfaction in digital banking. Findings reveal that banks deploying data-driven, agile, and empathy-oriented leadership demonstrate superior customer satisfaction scores. The paper concludes with strategic recommendations for banking leaders aiming to sustain competitive advantage through customer centricity.
Area: Department of Business and Leadership Management
Author: Pragashani Reddy
DOI: MJAP/05/0555
Abstract:
Dynamics and control form the backbone of modern engineering systems, enabling the design, analysis, and operation of complex, interconnected, and autonomous systems. This review presents a comprehensive analysis of the evolution of control methodologies and dynamic modeling techniques from 2000 to 2025. Early studies focused on classical control approaches, including PID, state-space, and robust control, while mid-2010s research emphasized predictive and networked control systems for distributed applications. In recent years, data-driven, intelligent, and hybrid control strategies have emerged, integrating machine learning and adaptive methods with classical frameworks to address nonlinearities, uncertainties, and high-dimensional dynamics. The paper highlights applications across robotics, autonomous vehicles, industrial automation, and smart transportation systems. Key research gaps are identified, including the integration of model-based and data-driven methods, scalability of networked systems, real-time implementation, robustness under uncertainty, and the lack of standardized benchmarking. This review provides insights into trends, challenges, and future directions, guiding researchers toward the development of intelligent, scalable, and resilient control systems for complex engineering applications.
Area: Department of Mechanical Engineering
Author: Rahul Soma Deshmukh1, Siddhant N. Patil2, Shraddha Lohakare3, Mohan T. Patel4, Pragati Patil5
DOI: MJAP/05/0554
Abstract:
The study investigates the mechanical behavior, structural performance, and durability of conventional and modern construction materials, including steel, concrete, timber, fiber-reinforced polymers (FRP), and self-healing concrete. Experimental testing and comparative analysis were conducted to assess tensile strength, compressive strength, ductility, toughness, and environmental durability under standard and extreme loading conditions. Results indicate that steel exhibits superior tensile capacity and ductility, while concrete demonstrates high compressive strength but limited tensile resistance. FRP-reinforced concrete and self-healing concrete offer enhanced durability, energy absorption, and corrosion resistance, making them suitable for resilient and long-lasting structures. Hybrid systems combining traditional and advanced materials demonstrated improved load-bearing capacity and reduced crack propagation. The findings highlight the potential of integrating modern composites and smart materials with conventional systems to achieve safer, more sustainable, and high-performance infrastructure. The study also identifies key research gaps, including long-term performance, multi-hazard resilience, large-scale implementation, and standardization of innovative materials, suggesting directions for future research in structural engineering.
Area: Department of Mechanical Engineering
Author: Rahul Soma Deshmukh1, Siddhant N. Patil2, Shraddha Lohakare3, Mohan T. Patel4, Pragati Patil5
DOI: MJAP/05/0553
Abstract:
Over the past twenty-five years, significant advancements have been achieved in the fields of structural vibrations, acoustic radiation, and fluid–structure interaction (FSI). This review presents a comprehensive assessment of research developments from 2000 to 2025, focusing on methodological evolution, computational innovations, and emerging intelligent control strategies. Early contributions emphasized classical modal analysis, finite element modeling, and partitioned FSI frameworks. Between 2010 and 2020, research expanded toward strongly coupled multi-physics simulations, smart material–based vibration control, and uncertainty quantification in vibro-acoustic systems. Recent developments (2020–2025) demonstrate a paradigm shift toward artificial intelligence (AI)-driven surrogate modeling, physics-informed neural networks, high-performance computing (HPC), and digital twin applications. The review identifies key achievements, including improved numerical stability in monolithic FSI solvers, hybrid finite element–boundary element acoustic methods, and adaptive vibration suppression using smart materials. However, several challenges persist, such as computational cost in high-frequency acoustic simulations, limited interpretability of AI-based models, insufficient experimental validation, and the absence of fully integrated multi-physics digital twin frameworks. The study concludes that future research should focus on hybrid physics–AI methodologies, scalable energy-efficient computational strategies, and robust uncertainty-aware modeling to address the increasing complexity of modern engineering systems.
Area: Department of Mechanical Engineering
Author: Rahul Soma Deshmukh1, Siddhant N. Patil2, Shraddha Lohakare3, Mohan T. Patel4, Pragati Patil5
DOI: MJAP/05/0552
Abstract:
Artificial intelligence integration into power grid fault management is a groundbreaking strategy of improving the reliability of electrical infrastructure and its efficiency of operations. This work explores the use of AI-based techniques in fault detection and localization in sophisticated power grid models, especially the focus on machine learning algorithms, deep neural networks, and smart monitoring systems. The main goals are to compare the effectiveness of different AI methods, determine the accuracy of fault detection in various grid configurations, and determine the difficulties involved in implementing AI in developing countries such as India. The research approach is a system review and a quantitative analysis of secondary sources gathered in form of grid operators, research departments, and international energy organizations. The hypothesis is that AI-powered systems represent much better fault detection and localization resistance than traditional schemes of protection. The findings have revealed that convolutional neural networks have a fault classification accuracy of over 98% whereas the support vector machines have a high fault localization in a transmission line with less than 1.2 errors. A discussion shows that there were significant gains in response time, decrease in outage time, and increased grid stability using AI. The conclusion has made it clear that AI-based solutions provide scalable answers to modernizing the power grid protection infrastructure in a wide range of contexts.
Area: Department of Electrical Engineering
Author: Sweta Sharma1, Mr. Shrikant Namdeo2, Mr. Rishikant Garg3
DOI: MJAP/05/0551