Abstract:
The rapid demographic transition in India has resulted in a significant increase in the elderly population, creating unprecedented challenges for mental health services and social support systems. This cross-sectional comparative study was conducted to examine mental health status, perceived social support, and quality of life among elderly residents of old age homes and community-dwelling elderly in and around Guwahati, Assam. The objectives included assessing depression prevalence, evaluating social support levels, comparing quality of life across settings, and identifying correlating factors between these variables. A total of 200 participants (100 from old age homes and 100 from communities) aged 60 years and above were selected using purposive sampling. Standardized instruments including the Geriatric Depression Scale-15, Multidimensional Scale of Perceived Social Support, and WHOQOL-BREF were administered. It was hypothesized that old age home residents would exhibit higher depression rates, lower social support, and diminished quality of life compared to community-dwelling elderly. Results revealed significantly higher depression prevalence among old age home residents (58%) versus community elderly (31%), with substantially lower perceived social support and quality of life scores in institutional settings. Correlation analysis demonstrated significant negative associations between depression and both social support and quality of life. The study concludes that institutional care settings require comprehensive mental health interventions and enhanced social support mechanisms to improve elderly wellbeing
Area: Department of Social Sciences
Author: Pari Borkakati1, Dr. Amalesh Adhikary2
DOI: MJAP/05/0077
Abstract:
Cloud computing has emerged as the backbone of modern digital infrastructure, providing scalable and flexible computing resources to users worldwide. However, efficient resource allocation remains a key challenge due to fluctuating demand, heterogeneous workloads, and the dynamic nature of cloud environments. Traditional resource allocation strategies often suffer from inefficiencies, leading to resource wastage, increased operational costs, and performance degradation. This paper proposes a hybrid machine learning approach that integrates supervised learning for demand prediction and reinforcement learning for dynamic allocation to optimize resource distribution in cloud environments. The proposed model aims to enhance scalability, adaptability, and cost-efficiency while ensuring Quality of Service (QoS) compliance. Through extensive simulations and real-world cloud workload datasets, we demonstrate that the hybrid model significantly improves resource utilization, task scheduling efficiency, and energy consumption compared to traditional methods.
Area: Department of Computer Science and Engineering
Author: Kirti Khanderao Gambhire1, Dr. Bechoo Lal2
DOI: MJAP/05/0076
Abstract:
One of the best strategies to lower energy usage and electric costs is to employ a demand control management system to use electricity more effectively. Effective monitoring and management of power use is accomplished through the use of an electrical demand control system. Additionally, it's a helpful tool to help you prevent fines above the negotiated value of power demand with each electricity provider. In this research, we will create a predictor based on Taguchi-Grey to estimate the value of power demand online.The most recent information on systems for managing energy (EMS) and prosumer structure of organizations is provided in this review. When sustainable energy sources (RES) are integrated into a home, recent difficulties arise for power system stability, optimal operation, power quality, and market involvement. Creating an EMS with unique prosumer organisational structures is a common way to handle these problems. Several facets of observation must be included in the interdisciplinary process of developing an emergency management system. In addition to input parameter prediction strategies, optimisation methodologies, goal roles, constraints, and market conditions, this study provides an overview of the types, components, organisational, and control systems of prosumers. To mitigate the effects of market prices, creation, and consumption uncertainties associated with renewable energy sources running at maximum efficiency, Particular care is taken in forecasting input parameters. And the optimisation framework, which exhibits development potential.
Area: Department of Electrical Engineering
Author: Mr. Mohan T. Patel*, Dr. Nikhil Rathod2, Mr. Sidhhant Patil3
DOI: MJAP/05/0075
Abstract:
This study investigates effective methods for enhancing communicative English skills among rural students in the Rampurhat sub-division of Birbhum district, West Bengal, India. Despite English being recognized as an essential skill for educational and professional advancement, rural students in this region face significant challenges in developing communicative competence. Using a mixed-methods approach involving 250 students and 25 teachers from 10 rural schools, this research examined current pedagogical practices, identified key barriers to English acquisition, and implemented targeted interventions over a six-month period. Data collection included classroom observations, proficiency tests, attitude surveys, and interviews. Results revealed that interactive teaching methodologies incorporating local cultural contexts, peer learning strategies, and digital learning resources significantly improved students' communicative competence. Performance improvements were particularly notable in speaking (37.2%) and listening (41.5%) skills. The findings suggest that contextualized language teaching approaches combined with periodic reinforcement activities and parental involvement can effectively address the unique challenges faced by rural learners. This research contributes to developing culturally appropriate English language teaching methodologies for similar socioeconomic contexts.
Area: Department of English
Author: Kalyan Datta1, Dr. A. K. Pal2
DOI: MJAP/05/0074
Abstract:
Feeding difficulties and self-regulation problems are highly prevalent among premature infants, high-risk neonates, and young children with neurodevelopmental disorders. Sensory processing plays a critical role in feeding readiness, suck–swallow–breathe coordination, and the maintenance of a calm–alert behavioral state necessary for successful feeding. Occupational therapy and physiotherapy-led sensory-based interventions, including oral tactile stimulation, deep pressure, swaddling, kangaroo care, vestibular inputs, and multimodal sensory programs, are widely implemented in neonatal and pediatric rehabilitation settings. However, despite widespread clinical usage, the empirical evidence supporting these interventions remains scattered and heterogeneous. The present study undertakes a scoping review to systematically map available research on sensory-based interventions aimed at improving feeding performance and self-regulation in infants and young children. The review identifies dominant intervention categories, outcome indicators, effectiveness trends, and major research gaps. The findings demonstrate promising short-term benefits of sensory-based interventions on feeding efficiency, state regulation, and caregiver–infant interaction; however, methodological limitations, lack of longitudinal studies, and inconsistent outcome measures restrict definitive conclusions. This review highlights the urgent need for standardized protocols, robust randomized trials, and integration of neurophysiological markers in future research.
Area: Department on Physiotherapy
Author: Kamat Wagh Gauri Ashok1, Dr. Apoorv Narain2
DOI: MJAP/05/0073
Abstract:
The exponential growth of Internet of Things (IoT) devices has led to a massive volume of time-sensitive data that need real-time processing at the network edge. Cloud-centric incumbent architectures are not only imposing unacceptable time delays for applications such as autonomous vehicles, industrial automation and augmented reality, but also saturating network bandwidth to relay infinite amount of data. Edge computing is an alternative paradigm that processes data closer to the source, and existing frameworks have limitations in dynamic resource assignment, heterogeneous device administration and work distribution within a distributed edge infrastructure. In this paper we present DeepEdge, an intelligent distributed computing framework that tackles these challenges through the following key innovations: (i) A deep reinforcement learning-based resource orchestrator achieving 43.7% reduction in task completion time by anticipative workload placement and continual resource scaling; (ii) a hierarchical computation offloading algorithm that minimizes deadline-violation rates considering both latency bounds, energy consumption constraints and computational complexity for tiered device-edge-cloud distribution of tasks and (iii) an integrated predictive maintenance solution employing temporal convolutional networks to predict device failures with 94.2% accuracy allowing proactive reallocation of resources. Comprehensive experiments on a 500 node testbed with scenarios including smart city, industrial IoT and healthcare monitoring show that DeepEdge reduces end-to-end latency by 67.3% when comparing against cloud-only approaches and by 34.8% with respect to state-of-the-art edge frameworks whilst achieving an energy efficiency gain of 41.2%. The system can handle more than 2.3M events per second with less than 10ms latency at the p99 horizon, which sets new standards for the distributed IoT analytics.
Area: Department of Information Technology
Author: Dr. Anjali A. Bhadre1, Dr. Harshvardhan P. Ghongade2
DOI: MJAP/05/0072
Abstract:
Combinatorial optimization problems are pervasive in such important areas as logistics, finance, drug discovery and machine learning; however, most of them are NP-hard when formulated for classical computers which makes them prohibitively complex to solve. Quantum computing promises theoretically exponential speedup for some classical problem classes through algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE). However, state-of-the-art Noisy Intermediate-Scale Quantum (NISQ) computers here matured in a niche with a few dozens of qubits and coherence times to measure this contest time in sub-second precision are presently limited by noisy sensor performance and have yet short coherence times. In this work we present the adaptive layered variational optimization (ALVO) framework, a new hybrid quantum-classical paradigm that overcomes these shortcomings with three major technical innovations: 1) An adaptive ansatz construction technique that dynamically grows circuit depth according to an analysis of the underlying optimization landscape, reducing gate needs by 47.3% while maintaining solution quality; 2) A hierarchical problem decomposition method for mapping large-scale problems onto available quantum resources through intelligent partitioning with classical coordination; and 3) A comprehensive error mitigation apparatus composed of zero-noise extrapolation, probabilistic error cancellation and post-selection to achieve achievable effective error rates below 10⁻³. Extensive experiments on IBM Quantum and Google Sycamore processors on benchmark problems such as MaxCut (up to 127 qubits), portfolio optimization (50 assets) and traveling salesman (20 cities) show that ALVO achieves approximations ratios within 3.2% of classical state-of-the-art using 5.4× less quantum operations. The framework provides a realistic set of guidelines for using quantum optimization on state-of-the-art hardware and predicts perf
Area: Department of Information Technology
Author: Dr. Anjali A. Bhadre1, Dr. Harshvardhan P. Ghongade2
DOI: MJAP/05/0071
Abstract:
The explosion of healthcare data in distributed medical facilities provided an unprecedented opportunity to create reliable artificial intelligent models, yet the trade-off for privacy has been unavoidable. In this paper, we present Adaptive-DP-FL, a federated learning framework tailored for healthcare applications integrating adaptive differential privacy methods. Our framework tackles three key challenges: (1) achieving clinically acceptable model accuracy under privacy constraints; (2) controlling communication overhead in healthcare networks that have limited bandwidth; and (3) reaching fair levels of performance across a diverse collection hospital data distributions. We introduce a dynamic budget allocation where the magnitude of noise injection scales according to gradient sensitivity and training convergence measures, resulting in 34% accuracy improvement over fixed- budget differential privacy strategies at matched privacy guarantees (ε=1.0). We also present a hierarchical privacy-preserving aggregation protocol and TopK-DP gradient compression, which decrease the communication overhead by 87.3% without degradation to model quality. Comprehensive experimental results on five real-world healthcare datasets with 511,893 patient records show that Adaptive-DP-FL achieves AUC-ROC of 0.923 for the task of mortality prediction, which reflects a performance degradation by only 2.1% compared to its centralized baselines; and guarantees formal (ε,δ)-differential privacy.
Area: Department of Information Technology
Author: Dr. Anjali A. Bhadre1, Dr. Harshvardhan P. Ghongade2
DOI: MJAP/05/0070
Abstract:
In recent years, the blockchain has gained widespread attention in secure decentralized computing due to its features of trustless transaction and verifiable computation without relying on a trusted party. But inherent scalability limitations restrict the use of blockchain in high-throughput applications, with leading platforms today capable-to process only 7-30 transactions per second, while enterprises often require thousands. We present SecureChain, a new-architectured sharded blockchain system that is able to deliver unprecedented throughput while preserving security guarantees through three contributions: (1) An adaptive state-sharding mechanism with dynamic rebalancing distributes blockchain state across multiple parallel execution environments, leading to linear scaling of the throughput up to 23,847 transactions per second over the 64 shards; (2) A cross-shard atomic commitment protocol using optimistic execution with zero-knowledge validity proofs maintains integrity while allowing for high parallel processing without incurring significant performance penalties; by factoring out data dependencies and reducing cross-shard transaction latency by 73.2% compared to two-phase committing schemes; and (3) A novel zkSNARK-based verification scheme enables succinct and constant-time verification for arbitrary computations regardless their complexity, reducing the overhead of proof verification by factor 94.7%. Through extensive evaluation on a 1,024-node geographically distributed testbed, we show that SecureChain can achieve high throughput and low latency: for the number of transactions per second (TPS) at up to 23,847 with finality as low as 2.3 seconds; meanwhile it also is resilient to Byzantine faults of up to f < n/3 malicious nodes. Security analysis in the adaptive adversary models verifies they are secure against known attacks such as grinding, long-range and cross-shard double spending attacks. The framework sets new standards for secure distributed co
Area: Department of Mechanical Engineering
Author: Dr. Harshvardhan P. Ghongade1, Dr. Anjali A. Bhadre2
DOI: MJAP/05/0069
Abstract:
The rapid expansion of edge computing applications necessitates energy-efficient AI solutions which can work under strict power constraints while embracing high inference accuracy. In this paper, we introduce a holistic hardware-software co-design for spiking neural networks (SNNs) on memristive crossbar arrays with the highest energy efficiency ever reported in literature towards edge intelligence applications. Our framework is built on three key innovations: (1) a novel Leaky Integrate-and-Fire with Adaptive Threshold (LIF-AT) neuron model which reduces spike activity by 43.7% through dynamically selecting its threshold based on network-level statistics, leading to significant energy savings without compromising classification accuracy; (2) a hardware-aware training approach that accounts for memristor device non-idealities such as conductance drift, stuck-at faults, and programming variability which achieves within 1.8% of software ideal accuracy even in the presence of 5% device fault rates; and (3) an optimized weight mapping strategy using ternary weight quantization accompanied by asymmetric thresholds reducing analog-to-digital converter (ADC) resolution requirements from 8 bits down to 4 bits while achieving the state-of-the-art network representation accuracy of $97.3\%$ CIFAR-10 classification. We construct and evaluate 1T1R memristive crossbar arrays with the HfO 2 -based resistive switching devices, achieving a 32×32 array fabrication with a successful yield of 97.2% and programmable conductance levels up to 4 bits. System-level experiments on image classification (CIFAR-10, ImageNet-subset), speech recognition (Google Speech Commands) and hand gesture recognition (DVS-Gesture) benchmarks show that our framework achieves 127.3 TOPS/W energy efficiency, which is a 23.4× improvement over state-of-the-art GPU implementations and 8.7× better performance than recent neuromorphic accelerators. The complete framework, including training algorithms, hardware mo
Area: Department of Mechanical Engineering
Author: Dr. Harshvardhan P. Ghongade1, Dr. Anjali A. Bhadre2
DOI: MJAP/05/0068
Abstract:
Advanced Persistent Threats (APTs) - the most sophisticated type of cyber-attacks, stealthy in long duration, multi-staged attack trains and nation-state level resources. Low-and-slow attack patterns and the use of legitimate tools for malicious activities make it difficult to detect APTs with traditional intrusion detection systems. This paper presents a new multi-stage deep learning model called DeepAPT-Shield for APT detection and attribution in enterprise networks. Our solution must deal with three main tasks: (1) capturing subtle behavioral deviations that may indicate the presence of an APT, (2) correlating alert messages and attack indications at multiple locations in a temporalspatial fashion, and (3) attributing found threats to specific known APT groups that can drive a precise reaction. It contains four related modules: 1) A GAT to model entity behavior; 2) A TCN with attention mechanisms for sequence analysis; 3) A Heterogeneous Graph Neural Network for attack chain correlation and a Siamese Network for threat attribution. We make three primary contributions: (1) an adaptive threshold mechanism that reduces false positives by 67% with minimal effect on detection rates; (2) a new kill-chain aware loss function that heavily penalizes the inability to detect stages of the attack that enable other stages to occur, even if those "enabling" stages are harmless per se; and (3) semi-supervised learning for training the model on limited labeled APT data. Extensive experiment on DARPA OpTC dataset (17.4B events), LANL Unified Host and Network Dataset (58-days enterprise activity) and a proprietary dataset from 5 fortune-500 companies indicates the proposed approach outperforms all competitors. DeepAPT-Shield obtains 94.7% detection rate of APT campaigns with just 0.003% false positive rate, and detects attacks average at 18.3 days earlier than the state-of-the-arts commercial solutions. The attribution module correctly attributes APT groups in 89.2% on a
Area: Department of Mechanical Engineering
Author: Dr. Harshvardhan P. Ghongade1, Dr. Anjali A. Bhadre2
DOI: MJAP/05/0067
Abstract:
The accelerated expansion of the universe suggests the existence of dark energy, a component with negative pressure constituting nearly 70% of the cosmic energy budget. While the cosmological constant Λ with equation of state w=-1 remains the simplest explanation, persistent theoretical challenges and increasingly precise observations motivate the study of dynamical dark energy models in which w(z) evolves with cosmic time. This review provides a comprehensive analysis of parametric and non-parametric methods of modeling w(z), theoretical frameworks including quintessence, phantom fields, k-essence, coupled dark sectors, and modified gravity theories, and the latest observational constraints from DESI, CMB, BAO, supernovae, and large-scale structure. Special attention is devoted to the phenomenon of phantom-divide crossing (w=-1), its theoretical challenges, and mechanisms that allow such behavior without pathological instabilities. The review concludes with prospects from upcoming cosmological surveys and the implications for fundamental physics.
Area: Department of Physics
Author: Suffiya Parvez1, Avinash Singh2
DOI: MJAP/05/0066
Abstract:
Industrial waste heat represents a critical untapped energy resource, with approximately 60-72% of global primary energy consumption dissipated as thermal losses across manufacturing sectors. This research investigates next-generation waste-heat recovery solutions, focusing on compact heat exchangers and thermoelectric generator applications in industrial settings. The primary objectives encompass evaluating the thermal efficiency of compact heat exchangers compared to conventional systems, analyzing thermoelectric generator performance parameters for low-to-medium temperature waste heat recovery, and examining integration strategies for industrial implementation. The study employs a mixed-methods research design incorporating secondary data analysis from industrial installations across steel, cement, and chemical sectors globally. The hypothesis posits that integrated compact exchanger-thermoelectric systems achieve superior recovery efficiency compared to standalone technologies. Results demonstrate that printed circuit heat exchangers achieve effectiveness values of 97.9%, while bismuth telluride thermoelectric modules attain 8% conversion efficiency at temperature differentials of 230°C. The synergistic integration of these technologies yields 25-40% energy cost reductions in heavy industries. This research concludes that next-generation waste-heat recovery systems offer transformative potential for industrial decarbonization, with compact exchangers and thermoelectric generators representing commercially viable pathways toward sustainable manufacturing.
Area: Department of Thermal Engineering
Author: Mr. Navin Kumar1, Dr. Sachin Baraskar2
DOI: MJAP/05/0065
Abstract:
The current research examines the beliefs of primary teachers in Goa for CWSN. Internationally, the Salamanca Statement 1994 and nationally, the Rights of Persons with Disabilities Act 2016, encourages inclusive education and calls upon all general educational institutions to ensure barrier-free access and learning settings. This study primarily aims to investigate (1) cognitive, affective and behavioral dimensions of teachers' attitudes (2) demographic variables affecting these attitudes and (3) perceived barriers to inclusive practices. The study assumes that the teachers who are specially trained and previously exposed to CWSN have more positive attitude than the other teachers. 180 primary school teachers from government and private schools in their respective districts, North and South Goa, were chosen for the study using a stratified random sample strategy and a descriptive survey methodology. In this research, the primary data collection tool was the Teacher Attitude Scale toward Inclusive Education (TASTIE). Goan primary teachers have a moderately positive attitude toward inclusive education, according to the T-test and ANOVA analysis. However, there is a substantial variation in their training, teaching experience, and school type. Based on favorable attitudes, the study networks recommend government, infrastructural, teacher education, and social policy formulation to close the gap between student and teacher attitudes about the implementation of MLE, and their challenges such as infrastructure, training and large class sizes.
Area: Mrs. Siddhi H. Joshi1, Dr. Sarita Singh2
Author: Mrs. Siddhi H. Joshi1, Dr. Sarita Singh2
DOI: MJAP/05/0064
Abstract:
Chhattisgarh, a resource-rich state in central India, plays a pivotal role in national energy production due to its vast coal reserves and rapidly expanding industrial sectors such as steel, mining, cement, and thermal power generation. While this industrialization has ensured strong energy availability and economic growth, it has simultaneously intensified environmental challenges, including rising carbon emissions, air pollution, and ecological degradation. With a population of nearly 30 million and a climate characterized by high solar insolation and abundant biomass resources, the state possesses significant potential for renewable energy development. This study presents an integrated renewable energy modeling and scenario analysis framework aimed at achieving a low-carbon energy mix in Chhattisgarh by 2035. Using statistical datasets and simulated projections, three scenarios Business-as-Usual (BAU), Moderate Transition (MT), and Aggressive Green Transition (AGT) were constructed to assess renewable integration pathways and their environmental impact. Results indicate that the AGT scenario, supported by large-scale solar expansion, biomass utilization, and energy storage deployment, can reduce carbon emissions by 48–55% while increasing the renewable share to 60–65% of the total energy mix. The findings demonstrate that a structured shift toward renewable energy can mitigate industrial pollution, enhance long-term energy security, and support India’s national goal of achieving net-zero emissions by 2070. The study provides policymakers with a strategic roadmap for optimizing renewable resources, reducing coal dependency, and enabling sustainable industrial growth in Chhattisgarh.
Area: Department of Mathematics
Author: Payal Goswami1, Aloke Verma2
DOI: MJAP/05/0063
Abstract:
Rapid urbanization, growth of the Internet of Things (IoT) and expansion of smart city initiatives in India have created a strong demand for low-power, autonomous electronic systems. Ambient energy harvesting from renewable sources particularly solar, biomass, micro-hydro, and environmental vibrations offers a pathway to power such smart systems without overburdening the conventional grid. Chhattisgarh, a resource-rich state with high solar insolation and strong biomass availability, is still dominated by coal-based electricity generation, but its renewable energy capacity has grown steadily in the last decade. Recent assessments report a total installed renewable energy capacity of around 1.6 GW, of which solar contributes nearly three-quarters. This paper examines the potential of harvesting ambient renewable energy for sustainable smart systems in Chhattisgarh with a focus on applications in smart cities (Nava Raipur Atal Nagar and Raipur), rural smart villages, precision agriculture, and environmental monitoring. A review of state-level energy transition reports, ambient energy-harvesting literature, and policy documents is combined with a conceptual framework for integrating harvested energy into IoT-based smart infrastructures. Key findings indicate that rooftop solar, small off-grid photovoltaic systems, and biomass-supported microgrids already promoted by the Chhattisgarh State Renewable Energy Development Agency (CREDA) can be effectively combined with ultra-low-power electronics and energy-aware communication protocols to realize energy-neutral smart systems. The paper proposes a multi-tier architecture linking ambient energy harvesters, local storage, edge computing nodes, and cloud platforms. It highlights case examples such as solar-powered street lighting, sensorised water-supply schemes, and tourism-oriented smart villages that already rely on renewable energy, and discusses how these can be scaled into a state-wide digital energy-harvesting ecosystem
Area: Payal Goswami1 Aloke Verma2
Author: Payal Goswami1 Aloke Verma2
DOI: MJAP/05/0061
Abstract:
Urban expansion represents a critical challenge for sustainable development, necessitating advanced analytical frameworks to monitor ecological transformations and environmental service provision. This review synthesizes current research on Geospatial Artificial Intelligence (GeoAI) applications for spatio-temporal urban growth assessment and ecosystem service evaluation. The study examines machine learning algorithms, deep learning architectures, and remote sensing technologies employed for land use/land cover change detection, urban morphology analysis, and environmental impact assessment. Through comprehensive literature analysis, we identify Random Forest, Convolutional Neural Networks, and ensemble methods achieving accuracies exceeding 90% in urban classification tasks. Results demonstrate GeoAI's capacity to integrate multi-source geospatial data for real-time monitoring, revealing significant urban expansion patterns globally, with developing nations experiencing 200-300% growth in built-up areas over two decades. Discussion highlights trade-offs between urbanization and ecosystem services, emphasizing vegetation loss, urban heat island intensification, and biodiversity decline. The review concludes that GeoAI frameworks provide robust, scalable solutions for sustainable urban planning, though challenges persist regarding model interpretability, data integration, and ethical considerations in deployment.
Area: Department of Computer Science
Author: Sandeep Tikariha1, Dr. Diwakar Tripathi2
DOI: MJAP/05/0060
Abstract:
Water quality assessment is crucial for ensuring public health and environmental sustainability. This study evaluates the physico-chemical parameters of drinking water sources in the Manendragarh region of Chhattisgarh, India. The primary objective was to assess the potability of water by analyzing key parameters including pH, total dissolved solids, hardness, turbidity, chloride, fluoride, nitrate, and heavy metals. Samples were collected from various locations including hand pumps, bore wells, and municipal supply points during pre-monsoon and post-monsoon seasons. Standard analytical methods prescribed by Bureau of Indian Standards and World Health Organization were employed for analysis. The hypothesis posited that anthropogenic activities and geogenic factors significantly influence water quality in this region. Results indicated that several parameters exceeded permissible limits at certain locations, particularly total hardness, fluoride, and iron content. Statistical analysis revealed significant spatial and temporal variations in water quality parameters. The findings suggest urgent need for water treatment interventions and regular monitoring to ensure safe drinking water supply. This comprehensive assessment provides baseline data for policy makers and highlights areas requiring immediate attention for improving water quality and protecting public health in the Manendragarh region
Area: Department of Chemistry
Author: Anisha Sonker1, Dr. Sushrita Panayak2
DOI: MJAP/05/0059
Abstract:
The digital revolution in India has created unprecedented opportunities alongside significant cybersecurity challenges. This empirical study examines the contemporary digital crime landscape in India through a comprehensive analysis of cybercrime statistics, legal frameworks, and enforcement mechanisms from 2020. The research employs a mixed-method approach, analyzing data from the National Crime Records Bureau (NCRB), Ministry of Home Affairs reports, and judicial pronouncements. Findings reveal a 300% increase in cybercrime cases, with financial fraud constituting 65% of reported incidents. The study identifies critical gaps in the Information Technology Act, 2000, and highlights the inadequacy of existing legal provisions in addressing emerging digital threats such as deepfakes, ransomware, and cryptocurrency-related crimes. The analysis of 500 cybercrime cases across five states demonstrates significant regional disparities in investigation capabilities and conviction rates. The research reveals that while India has established a robust legal framework through amendments to the IT Act and introduction of the Digital Personal Data Protection Act, 2023, implementation challenges persist. The study concludes that India's digital crime landscape requires urgent policy interventions, enhanced cybersecurity infrastructure, and specialized judicial mechanisms to effectively combat the evolving nature of digital criminality in the digital age
Area: Department of Law
Author: Shivani Singh1, Dr. Jyoti Singh2
DOI: MJAP/05/0062
Abstract:
HIV/AIDS remains a significant public health challenge in India, with educational disparities playing a crucial role in disease vulnerability. This study examines the relationship between educational attainment and HIV/AIDS awareness in tribal and coastal communities of Odisha. The research objectives include assessing educational levels, evaluating HIV/AIDS knowledge, comparing awareness patterns, and identifying intervention needs across both populations. A cross-sectional comparative design was employed with 400 participants from tribal districts (Koraput, Mayurbhanj) and coastal regions (Puri, Ganjam). Data collection utilized structured questionnaires and knowledge assessment tools. The hypothesis posited that lower educational attainment correlates with reduced HIV/AIDS awareness in tribal areas compared to coastal regions. Results revealed significant educational disparities, with 68% of tribal participants having primary or no formal education versus 42% in coastal areas. HIV/AIDS awareness was markedly lower among tribal populations (45% comprehensive knowledge) compared to coastal communities (71%). Discussion highlights the intersection of educational marginalization and health vulnerability. The study concludes that targeted educational interventions and culturally appropriate awareness programs are essential for bridging knowledge gaps and reducing HIV/AIDS susceptibility in educationally disadvantaged tribal populations.
Area: Department of Psychology
Author: Rasmita Mohanty1, Dr. Soniya Rani2
DOI: MJAP/05/0058
Abstract:
This study investigates the multiferroic properties of bismuth-based metal oxide nanostructures, focusing on BiFeO₃ (BFO), Bi₂Fe₄O₉, and Bi₅Ti₃FeO₁₅ systems. Multiferroic materials, which simultaneously exhibit ferroelectric and magnetic ordering, represent a frontier in advanced functional materials with significant potential for next-generation memory devices and sensors. Through comprehensive structural, magnetic, and electrical characterization, we examine how nanostructuring influences the coupling between ferroelectric and magnetic properties in these systems. Hydrothermal synthesis methods were optimized to produce nanoparticles, nanorods, and thin films with controlled morphology and composition. X-ray diffraction, scanning electron microscopy, transmission electron microscopy, vibrating sample magnetometry, and ferroelectric measurements revealed size-dependent enhancement of multiferroic properties, with critical dimensions below 100 nm showing significant improvements in magnetoelectric coupling coefficients. Notably, the BiFeO₃ nanoparticles with average diameter of 45 nm exhibited a magnetoelectric coupling coefficient of 18.5 mV/cm•Oe, representing a 37% enhancement compared to bulk counterparts. Temperature-dependent measurements confirmed room temperature multiferroicity, while substitutional doping with rare-earth elements demonstrated further property optimization potential. These findings provide critical insights for rational design of bismuth-based multiferroic nanostructures for technological applications.
Area: Department of Physics
Author: Purushottam Lal Verma1, Dr. Nitta Kumar Swamy2
DOI: MJAP/05/0056
Abstract:
The criminal justice system in India has undergone a historic transformation with the enactment of three landmark legislations in 2023: the Bharatiya Nyaya Sanhita (BNS), Bharatiya Nagarik Suraksha Sanhita (BNSS), and Bharatiya Sakshya Adhiniyam (BSA). These Acts, which came into force on 1st July 2024, replace the colonial-era Indian Penal Code 1860, Code of Criminal Procedure 1973, and Indian Evidence Act 1872 respectively. This comprehensive reform marks a paradigm shift from a punishment-oriented retributive model to a reformative, rehabilitative, and victim-centric approach to criminal justice. The new framework introduces progressive measures including community service as punishment, mandatory forensic investigation, digital evidence integration, and stricter penalties for organized crime and terrorism. This paper critically examines the reformative philosophy underlying these changes, analyzes key provisions, evaluates their alignment with international best practices, and assesses the challenges in implementation. Drawing comparisons with successful rehabilitative models such as Norway's prison system, this research explores whether India's reformed criminal justice system can effectively balance deterrence with rehabilitation while ensuring constitutional protections and human rights.
Area: Department of Law
Author: Parvatkar Krishna Rishikesh1, Dr. Sunil Kulhare2
DOI: MJAP/05/0057
Abstract:
The 'Narva' initiative, a flagship water conservation program implemented by the Chhattisgarh government, represents a significant effort to restore natural water streams and improve groundwater levels across the state. This empirical study examines the ecological and socioeconomic impacts of this initiative on the Bhoramdeo Wildlife Sanctuary (BWS) and its surrounding landscape. Situated in the Kabirdham district and part of the critical Kanha-Achanakmar corridor in the Central Indian landscape, BWS faces complex conservation challenges involving biodiversity preservation, water resource management, and community livelihoods. Through comprehensive field surveys, hydrological assessments, biodiversity monitoring, and socioeconomic evaluations conducted from 2020-2024, this research documents tangible improvements in water availability, habitat quality, and wildlife populations following Narva interventions. The initiative has shown positive correlations with groundwater recharge, increased vegetation diversity, and enhanced wildlife sightings, particularly during dry seasons. Socioeconomically, local communities, including the indigenous Baiga and Gond tribes, have experienced improved agricultural productivity, reduced water scarcity, and new ecotourism opportunities. However, challenges remain in balancing conservation goals with community needs, particularly regarding potential land-use conflicts and ensuring equitable distribution of benefits. This study provides critical insights for adaptive management of integrated conservation-development initiatives in similar landscapes across India.
Area: Department of Forestry
Author: Dilraj Prabhakar1, Dr. Anjana Pant2
DOI: MJAP/05/0055
Abstract:
The National Rural Livelihoods Mission (NRLM), implemented as Bihan in Chhattisgarh, represents a paradigm shift in rural development strategies aimed at poverty alleviation through women's Self-Help Groups (SHGs). This review paper examines the impact of Chhattisgarh NRLM on women empowerment in Fingeshwar Block through a comprehensive meta-analysis of past research work. The study synthesizes existing literature on NRLM's implementation, focusing on economic, social, and political dimensions of women's empowerment in rural Chhattisgarh. Through systematic review of empirical studies, policy documents, and field research conducted between 2011-2024, this paper analyzes the effectiveness of various NRLM interventions including microfinance, capacity building, skill development, and institutional strengthening. The critical analysis reveals significant positive impacts on women's economic independence, decision-making abilities, and social mobility, while also identifying implementation challenges such as caste-based exclusion, limited financial literacy, and patriarchal constraints. The meta-analysis demonstrates that NRLM has successfully created sustainable livelihood opportunities and enhanced women's agency in Fingeshwar Block, though geographical and social disparities persist. This review contributes to understanding the sociological implications of development interventions and provides evidence-based recommendations for strengthening women-centric rural development programs.
Area: Department of Sociology
Author: Swechcha Singh1, Dr. Yogmaya Upadhyay2
DOI: MJAP/05/0054
Abstract:
Malnutrition remains a persistent challenge in rural communities worldwide despite decades of intervention programs. This empirical study investigates the interrelationship between three key social determinants land rights, water sanitation, and household income and their collective impact on malnutrition prevalence in rural settings. Using a mixed-methods approach, data was collected from 2,438 households across 78 rural communities in four regions with historically high malnutrition rates. Multiple regression analysis and structural equation modeling revealed that insecure land tenure significantly limits agricultural productivity and dietary diversity (β=-0.42, p
Area: Department of Sociology
Author: Vijay Rai1, Dr. Yogmaya Upadhyay2
DOI: MJAP/05/0053
Abstract:
Nickel ferrite (NiFe₂O₄) represents a significant class of spinel ferrite materials exhibiting remarkable structural, spectral, and magnetic properties that make them invaluable for diverse technological applications. The strategic incorporation of transition metal dopants, particularly zinc (Zn) and cobalt (Co), has emerged as a powerful approach to engineer and optimize these intrinsic properties for specific applications ranging from magnetic storage to biomedical devices. This comprehensive review examines the extensive body of research focused on Zn and Co doped NiFe₂O₄ systems, providing critical analysis of synthesis methodologies, characterization techniques, and property modifications. The survey encompasses various synthesis routes including sol-gel, co-precipitation, hydrothermal, and combustion methods, while systematically analyzing their influence on crystallographic structure, lattice parameters, grain morphology, and magnetic behavior. Particular emphasis is placed on understanding how dopant concentration affects cation distribution between tetrahedral and octahedral sites, consequently influencing magnetic interactions and overall material performance. Spectroscopic investigations through X-ray diffraction, Fourier-transform infrared spectroscopy, and Raman spectroscopy reveal significant insights into structural modifications and bonding characteristics. The review critically evaluates magnetization mechanisms, coercivity variations, and saturation magnetization trends as functions of dopant composition. This meta-analysis identifies research gaps, discusses conflicting findings in existing literature, and proposes future directions for advancing fundamental understanding and practical applications of doped nickel ferrite systems.
Area: Department of Physics
Author: Priyanka Sinha1, Dr. Nitta Kumar Swamy2
DOI: MJAP/05/0052
Abstract:
This review paper presents a comprehensive meta-analysis of scholarly research examining the literary contributions of four prominent Bengali writers: Rabindranath Tagore, Bibhutibhushan Bandyopadhyay, Mahasweta Devi, and Buddhadeva Basu. Through systematic examination of existing literature, this study investigates the thematic preoccupations, narrative techniques, and cultural representations that characterize their works. The analysis reveals distinct yet interconnected literary trajectories shaped by historical contexts ranging from colonial Bengal to post-independence India. Tagore's universalist humanism, Bandyopadhyay's pastoral realism, Devi's activist documentation, and Basu's modernist experimentation represent four paradigmatic approaches to Bengali literature. This comparative framework identifies convergences in their treatment of social hierarchies, gender dynamics, and cultural identity while highlighting divergences in stylistic choices and ideological positioning. The meta-analysis synthesizes findings from literary criticism, cultural studies, and historical scholarship to construct a multidimensional understanding of Bengali literary evolution. This research contributes to comparative literature discourse by demonstrating how individual authorial voices collectively constitute a regional literary tradition while simultaneously engaging with global modernist movements. The findings have implications for understanding the relationship between literature and social transformation in colonial and postcolonial contexts.
Area: Department of English
Author: Mukul Thakur1, Dr. Padmalochan Rout2
DOI: MJAP/05/0051
Abstract:
Composite multiferroics represent a paradigm shift in functional materials research, combining multiple ferroic orders ferromagnetism, ferroelectricity, and ferroelasticity within a single material system to enable unprecedented magnetoelectric coupling effects. This comprehensive review examines the synthesis methodologies, characterization techniques, and fundamental properties of composite multiferroic materials through systematic meta-analysis of recent advancements in the field. The study critically evaluates various synthesis routes including solid-state reactions, sol-gel methods, chemical vapor deposition, and advanced thin-film fabrication techniques, correlating processing parameters with resultant material properties. Detailed characterization approaches encompassing structural, magnetic, electric, and magnetoelectric coupling measurements are systematically reviewed. The investigation reveals that composite multiferroics offer superior magnetoelectric coefficients compared to single-phase systems, with values reaching 10-1000 mV/cm•Oe depending on composition and microstructure. Critical analysis identifies key challenges including interface quality control, strain engineering, and phase purity optimization that significantly influence device performance. The review synthesizes emerging trends in nanostructured composites, core-shell architectures, and vertically aligned nanocomposite thin films, demonstrating their potential for next-generation spintronic devices, magnetic field sensors, energy harvesting systems, and multistate memory applications. This work provides researchers with a consolidated framework for understanding structure-property relationships in composite multiferroics and identifies promising directions for future investigation.
Area: Department of Physics
Author: Ashvan Kumar Sahu1, Dr. Nitta Kumar Swamy2
DOI: MJAP/05/0050