Abstract:
Stored pulses constitute a vital source of dietary protein across the developing world, yet their post-harvest value is severely compromised by infestation from bruchid beetles, principally Callosobruchus maculatus (Fabricius) and Callosobruchus chinensis (Linnaeus). This review synthesises the published literature on the developmental and infestation stages of these two species and critically appraises the spectrum of treatments developed to suppress them. Drawing on a structured survey of work spanning several decades, the paper examines the holometabolous life cycle egg, four larval instars, pupa and adult and clarifies the distinction between latent field infestation, in which oviposition begins on maturing pods, and the explosive multiplication that follows under storage conditions. The concealed feeding of larvae within the seed cotyledon, the production of characteristic circular emergence windows, and the consequent losses in seed weight, germinability and nutritional quality are documented in detail. The survey collates evidence on physical interventions such as solar heating, cold storage, hermetic and modified-atmosphere systems and irradiation; on chemical fumigation and insecticidal treatment; on botanical preparations including plant oils, inert seed coatings, leaf powders and essential oils; on biological control through hymenopteran parasitoids; and on host-plant resistance. A meta-analytic reading of reported efficacy reveals considerable heterogeneity attributable to dose, beetle species, seed type and the developmental stage targeted. Critical analysis exposes recurrent methodological weaknesses, including inconsistent reporting of stage-specific mortality and the frequent neglect of egg and pupal stages. The review concludes that durable management of Callosobruchus demands integrated, stage-aware strategies that combine non-chemical methods with the judicious use of safer compounds, and it identifies clear priorities for future research.
Area: Department of Zoology
Author: Gyanendra Pratap¹, Dr. Sumer Singh², Dr.B. S. Azad³
DOI: MJAP/05/1037
Abstract:
Performance appraisal systems (PAS) serve as cornerstone mechanisms in human resource management, linking organizational objectives with individual employee contributions. This empirical study investigates the intricate relationships among fairness perceptions, feedback quality, motivational outcomes, and overall employee performance within diverse organizational contexts. The study employed a quantitative research design, collecting primary data from 320 respondents across manufacturing, service, and information technology sectors using structured questionnaires. Statistical analyses including descriptive statistics, Pearson correlation, multiple regression analysis, and Analysis of Variance (ANOVA) were applied to examine the hypothesized relationships. Findings reveal that procedural fairness, distributive fairness, and interactional fairness collectively exert significant positive influences on employee performance (R² = 0.612). Feedback frequency and feedback quality demonstrated strong positive correlations with both intrinsic and extrinsic motivation (r = 0.71 and r = 0.68, respectively). Motivation emerged as a significant mediating variable between appraisal fairness and employee performance outcomes. Sector-specific variations in performance appraisal effectiveness were observed, with the IT sector reporting the highest satisfaction levels. The study concludes that organizations must design and implement appraisal systems that prioritize transparency, constructive feedback, and equitable reward mechanisms to foster sustained employee performance improvements. These findings carry substantial implications for HR practitioners, organizational leaders, and policymakers aiming to leverage performance appraisal as a strategic tool for workforce development and organizational excellence.
Area: Department of Management
Author: Dr V Suresh Pillai
DOI: MJAP/05/1036
Abstract:
Pre-engineered buildings PEBs are a unique construction methodology that provides structural efficiency, cost savings and fast-track delivery compared to traditional steel and reinforced concrete systems. This survey paper ergs a Literature Based Metanalysis of PEB Structural Analysis Design Optimization with respect to most advanced Computation Software Tools like STAAD Pro This area will not take big measure tools eg ETABS, ANSYS, SAP2000, Tekla Structural Designer Portal frame geometry, tapered section design, portal gable framing, behaviour of eave strut systems, optimal roof slope of freely supported buildings for self-weight or lateral loads resistance (or both), wind load distribution and seismic performance were; the systematic survey carried out among studies published from 2005 to 2024. The review shows that software based design workflows consistently achieve reductions in steel consumption ranging from 15–30 % above the quantities of steel that can be achieved, without significant loss of structural resistance when considering codified load combinations per IS 800, AISC 360 and Eurocode 3. Dynamic soil–structure interaction modelling, lifecycle performance assessment and the integration of Building Information Modelling (BIM) frameworks with structural optimization algorithms are critical gaps. We review issues with methodological inconsistencies across studies and outline standard benchmarking criteria to enhance clarity in future research. A meta-analysis of 30 studies shows a pooled reduction in steel weight of 18.6% (95% CI : 14.2–23.0%) with high between-study heterogeneity (I² = 74.3%), confirming the vital need for context specific optimization assessment. Results confirm that state-of-the-art software-enabled design may be regarded as the key driver for economical, structure-satisfying PEB systems in industrial, commercial and logistic applications.
Area: Department of Civil Engineering
Author: Vandana Amole¹, Prof. Sachin sironiya²
DOI: MJAP/05/1035
Abstract:
The construction industry in India remains one of the most hazardous sectors, accounting for a disproportionate share of workplace fatalities, injuries, and occupational diseases. Residential construction sites, particularly in semi-urban and rural districts of Madhya Pradesh, are characterized by informal labor arrangements, minimal regulatory oversight, and a near-complete absence of formal safety training. This review meta-analysis examines the published literature pertaining to occupational health risks, safety management systems, and labor awareness levels at construction sites, with a focused contextual application to the Raisen District of Madhya Pradesh. Drawing on a systematic aggregation of empirical findings from Indian and global studies published between 2000 and 2024, the paper synthesizes evidence on the prevalence of musculoskeletal disorders, respiratory hazards, fall-related injuries, heat stress, and psychosocial stressors among construction workers. The review further evaluates the effectiveness of existing statutory frameworks, including the Building and Other Construction Workers (Regulation of Employment and Conditions of Service) Act, 1996, the Factories Act, 1948, and the National Building Code of India, in operationalizing safety compliance at the ground level. Critical analysis reveals systemic gaps in implementation, worker awareness, contractor accountability, and government enforcement mechanisms.
Area: Department of Civil Engineering
Author: Muskan Kushwaha¹, Dr. Bikram Prasad²
DOI: MJAP/05/1034
Abstract:
Modern power grids are now equipped with Pharos Measurement Units (PMUs) sensors that provide real-time, high-resolution measurements of transmission line behaviour like never before. This paper discusses an extensive empirical analysis of artificial intelligence powered Fault detection and classification frameworks using real time PMU data streams from 400 kV and 220 kV transmission corridors in the Indian power grid. A hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) networks, and a Random Forest classifier-based fault detection system is proposed in which overall detection of accuracy of 98.76% and classification of the accuracy of 97.43% are achieved against ten random selected fault categories SLG, LL, DLG and all three-phase (3P) faults classifiers. We used a dataset of 52,800 fault event records from fourteen substations which have been monitored via PMU in a given area over a window of twenty-four months. Model robustness and generalizability over the conclusions were confirmed with statistical validation using one-way ANOVA (F = 142.67, p
Area: Department of Electrical Engineering
Author: Pooja Tirpathi¹, Raghunandan Singh Baghel²
DOI: MJAP/05/1033
Abstract:
Modern power grids are now equipped with Pharos Measurement Units (PMUs) sensors that provide real-time, high-resolution measurements of transmission line behaviour like never before. This paper discusses an extensive empirical analysis of artificial intelligence powered Fault detection and classification frameworks using real time PMU data streams from 400 kV and 220 kV transmission corridors in the Indian power grid. A hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) networks, and a Random Forest classifier-based fault detection system is proposed in which overall detection of accuracy of 98.76% and classification of the accuracy of 97.43% are achieved against ten random selected fault categories SLG, LL, DLG and all three-phase (3P) faults classifiers. We used a dataset of 52,800 fault event records from fourteen substations which have been monitored via PMU in a given area over a window of twenty-four months. Model robustness and generalizability over the conclusions were confirmed with statistical validation using one-way ANOVA (F = 142.67, p
Area: Department of Electrical Engineering
Author: Pooja Tirpathi¹, Raghunandan Singh Baghel²
DOI: MJAP/05/1032
Abstract:
Modern power distribution networks have seen the widespread deployment of smart meters, resulting in an unprecedented level of operational visibility and also significant cyber security vulnerabilities. The smart grid infrastructure hinges on advanced metering infrastructure (AMI) that constantly gathers, transmits and processes high-frequency consumption data meaning there is an immense attack surface vulnerable to energy theft, false-data injection, denial-of-service attacks and even meter tampering. Machine Learning (ML) and artificial intelligence (AI)-based techniques have proven to be the best-suited paradigms for detecting anomalies in smart meter data as they can model complex, non-linear consumption patterns while adapting to changes in threat factors without any explicit rule-based programming. In this reviews paper, we provide the most comprehensive meta-analysis of literature for AI enabled anomaly detection frameworks that have deployed within smart grid cybersecurity contexts. This work synthesizes results from thirty peer-reviewed studies published from 2015 to 2024, analyzing the most effective deep learning architectures, ensemble methods, federated learning approaches and hybrid AI models applied to smart meter anomaly detection. The review categorizes four major threats energy theft, false data injection attacks (FDIA), meter malfunction, and communication layer intrusions and analyzes existing detection algorithms in terms of their performance for each threat. We characterise them on several important performance metrics, including detection accuracy, false positive rates, computational overhead and scalability. The analysis indicates that deep learning models, especially long short-term memory (LSTM) networks and convolutional neural networks (CNN), perform better than traditional machine-learning techniques on controlled benchmarks with over 97% detection accuracy achieved, whereas federated AI and privacy-preserving AI architectures are emergi
Area: Department of Electrical Engineering
Author: Akanksha Bulbake¹, Raghunandan Singh Baghel²
DOI: MJAP/05/1031
Abstract:
The advanced power systems trends, which are becoming increasingly complex and scale in nature, have made it necessary to develop automation frameworks with high-performance and confidence for real-time monitoring, control, and protection. The current work is an empirical investigation of the use, performance and effect of automation enabled technology, namely Supervisory Control and Data Acquisition (SCADA), Phasor Measurement Units (PMU), Intelligent Electronic Devices (IED), Energy Management Systems (EMS), and AI/ML algorithms in contemporary electrical power infrastructure. We collected data over a time a period from 2018–2023 from six geographically distributed power system zones across three continents. Through statistical analysis like ANOVA, regression modeling and comparative benchmarking, integrated automation shows improved performance in fault detection time (mean=42 ms) and increased reliability of the system (SAIDI reduction=45.8%) with up to 56.4% lower technical energy losses compared to conventional systems. The paper also benchmarks these results against existing work to validate performance improvements.
Area: Department of Electrical Engineering
Author: Nainee Adav¹, Raghunandan Singh Baghel²
DOI: MJAP/05/1030
Abstract:
Solar energy forecasting has emerged as a critical technology for enabling grid integration of renewable power sources and optimizing demand-supply management in smart grids. This review synthesizes contemporary advances in machine learning methodologies applied to solar irradiance prediction and photovoltaic power generation forecasting across temporal horizons ranging from very-short-term (minutes) to seasonal scales. We examine supervised learning paradigms including support vector machines, neural networks, ensemble methods, and deep learning architectures, alongside emerging graph neural networks and transformer-based approaches. The review evaluates data integration strategies incorporating satellite imagery, weather variables, and temporal features, while critically analyzing forecasting accuracy metrics and model evaluation frameworks. Recent advances demonstrate that hybrid ensemble methods combining multiple algorithms achieve mean absolute percentage errors below 15% for hour-ahead forecasting in favorable atmospheric conditions. However, significant challenges persist in handling cloud transience effects, rare extreme weather events, and transferability across geographic locations with dissimilar solar climatologies. This paper identifies research gaps in explainable artificial intelligence for forecasting models, uncertainty quantification methodologies, and cost-benefit analyses for large-scale deployment. Future directions emphasize physics-informed neural networks, federated learning for distributed data, and integration with grid-scale energy storage optimization frameworks to maximize renewable energy penetration in global electrical networks.
Area: Department of Electrical Engineering
Author: Pushpendra Joshi¹, Raghunandan Singh Baghel²
DOI: MJAP/05/1029
Abstract:
Corruption continues to be one of the most stubborn barriers to good governance, sustainable economic development and public trust in authorities worldwide, with traditional administrative, legal and audit-based mechanisms having repeatedly shown they have limited ability to detect and address it in real-time [1]. The domain of artificial intelligence (AI) has recently become the latest crosscutting tool, spanning aspects from computer science to public administration as well as law and behavioural economics. This research studies the application of artificial intelligence tools such as machine learning, natural language processing, anomaly detection and predictive analytics for detecting anomalies in procurement, tax collection, public service deliverance and monetary transaction [3]. The study utilizes a multiple methods approach, including a structured survey questionnaire with government officials, IT professionals and citizens as well as secondary data analysis of e-governance and AI-based anti-corruption initiatives carried out in different jurisdictions[4]. We then created five analytical tables (awareness levels, perceived effectiveness, sectoral applicability, barriers to adoption and trust in AI driven systems) summarizing the results of each area presented followed by an individual interpretation. The results show that even if AI improves transparency and make discretionary decision-making less, its potential is limited due to the availability and quality of data, lack of technical capacity at the implementation stage, algorithmic bias and low strength laws on accountability in emerging technologies [5].
Area: Department of Computer Science & Engineering
Author: Indrani Sarkar1, Mohan Kumar Patel2
DOI: MJAP/05/1028
Abstract:
Hydraulic structures such as barrages, weirs, and diversion dams are critical components of water resource infrastructure, and their long-term performance depends on both structural integrity and resistance to hydraulic failure mechanisms such as piping and uplift pressure. This paper presents an integrated approach combining Khosla's theory of independent variables for seepage and uplift pressure analysis with finite element-based structural assessment using ANSYS, supplemented by a Life Cycle Assessment (LCA) framework to evaluate the sustainability of hydraulic infrastructure over its service life. The study examines exit gradient, uplift pressure distribution, and floor thickness requirements using Khosla's method, while ANSYS simulations provide stress, deformation, and factor-of-safety data under varying hydraulic loading conditions [1][2]. Five analytical datasets are presented covering uplift pressure variation, exit gradient safety factors, floor thickness optimization, ANSYS stress-strain results, and embodied carbon/LCA indicators. Results indicate that combining classical hydraulic design theory with modern computational structural analysis produces more reliable and materially efficient designs than either method used independently. The paper concludes that integrating Khosla's analytical rigor with ANSYS-based structural verification and LCA-driven material optimization offers a comprehensive pathway toward sustainable, durable, and economically viable hydraulic infrastructure.
Area: Department of Civil Engineering
Author: Siddharath Patel1, Sakshi Sahu2
DOI: MJAP/05/1027
Abstract:
The escalating energy consumption associated with modern high-rise buildings presents a critical challenge in the context of global climate change and rapid urbanization. This review paper undertakes a comprehensive meta-analysis of existing literature pertaining to climate-responsive design strategies employed in high-rise buildings across diverse climatic zones, encompassing tropical humid, arid, temperate, and cold climates. The study synthesizes empirical findings from peer-reviewed research, case studies, and simulation-based investigations to evaluate the energy mitigation potential of passive and active design strategies including envelope optimization, natural ventilation, solar shading, green roofs, double-skin facades, and building-integrated renewable energy systems. The comparative analysis reveals substantial disparities in energy performance outcomes contingent upon climatic context, building morphology, and material selection. Studies indicate that climate-responsive passive design strategies alone can reduce building energy consumption by 20–45% relative to conventional design baselines, with hybrid passive-active systems achieving reductions exceeding 60% in certain climatic conditions. The paper further critically examines the methodological approaches adopted in prior work, identifies recurring gaps in long-term performance validation, and highlights the insufficiency of cross-climatic comparative frameworks. Findings underscore the necessity of integrating microclimate analysis, occupant behaviour modelling, and computational fluid dynamics into the design decision-making process. This review contributes to the body of knowledge by offering a structured taxonomy of climate-responsive strategies and their efficacy across varied climatic contexts, thereby informing architects, engineers, and policymakers in the pursuit of sustainable high-rise building design.
Area: Department of Civil Engineering
Author: Reeta Sharnagat1, Shrishti Verma2
DOI: MJAP/05/1026
Abstract:
Sustainable anicut structures represent critical hydraulic infrastructure components designed to manage water flow, facilitate irrigation, and support environmental conservation in arid and semi-arid regions. This empirical study focuses on the comprehensive design and structural analysis of anicut structures utilizing advanced finite element modeling through STAAD.Pro software. The research investigates the performance characteristics of sustainable anicut configurations under varying hydrological and geological conditions. Through detailed computational analysis, the study evaluates structural behavior under different load scenarios, material properties, and environmental stressors. The findings demonstrate that optimized anicut designs incorporating sustainable materials and principles can achieve a 34% reduction in material consumption while maintaining structural integrity. Data-driven analysis reveals significant correlations between foundation soil bearing capacity and structural stability coefficients. The implementation of green construction methodologies further reduces environmental impact by 28% compared to conventional approaches. This research contributes to the advancement of sustainable water management infrastructure by providing quantitative evidence supporting the adoption of STAAD.Pro modeling for anicut design optimization, cost reduction, and environmental sustainability. The outcomes facilitate improved decision-making processes for engineers and water resource managers implementing modern sustainable infrastructure solutions across developing regions.
Area: Department of Civil Engineering
Author: Ramcharan Banshe1, Durgesh Sahu2
DOI: MJAP/05/1025
Abstract:
The accelerating global transition toward renewable energy sources has brought smart grid-connected inverters to the forefront of power electronics research. This paper presents a comprehensive review and meta-analysis of the design strategies, control architectures, and operational challenges associated with grid-connected smart inverters deployed in photovoltaic (PV), wind energy, and hybrid renewable energy systems. The review systematically examines literature published between 2010 and 2024, synthesizing findings from over 120 peer-reviewed studies to identify dominant design paradigms, prevailing control methodologies, and emerging technologies. Key aspects investigated include pulse width modulation (PWM) techniques, model predictive control (MPC), droop control, virtual synchronous generator (VSG) concepts, and artificial intelligence-based adaptive control schemes. The meta-analysis reveals a progressive shift from conventional PI-based control toward advanced predictive and learning-based algorithms, driven by the need for improved dynamic response, harmonic distortion reduction, and enhanced grid stability. Power quality compliance with IEEE 1547 and IEC 61727 standards emerges as a persistent design constraint across all reviewed works. The paper further identifies critical research gaps in fault ride-through capability, multi-objective optimization, and real-time digital twin integration for inverter management systems. Findings indicate that hybrid control frameworks combining MPC with machine learning exhibit the highest potential for future smart inverter deployment in high-penetration renewable energy grids. This review provides a structured foundation for researchers, engineers, and policymakers engaged in the design, standardization, and deployment of next-generation inverter technologies.
Area: Department of Electrical and Electronics Engineering
Author: Preeti Sharma1, Shailesh M. Deshmukh2
DOI: MJAP/05/1024
Abstract:
This empirical study investigates the structural, economic, and sustainability implications of incorporating Lightweight High-Strength Concrete (LWHSC) into residential building design across urban and semi-urban construction contexts. As contemporary housing demands increasingly prioritize cost efficiency, environmental sustainability, and structural resilience, LWHSC presents a compelling alternative to conventional Normal Weight Concrete (NWC). The study employs a mixed-methods empirical framework involving laboratory-based mechanical testing, field surveys across 12 residential project sites, and statistical analysis of 180 validated data samples. Key performance indicators examined include compressive strength (ranging from 42–68 MPa), unit weight (1,450–1,850 kg/m³), thermal conductivity, load-bearing efficiency, and life-cycle cost metrics. Results demonstrate that LWHSC achieves an average 28.6% reduction in dead load, a 21.4% improvement in thermal resistance, and a 14.7% reduction in total construction cost over conventional systems across a 30-year life cycle. Statistical analysis using ANOVA (F = 18.43, p < 0.001) confirms highly significant differences in structural efficiency between LWHSC and NWC mixes. The findings establish strong empirical support for LWHSC adoption in residential construction, particularly for multi-storey low-to-mid-rise structures. These results align with global sustainable construction goals, offering a technically sound and economically viable pathway for next-generation residential design.
Area: Department of Civil Engineering
Author: Dhaneshwar1, Sakshi Sahu2
DOI: MJAP/05/1023
Abstract:
The application of Building Information Modeling (BIM) has fundamentally transformed the way multi-story buildings are designed, coordinated, and analyzed across the global Architecture, Engineering, and Construction (AEC) industry. This empirical study presents a comprehensive BIM-based framework developed and validated through its application on five real-world multi-story building projects ranging from G+4 residential structures to G+22 high-rise complexes. The framework integrates BIM Level 2 and Level 3 maturity tools to address critical challenges in structural coordination, MEP (Mechanical, Electrical, and Plumbing) clash detection, cost estimation accuracy, and project schedule adherence. Data were collected from 85 respondents including architects, structural engineers, MEP consultants, project managers, and contractors, supplemented by quantitative project performance records across all case study sites. Findings reveal that BIM Level 3 implementation achieved clash detection accuracy of up to 94.8%, reduced cost variance to as low as 3.2%, and decreased Requests for Information (RFIs) by up to 71.2% compared to conventional design workflows. MEP coordination using BIM led to an 85.2% overall reduction in pre-construction clashes, translating to a combined cost saving of USD 127,100 across a representative project. Statistical analysis confirms significant improvements in structural redundancy, load path efficiency, and material optimization when BIM is integrated at the design stage. These results substantiate the efficacy of the proposed framework and its scalability for high-density urban construction environments. The study contributes an evidence-based, replicable BIM deployment model that aligns with international standards including IFC, ISO 19650, and LOD 300–400 specifications, offering actionable guidance for practitioners and policy-makers in the construction domain.
Area: Department of Civil Engineering
Author: Dev Lal Sahu1, Himanshi Meshram2
DOI: MJAP/05/1022
Abstract:
This study presents a comprehensive finite element analysis (FEA) framework for smart structures integrated with piezoelectric sensors and actuators, aimed at enabling real-time structural health monitoring (SHM) using the ABAQUS simulation environment. Smart structures incorporating piezoelectric transducers such as PZT-5A and PVDF films have emerged as highly viable candidates for continuous damage detection in aerospace, civil, and mechanical engineering applications. The present investigation employs ABAQUS/Standard and ABAQUS/Explicit solvers to simulate Lamb wave propagation, modal frequency shifts, strain energy density distributions, and electromechanical coupling responses across five distinct damage scenarios on aluminium alloy and carbon fibre reinforced polymer (CFRP) substrates. A mesh convergence study involving five levels of mesh refinement from 1,200 to 52,000 elements confirmed numerical stability at a mesh density of 14,200 elements using C3D20R formulation. Damage indices (DI) derived from time-of-flight shifts, amplitude attenuation, and reflection coefficients demonstrated a statistically consistent classification accuracy ranging from 88.7% for critical damage to 99.2% for intact structural states. The FEA-predicted piezoelectric voltage outputs correlated strongly with experimental bench data, yielding R² values between 0.976 and 0.994 across all load cases, with percentage errors not exceeding 5.6%. The findings validate ABAQUS as a robust simulation platform for SHM-oriented smart structure design, and the proposed damage index classification framework offers a reliable, low-cost pre-experimental tool for sensor placement optimisation and damage severity assessment.
Area: Department of Civil Engineering
Author: Deepak Kumar Ratre1, Shrishti Verma2
DOI: MJAP/05/1021
Abstract:
The sponge iron or Direct Reduced Iron (DRI) industry has emerged as a critical alternative to conventional blast furnace iron production, particularly in countries with limited coking coal reserves [1]. This research paper presents a comprehensive design and structural analysis of a Sponge Iron (DRI) plant using AutoCAD for architectural and layout drafting and STAAD-Pro for structural analysis of steel framed industrial structures. The study focuses on the design considerations for kiln supporting structures, cooler foundations, raw material handling sheds, and ancillary buildings that constitute a typical DRI plant. Loads such as dead load, live load, wind load, seismic load, and crane load were applied as per IS 875 (Part 1, 2, 3) and IS 1893 guidelines [2][3]. The structural members were analyzed for axial force, bending moment, shear force, and deflection, and the results were checked against permissible limits as per IS 800:2007 [4]. The AutoCAD drawings provided detailed plant layout, equipment positioning, and clearances required for operation and maintenance, while STAAD-Pro provided accurate analysis of steel and RCC members under various load combinations. Five analysis tables summarizing nodal displacements, member forces, support reactions, steel section utilization ratios, and load combination results are presented and discussed. The results indicate that the proposed structural system is safe, economical, and within serviceability limits. The integration of CAD-based layout planning with structural analysis software significantly reduces design time, minimizes errors, and improves coordination between civil, structural, and process engineering disciplines. The paper concludes that a combined AutoCAD–STAAD-Pro workflow is highly effective for the design of industrial DRI plants and can be extended to other heavy industrial facilities.
Area: Department of Civil Engineering
Author: Ananya Chatterjee1, Shree Ram Malani2
DOI: MJAP/05/1020
Abstract:
Ultra-High-Performance Concrete (UHPC) has emerged as a transformative material in modern structural engineering, distinguished by its exceptional compressive strength (exceeding 150 MPa), remarkable ductility, and superior durability characteristics compared to conventional concrete. This review paper presents a comprehensive meta-analysis of existing experimental, analytical, and numerical studies focusing on the structural behavior of UHPC members subjected to combined loading conditions, including flexure-shear, axial-flexure, torsion-flexure, and multi-axial stress states. A systematic examination of over sixty peer-reviewed investigations conducted between 2000 and 2024 reveals consistent trends in failure mode transitions, interaction surface formulations, and ductility enhancement mechanisms attributable to the steel fiber-reinforced microstructure of UHPC. The meta-analysis consolidates data across beam, column, and slab elements to identify critical parameters governing combined load response, including fiber volume fraction, matrix composition, reinforcement ratio, and slenderness effects. Significant discrepancies between current design code provisions and experimentally observed capacities are identified, particularly for shear-dominant combined loading scenarios. The review critically evaluates the adequacy of existing analytical frameworks and proposes directions for more robust interaction surface models. Findings indicate that UHPC members under combined loading exhibit markedly different failure mechanisms than those observed in normal-strength concrete, necessitating revised design philosophies. This paper provides a consolidated reference for researchers and practicing engineers and establishes a foundation for future experimental programs and codification efforts in UHPC structural design.
Area: Department of Structural Engineering
Author: Abhishek Kashyap1, Mr. Vivek Shukla2, Dr Jyoti Yadav3
DOI: MJAP/05/1019
Abstract:
Coastal building construction faces severe challenges due to persistent groundwater seepage, driven by tidal fluctuations, hydrostatic pressure, and saline soil conditions that accelerate structural degradation. Conventional waterproofing materials such as bituminous membranes and cementitious coatings have demonstrated limited durability under the dynamic conditions prevailing in marine and estuarine environments. Polyurethane (PU) technology has emerged as a highly effective solution for groundwater seepage prevention owing to its exceptional elasticity, adhesion to diverse substrates, chemical resistance to saline water, and long-term impermeability. This review paper synthesizes existing literature on the application of polyurethane-based systems including spray-applied coatings, injection grouts, foam sealants, and hybrid composites in coastal construction scenarios. A meta-analysis of past experimental and field studies reveals consistent performance advantages of PU systems over conventional alternatives in terms of crack-bridging capacity, bond strength, and resistance to hydrostatic pressure. The paper critically evaluates methodological approaches, material formulations, and performance metrics employed across the reviewed studies. Findings indicate that two-component PU injection systems achieve pore closure efficiencies exceeding 95% in fractured substrates, while spray-applied membranes sustain waterproofing integrity for over 20 years under saline exposure. Research gaps concerning long-term environmental impact, bio-fouling, and temperature-driven degradation in tropical coastal zones are identified. This review provides engineers, architects, and construction professionals with a comprehensive evidence base for selecting and deploying polyurethane waterproofing technologies in coastal infrastructure projects.
Area: Department of Physics
Author: Rajendra Kasturi Pai1, Dr. Avdesh Kumar Sharma2
DOI: MJAP/05/1018
Abstract:
Road traffic accidents represent one of the most critical public safety challenges worldwide, causing millions of fatalities and injuries annually. The increasing availability of large-scale accident datasets, combined with rapid advances in machine learning (ML) and artificial intelligence (AI), has catalyzed a surge of research efforts aimed at understanding, predicting, and mitigating road accident occurrences. This review paper presents a systematic and comprehensive meta-analysis of past work on road accident analysis using machine learning techniques, synthesizing findings from over three decades of published literature across diverse geographic, environmental, and technical contexts. The paper examines the evolution of ML approaches applied to accident prediction, severity classification, hotspot detection, causal factor identification, and post-accident response optimization. Key algorithms reviewed include decision trees, random forests, support vector machines, artificial neural networks, gradient boosting methods, deep learning architectures, and ensemble models. The study critically evaluates dataset sources, feature engineering strategies, model evaluation metrics, and the translational impact of findings on traffic policy. Findings reveal that ensemble methods and deep learning models consistently outperform traditional statistical approaches, achieving accuracy rates of 80 to 96 percent across various sub-tasks. However, significant gaps remain in model interpretability, real-time deployment, cross-regional generalizability, and integration with intelligent transportation systems. This paper provides a structured synthesis of existing knowledge and highlights priority research directions for future work in ML-driven road safety analysis.
Area: Department of Computer Science and Engineering
Author: Deepali1, Dr. Niresh Sharma2
DOI: MJAP/05/1017
Abstract:
Road traffic accidents represent one of the most critical public safety challenges worldwide, causing millions of fatalities and injuries annually. The increasing availability of large-scale accident datasets, combined with rapid advances in machine learning (ML) and artificial intelligence (AI), has catalyzed a surge of research efforts aimed at understanding, predicting, and mitigating road accident occurrences. This review paper presents a systematic and comprehensive meta-analysis of past work on road accident analysis using machine learning techniques, synthesizing findings from over three decades of published literature across diverse geographic, environmental, and technical contexts. The paper examines the evolution of ML approaches applied to accident prediction, severity classification, hotspot detection, causal factor identification, and post-accident response optimization. Key algorithms reviewed include decision trees, random forests, support vector machines, artificial neural networks, gradient boosting methods, deep learning architectures, and ensemble models. The study critically evaluates dataset sources, feature engineering strategies, model evaluation metrics, and the translational impact of findings on traffic policy. Findings reveal that ensemble methods and deep learning models consistently outperform traditional statistical approaches, achieving accuracy rates of 80 to 96 percent across various sub-tasks. However, significant gaps remain in model interpretability, real-time deployment, cross-regional generalizability, and integration with intelligent transportation systems. This paper provides a structured synthesis of existing knowledge and highlights priority research directions for future work in ML-driven road safety analysis.
Area: Department of Computer Science and Engineering
Author: Deepali1, Dr. Niresh Sharma2
DOI: MJAP/05/1017
Abstract:
The rapid proliferation of cyber threats and sophisticated attack vectors in modern networked environments has necessitated the development of intelligent, adaptive defense mechanisms beyond the capabilities of traditional rule-based security systems. Machine learning (ML) and deep learning (DL) have emerged as transformative paradigms in cybersecurity, offering unprecedented capacities for threat detection, anomaly identification, malware classification, and intrusion prevention. This review paper presents a comprehensive meta-analysis of the existing literature on the application of ML and DL techniques in cybersecurity domains published over the past decade. Through systematic analysis of over 150 peer-reviewed studies, the paper synthesizes key findings across sub-domains including network intrusion detection systems (NIDS), malware analysis, phishing detection, vulnerability assessment, and adversarial robustness. The review critically evaluates the performance of widely adopted algorithms such as Random Forest, Support Vector Machines, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), and Generative Adversarial Networks (GAN) across benchmark datasets including NSL-KDD, CICIDS2017, UNSW-NB15, and DREBIN. Methodological gaps, class imbalance issues, adversarial vulnerabilities, and real-world deployment challenges are identified.
Area: Department of Computer Science and Engineering
Author: Deepa1 , Dr. Niresh Sharma2
DOI: MJAP/05/1016
Abstract:
While the banking sector has always been a pioneer in terms of technology, the transformation in employee development and performance management systems as a result of digitization is unprecedented. This meta-analysis summarizes the existing literature over the past 10 years to aggregate meaningful insights about how digital platforms, artificial intelligence, machine learning, and analytics influence employee skill, employee engagement, and organizational and performance outcomes. By systematically reviewing 30 peer-reviewed studies and research papers, this paper unveils critical success factors for digital transformation in banking in terms of technology adoption, training, and performance measures. The results show that digital technologies significantly improve employee skills in the presence of solid training infrastructure and change management. But slow uptake of this tech stems from challenges faced in implementation including resistance to change, skill gaps and cybersecurity fears. In conclusion, the meta-analysis leads to the assertion that a holistic meta approach of pariter digital technologies into the economy must integrate technological infrastructure, development of complementary organizational culture and creation of the continuous learning ecosystems. This study adds to the understanding of the routes through which digital technologies convert into enhanced employee performance and firm performance in banking.
Area: Department of Commerce
Author: Siddhartha Chatterjee1, Dr. Aiman Fatma2
DOI: MJAP/05/1015
Abstract:
Accurate wind speed forecasting remains one of the most intractable challenges in renewable energy integration and atmospheric science. This paper introduces WuWeiDL, a novel hybrid deep learning architecture that synthesizes classical Chinese Daoist philosophy—specifically the concept of wu wei (non-forcing action) and feng-qi (wind-energy dynamics)—into a principled algorithmic framework for wind speed prediction at multiple horizons (1 h to 48 h). Inspired by Liezi's mythological "riding the wind" and the philosophy of effortless alignment with natural flow, WuWeiDL incorporates a Wu Wei Gating (WWG) module that adaptively suppresses over-parameterised attention in turbulent regimes, a Feng-Qi Turbulence Encoder (FQTE) that maps atmospheric boundary layer dynamics into latent embeddings, a bidirectional LSTM (BiLSTM) for capturing long-range temporal dependencies, and a transformer-based self-attention decoder. Evaluated on five benchmark wind farm datasets (inner Mongolia steppe, Danish North Sea, Texas Panhandle, Arabian Peninsula, and Tibetan Plateau), WuWeiDL achieves root-mean-square errors (RMSE) of 0.312–0.921 m/s across forecast horizons, improving upon state-of-the-art baselines by 20.4–46.8%. Ablation studies confirm synergistic contributions from all four components, with the Wu Wei Gate alone contributing a 19.8% RMSE reduction in high-turbulence conditions. The framework is openly interpretable via attention visualisations and offers a fresh epistemological bridge between Eastern philosophy of natural harmony and Western data-driven modelling, suggesting that "yielding to the wind's nature" rather than forcing predictions may be the key to ultra-accurate wind forecasting.
Area: Department of Computer Science and Engineering
Author: Er. Rishabh Aryan¹ , Manimozhi I²
DOI: MJAP/05/1014
Abstract:
Human–wildlife conflict (HWC) is an increasingly pressing concern along the forest–farmland interface of the Terai region, where dense protected areas adjoin densely settled agrarian communities. While the ecological and economic dimensions of crop raiding and livestock loss are widely documented, the gendered distribution of conflict exposure has received comparatively little attention. This paper examines the specific roles played by women in two everyday, conflict-prone activities—night-time crop protection and the collection of water, fuelwood and fodder—and argues that the prevailing socio-cultural division of labour systematically channels women into the spatial and temporal zones where encounters with wild animals are most likely. Drawing on a synthesis of secondary literature, regional conflict records and field observation in the Balrampur–Suhelwa landscape of the Indo-Nepal Terai, the study develops a conceptual framework of gendered exposure pathways and presents an indicative, multidimensional profile of the burdens women bear. The findings indicate that women are disproportionately responsible for water and fuelwood collection (about four-fifths of household effort) and contribute substantially to crop vigils, yet are markedly under-represented in conflict reporting and compensation processes. Beyond physical injury, women face acute time poverty, restricted mobility, psychological stress and hidden economic costs that conventional damage assessments overlook. The paper concludes that gender-responsive mitigation safer water access, lighting and escort arrangements, inclusive compensation, and the participation of women in conflict-management institutions is essential for any equitable and durable approach to human–wildlife coexistence in the Terai.
Area: Department of Geography
Author: Vipul Kumar Mishra
DOI: MJAP/05/1011
Abstract:
Over the last two decades, technological transformation has resulted in a massively enriched supply of Unmanned Aerial Vehicles (UAVs); structural configuration is being hierarchically identified as the most decisive factor in determining both operational capability and mission efficiency. This review and meta-analysis studies the very basic theoretical design, development and structural analysis of the Delta shaped UAV, a special type of configuration that has been gaining increased attention for its unique aerodynamic and structural advantages [4]. The effective performance of delta wing configuration based on -specific changes to geometry, materials, and their portrayal across wind tunnel experiments is consolidated through systematic synthesis of 30 peer-reviewed sources across structural design methodologies, computational analysis methods, and experimental validation campaigns. The delta plan form, characterized by its characteristic swept-wing geometry and fuselage-integrated configuration, offers outstanding maneuverability and stability compared to more conventional UAV layouts. Higher aerodynamic efficiency is also achieved through specific surface treatments and stiffened structures, allowing for modifications. Literature survey documents improvements in lift to drag ratio of 15–30%, structural weight reductions of 20–25% and measurable improvements in maneuverability indices [5]. In this review, the most significant gaps in the literature is also identified and it includes limited research on fatigue behavior of composite structures under varying cyclic load and thermal stress distribution under extreme operating conditions and multi-objective optimization algorithms on FDM parts. So, what does the study mean for the future of delta UAVs Emerging technologies such as additive manufacturing, automated fiber placement, physics-informed neural networks, and structural health monitoring are identified as key enablers to help facilitate the next genera
Area: Department of Aeronautical Engineering
Author: Sakharam Chouhan 1, Dr. Vishwjeet Ambade2, Dr. Arepally Shushrutha3
DOI: MJAP/05/1010
Abstract:
This work provides the structural optimization, design and high-fidelity parametric assessment of a new low-volume, high-power-density DC-DC resonant power converter specifically designed for future electric vehicle (EV) battery charging infrastructure. Facing the pressing requirements for compactness and thermal efficiency in fast-charging topologies, this work explores a silicon carbide (SIC) based P–I coupled interleaved full-bridge LLC resonant topology with an elevated switching frequency of 500 kHz. Weconducteda multi-parametric empirical study along a range of loading conditions (10% to 110% of the nominal36 kW output rating) and an input DC-link voltage between 400 V and 800 V in order to characterization experimental boundaries of efficiency profiles. In order to achieve this, raw operational data comprised of switching transitions, magnetizing loop dynamics, core losses, and synchronous rectification parameters were carefully logged and collated over various stages. The experimental results analyzed exhibit a maximum conversion efficiency of 97.85% at full-load allowing the proposed system to give an impressive volumetric power density of 2.14 kW/L making it a considerable benchmark improvement over traditional silicon based systems. Mathematical formulations and statistical regression analyses give us confidence that high-frequency switching transitions are able to achieve ZVS across the full operating envelope without incurring thermal or electromagnetic interferences that would be prohibitive. This work provides a repeatable data-driven framework to church-tune the core geometries and bridge-switching paradigms, showing how wide-bandgap co-integration with efficient planar magnetics meets the promises of ultra-compact pervasive infrastructure for sustainable automotive-based transportation.
Area: Department of Electrical Engineering
Author: Sulochana Gendre¹, Dr. Mithilesh Singh²
DOI: MJAP/05/1009
Abstract:
The power distribution systems are the key final link to provide end-users with access to the electrical grid, making their operational reliability one of the primary drivers influencing global power quality and customer satisfaction. This review paper tells a comprehensive higher-fidelity meta An analysis of the historical work associated with systematic normalization of distribution network reliability indices. This study evaluates and classifies the previous success of various grid improvement approaches by synthesizing both empirical datasets and architectural frameworks from thirty foundational studies printed over the previous two decades. In particular, the analysis focuses on key performance indicators such as System Average Interruption Frequency Index (SAIFI), System Average Interruption Duration Index (SAIDI) and Momentary Interruption Frequency Index (MAIFI). Historical literature, you can be categorized in the field of three main types of technical intervention: optimal positioning of automatic transformers switches, integration of distribution price generator and network reconfiguration strategies. This paper makes a statistical synthesis of case study outcomes to identify historical performance correlations, demonstrating that the highest levels of incremental reduction in SAIDI and SAIFI, with an average mitigation between 35% and 52%, corresponded to hybrid topologies with automated sectionalizing links deployed along with decentralized DG resources. Moreover, this review methodologically dissects the predictive frameworks developed previously by inspecting their evolution from analytical models to state-of-the-art meta-heuristic optimization algorithms and deep learning architectures. This work reviews the methodological pitfalls highlighted in earlier research, including insufficient modeling of stochastic operational parameters and the omission of high-impact, low-probability (HILP) weather disturbances. In conclusion this systematic review provi
Area: Department of Electrical Engineering
Author: Punam Gendre¹, Dr. Mithilesh Singh²
DOI: MJAP/05/1008
Abstract:
The digital era has fundamentally transformed pedagogical practices globally, forcing a shift from traditional face-to-face instruction to flexible, technology-driven approaches. Among these, the hybrid learning model—combining physical classroom interaction with asynchronous and synchronous online learning—has emerged as a core standard in higher education. This research paper evaluates the effectiveness of hybrid learning models specifically concerning English language comprehension and communication skills among university-level students. Through a structured review of ten authentic academic studies and an analysis of current pedagogical practices, this study examines how blended environments affect reading comprehension, listening skills, spoken fluency, and written communication. The findings indicate that while hybrid models significantly boost self-paced vocabulary acquisition and multi-modal comprehension, they present challenges regarding authentic conversational spontaneity and digital equity. The paper concludes with actionable recommendations for curriculum designers to optimize hybrid English language delivery.
Area: Department of Education
Author: Dr. Kanchan Jain
DOI: MJAP/05/1007
Abstract:
It is the surface integrity the total of surface roughness, microhardness, residual stress, and wear resistance that largely controls the in-service performance of finished aluminum aerospace components rather than any single response as previously considered in isolation. This study is a comparison of surface integrity of three commercial aluminum alloys when roller burnished with five nanofluid blends. These include: four binary formulations (Al₂O₃–CuO [75:25(c); 25:75(wt.%)], Al₂O₃–graphene [60:40(c); 40:60(wt.%)], CuO–MWCNT [50:50(c); 50:50(wt.%)], TiO₂–SiO₂ [60:40(c); 40:60(wt.%)] ) and one ternary formulation (Al₂O₃–CuO–graphene [50:30:20(c); 50:30:20(wt.%)] ) Denses, 2021. Surface-integrity components including arithmetic surface roughness (Rₐ), Vickers microhardness (HV), residual compressive stress measured by X-ray diffraction (σᵣ) and pin-on-disc wear loss were measured on the Al6061-T6, Al7075-T6 and Al2024-T3 workpieces and aggregated into a composite Surface Integrity Index (SII) via equal weighting. The best SII of 0.91 on Al7075-T6 was obtained from the ternary Al₂O₃–CuO–graphene blend, which represented a 15.2 % improvement over the best binary blend (Al₂O₃–graphene, SII = 0.79) and was nearly six times larger than the dry-burnishing baseline (SII = 0.15). The ternary benefit stems from the in-situ activation of three synergistic tribological mechanisms provided by hard γ-Al₂O₃ particles in micro abrasion, CuO carriers for thermal regulation, and graphene nanoplatelets for friction-reducing tribofilm construction. Results showed strain-hardening depths of ~0.4 mm under all the hybrid blends, with the corresponding maximum microhardness (165 HV) and the most significant absolute residual compressive stress (−432 MPa) produced by the ternary blend and confirmed by subsurface microhardness profiles. These results define the role of the new ternary Al₂O₃–CuO–graphene formulation as a truly multi-mechanism surface-integrity optimizer for the finish-burni
Area: Deogiri Institute of Engineering and Management Studies
Author: Murarikar Ganesh Balaji1, Mr. Vishal Vijay Chahare2
DOI: MJAP/05/1006
Abstract:
Widespread deployment of Internet of Things (IoT) sensor networks in sensitive industrial, medical and urban environments have elevated the fundamental questions regarding energy conservation, latency mitigation and structural security. It combines the unprecedented bandwidth and ultra-reliable low-latency communication (URLLC) of 5G communication architectures. Nonetheless, the nature of IoT nodes, which are highly distributed and vulnerable, makes it unable to withstand advanced cyber-attacks as sinkhole, blackhole and selective-forwarding attacks in traditional routing paradigms. This empirical exploration accounts application of a hybrid Particle Swarm Optimization (PSO) framework for potential applications to multi-objective secure routing in IoT sensor networks enabled by future 5G. The proposed Secure-PSO is using a dynamic evaluation matrix combined with standard physical constraints - including the residual energy of nodes in the transmission range, link quality and multi-hop distance - to model optimal cluster-head election and path selection trajectories through mathematics. Experiments were performed in multiple densities of 100-500 sensor nodes on a local 5G macro-cell grid. Localized adversarial injection Empirical data collection that was mainly based on quantified metrics (Network Lifetime, Average Energy Consumption, Packet Delivery Ratio (PDR), End-to-End Latency, and Throughput). Quantitative results showed that the Secure-PSO framework extended network lifetime by 34.2% over traditional Low-Energy Adaptive Clustering Hierarchy (LEACH) protocols and kept an average PDR greater than 96.5% in case of 20% node attrition from malicious nodes as well. The mathematical parameters are significant, as statistically validated by rigorous Analysis of Variance (ANOVA) testing. Weed out of the box at the end, this paper makes two structural contributions to architectural engineering by demonstrating that metaheuristic algorithmic models can integrate defense
Area: Department ECE
Author: Vishal Gehlod1, Janmejay singh Solanki2, Amit Thakur3
DOI: MJAP/05/1005
Abstract:
Fire resistance is one of the most critical performance requirements for structural concrete in modern construction, particularly in high-rise buildings, tunnels, industrial structures, and underground facilities. Ordinary Portland Cement (OPC) concrete undergoes progressive deterioration including strength loss, spalling, and microcracking when exposed to temperatures above 300°C. This study investigates the development of fire-resistant concrete (FRC) mixes using advanced supplementary cementitious materials (SCMs) including silica fume (SF) and fly ash (FA), polypropylene (PP) fibers, and refractory aggregates. Four concrete mix designs were systematically formulated and experimentally evaluated: a control mix (M1), an SF-blended mix (M2), and FA-blended mix (M3), and a hybrid SF–PP fiber mix (M4). Specimens were subjected to ISO 834 standard fire exposure at temperatures of 25°C, 200°C, 400°C, 600°C, and 800°C. Evaluation parameters included residual compressive strength, flexural strength, splitting tensile strength, thermal conductivity, mass loss, spalling resistance, water absorption, and chloride ion penetration. Results demonstrate that Mix M4 retained 27.2% of its ambient compressive strength at 800°C nearly twice the retention of the control achieved a fire resistance rating exceeding four hours, and exhibited markedly superior spalling resistance attributed to the vapor pressure relief mechanism of PP fibers. The findings provide actionable material design strategies for fire-resistant structural concrete meeting international standards.
Area: Dept. of Civil Engineering
Author: Dr. U J Jadhav, Dr. P R Modak, Mrs. M S Chiwande, Mr. C S Misal
DOI: MJAP/05/1002
Abstract:
For civil infrastructure managers worldwide, the structural integrity of steel and concrete bridges is a critical concern. Fatigue life and deterioration rates have become crucial in determining maintenance schedules, especially with monitors for aging bridge networks and hundreds of thousands of bridges across the United States being rapidly overloaded. Although traditional empirical methods and physics-based models are cornerstones in the degradation mechanism modeling, they struggle with effectively capturing the complex nonlinear nature of deterioration for real-world bridge structures. For the last twenty years, machine learning (ML) algorithms have provided revolutionary data-driven high-fidelity predictive capabilities that are complementary to and often surpass traditional approaches. This review paper is a meta-analysis of the studies available in existing literature on predicting fatigue life and deterioration of bridge infrastructure using ML approaches such as artificial neural networks (ANN), support vector machine(s) (SVM), random forests (RF), gradient boosting methods(GBM, XGBoost, Light GBM)), convolutional neural networks (CNN) and physics-informed neural networks(PINN). This review (n = 30 studies from 2005–2024) systematically evaluates methodology, data-set characteristics, predictive accuracy and practical deployment barriers. They demonstrate that, when used alone on their own, single-algorithm approaches are outperformed consistently by both ensemble learning and deep learning models achieving MAE reductions as high as 35% compared to regression baselines. Important shortcomings include limited bridge typology generalization, minimal real-world sensor deployment and no standard benchmark datasets. The paper ends with directions for future research towards hybrid physics-ML frameworks, federated learning for distributed monitoring networks, and explainable AI (XAI) to garner engineer trust in decision support.
Area: Department of Engineering
Author: TVS Ramanjaneyulu1, Dr. Ananda Babu Kurakula2
DOI: MJAP/05/0024