Abstract:
Maintenance in discrete manufacturing is still dominated by reactive repair and calendar-based preventive replacement, a combination that produces unpredictable stoppages, inflated spare-part inventories and persistent erosion of overall equipment effectiveness (OEE). This paper reports an empirical evaluation of an artificial-intelligence-based predictive maintenance (AI-PdM) framework deployed across forty-two machining, press and robotic welding cells in a component plant over twenty-four months. Multisensor telemetry-triaxial vibration, spindle current, bearing temperature, acoustic emission and hydraulic pressure, acquired over 198 channels in twelve-minute windows-was streamed through an edge gateway into a time-series data lake. A gated ensemble coupling a bidirectional long short-term memory network for remaining-useful-life regression with a gradient-boosted classifier for short-horizon failure detection was trained on 18,470 machine-weeks of labelled history and validated on a held-out 650 machine-weeks. Against the pre-deployment baseline the framework reduced unplanned downtime by 37.7 percent, from 11.4 to 7.1 hours per machine per month; raised mean time between failures by 28.6 percent, from 168 to 216 hours; cut mean time to repair by 31.1 percent, from 4.37 to 3.01 hours; and lifted OEE from 71.3 to 84.9 percent. Detection precision, recall and F1-score were 0.90, 0.92 and 0.91, with a receiver operating characteristic area of 0.96 and a median warning lead time of 214 minutes. Paired t-tests confirmed that every OEE component improved significantly at p < 0.01. The evidence shows that AI-PdM delivers warning long enough to be acted upon, and that reliability gains convert into measurable productivity and cost benefits rather than merely relocating maintenance effort earlier.
Area: Department of Industrial Engineering
Author: Mr. Jha Keshav Gopal1, Dr. Priyanka Jhavar2, Dr. Sachin Baraskar³
DOI: MJAP/05/2070
Abstract:
Battery-pack thermal management must limit peak temperature, suppress cell-to-cell gradients, and recover heat over repeated cycles. This paper presents an empirical secondary-data synthesis of published experimental and computational studies of phase-change-material (PCM) cooling combined with liquid cooling. No new cells were tested and no CFD runs were performed; reported values were extracted without pooling incompatible tests. Evidence includes triplicate 2C tests on three parallel 18650 cells at 20 °C, a validated channel-resolved model, a 96-cell 21700 pack simulated over three 2C-discharge/0.5C-charge cycles, and experiments on five-cell prismatic modules. In the matched test, maximum surface temperatures were 126.1 °C for natural convection, 73.6 °C for silicone oil, 59.4 °C for thermal adhesive, and 52.7 °C for PCM. PCM sustained the reported post-discharge 30–50 °C interval for 56 min, although uniformity depended on direction and melting progression. A numerical channel-D case reported 41.2 °C after 30 min and identified 0.108 L/min as an efficient flow, with smaller gains at higher rates. In the 96-cell model, cycle peaks rose from 46.22 to 49.93 °C; pack temperature was reported below 30 °C at cycle endpoints. Evidence supports a mechanism-level relationship: PCM buffers transient heat, while liquid circulation rejects heat and restores latent capacity. Cell formats, loads, and metrics differ, precluding a universal effect estimate. Source inconsistencies, unavailable raw records, and the distinction between PCM-model validation and numerical hybrid-channel testing are documented. The synthesis identifies design implications and validation needs for future design.
Area: Department of Thermal Engineering
Author: Md Ahlullah Khan¹, Dr. Sachin Baraskar²
DOI: MJAP/05/2069
Abstract:
Tall reinforced concrete (RC) buildings must resist two dominant lateral actions, earthquake ground motion and strong wind, and the governing action depends on height, stiffness and seismic hazard. This empirical study analyses how these demands change with building height through a data-driven, code-based parametric investigation. Five RC frame-core models with 10, 15, 20, 25 and 30 storeys, each on a 24 m x 24 m plan, were analysed using the seismic provisions of IS 1893 (Part 1):2016 and the wind provisions of IS 875 (Part 3):2015. Fundamental periods, seismic coefficients, design base shear, wind base shear, overturning moments and serviceability drifts were computed and organised into five tabulated datasets. The results show that wind base shear grows from 1052 kN to 4550 kN as height increases from 10 to 30 storeys, whereas Zone V seismic base shear grows only from 2588 kN to 3406 kN because the lengthening period reduces spectral acceleration. Wind therefore governs beyond about 20 to 25 storeys in Zone V and beyond 10 to 15 storeys in Zone III. Top wind displacement rises from 0.4 mm to 45.1 mm, yet all serviceability limits are satisfied. The conclusions confirm that the governing design criterion shifts with height and that core stiffness is the key control parameter for slender towers. These findings form the basis of the design recommendations presented in the conclusion. Wind-to-seismic demand ratios are reported so that engineers can interpolate the results to intermediate heights, and the study is intended as a screening tool for preliminary design.
Area: Department of Structural Engineering
Author: Himanshu Naresh Selukar¹, Mr. Vivek Shukla², Dr. Jyoti Yadav³
DOI: MJAP/05/2068
Abstract:
Voluntary compliance is the backbone of any personal income tax system, and India's reliance on it has grown as the base of filers has widened under digital administration. This study examines how knowledge of tax rules, perceived fairness, tax morale, perceived detection probability, perceived complexity and satisfaction with electronic filing shape the compliance behaviour of individual taxpayers in India. A cross-sectional survey was conducted among 420 individual taxpayers drawn from salaried, business and professional, and other income groups, using a structured five-point Likert questionnaire whose scales showed good internal consistency (Cronbach's alpha between 0.79 and 0.88). Data were analysed with descriptive statistics, Pearson correlation, multiple linear regression and one-way analysis of variance. Respondents reported moderately high compliance (mean 3.83 out of 5). Tax morale showed the strongest association with compliance (r = 0.58) and the largest standardised coefficient (β = 0.34), followed by tax knowledge, e-filing satisfaction and perceived fairness, while perceived complexity reduced compliance. The six predictors jointly explained 52 per cent of its variance. Salaried taxpayers, whose income is reported through deduction at source, complied significantly more than business and professional respondents (F = 22.7, p < 0.001), and compliance rose steadily across income bands. The results suggest that enforcement alone is an incomplete explanation of behaviour in India; attitudes, understanding and the quality of the filing experience matter at least as much. Simplified procedures, taxpayer education and visible, fair use of revenue are likely to yield more durable compliance than additional penalties. Self-reported data limit causal inference.
Area: Department of Commerce
Author: CA. Dr. Shankar Ghanshamdas Andani
DOI: MJAP/05/2067
Abstract:
Rapid vertical urbanisation in seismically active Indian cities has produced dense clusters of buildings separated by gaps far narrower than code-mandated values, while the same clusters are increasingly exposed to accidental and malicious explosion events in congested commercial precincts. This paper presents an empirical, data-driven investigation of the dynamic performance and impact-mitigation behaviour of closely spaced reinforced-concrete buildings subjected to seismic and explosion-induced loading, considered both separately and in combination. A parametric database of 216 nonlinear time-history simulations was assembled for three building pairs of 10, 15 and 20 storeys, nine separation gaps between 0 and 160 mm, eight recorded ground motions scaled to four intensity levels, and blast scenarios generated for charge weights of 50–250 kg TNT at standoff distances of 10–20 m. Impact was represented by a nonlinear Kelvin–Voigt contact element calibrated against reported experimental restitution coefficients, and blast demand was applied as a Friedlander pressure pulse on the exposed façade. Results are reported through five statistically analysed datasets covering peak pounding force, inter-storey drift, floor acceleration amplification, mitigation-device effectiveness and fragility. The data show that pounding amplifies drift by up to 111 percent and floor acceleration by up to 3.4 times at the impact level, that the combined seismic-plus-blast case shifts the median extensive-damage capacity from 0.31 g to 0.22 g, and that a viscoelastic coupling link reduces impact force by 67 percent and drift by 51 percent. Regression analysis yields a gap-demand relation of practical value, and the study concludes that gap provision alone is an inefficient mitigation strategy compared with energy-dissipating structural coupling.
Area: Department of Civil Engineering
Author: Amit Kumar Singh¹, Dr. Nitin Y. Patil²
DOI: MJAP/05/2066
Abstract:
Early childhood and primary education represent foundational developmental windows during which institutional governance and pedagogical climate profoundly shape lifelong learning trajectories. Despite extensive scholarship examining principal effectiveness in secondary schooling, empirical evidence quantifying how multidimensional school leadership directly and indirectly shapes holistic development among young learners remains fragmented. This study empirically investigates the impact of four core school leadership dimensions Instructional Leadership (IL), Transformational Leadership (TL), Distributed Leadership (DL), and Inclusive-Relational Leadership (IRL) on the holistic development of young learners aged five to nine years across cognitive-literacy, numeracy and executive function, socio-emotional learning, and behavioral self-regulation domains. Using a stratified multi-stage design, triangulated quantitative data were collected over a nine-month academic year from 28 principals, 84 primary teachers, and 420 young learners (Grades K–3) across urban, suburban, and rural schools. Psychometric evaluation confirmed high internal consistency across instruments (Cronbach's α = 0.86 to 0.93). Zero-order Pearson correlations revealed robust positive associations between the Composite School Leadership Index (CSLI) and Composite Holistic Learner Development (CHLD; r = 0.68, p < 0.001), with Distributed Leadership (r = 0.68) and Instructional Leadership (r = 0.62) exhibiting the strongest bivariate links. One-way variance analysis demonstrated substantial developmental superiority in Distributed-Integrated leadership regimes (M = 83.30) over administrative baseline schools (M = 64.63; F = 48.72, p < 0.001, η² = 0.260). Hierarchical regression and structural bootstrapping established that leadership and school-process mediators explain 58.4% of variance (R² = 0.584), with teacher efficacy (β = 0.29) and classroom climate (β = 0.25) mediating 57.1% of leaders
Area: Department of Education
Author: Shubha Kiran
DOI: MJAP/05/2065
Abstract:
Generative artificial intelligence (GenAI) and large language models (LLMs) have moved from research laboratories into everyday computer science practice, yet empirical evidence on how they are used, what benefits they deliver and which risks they introduce remains fragmented. The study is motivated by the need to connect adoption, efficiency, quality and perceived risk within a single analytical frame. This paper presents a data-driven analysis of the applications and challenges of GenAI and LLMs in computer science. A structured questionnaire was administered to 240 respondents comprising undergraduate students, postgraduate students, software developers and researchers. It was complemented by a timed comparison of five programming tasks and a quality audit of 300 LLM-generated code snippets. Descriptive statistics, paired comparisons and correlation analysis were applied to five datasets. Code generation (87.5%), debugging (72.1%) and documentation (68.3%) emerged as the most adopted applications, and assisted task completion times fell by 27.3% to 52.4%. Weekly usage hours correlated positively with perceived productivity gain (r = 0.64, p < 0.001). However, only 72.3% of generated snippets were functionally correct, 16.0% carried flagged vulnerabilities, and 78.3% of respondents identified hallucination as a major concern. Correctness declined steadily with complexity and security sensitivity, from 91.3% for simple functions to 47.5% for security-sensitive code. These findings indicate that LLMs are strong accelerators of routine work but unreliable autonomous agents for complex or security-critical development. Human verification, secure prompting, retrieval grounding and curriculum reform are therefore essential for responsible adoption. Implications for software practice, education and research governance are outlined.
Area: Department of Computer Science & Artificial Intelligence
Author: Krishna Kumar Singh
DOI: MJAP/05/2064
Abstract:
This paper evaluates how perceived work–life balance influences employees’ job satisfaction in India’s service sector. Because no field dataset was supplied, the study uses a generated synthetic cross-sectional dataset; its estimates demonstrate a workflow and must not be represented as observations from real respondents. The sample contains 384 profiles spanning information technology, banking and financial services, education, healthcare, and retail and business services. Multi-item five-point measures capture work–life balance, job satisfaction, supervisor support, schedule flexibility, and workload. Descriptive statistics, reliability tests, Pearson correlations, analysis of variance, and hierarchical ordinary least-squares regression assess the proposed relationship. Work–life balance correlates positively with job satisfaction (r = 0.649, p < 0.001). Controls explain 15.8% of satisfaction variance; adding work–life balance increases explained variance by 27.4 percentage points. In the full model, work–life balance remains the strongest predictor (standardized β = 0.451, p < 0.001), while supervisor support (β = 0.198) and schedule flexibility (β = 0.116) contribute independently. Employees in the highest balance quartile report greater satisfaction than those in the lowest quartile (3.875 versus 2.573), with a large omnibus effect (η² = 0.373). These convergent results support the proposition that balance is a job resource linking manageable boundaries to favorable work attitudes. Organizations should combine realistic workload norms, employee-controlled flexibility, and supportive supervision rather than rely on isolated benefits. Longitudinal field research is required to test causality, measurement invariance, and sector differences. The conclusion returns to these estimates and their managerial meaning.
Area: Department of Human Resources Management
Author: Arvind Kumar
DOI: MJAP/05/2063
Abstract:
Artificial intelligence (AI) is reshaping how organisations plan, schedule, forecast and control operations, yet empirical evidence linking AI adoption to both managerial decision quality and measurable operational efficiency remains fragmented. This paper examines how AI adoption intensity influences decision-making quality and operational efficiency, and whether decision quality transmits the efficiency benefits of AI. A cross-sectional dataset of 240 observations from manufacturing, logistics, retail, healthcare and IT/BFSI service firms was analysed using reliability testing, correlation, hierarchical regression, bootstrapped mediation and paired pre/post comparisons of five operational indicators. The dataset is simulated for illustration, with parameters informed by published literature. All constructs showed satisfactory reliability (Cronbach's alpha 0.78–0.86). AI adoption intensity was strongly associated with decision-making quality (β = 0.54, p < 0.001) and operational efficiency (β = 0.44, p < 0.001). Decision quality partially mediated the AI–efficiency relationship, accounting for 39% of the total effect (indirect effect = 0.17, 95% CI 0.09–0.25). After deployment, forecast error fell by 19.8%, order cycle time by 14.4%, inventory holding by 12.0% and unit cost by 6.4%, while on-time-in-full delivery rose by 5.4 percentage points. High adopters outperformed low adopters on efficiency, and data quality and top-management support emerged as key antecedents of adoption. Findings are robust to controls for firm size and sector. The findings suggest that AI creates value mainly by improving decisions rather than by automation alone, and that complementary organisational capabilities are decisive. The paper offers practical guidance for operations managers and a replicable analytical template for future primary studies.
Area: Department of Operations Management
Author: Prerana Ghosh
DOI: MJAP/05/2062
Abstract:
Database security is still evaluated largely through infrastructure checklists, while the software artefacts and engineering processes that produce databases remain weakly linked to measurable security outcomes. This paper reports an empirical study that measures the performance of database security using software engineering (SE) constructs. Data were collected over twenty-four months from 240 database instances operated by eight engineering teams in the Sultanate of Oman, spanning e-commerce, banking/fintech and healthcare, across five DBMS platforms. Ten process and code metrics, including security defect density, code churn, complexity, review and static-analysis coverage, patch latency, encryption coverage and compliance maturity, were instrumented, and a composite Security Performance Index (SPI) was built from six weighted dimensions. Analysis combined descriptive statistics, correlation, paired-samples t-tests with Cohen's d, and standardised regression (R-squared = 0.568, F(6, 233) = 51.1, p < 0.001). Security defect density correlated strongly with the number of disclosed vulnerabilities (r = 0.646, p < 0.001), while patch latency (beta = -0.413) and encryption coverage (beta = +0.369) were the strongest determinants of the SPI. After adoption of the SE-based framework, mean patch latency fell from 13.2 to 4.4 days (-66.7 percent, d = 2.19), security defect density fell from 0.80 to 0.35 defects per KLOC (-56.3 percent, d = 2.11), and the SPI rose from 0.651 to 0.869 (+33.5 percent, t = 65.61, p < 0.001), with 93.1 percent throughput retention and only 6.9 percent added query latency. Software engineering metrics are therefore valid, actionable predictors of database security performance, and measurable gains are obtainable without prohibitive runtime cost. The results are read against Oman's Personal Data Protection Law (Royal Decree 6/2022), enforceable since 5 February 2026, and the Central Bank of Oman's cyber security requireme
Area: Department of Software Engineering
Author: Ahmed Hassan Elsafi Mohamed
DOI: MJAP/05/2061
Abstract:
Strategic marketing and brand management have emerged as critical determinants of organizational success in the contemporary hyper-competitive business environment. This empirical study investigates the intricate relationship between strategic marketing dimensions and brand management outcomes with specific reference to the Omani FMCG and D2C (direct-to-consumer) sector. The primary objective is to empirically validate how market segmentation, positioning strategy, integrated marketing communication (IMC), and digital marketing orientation influence brand equity, brand loyalty, and market performance. A quantitative research design was adopted, employing a structured questionnaire administered to 384 marketing managers, brand managers, and consumers in the Sultanate of Oman, covering Muscat and other governorates including Dhofar, North Al Batinah, and Ad Dakhiliyah. Data were collected using stratified sampling, both online and in person, between January and March 2026. Reliability was established through Cronbach's alpha (0.82-0.91), and validity through confirmatory factor analysis with Average Variance Extracted values exceeding 0.50. Statistical analysis included descriptive statistics, Pearson correlation, multiple regression, ANOVA, and structural equation modeling (SEM) using SPSS 26.0 and AMOS 24.0. Results indicate that all four strategic marketing dimensions correlate positively with brand equity (r=0.67 to 0.78, p
Area: Department of Business Administration
Author: Ayman Degheidy
DOI: MJAP/05/2060
Abstract:
Rainfall-runoff (RR) modeling forms the analytical backbone of sustainable water resources management, underpinning flood forecasting, reservoir operation, drought planning, and watershed-scale decision making. This review and meta-analysis synthesizes findings from more than ninety peer-reviewed studies published between 2000 and 2024, spanning empirical, conceptual, physically-based distributed, and data-driven artificial-intelligence approaches to RR modeling. The paper traces the historical evolution of modeling paradigms, from the unit hydrograph and Soil Conservation Service Curve Number methods to contemporary machine-learning architectures such as artificial neural networks, support vector machines, and long short-term memory networks, and examines how each paradigm addresses the persistent challenges of parameter uncertainty, spatial heterogeneity, and non-stationarity induced by climate and land-use change. A structured methodology, informed by PRISMA-style screening, was used to identify, filter, and synthesize the literature quantitatively and qualitatively. The meta-analysis reveals that hybrid physically-informed machine-learning models consistently outperform purely empirical or purely data-driven counterparts in terms of Nash-Sutcliffe efficiency, particularly in data-scarce and ungauged basins. The critical analysis further highlights recurring limitations across the literature, including overreliance on single-basin validation, inadequate uncertainty quantification, and limited transferability of trained models across climatic regimes. The discussion situates these findings within the broader goals of sustainable water governance, arguing for greater standardization of benchmarking datasets and increased integration of remote-sensing inputs. The review concludes that future RR modeling research must prioritize explainability, transfer learning, and coupling with decision-support frameworks to translate modeling advances into actionable water resour
Area: Department of Civil Engineering
Author: Ravi Kumar Sonwani¹, NK Dhapekar²
DOI: MJAP/05/2059
Abstract:
Grid-interfaced renewable-energy systems require power electronic converters that deliver high waveform quality with low semiconductor stress and low component count. The cross-connected multilevel inverter (CC-MLI) recently reported by Pathak et al. is an attractive reduced-device-count baseline: it produces nine single-phase output levels using only eight semiconductor switches, four capacitors and two DC sources, without a polarity-changing H-bridge, and it has been validated in MATLAB/Simulink for a standalone 24 V + 24 V laboratory-scale load. Three measurable limitations motivate this work: the nine-level resolution caps the low-order staircase total harmonic distortion (THD) of the output voltage at approximately 11.5 % at a modulation index of 0.85 (analytically derived in this paper); the capacitor self-balancing property is asserted but neither modelled nor proven; and no grid-connected control, filtering, synchronization or robustness study is presented. This paper develops a single, verifiable advancement over that baseline: an asymmetric-ratio cross-connected multilevel inverter (AsCC-13L) for grid-connected renewable-energy applications. By re-rating the right-hand module of the unchanged eight-switch skeleton to V2 = 2·V1, the identical hardware eight switches, four capacitors, two DC sources synthesises thirteen equidistant voltage levels of step V1/2, lowering the analytically-derived staircase THD floor to approximately 7.1 % at the same modulation index while preserving the total-standing-voltage-to-peak-output ratio of 4.5 and the count of three conducting devices per state. A charge-integral analysis proves that, under the proposed state assignment, the per-fundamental-cycle net charge drawn from each capacitive divider midpoint is identically zero for any power factor; hence the baseline's self-balancing property survives the asymmetrisation exactly. A complete grid-connected design is additionally developed: dwell-integral capacitor sizi
Area: Electrical Engineering Department
Author: Vinay Pathak¹, Kishor Thakre², Prateek Nigam³
DOI: MJAP/05/2058
Abstract:
This paper presents an empirical, data-driven investigation of a novel StrongARM latch-based dynamic comparator designed and characterized in 20 nm CMOS technology. Dynamic comparators are the decision-making core of high-speed analog-to-digital converters (ADCs) and their delay, power, offset, and noise performance directly dictate converter accuracy and throughput. Building on the classical StrongARM regenerative topology, the proposed design introduces optimized transistor sizing, a modified tail-switching scheme, and a redistributed cross-coupled load to jointly reduce propagation delay, dynamic power, and kickback noise while limiting random offset voltage. Empirical characterization was carried out through simulation-based data collection across supply voltage sweeps, clock-frequency sweeps, 1000-run Monte Carlo mismatch analysis, and common-mode voltage sweeps, and the resulting datasets are summarized in five tabulated experiments. Quantitatively, the proposed 20 nm comparator achieves a 28.8% reduction in propagation delay, a 32.4% reduction in average power at 1 GHz, a 33.3% reduction in 3σ input-referred offset, and a 32.4% reduction in peak kickback noise relative to a 28 nm reference StrongARM design. These empirical results are discussed critically against prior published comparator architectures, confirming that node scaling combined with topology-level optimization yields compounding benefits beyond scaling alone. The findings validate the central claim of this study: architectural refinement of the StrongARM comparator at advanced 20 nm nodes provides a favorable, empirically verifiable trade-off among speed, power, and precision suitable for next-generation high-speed, low-power SAR and pipeline ADCs.
Area: Department of Electronics and Communication
Author: Anil Anant¹, Mr. Dhananjay Ingle²
DOI: MJAP/05/2057
Abstract:
Reinforced concrete (RC) structural elements are fundamental components in modern construction, bearing critical importance in determining the overall safety and durability of infrastructure. This empirical research paper presents a comprehensive investigation into the strength characteristics and behavioral patterns of reinforced concrete structural elements through both experimental testing and numerical simulation methodologies. The study encompasses a systematic evaluation of forty specimens under varying load conditions, incorporating different concrete grades (M20, M30, M40), reinforcement ratios, and geometric configurations. Advanced testing protocols were employed to monitor deflection, strain distribution, and failure mechanisms across all specimens. Parallel finite element analysis was conducted using industry-standard software to validate experimental observations and predict structural response under extreme loading scenarios. The research reveals significant correlations between concrete strength, reinforcement configuration, and overall element performance. Critical findings indicate that the interaction between concrete compressive strength and steel reinforcement provides a complex nonlinear relationship affecting ultimate load capacity. Numerical predictions demonstrated 94.3% accuracy when validated against experimental data, confirming the efficacy of the proposed modeling approach. The investigation identifies optimal reinforcement ratios and concrete compositions for enhanced structural efficiency. Results from this study contribute substantially to existing knowledge regarding RC structural behavior and provide practical guidelines for engineering design and optimization. The findings have direct applications in seismic-resistant design, sustainable construction, and bridge infrastructure development.
Area: Department of Structural Engineering
Author: Sachin Goswami1, Mr. Vivek Shukla2, Dr Jyoti Yadav3
DOI: MJAP/05/2056
Abstract:
Fifth-generation (5G) wireless communication networks represent a foundational architectural paradigm shift, engineered to deliver multi-gigabit per second peak data rates, sub-millisecond end-to-end latency, ultra-high reliability, and massive device connectivity across heterogeneous radio frequency spectra. This empirical research paper presents an exhaustive, quantitative performance analysis of standalone 5G New Radio (NR) deployments operating across mid-band Sub-6 GHz (3.5 GHz, Band n78) and millimeter-wave (28 GHz, Band n258) frequency spectra. Leveraging an enterprise-grade hardware testbed coupled with extensive metropolitan field drive-test campaigns, we systematically evaluate physical and transport layer key performance indicators, including downlink and uplink throughput, user-plane latency, jitter, block error rate (BLER), spectral efficiency, beamforming gains under massive Multiple-Input Multiple-Output (MIMO) array configurations, and radio frequency energy efficiency. Empirical findings reveal that mm Wave carriers achieve a peak downlink throughput of 4.18 Gbps over 400 MHz bandwidths under Line-of-Sight (LOS) conditions, but suffer steep propagation attenuation beyond 140 meters, exhibiting a 78.4% throughput reduction under Non-Line-of-Sight (NLOS) conditions. On the other hand, Sub-6 GHz carriers build better coverage: 620 Mbps at 450 meters The Ultra-Reliable Low-Latency Communication (URLLC) slice reached a mean one-way latency of 0.84 ms with 99.999% packet delivery reliability, meeting the strictest musts of industrial automation. Also, as massive MIMO antenna arrays were scaled from 8x8 to 128x128 elements, a SINR gain of 15.8 dB and spectral efficiency gains from approximately 5.8 bps/Hz to 38.1 bps/Hz were observed respectively. Finally, the integration of dynamic AI-enabled sleep modes achieved upto a 68.2% cut in base station energy consumption across low-traffic regimes, setting foundational empirical thresholds for future evolutions
Area: Department of Digital Communication
Author: Sachin Aggarwal¹, Dr. Bhawani Singh²
DOI: MJAP/05/2055
Abstract:
The P-Delta (P-Δ and P-δ) effect represents a critical second-order phenomenon influencing the stability, strength, and serviceability of steel frames subjected to combined gravity and lateral loads. While modern design codes mandate consideration of second-order effects, the differential impact of P-Delta on braced versus unbraced steel frames remains inadequately synthesized in existing literature. This review paper presents a comprehensive meta-analysis of past research spanning from 1990 to 2024 focusing on the comparative performance of braced and unbraced steel frames under lateral loading considering geometric nonlinearity[2]. A systematic search across Scopus, Web of Science, and ASCE Library identified 450 studies, from which 30 high-quality experimental, analytical, and numerical investigations were selected based on predefined inclusion criteria emphasizing frame typology, loading protocol, and consideration of P-Delta effects. Quantitative synthesis reveals that P-Delta effects amplify inter-story drift by 8-15% in concentrically braced frames (CBFs) and 18-42% in unbraced moment-resisting frames (MRFs) under design-basis earthquakes. The review critically evaluates evolution of analytical methods from conventional amplification factor approaches to direct analysis methods (DAM), and highlights inconsistencies in modeling assumptions, bracing configuration effects, and large-scale experimental validation. The findings underscore the necessity for enhanced stability design provisions, particularly for high-rise unbraced frames in seismic zones, and propose directions for integrated sustainability and robustness considerations[1].
Area: Department of Civil Engineering
Author: Ramsevak Vamne¹, Dr. Anudeep Nema²
DOI: MJAP/05/2054
Abstract:
Power electronic converters are the principal actuating and interfacing hardware of modern smart grids, coupling renewable generators, energy storage, electric vehicles, microgrids, and the bulk network through controlled dc-dc, dc-ac, and ac-dc stages. As converter-interfaced resources multiply, conventional fixed-gain and strictly model-based control methods struggle to preserve dynamic performance, efficiency, power quality, and grid-support functions under uncertainty, degradation, and rapidly changing operating conditions. This paper presents a systematic review and critical meta-analysis of past research on artificial-intelligence (AI)-based control and optimization of power electronic converters for smart-grid applications, covering 192 peer-reviewed studies published between 1990 and 2025. The corpus is organized into four methodological families: fuzzy logic control; neural-network and machine-learning methods; metaheuristic optimization; and (deep) reinforcement learning, with model predictive control treated as a hybrid bridge between model-based and learning-based design. Application domains spanning maximum power point tracking, grid-connected inverter control, microgrid and storage coordination, voltage regulation, and condition monitoring are consolidated, and reported performance gains are cross-compared. Beyond mapping the landscape, the review grades each study against five methodological quality criteria, ranging from hardware validation to statistical rigor, and quantifies how evidence quality varies across method families and application domains. Implications for researchers, developers, and standards bodies are drawn throughout. The critical meta-analysis exposes recurring weaknesses in past work: limited hardware validation, non-standardized benchmarks, weak reproducibility, insufficient statistical rigor, and inadequate safety guarantees. Prioritized research directions are identified to steer future efforts toward verifiable, certifiable, a
Area: Department of electrical and electronics engineering
Author: Hemraj Patel¹, Prabodh Khampariya²
DOI: MJAP/05/2053
Abstract:
Multilevel inverters (MLIs) are the key power-electronic interface for converting the direct-current (DC) power generated by variable renewable sources, in particular photovoltaic (PV) arrays and wind turbines, into grid-quality alternating-current (AC) power. This paper presents a comprehensive review and a quantitative meta-analysis of past work on the design and optimization of high-efficiency multilevel inverters for renewable energy applications. A systematic search of IEEE Xplore, Scopus, Web of Science, and ScienceDirect identified peer-reviewed studies published between 2000 and 2025, which were screened and coded for topology family, modulation strategy, optimization algorithm, validation method, and reported performance metrics; a total of 62 studies were included in the meta-analysis. Across all sampled designs, switching-angle schemes optimized with metaheuristic algorithms, including the genetic algorithm, particle swarm optimization, grey wolf optimizer, and their hybrids, consistently reduced total harmonic distortion (THD) by between 3.2 and 41.6 percent relative to conventional sinusoidal pulse-width-modulation baselines, while efficiency gains over unoptimized single-stage two-level converters ranged from 1.8 to 6.4 percentage points. Cascaded H-bridge topologies dominate low- and medium-power PV systems because of their modular structure, string-level maximum power point tracking, and low switching losses, whereas neutral-point-clamped and T-type configurations are preferred where a low component count and neutral-point balance are critical. The paper also synthesizes the evidence on voltage-stress reduction, soft-switching techniques, and digital-twin and hardware-in-the-loop validation practices, and it identifies the methodological gaps, notably the scarcity of published experimental validation and the absence of standardized efficiency metrics, that future research should address.
Area: Department of Electrical and Electronics Engineering
Author: Sumit Kumar Verma¹, Dr. Prabodh Khampariya²
DOI: MJAP/05/2052
Abstract:
The Sundarbans, the world's largest contiguous mangrove ecosystem shared between India and Bangladesh, has attracted sustained scholarly attention owing to its ecological, socio-economic, and climatic significance. Despite the growing volume of scientific output, a comprehensive quantitative mapping of this literature has remained limited, creating a gap between the pace of publication and the field's self-understanding of its own structure. This study addresses that gap by conducting a systematic bibliometric review of 1,102 peer-reviewed documents indexed in Scopus and Web of Science between 2005 and 2024, using performance analysis and science mapping techniques implemented through VOSviewer and Biblioshiny. The analysis quantifies annual publication growth, leading contributing countries and institutions, dominant subject categories, keyword co-occurrence networks, and citation patterns across the most influential journals. Results reveal an exponential rise in publication output after 2015, driven primarily by India and Bangladesh, with climate change, salinity intrusion, remote sensing, biodiversity conservation, and ecosystem services emerging as the dominant thematic clusters. The findings establish quantitative evidence for a shift in research emphasis from descriptive ecological surveys toward climate-vulnerability and geospatial modelling approaches over the past decade. By synthesizing three decades of scholarship into structured, tabular, and visual evidence, this paper builds a direct empirical link between historical research trajectories and future priorities, offering a data-grounded foundation for the conclusions and recommendations presented. The study contributes a replicable methodological framework for bibliometric assessment of ecologically critical but understudied regions and identifies concrete gaps for future interdisciplinary research on the Sundarbans.
Area: Department of Geography
Author: Dr. Anukul Ch. Mandal¹, Dr. Th. Devala Devi², Dr. Gouri Sankar Bhunia³
DOI: MJAP/05/2051
Abstract:
Miami-Dade County presents a critical case for understanding how organizations embed environmental management into operations within a coastal, climate-vulnerable, hyper-diverse metropolitan region. Rapid population growth, sea-level rise, saltwater intrusion into aquifers, and heat island intensification have elevated environmental governance from compliance exercise to existential infrastructure challenge. This empirical study examines SEM adoption across 320 organizations spanning manufacturing, hospitality, real estate development, municipal services, and port operations across Miami-Dade's three tiers: the central urban core (Miami proper), the intermediate suburban belt (Kendall, Westchester, Hialeah), and peripheral exurban communities (Florida City, Homestead, Princeton). Drawing on structured questionnaires, the analysis measures eight practice dimensions environmental policy, EMS certification, green procurement, waste and circular-economy initiatives, stormwater and water conservation, renewable energy, community/environmental-justice engagement, and staff training and regresses these against a sustainability performance index capturing resource efficiency, pollution reduction, regulatory compliance, climate adaptation capacity, and environmental justice outcomes. Results reveal uneven adoption (mean = 3.35), with significant performance gains for high adopters (r = 0.71; R² = 0.50), but stark disparities between affluent central Miami neighborhoods and underserved peripheral areas where industrial facilities, landfills, and port operations concentrate. The paper argues that SEM in Miami-Dade cannot succeed without explicit attention to environmental justice and climate resilience principles largely absent from the Western SEM literature. Policy recommendations center on building equitable, place-sensitive environmental capacity while integrating adaptation planning into municipal development frameworks.
Area: Department of Environmental Engineering
Author: Olaf Riedel
DOI: MJAP/05/2050
Abstract:
The increasing demand for renewable energy sources has intensified the focus on optimizing the performance of solar photovoltaic (PV) systems. A critical component for maximizing energy extraction is the Maximum Power Point Tracking (MPPT) system, which continuously adjusts the operating point of the PV array to harvest maximum power under prevailing conditions. This paper presents a comprehensive review of Adaptive Neuro-Fuzzy Inference System (ANFIS)-based MPPT strategies, a class of intelligent control algorithms that have demonstrated significant potential in enhancing PV efficiency. The review synthesizes findings from recent research, highlighting the evolution from conventional MPPT techniques like Perturb and Observe (P&O) and Incremental Conductance (IncCond) to more advanced AI-driven approaches. ANFIS, by integrating the learning capabilities of neural networks with the rule-based reasoning of fuzzy logic, offers superior adaptability to dynamic environmental conditions such as varying solar irradiance and temperature, and is particularly effective in mitigating issues like partial shading. Studies consistently report high tracking efficiencies, often exceeding 99%, for ANFIS-based MPPT controllers, with advantages including faster convergence times, reduced steady-state oscillations, and improved global maximum power point (GMPP) detection compared to traditional methods. This review critically analyzes various ANFIS implementations, discussing their architectures, training methodologies, and performance metrics. It also explores hybrid approaches combining ANFIS with metaheuristic algorithms and advanced control strategies, and identifies avenues for future research to further refine these advanced MPPT strategies for robust and efficient solar energy harvesting in real-world applications.
Area: Department of Electrical and Electronics Engineering
Author: Varsha Nagle¹, Prabhodh Khampariya²
DOI: MJAP/05/2007
Abstract:
Aging reinforced concrete (RC) multi-storey infrastructure across high-seismic zones in India exhibits severe vulnerability under revised seismic demand criteria established by IS 1893 (Part 1):2016. This empirical investigation evaluates the seismic adequacy of an existing G+10 residential RC frame building situated in Seismic Zone IV (peak ground acceleration PGA = 0.24g) on medium soil (Type II), originally constructed in accordance with legacy IS 1893:1984 provisions. In-situ destructive and non-destructive material evaluations, including concrete core extraction and ultrasonic pulse velocity testing, established a baseline concrete compressive strength of 20 MPa and rebar yield strength of 415 MPa. Three-dimensional finite element modeling and non-linear static pushover analysis conducted in accordance with ATC-40 and FEMA 356 protocols revealed that the existing bare frame experiences an excessive roof lateral displacement of 66.8 mm and a maximum inter-storey drift ratio of 0.54%, directly violating the codal limit of 0.40% and locating the building at the Collapse Prevention (CP) performance limit state. To remediate these structural deficiencies, three distinct seismic retrofit strategies were empirically appraised: Carbon Fiber Reinforced Polymer (CFRP) confinement, Reinforced Concrete (RC) column jacketing, and concentric Steel X-bracing. Structural response data demonstrate that concentric Steel X-bracing achieves superior performance, reducing peak lateral displacement to 31.2 mm (a 53.3% reduction) and maximum drift to 0.21%, while elevating the base shear capacity by 97.2% (from 1410 kN to 2780 kN) and shifting the global performance state to Immediate Occupancy (IO) with a modest structural mass increase of 3.2%, establishing it as the optimal techno-economic retrofit solution for Indian urban building stock.
Area: Department of Structural Engineering
Author: Abhiranjan Kumar¹, Mrs. Kamni Laheriya²
DOI: MJAP/05/2006
Abstract:
This review surveys and meta-analyses five decades of work at the confluence of three programmes: character sheaves on commutative group schemes and group ind-schemes, the local Langlands correspondence for an algebraic torus T over a non-archimedean local field F, and the inertial refinement classifying smooth characters by the restriction of their parameter to the inertia subgroup I_F. Writing LT for the loop ind-scheme colim_n T_n attached to T and CS(LT) for the Picard groupoid of multiplicative rank-one local systems on it, the central object of study is the comparison square asserting that the sheaf-function dictionary applied to the Contou-Carrère Fourier transform agrees with Langlands' isomorphism composed with dualisation. We formalise thirty contributions through a coding of formal invariants: object class, ambient triangulated category, dualising functor, hypotheses on residue characteristic and on the splitting field, and the type of inertial output. On that basis we reconstruct the logical dependency skeleton of the field, quantify its thematic and chronological structure, and audit which asserted compatibilities carry explicit proofs. Our principal mathematical synthesis is the statement that for a split torus in equal characteristic the depth filtration on CS(LT) matches the upper-numbering ramification filtration on H^1 (W_F,T-dual), so that the inertial correspondence is realised by a filtered equivalence of Picard groupoids rather than by an imported bijection. Four gaps are isolated: wild ramification in mixed characteristic, non-standardised normalisations, the absent geometry of norm maps for non-split tori, and the missing comparison functor to categorical local Langlands on the Fargues-Fontaine curve.
Area: Department of Mathematics
Author: Pankaj Kumar Tiwari¹, Dr. Praveen Kumar Mathur²
DOI: MJAP/05/2005