International Journal of Technical Research Studies (IJTRS)
The International Journal of Technical Research Studies (IJTRS) offers a diverse range of innovative research articles covering various Technical disciplines, establishing itself as a leading platform in the field. This prestigious journal serves as a central platform for scholars, researchers, and professionals from around the globe to share their innovative ideas, significant discoveries, and Technical expertise. This journal adheres to the policies and ethical standards prescribed by the UGC-CARE. Through a meticulous double-blind peer-review process and the collaboration of renowned editors, the International Journal of Technical Research Studies ensures the highest standard of published research. This platform provides a unique opportunity for researchers to showcase their work to a global audience and make valuable contributions to the advancement of scientific knowledge in their respective fields of expertise. Featuring a wide array of technical disciplines, the International Journal of Technical Research Studies is a valuable resource for both academics and experts. It encompasses a wide range of subjects including Engineering, Technology, Computer Science, and Applied Sciences, offering valuable insights for curious and analytical individuals. By fostering collaboration and facilitating the exchange of ideas, this journal plays a crucial role in shaping the future of technical research and innovation. Discover the latest trends and advancements in Technical Research by submitting your work to the International Journal of Technical Research Studies. Be a part of this dynamic community and make a lasting impression in the ever-evolving realm of Technical Studies. Title International Journal of Technical Research Studies (IJTRS) Frequency Quarterly ISSN 3139-1788 Publisher Eduschool Academic Research and Publishers Chief Editor Dr. Krishna Prasad K Copyright Eduschool Academic Research and Publishers Starting Year 2025 Subject Engineering Language English Publication Format Online Phone No 0484-2929383 Email Id ijtrs@eduresearchpublication.com, info@eduresearchpublication.com Website www.eduresearchpublication.com Address Eduschool Academic Research and Publishers, Airport Road, Nayathode Kavala, Angamaly -683572, Kerala, India.
Eduschool Academic Research and Publishers
It is our pleasure to present the latest issue of the International Journal of Technical Research Studies (IJTRS), a scholarly platform dedicated to disseminating innovative research and technological advancements across engineering, applied sciences, and emerging interdisciplinary domains. This issue brings together a collection of high-quality contributions that reflect the transformative impact of digital technologies, intelligent systems, and sustainable engineering solutions in addressing the complex challenges of modern industry and society.
Thermal Management of Lithium-Ion Battery Packs in Electric Vehicles
The performance, safety, and service life of electric-vehicle (EV) battery packs are governed by their operating temperature. Lithium-ion cells deliver optimal efficiency within a narrow window of roughly 15 to 35 degrees Celsius and demand a cell-to-cell temperature spread below about 5 degrees Celsius to avoid accelerated and uneven ageing; excursions beyond 50 degrees Celsius can trigger thermal runaway. This paper presents a comparative thermal analysis of four battery thermal management system (BTMS) architectures forced air, liquid cold-plate, phase-change-material (PCM), and a PCM–liquid hybrid for a representative pouch-cell module subjected to discharge rates from 0.5C to 5C. A coupled electrothermal model, validated against published cell calorimetry, was used to predict peak temperature, cell-to-cell uniformity, and parasitic energy consumption. At a 3C discharge the hybrid system limited the maximum cell temperature to 33.4 degrees Celsius and the cell-to-cell spread to 1.9 degrees Celsius, against 44.8 degrees Celsius and 8.6 degrees Celsius for forced air, while consuming 38 percent less pump power than standalone liquid cooling. The study quantifies the trade-offs among cooling capacity, temperature uniformity, parasitic load, and system complexity, providing design guidance for next-generation high-power EV battery packs.
Green Hydrogen Production Using Proton-Exchange-Membrane Water Electrolysis
Green hydrogen produced by water electrolysis powered by renewable electricity is widely regarded as an indispensable energy carrier for decarbonizing sectors that resist direct electrification, including steel, ammonia, refining, and heavy transport. Among the electrolysis technologies, proton-exchange-membrane (PEM) water electrolysis is especially well suited to coupling with intermittent renewables owing to its high current density, compact footprint, rapid dynamic response, and ability to operate at high differential pressure. This paper reviews the operating principles of PEM electrolysis and develops an electrochemical model that resolves the cell voltage into its reversible, activation, ohmic, and concentration components to quantify the trade-off between energy efficiency and hydrogen-production rate. The model is used to compare PEM with alkaline and solid-oxide electrolysis and to estimate the levelized cost of hydrogen (LCOH) as a function of electricity price and capacity factor. At a current density of 2 A/cm-squared the modeled PEM cell operates at a voltage efficiency of about 74 percent, and the LCOH analysis shows that hydrogen approaches the cost-competitiveness target of roughly two US dollars per kilogram only when low-cost electricity is available at a high capacity factor. The study identifies efficiency, durability, and electricity cost as the principal levers for scaling green hydrogen.
Grid-Forming Inverter Control for Renewable-Dominated Power Systems
The accelerating displacement of synchronous generation by inverter-based renewable resources is eroding the rotational inertia and frequency-regulating capability that have historically maintained power-system stability. Conventional grid-following inverters behave as controlled current sources and depend on a stiff external grid for synchronization, rendering them inadequate as the dominant resource. Grid-forming (GFM) inverters, which regulate their own voltage magnitude and frequency and can operate without a pre-existing grid reference, are widely regarded as the enabling technology for very high renewable penetration. This paper reviews and comparatively evaluates the principal grid-forming control strategies droop control, the virtual synchronous machine, dispatchable virtual oscillator control, and the synchronverter within a unified small-signal and electromagnetic-transient simulation framework. Using a modified IEEE benchmark network, the study quantifies frequency nadir, rate of change of frequency (RoCoF), and transient settling time under load and generation disturbances. Results show that a network with 30% grid-forming capacity limits the maximum RoCoF to 0.58 Hz/s at 100% inverter penetration, compared with 2.9 Hz/s for an all-grid-following system, keeping the frequency excursion within statutory limits. The findings provide system planners with practical guidance on the selection of grid-forming control and the minimum grid-forming share required for secure operation of low-inertia networks.
Edge-AI Enabled Structural Health Monitoring of Civil Infrastructure
Ageing bridges, buildings, and other civil infrastructure require continuous condition assessment to prevent catastrophic failure, yet conventional structural health monitoring (SHM) systems stream raw vibration data to a central server, incurring prohibitive bandwidth, latency, and energy costs that limit scalability. This paper proposes an edge-artificial-intelligence (edge-AI) SHM framework in which lightweight one-dimensional convolutional neural networks execute damage detection directly on resource-constrained sensor nodes, with a fog layer performing damage localization and a cloud layer maintaining the long-term structural record. The hierarchical edge–fog–cloud design draws on architectural principles established for fog-computing-enabled intelligent transport systems. Evaluated on a benchmark bridge-damage dataset and a numerical girder model, the on-device network attained 95.5 percent damage detection accuracy at a 15 dB signal-to-noise ratio while reducing end-to-end detection latency from 1850 milliseconds for a cloud-only pipeline to 95 milliseconds and cutting uplink bandwidth by 93 percent. The framework localized damage on a thirty-metre girder to within one metre. These results demonstrate that pushing inference to the edge makes dense, real-time, and energy-efficient monitoring of large infrastructure portfolios technically and economically viable.
Digital Twin-Driven Predictive Maintenance for Smart Manufacturing Systems
Unplanned equipment failure remains a principal source of productivity loss in modern manufacturing, accounting for a substantial fraction of total operating cost. This paper presents a digital twin-driven predictive maintenance (PdM) framework that couples a high-fidelity virtual representation of rotating machinery with data-driven prognostics for remaining useful life (RUL) estimation. Multivariate condition data comprising vibration, temperature, and motor-current signatures are streamed from the physical asset through a cloud-native microservice pipeline into the digital twin, where a hybrid convolutional-recurrent model fuses physics-based degradation indicators with learned temporal features. Validated on the NASA C-MAPSS turbofan degradation benchmark and an in-house bearing test-rig dataset, the proposed model attains a root-mean-square error of 11.3 cycles and a PHM08 penalty score of 410, outperforming standalone LSTM (15.2 cycles) and CNN-LSTM (13.6 cycles) baselines. Field emulation indicates a 42% reduction in unplanned downtime, a 28% reduction in maintenance cost, and a 16% improvement in overall equipment effectiveness relative to a reactive maintenance baseline. The results confirm that integrating digital twin modeling with scalable cloud-native analytics provides a deployable decision-support tool for condition-based maintenance in Industry 4.0 environments.
The forthcoming issue of the International Journal of Technical Research Studies (IJTRS) presents a diverse collection of research contributions that reflect the expanding scope of contemporary technical innovation. The articles in this volume explore advancements across several key domains of modern engineering and applied technology, including artificial intelligence, cloud-native computing architectures, smart manufacturing, power electronics, and geospatial technologies for sustainable development. Collectively, these studies demonstrate how interdisciplinary technical research continues to drive solutions to complex industrial, computational, and environmental challenges.
GIS-Based Land-Use Planning For Sustainable Urban Growth
Uncontrolled urban expansion threatens agricultural productivity, biodiversity corridors, and flood resilience across rapidly growing cities in Sub-Saharan Africa. This study applies Geographic Information System (GIS) based multi-criteria decision analysis (MCDA) and Cellular Automata–Markov (CA-Markov) modelling to evaluate land-use change and guide future development in the Kumasi Metropolitan Area, Ghana. Landsat imagery from 2000, 2010, and 2020 was classified into five land-cover categories using supervised Maximum Likelihood classification (overall accuracy > 87%, Kappa > 0.82). Between 2000 and 2020, built-up area expanded from 42.3 km² to 95.6 km², consuming 38.5 km² of vegetation and 20.2 km² of agricultural land. An Analytic Hierarchy Process (AHP) weighted overlay identified zones with high development suitability based on slope, road proximity, soil type, and flood risk. CA-Markov simulations projected three 2030 scenarios: business-as-usual (BAU), which predicts a 38% increase in built-up area; planned growth, limiting expansion to high-suitability zones (22% increase); and conservation, which designates ecological no-go zones (9% increase). The planned-growth scenario preserves 65% more agricultural land than BAU while accommodating projected population demand.
Multi-level Inverters For High-Power Industrial Drives
This paper presents a comparative analysis of three multi-level inverter (MLI) topologies — Neutral-Point Clamped (NPC), Flying Capacitor (FC), and Cascaded H-Bridge (CHB) — for driving high-power three-phase induction motors in the 1–10 MW class. Five-level configurations of each topology were modelled in MATLAB/Simulink and evaluated under identical load conditions (4-kW, 400-V, 50-Hz induction motor) with Phase-Shifted PWM and Level-Shifted PWM modulation strategies. The output voltage total harmonic distortion (THD), switching losses, conduction losses, and component count were compared across a modulation index range of 0.3 to 1.0. The CHB inverter achieved the lowest THD of 3.8% at a modulation index of 0.9, meeting the IEEE 519 harmonic limit without additional filtering. Switching losses were 20% lower in the CHB topology due to lower device voltage stress. The NPC topology offered the most compact solution for back-to-back converter configurations, while the FC design provided inherent fault tolerance through redundant switching states. These findings guide topology selection for specific industrial drive requirements.
ML-Based Optimization OF CNC Machining Parameters For Complex Geometries
This paper presents a machine-learning-based approach to optimize CNC milling parameters for workpieces with complex three-dimensional geometries. Three regression models Random Forest (RF), Support Vector Regression with radial basis function kernel (SVR-RBF), and a feedforward Artificial Neural Network (ANN) were trained on experimental data collected from an L27 orthogonal array of machining trials on Al 6061-T6 alloy. The input parameters comprised spindle speed, feed rate, depth of cut, and step-over ratio; the target responses were surface roughness (Ra) and flank tool wear (VB). Among the three models, RF achieved the highest predictive accuracy, with R² values of 0.941 for Ra and 0.923 for VB. A subsequent multi-objective optimization using NSGA-II on the trained RF surrogate produced a Pareto-optimal set of machining configurations. The best compromise solution reduced surface roughness by 18.3% and tool wear by 14.7% relative to the centre-point condition of the experimental design. These results demonstrate that data-driven surrogate models can replace computationally expensive finite-element simulations and trial-and-error approaches for parameter selection in multi-axis CNC machining of complex parts.
Cloud-Native AI Workloads Using Microservice Architectures
Production machine learning systems often run as monolithic applications where data preprocessing, model inference, and post-processing share a single container and scale as a unit. This coupling wastes compute resources when workloads are heterogeneous a GPU-bound inference stage idles CPU-bound preprocessing capacity, and vice versa. This paper presents a microservice architecture deployed on Kubernetes that decomposes an ML serving pipeline into six independently scalable services: API Gateway, Model Registry, Feature Store, Inference Engine (NVIDIA Triton), Training Pipeline, and Monitoring. Experiments on a five-node cluster with three workloads (ResNet-50 image classification, BERT-base text classification, XGBoost tabular prediction) show that the microservice design achieves 3.2× the peak throughput of an equivalent monolithic Flask deployment under 10× bursty loads. Tail latency (p99) drops by 45%, and GPU utilisation increases from 52% to 78%. Horizontal Pod Autoscaler (HPA) driven by inference-queue-depth metrics provisions additional pods within 18 seconds of load onset, containing throughput degradation to under 5% during burst transients.
Real-Time Emotion Recognition Using Lightweight Deep Learning
Facial emotion recognition (FER) systems built on deep convolutional neural networks achieve strong accuracy but typically require millions of parameters and substantial compute, making them impractical for deployment on resource-constrained edge devices. This paper proposes MicroExpNet, a lightweight architecture that pairs a MobileNetV3-Small backbone with a channel-wise Squeeze-and-Excitation attention block and a single fully connected classification head. The model is trained through knowledge distillation from a ResNet-50 teacher pretrained on VGGFace2. On the FER2013 benchmark, MicroExpNet reaches 71.2% accuracy with 1.2 million parameters a 15-fold reduction compared to ResNet-50 (25.6 M) and a 110-fold reduction compared to VGG13 (133 M). Deployed on a Raspberry Pi 4 via ONNX Runtime, the model processes 28 frames per second at 48 × 48 pixel resolution. On AffectNet (8 classes), accuracy is 58.4%. Ablation experiments confirm that knowledge distillation contributes 2.8 percentage points and the attention module adds 1.4 points over the plain MobileNetV3-Small baseline. These results position MicroExpNet as a viable option for real-time affective computing on embedded hardware.
Lightweight convolutional neural network
It is with great pleasure that we present the latest issue of the International Journal of Technical Research Studies (IJTRS), a collection that reflects the rapid technological advancements shaping today’s engineering and computational landscape. The articles in this issue highlight cutting-edge research across multiple domains, capturing both theoretical innovations and practical applications that address real-world challenges. This edition brings together significant contributions ranging from AI-augmented software testing frameworks that revolutionize large-scale system validation, to advanced navigation strategies for autonomous multi-agent environments. The issue also explores the transformative role of fog computing in creating resilient smart transportation systems, offering crucial insights into latency reduction, system reliability, and scalable urban mobility solutions.
Smart Surface Texturing for Improved Tribological Performance in Automotive Engines
Surface texturing has emerged as a promising technique for enhancing tribological performance in automotive engine components, where friction reduction and wear minimization are critical for fuel efficiency and component longevity. This paper presents a comprehensive investigation of smart surface texturing strategies applied to automotive engine tribological interfaces, including piston rings, cylinder liners, and journal bearings. We analyze the hydrodynamic and mixed lubrication regimes governing these interfaces and evaluate various texturing patterns including dimples, grooves, and hybrid configurations. Through systematic review of experimental and computational studies, we demonstrate that optimized surface textures can reduce friction coefficients by 15-40% and extend component life by 25-60% compared to conventional smooth surfaces. The paper establishes design criteria for texture geometry, considering parameters such as dimple depth (5-20 μm), diameter (50-200 μm), and area density (5-30%). We present a framework for adaptive texturing that responds to varying operating conditions including load, speed, and temperature. The findings indicate that laser surface texturing (LST) combined with advanced coatings provides the most promising pathway for next-generation engine tribology. Implementation challenges including manufacturing scalability, cost considerations, and integration with existing engine architectures are critically evaluated. This work contributes to the theoretical understanding of texture-enhanced lubrication mechanisms and provides practical guidelines for automotive engineers implementing surface texturing technologies.
Design of High-Efficiency Inductive Charging Systems for EVs
This paper presents a comprehensive investigation into the design and optimization of high-efficiency inductive charging systems for electric vehicles (EVs). The proliferation of EVs necessitates advanced charging infrastructure that addresses limitations in conventional plug-in systems. Inductive power transfer (IPT) offers a wireless alternative through electromagnetic coupling between transmitter and receiver coils. This research examines critical design parameters including resonant frequency optimization, coil geometry configuration, magnetic core materials, and compensation network topologies. A systematic analysis of power transfer efficiency across varying air gap distances (100-300mm) and lateral misalignment conditions (±100mm) is conducted. The proposed system employs series-series (SS) compensation with ferrite-based magnetic shielding, achieving 94.2% efficiency at 150mm air gap with 7.7kW power transfer capability. Experimental validation demonstrates tolerance to ±75mm lateral misalignment while maintaining >90% efficiency. The findings provide actionable design guidelines for deploying practical IPT systems in residential and commercial EV charging applications.
Copyright (c) 2025 International Journal of Technical Research Studies (IJTRS)
Neuromorphic Hardware Systems for Ultra-Low-Power Computing
Neuromorphic computing represents a paradigm shift in computational architecture, offering unprecedented energy efficiency through brain-inspired hardware implementations. This paper provides a comprehensive analysis of neuromorphic hardware systems designed for ultra-low-power computing applications. We examine the fundamental principles underlying neuromorphic architectures, including spiking neural networks (SNNs), event-driven computation, and synaptic plasticity mechanisms. Through systematic evaluation of contemporary neuromorphic platforms including IBM TrueNorth, Intel Loihi, BrainScaleS, and SpiNNaker we demonstrate power consumption reductions of 3-5 orders of magnitude compared to conventional von Neumann architectures for specific computational tasks. Our analysis reveals that neuromorphic systems achieve energy efficiencies ranging from 20 pJ to 50 pJ per synaptic operation, approaching biological neural network performance. We present detailed comparisons of analog, digital, and mixed-signal implementation strategies, examining their respective advantages in terms of power efficiency, scalability, and computational accuracy. Furthermore, we discuss emerging applications in edge computing, sensor networks, and autonomous systems where ultra-low-power operation is critical. The paper concludes with an examination of current challenges including limited programming frameworks, hardware-software co-design complexity, and scalability constraints and identifies promising research directions for next-generation neuromorphic systems.
Fog-Computing-Enabled Smart Transportation Systems: Architecture, Implementation, and Performance Analysis
Smart transportation systems represent a critical infrastructure paradigm for modern urban environments, yet traditional cloud-centric architectures introduce latency constraints incompatible with real-time vehicular applications. This paper presents a comprehensive analysis of fog-computing-enabled smart transportation systems, examining architectural frameworks, implementation strategies, and performance characteristics. We investigate the integration of fog computing nodes at the network edge to support latency-sensitive applications including collision avoidance, traffic management, and autonomous vehicle coordination. Through systematic analysis of distributed processing architectures, we demonstrate that fog-enabled systems reduce average response latency by 73% compared to cloud-only implementations while maintaining 99.7% system availability. Our evaluation framework encompasses network topology design, resource allocation algorithms, and quality-of-service guarantees for vehicular applications. Results indicate that three-tier fog architectures optimally balance computational overhead, communication latency, and energy efficiency. We further analyze security considerations, scalability challenges, and interoperability requirements for large-scale deployment. This work contributes architectural guidelines, performance benchmarks, and implementation strategies for next-generation intelligent transportation infrastructure.
Autonomous Multi-Agent Navigation in Crowded Environments: A Comprehensive Survey and Analysis
This paper presents a comprehensive survey and analysis of autonomous multi-agent navigation in crowded environments, addressing the fundamental challenge of coordinating multiple mobile agents to achieve collision-free, efficient, and socially-compliant motion in dynamic spaces shared with humans. We examine the theoretical foundations spanning collision avoidance algorithms, social force models, and machine learning approaches. Through systematic analysis of velocity obstacles, reciprocal velocity obstacles, optimal reciprocal collision avoidance, and deep reinforcement learning methods, we identify key advantages and limitations of current approaches. The paper critically evaluates computational complexity, scalability constraints, safety guarantees, and real-world deployment challenges. We present comparative performance metrics across simulation and physical implementations, demonstrating that hybrid approaches combining classical geometric methods with learned policies achieve superior performance in dense crowds. Our analysis reveals that while reinforcement learning methods show promise for social compliance, they face challenges in safety certification and sim-to-real transfer. We conclude with recommendations for future research directions, emphasizing the need for unified frameworks that integrate predictive modeling, multi-modal learning, and formal verification methods to enable robust deployment in safety-critical applications.
AI-Augmented Software Testing for Large-Scale Systems: A Comprehensive Framework and Empirical Analysis
The exponential growth in software system complexity necessitates innovative testing methodologies that transcend traditional approaches. This paper presents a comprehensive framework for AI-augmented software testing specifically designed for large-scale distributed systems. We introduce a hybrid architecture integrating deep learning models, reinforcement learning agents, and evolutionary algorithms to automate test case generation, execution, and defect prediction. Our empirical evaluation across 15 enterprise-level applications demonstrates a 34.7% improvement in defect detection rates, 42.3% reduction in testing time, and 28.9% increase in code coverage compared to conventional testing frameworks. The proposed system employs transformer-based models for test oracle generation and graph neural networks for dependency analysis. We validate our approach through controlled experiments involving 2.3 million test cases across systems ranging from 500K to 5M lines of code. Results indicate significant improvements in regression testing efficiency, with the AI system identifying 87.6% of critical bugs within the first 20% of test execution time. This research contributes both theoretical foundations and practical implementation strategies for next-generation software quality assurance.