KEC Journal of Science and Engineering is a joint publication of the KEC Faculty of Electronics and Communications, Computer Engineering and Electrical Engineering, Kalimati, Nepal. This journal publishes original, empirical and innovative works in basic, functional and support areas of science, technology and engineering disciplines.
A Review and Thermal Analysis of a Heat Exchanger with Conical Cross Flow Arrangement
The frequently used device for transfer of heat energy is the heat exchanger which allows the heat transfer from one fluid to another. The heat exchanger with cross flow arrangement has tube-side and outside fluid flows perpendicular to each other. It often occurs that in the cross-flow arrangements due to perturbations of fluid flow and heat transfer conditions, some parts of tubes in bundle are excluded from an intensive heat transfer process. This results in the effects as if the tubes were progressively shorter in comparison to their real length and the thermally active tube bundle were conical in shape (trapezium). The thermal analysis of the cross-flow arrangement for any number of tubes in bundle with progressive changes (decrease / increase) in tube length has been carried out and presented in this paper. To solve this problem the method of the "weighted mean value of outside fluid temperature" was applied. The numerical results are presented in diagrams with the effectiveness of tube side fluid versus the effectiveness of the outside fluid. The outside liquid flows one time in direction with narrowing cross-section that results in increasing of its velocity, and the second time it flows in opposite direction where the stream expands and liquid slows down its velocity. It was found that for assumed equal number of transfer units (NTU) of a heat exchanger for these both cases the second one leads to higher values of effectiveness.
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A Telecommunication System Based on Wireless Relay Network
A wireless relay network is made up of multiple low-power nodes that pass messages between two endpoints by forwarding them through intermediate helper nodes. This relay process greatly increases the communication range of small wireless devices but requires a custom communication protocol rather than traditional centralized routing methods. In this work, we present the FMTG (Find Me That Guy) protocol, a multi-hop system that uses simple broadcast discovery and chained acknowledgements to build communication paths. It removes the need for periodic control messages entirely, cutting idle overhead by 100% compared to protocols like OLSR and BATMAN and uses a compact 28-byte packet with an 11-byte header to keep communication efficient. Practical experiments with microcontroller-based nodes show that this approach extends communication distance from about 15 m to 38 m across four hops. These results demonstrate that it is a low-overhead and power-efficient option for building distributed communication networks.
Accessing Battery Thermal Safety and Adoption of Socio-Technical Influence of Electric Vehicles in Nepal
The adoption of electric vehicles (EVs) in Nepal faces critical challenges due to lithium-ion battery thermal instability, aggravated by steep terrain and high ambient temperatures. This study investigates a dual-enhancement passive Battery Thermal Management System (BTMS) using octadecane-based Phase Change Material (PCM) modified with graphite for thermal conductivity and polyurethane for flame retardancy. A 3D computational fluid dynamics (CFD) model of a 20-cell module was developed in ANSYS Workbench to evaluate discharge rates (1C–3C) under 25–40 ℃ ambient conditions. Results demonstrate that 10% graphite loading improved PCM conductivity by ~42%, achieving temperature uniformity within ±2 ℃, while a 5 mm polyurethane layer reduced simulated flame propagation by ~60%. Complementary stakeholder consultations revealed overheating and safety concerns as primary barriers to EV adoption, with a strong preference for passive, energy-efficient cooling solutions. By integrating technical optimization with socio-technical perspectives, this research provides a scalable framework for safer EV deployment in emerging economies. The findings highlight the potential of composite PCM-based BTMS to enhance user trust, support Nepal’s Net Zero 2045 target, and advance sustainable mobility, though experimental validation and long-term material stability remain essential future directions.
Adaptive Reuse: The Cases of Kathmandu Valley
In the time of environment urgency and cultural loss, adaptive reuse has emerged as an important sustainable architecture. This research explores how innovative materials and design techniques supports adaptive reuse that not only addresses ecological concerns but also respects and preserves the historical and cultural identity focusing on adaptive reuse in Kathmandu valley. The study evaluates adaptive reuse in two cases: Hotel Shanker and Babar Mahal Revisited. Through these case studies, it examines how context specific materials and design strategies have been applied and what outcomes they produced. Overall, it was found that constructing a new building of similar size would use 2.15 to 3 times more embodied energy and emit 2.62 to 3.7 times more carbon.
Artificial Intelligence in Architecture: Its Influence on Design Processes
The paper investigates how Artificial Intelligence (AI) is affecting architecture, specifically through its effects on design, creativity and making architectural work more effective. It investigates how AI can be used in architectural design, for creating sketches, analysing the site and automating records. The paper discusses uses of AI in examples such as Zaha Hadid Architects and Foster + Partners, along with revealing problems related to data, ethics, and skills development. According to the study, AI should be taught in architectural programs, appropriate tools made and principles for ethical use put in place.
Artificial Neural Network Based Shunt Active Power Filter for Power Quality Improvement
The increase in non-linear loads in the electrical distribution system has resulted in harmonics, which affect the power system’s stability and performance. The project focuses on mitigating the current harmonics of the system by implementing a Shunt Active Power Filter (SAPF), optimized using an Artificial Neural Network (ANN). The proposed system ensures the generation of compensating current through the Shunt Active Power Filter (SAPF), which helps in mitigating the harmonics present in the system. The project showcases the gradual improvement in power quality using different techniques for optimization in the conventional Shunt Active Power Filter (SAPF). This paper presents a comparative analysis of harmonic reduction techniques for a non-linear load system. The uncompensated system shows a Total Harmonic Distortion (THD) of 28.34%. Applying a conventional Shunt Active Power Filter (SAPF), PI-tuned SAPF, and PSO-optimized PI-SAPF reduces THD to 14.01%, 3.43%, and 1.64%, respectively. An Artificial Neural Network (ANN) further enhances the PSO-PI controller through adaptive real-time optimization. MATLAB/Simulink simulations demonstrate the proposed ANN-based SAPF achieves a THD of 1.41%, offering superior harmonic suppression compared to traditional methods. This demonstrates the significant improvement in the power quality by effectively mitigating the harmonics present in the system.
Building Facade Design through Paired Image-to-Image Translation using Pix2Pix
This project leverages the pix2pix image-to-image translation framework to generate realistic building facades from semantic label maps using the CMP Facade dataset. The primary objective was to reproduce the original conditional GAN architecture and validate its performance on the facade-generation task. Beyond model reproduction, we developed a lightweight web-based interface using a standard JavaScript canvas that allows users to draw block-level layout sketches. These sketches are then processed by the trained model to generate corresponding facade images, providing a simpler and faster method for prototyping architectural facades compared to traditional manual sketching techniques. The interactive workflow preserves the structural and textural fidelity of the generated buildings, while making facade visualization more accessible and user-friendly. Experimental results demonstrate that the reproduced model effectively captures building features, and the interface offers a practical tool for rapid architectural concept development and visualization. Overall, this work presents an application-focused replication of pix2pix that bridges the gap between theoretical model reproduction and practical, interactive design workflows, highlighting its potential for supporting early-stage architectural prototyping and creative experimentation.
Chord Classification Using Machine Learning
Chord classification is a fundamental task that enables automated music transcription and analysis in Music Information Retrieval. This paper presents a comparative study of the machine learning approaches for chord recognition from audio signals. The proposed framework deals with two feature extraction methods– Pitch Class Profile (PCP) and spectrograms, which are evaluated across three classification models: Support Vector Machines (SVM), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNN), and these models are trained to classify 24 chord classes comprising 12 major and minor triads. Upon experiment, the result demonstrates that PCP- based feature extraction significantly out performs spectrogram-based methods, particularly with limited training data. The MLP classifier achieves the highest performance with 0.99 precision, recall, F1-score, and accuracy, though these metrics indicate potential overfitting. The SVM model yields 0.96 accuracy with better generalization capability on unseen data. The CNN model achieves up to 0.97 training accuracy but exhibits poor generalization. The finding reveals that with PCP-enabled method can be used effectively and develop a robust chord classification system. This work highlights the potential of machine learning in improving chord classification accuracy and sets the stage for future development by refining models and expanding the dataset.
CNC Plotter: An Educational Prototype for Intelligent Automation
This paper illustrates the development of a CNC plotter as an educational prototype for intelligent automation. The system illustrates how computer vision, G-code execution, and AI-based decision making can all be integrated together into a CNC platform. The system has two modes: a drawing mode, where you can create text and pictures, and a gaming mode, where the plotter plays tic-tac-toe against a person. The hardware setup relies on an Arduino Uno, NEMA 17 stepper motors, and an SG90 servo motor, while the software setup includes Inkscape, Universal G-code Sender, and GRBL firmware. A custom-trained YOLOv11 model detects board states, while the Minimax algorithm with Alpha-Beta pruning calculates the most optimal moves. The experimental results show that user designs are accurately reproduced, gameplay performance is reliable, and symbols are classified nearly perfectly. The potential of integrating CNC technology and machine learning for the purpose of education and prototyping in intelligent automation is showcased in the project.
Comparative Analysis of Distributed and Centralized PV Integration in a Distribution Feeder Using DIgSILENT Power Factory
With the rapid growth of photovoltaic (PV) integration into distribution networks, maintaining voltage stability and minimizing feeder overloading have become significant technical challenges for utilities. This study aims to analyze the impact of distributed and centralized PV system integration on the operational performance of a distribution feeder modeled in DIgSILENT PowerFactory and categorized into four different zones. The network was simulated under two configurations: in the distributed PV system, nine PV plants with different generating capacity were strategically allocated across different buses to represent decentralized generation, while in the centralized PV system, the same total capacity was concentrated at the approximate load center. The feeder’s performance is analyzed at 12 different level of PV penetration from 0 to 50% of total generation (14.11 MW), where the feeder was loaded at 3.34 MW. Through detailed load flow analysis, system performance was evaluated based on line loading, voltage profile, and PV bus characteristics. In the distributed system, the feeder’s minimum loss was at 22% PV penetration with over 63% loss reduction. One the other hand, centralized system had minimum loss at 15% PV penetration with over 50% loss reduction. The large number of lines were operating under heavy overloading condition due to large localized power flow in the centralized system compared to that in distributed system under high level of PV penetration. The voltage profile of the distributed PV system is found to be uniformly improving with the increase in PV injection level. The results indicate that distributed PV integration provides more balanced voltage profiles along the feeder, significantly reducing voltage drops, line loading, and power loss. Conversely, the centralized PV configuration caused noticeable voltage rise near the point of connection and higher upstream line loading. The comparative findings demonstrate that distributed PV systems enhance network efficiency and voltage stability, while centralized PV setups offer simpler control but are more prone to localized overvoltage and uneven power distribution. Study also demonstrate that the hosting capacity of the feeder is more in distributed system. The study provides insights into how optimal PV placement strategies can improve the technical performance and reliability of modern distribution networks.
Control of Isolated Micro-Hydro Power Plant with ELC for Un-balanced Load
In isolated micro-hydro power (MHP) systems, maintaining frequency and current balance under varying and unbalanced loads is essential for stable and reliable operation. Conventional Electronic Load Controllers (ELCs) regulate generator frequency by diverting excess power to dump loads; however, they activate dump loads equally across all phases, disregarding load asymmetry. This approach leads to current imbalance, zero-sequence currents, and excessive heating in the neutral conductor, which can compromise generator performance and lifespan. This paper proposes a novel phase-wise ELC control strategy that dynamically adjusts the number of dump loads in each phase independently based on real-time current measurements. By comparing each phase current with a reference full-load value, the controller ensures balanced generator operation under unbalanced consumer loads. The proposed system was modeled and simulated in MATLAB/Simulink under three scenarios: conventional ELC with balanced load, conventional ELC with unbalanced load, and the proposed ELC with unbalanced load. Simulation results show that the proposed ELC significantly reduces current imbalance, minimizes neutral current, and stabilizes rotor speed, thereby improving system efficiency and operational reliability in isolated MHP applications.
Design and Empirical Validation of a Mid-Range Wireless Power Transfer System Using Magnetic Resonance Coupling
Conventional inductive power transfer is fundamentally limited by the rapid decay of magnetic flux density, restricting efficient energy transmission to near-contact distances. This research addresses this spatial limitation through the design, simulation, and empirical validation of a mid-range wireless power transfer system based on magnetic resonance coupling. The system employs a Class-B push-pull power amplifier driving high-quality factor helical coils with a diameter of 30 centimeters and 6 turns, tuned to a nominal resonant frequency of 1 megahertz. Through integrated finite element method magnetostatic simulations and circuit analysis, the system was optimized to maximize flux linkage while minimizing skin effect losses. Experimental validation demonstrated that the magnetic resonance coupling topology significantly extends transmission range compared to non-resonant inductive coupling. At a separation of 20 centimeters, the resonant system delivered 5.4 volts to the load, whereas the inductive baseline achieved only 0.33 volts. The system achieved a peak end-to-end efficiency of 19.01 percent and maintained functional power transfer up to 40 centimeters. These findings confirm that resonant impedance matching provides a scalable pathway for mid-range charging applications when frequency tracking mechanisms compensate for dynamic coupling variations.
Design and Implementation of a Multi-Purpose Low-Cost Hall-Effect Sensor Glove for Sign Language Recognition
Despite the prevalence of severe hearing loss affecting over 430 million people globally, access to sign language interpretation remains critically scarce, particularly in low-resource settings like Nepal. Assistive technologies divide into two flawed categories: prohibitively expensive commercial gloves (often exceeding $3,000) or fragile research prototypes reliant on flex sensors that degrade rapidly under mechanical stress. This paper introduces a robust, cost-effective sign language recognition system tailored for the Nepali Sign Language (NSL) community. Departing from traditional resistive sensing, we implement a non-contact Hall-effect architecture that correlates magnetic field intensity with finger flexion, eliminating mechanical wear and signal drift. The system integrates 14 sensor nodes across the DIP, PIP, and MCP joints, augmented by an MPU6050 IMU for wrist orientation. An embedded Multi-Layer Perceptron, executed locally on an Arduino Mega, performs gesture classification, negating the need for cloud dependencies. With a Bill of Materials between $80 and $100, this solution is approximately 30 times more affordable than market alternatives. Validation trials across five subjects yielded 96% accuracy on a fundamental NSL vocabulary. As a proof-of-concept demonstration, this work validates the approach using 11 common NSL words, establishing a foundation for future vocabulary expansion. Stress testing confirmed that the Hall-effect configuration maintains signal fidelity over repeated cycles where traditional sensors fail. This study demonstrates that high-precision recognition is achievable through strategic engineering rather than premium components, offering a scalable pathway for deployment in Nepal's deaf schools.
Designing With Clay: Architectural Evaluation of Hollow and Solid Brick for Sustainable Construction
This paper evaluates the environmental, thermal, and structural performance of solid and hollow clay bricks in a building. We evaluate compressive strength, thermal insulation, and carbon footprint using material testing and case studies (such as Auroville Visitors Centre, IIT Hyderabad). According to the findings, hollow clay bricks preserve structural integrity while using 20–30% less energy than solid bricks. There are suggestions for the best brick choices based on climate zones.
Dual-Loop Virtual Synchronous Machine to Enhance Frequency and Voltage Stability in PV-Hydro Microgrids
This paper investigates a dual-loop Virtual Synchronous Machine (VSM) to enhance frequency and voltage stability in a PV-Hydro microgrid supplemented by a lithium-ion storage battery system. The proposed VSM simultaneously emulates the inertia and damping property of a synchronous generator and mitigates the voltage dip problem during transient period due to the large perturbation of P-Q load. The VSM injects active and reactive power during transient period through an inverter with a storage battery at dc side. MATLAB/Simulink simulations were carried out under severe load disturbances for two cases: (i) PV-Hydro micro-grid without VSM, (ii) PV-Hydro micro-grid with proposed dual-loop VSM. Without VSM support, frequency and voltage dropped to 0.57 pu and 0.78 pu respectively during transient period, exhibiting unstable behaviour. The proposed dual-loop VSM improved stability, limiting frequency deviation to 0.86 pu and voltage sag to 0.9 pu with faster recovery during transient period.
Early Prediction of Chronic Kidney Disease Using Extra Trees and LightGBM with Sharp Visualization
This research paper aims to detect Chronic Kidney Disease (CKD) at the initial stages by using blood and urine test parameters leveraging computationally efficient machine learning algorithms such as LightGBM and Extra Trees. The UCI Chronic Kidney Disease dataset with 400 instances and 24 features along with binary “ckd” and “notckd” target class, is preprocessed by imputing missing values, clipping outliers and normalization. Validation was done by 5-fold cross validation technique. Extra Trees and LightGBM achieved accuracies of 0.993 and 0.985 respectively. SHAP visualization showed albumin, hemoglobin and specific gravity (sg = 1.025) as key features using Extra Trees whereas LightGBM showed albumin, hemoglobin and serum creatinine as key features. Albumin, hemoglobin, specific gravity = 1.025, hypertension and serum creatinine are significant indicators of CKD. Specific value of sg i.e. 1.025 being a significant contributor can be a significant area of medical study. Hence, this study offers an interpretable, lightweight framework suitable for integration into routine clinical blood testing for early CKD detection and can be emulated as a method of identifying features that contribute towards a disease, which with high model accuracy can be put into consideration for clinical research to uncover the feature’s biological meaning and potential as a hidden biomarker as well. Notably, low hemoglobin, linked to reduced production of erythropoietin as seen in impaired kidneys, strongly predicted “ckd”, displaying one such critical biomarker. This shows a strong potential of using this method for other diseases as well.
Fuzzy Logic Controller Implementation for Induction Motor Soft Start
Direct-on-line starting of three-phase induction motors causes high inrush current, voltage dips, and torque oscillations, reducing motor life and system stability. Conventional soft starters like auto-transformers and star-delta systems offer limited efficiency and control. This paper proposes a fuzzy logic controller (FLC)-based soft starter for induction motors driving fan-type loads to achieve smooth, adaptive, and energy-efficient operation. The system, modelled in MATLAB/Simulink with optimized membership functions and rule bases, uses weighted average defuzzification for real-time voltage control. Simulation results show that the FLC reduces peak starting current to about 41 A, compared to 80 A in direct-on-line and 54 A in PI-controlled methods. The fuzzy-based system provides smoother torque and speed response, reducing mechanical stress and improving performance. Thus, FLC-based soft starters offer an efficient, scalable, and intelligent solution for industrial applications such as HVAC systems, pumps, and conveyors, with potential for future hardware implementation.
ImageCloak: An CNN Based Stegenography System
ImageCloak is an advanced deep learning-based image steganography system that enables secure embedding and extraction of hidden data within digital images using Convolutional Neural Networks (CNNs). The system achieves high performance, with 91.48% testing accuracy in text encoding and image encoding results of SSIM: 0.8898 and PSNR: 31.12 dB, ensuring that the concealed data remains imperceptible while preserving the visual quality of the cover image. To enhance scalability and usability, ImageCloak integrates MongoDB for efficient storage and retrieval of stego-images, allowing users to manage hidden data securely over time. The system also employs robust security measures to prevent unauthorized access, ensuring that sensitive information remains protected. Additionally, it supports seamless sharing of encoded images among authorized users, making it an effective solution for private communication, secure data exchange, and long-term confidential storage. By leveraging deep learning techniques and advanced embedding methods, ImageCloak provides a reliable and scalable approach to modern steganographic applications, enhancing data privacy and security in digital communication.
Nepali Sign Language Characters Recognition: Dataset Development and Deep Learning Approaches
Sign languages serve as vital communication systems for individuals with hearing or speech impairments, yet digital resources for underrepresented languages such as Nepali Sign Language (NSL) remain scarce. Although tens of thousands of people in Nepal rely on NSL, the lack of systematically curated datasets has slowed progress in computational assistive technologies. This study introduces a large-scale image-based dataset for NSL, consisting of 54,000 images spanning 36 gesture classes, with 1,500 samples per class. The dataset includes samples captured against both plain and varied backgrounds to enhance robustness and generalization. To establish baseline recognition performance, we fine-tuned MobileNetV2 and ResNet50 Convolutional Neural Network (CNN) models using transfer learning. Experimental results indicate that MobileNetV2 outperforms ResNet50, achieving a classification accuracy of 90.45% compared to 88.78%, suggesting that lightweight architectures generalize more effectively in low-resource settings. To the best of our knowledge, this dataset represents one of the first large-scale image-based resources for NSL, providing a foundation for advancing research on underexplored sign languages.
Neuronex: An Immersive Learning Experience Using Augmented Reality
Augmented Reality (AR) technology has emerged as a powerful tool in education, offering immersive and interactive learning experiences. This study presents Neuron-X, an e-learning application designed to bridge the gap between traditional instructional methods and modern, technology-enhanced education. The application integrates 3D models created in Blender with AR functionalities developed in Unity to visualize and interact with digital content in real-world contexts. Through interactive 3D models, diagrams, and simulations, Neuron-X aims to simplify complex concepts, promote active engagement, and improve knowledge retention among middle school students. The system’s flexibility allows it to be applied across multiple subject areas, making it suitable for both educators and learners. By incorporating AR into the learning process, this research seeks to enhance student engagement, support diverse learning needs, and demonstrate the potential of AR-based tools in improving academic performance.
Performance Analysis of AC Voltage Controller Using Firing Angle and PWM Control for Various Load Conditions
This research focuses on the performance analysis of an AC Voltage Controller (ACVC) utilizing various control techniques—specifically firing angle control and Pulse Width Modulation (PWM) control for different load conditions. The AC Voltage Controller is a power electronic device that regulates AC voltage magnitude without altering its frequency. Simulation models were developed using MATLAB/Simulink for resistive, inductive and single-phase induction motor loads under both firing angle and PWM control techniques. The results highlight that PWM control significantly reduces Total Harmonic Distortion (THD) and improves power factor compared to conventional firing angle control, thereby ensuring enhanced performance and efficiency.
Regional Variability in Geotechnical Properties and Slope Stability Mechanisms Across Climatic and Geological Zones of Nepal
Nepal’s Himalayan terrain, characterized by extreme elevational gradients and heterogeneous lithology, presents region-specific challenges for slope stability and infrastructure resilience. This study evaluates the geotechnical properties of soils from five climatically distinct regions—Kathmandu Valley (subtropical urban basin), Pokhara (humid alluvial valley), Chitwan (forested sub-Himalayan tract), Mustang (arid trans-Himalayan zone), and Terai Plains (tropical lowland)—to establish predictive relationships between soil behaviour, environmental factors, and slope failure mechanisms. Laboratory analyses, including triaxial shear testing under unsaturated conditions and advanced permeability profiling were paired with limit equilibrium stability modelling. Results demonstrate that moisture content (R² = 0.87, p < 0.01) and clay mineralogy dominate stability outcomes, with safety factors (FoS) ranging from 0.8 (Terai) to 2.5 (Mustang). A novel regional classification framework is proposed to guide slope management in Nepal’s rapidly developing landscapes.
Sensor less Control of BLDC Motor Using Anfis
This study presents a sensor less control strategy for brushless DC (bldc) motors using an adaptive neuro fuzzy inference system (anfis) to generate virtual hall effect signals from the measured three-phase back-emfs. The proposed approach eliminates the need for physical position sensors by producing digital hall equivalent signals that directly interface with conventional commutation logic circuits, providing a seamless drop-in replacement for the hall sensors. The controller was modelled and simulated in MATLAB/Simulink with a closed-loop PI speed controller (K­p = 0.013, Ki = 16.61). The anfis network was trained on data collected at a reference speed of 3000 rpm from a sensor equipped bldc test bench in Simulink. The trained model was validated under both loaded and unloaded operating conditions. A unit step load torque was applied at t=0.1s to evaluate dynamic performance. The results demonstrate that the anfis based system exhibits a dynamic response comparable to the conventional hall sensor-based control.
Spatial Influence on Form: Understanding How Space Shapes Architectural Expression
This research explores how space influences the way architectural form is shaped. It looks at the idea that form is not only used to define space, but can also grow out of how space is used, experienced, and understood. The study includes both theory and real examples to show how spatial needs and perceptions guide design. It also looks at how people experience these spaces after they are built. The aim is to understand how architecture can express meaning by letting space lead the design process.
Speech Recognition-Driven Language Learning: The Case of Tiny Talks for Nepali Using Wav2Vec2
The TinyTalks project presents a mobile based system designed to support Nepali language learning among children aged 4 to 8. The application addresses the challenge of language preservation by offering an engaging and interactive learning environment tailored to young learners. TinyTalks integrates automatic speech recognition using a fine-tuned Wav2Vec2 model to evaluate children’s pronunciation in real time. Nepali speech samples collected from multiple speakers were used to train the model through supervised learning, resulting in a validation accuracy of about 75 percent. The final system provides interactive lessons, pronunciation feedback and simple quizzes that support early language acquisition. The study demonstrates the feasibility of combining mobile technology and speech recognition to assist foundational Nepali language learning for young children.
State Estimation of IEEE 5-Bus and 14-Bus Power System
This paper investigates techniques for precisely estimating the state (angles and voltages) of electrical networks using the 5-bus and 14-bus test systems. Two methods were examined: Weighted Least Squares (WLS), a statistical technique used to minimize measurement errors; and the Artificial Neural Network (ANN), a machine learning model trained to estimate the system states. The results showed that WLS provided reliable estimates with fewer measurements, while ANN yielded the most accurate results overall. This study contributes to the enhancement of real-time power grid monitoring, thereby supporting a stable and consistent electricity supply.
Study of Shading Photovoltaic Module and Fabrication of Solar Data Logger
Solar photovoltaic (PV) systems are essential for renewable energy production, yet their performance is greatly influenced by shading. The performance of photovoltaic (PV) modules is highly influenced by environmental conditions, with shading being one of the most critical factors causing significant power losses. To investigate this effect, nine different shading strategies were designed based on real-case scenarios commonly encountered in rooftop and field installations, such as partial obstruction by buildings, trees, and nearby structures. This research examines the influence of shading on photovoltaic systems through MATLAB/Simulink simulations and practical experiments. A solar data logger was created to track essential performance metrics, such as voltage, current, temperature, and irradiance. The research confirms the issue of power loss in PV modules due to shading, which results in decreased energy production and efficiency. The main objective is to investigate the impact of various shading strategies, assess their effect on the I-V and P-V characteristics of PV modules, and fabricate a solar data logger system. This system records and analyses real-time data to facilitate performance evaluation and predictive maintenance of PV modules. The findings indicate that shading significantly reduces the output of PV modules, with some shading patterns causing power losses of up to 100%. The hardware results closely match the simulation, confirming the accuracy of the analysis. Additionally, the fabricated solar data logger effectively captures essential PV parameters, providing a reliable tool for long-term system monitoring. This study highlights the impact of shading on PV modules and emphasizes the role of real-time data logging in performance evaluation and predictive maintenance.
Tool And Die Design for the Prototype Fabrication of Automobile Pleated Air Filter
Air filter is one of the important components of internal combustion engine driving system. In this study tool, die and fixtures that can be designed and assembled for the fabrication of air filter is presented. Dies were further heat treated for better hardness property so that it can press sheet metal. Then sheet metal was sandwiched in between these two dies and pressed together using flywheel press to fabricate pleat base. Effect of dry pressing, clearance, lubrication and removal of shearing angle for achieving quality finishing were thoroughly presented. Dry pressing result provided distorted pleat shape as shearing and wrinkles were obtained in the bending portion of pleat base. Due to this reason dies were redesigned and lubrication was introduced during pressing. Similarly, clearance and shearing angle were modified to achieve pleat base. After that perforated support and pleated air filter were fabricated and assembled together to fabricate an air filter.
Traditional Nepali Object Detection using Fine-Tuned YOLOV8
Visual learning supports comprehension by using images and videos to present information in an intuitive way. This study develops and optimizes an object detection model for integration into Drishti Kosh, a mobile visual dictionary designed to identify and translate traditional Nepali objects. A dataset containing 3,591 images across twelve culturally significant classes was used to fine-tune the YOLOv8n model. Three optimization algorithms—Stochastic Gradient Descent, Adaptive Moment Estimation, and AdamW—were evaluated to determine the most effective configuration for real-time use. Among them, the model trained with the AdamW optimizer demonstrated superior performance, achieving the highest recall value of 0.8753 and a mean Average Precision at 50 percent of 0.6545. It also produced the fastest inference time of 2.15 milliseconds and the lowest post-processing time of 0.98 milliseconds, along with a low training loss of 0.00087, indicating stable convergence. The results show that a lightweight and culturally aware object detection model can support real-time mobile deployment and enhance user interaction in multilingual environments. This contributes to improved accessibility for learners, tourists, and general users engaging with traditional Nepali objects in everyday contexts.
Transformation of Courtyards in the Historic Core of Kathmandu: Typologies, Functions, and Socio-Economic Shifts
Courtyards have been at the heart of Kathmandu’s spatial, social and spiritual life. This paper examines how these traditional spaces- once integral to the collective identity, religious practice, and urban ecology are changing under the forces of modernization, migration and economic transformation. Based on the case studies of three historic bahals: ItumBahal, Shree Gha Bahal and TeBahal the research examines changes in typology, function and meaning. Using participants observation, semi-structured interviews, and site documentation, the research illustrates how some courtyards remain strong communal and religious spaces, while others are being degraded or commercialized. The research identifies the need for reconciliation between heritage conservation and evolving urban conditions.
Vibration Analysis and Control: A Cantilever Beam with a Tuned Mass Absorber Under Harmonic Loading and Mass at the Free End
Vibration of a cantilever beam subjected to harmonic loading with a tip mass is mitigated by employing a tuned mass absorber at the free end. The beam is modeled using the Euler-Bernoulli theory to establish equations of motion for both the beam and the absorber. Stiffness and mass values ensuring zero deflection at the free end are determined via Galerkin’s Method with a polynomial trial function. The analytical results are validated through FEM simulations in ANSYS Mechanical. The close agreement between computational and analytical outcomes establishes a foundation for advanced computational investigations.
Wireless Power Transfer through Inductive Coupling
This paper proposes the wireless power transfer (WPT) through inductive coupling (IC). WPT can be described as the transfer of electrical energy from the source to the electrical load without using conductors connecting between transmitter and receiver coil. This paper highlights significant studies on the short-range inductive coupling and challenging in achieving high efficiency and safety over long distances.
Editor-in-Chief’s Message Dear Readers, It is with great pride and enthusiasm that I present Volume 10, Issue I of the KEC Journal of Science and Engineering (KJSE), a significant milestone that marks a decade of our continued commitment to academic excellence and knowledge dissemination. Since its inception in 2013 under the Office of KEC Research and Publication (OKRP), KJSE has steadily evolved into a credible platform for fostering research, innovation, and scholarly dialogue within Nepal and beyond. Over the years, engineering has undergone rapid transformation, driven by advancements in artificial intelligence, sustainable technologies, data science, and interdisciplinary integration. As we step into this new volume, we reaffirm our mission to bridge theory and practice by promoting research that not only advances scientific understanding but also addresses real-world challenges. In doing so, we aim to cultivate a culture of inquiry, creativity, and responsible innovation among students, faculty, and professionals. This issue brings together a diverse collection of research articles and technical contributions that reflect the dynamic and multifaceted nature of engineering and applied sciences. The works featured herein demonstrate rigorous analysis, innovative thinking, and a strong commitment to solving contemporary problems. We are particularly encouraged by the growing participation of young researchers and students, whose curiosity and dedication continue to shape the future of engineering. As KJSE enters its tenth volume, we are also focused on strengthening our academic reach and impact in the society. Building upon our inclusion in platforms such as NEPJOL, we aspire to expand collaborations with national and international institutions, enhance the quality of peer review, and encourage interdisciplinary research that transcends traditional boundaries. We remain equally committed to engaging with industry stakeholders, recognizing the importance of practical insights and real-world applications in enriching academic discourse. I extend my heartfelt gratitude to all authors, reviewers, editorial board members, and the OKRP team for their unwavering support and dedication. Your contributions are the foundation of this journal’s success. I also encourage our readers to actively engage with the published work, share insights, and contribute to future editions. As we celebrate this milestone, let us continue to push the boundaries of knowledge, inspire innovation, and work collectively towards engineering solutions that are sustainable, inclusive, and impactful. Thank you for being a valued part of the KJSE community. Sincerely, Pratima Pradhan, PhD Editor-in-Chief KEC Journal of Science and Engineering (KJSE) Office of KEC Research and Publication (OKRP) April, 2026