Journal of Advanced College of Engineering and Management
Published by the Advanced College of Engineering and Management, Kalanki, Kathmandu, Nepal
Advanced College of Engineering and Management
JACEM reserves the copyright for the published papers. Author will have right to use content of the published paper in part or in full for their own work.
Balancing Privacy And Accuracy In Nepali Sentiment Analysis: Fine-Tuning Nepalibert With Differential Privacy
The increasing volume of user-generated Nepali text has enabled the development of sentiment analysis systems, but training large language models on real data introduces significant privacy risks, including potential exposure through membership inference attacks. This study examines the balance between accuracy and privacy in Nepali sentiment analysis by fine-tuning NepaliBERT with and without Differential Privacy. A high-performing non-private baseline model was trained on approximately 7,000 labeled samples, achieving near-perfect classification performance (Accuracy up to 99.88–100% and Macro F1 up to 1.00), and was subsequently evaluated for vulnerability using membership inference and canary-based privacy assessments. To mitigate privacy risks, Differentially Private Stochastic Gradient Descent was applied under varying privacy budgets (ε), and the resulting models were systematically analyzed to measure performance degradation and resistance to privacy attacks. The findings establish an empirical benchmark for the privacy–utility trade-off in low-resource Nepali NLP and provide practical guidance for building sentiment analysis systems that are both accurate and privacy-preserving.
Copyright (c) 2026 Journal of Advanced College of Engineering and Management
Child Seizure Prediction Using Temporal Features with Temporal Convolutional Neural Network (TCNN)
Early prediction of epileptic seizures is clinically important because it allows for immediate treatment, reduce injury risk, and improve the quality of life of pediatric patients. This paper presents a subject-specific three-class seizure prediction framework using multichannel electroencephalography (EEG) and a Temporal Convolutional Neural Network (TCNN). The proposed method is evaluated on the publicly available CHB-MIT scalp EEG database. During preprocessing, the raw EEG signals were band-pass filtered between 0.5--40~Hz, notch filtered at 60~Hz, segmented into fixed 5-second windows, and labeled as interictal, pre-ictal, and ictal using seizure annotations, a seizure prediction horizon of 5 minutes, and a seizure occurrence period of 30 minutes. The proposed TCNN learns temporal EEG patterns directly from multichannel inputs, while weighted sampling and focal loss are used to address class imbalance. The main contribution of this study is the development of a lightweight subject-specific three-class framework with class-wise, threshold-independent evaluation. For a representative validation subject, the model achieved 89.03\% accuracy, a macro F1-score of 0.8239, ROC-AUC values of 0.958, 0.965, and 0.997 for interictal, pre-ictal, and ictal classes, respectively, and average precision values of 0.988, 0.874, and 0.952. The results indicate that the proposed TCNN is effective for seizure-state discrimination and near-onset warning, while pre-ictal prediction remains the most challenging task. Overall, this study demonstrates that temporal convolution is a promising approach for pediatric EEG-based seizure prediction, with future work needed to improve pre-ictal specificity.
Comparative Study of CNN and MobileNetV2 for Forest Fire Detection with Data Augmentation
Forest fires pose a significant environmental threat, and quick, reliable detection is essential to reduce damage. This paper compares lightweight custom Convolutional Neural Network (CNN) with a fine-tuned MobileNetV2 for automated forest fire detection. Both models were trained on a Kaggle dataset containing 5,050 labeled forest images. The images underwent preprocessing that included resizing and normalization, and they were improved through extensive data augmentation like rotation, zoom, shifting, and flipping. The CNN achieved an accuracy of 92% with an F1-score of 0.90 for detecting fire. In comparison, MobileNetV2 achieved a higher accuracy of 95% with an F1-score of 0.94, cutting false negatives by 39%. Both models reached a ROC-AUC of 0.99, but MobileNetV2 showed better sensitivity, making it more suitable for real-time monitoring systems like drones and satellites. The results show that while CNN is robust with fewer false positives, MobileNetV2 strikes a better balance between precision and recall, making it the preferred choice for forest fire detection.
Comparison of YOLO Algorithms for Sitting Posture Assessment of Office-Based Workers in Construction
Improper sitting posture is a significantly contributes to physical and psychological damages to office-based workers particularly where there is a lack of continuous ergonomic monitoring. This study analyses four automated sitting posture detection models based on recent versions of YOLO object detection algorithm. The models demonstrated reliable sitting posture classification by accurately detecting sitting posture variation. The proposed models provided an efficient solution for office-based workers siting posture monitoring compared to existing sensor based and computer vision-based models. The models were evaluated based on key performance matrices such as true positive values, precision score, recall score, mAP values and inference speed. Among the four models, YOLOv11-s was selected as the optimal model due to its performance in accuracy and computational efficiency. The model can be further developed by integrating real-time feedback mechanisms, such as automated posture alerts to improve workplace ergonomics, worker wellbeing, and overall safety practices.
DeepTrust: A Hybrid Transformer-CNN Model for DeepFake Detection With Zero-Knowledge-Based Blockchain Authentication
Deepfake technology poses a growing threat to digital trust across journalism, law, and politics. Current CNN-based detectors capture local artifacts but struggle with high-quality fakes and offer no way to prove their predictions are genuine. This paper presents DeepTrust, a framework combining a hybrid CNN–Transformer detector with Zero-Knowledge Proof (ZKP) verification and blockchain-based record-keeping. The detection model fuses spatial features from an attention-enhanced Xception network, global context from ViT-B/16, and spectral cues from a Frequency Encoder through a cross-attention mechanism. Predictions are cryptographically committed using a Pedersen scheme with the Fiat-Shamir heuristic, then stored on a proof-of-work blockchain. Evaluated on FaceForensics++, Celeb-DF, DFD, and 140K Real vs Fake, DeepTrust achieves 97.00% accuracy and 0.999 AUC on FaceForensics++, with balanced per-class accuracy despite imbalance ratios up to 1:8.5. ZKP overhead remains below one millisecond per prediction.
Design And Development of An AI-Driven Solution For Rhesus Macaque
In much of the farming land of rural Nepal, wild animals, most notably the Rhesus Macaque, a widely distributed monkey species found in Nepal, pose a significant issue. These animals frequently find their way into the farmland and destroy the farm crops, endangering the efforts and earnings of the farm- ers. The research aims to create a smart solution that uses Artificial Intelligence-based detection and ultrasonic-based repellents. The system enables the use of the AI-powered cameras to scan and monitor the target area and identify the Rhesus Macaque in real-time using the YOLO algorithm, trained using a custom dataset of monkey images, and upon detection, use an ultrasonic deterrent system to repel the Rhesus Macaque. The detection model was trained on the dataset collected from the Pashupatinath and Swayambhunath area, as the farmland shares similar species, to enhance the detection performance of the model under local settings. Upon identification of the species, the detection model activates the ultrasonic deterrent subsystem. The YOLO based detection model achieved a precision of up to 1.00, an F1-score of 0.97, and a recall of 0.99. The 555 Timer IC-Based ultrasonic generator was validated at the prototype stage, with its generated output frequency measured and verified using an oscilloscope.
Explainable Pneumonia Detection from Chest X-Ray Images Using a ResNet-50 Based Deep Learning Framework
Pneumonia is a serious lung infection responsible for approximately 2 million deaths in children annually and 50,000 adult deaths each year, placing a significant burden on global healthcare. Early and accurate detection is critical, particularly in resource-limited settings. This paper proposes an automated pneumonia detection framework based on the ResNet-50 deep learning architecture applied to chest X-ray images. The model was trained on the publicly available Chest X-Ray Pneumonia dataset (Kaggle) using transfer learning, with 70% of data used for training and 30% for testing. To address model interpretability, a critical requirement for clinical adoption of Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated to visually highlight lung regions influencing classification decisions. The proposed model achieved an overall accuracy of 81.9%, a recall (sensitivity) of 96.7%, a precision of 79.0%, a specificity of 57.3%, and an F1-score of 87.0% on the test set. The high recall is prioritized given the clinical importance of minimizing false negatives in pneumonia screening. Grad-CAM visualizations confirm that the model focuses on anatomically relevant lung regions. This system is intended as a decision-support tool to assist clinicians rather than replace them.
Hybrid GAN-Transformer For Synthetic Medical Text Generation To Address Data Scarcity In Healthcare
Medical research often struggles to access patient data because privacy laws like HIPAA and GDPR protect sensitive health information. This study proposes a system that generates realistic synthetic medical notes without risking patient privacy. We developed a hybrid model using two neural networks: DistilGPT-2 as the text generator and BioBERT as the evaluator. BioBERT was trained on clinical notes from the MIMIC-III database. Training was completed in three phases. First, the generator learned basic medical writing and achieved a best validation perplexity of 8.87 (from an initial 9.34), with training perplexity reducing from 19.55 to 6.90. Second, we intentionally weakened the evaluator to maintain balance between the models. Third, both networks were trained at full strength with added controls. However, the evaluator became too accurate, which disrupted the training process. The generator’s validation perplexity increased to 47.05, and text diversity (Distinct-1) decreased from 0.271 (Phase 2 start) to a minimum of 0.110 (Phase 3, epoch 8).
Interpretable Model For Anomaly Detection In P2P Financial Transactions
The rapid growth of peer-to-peer (P2P) digital transactions has increased the risk of transaction errors and anomalous activities, which presents a requirement for a robust and understandable system for detecting anomalies. Although machine learning models have shown promising results in anomaly detection, their integration in real world setting is limited due to lack of transparency. This study focuses on developing an anomaly detection framework for P2P financial transactions utilizing a set of contextual, behavioral, network, and temporal features using financial fields. Additionally, several preprocessing techniques are applied to deal with class imbalances. This study utilizes Machine Learning models such as Logistic Regression, Decision Trees, Random Forest, and XGBoost. XGBoost achieves the best overall performance with a ROC-AUC of 0.968 and a PR-AUC of 0.076, representing a 69-fold improvement over the random baseline. A recall of 0.943 indicates that 94.3% of anomalous transactions are correctly identified. Furthermore, to enhance model interpretability, Explainable AI (XAI) techniques such as SHAP, LIME, and CIU are applied to the best performing model (XGBoost). Spearman rank correlation analysis confirms strong inter-method agreement between SHAP and CIU (ρ = 0.745, p < 0.001), providing quantitative evidence of explanation consistency.
Malaria Parasite Classification in Thin Blood Smears Based on CNN with SpinalNet and GAN
This research presents a novel framework integrating Generative Adversarial Networks (GANs) with a parameter-efficient Convolutional Neural Network (CNN) to automate malaria parasite classification from thin blood smear microscopy images. A major bottleneck in automated malaria diagnosis is severe clinical data imbalance, where minority species (e.g., Plasmodium ovale, P. malariae) are heavily underrepresented, causing standard deep learning models to default to majority-class predictions. To address this, our methodology employs a Wasserstein GAN with Gradient Penalty (WGAN-GP) to synthesize high-fidelity microscopic images, effectively balancing the training distribution prior to classification. Following rigorous preprocessing---including Contrast Limited Adaptive Histogram Equalization (CLAHE) and aggressive data augmentation to mitigate staining variability---the balanced dataset is processed by a CNN for feature extraction. Crucially, traditional fully connected layers are replaced with a SpinalNet classification head. This architectural innovation processes extracted features sequentially, which drastically reduces the number of trainable dense parameters while acting as a structural regularizer against overfitting on the synthetic data. Evaluated on strictly real, hold-out clinical test sets, the proposed GAN-CNN-SpinalNet pipeline demonstrates a strong capability to identify rare parasite species without sacrificing performance on the P. falciparum majority class. Assessed across standard metrics, including accuracy and Macro F1-score, this approach successfully overcomes data scarcity to deliver a robust, and computationally efficient diagnostic tool for resource-limited settings.
Nepali Speech Emotion Recognition Using Variational Quantum Circuits
Speech emotion recognition (SER) is an active area of research, yet existing work has focused almost exclusively on high-resource languages, leaving Nepali — with over 32 million first- and second-language speakers worldwide — without any published SER study or emotional-speech corpus. This paper addresses that gap along two dimensions. First, we construct a Nepali emotional-speech dataset comprising 600 utterances across three emotion classes (happy, sad, neutral), validated by 117 native listeners whose mean recognition accuracy is 91.5%. Second, on this corpus, we evaluate a fully quantum data-reuploading variational quantum circuit (VQC) classifier with trainable SU(2) encoding on nine qubits, and compare it directly against two classical baselines — a random forest and a multilayer perceptron — on the same PCA(27) feature pipeline and stratified 480/120 split. A staged hyperparameter search covering circuit depth, learning rate, optimizer, and batch size identifies an optimal VQC configuration of ten layers and 540 trainable parameters, which attains 90.83% test accuracy and a macro-F1 of 0.908. Gradient-norm analysis confirms the absence of barren plateaus during training. Both classical baselines outperform the VQC under this protocol (Random Forest 95.00%, MLP 99.17%); however, a leave-one-speaker-out robustness check shows that classical accuracy collapses by approximately one-third under this evaluation, indicating that a substantial portion of the classical advantage reflects speaker-level information leakage.
Predictive maintenance (PdM) is crucial in vital sectors like aviation. It uses data analytics and machine learning to optimize maintenance plans, lower operating costs, and eliminate unscheduled downtime. To create a PdM framework for forecasting the Remaining Useful Life (RUL) of aviation components, this study uses a publicly accessible aircraft engine dataset, which includes time-series data of operating settings and sensor readings. This study uses advanced machine learning model, such as Random Forest to show how PdM systems can predict component failures. The methodology includes data preparation, feature engineering, and rigorous evaluation utilizing measures. Though the dataset is not specific to Nepal, the findings show how this method can improve aviation safety in regions like Nepal, where difficult operating circumstances and a high frequency of aviation incidents demand better maintenance practices. This study raises the way for predictive maintenance’s increased adoption in the aviation industry by showing that it improves safety, lowers planned downtime, and promotes operational efficiency.
Satellite Attitude Control using Reinforcement Learning and State Space Model
This paper presents a reinforcement learning (RL) based framework for satellite attitude control using a state-space model. The Proximal Policy Optimization (PPO) algorithm is used to train an agent for three-axis satellite reorientation in a simulation environment governed by Euler's rotational equations and quaternion kinematics. Real-world Inertial Measurement Unit (IMU) data collected from Micro-Electro-Mechanical Systems (MEMS) Accelerometer and Gyro sensors was used to characterize noise parameters and validate simulation fidelity. The trained PPO agent was evaluated against an untrained baseline and a cascade Proportional-Integral-Derivative (PID) controller over 500 randomized episodes. The trained RL agent achieved a 96% success rate with 18.3° mean pointing error and 98.8% alignment score, closely competitive with PID which achieved 100% success rate, 26.0° mean pointing error, and 99.8% alignment score, while using comparable control effort 276 Rate Per Minute (RPM) for RL agent vs 287 RPM for PID. A 3D interactive visualization system was developed for real-time trajectory inspection. Results confirm the feasibility of RL-based attitude control and identify clear directions for improvement.
Visual Interpretation and Classification of Apple Leaf Diseases via Grad-CAM and Convolutional Neural Networks
Apple cultivation is a crucial agricultural activity in various mountainous regions, playing a vital role in supporting the local economy and sustaining the livelihoods of farmers. Several prominent mountain districts are known for leading apple production. However, apple orchards in these areas are often threatened by numerous diseases that reduce fruit yield and quality. In this research, we suggest a machine learning-based technique to automate the detection and classification of common apple diseases based on images of apple leaves collected from various regions. Through the use of Convolutional Neural Networks (CNN), the system can classify diseases with 97.36% precision. For post hoc explainability, Grad-CAM is used, which highlights the important regions that influenced CNN’s decision. The automated disease detection tool provides farmers in Nepal’s rural mountain areas with an affordable real time solution to monitor orchard health, minimize crop loss, and improve apple production. The dataset used in this study is originally derived from the United States based PlantVillage dataset, which is widely used for apple leaf disease classification research. Although the dataset is not collected from Nepal, the visual characteristics of apple leaf diseases remain largely consistent across regions due to similar biological infection patterns. Therefore, the model trained on this dataset is applicable to Nepali apple cultivation environments as well. At present, a publicly available or annotated Nepali specific apple leaf disease dataset is not available, which limits region-specific training and evaluation.
When Should Educational AI Stay Silent? Abstention-Aware Student Risk Prediction Using Learning Analytics
Universities increasingly rely on machine learning systems to identify students at risk of academic failure or withdrawal, where such predictions directly influence intervention decisions, resource allocation, and students’ academic trajectories. However, in these high-stakes educational settings, conventional models produce predictions for all students regardless of confidence, which can result in incorrect risk identification, unnecessary student anxiety, and misallocation of academic support. This study introduces an abstention-aware prediction approach that allows the model to withhold decisions when confidence is insufficient. The approach is evaluated using the Open University Learning Analytics Dataset (OULAD). Logistic regression is adopted due to its well-calibrated probabilistic outputs, achieving a ROC-AUC of 0.889. Comparisons with Random Forest and XGBoost show that while these models achieve slightly higher discrimination, logistic regression provides more consistent probability estimates for uncertainty-aware prediction. The results demonstrate a clear risk–coverage trade-off, where uncertain cases are deferred while confident predictions remain accurate. The findings support the use of selective prediction as a practical mechanism for improving the reliability of AI-assisted decision-making in education.
An Analysis of Relationship between Los Angeles Abrasion Value and Aggregate Crushing Value
The physical properties of materials used in the base course of pavement play a critical role in determining the quality of road construction. Aggregates from different river sources exhibit varying physical, chemical, and geotechnical properties. The objective of the study is An Analysis of Relationship between Los Angeles Abrasion Value and Aggregate Crushing Value. Focusing on strength and durability based on laboratory test results to evaluate their suitability according to the "Standard Specification for Road and Bridge Works 2016 (2nd Amendments 2022)". Base course aggregate samples from within Major River sources in Madesh Province of Nepal was collected for this research. According to the IS code and the manual of standard tests by the department of roads, laboratory tests was conducted on 66 samples of 22 location of 22 river to determine Los Angeles abrasion value and aggregate crushing value. Out of these, 21 samples met the specified requirements. Fifteen suitable samples was selected for regression analysis to evaluate the relationship and establish the relationship between the Los Angeles abrasion value and aggregate crushing value. The remaining six suitable samples was used for model validation. The results show strong relationship between Los Angeles abrasion value and Aggregate Crushing Value as: LAAV= 1.2811× ACV + 8.0895 The R - square values was found to be 0.841 respectively. From model validation, the R - square values was found 0.7646 the predicted value of Los Angeles abrasion value against actual values of Los Angeles abrasion value, respectively. Therefore, the Aggregate Crushing Value test is used as a substitute test for the Los Angeles abrasion value tests.
Assessment of Design Strategies and Economic Viability for Sanitary Landfill Planning Using GIS and AHP: A Case Study of Shuklagandaki Municipality, Nepal
The planning of sanitary landfills in emerging municipalities repeatedly hurts from a disjointed approach, where site suitability analysis is dissociate from comprehensive context-specific design strategies and economic viability assessment, leading to plans that are technically infeasible or economically unviable owing to limited integration between spatial appropriateness, engineering design, and lifecycle cost assessment . This research targets to bridge this gap by evolving an integrated framework for sanitary landfill planning, employed to Shuklagandaki Municipality, Nepal. The methodology integrates Geographic Information Systems (GIS) and the Analytical Hierarchy Process (AHP) for multi-criteria site selection, along with waste generation projections, engineering design calculations, and a lifecycle cost analysis. The most noteworthy results show that only 0.89% (1.489 km²) of the municipal area is highly appropriate for landfill planning with land use, residential proximity, and water body protection being the most weighted criteria. The design for a 15-year facility approves a mineral liner system and estimates a peak leachate generation of 0.125 m³/day, while the economic assessment reveals a serious dependency on establishing a dedicated waste management fee to bear substantial annual operation and closure costs. The major conclusion is that while suitable sites and feasible designs exist, sustainable implementation is reliant upon integrating spatial planning with pragmatic engineering and securing institutional commitment to long-standing financial mechanisms, providing a replicable model for evidence-based landfill planning in analogous constrained environments.
Comparative Assessment Of Raw And Downscaled CMIP6 Multi-Model Ensembles For Projecting Temperature And Precipitation Dynamics In The Tamor River Basin
This study presents a comparative assessment of raw and downscaled CMIP6 multi-model ensembles for projecting temperature and precipitation dynamics in the Tamor River Basin. Utilizing a six-model ensemble under SSP245 and SSP585, the research quantifies the added value of high-resolution NEX-GDDP data and secondary local bias correction. Results indicate a consistent warming trend, with minimum temperatures rising faster than maximums, narrowing the diurnal range. Under SSP585, the downscaled ensemble projects a +4.22°C temperature increase, surpassing +3.72°C projected by raw GCMs. Similarly, downscaling amplifies the wetting signal, projecting a 54% increase in annual precipitation compared to 46% in raw outputs, with significant intensification during monsoon (JJAS) and post-monsoon (ON). These findings suggest that raw GCMs may underestimate thermal stress and hydrological intensification in complex Himalayan terrains. Overall, downscaled projections provide improved spatial representation and more consistent climate signals, offering a reliable basis for hydroclimatic assessment.
Comparative Assessment of RCC Buildings with Special Moment-Resisting Frame Using Static and Dynamic Procedures of Both Linear and Nonlinear Seismic Analysis
Advances in seismic design concepts have led to the development of sophisticated methods. The goal of this paper is to analyse the structure using different analytical procedures and compare the structural responses obtained. Linear Seismic Analysis (Response Spectrum Analysis) and Nonlinear Seismic Analysis (Pushover Analysis and Nonlinear Time History Analysis) methods were used as seismic analysis procedures. Seismic analysis was performed on the two symmetric Bare Special Reinforced Moment Frame models, having storey numbers of 5 and 10, respectively, using ETABS(v22) software. Provisions of IS 1893:2016 and ATC-40 are considered for performing Response Spectrum and Pushover analysis, respectively. Three ground motion records are provided to structural models as input to perform NTHA. The paper provides global seismic responses like storey drift and storey displacement from all procedures and concludes with a comparison of results among these procedures.
Comparative Focal Mechanism and Fault Plane Ambiguity Analysis of the 2015 Gorkha Earthquake Using Global CMT and DMG Catalogs
On April 25, 2015, Nepal was struck by an Mw 7.8 earthquake. This study characterizes the source mechanism using focal mechanism solutions (beach balls) derived from the Global Centroid Moment Tensor (CMT) and Department of Mines and Geology (DMG) catalogs. The results confirm a thrust-faulting mechanism with strike, dip, and rake angles consistent with the regional tectonic framework. The rupture initiated near the hypocenter and propagated southeastward along the dip direction, a trajectory supported by finite-fault modeling. As no shallow surface rupture was observed, this rupture direction is supported by aftershock distribution, stereonet P-axis orientation, and finite-fault modeling studies. These findings improve understanding of rupture geometry and help identify potential seismic hazard zones in central Nepal.
Comparison Of Seismic Performance Of High Rise Rcc Building For Various Configurations Of Fluid Viscous Damper
The vulnerability of reinforced concrete (RC) buildings to earthquake forces specially in seismically active region like Nepal highlights the need for improved seismic performance through the use of energy dissipation devices. This study investigates the effectiveness of fluid viscous dampers in improving the seismic behavior of a multi-storey RC building by comparing different damper configurations. A G+12 reinforced concrete building located in Kathmandu (Seismic Zone V) was modeled and analyzed using ETABS v19 in accordance with the provisions of NBC 105:2025. Response spectrum analysis was performed to evaluate the seismic response of five different structural models: a building without dampers and four models with fluid viscous dampers arranged in different configurations, including alternate bay all-storey, corner bay all-storey, alternate bay alternate-storey, and corner bay alternate-storey arrangements. The seismic performance of the building models was evaluated in terms of time period, storey displacement, storey drift, and storey shear. The results indicate that the installation of fluid viscous dampers significantly reduces storey displacement by about 45% compared to the building without dampers. Among the studied configurations, the alternate bay alternate-storey damper configuration demonstrated the most effective reduction in displacement and drift, providing better control of structural response while using fewer dampers compared to the all-storey configurations. Although the inclusion of dampers slightly increases storey shear due to additional damping forces, the overall structural performance is significantly enhanced. The findings of this research may contribute to the adoption of supplemental damping systems for improving earthquake resilience of buildings in seismic regions like Kathmandu.
Impact of Hydro Peaking Operation on Downstream RoR Energy Generation in West Seti River Basin
Nepal’s power system is dominated by run-of-river (RoR) hydropower projects, challenging load management during peak hours. Amidst prioritization of government policy to Peaking RoR scheme, there is a lack of proper framework for analyzing downstream linkages of hydro-peaking in a river basin. Daily peaking operation of a RoR project will have substantial impact on downstream project but has been less considered in regional studies. This study analyzes the impact of peaking operation on flow availability and subsequent energy generation considering two projects; Chainpur Seti and Seti-Nadi3 HPP of West Seti river basin. This study analyzes two operational scheme; one with complete stoppage of upstream plant for reservoir filling and another reservoir filling by continuous withdrawn on non-peak hours for storage. For the operation Scheme 1, during normal condition the generation loss is 17%, 19% while considering the flushing effect, 16.9% when turbine operation is considered, and 19.2% when both flushing and turbine operation are taken in consideration. Similarly, for operation Scheme 2, the generation loss is 0.97% for normal condition, 3.3% when flushing effect is considered, 0.36% when turbine efficiency only is considered, and 2.8% when both the flushing effect and turbine operation is taken into account. Also, the generation of downstream project shows increasing trend only when the peaking hours is greater than or equal to 2hr. Our study necessitates the multi-stakeholder collaboration in planning and operation at project and basin level for sustainable water resources management.
Performance of Base Isolated RCC High-rise Building with Fluid Viscous Damper considering Soil-Structure Interaction
Soil-Structure Interaction (SSI) influences how base-isolated buildings respond to earthquakes, yet it is often neglected in practice due to analysis complexity. This study shows a research gap exists regarding hybrid systems combining base isolation with fluid viscous dampers on soft soil. This study uses nonlinear time history analysis on a RC structure to evaluate performance under three earthquake records. The results demonstrate that all advanced model system (II-V) significantly outperform the conventional fixed-base model. While base isolation achieved the good base shear reduction (61%) compared to Model-I. The novelty lies in evaluating the hybrid Model-V under SSI; while it maintained force reduction, it amplified foundation level acceleration to 2.10 m/s2 an 80% increases relative to the rigid-base hybrid proving that neglecting SSI is unconservative. Conversely, Model-II (fixed-base with dampers) is most robust, yielding 72% drift and 33% force reduction compared to Model-I. These findings emphasize that soft soil sites, SSI must be integral to seismic design.
Performance-Based Finite Element Investigation of Concrete-Filled Steel and FRP Tubular Columns under Axial Compression
Reinforced concrete columns are prone to brittle failure under larger axial and seismic loads, highlighting the need for improved structural systems. Although concrete-filled steel tube (CFST) and concrete-filled FRP tube (CFFT) columns offer enhanced performance due to confinement effects, limited studies provide a direct comparison within a unified framework. This paper presents a comparative evaluation of CFST and CFFT columns using finite element modelling in Abaqus, based on confined concrete damage plasticity model for infilled concrete, a plasticity model for steel tubes, and the Hashin damage model for FRP tubes, with validation against experimental results available in the literature. A parametric study was conducted by varying tube thickness, rebar diameter, tube cross-sectional shape, and slenderness ratio. The results showed that increasing the tube thickness from 2 mm to 8 mm enhanced the peak strength of CFST and CFFT columns by 69.54% and 55.6%, respectively, while increasing the rebar diameter from no reinforcement to 16 mm improved the peak strength by 45.6% and 58.36%, respectively. Columns with circular sections exhibited superior performance due to uniform confinement, whereas non-circular sections showed reduced strength. Increasing the slenderness ratio reduced load-bearing capacity and flexural stiffness, thereby increasing buckling susceptibility. Overall, CFST columns exhibited higher strength and stiffness, whereas CFFT columns showed greater ductility because the FRP tube provides effective lateral confinement, allowing greater expansion of the concrete core before rupture. However, due to the relatively low elastic modulus of FRP, its contribution to axial stiffness remains limited.
Utilization and Effectiveness of Grade-Separated Pedestrian Crossings: A Case Study of Kathmandu
This study examines the utilization and effectiveness of grade-separated pedestrian crossings in underdeveloped urban settings, focusing on Kathmandu. Observations of 47,783 pedestrian crossings across five sites (four overhead bridges and one underpass) were conducted over 15 days, collecting data on age, gender, and crossing functionality using manual and video methods. The analysis revealed a high overall utilization rate of 83.20%. The underpass had the highest usage at 97.92%, compared to 93.67% for overhead bridges. Age and gender significantly influenced usage patterns, with individuals under 25 and males utilizing crossings more frequently. Site selection considered factors such as lane count, median barriers, and facility type, highlighting their impact on utilization. Median barriers and the ergonomic design of facilities, particularly the number of steps, emerged as key determinants of utility. The findings underscore the importance of strategic planning and design in enhancing pedestrian safety and usability in underdeveloped urban areas. Median barriers and ergonomic considerations, such as minimizing physical effort, play a pivotal role in encouraging usage. This research provides valuable insights for urban planners and policymakers to develop pedestrian-friendly infrastructure, addressing safety concerns and demographic needs in similar urban contexts.
A Genetic Algorithm Approach for Optimal Allocation and Sizing of Distributed Generation in Distribution Networks
Numerous advantages attained by integrating Distributed Generation (DG) in distribution systems. These advantages include decreasing power losses and improving voltage profiles. Such benefits can be achieved and enhanced if DGs are optimally sized and located in the systems. This study presents a distributed generation (DG) allocation strategy to improve node voltage and power loss of radial distribution systems using genetic algorithm (GA). The objective is to minimize active power losses while keep the voltage profiles in the network within specified limit. In this study, the optimal DG placement and sizing problem is investigated using two approaches. First, the optimization problem is treated as single-objective optimization problem, where the system’s active power losses are considered as the objective to be minimized. Secondly, the problem is tackled as a multi-objective one, focusing on total power loss as well as voltage profile of the networks. This approach finds optimal DG active power and optimal OLTC position for tap changing transformer. The simulation study is carried out on a 33-bus radial Distribution System. The results are presented, compared and analyzed in this paper.
A Nepal Perspective on Closing the Carbon Cycle Pathways to De-fossilize Difficult-to-Electrify Sectors in a Hydropower-Dominated Economy
Urgent decarbonization of energy systems, industrial processes and transportation is necessary to mitigate GHG emissions. Though Nepal generates more than 95% of electricity from hydropower, still there are difficult-to-electrify sectors like transportation, fertilizer production, cement and brick industries, which are fossil fuels dominated. This dependency causes increase in fuel imports, emissions, and climate risks. This paper studies how Nepal can move forward towards a circular carbon economy by adopting the principle of closing the carbon cycle, originally developed in the US context, to Nepal’s hydropower-dominated and resource-constrained economy. Using a qualitative and systems-oriented approach based on secondary literature, policy documents, and sectoral reports, the study identifies key scientific and technological opportunities, including green hydrogen and ammonia production from surplus hydropower, carbon capture and utilization in cement and brick industries, and waste-to-energy systems for urban resilience. Based on this adaptation, a Nepal-specific conceptual framework is proposed to connect energy, industry, agriculture, and waste systems so that carbon can be reused across multiple applications before final disposal. The paper also highlights policy implications, investment considerations, and a phased research and innovation roadmap emphasizing enabling regulations, financial incentives, pilot projects, and inclusive governance. The study concludes that Nepal can position itself as a model for decentralized, low-carbon development by strategically aligning renewable electricity, carbon reuse pathways, and sector-specific transition policies.
Comparative analysis of direct, diffuse and global Irradiance at two different locations in Nepal
Analysis of solar radiation on different geographical location is important to understand their solar potential and possible energy harvest that can be turned to financial benefits for investors. Nepal has a huge solar potential but large scale solar installations are still minimal. In this regard, this study is an attempt to compare solar irradiances of two different geographical locations (Kathmandu & Nepalgunj) to observe the possible energy output. Real ground measurements of global and diffuse horizontal irradiances (GHI and DHI) along with direct normal irradiance (DNI) data are obtained for six months (Jan to June, 2019) from both sites. Analysis is presented based on comparison of daily and monthly average irradiances along with min by min plot of GHI for particular day of winter and summer season. Results suggest that average daily and monthly irradiance (GHI and DNI) for four out of six months (March toJune) are higher in Nepalgunj as compared to Kathmandu. The six month averages of GHI=350.38 W/m2 and DNI=296.51 W/m2 in Nepalgunj are found to be slightly higher than that of Kathmandu (GHI=321.9 W/m2, DNI=292.25 W/m2). Both locations have very good sunshine of 5 hours/day in winter and excellent sunshine of 8 hours/day in summer. Also during summer, the average GHI greater than 500 W/m2 that would generate more energy output from the solar collector is higher for Nepalgunj (~71%) as compared to Kathmandu (~50%) whereas during winter, it is nearly 60% in Kathmandu and only about 39% in Nepalgunj.
Optimal Recloser Placement in Rural and Urban 11 kV Distribution Feeders of Nepal Based on Reliability and Cost–Benefit Analysis
Reliability improvement of distribution systems is a major concern for utilities in Nepal due to frequent interruptions in both rural and urban feeders. This study investigates the optimal placement of auto-reclosers in two practical 11 kV feeders: Feeder-4 (urban) under the Birgunj Distribution Center and the Khoplang feeder (rural) under the Gorkha Distribution Center. Base-case reliability indices (SAIFI, SAIDI, CAIDI, and EENS) were evaluated using analytical modeling and ETAP simulation. A Genetic Algorithm (GA)-based optimization framework was developed to minimize Expected Energy Not Supplied (EENS) and maximize economic benefits. The results indicate that 2 reclosers are optimal for the Feeder-4 (urban) feeder and 4 recloser for the Khoplang (rural) feeder. SAIDI and EENS were reduced by more than 55% in both feeders. Cost–benefit analysis yielded benefit–cost ratios greater than 2.0 and payback periods of less than four years, confirming economic feasibility.
Performance of Armoured Steel Plate against High-Speed Projectile using Finite Element Analysis and Lab Experiment
A comprehensive understanding of the mechanical behavior of armored plates used in defense sectors under high-velocity impact is fundamental in optimizing material selection and design for specific operational requirements. This research investigates the ballistic performance of armoured steel plate through integrated finite element analysis and experimental testing. The study began with the mechanical property characterization of the armoured steel plate, from which Johnson-Cook (JC) constitutive parameters were derived by correlating with established literature. Using a finite element analysis explicit dynamic solver for the simulation of multiple impact scenarios, with simulation predictions validated against experimental test data. The experimental data of high-velocity impact from a leaded projectile at 300, 600, and 900 incident angles correlated closely with the simulation of impact on Armox 500 armoured plate, with a maximum deviation of 8.5%. Following validation, an optimized material model was implemented to further assess the armoured plate's ballistic resistance. The findings provide valuable insights into the resistance capabilities of the armoured plate, which can contribute to the defense sector.
Three-Dimensional Transient Heat Conduction in a Solid Block with Fully Implemented Convective Boundary Conditions
Accurate prediction of transient cooling in three-dimensional solid bodies is essential for thermal processing and electronic cooling applications. This study develops a physically consistent 3D transient heat conduction model that explicitly estimates convective heat transfer on all external boundaries, including faces, edges, and corners, using an explicit finite-difference scheme on a uniform Cartesian grid. Unlike conventional models that simplify convective boundary conditions to planar surfaces, this approach implements directionally consistent Robin conditions to allow multi-directional heat loss at boundary intersections. The model was applied to an aluminum block cooling from 180°C to an ambient temperature of 25°C. Results reveal that that enhanced cooling at edges and corners, exposed to convection in two and three orthogonal directions, respectively, results in significant local temperature variations. A grid independence study comparing spatial step sizes from 4 mm to 2 mm confirmed that the numerical solution is robust, with a relative difference in center cooling time of less than 2% between the two finest grids. Quantitative analysis indicates that neglecting these edge and corner effects can result in a significant underestimation of local heat loss and inaccuracies in total cooling time predictions. This framework provides a transparent numerical approach for analyzing transient heat transfer where surface-to-volume ratios and localized cooling are critical.
Redshift-Dependence of the Cosmic Expansion Rate: A Data-Driven Analysis of Observational H(z) Measurements
Observational measurement of the Hubble parameter provides direct information about the expansion history of the Universe (Hubble, 1929; Weinberg, 2008). While most studies rely on fitting observational data specific cosmological models, complementary information can be obtained through direct, model independent data analysis (Seikel et al., 2021). In this work, observational Hubble parameter data are examined using a normalized expansion gradient that quantifies the relative change of the expansion rate across consecutive redshift intervals. This simple diagnostic approach allows local variability in the cosmic expansion history to be explored without invoking assumptions about the underlying cosmological model. The analysis reveals non-uniform behavior of the expansion rate across redshift, demonstrating that gradient based methods can serve as a useful exploratory tool for identifying redshift ranges of enhanced dynamical evolution in observational cosmology (Moresco et al., 2016).
Sustainable Management Of Household Organic Waste Through Vermicomposting: Evaluating The Nutrient Potential And Financial Sustainability
Solid waste management is becoming a major environmental challenge for sustainable development, especially in developing countries due to urbanization, industrialization, and improper dumping practices. Waste management techniques such as recycling, incineration, landfilling, and composting are designed to manage waste materials to reduce environmental impact. This study examines the nutrient composition and economic viability of household-scale vermicomposting using organic waste in Shuklagandaki Municipality, Tanahun. A 90-day bin experiment was carried out using Eisenia fetida earthworms, with kitchen waste, garden residues, and paper serving as feedstock. The findings indicate that vermicompost meets standard nutrient criteria for nitrogen, phosphorus, and potassium, though moisture content and pH are slightly above recommended levels. The economic analysis showed strong financial feasibility, with a net present value of NRs. 801,460 over five years at a 6.05% discount rate. Sensitivity analysis identified investment cost and annual benefits as the key factors influencing project viability. Household survey results (n = 109) indicated an estimated daily organic waste generation of 8,866 kg. Overall, the findings suggest that vermicomposting is a practical, environmentally sustainable, and economically viable approach for managing household organic waste in municipalities.