International Journal on Engineering Technology
The International Journal on Engineering Technology (InJET) is a multidisciplinary journal that endeavors to publish innovative and state-of-art research in the field of engineering and technology. The primary objective of the journal is to provide platform for researchers to share their valuable insights and contribute to the advancement of engineering and technology.
This license enables reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
Editorial from Editor in Chief - 8th KEC Conference 2026
Copyright (c) 2026 International Journal on Engineering Technology https://creativecommons.org/licenses/by/4.0
Hybrid CRNN with Seq2Seq Attention Mechanism for Handwriting Recognition
Handwritten Text Recognition (HTR) faces significant challenges including limited annotated data, high handwriting variability, and complex character formations. This paper proposes a hybrid CRNN with Seq2Seq Bahdanau attention for robust HTR. The encoder employs a ten-layer CNN with residual connections for spatial feature extraction and Bidirectional LSTM for temporal modeling. The decoder uses Bahdanau attention to dynamically generate context vectors by focusing on relevant image regions at each decoding step, combining character embeddings and context vectors to produce character probabilities via SoftMax. To prevent overfitting with limited data, comprehensive regularization is applied: dropout (0.3), weight decay (1e-4), label smoothing (0.1), stochastic depth (0.1), exponential moving average (EMA), stochastic weight averaging (SWA), adaptive teacher forcing decay, cosine annealing, and layer-wise learning rate decay, supplemented by light augmentation. Evaluation on the IAM handwriting dataset demonstrates 13.59% WER and 4.28% CER (86.41%-word level accuracy, 95.72%-character level accuracy), outperforming recent comparable CRNN-based methods. Attention visualizations confirm meaningful spatial-sequential alignment, with diagonal attention patterns indicating systematic left-to-right character progression. These results validate that attention-based sequence modeling combined with systematic regularization achieves robust HTR performance in data-limited scenarios without relying on synthetic data or external lexicons.
Comparative Analysis of Ensemble Machine Learning and Hybrid Deep Learning Models for Intrusion Detection
The emergence of cloud computing and corporate data centers has increased traffic on the Internet, making them more vulnerable to cyberattacks. Signature-based IDSs are not capable of recognizing emerging threats due to the dependence on known patterns of attacks. Therefore, machine learning and deep learning can be used as tools for intrusion detection in the modern network environment. This paper examines three popular machine learning and hybrid IDS models: Random Forest (RF), XGBoost, and CNN-LSTM. Their effectiveness was evaluated based on the use of the CSE-CIC-IDS2018 dataset containing real network traffic and such attacks as brute force attacks, DDoS, botnets, and others. Such techniques as data cleaning, feature encoding, normalization, and feature selection were used in the preprocessing step. The models were compared using several quality metrics such as accuracy, precision, recall, F1-score, false positive rate, and receiver operating characteristic. Experimental results prove that XGBoost shows the best performance with 97.97% accuracy, 0.9797 F1-score, and 0.0051 false positive rate, followed by Random Forest with the accuracy of 97.90%. At the same time, CNN-LSTM has shown an excellent result in recognizing temporal patterns in network traffic with the accuracy of 97.40%. These findings illustrate the efficacy of ensemble-based approaches on tabular datasets and the power of hybrid deep learning algorithms for sequential data. In general, this research stresses the significance of choosing the right IDS models by considering accuracy, speed, and deployability aspects.
Nepali Music Genre Classification Using CNN-SVM Hybrid Architecture
Nepali music genre classification using CNN–SVM hybrid model was developed to address the challenge of categorizing local genres such as Gazal, Lok Dohori, Nephop, and Pop. A dataset of 1,000 manually curated songs (250 per genre) was collected from YouTube, segmented into 30‑second clips at 25%, 50%, and 75% of each track’s duration, resulting in approximately 3,000 audio segments. Each segment was converted into a 128×128 Log‑Mel spectrogram. A four‑layer CNN extracted a 64‑dimensional embedding, which was then passed to an SVM classifier. Experiments showed that the CNN–SVM hybrid with an RBF kernel achieved 88.29% accuracy, outperforming the standalone CNN baseline (84.28%). Among evaluated kernels, RBF and Linear both achieved the highest accuracy of 88.29%, while the Sigmoid kernel performed worst at 79.60%. The results demonstrate that combining deep learning feature extraction with a traditional machine learning classifier is effective for Nepali music genre classification on moderate‑sized, domain‑specific datasets.
A Two-Stage Attention-Enhanced ConvNeXt Framework for Skin Cancer Detection and Classification
The rising global incidence of skin cancer has positioned automated dermatological screening as a critical technology for modern healthcare diagnostics. This study introduces a two-stage deep learning framework for skin cancer detection and classification, demonstrating strong performance across diverse dermatoscopic imaging conditions. The proposed approach employs a ConvNeXt-Tiny backbone enhanced with Squeeze and Excitation (SE) and Convolutional Block Attention Module (CBAM) to improve feature recalibration and spatial channel attention. In Stage 1, a binary classifier distinguishes cancerous lesions from normal skin, achieving near-perfect validation accuracy of approximately 100% and an F1-Macro score consistently exceeding 95%, confirming robust generalization with minimal overfitting. In Stage 2, a multi-class classifier categorizes lesions into three clinically significant classes Melanoma (MEL), Basal Cell Carcinoma (BCC), and Vascular Lesions (VASC) achieving a validation accuracy of 94.62% with a weighted F1-score of 95.16%. On the held-out test set, Stage 2 attained an overall accuracy of 90.64%, a macro F1-score of 88.14%, and a weighted F1-score of 90.68%.The proposed two-stage framework was evaluated on a combined dataset sourced from Harvard Dataverse and Kaggle, achieving a testing accuracy of 100% on the Stage 1 binary screening task (cancer vs. normal skin) and a Stage 2 multi-class test accuracy of 90.64% with a macro F1-score of 88.14% across melanoma, basal cell carcinoma, and vascular lesions. These results highlight the framework's potential for reliable, automated skin cancer screening, providing a valuable tool for clinical decision support, early diagnosis, and accessible dermatological care.
3D Frequency-Difference Electrical Impedance Tomography for Early Subcutaneous Edema Detection Using Tikhonov Regularization and Physics-Informed Neural Networks
Subcutaneous edema is difficult to detect at an early stage because the associated conductivity changes are subtle and hard to localize using conventional imaging methods. This study presents the 3D Frequency Difference-Electrical Impedance Tomography (FD-EIT) framework for the detection of early-stage edema in the lower leg region. A 3D lower-leg model with 16 electrodes was simulated, and the boundary voltages and differential measurements were generated under a broader set of synthetic edema scenarios representing varied anomaly extent, irregularity, conductivity, contrast, and measurement difficulty. Reconstruction was evaluated using two approaches: the baseline method using Tikhonov regularization and the Physics-Informed Neural Network (PINN) method. The results show that the baseline method produces stable but spatially diffuse reconstructions, whereas the PINN method provides more localized and structured anomaly patterns that are aligned more closely with the ground-truth edema regions. Quantitative evaluation with RMSE, MAE, relative error, and Dice score showed better reconstruction accuracy and edema-mask localization for the PINN compared to the baseline method. However, the results should be interpreted as a controlled synthetic proof-of-concept because the PINN was trained and evaluated using scenario-specific simulated data. Therefore, the reported localization performance does not represent clinical diagnostic accuracy. Future work will focus on realistic anatomical modeling, electrode contact effects, phantom validation, and experimental lower-leg measurements.
A Hybrid Cross-Lingual News Aggregator for Nepali Media using mT5 and Dense Vector Embeddings
The rapid proliferation of digital journalism in Nepal has created a fragmented information landscape, where navigating hundreds of independent portals leads to significant cognitive load and a widening semantic gap in information retrieval. A hybrid search engine is featured in the system’s core architecture, through which lexical precision (BM25) is fused with deep conceptual understanding via dense vector embeddings. Seamless cross-lingual accessibility is enabled by this dual-path retrieval mechanism, by which semantically relevant Nepali content is accurately retrieved using English queries, achieving an average Cosine Similarity score exceeding 0.72. To alleviate information density, an automated synthesis layer is implemented using a fine-tuned mT5 (Multilingual Text-to-Text Transfer Transformer) model, through which long-form journalism is distilled into concise abstractive summaries with ROUGE-1 of 0.33. Furthermore, Zero-Shot Classification based on Natural Language Inference (NLI) is integrated into the platform so that unstructured news streams are dynamically categorized into thematic verticals without the requirement for manual labeling. It is demonstrated by experimental results that retrieval recall and organizational efficiency are significantly improved by the proposed framework, and a scalable solution for modernizing regional news consumption in low-resource linguistic environments is provided.
Hallucinating Health: Assessing the Clinical Reliability of LIME, Grad-SHAP and Grad-CAM in Small-Scale Medical Imaging
Explainable Artificial Intelligence (XAI) methods such as Grad-CAM, SHAP, and LIME are increasingly employed in medical imaging to provide post-hoc interpretations of deep learning models. However, their reliability under data-scarce conditions remains insufficiently understood, despite being critical for real-world clinical deployment. In this work, we investigate the phenomenon of hallucinated explanations wherein XAI methods produce visually plausible but clinically ungrounded saliency maps due to models learning spurious correlations from limited training data. We conduct a controlled study on pediatric chest X-ray pneumonia classification by systematically reducing training set size (N = 1000 → 500 → 200 → 100 → 50) and evaluating explanation quality using perturbation-based faithfulness metrics, localization consistency, and qualitative clinical alignment. Our results demonstrate a non-linear degradation in explanation of reliability as dataset size decreases, with both gradient-based and perturbation-based methods exhibiting distinct failure modes. Notably, high model confidence persists even as explanation of faithfulness collapses, highlighting a critical decoupling between predictive performance and interpretability. We further evaluate mitigation strategies including data augmentation and transfer learning, finding that transfer learning partially stabilizes explanation fidelity but does not eliminate hallucination effects at very low sample sizes. These findings underscore the need for rigorous, quantitative evaluation of XAI methods prior to clinical adoption and suggest that commonly used saliency techniques may be unreliable in low-data regimes. This work contributes to a systematic framework for auditing XAI reliability in medical imaging and provides practical insights toward safer deployment of interpretable AI systems.
Hybrid NLP Architecture for Crisis-Aware Chatbot Integrating Emotion Classification and Retrieval-Augmented Legal Response Generation
Survivors of domestic violence frequently face barriers to accessing timely legal and emotional support, underscoring the need for intelligent, accessible assistance systems. This paper presents a legally capable and emotionally aware conversational system designed to classify user-generated text and provide contextually rich responses in the domain of domestic violence. The system employs BERT-based transformer models to perform three layers of classification on pre-processed user input: crisis classification (safe, urgent, or emergency), intent classification (legal or emotional), and emotion classification across seven fine-grained psychological categories. A Retrieval-Augmented Generation (RAG) module then retrieves relevant content from a curated legal knowledge base to ground legal responses in verified documents, reducing hallucination and improving factual reliability. Experimental results demonstrate that the hybrid architecture supports reliable, safety-aware response generation and shows promise as a supportive tool for survivors in crisis situations.
Deep-Learning Based Tomato Leaf Disease Classification using CNN, ConvNeXt, Vision Transformer, and Swin Transformer
Deep-learning-based tomato leaf disease classification remains challenging because it must address visually similar disease patterns, non-target inputs, and computational constraints. This paper presents a deployment-aware comparative study of four deep learning model families—Convolutional Neural Networks (CNN), ConvNeXt, Vision Transformer (ViT), and Swin Transformer—for tomato leaf disease classification. We used a dataset comprising 31,042 training images and 5,643 validation images and include a not_tomato rejection class to better reflect real-world deployment beyond closed-set classification. We also applied HSV-based leaf-focused background removal and evaluated model accuracy, loss behavior, precision, recall, and F1-score. Results showed that Swin Transformer achieved the best overall performance with 98.69% validation accuracy and superior plant detection capability. ViT demonstrated the most stable generalization behavior, while ConvNeXt remained competitive but computationally expensive. The custom CNN offered high efficiency but lower accuracy. These findings highlight the importance of balancing accuracy, generalization, and deployment constraints in agricultural AI systems.
Self-Supervised and Semi-Supervised Learning for Nepali ASR with Limited Labeled Data
Nepali ASR suffers from a severe lack of manually transcribed data. This paper proposes a hybrid framework combining Self-Supervised Learning (SSL) and Semi-Supervised Learning (Semi-SL) to develop high-performance ASR systems under low-resource conditions. Three pre-trained transformer architectures, Wav2Vec2, XLSR-53, and CLSRIL-23 are adapted to the Nepali domain through a two-stage strategy: supervised CTC fine-tuning on limited labeled data, followed by an iterative pseudo-labeling loop that progressively incorporates unlabeled data. Experiments are conducted across four supervised training budgets (1h, 5h, 10h, 20h), evaluated on 16,136 test utterances from the SLR54 Nepali speech corpus. Results demonstrate that the proposed framework achieves competitive, with XLSR-53 reaching 23.05% WER and 5.05% CER at 20h, comparable to a strong supervised baseline. Critically, the proposed method shows measurable data-efficiency gains at intermediate training sizes, where for the XLSR-53 model at 20 hours, the proposed method reduces WER by 17.1% relative to the supervised baseline. Linguistic error analysis reveals that consonant confusion and vowel matra errors consistently account for 66 to 68% of all character-level errors, pointing to language-model integration as the highest-impact next step.
Structural Analysis of the COVID-19 Infodemic: Motif-Based Detection of Echo Chambers and Geopolitical Hijacking in Global News Networks Using GDELT
The COVID-19 pandemic produced a global infodemic that evolved structurally across time. This paper presents an automated informatics pipeline that transforms high-velocity GDELT 2.0 Global Knowledge Graph (GKG) data into a mathematically validated model of narrative evolution across five pandemic milestones. The pipeline integrates elite-domain authority filtering across 21 globally recognised news sources, BERTopic-based semantic node extraction, and Heterogeneous Information Network (HIN) construction. Two network motifs, Narrative Stars (broadcast hubs) and Sociosemantic Triads (echo chambers), are enumerated and validated against 1,000 degree-preserving null models using Monte Carlo permutation testing. BERTopic consistently outperforms the LDA baseline (Cv = 0.58) with coherence scores above Cv = 0.70 at all milestones, peaking at Cv = 0.7777 during M2 Lockdown. Motif analysis reveals a statistically significant Star-to-Triad crossover, with Stars peaking at Z = 156.65 in M2 and Triads peaking at Z = 210.55 in M5 (p < 0.001). Longitudinal Louvain-Jaccard tracking identifies near-zero community survival (J = 0.0075 at the M2-to-M3 transition), confirming structural collapse rather than gradual evolution. During M4 Delta, betweenness centrality shifts toward geopolitical entities, providing quantitative network evidence of narrative displacement. The paper contributes a reproducible topological framework for infodemic surveillance.
Design and Development of a Low-Cost sEMG Acquisition System with Adaptive DSP Pipeline
Surface electromyography (sEMG) is a widely used non-invasive technique for capturing muscle electrical activity, with applications in prosthetics, rehabilitation, and gesture-based human-computer interaction. Progress in low-cost sEMG research is hindered by the high cost of laboratory-grade acquisition hardware. This paper presents the design and development of a low-cost sEMG acquisition system validated on hand grip recordings from six healthy adult subjects (S1–S6). The system integrates a custom Analog Front-End (AFE) using an AD620 instrumentation amplifier and LM324 operational amplifiers with an ESP32 microcontroller for real-time digitization and wireless transmission. A six-stage digital signal processing (DSP) pipeline — comprising DC removal, adaptive notch filtering, fourth-order Butterworth bandpass filtering (20–450 Hz), Daubechies-4 wavelet denoising (VisuShrink, Level 5), full-wave rectification, and linear envelope extraction — is applied offline. Across all six subjects, M1 SNR ranges from 50.2 to 60.6 dB (mean ± SD: 56.1 ± 4.3 dB), M3 SNR ranges from 49.1 to 58.6 dB (mean ± SD: 54.8 ± 4.1 dB), and noise floor RMS remains below 0.215 µV in every session — well below the 5 µV hardware specification benchmark. The total system cost is below NPR 15,000, demonstrating that research-grade sEMG quality is achievable with off-the-shelf components across a diverse set of subjects.
Nepali Speech Emotion Detection Using Deep Learning
Emotionally intelligent human-computer interaction solutions depend on Speech Emotion Recognition (SER), which attempts to recognize emotional states from speech. There is still little research on SER for languages with limited resources, like Nepali. In this work, a one-dimensional Convolutional Neural Network (1D-CNN) and Mel-Frequency Cepstral Coefficients(MFCCs) are used in a deep learning-based Nepali speech emotion detection system. 1,810 audio samples of 632 happy, 560 neutral, and 618 sad utterances were gathered from studio recordings, mobile recordings, podcasts, and broadcast sources to create a specific Nepali emotional speech dataset. Every audio sample underwent preprocessing, resampling to 16 kHz, and conversion to mono. A 1D-CNN model was fed MFCC features that had been retrieved. The suggested model yields an overall accuracy of 88% on the Nepali dataset, according to experimental results. With a precision of 0.96, recall of 0.92, and F1-score of 0.94, the Sad emotion class performed the best. The Neutral class received a precision of 0.89 and an F1-score of 0.81, but the Happy class received a recall of 0.98 and an F1-score of 0.89. Strong discrimination was shown by ROC analysis, with AUC values of 0.97 for neutral and 0.99 for happy and sad.
Edge-Deployed Waste Classification System Using Transfer Learning on Custom IoT Camera Data with Data Augmentation
Rapid urbanization and inefficient waste segregation have intensified the global waste management crisis, with Municipal Solid Waste projected to reach 3.8 billion tonnes annually by 2050. This study proposes a smart automated waste classification system integrating a lightweight MobileNetV2 convolutional neural network with an IoT-based Raspberry Pi 4 platform to enable real-time, cloud-independent automated waste sorting across five categories which are Metal, Plastic, Paper, Organic, and None (not_waste). A dataset of 671 manually collected images was used to train and compare two models: a baseline model achieving 71.20% accuracy and an augmentation-enhanced model achieving 93.60% accuracy with a macro F1-score of 0.9344, where offline data augmentation proved to be the single most important factor, dramatically improving Organic class recall from 0.22 to 0.96. The deployed system captures waste images in real time, classifies them on-device, and automatically activates a sorting mechanism, confirming that lightweight CNN architectures combined with balanced training data offer a practical and low-cost solution for smart waste management in resource-constrained real-world environments.
Vision-Based Smart Waste Segregator: An Embedded System
Improper waste disposal is a critical environmental challenge, particularly in urban areas where manual segregation remains inefficient, unsafe, and inconsistent. This paper presents a Vision-Based Smart Waste Segregator, an automated system that classifies and sorts waste into five categories: glass, metal, paper, plastic, and organic waste. The system employs a Raspberry Pi 4B as the central processing unit, which is triggered by a Time-of-Flight (ToF) sensor upon waste detection. A Pi Camera Module captures an image of the waste item, which is classified using a novel feature-fusion deep learning model combining EfficientNetB0 and DenseNet121. The fused feature vectors from both architectures are passed through fully connected layers with a Softmax output for five-class classification. Based on the results, a dual-motor mechanism, a stepper motor for bin alignment and a servo motor for waste release, directs the waste into the appropriate bin. Bin fill-level monitoring via a second ToF sensor transmits real-time status to a web-based interface through HTTP communication. The model achieved a validation accuracy of 94-95% and was optimized using TensorFlow Lite post-training quantization for efficient edge deployment.
Predicting Fertilizer Quantity Using Geo-Spatial and Physicochemical Properties of Soil
Nepal's economy is highly dependent on agriculture, yet smallholder farmers benefit far less than they could because they rely on traditional practices or one-size-fits-all recommendations that ignore local soil and topographic variation. To address this, precision farming must be adopted nationwide, and the precise use of fertilizers for the right crop type is a critical part of this transition. This study presents a novel approach to recommend fertilizer quantities for the three major cereal crops, rice, maize, and wheat, including their OPV and hybrid subtypes, by modeling the problem as a multi-output regression task. The model uses the soil's physicochemical properties (pH, organic matter, total nitrogen, available phosphorus, available potassium, sand, silt, parent soil, clay, zinc, and boron) and geospatial properties (latitude, longitude, and elevation) as inputs. Around 386,000 soil data points from all provinces of Nepal were collected from the National Soil Science Research Center of Nepal Agricultural Research Council through a custom data extraction and transformation pipeline. A comparative analysis of Random Forest, XGBoost, and LightGBM was conducted against Linear and Ridge Regression baselines. Tree-based ensemble models consistently outperformed linear approaches, with LightGBM, XGBoost, and Random Forest achieving R² values of 0.976, 0.975, and 0.972, respectively, while both linear models only achieved 0.755. 10-fold cross-validation on a small subset suggests that performance across individual folds is consistent, rather than being overfit to the data. The proposed framework offers a scalable, data-driven path toward site-specific fertilizer recommendation across Nepal's diverse topography.
ExerLiteNet: Lightweight CNN-LSTM Architecture for Binary Exercise Recognition from Webcam RGB Video
Exercise recognition from video is important for building digital workout tracking systems and automated fitness monitoring tools that can provide coaching without the need for expensive equipment. This paper presents ExerLiteNet, a lightweight deep learning model designed for resource-constrained devices, which uses standard RGB webcam video to classify two common resistance exercises, squats and bicep curls. The proposed model combines a fine-tuned MobileNetV3Small CNN wrapped in a Time Distributed architecture to extract spatial features from each video frame, and a Long Short-Term Memory (LSTM) network to capture the motion patterns across a sequence of 15 frames. The training data consisted of 299 videos across two exercise classes, each averaging 10 seconds in length, collected from both stock video sources and webcam recordings. The model achieved a classification accuracy of 92.08% on the test dataset after fine-tuning, outperforming a frozen ResNet50 baseline (82.31%) and an intermediate frozen MobileNetV3Small + LSTM configuration (87.08%). In terms of computational efficiency, the proposed model has a total size of approximately 178 MB and requires only 1.8 GFLOPs per inference sequence, which is significantly lower than VGG16+LSTM at approximately 553 MB and 15.43 GFLOPs, and ResNet50 at approximately 3.8 GFLOPs with no temporal modeling capability. The model also runs at approximately 15 frames per second with an end-to-end inference latency of 2.3 seconds per sequence on a standard webcam setup. These results show that combining a lightweight convolutional architecture with sequence modeling can achieve competitive accuracy while remaining practical for deployment on everyday hardware without any specialized sensors or depth cameras.
Glaucoma Disease Detection System Using Hybrid Deep Learning Architecture
Glaucoma is one of the major causes of permanent loss of vision in the world. Blindness can best be prevented by mass screening of the disease at its early stages. Nonetheless, the manual checking of eye photos is time consuming, and it is also vulnerable to human error. This paper describes a hybrid deep learning architecture that can be used to detect glaucoma by examining eye photographs. The suggested system isolates the procedures of locating the eye anatomy as well as categorizing the disease to simplify the outcomes and make them easy to comprehend. First, an Attention U-Net masks Optic Disc and Optic Cup to produce accurate black and white masks. The masks are added to the original color image to form a 5-channel input. The resulting combined input is then used by a Data-efficient Image Transformer (DeiT-Tiny) that then determines the physical distances between the eye structures. The hybrid architecture was tested on the SMDG-19 dataset of 12,449 images with accuracy of 89.77%, sensitivity of 84.41% and Area Under the Curve (AUC) of 0.9564. The results of the experiment indicate that the local feature extraction and global classification should be separated to produce an extremely useful and effective medical screening program tool. This study specifically focuses on glaucoma detection rather than general retinal diseases.
Hybrid Text Summarizer Using SBERT Extractive Filtering and Fine-Tuned BART Abstractive Generation on a Custom Dataset
The exponential growth of digital information has made efficient extraction of key insights from large text corpora an increasingly critical challenge. Traditional extractive summarization methods often yield disjointed, incoherent summaries, while purely abstractive approaches, despite their fluency, are prone to hallucination and demand considerable computational resources. This paper presents a hybrid deep learning framework that integrates the complementary strengths of both paradigms. The system employs DistilRoBERTa, an encoder-only transformer, to identify the most semantically relevant sentences through a greedy labeling strategy. A Sentence-BERT (SBERT) semantic filtering module then re-ranks the extracted candidates using cosine similarity before serializing them as input to the abstractive module. The abstractive module is built upon the Facebook/BART-Large-CNN architecture, fine-tuned on a custom hybrid dataset of 18,000 samples constructed programmatically from CNN/DailyMail. Evaluation using ROUGE metrics yielded a ROUGE-1 score of 0.4935 and a ROUGE-2 score of 0.2421 at Epoch 2. The final system is deployed with a graphical user interface enabling users to upload documents and receive high-quality, factually grounded summaries.
Comparative Analysis of Characteristics of Modified Masonry Unit with Infused Additives: Coal, RHA, and Sawdust
Brick is one of the most commonly used construction materials. The performance, age, and economy of a project are hugely impacted by brick characteristics. This study aimed to investigate the behavior of modified bricks in terms of compressive strength, porosity, and economic perspectives. Fifteen brick formulations were investigated: a Coal-based series (five variants: 2–10% coal by weight), RHA+Sawdust combinations (four variants with varying RHA:SD ratios totaling 8%), a Sawdust-only series (four variants: 2–8% sawdust), and reference samples (Traditional Dachii Appa and Modern machine-made bricks). All specimens were tested in the laboratory as per IS 3495:1992 for compressive strength, water absorption, porosity, bulk density, and durability. Compressive strength was enhanced by adding 6% coal, yielding 8.282 MPa; the RHA+SD combination of 6% RHA and 2% SD achieved the highest modified-brick strength of 9.662 MPa. All formulations met the minimum strength requirement (≥ 3.5 MPa) of NBC 109:1994. The average water absorption of coal-based bricks was 20.359%, with an average group porosity of 29.469%, whereas the RHA+SD group showed an average porosity of 36.189% (individual values ranging up to 39.3%) with enhanced strength but higher porosity. By integrating industrial and agricultural by-products into bricks, structural integrity can be maintained while achieving sustainable solutions for brick manufacturing. Additionally, these bricks contribute to resource efficiency and eco-friendly building practices in the Nepalese construction industry.
Comparative Seismic Performance Evaluation of RC Dual System Buildings Under NBC 105:2020 and IS 1893:2016 Across Varying Heights
This paper presents a comparative seismic performance evaluation of reinforced concrete (RC) dual system buildings, comprising shear walls and moment-resisting frames, of varying heights (4, 8, and 16 storeys) analyzed under NBC 105:2020 and IS 1893 (Part I):2016. Key seismic performance indices base shear, lateral storey displacement, inter-storey drift, and fundamental time period are systematically compared using the Response Spectrum Method. NBC 105:2020 yields significantly higher seismic demands, with base shear ratios of approximately 1.6, 3.0, and 5.2 times that of IS 1893:2016 for 4-, 8-, and 16-storey buildings, respectively. These differences arise from NBC 105:2020's probabilistic seismic hazard framework, higher spectral accelerations, and the combined application of ductility and overstrength factors in place of a single response reduction factor. The study highlights the conservative nature of NBC 105:2020 and its implications for structural design economy and safety in seismically active regions like Nepal.
3D Finite Element Analysis of Geogrid-Reinforced Pavement Performance under Heavy Vehicle Loading in a Curved and Graded Highway Section over Weak Subgrade: A Case Study of Bishaltar, Nepal
The Bishaltar section of Prithvi Highway in Dhading District, Nepal, is a critically distressed pavement segment characterized by combined horizontal curvature (R=40m) and longitudinal gradient (G=4.56%), along with high super elevation (e=6.8%) and a very weak subgrade (california bearing ratio, CBR=2.83%, modulus, E=2.83×10⁴ kPa). Despite being designed using IRC and Department of Roads (DoR) guidelines for an 80kN standard axle load and 30 million standard axle (MSA) through IITPAVE software, the pavement has exhibited persistent surface distresses, early rutting, and premature fatigue cracking, often rendering it unrepairable due to recurring structural deficiencies. A detailed 3D finite element analysis using PLAXIS 3D with Mohr-Coulomb material modeling was conducted, incorporating 27 loading phases to simulate the complex stress path induced by a 12-wheeled 33 Ton gross weight truck under curved and upgraded conditions. Results show that geogrid placement at the subgrade-base interface (SB) is most effective for rutting control, achieving service life ratio (SLR) values of 1.576 (vertical compressive strain, εzz) and 1.588 (Principal compressive strain, ε1). The combined SB and base-asphalt (BA) configuration provides maximum transverse strain reduction (54.7%, SLR=2.207), whereas BA alone is detrimental, increasing subgrade strain. Geogrid stiffness influence is minimal, with EA=300-600 kN/m identified as performance optimal. The study highlights the limitations of conventional design approaches and underscores the necessity of reinforcement-based design for critical curved and graded highway sections.
Intersection Performance in the Kathmandu Valley: A Systematic Review of Capacity, Level of Service, and Intervention Effectiveness
Urban intersections in rapidly motorising South Asian cities routinely operate beyond design capacity, yet the severity of failure and the comparative effectiveness of remedial interventions remain poorly characterised for heterogeneous, motorcycle-dominated traffic streams. This systematic review synthesises quantitative findings from 32 peer-reviewed intersection performance studies and six contextual studies examining capacity, level of service (LOS), and intervention effectiveness across 25 intersections in the Kathmandu Valley, Nepal, spanning 2014–2026. Five principal findings emerge. LOS F prevails at most of studied signalised intersections while DOS among LOS F intersections ranges from approximately 1.3 to 17.37. The absence of a unified motorcycle PCU standard produces a 22–48% divergence in estimated traffic demand, shifting LOS classifications by up to two tiers from the same field data; a parallel inconsistency in simulation calibration — with VISSIM and SIDRA parameters routinely applied without reference to prior site-specific derivations — compounds cross-study incomparability. Intervention effectiveness is strongly DOS-stratified: signal optimisation yields meaningful delay reduction only below DOS ~2.5; grade separation achieves LOS C with 87–90% delay reductions in all modelled flyover scenarios; and a 50% modal shift from motorcycles to public transport produces a 74% corridor delay reduction without infrastructure investment. Signal coordination is spacing-dependent: beneficial at 180 m (Shital Nivas–Kanti, −75% and −50% delay reductions) and counterproductive at 1,130 m (Satdobato–Gwarko, +4.7% network queue increase). Across all 32 studies, only one provides genuine before-after field measurement of a recommended intervention; five independent studies at New Baneshwor remain unvalidated following implementation, confirming that the primary barrier to performance improvement is institutional rather than technical. These findings collectively identify PCU and calibration parameter standardisation, simulation-to-field validation protocols, and cumulative knowledge production as the three priorities for advancing intersection management in the Valley.
Performance Evaluation Of Shankarnagar Water User’s Committee, Rupandehi, Nepal
National coverage of basic water supply and sanitation in Nepal improved significantly between 2014 and 2019, increasing from 83.59% to 87.88% for water supply and from 72.22% to 98.32% for sanitation. However, many completed water supply schemes face functionality challenges: only 28.14% are functioning well, while the rest require varying levels of repair, rehabilitation, or reconstruction. This study assesses the performance of the Shankarnagar Water Supply Consumers and Sanitation Association (SWSCSA) as a representative water utility. Performance was evaluated in terms of physical condition, functionality, quality of service, and operational and financial management. Primary data were collected through household surveys, key informant interviews, focus group discussions, site visits, and water quality testing, while secondary data were obtained from official documents, reports, and published literature. Key performance indicators were grouped into three categories: quality of service, operational performance, and financial performance. Quality of service indicators included water quality, quantity, and affordability. Operational performance was assessed using metering ratio, staff productivity, energy consumption, and non-revenue water (NRW). Financial performance was evaluated using the cost recovery ratio (CRR), maintenance cost as a proportion of operating cost, and revenue collection ratio (RCR).
Functionality of Community Managed DWSS: A Case Study of Water Supply Schemes in Simta Rural Municipality, Surkhet, Nepal
Community-managed drinking water supply systems (DWSS) constitute the backbone of rural water service delivery in Nepal. Despite substantial investments and high nominal coverage, the long-term functionality of these systems remains a persistent challenge. This study assesses the functionality status of community-managed DWSS and identifies key factors influencing their performance in Simta RM of Surkhet District, Nepal. A mixed-methods approach was adopted, combining a quantitative scheme-level survey of ten water supply schemes with focus group discussions, key informant interactions, and field observations. Functionality was evaluated using nationally recognized indicators encompassing technical, environmental, institutional, and financial dimensions, while influencing factors were ranked using a RII based on Likert-scale responses. The results indicate that 70% of the surveyed schemes were at no or low risk of functionality failure, 20% were at some risk, and 10% were at high risk. Institutional and financial factors—particularly the activeness of UCs, availability of O&M funds, and presence of trained VMWs—were found to exert the strongest influence on functionality, followed by technical and environmental factors. The findings highlight that physical infrastructure alone does not ensure sustainable service delivery; rather, continuous post-construction support, transparent governance, and adequate financing mechanisms are critical. The study concludes with policy-relevant recommendations aimed at strengthening community management, enhancing post-construction support, and improving the long-term sustainability of rural water supply services in Nepal.
Design–Cost Nexus of Rapid Sand Filter Systems in Water Supply Projects: A Comparative Construction Management Analysis from Nepal
Rapid Sand Filters (RSFs) are the dominant treatment technology in Nepalese water supply projects, yet similar-capacity systems exhibit markedly different construction costs, suggesting inconsistent design practices and poor cost efficiency. Methods: This study employs a mixed-methods approach—combining quantitative analysis of design drawings, hydraulic data, Bills of Quantities (BOQs), and cost estimates with qualitative insights from field observations and expert interviews—across four completed water quality improvement projects in Sindhupalchowk and Kavrepalanchowk districts. Key design parameters (filter bed area, structural depth, RCC volume, reinforcement quantity, filter media, underdrain system) were systematically compared and a simple linear regression was performed to quantify the reinforcement–cost relationship. Results: Total construction costs ranged from NPR 1.91 million to NPR 6.11 million. Civil works constituted 48–58% of cost in three projects; mechanical and electrical works dominated Project D at 77.8%. Reinforcement quantity explained 96.9% of cost variation (R² = 0.969; p = 0.016), with a unit rate of NPR 150 per kg. Conservative structural detailing and absence of standardized design norms are identified as the primary sources of cost disparity. Conclusion: Rationalising structural depth and reinforcement specifications through context-sensitive design guidelines can substantially reduce RSF construction costs without compromising treatment performance.
Problems Associated with the Public Procurement Process with Special Focus on public Building Construction Projects of Dhulikhel municipality, Kavreplanchowk District, Nepal
This study investigates challenges in public procurement processes, focusing on public building construction projects in Dhulikhel Municipality, Kavrepalanchowk District, Bagmati Province, Nepal. The research aimed to identify operational and administrative issues affecting procurement implementation at the municipal level. A mixed-method approach was adopted, combining qualitative and quantitative data collected through structured questionnaires, field observations, in-depth interviews, and focus group discussions. Findings indicate that procurement in Dhulikhel Municipality faces multiple challenges, including staff absenteeism, inadequate compliance with procurement laws, and limited use of the electronic government procurement (e-GP) system due to low ICT capacity. These issues have led to inefficiencies and delays, negatively impacting project performance and the quality of public construction works. To address these challenges, the study recommends enhancing institutional accountability, expanding digital procurement adoption, promoting ethical practices, and strengthening monitoring and evaluation mechanisms. Additionally, capacity building and clear procedural guidelines are emphasized to ensure fair, efficient, and transparent procurement operations. The insights from this research are intended to support local authorities, policymakers, and practitioners in improving procurement management and governance, ultimately contributing to more effective public infrastructure development.
Assessment On Losses Due To The 2080/2023 Earthquake In Jajarkot District, Nepal
Nepal, positioned along the tectonically active Himalayan zone, is particularly prone to earthquakes. The Jajarkot earthquake of November 3, 2023 (Mw 5.7, ML 6.4) caused significant loss of life and property in Chhedagad Municipality of Jajarkot District. This study aimed to assess the losses and their underlying causes in the aftermath of this earthquake. Data were collected through Primary and Secondary method. Quantitative data were analyzed using Microsoft Excel with Relative Importance Index (RII) applied to Likert-scale items. Results indicated that the most significant losses included damages to public infrastructure with RII = 0.80. The dominant causes was structural vulnerabilities of traditional stone-wall construction (RII = 0.80. Recommended mitigation measures include accurate mortality assessment, integrated planning with resilient infrastructure design, and multi-agency collaboration among local governments, NGOs, and international bodies. This study provides a critical evidence base for disaster risk reduction policy and practice in seismically active remote regions of Nepal.
Experimental Investigation of Compressive and Flexural Strength of Lime-Surkhi Mortar
Lime-surkhi mortar is a traditional binder used in historic masonry structures in Nepal. Despite its widespread application, compressive and flexural properties of lime-surkhi mortar remain poorly quantified, limiting its reliable use in heritage conservation and structural assessment. This study experimentally investigates the compressive and flexural behavior of lime-surkhi mortar prepared with quicklime-based lime putty, varying surkhi and sand proportions at a constant lime content. Following initial screening of thirteen mix ratios, the 1:1:2 and 1:2:2 mixes were selected for detailed analysis. Specimens were air-cured at 70–80% relative humidity and tested at 7 and 28 days. Cube compressive strength increased by approximately 22 times for the 1:1:2 mix and 14 times for the 1:2:2 mix between 7 and 28 days. Masonry prism compressive strength increased by about 1.7 times for both mixes. While the 1:2:2 mix showed higher cube compressive strength, the 1:1:2 mix exhibited superior prism performance, highlighting the significance of mortar–unit interaction. The flexural strength of the 1:1:2 mix increased slightly with curing, whereas the 1:2:2 mix showed a decline at 28 days. These findings provide empirical data for selecting lime-surkhi mortars in heritage repair and low-load masonry applications.
Energy Optimization of Building in Revit with Passive Design Strategies for Kathmandu, Nepal
The building sector is a major contributor to global energy consumption. Innovations aimed at reducing energy use in this sector can significantly advance the achievement of Sustainable Development Goals. Climate-responsive buildings leverage natural climatic elements, such as air temperature, relative humidity, wind, solar irradiation, and rainfall to minimize energy demand for heating, cooling, and lighting. This study optimized building strategies for the warm-temperate climate of Kathmandu, Nepal, using Autodesk Revit. The result obtained from building energy simulation of the baseline and passive building showed significant reduction in Energy Use Intensity and Heating, Cooling, Lighting and Equipment load. The building optimized with passive design strategies consumed 21% less energy and performed 22% better in total site energy reduction and Energy Use Intensity Reduction compared to the normal building, demonstrating the effectiveness of climate-adaptive design in reducing operational energy demand. These findings contribute to the achievement of SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action).
Single-Camera AI-Based Traffic Analytics for Heterogeneous Intersections: A Deep Learning Framework for Microscopic Data Extraction from Smartphone Video
In countries like Nepal, where traffic is heterogeneous, mixed, and non-lane-disciplined, the data collection methods used in transportation planning remain outdated and incapable of producing the microscopic parameters needed to understand local driving behaviors. This paper demonstrates a computer vision framework that converts footage from a single elevated smartphone camera (4K, 24 fps) into microsimulation-ready traffic data at the Koteshwor intersection, a busy three-legged junction in Kathmandu. The framework deploys YOLOv8x for vehicle detection, ByteTrack for multi-object tracking supported by a three-tier identity recovery mechanism, and per-approach planar homography, along with a post-processing pipeline for axial-length reclassification, speed correction, Wiedemann 74 safety-distance extraction, and complementary acceleration/deceleration metrics. From two independent peak-hour datasets yielding 31,882 total vehicle trajectories, 29 stratified validation clips covering 1,805 manually counted vehicles resulted in a 100% GEH pass rate and a class-wise Mean Absolute Percentage Error (MAPE) of 2.48%. The framework outputs volumetric counts across seven vehicle classes, Origin-Destination (OD) matrices, speed distributions, and trajectory-level microscopic parameters, demonstrating that a single smartphone setup can produce richer and more reliable traffic data than traditional manual surveys.
Analyzing Vehicle-Pedestrian Conflicts at Satdobato Intersection
This study examines vehicle–pedestrian conflicts at the Satdobato signalized intersection in Lalitpur, Nepal, a critical urban node characterized by high-volume mixed traffic and high pedestrian activity. Utilizing primary data gathered via three-day video-based field surveys, the research captured peak-hour traffic volumes, pedestrian movements, and signal timings to develop a microscopic simulation model within VISSIM. Following calibration and validation against field conditions, conflict analysis was performed using the Surrogate Safety Assessment Model (SSAM), specifically employing Time to Collision (TTC) and Post Encroachment Time (PET) as primary indicators. The results reveal a high frequency of conflicts peaking at 2,680 incidents during the morning rush primarily driven by signal violations and inadequate pedestrian infrastructure. The study evaluates two mitigation strategies: redirecting pedestrians from Mahalaxmi lane to an existing overhead crossing, yielded a 13.88% reduction in conflicts; and construction of a new overhead crossing at Gwarko lane, achieved a more substantial 39.74% reduction. These findings demonstrate that targeted infrastructural interventions can significantly enhance intersection safety, though their efficacy remains contingent upon the specific operational characteristics of the diverse vehicle types present in the traffic stream.
Spell Correction using N-Gram Modeling and Zero Shot Learning
This paper presents an integrated spell-correction system that combines n-gram language modeling with zero-shot contextual inference using a pretrained LLaMA-2 7B model. The n-gram component efficiently generates correction candidates from local word-sequence statistics, while the zero-shot stage re-ranks those candidates by evaluating contextual and semantic plausibility without task-specific fine-tuning. The system is evaluated on two established benchmarks—BEA-60K and JFLEG—and compared against Hunspell, pyspellchecker, a standalone n-gram baseline, a standalone LLaMA-2 baseline, and the NeuSpell (BERT) toolkit. On BEA-60K, the integrated model achieves an F₁-score of 90.7%, improving over the n-gram-only baseline (59.6%) and the standalone LLaMA-2 model (80.9%). On JFLEG, the system obtains a GLEU score of 58.6, outperforming all individual baselines. An error analysis shows that the integrated model handles non-word errors with 94.1% accuracy and real-word context-sensitive errors with 85.9% accuracy. These results demonstrate that hybrid statistical–neural architectures can deliver strong correction performance while preserving the efficiency of the n-gram front end.
Enhancing Tennis Player Tracking Accuracy Using a Vision-Based Framework with YOLOv8, Adaptive Kalman Filtering and Homography-Based Court Mapping
Manual player monitoring in sports is error-prone, and existing automated systems are economically constrained by the need for high-speed cameras. This paper presents a cost-effective tennis player tracking system leveraging YOLOv8, which achieves 94.90% training accuracy and 97.87% validation accuracy. The system employs a vision-based framework combining a key point-based approach for court landmark detection, Homography transformation to map image coordinates to real-world court positions, and a Kalman filter for robust tracking during fast movements and occlusion. Together, these components enable quantitative analysis including trajectory visualization, distance coverage, speed estimation, and heatmap generation.
Performance Improvement through Signal Optimization: A Case Study of Ekantakuna Intersection
Traffic congestion has long been one of the most persistent challenges in Kathmandu Valley. Intersections have emerged to be a primary factor contributing to this problem where congestion largely undermines efficiency in daily travel. This study focuses on assessing the present operational conditions at critical junction in the ring road, namely Ekantakuna and exploring measures to improve performance at intersection using SIDRA 8.0. Video graphic survey was conducted for 3 consecutive weekdays. To determine peak traffic periods, a 12-hour volume count was extracted from one day of the data, establishing peak ranges for both morning and evening. From this analysis, volume count within the range was carried out for remaining days and maximum peak hour from the three days was taken for the study. Data collected from the Peak hour which includes; Traffic volume, Back of Queue, Saturation flow etc. were applied for the formulation of model to assess the present condition of intersection and recommend different improvement measures. The current operational performance at Ekantakuna showed an average delay of 72.7 seconds and Level of Service (LOS) of E. Two alternatives were approached among which, Alternative 1 targeted Signal Optimization, reducing average vehicular delay from 72.7 seconds to 53.6 seconds and improving LOS to D. Similarly, Grade-Separated intersection was introduced in Alternative 2, it further reduced Average delay from 72.7 seconds to 40.1 seconds while maintaining an improved LOS of D. The findings from the study conclude that targeted model-based optimization can substantially enhance the operational performance of intersection.
School Zone Road Safety Assessment Along Urban Roads with Different Traffic Characteristics: A Case Study of Kathmandu Valley
Kathmandu Valley has emerged as Nepal’s major educational and socio-economic center and it has experienced rapid urban expansion and unplanned growth of educational institutions, resulting in increased road safety concerns around school areas. Inadequate road and pedestrian infrastructure, high traffic volumes, and high vehicle operating speeds have significantly increased the risk for school children. This study, titled “School Zone Road Safety Assessment Along Urban Roads with Different Traffic Characteristics: A Case Study of Kathmandu Valley”, assesses the safety performance of selected school zones using the International Road Assessment Program (iRAP)'s Star Rating for Schools (SR4S) methodology. Field observation was conducted to determine the key safety parameters as described by the SR4S coding guide. The assessment revealed that both school zones performed below the internationally recommended 3-star safety benchmark. It is majorly due to high traffic volume, uncontrolled vehicle speed, and sub-standard infrastructure.Based on the assessment, the study recommends scenario-based interventions that are easy to implement, such as installing speed limit signs, enforcing stricter parking regulations, enhancing curve geometry, and improving road markings and signage, to improve overall road safety.