Journal of Himalaya College of Engineering

This study implements direct speech to speech translation eliminating the conventional need for Automatic Speech Recognition (ASR) and Machine Translation (MT) by leveraging HuBERT for discrete unit extraction, a Transformer-based encoder-decoder for translation, and HiFi-GAN for speech reconstruction. Inspired by recent advances in unit-based S2ST, the model is trained on the multilingual FLEURS dataset for Nepali to English language translation. It achieved a BLEU score of 42.80, demonstrating both translation accuracy and naturalsounding speech synthesis. This work offers a scalable and effective solution for low-resource languages like Nepali.

The rapid growth of digital information has led to the storage of large volumes of textual data in PDF documents, making manual information extraction time-consuming and inefficient. This paper presents an automated question answering system for PDF text analysis using Natural Language Processing (NLP) and transformer-based models. The proposed system extracts textual data from PDF documents using the PyPDF2 library and preprocesses the extracted text through tokenization and structured formatting to match the input requirements of the T5 transformer model. The model is fine-tuned on question–context– answer datasets to generate context-aware responses to user queries. The performance of the system was evaluated using ROUGE metrics, including ROUGE-1, ROUGE-2, and ROUGE-L, which measure unigram overlap, bigram similarity, and sequence-level similarity between generated and reference answers. Experimental results show that the proposed model achieved a ROUGE-1 score of 0.5263, ROUGE-2 score of 0.3529, and ROUGE-L score of 0.5253, indicating that the system is capable of generating relevant and contextually accurate responses from PDF-based textual data.

This research introduces a mobile application aimed at improving public accessibility to Nepal’s constitutional provisions through an intelligent virtual assistant. The system employs Natural Language Processing (NLP) to deliver simplified summaries, accurate information retrieval, and interactive responses to user queries. By overcoming the shortcomings of existing platforms—such as limited interactivity, poor accessibility, and the complexity of legal language—the proposed solution facilitates easier exploration of constitutional content for citizens, students, and educators. The approach involves developing a structured legal corpus, implementing advanced deep learning models for text summarization and question-answering, and deploying the application with real-time interactive capabilities. Experimental evaluations confirm the model’s effectiveness in generating precise, comprehensible, and user-centered outputs, supported by quantitative metrics and user assessments. The results underscore the transformative potential of AI-driven tools in democratizing legal understanding, fostering civic awareness, and enhancing public engagement in democratic governance.

Mithila Architecture, found in the Mithila region of India and Nepal, showcases the rich cultural and artistic traditions of the Maithili people. This paper explores different types of Mithila Architecture, focusing on their unique features and cultural importance. Traditional Mithila houses are known for their distinct features such as mud walls adorned with vibrant wall paintings, thatched roofs, and intricate wood carvings. These houses are not only aesthetically pleasing but also built using sustainable practices that harmonize with the local environment. The findings of this study indicate that Mithila’s architectural style is closely linked to its culture and everyday life. The traditional architecture and artistic elements of Mithila houses reflect the daily lifestyle, customs, and cultural identity of the Maithili people.

This research investigates the socio-spatial dynamics, the reciprocal relationship between physical space and social behavior of Kathmandu Durbar Square, a UNESCO World Heritage Site, through the lens of liminality, a term derived from the Latin word limen, which translates to ”threshold”. Focusing on Basantapur Dabali, situated at the eastern entrance of the Kathmandu Durbar Square, the study examines how traditional urban public platforms function as ’threshold spaces’ (often referred to as ”liminal zones” which are transitional areas that mediate between two distinct conditions, typically moving from the public street to a private building, or from a secular area to a sacred one) that facilitate both scripted rituals like the daily morning Puja (offerings), the ceremonial processions of the Living Goddess Kumari, the seasonal construction of the Lingo (ceremonial pole) during Indra Jatra and spontaneous social interactions. Utilizing a mixed-methods approach, combining behavior mapping, user surveys, and morphological analysis the research identifies a spectrum of activities ranging from necessary religious rites to ’optional’ social engagements, a foundational concept in urban design, developed specifically by the Danish architect and urban designer Jan Gehl. By analyzing these activities within a framework of urban transition, the study moves beyond generic public space discourse to highlight how liminality sustains cultural continuity and social cohesion in a dense, modernizing heritage context. The findings suggest that the vibrancy of the Dabali is not merely a product of its historical significance, but of its capacity to act as a meaningful ‘urban common’ that bridges the sacred and the secular, providing a model for socially responsive public architecture in growing cities.

Location-Based Futsal Opponent Matching and Game Scheduling Using Collaborative Filtering present a mobile application that addresses the growing demand for efficient futsal game organization in Nepal. Traditional methods rely heavily on personal networks and manual scheduling, often leading to inefficiencies and missed opportunities. The proposed application addresses this challenge by incorporating location-based services and intelligent matchmaking through advanced algorithms. Using cosine similarity in collaborative filtering, the app recommends players with similar skill levels and preferences, enhancing fair and balanced team formation. Simultaneously, the Haversine algorithm is employed to suggest the nearest futsal venues based on the user’s live location, optimizing travel convenience. Real-time push notifications further improve user engagement by updating them on bookings, team status, and match schedules. Built using Flutter for the frontend and Django for the backend, the application is cross-platform and scalable. A MySQL database ensures reliable data storage and retrieval. Initial testing indicates that the application significantly reduces coordination time and improves the futsal experience for players. The system also promotes community involvement by simplifying team creation and venue booking. Overall, the proposed application enhances accessibility, efficiency, and engagement in Nepal’s futsal community through smart technology integration.

Deafness profoundly influences how individuals experience, communicate with, and interact with the world around them. For many, this is a reality that profoundly shapes their experiences and interactions. Deafness is not merely a limitation; it represents a unique journey filled with rich communication, vibrant expression, and deep connections. Contrary to common perceptions, the world of the deaf is alive with visual language, where every gesture and sign conveys meaning, emotion, and a sense of community. This study explores the architectural parameters that shape spatial accessibility, visual connectivity, and sensory legibility for Deaf and hard-of-hearing users in educational environments. Employing a comparative case-study, the research analyzes spatial configurations, circulation geometries, material, and lighting conditions at Gallaudet University (Washington, D.C., USA), Isinya Boarding School for the Deaf (Kajiado County, Kenya)and Central Secondary School for the Deaf(Naxal, Kathmandu, Nepal). Field observations in Nepali Deaf schools further expose systemic gaps in space and proximity, sensory reach, mobility and proximity, light and color, acoustics. The findings demonstrate that design interventions such as U-shaped classroom arrangements, widened and visually continuous corridors, acoustically absorptive surfaces, and diffused illumination significantly enhance visual communication, safety, and spatial comprehension for Deaf users. Design standards from Deaf Space guidelines, Universal Design principles, ADA design standards, International Building Code provisions, and National Building Codes of India and Nepal, the study formulates a set of context-responsive design recommendations applicable to low-resource educational settings. The article contributes to the theoretical and practical discourse on Deaf-friendly architecture by proposing an evidence-based framework for integrating sensory-oriented design within Nepal’s evolving educational infrastructure.

This research work presents an AI-powered football highlight generation application that automates the process of detecting key events in a match using deep learning. Traditional methods of highlight creation are time-consuming and rely on human effort, often delaying access to important moments of the game. To address this limitation, the proposed system utilizes the YOLOv8 object detection algorithm to identify and track essential elements such as players, the ball, and goalposts. The proposed system automatically detects football match events and generates highlight reels through a web-based platform. Users upload a full match video, which is then processed to detect significant events, particularly goals, by analyzing the interaction between the ball and the goal line. A window of 125 frames before and 125 frames after the detected event is extracted to generate a 10-second clip for each event. These clips are stored and compiled into a highlight reel. These highlight reels can be viewed and downloaded through a user-friendly web interface. The research work demonstrates the potential of deep learning in sports highlights generation and analysis by providing a fast, accurate, and scalable solution for automatic video summarization.

In this digital era, AI-generated faces have become so realistic that distinguishing real from fake has become almost impossible. To address this issue, the findings presented in this paper were utilized to develop an advanced digital detection mechanism. The paper presents a DenseNet-121-based neural network trained on 65,000 real and synthetic facial images. The paper implemented the detection into a practical, user-friendly web application based on Flask and React, where users can upload their images, do necessary cropping, and instantly receive predictions along with confidence scores, essentially democratizing deepfake detection to the masses. When evaluating on 13,000 entirely new sets of images, it performed 76 errors (65 real faces wrongly flagged as fake, 11 fakes wrongly accepted), attaining 99.42% accuracy in just seven training epochs by using techniques such as data augmentation and check pointing, including image augmentation and adaptive check pointing. These results confirm the model’s effectiveness in distinguishing between genuine and AI-generated faces. The paper then concludes with future directions, including other video deepfake detection and ethical considerations concerning this technology, asserting that without enhanced digital literacy and public awareness programs, building safer and more trustworthy digital worlds would only be possible by having effective detection tools.

Rapid urbanization in Nepal has made it more difficult to enforce building bylaws, which has led to unchecked growth and deteriorated urban quality. The challenges in implementing the building bye laws and its impact on urban development and their efficacy are assessed in this study. By employing a qualitative approach that includes a literature review, comparative analysis, and interviews with twelve professionals, the study points important gaps in enforcement, low public awareness, and institutional inefficiencies. The results emphasize the necessity of better coordination between communities, technical experts, and local authorities. The study gives practical recommendations to strengthen regulatory frameworks and promote more sustainable urban practices. By situating Nepal’s byelaws within regional realities and international norms, the study contributes to ongoing discussions on resilient urban planning. Strengthening implementation remains essential to ensuring safety, sustainability, and inclusive growth in Nepal’s evolving urban landscape.

Cloud computing’s inherent scalability and flexibility necessitate robust Identity and Access Management (IAM) systems to ensure secure access to resources. This paper focuses on developing an identity-centric cloud security framework, with the primary goal of streamlining user authentication and access control. The project involved integrating Zendesk with the AWS console and implementing Single Sign-On (SSO) based on Role-Based Access Control (RBAC). AWS Cognito was employed to manage user identities, enabling secure authentication and authorization, while distinct roles (admin and basic) were defined to enforce granular access permissions. The implemented system successfully demonstrates secure user authentication and the effective use of RBAC to control resource access. By leveraging AWS services, the architecture provides scalability and facilitates real-time monitoring through CloudWatch, enhancing the ability to detect and respond to security incidents. The findings underscore the importance of an identity-centric security model in cloud environments and highlight the benefits of centralized identity management. The paper emphasizes the necessity of robust authentication and authorization mechanisms to secure cloud-based applications. The study advocates for the adoption of Identity Providers (IdPs) like AWS Cognito, which offer scalable and secure solutions for managing user identities. Furthermore, it recommends the careful definition of roles and permissions aligned with organizational security policies and continuous monitoring to maintain a strong security posture in the cloud.

This study proposes an integrated solution for online eyewear shopping that combines face shape–based eyewear recommendations with dynamic price optimization to improve customer satisfaction and increase revenue. The first objective employs computer vision techniques, starting with Haar Cascade for face detection and Dlib for facial landmark identification and placement. The system then classifies face shapes using a Swin Transformer model trained on a dataset of diverse facial features. To enhance the virtual try-on experience, 3D rendering is achieved through Markerless Augmented Reality (AR) technology and Blender, allowing customers to visualize eyewear options that best fit their face shape. For the second objective, dynamic price optimization leverages machine learning models such as XGBoost, Random Forest, and Linear Regression to analyze historical sales data and determine optimal pricing strategies. By integrating Price Elasticity of Demand (PED) analysis, the project fine-tunes pricing, adjusts discounts, and maximizes profit margins. This dual approach not only enhances the customer experience but also contributes to the overall profitability of the eyewear company.

The need for intelligent and easily accessible medical support systems has increased as a result of limited access to timely healthcare, particularly in underserved and rural areas. A web-based Virtual Health Assistant (VHA) is presented in this work with the goal of improving patient-centred, data-driven, and efficient healthcare. The platform incorporates two essential features: an AI-driven chatbot that predicts diseases and provides medication recommendations, as well as an appointment scheduling system that links patients with qualified medical professionals. The system architecture employs a React.js frontend, Flask and Node.js backends, and MongoDB for secure storage of user profiles, symptom inputs, predictions, and appointment records. The dataset, which included 41 different diseases, 132 binary symptom features, and 4,920 records, was used to train a Support Vector Classifier (SVC) model. A one-vs-rest classification scheme was used to implement the model after pre-processing (feature encoding, label encoding, missing-value handling, followed by a 70/30 train-test split. Precision, recall, F1-score, and support metrics were used to assess the model. By efficiently fusing real-time appointment scheduling with AI-based diagnostics, the integrated VHA lowers patient wait times and enhances access to medical care. The dataset will be expanded, NLP-based symptom interpretation will be improved, and telemedicine features for direct patient-doctor interaction will be incorporated in future improvements. All things considered, this system shows great promise to revolutionize healthcare delivery by utilizing intelligent automation to make it more individualized, efficient, and accessible.

Hyperbolic geometry is a non-Euclidean geometry defined by its violation of Euclid’s parallel postulate, which states that for any point not on a given line, exactly one line can be drawn through that point parallel to the given line. In contrast, hyperbolic geometry permits infinitely many lines through a point that do not intersect a given line. This geometry is visualized on surfaces known as the hyperbolic plane, represented through models such as the Poincare Disk Model, the Upper Half-Plane Model, and the Klein Model, each offering a distinct representation of hyperbolic space. The hyperbolic metric, which defines distances between points, differs significantly from the Euclidean distance. Notably, triangles in hyperbolic geometry have sum of interior angles less than 180°, and the concept of parallel lines differ from Euclidean principles. These unique characteristics make hyperbolic geometry essential for understanding spaces with distinct properties. Distances in hyperbolic models are calculated using their respective metrics, and the analytical representation of hyperbolic lines has been established through examples in Poincare Disk Model, the Upper Half-Plane Model.

In the modern big data environment, a deep learning approach for intrusion detection was seen as more effective and efficient to counter known as well as unknown attacks. Much research has been conducted on intrusion detection systems using a deep learning approach, as well as a hybrid model of deep learning, and continues to be conducted to minimize the risk of intrusion. In this paper hybrid approach is used using variational autoencoders and DenseNet convolutional neural networks to model an intrusion detection system. An experiment is performed using the NSL-KDD dataset to calculate the performance metrics of the model, which contains four types of attacks: probe attack, DoS attack, user-to-root attack, and remote to local attack. The result obtained is compared with the results of other related works. The result obtained from the experiment is quite satisfactory as well, and in comparison with other related works, the model seems comparatively efficient. An intrusion detection system using a VAE-DCNN hybrid model can detect malicious activities very efficiently and correctly, as the performance metric of this model is quite good. The possibility of a false alarm will be reduced using this hybrid model, as the false alarm rate is low.

In electronics education, students frequently encounter difficulties bridging theoretical concepts from textbooks to practical applications, exacerbated by limitations in traditional lectures, static simulations, and resource-intensive physical labs (Bravo et al., 2021). This study introduces an Augmented Reality (AR) Circuit Simulator designed to enhance interactive learning by overlaying 3D virtual components onto real-world environments via mobile devices. The methodology integrates Unity 3D for AR rendering with ARCore for camera-based tracking, and Roboflow for detecting hand-drawn circuit elements. Users can manually place and connect components like batteries and resistors or import sketches, enabling real-time simulation of DC circuits using algorithms based on Ohm’s Law and Kirchhoff’s Laws. Quantitative evaluation of the prototype demonstrated 99% accuracy in simulating basic configurations, such as a 5V battery with a 100Ω resistor yielding 0.05A current, and series-parallel networks. Image recognition achieved 54.3% precision, identifying components reliably in clean sketches but requiring dataset expansion for robustness. This tool supports five core components (battery, resistor, wire, multimeter, nodes) and visualizes current flow through particle animations, outperforming conventional 2D tools in engagement by providing tangible, immediate feedback. The system promotes deeper conceptual understanding, particularly for visual learners, and paves the way for expansions to AC components and user progress tracking.

The growth of e-commerce has led to rise in user-generated reviews, many of which in Nepal are written in Romanized Nepali a non-standard form with inconsistent spelling, grammar and code-switching with English. These irregularities challenge traditional sentiment analysis methods. This study presents NEPTUN(NEpali Phonetic Translation-Based Unified Normalization), a novel module for normalizing Romanized Nepali, NEPTUN uses phonetic transliteration to map Romanized words to Devnagari, verifies them via a Nepali dictionary, and then back-transliterates them into standardized Romanized forms. It also applies frequency-based filtering to retain common variants, improving consistency. While similar techniques exist for Romanized Hindi and Urdu, NEPTUN is the first tailored to Romanized Nepali. Its effectiveness was tested using various sentiment classifiers- Logistic Regression, Naive Bayes, K-Nearest Neighbors, and BERT. NEPTUN-enhanced preprocessing improved model accuracy, with BERT achieving the highest at 87.56%. These results emphasize the need for domain-specific preprocessing in low-resource language like Nepali.

Solid waste disposal is a major challenge faced by communities across the world. Unplanned and unscientific way of dumping is leading to adverse effect in the environment and health factors. This study is carried out in Madhyabindu Municipality, located in Nawalparasi-East, Nepal where the one and only dumping site in Arunkhola river creating the urgency for a sustainable solution to reduce landfill dependency in a long term. An integrated approach combining Fuzzy Analytical Hierarchy Process (FAHP); Geographic Information System (GIS); and Material Recovery Facility (MRF) were employed. Seven criteria namely: Slope; Elevation; Land use; Land cover; Proximity to rivers; Forest; Roads; and Built-up areas were analyzed for ranking the criteria using FAHP. The study showed that an average waste generation rate at base year 2023 is 0.303kg/capita/day of which 61% is organic waste. By using weighted overlay suitability method in MRF only 0.9% of the total land area is found suitable and only 0.001% is highly suitable area for disposal. The way of using integrated approach to identify suitable site for waste disposal problems is highly useful to other sites also where similar condition exist.

In this study, we examine how the integration of market sentiment with traditional financial indicators can improve the accuracy of stock price forecasts in Nepal’s small-cap equity market. With the NEPSE Index as our point of focus, we examined market trends from July 2024 to January 2025, with Nepal Finance Limited as a prominent case. To measure investors’ sentiment, we applied natural language processing to diverse local data sources, including the financial news from Sharesansar, Floorsheet insights from Merolagani, and street-level views from the r/nepalstock community at Reddit. Those sentiment drivers were then combined with conventional technical drivers in a two-model forecasting model with a combination of LSTM neural networks and XGBoost. The findings are substantial: sentiment models outperformed technical analysis-only models in all tests, lowering Mean Squared Error by 17.3% and doing much better on directional forecasting. Amongst all inputs, previous-day stock prices and movement of the NEPSE index were the most significant predictors, whilst sentiment inputs provided a rich source of leading data. By illustrating the efficacy of alternative data in a frontier market environment, this study presents both scholarly timeliness and pragmatic insight for Nepali investors pursuing an advantage within the country’s emerging capital markets.

A GPS-Integrated Fitness Tracking System with Exercise Classification and Personalized Health Recommendations is a mobile app for increasing fitness through real-time activity tracking, exercise classification, and personalized health recommendations. Using GPS, it monitors walking and running, calculates distance and calories burned. Machine learning model, leveraging the device’s camera, classify exercises (e.g., push-ups, squats) in real-time, also it counts the exercises with the algorithm analyzing calories burns providing immediate feedback. Also, with the data synchronization, with notifications delivering a daily health reminder. Addressing limitations of existing apps such as limited real-time classification and generic guidance this system integrates tracking, machine learning, and dynamic recommendations to promote sustained engagement and healthier lifestyles.

The research explores the spatial layout of theaters in Kathmandu and Pokhara valley, centering on the needs and preferences of users—both performers and audiences. It examines how theater spaces interact with actors’ needs and audience expectations, employing a mixed method approach that combines literature review, surveys, and on-site observations. By analyzing existing challenges and opportunities, the research offers insights into designing theaters that respond directly to user requirements, enhancing performance quality, comfort, and engagement. The findings contribute to the enrichment of the two cities’ theatrical landscape by guiding the creation of user-centric spaces that foster creativity and artistic expression.

Despite Nepal’s cryptocurrency ban, blockchain technology has considerable potential for improving transparency, security, and traceability in the country’s supply chain systems. This study demonstrates the viability of applying a blockchain-based framework in the Fast-Moving Consumer Goods (FMCG) industry to increase product authenticity and prevent counterfeiting. By giving unique digital identifiers to products, the technology enables verification at each level of the supply chain via an immutable decentralized ledger. Implemented and tested on the Ethereum Sepolia testnet, the prototype achieved 96% traceability accuracy and decreased transaction latency to 7 seconds, demonstrating blockchain’s practical usability within Nepal’s legislative limits. The findings confirm that blockchain may run securely and efficiently without relying on cryptocurrencies, providing a foundation for future study, infrastructure development, and regulatory adaption.

The study refines an abstract Nepali news summarization system using natural language processing (NLP). The powerful multilingual T5 (mT5) model was fine-tuned in the collected data set. Pre-processing steps, including tokenization, punctuation removal, and special character removal, were applied to enhance performance. Using supervised learning, the model was trained to reduce overfitting. Evaluation was conducted using the ROUGE metric to assess the quality of the generated summaries. The extensive text is then provided to users in the form of concise and meaningful summaries, preserving the core meaning of the original content. The news articles were also extracted using an API, and the summaries are displayed accordingly. This paper highlights the transformer- based model for low-resource languages like Nepali. Moving forward, the plan is to secure more powerful computational resources and improve the scalability of the generated summaries.

This study presents the Digital Financial Advisor, a webbased application designed to provide personalized invest recommendations by analyzing user specific financial factors such as age, financial knowledge, risk tolerance, investment time horizon, and financial goals. The nsystem uses the Random Forest Regressor algorithm as the core methodology to generate optimal portfolio allocations across diverse asset classes like stocks, fixed deposits, SIPs, bonds, and commodities. The platform features interactive pie chart that makes the investment strategies easier to understand. Initial testing demonstrated system responsiveness and effective port- folio distribution. In conclusion, the system bridges the gap between expert financial advice and everyday users. Future enhancements include integrating real-time financial data for dynamic recommendations and expanding feature sets to incorporate additional financial factors, ensuring more comprehensive and accurate investment guidance.

Nepal, a developing and landlocked country with diverse geography and challenging terrain, relies heavily on infrastructure development to enhance regional connectivity and stimulate economic growth. However, government-led construction projects often suffer from systemic inefficiencies rooted in political instability, poor governance and bureaucratic hurdles. Frequent changes in government disrupt policy continuity and delay crucial decision-making processes, resulting in stalled project approvals and implementation gaps. Political interference in project selection, contract awarding and staffing contributes to favoritism and reduces accountability. Additionally, excessive bureaucratic procedures and weak institutional coordination lead to significant time delays, cost overruns and compromised construction quality. These challenges not only waste public resources but also hinder access to essential infrastructure, slowing down national development. This paper critically examines these effects and highlights the urgent need for policy reform, improved institutional capacity, and political stability to ensure successful infrastructure delivery in Nepal.

This research documents traditional copper smithing practices in Patan, Nepal, focusing on the methods, techniques, and spatial requirements of this craft. Traditional metal crafting is an essential facet of Nepal’s cultural heritage, as exemplified by the ornate Hindu and Buddhist temples and intricate metalwork found in the country. The study emphasizes the repousse technique, which involves shaping thin sheets of metals such as copper, silver, and gold into intricate relief patterns using hammering and specialized techniques. Through comparative documentation of a residential-scale workshop and an industrial-scale facility in Patan, this research examines spatial arrangements, environmental conditions, and safety provisions. While general building codes and occupational health standards exist in Nepal, these do not provide specific guidelines for traditional craft workshops. The study explores the occupational challenges faced by craftsmen working in workshop spaces that have evolved organically within existing structures. This research has certain limitations as it focuses exclusively on coppersmiths workshops using the repousse technique, and concentrates on spatial requirements rather than broader socioeconomic and cultural factors. Nevertheless, this study establishes foundational spatial documentation that can inform future development of appropriate guidelines for traditional metal craft workshop design.

This study develops a machine learning (ML)-based framework to assess and mitigate household carbon emissions in Nepal, leveraging a stacking ensemble model (Random Forest + Gradient Boosting meta regressor) that achieves high predictive accuracy (MSE: 112.17, R²: 0.98). By analyzing data from 4,000 households across energy use, transportation, waste, and dietary habits—collected via a structured Google Forms survey and processed using feature selection and Z-score normalization—the system provides personalized carbon footprints and reduction strategies, validated against IPCC benchmarks. The web-based FastAPI-React tool identifies high-impact factors (e.g., LPG consumption, bottled water usage, rainwater harvesting) and effective mitigation measures (e.g., solar adoption), offering actionable insights for households and policymakers to support Nepal’s climate goals. This work advances scalable, context-aware ML solutions for sustainability in developing regions.

The world's data collecting is growing and expanding. The infrastructure must be able to hold a large amount of data, which is becoming more crucial every day. Distributed File Systems could be used to accomplish this. For processing, storing, and analyzing massive amounts of unstructured data, the Distributed File System (DFS) is utilized. Google File System (GFS) and Hadoop Distributed File System are the most popular file systems (HDFS). We give a review of distributed file system design in this work. Scalability, availability, compatibility, extensibility, autonomy, and other factors are considered while comparing file systems.

E-government is a means for governments to use the most innovative information communication technologies (ICTs) through electronic network with more convenient access to government information and services. ICTs develop public participation by facilitating citizens to interact better with each other. The government services are made available to the citizens in a convenient, efficient and transparent manner through e-governance. The objective of this study is to present how public participation is significant to enhance quality government services in developing countries such as Nepal. A conceptual framework was developed based on extensive literature review. The theoretical foundations proposed in this study are policy network theory and theory of intrinsic motivation. The key benefits of e-governance include efficiency, improved services, better accessibility of public services, and transparency. The result of ethical implementation of e-governance is creation of good governance. The E-governance requires a considerable increase in regulation and policy making abilities, with all the expertise and opinion-shaping processes.

This paper analyzes present energy consumption by industrial sector within Kathmandu Valley. Initially, author has gone through intensive literature review and prepare questionnaire. Kathmandu Valley is considered as hub of economic sectors. There are large numbers of industries therefore Sample Distribution method is applied to select location. Out of 1,372 industries, 239 industries have been visited to collect data regarding various energy sources available, technologies used, manufactured final products etc. Collected data is compiled using MS-Excel. It was found that total energy consumption by manufacturing industries in Kathmandu Valley is 16.4 PJ. Diesel is the main energy sources which contribute about 37.21% of total energy consumption. The energy demand is projected from base year 2019 to 2050 using MAED energy model tool. The energy demand for manufacturing industries in Kathmandu Valley is estimated to grow from 16.4 PJ in 2021 to 50 PJ at low growth rate (3.95%), 116 PJ at medium growth rate (7%) and 524 PJ at high growth rate (12.7%). However, the demand of diesel and electricity will increase from 38% and 6% in base year to 60% and 14% respectively by 2050.GHG emission in 2021 was 1,249.6 thousand metric tons and it is expected to increase by 2050 at low growth, medium growth, and high growth is 3842.8 thousand metric tons, 8890 thousand metric tons, and 40,043 thousand metric tons respectively.

Food security in Nepal has been a challenge due to low productivity of agricultural crops. Access to fertilizer has been the bottleneck for increasing productivity in Nepalese agriculture. The ever increasing price for fertilizer and unreliable supply will put further pressure on Nepalese agriculture in the future. Therefore, an alternative fertilizer plan based on recovering fertilizer products from various agricultural and municipal organic wastes has been explored. Developing fertilizer products from a wide range of organic wastes has been a topic of interest world-wide, and recently there has been major progress on new methods of organic recycling. For example, Biotech Ltd has patented an aerobic thermophilic digestor technology that is in use for the rapid composting of various organic wastes with high fertilizer value. Rapid digestion of varied organic wastes, including poultry wastes, and their codigestion with other organic wastes (e.g., food, sludge, green waste) were evaluated to produce a wide range of composted products that can be further composted or to produce dry organic pallets, granules or to develop custom blends of fertilizer in an organo-mineral mix as a complete fertilizer suitable in different cropping industries. Palletization and dry organo-mineral fertilizer products have longer storage, are cost effective to transport and in land application. Innovation and practice change in the production of organo-mineral fertilizers complemented with best practice management in crop production, are expected to result in transformational gain of agriculture productivity and profitability in Nepal.

Sudoku is logic-based puzzle and quite a popular game. The 9 by 9 square grid Sudoku puzzle is the one that we have taken into an account. For solving sudoku puzzle, we have used image processing along with genetic algorithm. In the first phase, the raw and unsolved image is first gray scaled and then Gaussian blur is applied to reduce noise. Then it goes through a main frame extraction stage where adaptive thresholding, maximum contour detection, perspective transformation and small grid segmentation are performed respectively. The individual 81 grids are passed to the CNN model for digit recognition. Then finally, genetic algorithm is used to find the optimal solution of Sudoku problem.

This study analyses the biogas for clean energy consumption and GHG emission reduction in two ecological regions - Hill and Terai – of Nepal. Biogas is methane-rich gas mostly used for cooking in rural households in Nepal. Biogas is interrelated with energy consumption and associated greenhouse gas emissions. The unsustainable use of emission-intensive biomass cooking fuel and other commercial fuels are the major sources of GHG emissions. If biogas is produced and used to its full capacity, it can replace other cooking fuels in the household. The main objective of this study was to observe the existing production and use status of biogas to explore the new pattern of energy consumption and associated GHG emission reduction. This research was carried out in Chitwan and Lamjung districts, representing both Terai and Hill regions of Nepal. Biogas household survey, key informant interview, and direct field observations were used for data collection, while Statistical Package for Social Sciences (SPSS), Volumetric Methane Prediction (VMP), and Long-Range Energy Alternative Planning System (LEAP) Energy Environment Modal were used for data analysis. The result showed that if the biogas completely replaces other cooking fuels, it reduces energy consumption by 40% and GHG emission up to 19 tCO2e per year. It is mostly from saving on fuelwood burning, by 31.8 GJ/household in the Chitwan/Terai and 19.3 GJ/household in Lamjung/Hill from 2012-2040 compared with a non-biogas household.

In Nepal, the level of building code implementation is different in different municipalities and each municipality has a different building permit process. No research is done about the effectiveness of the building permit approval system in the local authority in Nepal. The study was conducted to review the effectiveness of the building permit approval system in Kirtipur Municipality. The research presents and discusses the findings from one of the methods used to measure effectiveness of the system. Fifty (50) questionnaires have been made to the house owner or the Principal Submitting Person (PSP) of the private building registered on FY 2076/77 or later. Four problems were identified from literature review relevant for Kirtipur Municipality and two problems were identified from group discussion with the house owners of ward number nine of Kirtipur Municipality. Most of the respondents felt that ineffectiveness of the building plan approval process was due to lack of transparency, information gap regarding Building permit process and delay to send notice by the ward. The causes of lack of transparency were found to be deficiency of complaint mechanism, discrepancy of the basis of designer, step of floor plan approval is not disclosed and delay in decision. The causes of the problem with the information gap regarding building permit process were building permit procedures not accessible via phone, responsible officials are not friendly to give information and building permit procedures not accessible via website. The problem of delay to send notice by the ward was delay to make site visits by the ward engineer, delay to send report by the ward engineer and due to reshuffling of engineers in ward offices.

The functionality and sustainability of the water supply project is a major challenge in development in rural areas of Nepal. This study primarily focuses on water supply projects built around Dordi Rural Municipality and Sundarbazar Municipality of Lamjung district. A total of 250 water projects were studied. Data was extracted from NWASH-MIS. This research commences by analyzing various components to calculate the functionality score and assess the maintenance needs of key structures of each project. With these two derivations, the relation between these two metrics is studied. It is elaborated through different charts to effectively derive a conclusive dependency between these two components. It was found that the repair condition of an intake is not related to the functionality of a water supply scheme and there is a negative linear relation between functionality with the condition of Reservoir Tank and Collection Chamber in the scheme. Using the Inferential Statistical Analysis approach such as Spearman’s rank correlation coefficient test, we were able to determine if there were a significant correlation between Institutional Capacity with the Functionality Score of schemes under study. There is a strong positive relationship between management practices and functionality (ρ = 0.76, p<0.00). The mathematical relationship between them in low-risk zone of functionality is shown through the simple linear regression model. Result shows that the scheme with lower population (less than 100) are in the higher risk of functionality. Lastly, we found that there is a moderate positive relationship between functionality and the provision of remuneration for Village Maintenance Worker in the committee (ρ = 0.57, p-value = 9.5e-24<0.05).

Because of technological advancement and declining costs of the technologies, solar photovoltaic (PV) based mini-grids in the recent days are gaining popularity as a means to provide electricity to off-grid communities especially in the developing countries. Nepal has been implementing solar mini-grid projects to non-electrified rural areas to achieve the target of electricity for all by 2023. The scope of implementation and sustainable operation of such mini-grid projects in rural area is interdisciplinary in nature. This paper presents technical, social, financial aspects along with key issues of 100kWp solar mini-grid project in one of the rural villages of Nepal. The insights from the paper can support to design solar mini-grid program for sustainable electricity supply in remote areas.

This paper analyzes present energy consumption by industrial sector within Kathmandu Valley. Initially, author has gone through intensive literature review and prepare questionnaire. Kathmandu Valley is considered as hub of economic sectors. There are large numbers of industries therefore Sample Distribution method is applied to select location. Out of 1,372 industries, 239 industries have been visited to collect data regarding various energy sources available, technologies used, manufactured final products etc. Collected data is compiled using MS-Excel. It was found that total energy consumption by manufacturing industries in Kathmandu Valley is 16.4 PJ. Diesel is the main energy sources which contribute about 37.21% of total energy consumption. The energy demand is projected from base year 2019 to 2050 using MAED energy model tool. The energy demand for manufacturing industries in Kathmandu Valley is estimated to grow from 16.4 PJ in 2021 to 50 PJ at low growth rate (3.95%), 116 PJ at medium growth rate (7%) and 524 PJ at high growth rate (12.7%). However, the demand of diesel and electricity will increase from 38% and 6% in base year to 60% and 14% respectively by 2050.GHG emission in 2021 was 1,249.6 thousand metric tons and it is expected to increase by 2050 at low growth, medium growth, and high growth is 3842.8 thousand metric tons, 8890 thousand metric tons, and 40,043 thousand metric tons respectively.

An online teaching learning system is a digital-based technology for virtual classes by using computer type appliances. Digital-based Audio-Visual (A-V) aid is considered an effective tool for online teaching learning system as it stimulates, motivates, and focuses learners’ attention during the Keywords instructional process. This research explored the opinions and experiences of both teachers and students on the effective use of A-V aids in online teaching learning classes, based on their online teaching learning practices, in an Academic Institution in Nepal from June to December 2020. Google survey forms (Online) were made available to all the teachers and students from different programs/faculties at Himalaya College of Engineering, affiliated to Tribhuvan University, Nepal. The collected data from the survey were analyzed by using basic statistical tools and the data was presented the results graphically. The study showed that the majority of the teachers and students have positive perceptions on their impact of using A- V aids, however, the range of agreement was varied based on different course contents, faculties and perceptions of the respondents.

In this research comparative study of rectangular and trapezoidal sections of post tensioned box girder for same width, depth and loading conditions is done. For this research, a three-dimensional finite element (FE) model of two-lane Box Girder Bridge made up of prestressed concrete has been developed using commercially available software CSiBridge v23.3.1. Three dimensional 4-noded shell elements have been employed for discretization of domain and to analyze the complex behavior box girders. The linear analysis has been carried out for moving load with maximum eccentricity at mid span. Also. the study intends to present the parametric study for deflections, longitudinal bending moment, shear force, consumption of concrete and stiffness for these cross-sections. And draw fragility curve to identify the probability of exceedance for the defined damage state conditions.

This research aims for the assessment of the influence of cement and epoxy resin on the stabilization of silty soil. Cement is used as a primary binder and epoxy as an admixture to stabilize the soil by increasing its compressive strength. Disturbed soil samples near the bank of Bagmati river, Chyasal were sampled through excavation. Laboratory tests show that the soil is silt with low plasticity. A Standard Proctor test is done to determine the Optimum Moisture Content (OMC) and Maximum Dry Density (MDD) is done so that the quantity of cement and epoxy required for mixing with the dry soil can be calculated. Cement of proportions (5%,10%,15%) by total weight of dry soil have been used as a primary binder and epoxy resin of proportions (1.25%,2.5%,5%) by total weight of soil as an admixture to prepare soil samples for stabilization for a curing period of 7, 14, 28, 56 and 90 days and the unconfined compressive strength of those samples were determined accordingly. In this paper, the unconfined compressive strength of soil before and after stabilization have been compared. In this research, it is seen that the compressive strength of the stabilized soil drastically improves with the increase in proportions of combination of both cement and epoxy resin. Also, the research shows that the compressive strength increases with increase in curing period.