Pokhara University

Artificial Intelligence (AI) represents a knowledge-intensive technology with the potential to transform production processes, enhance intellectual capital, and accelerate the transition toward knowledge-based economic systems. In developing economies, however, the adoption of AI remains uneven and constrained by organizational, institutional, and policy-related factors. This study examines the readiness of industries in Kathmandu Valley, Nepal, to adopt AI, positioning AI adoption as a key mechanism of knowledge-based industrial upgrading in an emerging economy. Using survey data from 287 industries and employing Structural Equation Modeling (SEM), the study analyzes how technological, organizational, and environmental contexts influence AI adoption, with government intervention examined as a mediating factor. Descriptive results indicate limited readiness for AI adoption among Nepalese industries, with a majority expressing reluctance to adopt AI-based systems. The SEM results reveal that technological, organizational, and environmental contexts exert significant positive effects on AI adoption, while mediation analysis highlights the critical role of government intervention in shaping the environmental conditions that support knowledge-intensive technology uptake. AI adoption in Nepal reflects systemic knowledge economy constraints rather than technological scarcity, as evidenced by the SEM results, highlighting the pivotal role of intellectual capital, innovation infrastructure, and institutional support in knowledge-driven industrial transformation.

The rapid adoption of electric vehicles (EVs) globally demands expanded charging infrastructure; however, their unplanned integration into radial distribution systems (RDS) often causes voltage instability, high power losses, and system inefficiencies. This study optimizes the placement and sizing of solar photovoltaic-battery system (PV-BESS) in Nepal's 30-bus Byasi feeder to mitigate these issues. Employing particle swarm optimization (PSO) and MATLAB/Simulink, the proposed approach reduces active power loss by 56.18 % (477 kW-209 kW) and improves minimum bus voltage by 2.57 % (0.9404-0.9646 p.u.) through integration of 2576 kW PV and 1311 kW BESS. The application of novel energy management strategy further curtails peak demand by 38.3 % (3700 kW-2283 kW). The solution achieves financial viability with approximately 9-year payback period. This research pioneers a comprehensive technical-operational-economic assessment for EV charging-oriented PV-BESS planning in Nepal's RDS, demonstrating how weighted multi-objective optimization balances power loss reduction and voltage improvement while ensuring economic feasibility. The findings provide a replicable framework for sustainable EV infrastructure development in similar distribution networks, highlighting the critical importance of strategic renewable energy integration for grid stability. The study's methodology and results offer valuable insights for utilities and policymakers addressing EV charging infrastructures challenges in developing power systems.

Sustainable banking practices primarily focus on incorporating environmental, and social issues into banks' business operations. It helps to achieve sustainable development goals in any nation. In this context, this study attempts to assess the current sustainable banking practices and sustainability performance of commercial banks listed on the stock exchange of Nepal. For this purpose, this study computes a sustainability performance index using a standard framework and a set of established 50 indicators in the context of developing nations. The data was collected through content analysis of annual reports and websites of the banks from 2022 to 2024. Additionally, key informant interviews among managerial-level employees were undertaken. Differences in sustainability performance by bank type were examined using the Mann-Whitney U test. The results depict that although the commercial banks in Nepal are at the beginning level of sustainable banking practices, their sustainability performance is at a satisfactory level. Sustainability performance was found to be associated with bank size and ownership. Larger banks and banks with foreign ownership were found to have better sustainability performance. The sustainability dimensions in which the banks have performed relatively well were environmental indicators, social issues and development, policy and procedure, and internal socio-ethics. However, the banks need to improve their sustainable products and services, sustainable reporting, and ESG integration indicators. The findings of the study have practical and policy implications for advancing sustainable banking practices in the context of developing nations.

This primary research paper emphasizes cross-validation, where data samples are reshuffled in each iteration to form randomized subsets divided into n folds. This method improves model performance and achieves higher accuracy than the baseline model. The novelty lies in the data preparation process, where numerical features were imputed using the mean, categorical features were imputed using chi-square methods, and normalization was applied. This research study involves transforming the original datasets and comparative model analysis of four Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest (RF) cross-validation methodologies to heart disease open datasets. The objective is to easily identify the average accuracy of model predictions and subsequently make recommendations for model selection based on data preprocessing cross-validation model increased (5 to 14%) more than baseline model for best model selection. From comparing each model’s accuracy scores, it is found that the logistic regression and k-nearest neighbor models achieved the highest accuracy of 81% among the four models when single accuracy is a concern. However, the random forest model summary statistics attained an F1 score of 95%, precision (96%), and recall (97%), indicating the highest overall macro accuracy score. These findings can be further compared using learning curve validation. Conversely, the logistic regression model exhibited the lowest accuracy of 84% among the four machine learning models. However, this research does not cover hyperparameter optimization, which could potentially improve model performance.

Delays are a common problem in construction endeavors, greatly affecting projects and their stakeholders. This research delves into the reasons and repercussions of delays in Nepal’s municipal construction projects, offering crucial insights into contributing elements. The study utilized the Relative Importance Index (RII) to identify 54 causes and 20 impacts of delays. Significant causes include challenging soil conditions, delayed budget releases, and poor client-contractor communication. Data from clients, contractors, consultancies, and case studies revealed additional factors like price surges and equipment shortages. In road and bridge construction, bids averaging 37.52% below tender prices lead to increased costs, schedule overruns, disruptions, and resource inefficiencies. In Shuklagandaki Municipality, 89.81% of construction initiatives encountered delays, with the worst case reaching a 795% overrun for the Mankauri Motor Margha upgrade. The main causes of these delays include ineffective contract planning, COVID-19 effects, a lack of materials, and underbidding. The report offers targeted recommendations for clients, consultants, and contractors, stressing the importance of collaboration to reduce delays. The findings underscore the need for enhanced project management methods to achieve better construction outcomes in the area. The study advocates for a nationwide analysis to devise strategies that address the primary causes of delays in municipal construction projects across Nepal.