Earticle

This study investigates the critical determinants of success and prevailing challenges faced by women entrepreneurs leading family-controlled businesses (FCBs) in Uzbekistan. Given the nation's strategic emphasis on family enterprises for economic growth, employment, and social stability, and the supportive legislative frameworks enacted since 2019, understanding women's leadership dynamics within this context is crucial. The research focuses on managerial traits such as self-confidence, risk-taking, and entrepreneurial skills, and explores how familial support structures influence strategic decision-making. Recognizing the transformative impact of digital technologies globally, this study also examines how digitalization reshapes entrepreneurial roles, enhances market accessibility, and affects leadership practices among Uzbek women entrepreneurs. The insights provided offer valuable implications for policymakers, practitioners, and future research on women’s empowerment and entrepreneurship in transitional economies.

This research focused on Walton, Bangladesh's largest manufacturer in the electrical, electronics, automobile, and home appliance sectors. Walton mainly focused on its innovative and sustainable business practices. Its strategic emphasis on advanced R&D and Advanced manufacturing system has positioned it in leading position locally and helps it to compete global market. The company’s commitment to quality, affordability, and sustainability has made it competitive internationally. This research highlights Walton's technological, organizational, environmental approach to demonstrating how these elements contribute to its economic success and social impact. By exploring Walton's role in job creation, skill development, and export growth, the study offers insights into how emerging market firms can achieve global competitiveness. Walton's practices provide insights for integrating technological advancement and environmental responsibility, setting new standards in the industry and offering a model for other corporations in developing economies.

In this project, we present SpudScan, a comprehensive approach for the semantic segmentation of potato leaf diseases. Utilizing a custom dataset collected and meticulously annotated by our team, we trained and evaluated three state-of-the-art models: Segformer, Unet, and Deeplab V3. Our objective was to accurately identify and segment various disease-affected regions on potato leaves to facilitate early detection and treatment. Comparative analysis of the models highlights their respective strengths and weaknesses in terms of performance, processing time, and robustness. The results demonstrate significant potential for integrating deep learning techniques in agricultural disease management, paving the way for more efficient crop monitoring and health assessment.

Electricity demand management is most crucial part for Nepal Electricity Authority (NEA) in case of Nepal. Electricity demand is mainly affected by time of day, week of day, monthly season and ambient temperature of power substation customer, their population trained and their life style according to time period. Mostly in winter most of electricity consumer use heating appliances and in summer cooling appliances. Also, in daily pattern electricity demand is high during morning and evening time due to peoples used cooking, lighting and entertainment appliances. That varies the load on power grid. By leveraging historical electricity demand data, time and meteorological records, we have to identify correlations and seasonal and time series patterns using statistical and machine learning techniques and predict short time electricity demand on power substation (hourly). In this system we use multivariable LSTM based-short term electricity demand forecasting, which can predict with 99% maximum accuracy.