Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

Tomato (Solanum lycopersicon L.) is a highly valued crop in the world, particularly in Nigeria with high nutritional and economic benefits. However, its production in Iwollo, Southeast Nigeria, is constrained by unfavorable weather conditions. To address this, a study was conducted at the Teaching and Research Farm, Department of Horticultural Technology, Enugu State Polytechnic, Iwollo, Southeast Nigeria to evaluate and select the best cultivar for high tunnel production using the Rank Summation Index. Completely Randomized Design with three replications was used, and six high-yielding cultivars, namely Roma VF, BHN-1021, Supremo, Pomodro, Money maker, and Iwollo local, were evaluated. Data were collected on key agronomic characters and analyzed with Analysis of Variance (ANOVA) at a 0.05 level of probability. There were significant differences in the number of leaves per plant, plant height, number of branches per plant, days to fruit maturity, fresh fruit weight, number of harvested fresh fruits per plant, and fresh fruit yield per plant among the cultivars. These characters that showed significant differences were ranked and summed up to obtain the Rank Summation Index (RSI) score. The results revealed that the Supremo cultivar had the lowest and best score (18). This suggests Supremo as the best cultivar for high tunnel tomato production in the study area, based on its superior performance across key agronomic traits.

This study examined socio-economic factors affecting Micro, Small, and Medium Enterprises (MSME) e-commerce adoption, focusing on gender, income, and education. Using the 2022 National Socio-Economic Survey (Susenas) data, a logistic regression model was employed to analyze key determinants of e-commerce utilization. Additionally, an online survey of 550 MSMEs across 29 provinces was conducted to assess the impact of digitalization on business performance. In comparison, an offline study of 42 MSMEs with low digital adoption provided insights into the barriers hindering digital transformation. A natural experiment was conducted to evaluate the effectiveness of behavioral interventions in promoting the adoption of e-payments and e-commerce. The main contribution of this study lies in integrating large-scale national survey data with experimental approaches to provide a deeper understanding of digital adoption among MSMEs. Unlike previous studies focusing solely on socio-economic determinants, this research incorporated a digital nudging experiment to examine how targeted incentives influenced e-commerce participation. The findings revealed that digital transformation significantly enhanced MSME performance, particularly in turnover, product volume, customer base, and worker productivity. Socio-economic factors such as gender, household head status, and social media access significantly influenced digital adoption decisions. Behavioral nudging proved effective in increasing MSME participation in e-commerce. Although this study was limited to Susenas 2022 data and survey responses, it bridges a critical research gap by linking socio-economic factors with behavioral interventions in MSME digitalization. The findings offer key insights for policymakers in formulating evidence-based strategies to drive MSME digital transformation and e-commerce growth in Indonesia.

The internationalization of higher education began to take shape during the period of the Republic of China. This trend manifested in various forms and encompassed a rich array of activities, including the construction of teaching staffs, the exchange of international students, and the presence of overseas scholars giving lectures in China. Between 1899 and 1945, Japanese institutions sent nearly 200 academic overseas students to China. With the establishment and improvement of the internal system of universities in the Republic of China, these students were able to study and interact with Chinese scholars. The forms of communication were diverse, the content was rich, and the channels were smooth, making the process lively and interesting with distinct characteristics of the era. Consequently, this group became both participants and witnesses in the internationalization process of universities in the Republic of China. However, the full-scale Anti-Japanese War disrupted the internationalization of universities, causing it to deviate from its normal trajectory. Some Japanese academic overseas students who had previously studied in China became instruments of Japanese imperialism’s cultural invasion and educational colonization. These students played a significant role in promoting the “alternative internationalization” of universities in the Republic of China. In short, examining the involvement of Japanese academic overseas students providing us a unique insight into the general situation and processes of internationalization at universities in the Republic of China during different historical periods.

It is important for society to know the actions implemented by companies in the construction sector to reduce the environmental pollution generated by this industry and to contribute to the solution of economic and social problems in their environment; however, the variables that allow identifying their contributions and impacts are not known. Based on this problem, the study focuses on identifying the factors that influence sustainability management within the construction sector in Colombia. The research presents a predictive approach and uses a quantitative methodology, applying statistical modeling techniques. The sample corresponds to 84 Colombian companies. As a result, a system of equations of the form y=mx+b is presented to describe the deviation of the environmental, economic, social, compensation measures, management, indicators and sustainability reports. The analysis of the intersections constitutes a projective tool to evaluate the relationships and balance points between the dimensions analyzed, helping to identify strengths and opportunities for improvement.