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.

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.

This study presents a simple yet informative bibliometric analysis of servant leadership literature, aiming to provide a basic overview of its scholarly landscape and identify general trends. We conducted this analysis in September 2023. We focused solely on the Scopus database to understand the current state of servant leadership research. Despite extensive search efforts, we found no similar bibliometric analyses within the servant leadership domain during our study period. Therefore, our focus is to present a brief and straightforward analysis of current research in this field based on identification trends over time, connection between co-occurrence of author keywords, most and less discussed keyword, and areas of high and low concentration. Our findings show an increase in scholarly publications, reflecting a growing acknowledgment of servant leadership’s relevance in management practices. Interconnected keywords and themes such as leadership, transformational leadership, job satisfaction, work engagement, authentic leadership, ethical leadership, organizational citizenship behavior, trust, and leadership development emerge prominently. Additionally, less-discussed keywords such as accountability, core self-evaluations, educational leadership, stewardship, customer orientation, and psychological well-being provide alternative perspectives on these research results. While acknowledging limitations inherent in our bibliometric research, such as potential publication bias and language restrictions, our study offers valuable insights for scholars and practitioners interested in this area.

Short-form content has the potential for virality and broad sharing, allowing businesses to reach large audiences in a short period of time. This type of content has transformed traditional marketing approaches, capturing the attention and curiosity of Generation Z, thereby leading to the rise of digital marketing. As Generation Z is the next generation of consumers and their purchasing power increases as they enter the workforce, marketers need to understand the factors influencing their attitudes and purchase intentions. This study aims to explore the relationship between the growing presence of short-form advertising content in corporate marketing strategies and consumer behavioral intentions. To achieve this, the sub-characteristics of short-form content were categorized into expertise, ease of use, and entertainment value, while information reliability was set as a mediating variable. Data was collected through a survey of 256 adults residing in Busan and Gyeongnam, and analyzed using SPSS 28.0. The findings of the study revealed that most sub-characteristics of short-form content advertisements positively influenced both recommendation and purchase intentions. Additionally, information reliability was identified as a significant mediating factor between short-form content and consumer behavioral intentions. These results provide important insights for corporate marketers and advertising professionals, as they offer valuable guidance on how to influence consumer purchase intentions effectively.