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.

This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.

Using individual- and panel country-level data from 118 countries for the period 1981–2020, this study investigates the effects of national- and individual-level economic and environmental factors on subjective well-being (SWB). Two individual SWB indicators are selected: the feeling of happiness and life satisfaction. Additionally, two environmental factors are also considered: CO2 emissions by country level and personal perspective on environmental protection. The ordered probit estimation results show that CO2 emissions have a significant negative effect on SWB, and a higher perspective on environmental protection has a significant and positive effect. Compared with the average marginal effect of national income, CO2 emissions are a more important determinant of SWB when considering a personal perspective on protecting the environment. The estimation results are robust to various estimation model specifications: inclusion of additional air pollutants (CH4 and N2O), PM 2.5 and various sample groupings. This study makes a novel contribution by providing comprehensive insights into how both individual environmental attitudes and national pollution levels jointly influence subjective well-being.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.