This paper reviews and compares the opportunities and challenges in terms of port and intermodal development in China and India—the two fast-growing economic giants in the world. The study analyzes the future direction of these two countries’ port-hinterland intermodal development from the sustainability perspective. Both China and India face some major opportunities and challenges in port-hinterland intermodal development. The proposal of the Silk Road Economic Belt and the 21st-century Maritime Silk Road, also known as the Belt and Road Initiative (BRI), offers plentiful opportunities for China. A challenge for China is that its development of dry ports is still in the infancy stage and thus it is unable to catch up with the pace of rapid economic growth. As compared with China, India focuses more on the social aspect to protect the welfare of its residents, which in turn jeopardizes India’s port-hinterland intermodal development in the economic sense. The biggest challenge for India is its social institution, which would take a long time to change. These in-depth comparative analyses not only give the future direction of port-hinterland intermodal development in China and India but also provide references for other countries with similar backgrounds.

In this study, the development of rinnenkarren systems is analyzed. During the field studies, 36 rinnenkarren systems were investigated. The width and depth were measured at every 10 cm on the main channels and then shape was calculated to these places (the quotient of channel width and depth). Water flow was performed on artificial rinnenkarren system. A relation was looked for between the density of tributary channels and the average shape of the main channel, between the distance of tributary channels from each other and the shape of a given place of the main channel. The density and total length of the tributary channels on the lower and upper sections of the main channels being narrow at their lower end (11 pieces) and being wide at their lower end (10 pieces) of the rinnenkarren systems were calculated as well as their average proportional distance from the lower end of the main channel. The number of channel hollows was determined on the lower and upper sections of these main channels. It can be stated that the average shape of the main channel calculated to its total length depends on the density of the tributary channels and on the distance of tributary channels from each other. The main channel shape is smaller if less water flows on the floor for a long time because of the small density of the tributary channels and the great distance between the tributary channels. In this case, the channel deepens, but it does not widen. The width of the main channel depends on the number and location of the rivulets developing on channel-free relief. The main channel becomes narrow towards its lower end if the tributary rivulets are denser and longer on the upper part of the main rivulet developing on the channel-free, plain terrain and their distance is larger compared to the lower end. The channel hollows develop mainly at those places where the later developing tributary channels are hanging above the floor of the main channel. Thus, the former ones are younger than the latter ones. It can be stated that the morphology of the main channels (shape, channel hollows, and width changes of the main channel) is determined by the tributary channels (their number, location and age).

With the deep integration of artificial intelligence technology in education, the development of AI integration capabilities among pre-service teachers—as the core of future educational human resources—has become crucial for enhancing educational quality and driving digital transformation in education. Based on the AI-TPACK (Artificial Intelligence-Technological Pedagogical Content Knowledge) theoretical framework, this study employs questionnaire surveys and structural equation modeling to explore the structural characteristics, influencing factors, and formation mechanisms of AI-TPACK competencies among pre-service teachers in Chinese universities. Findings indicate that while pre-service teachers demonstrate moderately high overall AI-TPACK levels, their technical knowledge (AI-TK) and technological integration competencies (e.g., AI-TPK, AI-TCK) remain relatively weak. School technical support, technological attitudes, and technological competence significantly influence their AI-TPACK capabilities, with institutional level and teaching experience serving as important external moderating factors. Building on these findings, this paper proposes a systematic framework for developing pre-service teachers' AI integration capabilities from a human resource development perspective. This framework encompasses four dimensions: curriculum optimization, practice enhancement, resource support, and policy guidance. It aims to provide theoretical foundations and practical pathways for pre-service teacher training and teacher human resource development in higher education institutions.

This quantitative survey was non-experimental and had two goals. An evaluation of predictor variables of empowerment, motivation, teamwork, interpersonal skills, and training and development in project environments was one goal to help explain the industry’s high project failure rate. Second, this research tested Bandura’s social learning theory and tested the hypothesis that empowerment and motivation boost performance. Using a survey-based questionnaire, the data was collected from 212 employees working in different IT companies in Pakistan. The results revealed that empowerment, motivation, teamwork, and training and development have a significant impact on project performance. Using the results, this study proposes theoretical implications for the researchers and managerial implications for the organizations.