The sea level rise under global climate change and coastal floods caused by extreme sea levels due to the high tide levels and storm surges have huge impacts on coastal society, economy, and natural environment. It has drawn great attention from global scientific researchers. This study examines the definitions and elements of coastal flooding in the general and narrow senses, and mainly focuses on the components of coastal flooding in the narrow sense. Based on the natural disaster system theory, the review systematically summarizes the progress of coastal flood research in China, and then discusses existing problems in present studies and provide future research directions with regard to this issue. It is proposed that future studies need to strengthen research on adapting to climate change in coastal areas, including studies on the risk of multi- hazards and uncertainties of hazard impacts under climate change, risk assessment of key exposure (critical infrastructure) in coastal hotspots, and cost-benefit analysis of adaptation and mitigation measures in coastal areas. Efforts to improve the resilience of coastal areas under climate change should be given more attention. The research community also should establish the mechanism of data sharing among disciplines to meet the needs of future risk assessments, so that coastal issues can be more comprehensively, systematically, and dynamically studied.

In today’s fast-paced digital world, generative AI, especially OpenAI’s ChatGPT, has become a game-changing technology with significant effects on education. This study examines public sentiment and discourse surrounding ChatGPT’s role in higher education, as reflected on social media platform X (formerly Twitter). Employing a mixed-methods approach, we conducted a thematic analysis using Leximancer and Voyant Tools and sentiment analysis with SentiStrength on a dataset of 18,763 tweets, subsequently narrowed to 5655 through cleaning and preprocessing. Our findings identified five primary themes: Authenticity, Integrity, Creativity, Productivity, and Research. The sentiment analysis revealed that 46.6% of the tweets expressed positive sentiment, 38.5% were neutral, and 14.8% were negative. The results highlight a general openness to integrating AI in educational contexts, tempered by concerns about academic integrity and ethical considerations. This study underscores the need for ongoing dialogue and ethical frameworks to responsibly navigate AI’s incorporation into education. The insights gained provide a foundation for future research and policy-making, aiming to enhance learning outcomes while safeguarding academic values. Limitations include the focus on English-language tweets, suggesting future research should encompass a broader linguistic and platform scope to capture diverse global perspectives.

This research aimed to assess the results of two vendors used by the company in the shipping process of export goods. Two leading suppliers for one similar activity had caused more difficulties in the monitoring and controlling activities of DHL Global Forwarding Indonesia. This research used qualitative and quantitative methods, with the Analytic Hierarchy Process decision-making method using 36 internal staff members as the sample. Through a qualitative calculation method by distributing questionnaires to the existing suppliers, namely Monang Sianipar Kargo and Andima Transportindo, it was found that the weighted score for Monang Sianipar Kargo was 22.84 and for Andima Transportindo was 10.66. Subcriteria and indicators should be prioritized in the criteria of Price and service, significantly to improve the performance of problematic suppliers. This research recommended using the Analytic Hierarchy Process for assessment since it facilitated the research development by the opinion of the company’s experts. Such a finding implied that a policy from the management was needed in the assessment of suppliers. As an implication, it was necessary to assess all suppliers cooperating with DHL Global Forwarding Indonesia by using actual data from the current month.