To investigate the effect of the location of vacuum insulation panels on the thermal insulation performance of marine reefer containers, a 20ft mechanical refrigeration reefer container was employed in this paper, and the physical and mathematical models of three kinds of envelopes composed of vacuum insulation panels (VIP) and polyurethane foam (PU) were numerically established. The heat transfer of three types of envelopes under unsteady conditions was simulated. In order to be able to analyze theoretically, the Rasch transform is used to analyze the thermal inertia magnitude by calculating the thermal transfer response frequency and the thermal transfer response coefficient for each model, and the results are compared with the simulation results. The results implied that the insulation performance of VIP external insulation is the best. The delay times of each model obtained from the simulation results are 0.81 h, 1.45 h, 2.03 h, and 2.24 h, while the attenuation ratios are 8.93, 20.39, 20.62, and 21.78, respectively; the delay times calculated from the theoretical analysis are 0.78 h, 1.43 h, 1.99 h, and 2.20 h, respectively; and the attenuation ratios are 8.84, 20.31, 20.55, and 21.72, respectively. The carbon reduction effect of VIP external insulation is also the best. The most considerable carbon reduction is 3.65894 kg less than the traditional PU structure within 24 h. The research has a guiding significance for the research and progress of the new generation of energy-saving reefer containers and the insulation design of the envelope of refrigerated transportation equipment.

This study provides empirical data on the impact of generative AI in education, with special emphasis on sustainable development goals (SDGs). By conducting a thorough analysis of the relationship between generative AI technologies and educational outcomes, this research fills a critical gap in the literature. The insights offered are valuable for policymakers seeking to leverage new educational technologies to support sustainable development. Using Smart-PLS4, five hypotheses derived from the research questions were tested based on data collected from an E-Questionnaire distributed to academic faculty members and education managers. Of the 311 valid responses, the measurement model assessment confirmed the validity and reliability of the data, while the structural model assessment validated the hypotheses. The study’s findings reveal that New Approaches to Learning Outcome Assessment (NALOA) significantly contribute to achieving SDGs, with a path coefficient of 0.477 (p < 0.001). Similarly, the Use of Generative AI Technologies (UGAIT) has a notable positive impact on SDGs, with a value of 0.221 (p < 0.001). A Paradigm Shift in Education and Educational Process Organization (PSEPQ) also demonstrates a significant, though smaller, effect on SDGs with a coefficient of 0.142 (p = 0.008). However, the Opportunities and Risks of Generative AI in Education (ORGIE) study did not find statistically significant evidence of an impact on SDGs (p = 0.390). These findings highlight the potential opportunities and challenges of using generative AI technologies in education and underscore their key role in advancing sustainable development goals. The study also offers a strategic roadmap for educational institutions, particularly in Oman to harness AI technology in support of sustainable development objectives.

On the basis of the enlightenment of international engineering education accreditation for the reform and development of higher education in China, combined with the important measures of the national “double first-class” construction, new challenges have been proposed for innovative talent cultivation among engineering majors in the context of promoting national development. These challenges also promote the reform of science-oriented courses among engineering majors. As a core mandatory course for engineering majors, biochemistry plays a crucial role in the entire educational process at universities, serving as a bridge between basic and specialized courses. To address challenges such as limited course resources, insufficient development of students’ advanced thinking and innovation skills, and overly standardized assessment methods, the bioengineering major from Guilin University of Technology restructured the biochemistry course content. A blended teaching model termed “three integrations, three stages, one sharing”, was implemented. This effort has yielded significant results, providing a research foundation for constructing an innovative talent cultivation system that is oriented toward industry needs within modern industrial colleges. It also offers valuable insights into and reference points for the cultivation of engineering talents and curriculum reform in local universities.

The current with the rapid development of Internet and new media technology, the information openness and diversity makes ideological education is facing big challenge, in accordance with the "five a three-ring four law" teaching mode,the fundamental task of implementing ideological and political education, fostering values and cultivating talents is comprehensively carried out. We are advancing the resonance of the “three classrooms” and promoting the synchronous implementation of the “four transformations”, aiming to enhance the “five capacities” of students, according to the current construction of" big education courses "concept, change education thought and idea.

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.