This study aims to use dialectical thinking to explore the impacts and responses of Artificial Intelligence (AI) empowerment on students’ personalized learning. The effect of AI empowerment on student personalization is dissected through a literature review and empirical cases. The study finds that AI plays a significant role in promoting personalized learning by enhancing students’ learning effectiveness through intelligent recommendation, automated feedback, improving students’ independent learning ability, and optimizing learning paths, however, the wide application of AI also brings problems such as technological dependence, cheating in exams, weakening of critical thinking ability, educational fairness, and data privacy protection to students. The study proposes recommendations to strengthen technology regulation, enhance the synergy between teachers and AI, and optimize the personalized learning model. AI-enabled personalized learning is expected to play a greater role in improving learning efficiency and educational fairness.

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.

Modified chitosan hybrids were obtained via chemical reaction of chitosan with two pyrazole aldehyde derivatives to produce two chitosan Schiff bases, Cs-SB1, and Cs-SB2, respectively. FTIR spectroscopy and scanning electron microscopy confirmed both chemical structures and morphology of these Schiff bases. Thermal gravimetric analysis showed an improvement of thermal properties of these Schiff bases. Both chitosan Schiff bases were evaluated in a batch adsorption approach for their ability to remove Cu(II) ions from aqueous solutions. Energy dispersive X-ray for the Schiff bases adsorbed metal ions in various aqueous solutions was performed to confirm the existence of adsorbed metal ions on the surface substrate and their adsorptive efficiency for Cu(II) ions. Results of the batch adsorption method showed that prepared Schiff bases have good ability to remove Cu(II) ions from aqueous solutions. The Langmuir isotherm equation showed a better fit for both adsorbents with regression coefficients (R² = 0.97 and 0.99, respectively) with maximum adsorption capacity for Cu(II) of 10.33 and 39.84 mg/g for Cs-SB1 and Cs-SB2, respectively. All prepared compounds, pyrazoles and two chitosan Schiff bases, showed good antimicrobial activity against three Gram +ve bacteria, three Gram –ve bacteria and Candida albicans, with varying degrees when compared to the standard antimicrobial agents.

This study offers a new perspective on measuring the impact of village funds (DD) on rural development. Using a mixed-method approach, the qualitative analysis reveals that, like previous rural development programs, the DD program struggles to implement inclusive methods for capturing community aspirations and evaluating outcomes. Despite rural infrastructure improvement, many villagers feel they have not fully benefited and do not view it as offering economic opportunities. The econometric model confirms the qualitative findings, indicating no significant DD influence on the village development index (IPD). Instead, effective governance factors like Musdes, regulations, and leadership are essential for the IPD improvement. Thus, enhancing village governments’ institutional capacity is crucial for increasing the DD effectiveness. The paper recommends several measures: training village officials in financial management and project planning, providing guidelines for the DD allocation and usage, creating robust monitoring-evaluation systems, developing communication strategies, and fostering partnerships with local NGOs and universities.

Problem statement: An environmentally conscious consumer’s perspective can shift as they look for things that are gentler on the planet. Conversely, businesses engage in greenwashing when they try to cover up their lacklustre environmental initiatives. The current research was used the theory of rational choice behaviour to examine a model that connects corporate green washing and consumers’ green purchase intentions via the mediating roles of perceived risk, green trust and green confusion about food and beverage brands in Saudi Arabia. Research motivation: Sustainable business practices have been developed and adopted by corporations in response to the growing interest in environmentally friendly lifestyles and green products. However, green washing has become increasingly common as a means for businesses to give off the impression that they care about the environment when they really don’t. Research methodology: The online survey was used to obtain data directly from consumers about their views on green washing by corporations. Primary data was analysed using appropriate statistical tools and techniques in SPSS, AMOS and SmartPLS software, such as Correlation, Regression, Structural Equation Modelling (SEM), etc. Results: In terms of perceived greenness and confusion, the results showed that green wash mediates the relationship between green purchasing intention and greenness. There is a two-way correlation between consumers’ intentions to buy environmentally friendly products and their levels of green perception, and green confusion. The findings of this study were broadening our understanding of the consequences of green washing. Conclusions: All things considered, the study was encouraging more research on the subject and be a useful tool for academics, corporate managers, and students interested in environmental sustainability, product innovation, and green branding. According to the results, businesses can improve their green purchasing intentions by cutting down on green washing and focusing instead on building a positive reputation for their brand and encouraging customer loyalty. Corporate performance and social environment sustainability can both benefit greatly from this paper’s expansion of knowledge regarding the processes of individual customer psychological effects after perceptions of corporate greenwashing behaviour.

With the wide application of the Internet and smart systems, data centers (DCs) have become a hot spot of global concern. The energy saving for data centers is at the core of the related works. The thermal performance of a data center directly affects its total energy consumption, as cooling consumption accounts for nearly 50% of total energy consumption. Superior power distribution is a reliable method to improve the thermal performance of DCs. Therefore, analyzing the effects of different power distribution on thermal performance is a challenge for DCs. This paper analyzes the thermal performance numerically and experimentally in DCs with different power distribution. First, it uses Fluent simulate the temperature distribution and flow field distribution in the room, taking the cloud computing room as the research object. Then, it summarizes a formula based on the computing power distribution in a certain range by the numerical and experimental analysis. Finally, it calculates an optimal cooling power by analyzing the cooling power distribution. The results shows that it reduces the maximum temperature difference between the highest temperature of the cabinet from 5-7k to within 1.2k. In addition, the cooling energy consumption is reduced by more than 5%.