In China, ideological and political education is currently the hot direction of teaching reform in various colleges and universities, yet the development of appropriate teaching evaluation methods needs to catch up. This study addresses the pressing need for a preliminary investigation into the complex relationships among ideological and political education, the students’ learning satisfaction and teaching quality. This research examines the influence of teaching and ideological and political education quality on students’ satisfactions by designing a set of scales, collecting about 3800 questionnaires. Utilizing Structural Equation Modeling (SEM) and qualitative interviews, this study reveals that the teaching quality directly affects students’ learning satisfaction and ideological and political education. Notably, ideological and political education can also affect students’ learning satisfaction. The findings underscore the importance of including ideological and political education assessments in evaluating courses. This research contributes to the ongoing dialogue on effective teaching evaluation methods in the context of evolving educational practices.

Modelling and simulation have now become standard methods that serve to cut the economic costs of R&D for novel advanced systems. This paper introduces the study of modelling and simulation of the infrared thermography process to detect defects in the hydroelectric penstock. A 3-D penstock model was built in ANSYS version 19.2.0. Flat bottom holes of different sizes and depths were created on the inner surface of the model as an optimal scenario to represent the subsurface defect in the penstock. The FEM was applied to mimic the heat transfer in the proposed model. The model’s outer surface was excited at multiple excitation frequencies by a sinusoidal heat flux, and the thermal response of the model was presented in the form of thermal images to show the temperature contrast due to the presence of defects. The harmonic approximation method was applied to calculate the phase angle, and its relationship with respect to defect depth and defect size was also studied. The results confirmed that the FEM model has led to a better understanding of lock-in infrared thermography and can be used to detect subsurface defects in the hydroelectric penstock.

This study aims to explore the design and application of a learning achievement evaluation model, in order to improve the quality of teaching in the field of education and promote student development. This article starts with the importance of constructing a learning effectiveness evaluation model, and then clarifies the basic concepts and related theories of learning effectiveness evaluation, providing theoretical support for subsequent model design. In the model design section of learning effectiveness evaluation, propose the model design principles, indicator selection, and construction process to ensure the accuracy and comparability of the evaluation model construction. In the application and evaluation section of the learning effectiveness evaluation model, the application and evaluation methods of the main models in practical teaching were explored. Finally, the issues that need to be noted in the design process of the evaluation model were proposed in order to design a more high-quality evaluation system and promote the improvement of education quality.

Data mining technology is a product of the development of the new era. Unlike other similar technologies, data mining technology is mainly committed to solving various application problems, and the main means of solving problems are to use big data technology and machine learning algorithms. Simply put, data mining technology is like panning for gold in the sand, searching for useful information among massive amounts of information. Data mining technology is widely applied in various fields, such as scientific research and business, and also has its shadow in the education industry. Currently, major universities are applying data mining technology to teaching quality evaluation. This article first explains the impact of data mining technology on the education industry, and then specifically discusses the application of data mining technology in the evaluation of teaching quality in universities.

[Objective]In order to explore the sustainable food security level in the Yangtze River Economic Belt, ensure food security and sustainable development of agricultural modernization, it is necessary to establish a scientific food security evaluation system to safeguard local food security.[Methods]This paper takes the food system of the Yangtze River Economic Belt in China as the research object, based on the food security research results at home and abroad, based on sustainable development thinking, combined with a new perspective of dynamic equilibrium research: Beginning with food normalcy, a comprehensive analysis of food production, food economy, social development, ecological security, and technical support for sustainable development is presented using the entropy-weighted TOPSIS model to build a food security evaluation system for sustainable development. [Conclusion]After systematic analysis, it is concluded that (1) the average value of food security score of the Yangtze River Economic Belt from 2008 to 2021 is 0.429, and the overall food in the Yangtze River Economic Belt is in general security level (0.400 ≤ Q1 ≤ 0.600), and the overall situation of food security is not optimistic, (2) from the segmentation of the Yangtze River Economic Belt, the high and low level of food security are divided into sections: midstream > downstream > upstream, and each province and city is slowly rising to different degrees. In this way, we propose general countermeasures to ensure local food security from the perspective of sustainable development.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.