INTRODUCTION: Through in-depth study of Civics classroom evaluation, it can provide teachers with scientific evaluation indexes and methods, improve teaching quality and effect, promote the overall development of students, and also promote the professional growth of teachers. OBJECTIVES: Based on critical and fuzzy comprehensive evaluation methods, this study aims to study the effectiveness and accuracy of the evaluation methods in Civics and Politics classrooms. Based on the CRITIC method, the fuzzy comprehensive evaluation method was introduced to solve the problem of subjectivity and uncertainty in the evaluation process. METHODS: Various research methods were used, including observation, interviews, and questionnaires. By comprehensively analyzing the students' performance, feedback, and assessment, and the teachers' pre-course preparation, the advantages and improvement directions of Civics classroom teaching can be accurately evaluated using the CRITIC and fuzzy comprehensive evaluation methods. RESULTS: The study results show that the CRITIC and fuzzy comprehensive evaluation methods can provide a more comprehensive, accurate, and objective evaluation of the Civics classroom. The undefined complete evaluation method plays an essential role in dealing with ambiguity and uncertainty in the evaluation process, making the evaluation results more objective and reliable. CONCLUSION: The Civics classroom evaluation method based on the CRITIC and fuzzy comprehensive evaluation methods is adequate and accurate. These findings strongly support improving the quality of Civics classroom teaching and enhancing students' learning outcomes.

INTRODUCTION: At present, China's urbanization continues to develop, but the level of urban planning and design has not kept pace with the progress of development, especially the creation of street space in small cities, which is still not able to adapt to the current needs of people's cultural life. With the development of the economy and science and technology, artificial intelligence and other information technologies are constantly integrated into daily life. Therefore, utilizing artificial intelligence for street space design research is significant. OBJECTIVES: To improve the development level of artificial intelligence, information technology, and the application level of artificial intelligence in China; to solve the problem of the relatively low level of street space design in the current city, especially in small towns, to meet the needs of people's cultural life; to promote the improvement of the level of artificial intelligence in China for the convenience of civil life, and to further improve the level of street space design in China. METHODS: Firstly discusses the theories, such as the principles of street space design in small cities, to illustrate the necessity of integrating AI into street space design in small towns; secondly, the model established by AI is used to optimize the street space in small cities; finally, the feasibility of integrating AI design into street space design in small towns is illustrated through cases and examples. RESULTS: Street space design in small cities in China needs more design aesthetics, and artificial intelligence needs to be integrated into street space design. Artificial intelligence has a noticeable optimization effect on street space design, and using artificial intelligence can improve the design level of limited space. Also illustrates that artificial intelligence can be better integrated into the street space design of small cities through cases. CONCLUSION: In the context of the digital era, the street space design of small cities needs to apply artificial intelligence. In modernization-oriented urban construction, the street space design level of small towns should be further strengthened to improve the modernization level of cities.

This paper proposes a new data-driven method for power outage detection. By capturing the changes in data distribution of smart meters (SM), it can detect power outages in partially visible distributed systems. First, a mechanism based on breadth-first search (BFS) is proposed, which decomposes the network into a set of regions to find the location information where power outages are most likely to occur. Then, the SM data for each region, generating a generative adversarial network (GAN), is used in order to extract unsupervised manner implicit temporal behavior under normal conditions. After network training, anomaly scoring technology is used to determine whether the real-time measurement data is the data of a power outage event. Finally, in order to infer the location of a power outage in a multi-area network, a regional coordination process with interdependence be-tween cross-regions is used. At the same time, the concept of entropy is used to provide performance analysis for the algorithm in this paper. This method has been verified on the distribution feeder model with actual SM data. Experimental results show that the algorithm is effective and feasible.

Shell-and-tube heat exchangers are pivotal in thermal engineering, making the accuracy and quality of the heat transfer data obtained from them essential. Current data monitoring technologies face several challenges, such as increased complexity, noise, and inefficiency in handling the dynamic heat transfer process. This paper introduces a novel approach to enhancing the accuracy and precision of energy transfer data segmentation in shell-and-tube heat exchangers using a multi-pipeline segmentation algorithm. Our methodology integrates data collection with the algorithm's hands-on development, employing advanced techniques to segment and categorize energy transfer data based on real-time system parameters. This creates a robust definition of normal and anomalous operating conditions. Our approach was validated through extensive experiments and simulations, demonstrating superior data accuracy and noise detection compared to traditional methods. Moreover, this innovative segmentation algorithm has potential applications in maintenance forecasting and optimization strategies, ultimately improving energy efficiency. In the future, our algorithm could be extended to other types of heat exchangers or industrial systems, further enhancing their energy efficiency and operational lifespan.

With the rapid development of Internet technology, e-commerce has thoroughly infiltrated every aspect of commercial activities. It has not only altered traditional business models but also redefined market structures and consumer behaviors. However, existing e-commerce systems exhibit shortcomings in data file management and data export tasks, manifesting as functional gaps and redundant constructions. In response to this challenge, this study designs and implements an e-commerce file task system based on Domain-Driven Design theory and a microservices architecture. This system aims to provide a universal, efficient, and flexible solution for managing the complex and chaotic file and data export tasks inherent in the e-commerce domain. It contributes to enhancing the overall operational efficiency of e-commerce systems and optimizing the end-user experience. Finally, this study validated the effectiveness of the system through well-designed experiments.

This paper investigates the impact of cross-border e-commerce pilot zones on the optimization of the industrial structure in the Yangtze River Delta region, a pivotal area for China's economic development. It commences with a review of relevant literature and theoretical underpinnings concerning cross-border e-commerce and its influence on industrial structure. The study then provides a detailed account of the pilot zones' development and the region's current industrial structure, exploring the mechanisms that contribute to structural optimization. Utilizing a multi-period double difference (DID) method on panel data from 41 prefecture-level cities spanning from 2011 to 2020, the research meticulously assesses the impact of the pilot zones. The findings are validated through robustness tests and heterogeneity analyses, yielding significant insights. The study discerns that the establishment of pilot zones significantly bolsters the optimization of the industrial structure in Yangtze River Delta. The impact varies based on city size, administrative level, and the order of establishment. Three principal intermediary mechanisms are identified as catalysts for this optimization: the innovation-driven effect, which stimulates technological progress and innovative business models; the industrial agglomeration effect, which strengthens industry concentration and competitiveness; and the resource allocation effect, which enhances the efficiency of resource distribution across sectors

This paper employs portfolio optimization models based on second-order stochastic dominance and "super-convex" third-order stochastic dominance, comparing them with an equal-weight portfolio optimization model. Through the computation of out-of-sample indicators for portfolio evaluation, it is found that, in the Shanghai Stock Exchange A-share market, the portfolio based on stochastic dominance theory exhibits a significant momentum effect, leading to substantial excess returns when the formation period is relatively long compared to the benchmark returns.

With the continuous development of e-commerce, how to improve consumer stickiness has become the focus of e-commerce platforms. Increasing consumer satisfaction is one of the important ways to increase consumer stickiness. In recent years, with the development of the logistics industry, postponement has gradually become a hot spot for scholars. By using the postponement strategy that can solve the "Bullwhip effect" effectively, further consumer perception could be improved. However, some scholars believe that the postponement strategy improves consumer perception as well as reduces consumer satisfaction. Therefore, this paper uses questionnaire survey to collect data, and multiple regression models are used to analyze the causal relationship between delay strategy, like logistics delay strategy, design delay strategy, payment delay strategy, and consumer perception. According to the results of multiple regression analysis, it is found that different delay strategies have different impacts on consumers' perception, and the rational use of delay strategies can help increase consumers' perceived benefits and reduce consumers' perceived losses, which results in improving consumers' satisfaction.

This project emerged in the context of the metaverse. We combine 3D data and stereoscopic projection to collect data on traditional shopping and people's views on online shopping today. We launch the "Yuan" shopping plan and are committed to creating a WeChat mini program that combines 3D data and stereoscopic projection. Digitize the products, utilize data, and use stereo projection technology to allow users to visually see the products, thereby bringing people a wonderful and brand new shopping experience, allowing consumers to better understand the adaptability of the products and improve shopping efficiency.

E-commerce, as a required course for major of economic management, has the characteristics of strong practicality and wide scope. Based on the OBE education concept, the use of ChaoXing XueXitong to carry out online and offline combination of teaching means; Integrate the latest achievements of ideological, political and disciplinary development into the teaching content; By adopting flexible teaching methods such as thematic discussion and scenario simulation, the classroom teaching of E-commerce course is combined with after-school tutoring (Q&A), after-class exercises, practical teaching and innovation and entrepreneurship education, so as to continuously improve the e-commerce practice and innovation ability of students majoring in economics and management.