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With the development of digital technology, the curriculum design of art majors is also quite different from traditional courses. With the continuous development of information technology and the continuous maturity of campus network technology, teaching resources have become an important part of information resources. High-quality teaching resources play an important role in improving teaching quality and excavating educational development potential. However, traditional classroom teaching is difficult to meet people's needs. In the context of the current new normal, the way of social and economic management has changed. On the one hand, traditional course teaching resources store information in the forms of paper lesson plans, PPT courseware, discrete pictures, and video files. These forms of storage are not conducive to the distribution and sharing of resources. On the other hand, these teaching resources are scattered among the teaching teachers. As the curriculum develops, the courses taught by teachers change. Because schools lack effective means for unified management, these teaching resources are easily lost. The used method is not conducive to the accumulation of teaching resources or the continuation and development of courses. In order to present good results, these materials usually occupy a large amount of storage space. Therefore, some existing information-based teaching platforms cannot meet the demand. We use network and web technology to develop a teaching file management system, which is very necessary. This paper firstly compares the mature teaching resource management system on the market and some problems in its professional application. Secondly, the article analyzes the actual situation of teaching resource management of art design majors in China and generally describes the system development background and technology. Then, the paper analyzes the requirements of the system in detail by using the object-oriented method and gives the overall design and implementation method of the system. These methods include the use case diagram of the subsystem, the flow chart of the main module, the system architecture, and the design and application of UML. Finally, this paper designs a test case for the teaching resource management system. [ABSTRACT FROM AUTHOR]

This paper sorts out and discusses the different methods used in the analysis of China-related public opinion, builds a theoretical system of public opinion analysis methods on this basis, conducts case analysis and research on hot public opinion combined with corpus, and selects the semantic analysis method from the technical level supporting the public opinion analysis method. From the perspective of related technologies involving intelligent analysis methods, comparative analysis and improved applications are given, which provides an effective analysis basis for government public management departments to fully grasp and guide China-related public opinion. Sentiment tendency analysis is also an important direction of Chinese-related public opinion research. This paper sorts and analyzes China-related public opinion from the characteristics, applications, and text processing methods of different granularities and builds and improves the annotation model for the smallest granularity. The frequency, trend, and evolution characteristics of China-related public opinion events are sorted out and analyzed, and the trend analysis method is used to analyze the evolution trend of attention of public opinion events. The distribution of individual opinion acceptance, trust threshold, and opinion leaders are simulated by experiments. The impact on the evolution of China-related public opinion was determined. The experimental results show that the effect of the method proposed in this paper is improved by about 10% compared with the direct use of statistical learning methods for emotional orientation analysis. The SVM method also has obvious advantages over the Bayesian method, especially the combination of the SVM method and the bigram method which gives the best results. [ABSTRACT FROM AUTHOR]

China has proposed two major measures to address the "three rural issues": the first is to abolish the agricultural tax, which has been in place for over 2000 years; the second is to propose the construction of a new socialist countryside, which would mark the end of the old era and the beginning of the new. As a result, this paper employs in-depth learning technology to enhance rural tourism development and the creation of a new socialist countryside. This paper investigates deep-learning-based rural tourism and the creation of a new socialist countryside. Because MSE and MAE reflect the prediction error score, the lower the value, the better the recommendation accuracy. The MSE value of the machine learning algorithm is 2.456, the MSE value of the data mining algorithm is 2.324, and the MSE value of the convolution neural network algorithm is 2.102, when the number reaches 80. It can be concluded that the convolution neural network algorithm proposed in this paper has the lowest MSE and MAE values of the three methods, implying that the convolution neural network algorithm is the best of the three. The use of the convolution neural network algorithm to implement the scientific concept of development and the construction of a new socialist countryside is an important part of creating a harmonious society that fully meets the central government's objectives and requirements for the construction of a new socialist countryside. [ABSTRACT FROM AUTHOR]

Folk dance is a very unique local culture in China, and dances in different regions have different characteristics. With the development of 3D digital technology and human gesture recognition technology, how to apply it in folk dance is a question worth thinking about. So, this paper recognizes and collects dance movements through human body detection and tracking technology in human gesture recognition technology. Then, this paper writes the data into the AAM model for 3D digital modeling and retains the information by integrating the manifold ordering. Finally, this paper designs a folk dance learning method combined with the Few-Shot learning method. This paper also designs a data set test experiment, an algorithm data set comparison experiment, and a target matching algorithm comparison experiment to optimize the learning method designed in this paper. The final results show that the Few-Shot learning method based on gesture recognition 3D digital modeling of folk dances designed in this paper reduces the learning time by 17% compared with the traditional folk dance learning methods. And the Few-Shot learning method designed in this paper improves the dance action score by 14% compared with the traditional learning method. [ABSTRACT FROM AUTHOR]

As an important component of modern service industry, tourism is regarded as a "green" driver to regional economic growth and industrial upgrading. Applying the "quasi-natural experiment" advocated by the "The Belt and Road" initiative, this paper, based on the method of DID, assessed the influence of this initiative on regional tourism development by analyzing panel data collected from 286 prefecture-level cities in China over the period of 2007–2018. It is found that the "The Belt and Road" initiative has stimulated market dynamics and promoted tourism development in the regions along the route and has significantly improved indicators such as per capita domestic and inbound tourism revenue and per capita domestic and inbound tourist receipt. Also, the degree of contribution of domestic tourists to the tourism economy of the regions where the policy has been implemented is stronger than that of inbound tourists. In sum, the results of this paper not only confirm that the "The Belt and Road" initiative has significantly contributed to the high-quality tourism development of Chinese provinces along the routes but also provide important policy enlightenment for the upgrading of regional tourism industry and regional economic balance. [ABSTRACT FROM AUTHOR]

Owing to the rapid expend of the global economy and the increasingly fierce competition among enterprises, scientific and modern management has been gradually adopted in the transaction processing of enterprises. Especially for the show appraisal of enterprises, the traditional appraisal has a lot of subjective influence of managers, so the traditional show appraisal methods cannot meet the need for survival and expend of enterprises. The pressure of talent demand is increasing, which makes many enterprises pay more attention to whether their own mankind resource management system supports the company's strategic spend and can win advantages in the competition of talent structure, organizational structure, and management process. Numerous foreign enterprises use mankind resource audit to achieve this goal, but at present, there is little practice in China that enterprise show evaluation is an important basic work and core content of mankind resource management and expend. Improving the effectiveness of enterprise show evaluation is of great significance to increase the core competitiveness of enterprises and promote the sustainable expend of enterprises. Therefore, this paper designs a model based on collaborative filtering algorithm to analyze the show of mankind resource management of enterprises and has achieved good results. Compared with the original algorithm, the model designed in this paper has a 65.4% improvement in algorithm, which will greatly simplify the calculation process and improve the model processing efficiency. The numerical results show that there is an inevitable linear correlation between mankind resource management and enterprise show. This index can reflect that the accuracy of the model has been enhanced by 74.3%. [ABSTRACT FROM AUTHOR]

For many countries, the systemic risk of local government debt is a matter of concern, but at present, there is little analysis in this area. The complex network based on information analysis provides an effective tool for analyzing the system risk between networks. This paper takes China as an example and takes the provincial urban investment bonds as the object for research. The regional network constructed with the interest rate data of urban investment bonds represents the overall systemic risk of these bonds. The main conclusions are as follows: provincial nodes in western and central areas (underdeveloped areas) are more important in the system, and they are more easier to form debt defaults. For an interconnected urban investment bond network, the higher the default risk of an individual, the greater the risk of the entire system. Finally, based on the indicators obtained from the provincial urban investment bond network model, this paper puts forward policy suggestions for the prevention of urban investment bond risks. [ABSTRACT FROM AUTHOR]

Since the reform and opening up, China's economy has developed rapidly, becoming the second largest economy in the world. The state of macroeconomic development has a great influence on the government's policy introduction and the investment decisions of individual institutions. Therefore, forecasting the macroeconomy is of great significance to the country. This paper aims to study the macroeconomic forecasting model system based on digital information and blockchain technology. This paper proposes an artificial neural network prediction algorithm based on digital information and blockchain technology, and the artificial neural network and particle swarm algorithm are combined to become the hybrid artificial neural network algorithm, and the conception of the establishment of the macroeconomic forecast model is proposed. The experimental results of this paper show that according to the prediction results of artificial neural network, the prediction and actual error in 2010 is 0.10, while the new method proposed in this paper predicts the error of 0.056. In 2016, the prediction error based on artificial neural network was 0.14, while the prediction result of the new method proposed in this paper showed an error of 0.008. In each year's economic forecast, the neural network model of the new method proposed in this paper has higher prediction accuracy and smaller error. It can be seen that the neural network model based on artificial neural network and PSO algorithm proposed in this paper is beneficial to macroeconomic forecasting. [ABSTRACT FROM AUTHOR]

In order to fully implement systematic, continuous and effective supervision of financial institutions and promote the safe, steady, and efficient operation of China's financial system, this research needs to develop a fully intelligent financial supervision information system, so as to take measures to effectively prevent and resolve financial risks. In this paper, based on ML (machine learning), an LSTM (long short-term memory) model with good comprehensive performance is built. This model is different from the existing scorecard model which relies on statistical learning. It not only further reduces the dependence on financial experts, but also has the ability of rapid iteration. The post-loan risk early warning model based on RF (random forest) algorithm is designed. The model parameters are optimized, which makes the risk early warning model have higher accuracy. The results show that when the data amount is 10,000 to 50,000, the accuracy of the model is relatively high when it is input into the model. When the amount of data is low, the overall throughput of the model is still very high. It shows that the pre-warning model of post-lending risk constructed in this paper has a strong risk prediction ability. [ABSTRACT FROM AUTHOR]

With the establishment and rapid development of the China (Shanghai) Pilot Free Trade Zone (FTZ), the scale of enterprises in the zone has grown rapidly. This paper takes the actual needs of the Shanghai FTZ as the background, extracts key information such as entities from the Big Data related to Internet enterprises, further constructs the enterprise knowledge graph, and applies it to the supervision and service of the FTZ. The enterprise knowledge graph is constructed using the Neo4j graph database. To verify the effectiveness of the named entity identification and relationship extraction methods proposed in this paper, experiments were conducted to validate them, and both achieved good results. [ABSTRACT FROM AUTHOR]

In order to realize the research on vital signs monitoring based on the Internet of things, in view of the urgent needs for mobile health and universal medical treatment in China, this paper puts forward the platform design and implementation of exercise parameter health management and detection terminal based on the Internet of things. Firstly, the overall architecture of the exercise heart rate monitoring system is designed, and the design of the heart rate acquisition module is also put forward, in order to monitor human heart rate data conveniently, quickly, and in real time, considering the portability and low power consumption of the system, a wearable heart rate real-time monitoring system is designed in this paper. The experimental results show that the maximum individual average heart rate difference is 4.11 bpm and the overall average heart rate difference is 2.24 bpm. After processing with the adaptive hybrid filtering algorithm, the maximum individual average heart rate difference is 0.69 bpm, and the overall average heart rate difference is 0.42 bpm. The average standard deviation of the difference between the 10 groups of measured values and reference values with and without the filtering algorithm is 0.304 and 2.788, respectively, which is about 9 times higher. The accuracy and stability of the filtering algorithm are greatly improved. The whole system can run well and can be applied to practical sports training guidance. [ABSTRACT FROM AUTHOR]

In today's economic globalization and artificial intelligence modern society, the rapid economic growth mainly relies on the vigorous development of digital information technology. Simultaneously, digital technology promotes economic revolution and produces a digital economy, which has become an important concept of China's economic development. In order to more scientifically and accurately describe the development status of my country's industrial structure and digital economy industry, this paper takes each industry of the national economy as the research object and puts forward the idea of using complex network theory to study the relationship between input and output. Based on the complex network theory, this paper analyzes the interrelationships between various industries in the complex system. By referring to the relevant data of the national economic input and output in recent years, the input-output correlation network model between the various industries in the national economic system is established. Using the method of combining statistical analysis and comparative analysis, quantitative analysis and qualitative analysis, carry out detailed and in-depth statistical analysis from the input-output correlation network model and model attributes so as to point out the status and role of various industries in the national economy and so as to analyze and study the industrial structure of the national economy. [ABSTRACT FROM AUTHOR]

With the sustained and rapid development of China's economy, the business environment of enterprises is becoming more and more complicated. Coupled with the increasing international competitive pressure of enterprises, the management of enterprises needs more attention. The emergence of DSS provides an opportunity for enterprise management intelligent decision-making. Based on the in-depth study of RF algorithm, this paper proposes a new intelligent decision-making model for enterprise management, aiming at the shortcomings of traditional decision-making systems. In this paper, the decision-making process of the system for decision support is given. Through the quantification and evaluation of various data by subsystems, the best decision-making scheme with strong operability is provided for enterprises. And this model can effectively solve the problem of heterogeneity, duplication and clutter of data in traditional database, and avoid the loss of historical data. Through simulation, the accuracy of this system can reach 96%, which is about 17% higher than other systems. It has certain practicability and feasibility. It is hoped that the research in this paper will play an important role and service support for enterprise intelligent decision-making and further promote the development of intelligent enterprise management decision-making system in China. [ABSTRACT FROM AUTHOR]

Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]

The participation degree of community residents has become a yardstick to measure the community governance system and governance capacity in China. Since 2000, China has vigorously promoted community construction. The related research literature on community construction in China is also showing an explosive growth trend. Knowledge map refers to the analysis of complex domain knowledge through data mining, information processing, knowledge measurement, chart drawing, etc., so as to achieve the purpose of understanding the dynamic development law of knowledge domain through diagrams. In the face of complex knowledge fields, we can use knowledge maps to combine various information to obtain relational networks and analyze problems from the perspective of "relationships." Knowledge map is widely used in intelligent search, intelligent question and answer, and other fields. Using the intelligent decision support framework based on knowledge map can help us more clearly find the problems existing in the traditional decision support system and provide corresponding solutions. Combining big data, knowledge map, and other large-scale knowledge analysis and modeling technologies with decision support systems can strengthen the ability of problem analysis and processing and form a knowledge and relationship network. This paper provides direction and theoretical support for the research and development of community construction and governance in China in the future. [ABSTRACT FROM AUTHOR]

With the increasing investment in education in China, higher education institutions have higher requirements for the introduction of teachers. This research mainly discusses the development and training strategies of college teachers based on data mining technology. Data mining technology is dedicated to data analysis and understanding, and the technology of revealing the information contained in the data. It is a frontier research topic in the field of information and database technology. Therefore, the system of monitoring and evaluation of college teaching quality based on data mining is designed for the management of educational affairs in colleges provided convenience. This paper selects an unsupervised classification method: a cluster analysis. This method can not only obtain reasonable classification results but also give consideration to the comprehensiveness of employee development and give reasonable development suggestions for each employee through classification results. For a series of introduced teacher's information, personnel management module should provide the following functions: teacher information management, contract information management, resignation information management, and query teacher personnel information. The recruitment management module often collects the candidate information, registers and stores it, and then conducts a series of personnel screening for these candidates according to the recruitment criteria, and finally determines the possible candidates. Afterwards, a series of comprehensive evaluations are carried out on these selected candidates, and finally the candidates are selected for admission based on the comprehensive performance of the candidates. The K-means clustering algorithm in the cluster analysis method is adopted. This algorithm has the excellent characteristics of high computational efficiency and is suitable for the operation of large amount of data. Through the clustering algorithm, a reasonable assessment method is established, and it is effectively used in the human resources assessment management system. Among the introduced teachers, the number of teachers whose professional title is high, the highest degree is doctorate, and the number of teachers whose papers are published at SCI level accounts for 16%. The data tested by the data mining tool contains 1,400 rows of data. The minimum support is 5%, and the minimum confidence is 90%. This study is helpful for the rational planning of human resources and the promotion of comprehensive competitiveness of colleges and universities. [ABSTRACT FROM AUTHOR]

With the economic globalization continuous growth of China's socioeconomic level tends to be internationalized, China's attention to English has been significantly improved. However, the domestic English teaching level is limited, so it is impossible to correct students' English pronunciation and make a reasonable evaluation at all times so that oral training has certain disadvantages. However, the computer-aided language learning system at home and abroad focuses on the practice of words and grammar, and the evaluation indicators are less and not comprehensive. In view of the complexity of English pronunciation changes, traditional speech recognition is difficult to recognize speech speed and improve its accuracy. Furthermore, to strengthen the English pronunciation of domestic students, a nonlinear network structure is studied in depth to simulate the human brain to analyze a model of speech recognition is established Mel frequency cepstrum characteristic parameters of human ear model and deep belief network. In this paper, the traditional computer pronunciation evaluation method is improved in an all-round way, and a set of high-quality speech recognition system of speech recognition method is constructed. Aiming at the above problems, it takes the students as the research, which proves that the method adopted in this paper can give the learners accurate pronunciation quality analysis report and guidance and correct their intonation and improve the learning effect, and the experimental data verify that the improved speech recognition system model recognition ability is higher than the traditional model. [ABSTRACT FROM AUTHOR]

In 2019, the Chinese government has put forward comprehensive plan for reducing social insurance premium rate, which will affect the measurement of the pension security level. In order to evaluate the pension level, this paper uses CHIPs data from 1988 to 2018 to calculate "empirical" pension replacement rate by "empirical" method based on different wage calibers. The result shows that the pension replacement rate and income replacement rate of urban employees have gradually declined in the past 30 years. The pension level of nonprivate employees is lower than that of insured employees, and that of insured employees is lower than that of full-scale employees. The pension level of male, high-income groups, the older generation, organs, and institutions is much higher than that of female, low-income groups, the younger generation, and enterprises relatively. Compared with other countries, the pension replacement rate of urban employees in China is not low, but the income replacement rate is relatively low. This paper makes an in-depth analysis of the actual pension level under the new policy. As a result, it is necessary to vigorously develop multilevel and multipillar pension system such as enterprise annuity, occupational annuity, and commercial pension insurance, so as to improve the pension level of urban employees. [ABSTRACT FROM AUTHOR]

Connotative development of higher education is of great significance in China. To promote this endeavor, this paper has established a connotative teaching system based on the Kano model and has applied it in the teaching of statistics. Guided by the fundamental demands of students, this system is built from the four perspectives, including students, third-party evaluation institutions, schools, and morality and law. It also takes the advantages of the Kano model, featuring the three dimensions of essential demand quality, expected demand quality, and charismatic demand quality. The paper also describes a two-year experiment to test the effect of the Kano-model-based connotative teaching system. It has collected more than 200 effective samples and compared the performance of students in private colleges. The results are as follows: the connotative teaching of statistics guided by the Kano model can meet the demands of students in private colleges and improve their comprehensive scores. This model shows remarkable performance and will promote the reform on connotative teaching. [ABSTRACT FROM AUTHOR]

Due to the timeliness and short life cycle of news, the postrelease prediction is limited, and the prerelease prediction also faces huge challenges due to the diversity and difficulty of defining influencing factors. This paper uses the foreclosure. This paper proposes a news popularity prediction method based on GRU deep neural network. Firstly, a web crawler was designed to obtain news data of different types and structures from 10 information security portal websites in China. After data preprocessing, the Word2Vec method was used to extract features, extract key news sentences and construct a subset of content features. Establish a GRU neural network regression prediction model to predict hot news on the Internet. The experimental results show that, compared with the traditional processing method, the model can process the multi-source rough data set in this paper and greatly reduce the prediction error. At the same time, because the threshold recurrent unit structure used in this paper is simpler than the long-short-term memory network structure, it can shorten the prediction time and improve the computing performance. [ABSTRACT FROM AUTHOR]

As one of the most widely used languages in the world, English plays a vital role in the communication between China and the world. However, grammar learning in English is a difficult and long process for English learners. Especially in English writing, English learners will inevitably make various grammatical writing errors. Therefore, it is extremely important to develop a model for correcting various writing errors in English writing. This can not only be used for automatic inspection and proofreading of English texts but also enable students to achieve the purpose of autonomous practice. This paper constructs an English writing error correction model and applies it to the actual system to realize automatic checking and correction of writing errors in English composition. This paper uses the deep learning model of Seq2Seq_Attention model and transformer model to eliminate deep-level errors. Statistical learning is combined with deep learning and adopted a model integration method. The output of each model is sent to the n-gram language model for scoring, and the highest score is selected as output. [ABSTRACT FROM AUTHOR]

With the popularization of higher education, China's higher education has moved from the stage of elite education to the stage of universal education, and the number of graduates is also increasing. At present, college students are facing tremendous pressure on employment. One is the huge number of jobs, the other is the difference in professional needs, and the third is the spread of job information, which makes it difficult for college students to find a job that suits them. In order to solve this dilemma, this paper analyzes the related technologies and in-depth basic theories of data mining. After introducing several traditional recommendation algorithms, the traditional convolutional network method is improved from three aspects: activation function, pooling strategy, and loss function. Finally, using the hybrid convolutional neural network, a career recommendation model for college students based on deep learning and machine learning is proposed, and simulation experiments are carried out on it. The main research work is as follows: (1) a hybrid convolutional neural network is proposed, which uses convolution operation to learn high-level features to achieve personalized employment recommendation; (2) the training optimization strategy of the hybrid convolutional neural network is studied, aiming at the activation function, pooling processing, and loss function, and the feasibility of the optimization method is verified through simulation experiments; (3) finally, according to the evaluation index of the recommendation algorithm (recall rate and F1-Score), the recall rate of the algorithm in this paper is nearly 15% higher than that of the DNN model. The experiment is compared with the traditional commonly used recommendation algorithm, and the comparative analysis of the experimental results proves the effectiveness of the algorithm for the employment recommendation of college students. [ABSTRACT FROM AUTHOR]

The economic subsystem, social subsystem, and natural ecosystem of forest towns constitute forest towns' complex ecosystem. Forest towns' complex system provides good ecological welfare for the residents' survival and development, especially the beautiful ecological environment has played a positive role in promoting the residents' healthcare. First of all, this paper expounds on the concept of forest towns' ecological welfare from the construction goals of forest towns. Secondly, it analyzes the formation mechanism of forest towns' ecological welfare based on the DPSIR conceptual model. Finally, taking forest towns in Heilongjiang Province as an example, the index system for ecological welfare evaluation is constructed, and the influence degree of the evaluation index is analyzed through using the analytic hierarchy process. The results show that employment and income are the most important indicators that affect the ecological welfare of forest towns in Heilongjiang Province. This paper provides a theoretical basis for evaluating the level of ecological welfare level, level of healthcare level, and planning development path of Chinese forest towns in the future. [ABSTRACT FROM AUTHOR]

Given the phenomenon of "financing is difficult and expensive" for MSEs, this paper empirically investigated the influencing mechanism of the credit demand side characteristics on the financing constraints of MSEs based on the information conveyance perspective. The conclusions show that MSEs in China are severely suffering from financing constraints and 57.17% and 50.00% of MSEs with credit demand have not applied for loans from formal and informal financing channels, respectively. In terms of enterprise characteristics, MSEs have low asset size, short establishment history, weak profitability, and lack of tools such as fixed assets, complete financial management system, professional technicians, and private brands to convey risk information to financing institutions, which are key factors resulting in their financing constraints. In terms of owner characteristics, young owners lack financing experience and convey higher risk information to financing institutions; therefore, owners' age negatively influences the financing constraints of MSEs. These findings suggest that banks can use big data credit technology as a tool to obtain risk information about MSEs, and the government should implement diversified interventions to improve the information environment in financial markets. These findings provide empirical evidence for banks and governments to address the financing constraints of MSEs. [ABSTRACT FROM AUTHOR]

Under the condition of weakly cemented soft rock in Western China, the surrounding rock deformation of gob-side entry is obvious, especially under the influence of fault, and the problem of surrounding rock control becomes increasingly prominent. To solve this issue, this paper firstly studied and obtained the evolution law of the ground pressure appearance of the gob-side entry in the fault area, then put forward the concept of surrounding rock control in the fault area of the gob-side entry, put forward a targeted pressure relief + increase preload + passive reinforcement parallel to the roadway surrounding rock strengthening control scheme, and carried out field applications. The results show that the roof subsidence and mining side heave of the roadway in the fault area increase significantly, accompanied by the characteristics of broken cables, the length of the broken cable is 0.2 m–0.6 m, and the breaking process is divided into multiple breaks. The stress evolution of gob-side entry in fault area presents a stage characteristic of "rapid growth-stable." The surrounding rock control effect is improved under the bolt cable high pre-tightening support, and the roadway deformation is effectively controlled under the pressure relief + active + passive support, with a good application effect. [ABSTRACT FROM AUTHOR]

The magnitude of pretension force serves as an important indicator of the effectiveness of anchor bolt active support and is one of the significant factors influencing the stability of tunnel surrounding rock. Therefore, in practical engineering, it is crucial to establish the relationship between pretension torque and pretension force. However, current research in this field has some limitations. With a specific mine in western China as the engineering background, this paper first analyzed the influence factors of surrounding rock deformation and failure, established a mechanical model for pretension structure of anchor bolt support, and derived the quantitative relationship between pretension torque and pretension force based on hypothetical conditions. Finally, it proposed the loss effect of anchor bolt support and modified the pretightening loss coefficient for engineering application. The research results show that the actual pretightening force of anchor support is lower due to the influence of deep soft coal seam, which leads to failure of coal pillar under mining stress. The loss effect of surrounding rock on pretightening force was represented by a coefficient k, and the modified value of the coefficient was calculated as 0.19∼0.43. By applying the modified relationship between pretightening torque and pretightening force, it was found that the actual pretightening torque of 300 Nm applied to 11307 return air roadway effectively controlled surrounding rock deformation and failure, with coal pillar displacement less than 120 mm. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to explore the relationship between corporate social responsibility (CSR) and firm value in the Chinese market and identify factors that may influence it. We discuss the relationship between CSR engagement in Chinese firms and firm value from a nonlinear perspective. In addition, we examine how the degree of internationalization in a firm may affect the relationship between CSR and firm value. We employ the Hausman test to compare a random-effects and a fixed-effects model, and after testing and comparison, the fixed-effects model was chosen in our paper. Using data from 314 firms listed in China's A-shares market from 2010 to 2017, we verify the U-shaped relationship between CSR and firm value. Meanwhile, the degree of internationalization will affect firm value but cannot positively regulate the relationship between corporate social responsibility and firm value. [ABSTRACT FROM AUTHOR]

Research Background and Content. Intellectual property and high-quality economic development are mutually reinforcing and restrictive, China is a large but not a strong intellectual property country, and China's high-quality economic development still has problems of inadequacy and imbalance. Based on the above research background, this paper mainly studies the relationship between the intellectual property system and the high-quality economic development system from the perspective of coupled and coordinated development. Research Objective. The objective is to measure the coupling and coordination degree between the intellectual property development system and the high-quality economic development system and to explore the spatiotemporal characteristics and differences in the coupling and coordination between the two systems. Research Method. Based on the data of 30 provinces (autonomous regions and municipalities) from 2013 to 2020, the methods used in this paper to analyse include entropy weight method, grey correlation analysis, coupling coordination degree model, and Dagum Gini coefficient. Research Results. The development of economic openness and innovation are more closely related to the development of intellectual property, and the protection, utilization, and creation of intellectual property are more closely related to high-quality economic development; the coupling coordination degree of the two systems shows the characteristics such as "high in the east and low in the west" and "coastal area is better than inland," and the development of intellectual property lags behind the high-quality economic development; the coupling and coordination degree of the two systems shows a slight growth trend in most provinces, but the overall differences shows an expanding trend, and differences between regions are the main sources of overall regional differences. Research Innovation. Research innovation is to study the two-way relationship between intellectual property and high-quality economic development and to measure the coupling and coordination degree between the development system of intellectual property and the high-quality economic development system, and its spatiotemporal characteristics, regional differences, and sources of differences are analyzed. Research Value. Research value is to provide decision reference for the two-way empowerment of intellectual property and high-quality economic development and regional coordinated development. [ABSTRACT FROM AUTHOR]

The new development pattern is a strategic choice to reshape China's new advantages in international cooperation and competition. It is based on China's current development stage, changes in the environment, and other factors. Located deep within China's interior, Xinjiang is a crucial region for the development of the western region and the Belt and Road Initiative. Its agricultural resources are exported to numerous foreign nations. In the context of the new development pattern, this paper first examines the foreign trade issues in Xinjiang. Second, this paper analyzes the current state of Sino-Kazakhstani trade, in particular the current state of trade in agricultural products. In addition, the growth potential of Xinjiang's agricultural international trade is analyzed utilizing the gravity model and fuzzy algorithm. Finally, countermeasures for the mode of development of Xinjiang's international trade are proposed. [ABSTRACT FROM AUTHOR]

Green credit is an important manifestation of commercial banks' environmental responsibility, but few studies have examined the impact of green credit policies on the financial performance of commercial banks. Based on the panel data of 62 commercial banks in China from 2013 to 2020, this paper investigates the changes in financial performance due to the implementation of green credit policies in 2016 using the difference-in-differences (DID) method and explores the mediating mechanism of green credit affecting commercial banks' profit. The sample was divided into a treatment and a control group. The treatment group disclosed total green credit data during the sample period, while the control group did not. The findings of this paper are as follows: (1) the implementation of the green credit policy increases the profits of commercial banks. (2) The green credit policy improves the profits of commercial banks by increasing their noninterest income and reducing their nonperforming loan ratios. (3) The green credit policy does not improve commercial banks' profit by reducing their cost-to-income ratios. (4) The implementation of the green credit policy significantly improves the profits of banks with low (vs. high) nonperforming loan ratios. (5) Compared to large national banks, regional urban and agricultural commercial banks' profits improve more significantly after executing the green credit policy. The research contribution of this paper provides a quantitative basis for Chinese commercial banks to improve their financial performance through the implementation of green credit in recent years and for the government to further improve green credit policies to motivate banks to implement green credit and achieve sustainable development. Then, we further discuss the implications for the prominent theoretical and managerial policies, study's limitations, and future research directions. [ABSTRACT FROM AUTHOR]

Under the influence of COVID‐19, although upstream small‐ and medium‐sized enterprises (SMEs) in construction industry chain suffer from high operating costs and tight cash flow problems, their financing demands are even stronger. As an electronic and platform‐based comprehensive service, online supply chain finance can ease the financing problems of SMEs in the construction industry. However, it has become an important issue for financial institutions to effectively assess the credit risk in the process of online supply chain financing. In this paper, an online supply chain risk assessment method, which is based on a hybrid model chain, including eXtreme Gradient Boosting (XGBoost), Synthetic Minority Oversampling TEchnique for Nominal and Continuous (SMOTENC), and Random Forest (RF), is proposed to identify and control the credit risk of financial institutions. Specifically, we establish the financial credit risk assessment system with respect to the characteristics of financing enterprises in the construction industry supply chain, including the status of financing enterprises, the status of core enterprises, the operating status of the supply chain, and the status of assets under financing. On the basis of the system, the best index number of the assessment system and the minority samples are obtained by the XGBoost algorithm and the SMOTENC algorithm, respectively. The classification method based on RF is applied to judge the credit risk of financing enterprises in the supply chain of construction industry. In the simulation stage, we take upstream SMEs in the supply chain of construction industry in China as an example for empirical analysis to validate the effectiveness of our proposed method. The credit risk assessment method proposed in this paper has better performance than the commonly used ones in the academic field with an average improvement on assessment accuracy for 6.39% and an average increase of Area Under Curve for 6.95%. Our study provides meaningful exploration on the fund monitoring system of the financing service platform to improve financing efficiency and risk management level. [ABSTRACT FROM AUTHOR]

In China's rural credit system, the problem of credit constraints is prominent. Due to the imperfect credit market, a large number of rural residents have credit constraints. Rural credit constraint is a serious problem restricting China's rural economic development. Aimed at solving the rural credit constraints, this paper makes an optimization analysis on the rural credit system and loan decision-making. To more reasonably evaluate customers' borrowing ability, the credit risk based on farmers' data on the big data platform is evaluated in this paper. The stacked denoising autoencoder network is improved by adopting the deep learning framework to improve the accuracy of credit evaluation. For improving the loan decision-making ability of rural credit system, a loan optimization strategy based on multiobjective particle swarm optimization algorithm is proposed. The simulation results show that the optimization ability, speed, and stability of the proposed algorithm have achieved good results in dealing with the loan portfolio decision-making problem. [ABSTRACT FROM AUTHOR]

The "innovation-driven" development approach plays a crucial role in transforming and developing current China's national economy. Many innovation-driven mergers and acquisitions (M&As) emerge in the M&As activities of firms, and the acquiring firm's business strategy of conducting innovation-driven M&As has drawn significant attention. To study the impact of business strategy on innovation-driven M&As, this paper adopts the probit model to conduct logistic regression analysis on the 223 M&As data samples, exploring which business strategy is inclined to perform innovation-driven M&As. The results show that prospective firms are more likely to conduct innovation-driven M&As. Besides, the firms that conduct prospective business strategies are more likely to have a higher innovation level than firms operating defensive business strategies. The innovation level plays the role of a mediator variable in the model of business strategies influencing innovation-driven M&As. This paper analyzes the innovation-driven motivation from the perspective of business strategy, enriching the research of M&A and offering a method to predict the possibility of innovation-driven M&A by measuring business strategy. [ABSTRACT FROM AUTHOR]

Although the development time of cross-border e-commerce in China is very short, the scale of its transactions and the speed of development are amazing, and as a supporting foundation for promoting economic and trade globalization, cross-border e-commerce has an extremely important strategy for this guiding role. This not only brings new opportunities to cross-border e-commerce companies but also excavates a huge potential market for the logistics industry. Cross-border e-commerce not only breaks through the trade barriers between countries; it makes trade move towards borderlessness and at the same time triggers major changes in international trade. This paper introduces partial differential equations into the video surveillance image enhancement system of cross-border e-commerce logistics. Aiming at the shortcomings of the contrast enhancement method based on gradient field equalization, this paper proposes a partial differential enhancement method based on histogram equalization. By proposing a gradient transformation function, the edges and textures with relatively small gradient values are enhanced to make the original weaker texture details clearer. In order to better adjust the brightness and contrast of the image, combined with histogram equalization, we propose an inverse equalization transformation. When the histogram equalization and the inverse equalization transform are combined reasonably, the brightness and contrast of the image can be adjusted very well. In this paper, the finite difference method is used for discretization when solving partial differential equations, and Euler's equation is obtained by applying the principle of least squares. By introducing the heat equation, the direct solution of Euler's equation is converted into an iterative form, which greatly reduces the amount of calculation. This article uses statistical methods to obtain the empirical formula of the fractional differential order. This empirical formula makes the calculation of the order of the fractional derivative easy and can be extended to other fractional image enhancement models and overcomes the shortcomings of the traditional fractional derivative order obtained through experience or a large number of experiments. Experiments show that the proposed algorithm not only enhances detailed texture information but also improves image clarity, overall brightness, and contrast without color distortion. The objective evaluation indicators also show the superiority of the algorithm. [ABSTRACT FROM AUTHOR]

With the steady increase of air traffic column, an auxiliary decision tool is required to compensate the operation redundancy deficiency of more sectors of air traffic control. To solve the problem of nonconflict high-density departure and arrival traffic flow, this method is expected to rapidly establish and maintain safe separation with more flexible changing strategies for aircraft heading and speed. This paper proposes an improved reinforcement learning framework to achieve conflict detection and resolution. The proposed framework includes the first development of an air traffic flow model based on a multiagent Markov decision process. The goal reward function was then maximized by improved Monte-Carlo tree search combined with an upper confidence bound tree. Three simulation scenarios were designed for illustrating the improvements of the proposed algorithm, with the results indicating that the algorithm could establish and maintain safe separation between 20 agents in the simplified hexagon-shaped airspace of Huadong, China. Furthermore, the proposed method was demonstrated to reduce the number of conflicts between aircraft agents by up to 26.32% compared to previous research. [ABSTRACT FROM AUTHOR]

With the construction of a new-type power system under the China "double carbon" target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time. [ABSTRACT FROM AUTHOR]

With the rapid development of the real estate industry in China, urban air quality is inevitably affected. By using data of Chinese cities located in the Yangtze River Delta from 2009 to 2018, this paper is aim at examining the casual impact of house prices on air quality. By considering the endogenous problems, in this study, first, the birth rate of the previous six years and the area of state-owned land supply per capita in the previous year are taken as instrumental variables of house price. Second, the two-stage least squares method is used to assess the causal impact of house price on air quality. The findings of this study show that there is no significant relationship between urban house prices and air quality, and these results are obtained without considering the endogenous problem. While, after considering the endogenous problem, urban house prices showed a significant negative effect on overall air quality, especially for low administrative level cities. The increase in house prices has hindered the improvement of air quality mainly through the blind-scale expansion of real estate development investments and the inhibitory effect of innovation. [ABSTRACT FROM AUTHOR]

In this paper, a vehicle-bridge coupling dynamic model is established considering the vertical wheel-rail tight contact and the lateral simplified wheel-rail creeping. Through the site test and the numerical calculation, the dynamic deflections and the stresses of the U-shaped girder in the Nanjing S6 Urban Rail Transit in China are obtained, and the effectiveness of the established numerical model is verified. By changing the vehicle types and speeds, the dynamic amplification coefficients (DAFs) of the vertical deflections and the biaxial stresses at the girder bottom including the key points at the plate and section intersections are calculated. The research shows that the distribution of the lateral stresses is more complex than that of the deflection and the longitudinal stresses. Based on the calculation results considering various vehicle types, it is suggested that the DAFs of the vertical deflections and the longitudinal stresses are taken as 1.30, and the DAF of the lateral stresses remains at 1.40 as stipulated by the code. The research of this paper is to provide a reference for the structural design and size optimization of the U-shaped girders for urban rail transit. [ABSTRACT FROM AUTHOR]

Detection of out-of round (OOR) faults of metro vehicle wheels is very important to improve stationarity and stability in metro vehicles and avoid accidents caused by OOR faults. Diagnosis of OOR faults demands extracting useful information accurately from mass of vibration signals with poor signal-to-noise ratio (SNR) of metro vehicle wheels for complex running condition. In this paper, we proposed a diagnosis method on OOR faults of metro vehicle wheels combined with variational mode decomposition (VMD), kernel principal component analysis (KPCA), and deep belief network (DBN) to diagnose the OOR faults of metro wheels. Vibration signals of China metro vehicle wheels collected while the metro vehicle is running are used to train the diagnosis model and adjust parameters of DBN and KPCA based on testing accuracy. The different dimensions of KPCA, epoch number, and node number of DBN are compared, and the better parameters of diagnosis model based on vibration signals are concluded in this paper. The generalization of the diagnosis model is checked nine times by testing the calculation of each group of parameters and using an error declining process. The mean accuracy of diagnosis model proposed in this paper is 0.9136, and the diagnosis model presented in this paper is very significant to detect OOR faults online. [ABSTRACT FROM AUTHOR]

IoT networks and Teaching System have attracted much attention from researchers of industry and academia. As a typical application of IoT tworks and Teaching System, the system studied in this paper is a three-way platform for IoT technology, language teaching methods, and language teaching quality is incorporated into the framework of language teaching system. Based on the current stage of language teaching methods in China and its digital needs, the role of IoT-based language teaching models on language teaching quality improvement is investigated by establishing an IoT language teaching model and using conventional comparison methods to compare conventional language teaching quality improvement models with language teaching models based on IoT platforms to consider the impact of IoT technology on language teaching quality improvement. The results show that in the educational context where digitalization and intelligence are the trends, the intelligent language teaching model integrating IoT technology is much better than the language teaching model under the conventional model in terms of teaching quality, and through IoT technology, more language enthusiasts can become language learners and improve their learning ability. The work in this paper provides an important application guidance to IoT networks and Teaching System. [ABSTRACT FROM AUTHOR]

With the increasing import volume of China's oil and natural gas, the structure of China's energy supply chain (ESC) has become more and more complex. How to use the "Belt and Road" (OBOR) layout to promote China's regional energy cooperation and ensure China's energy security has become an important international issue facing China. This paper constructs an ESC network model in the context of the OBOR, and finds out the position of China in this network model. This paper also finds out the role law of each risk influencing factor, analyzes the degree of its influence, and puts forward some suggestions on the risk management of ESC according to the analysis results. This paper proposes to use decision tree algorithm to guide the development and construction of Sino-Lao economic and trade cooperation, which will help analyze and help the construction and research of Sino-Lao economic and trade cooperation and sustainable energy freight corridor. Based on the energy cooperation between China and the countries along the "Belt and Road," this paper analyzes the network structure of energy cooperation under the "Belt and Road" background based on the small-world network theory. This paper analyzes the important role of core countries in the ESC through the analysis of the basic parameters of the small-world network, the efficiency of energy cooperation, and the stability of energy cooperation. According to the value of each vertex degree obtained by the calculation formula of entropy value, the entropy value of the ESC network structure under the background of OBOR can be obtained as 2.408. It calculates that the maximum and minimum entropy of the ESC network structure at this time are 3.09 and 2.21 respectively. [ABSTRACT FROM AUTHOR]

Venture capital syndication (VCS), an investment model, is gaining increasing attention. This paper uses a new perspective on knowledge transfer to explore the influence mechanism of knowledge heterogeneity in VCS on the innovation performance (IP) of entrepreneurial firms. We use firm-year panel data from the Shenzhen Stock Exchange for 2000–2017 and find that the knowledge heterogeneity of VCS includes, at least, management knowledge heterogeneity, investment knowledge heterogeneity, and technical knowledge heterogeneity. In particular, management knowledge heterogeneity and technical knowledge heterogeneity positively affect the IP of entrepreneurial firms. We further find that the intellectual capital (IC) of entrepreneurial firms mediates these positive relationships. Moreover, the equity concentration of VCS positively moderates the relationship between knowledge heterogeneity and IP and negatively moderates the relationship between knowledge heterogeneity and IC. This paper provides a new theoretical explanation of the relationship between VCS and IP of entrepreneurial firms from a knowledge transfer perspective and explores the impact paths of this relationship. [ABSTRACT FROM AUTHOR]

The B&R strategy came into being to promote the free and orderly flow of economic factors, the efficient allocation of resources, and the depth of market integration. Logistics is the artery of element circulation and the basis of the B&R. The logistics capacity of core cities along the R&B will be an essential factor affecting the strategy. In this context, it is of practical significance to measure the logistics capacity of the core cities along the B&R. Since combing domestic and overseas research, this paper first uses clustering analysis to screen out nine core cities along the B&R and then uses fuzzy matter-element analysis to measure their urban logistics capacity and sort them. The results show that the urban logistics capacity in the eastern coastal areas of China is higher than that in northwest and southwest China. The logistics capacity of cities along "The 21st Century Sea Silk Road" is more robust than that of others and through the urban agglomeration could further achieve economies of scale. The logistics capacity of cities along the "Silk Road Economic Zone" should improve the construction of logistics infrastructure and enhance logistics information in the future. [ABSTRACT FROM AUTHOR]

Breast cancer is a cancer disease that seriously threatens women's health and occupies the first place in female cancer mortality. At present, the incidence rate of breast cancer in China is the first in the world and is on the rise. In view of the serious harm of breast cancer to life and health, researchers and institutions are making unremitting efforts to find a perfect diagnosis and treatment plan. With the improvement of computer performance and machine learning levels, intelligent algorithms have been able to replace human behavior and judgment in some fields. The traditional breast cancer diagnosis process requires medical experts to observe patient data repeatedly. In this case, the algorithm technology is used to quickly feedback a high probability reference result to doctors, which is particularly important to increase the diagnosis efficiency and reduce the burden of doctors. In order to improve the accuracy of existing breast cancer recognition methods, this paper proposes and implements a scheme based on a whale optimization algorithm to iteratively adjust the key parameters of the support vector machine to improve the accuracy of breast cancer recognition. In order to verify the performance of the WOA-SVM algorithm, this paper uses the Wisconsin breast cancer data in the UCI database for performance verification experiments. Experiments show that the WOA-SVM model has higher recognition accuracy than the traditional breast cancer recognition model. [ABSTRACT FROM AUTHOR]

With the rapid development of China's economy, power demand has been closely linked with economic development in order to analyze and predict the future power situation in China. Based on the historical data of China's electricity consumption, this paper analyzes the data characteristics of China's electricity consumption by using Eviews software. The long-term trend of power consumption sequence is eliminated by fitting the regression curve, and then the residual sequence is analyzed and identified according to the relevant theory of time series. Using the data of China's power demand from 2004 to 2019, the ARMA time series model is used to analyze China's future power situation finally according to the test statistics of the model. Finally, the exponential regression ARMA model is selected to predict China's electricity consumption. The index regression analysis method extracts the long-term characteristic information of the sequence, which is conducive to the stability processing and empirical analysis of long-term prediction. The result predicts China's power demand in 2020 more accurately, which can provide a reference for the future productivity layout of China's power. [ABSTRACT FROM AUTHOR]

Exploring the improvement of classroom teaching effect under the background of sports education integration in China has important practical significance and theoretical value. Integration of yard sports education and classroom course education in the school is a novel concept put forward by China's sports and education circles to deepen the combination of sports and education. Although it is only a word different from the combination of sports and education to integrating sports and course education in the school, it is a groundbreaking theoretical and practical innovation for China in terms of changing the competitive sports development model and cultivating exceptional athletes. It is a new way to promote the sustainable development of sports and education according to the scientific outlook on development. Its fundamental significance is to change the closed state of sports and education departments, put sports and education in the background of economic and social development in a certain region, and fundamentally reform the content and mode of education. Therefore, this paper proposes a comprehensive rating model of classroom teaching effects based on deep learning (DL) and machine learning (ML) techniques. In this paper, the Jaffe expression dataset is used to train and test the utilized ML and DL models such as ResNet50, random forest (RF), logistic regression (LR), K-nearest neighbor (k-NN), and decision tree (DT). Further, with the help of artificial intelligence (AI) techniques, the algorithms can objectively evaluate the classroom teaching effect after the integration of physical education with classroom education and provide important guidance for the modernization and intellectualization of China's educational methods in the future. [ABSTRACT FROM AUTHOR]

With the prosperity of the economy, China's foreign exchanges are becoming more and more frequent, and English is becoming more and more important as the common language of international communication. For decades, Chinese educators and scholars have achieved great success in teaching English, but there are also some problems, including neglecting the teaching of crosscultural knowledge in English classrooms. And in the crosscultural English teaching based on hierarchical correlation analysis, its specific structure system is not complete, and its application scope is also less. Therefore, this paper analyzes hierarchical correlation analysis, teaching mode, etc., which lays the foundation for subsequent research. In addition, combined with the current research status, this paper analyzes the past crosscultural knowledge system and proposes a new structural model. A large number of experiments have verified the application effect of the improved knowledge architecture model in crosscultural English teaching under the hierarchical correlation analysis. The new model can improve students' crosscultural awareness and crosscultural communication competence and improve the previous teaching system structure. There is a problem that both hierarchical teaching and overall planning cannot be taken into account. This paper studies the crosscultural issues in English teaching and conducts research from the following four aspects: first, it discusses the cultivation of crosscultural communicative competence of college students in English teaching and proposes some feasible solutions to the problem of neglecting the integration of crosscultural knowledge in English teaching. Sexual suggestions cultivate students' crosscultural awareness of English learning and improve the efficiency of English learning. The second is to point out that college school students should pay attention to the differences in cultural communication between China and foreign countries and develop the awareness and ability of crosscultural communication. The third is to explore ways to improve students' crosscultural communicative competence and cultivate all-round English talents for the country and society. The fourth is how to achieve crosscultural communication, so that people with different cultural backgrounds can communicate and learn smoothly. Based on the above problems, this paper adopts the method of case analysis to discuss the problem of imperfect cross-cultural knowledge system in English teaching in China, and the educational structure is incomplete. [ABSTRACT FROM AUTHOR]

In order to further develop and improve the management theory of public libraries, this paper studies the impact of library information on the utilization of regional environmental resources from the perspective of intelligent adaptive learning. Firstly, based on the data envelopment analysis method, this paper measures the efficiency of Public Libraries in various regions of China. Then, using Tobit model, this paper analyzes the environmental factors affecting the efficiency of public libraries from three aspects: financial allocation, per capita GDP, and per capita years of education. The results show that there is a significant correlation between per capita GDP, per capita years of education, and the efficiency of public libraries. There is a significant negative correlation between per capita GDP and public library efficiency (the coefficient is -5.73x10-6, and the probability of two-sided t-test is 0.0229); There is a significant positive correlation between the number of years of education per capita and the efficiency of Public Libraries (the coefficient is 0.0684, and the probability of two-sided t-test is 0.0169). Conclusion: This study provides a reference for the performance evaluation of provincial public libraries. [ABSTRACT FROM AUTHOR]

Economic growth has not only made life more comfortable for people but has also increased the demands and expectations of the living environment. Urban planning, layout, construction, transportation, environmental protection, etc. are increasingly attracting the attention of the government. Due to the increase in residents, there are many problems in cities, such as traffic congestion, smog, etc. The relevant departments hope to use intelligent means to effectively alleviate these problems, and many cities have achieved good results. As a part of urban economics research, analyzing the economic benefits of a city helps to study how to develop the city. Smart sensor and communication technology is an advanced technology for smart manufacturing and the Internet of Things. As an advanced sensor tool, it is of great significance for exploring the economic benefits of smart cities. This paper discusses the calculation method of urban economic benefit value based on smart cities and evaluates the calculation method. The purpose of this paper is to find out the calculation method applicable to the urban economic benefit and to propose an effective evaluation method for the urban economic benefit, so as to have a more complete and effective calculation and evaluation of the urban economic benefit. This paper introduces the smart city, combining intelligent sensing and communication technology, and designs the economic benefit calculation method and evaluation model. Finally, this paper designs an experiment of economic benefit calculation and evaluation. First, 18 cities in China are used as examples to explore the relationship between indicators and GDP. It is concluded that there is a long-term and stable relationship between GDP and household savings balance at the end of the year and that this does not exist with the average number of on-the-job workers. Then we took Wuhan Metro Line 5 as an example to evaluate its economic benefits. The experiment shows that the largest weight value is the investment return period, with a weight of 0.54, and the evaluation level of Wuhan Metro Line 5 is 4. [ABSTRACT FROM AUTHOR]

The comprehensive reasons for the cross-regional flow of enterprises in their own economy are studied in this paper. Most traditional enterprise migration research focuses on the impact of a single factor. Even the research on the influence of multiple factors often ignores the interaction of various factors. Therefore, this paper proposes and logicalises the "push and pull" mechanism of enterprise migration to address the limitations of previous research. Through bibliometric analysis and investigation, we systematically think about the factors that affect the corporate migration mechanism and improve the Push-Pull Theory. Besides, by deploying a conceptual system dynamics model, this paper explains how various factors affect enterprise migration. In addition, we discussed how to adopt systems thinking to promote the development of emerging economies. We selected and tested 6 companies that migrated in China, and the results are in line with reality. The conclusion supports the theory that systematic thinking and decision-making influence corporate behaviour. [ABSTRACT FROM AUTHOR]