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Digital twin technology assists physical entities better to achieve functional perfection through the interactive cointegration of physical entities and virtual spaces. In this view, this paper proposes a digital twin inspired intelligent diagnosis method for bearing faults based on adaptive correlation filtering and an improved stack autoencoder (SAE) classification model. First, the adaptive correlation filtering algorithm is designed to filter the engineering signal in physical space, in which the noise-free ideal bearing fault signal spectrum in the virtual space is used as the comparison. In this filtering, the optimal cut-off frequency is determined adaptively with the condition that the similarity between the simulated spectrum and the engineering spectrum is maximized. Second, the Tan activation function is used in the SAE classification model to enhance the signal differentiation and avoid the problem of large computation of the traditional sigmoid function. Finally, the accuracy of the method in this study is verified on the basis of engineering experiments and Case Western Reserve University bearing fault dataset. Experimental results show that the method can achieve 100% correct diagnosis rate of bearing faults. [ABSTRACT FROM AUTHOR]

This paper proposes a multiattribute group decision-making (MAGDM) based on the CoCoSo method under the intuitionistic fuzzy sets (IFSs) environment for quality evaluation of innovation and entrepreneurship education (IEE) in vocational colleges. First of all, this paper extends the CoCoSo to the IFSs environment. Second, a new MAGDM model for quality evaluation of IEE in vocational colleges based on the CoCoSo algorithm is built. In this algorithm, the attribute weight is derived by subjective weight and objective weight, and the objective weights are defined by using the CRITIC method. At the end of this given study, some comparisons are given to verify the decision effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

With the rapid development of the economy, the challenges faced by enterprises are becoming more and more severe. In order to enhance the core competitiveness of enterprises themselves, enterprises pay more and more attention to accounting cost accounting. However, for a long time, enterprises have been using traditional accounting costing methods, which greatly hampered the development of enterprises. The Fourier transform (FT) algorithm is a commonly used information transformation method, which can simplify the complex cost accounting process. Based on this, this paper proposes a centralized cost accounting method for enterprise accounting based on FT algorithm, aiming to fundamentally change the traditional cost accounting thought. The article first analyzes and summarizes the general characteristics of cost accounting methods. Then, starting from the cost centralized accounting method, the paper optimizes the traditional cost accounting formula by using the FT algorithm and proposes a new accounting cost-centralized accounting formula based on the FT algorithm. Finally, starting from the reality and taking W company as the research object, the article focuses on analyzing the application of the centralized accounting method of accounting cost relying on the FT algorithm in the production and operation activities of the actual enterprise. After a series of experiments and analysis, we found that the centralized cost accounting method of enterprise accounting based on FT algorithm has a very high degree of fit with the actual situation, and the accuracy of cost accounting is as high as 70.01%. Moreover, in the process of enterprise cost analysis, the quality of accounting information of enterprises has been comprehensively improved by 31.9%. This fully shows that the centralized accounting method of enterprise accounting cost based on the FT algorithm can bring certain economic benefits to the enterprise and optimize the internal resource allocation of the enterprise. [ABSTRACT FROM AUTHOR]

In order to further improve the stability of the composite axis aviation photoelectric stabilization platform, a control method of negative effect compensation of disturbance (NECOD) is proposed and applied to the voice-coil fast steering mirror adjusting the optical path of the platform. The experimental results show that the control accuracy and closed-loop bandwidth of the voice-coil fast steering mirror based on NECOD have been greatly improved, while its advantages of large stroke are not possessed by piezoelectric fast mirror. The experimental results of high-frequency turntable and vibration table show that NECOD proposed in this paper has better antidisturbance performance than DOB and ARC, and its parameters are less and easy to adjust. It is proved that the robustness of NECOD is very strong in the presence of 20% parameter perturbation. The final LOS stabilization accuracy experiment shows that the NECOD voice-coil fast steering mirror proposed in this paper fully meets the performance requirements of high-precision aviation photoelectric stabilization platform, and has high practical value for improving the antidisturbance performance of the control system of aviation photoelectric stabilization platform. [ABSTRACT FROM AUTHOR]

The development of a country's language and literature is the orientation of the country's spiritual strength, and it is also the embodiment of the national literary quality. Chinese language and literature has a very long history of development, the content of which is broad and deep, and there are countless literary works. It is impossible for one person to fully understand these works after spending his entire life. As a result, these literary resources are optimized and constructed and classified into various disciplinary contents, so that students can systematically learn literary knowledge according to their own interests. Therefore, this paper conducts a disciplinary analysis of the construction of Chinese language and literature resources by integrating the method of fuzzy mathematics. Through the study of fuzzy mathematics, the method of fuzzy cluster analysis is proposed to classify Chinese language and literature resources, and the method of fuzzy comprehensive evaluation is used to evaluate the contents of various subjects. This paper selected the richness of subject content, faculty strength, talent demand, and subject difficulty as evaluation indicators. By comparing the scores of various discipline indicators before and after resource optimization, the results showed that using fuzzy cluster analysis, disciplines 1 and 2 were popular disciplines. Disciplines 4 and 5 were general disciplines, and disciplines 3 and 4 were unpopular disciplines. Using the fuzzy comprehensive average, the comprehensive scores of the six disciplines were 84, 79, 80, 82, 83, and 75, respectively. The quality of resource construction in these disciplines was high. In addition, the scores given by experts were all above 70 points, with an average score of about 83 points. It showed that the method of using fuzzy mathematics to construct Chinese language and literature resources has achieved good results, and the results obtained by using the method of disciplinary analysis were reliable and feasible. [ABSTRACT FROM AUTHOR]

In recent years, the main grain-producing areas in the west of Jilin have gradually concentrated in the perennial irrigation areas and supplementary irrigation areas in the north, and the stress degree of grain irrigation water resources is gradually increasing. Therefore, this paper selects the arid and semiarid area in the west of Jilin Province as the research area and compares and analyzes the modeling process and simulation accuracy of the temporal and spatial distribution of food security production and total water resources. Based on the center of gravity model, this paper studies the temporal and spatial distribution of food security production and total water resources in Western Jilin. It can be seen that after 2018, rice and wheat show a fluctuating upward trend, and their values rise from 0.612 to 0.786 and 0.356 to 0.612, respectively; the rise of corn is small, showing an inverted "U" trend, which first increases from 0.693 in 2018 to 0.701 in 2019 and then decreases to 0.671 in 2021; By comparing the precipitation statistics in different years, the inverted "U" trend of corn irrigation water efficiency may be related to the sudden increase of precipitation. The center of gravity model is used to analyze the evolution trend of the center of gravity of grain production and water resources in Western Jilin from the national and regional levels, and the spatiotemporal coupling degree of the center of gravity of grain production and water resources is explored through the two-factor center of gravity coupling situation model. [ABSTRACT FROM AUTHOR]

Nowadays, with the vigorous development of information management technology, talent management has become a hot field that scholars pay attention to. The flow of talent between companies has become increasingly frequent. A large number of cooperative behaviors have produced a large number of cooperative results and subsequently brought a large amount of data on what to do. A huge network of collaborators has also been quietly formed, and how to mine valuable information from it has become a research hotspot, among which talent recommendation is one of the most important topics. Talent recommendation, when an enterprise introduces high-quality talents, provides valuable reference suggestions and selects candidates. When introducing talents, enterprises should not only consider the ability level of talents but also consider the cooperative relationship between them and enterprise personnel. Therefore, it is necessary to analyze the network of partners to find out the rules. There are only author nodes in the isomorphic collaborator network, and the connection between nodes is the cooperative relationship. On this basis, this paper constructs a heterogeneous collaborator network; that is, there are multiple types of nodes and connections in the network. The main research problem of this paper is to find an indicator to measure the strength of association between scholars in the collaborator network and to recommend potential academic talents for enterprises. Based on the data of academic research cooperation network, we carried out sufficient experiments to demonstrate the effectiveness of the heterogeneous cooperation network model proposed in this paper. [ABSTRACT FROM AUTHOR]

The rapid development of a new generation of information technology, the promotion of network technology, and the emergence of complex and diverse requirements for control objects make the structure of language learning models more and more distributed. Distributed learning theory emphasizes the central position of learners in the learning process and the universality of learning scenes. This paper explores the significance and value of various learning modes to improve students' learning effect. By analyzing the research data and explaining various effective language learning models, this paper aims to establish a theoretical framework of English language learning models and explore more effective language model matching schemes. This paper analyzes the adaptive multiagent, reward function, Markov model, probability function model, etc. and conducts experiments on the basis of the designed model. The linear correlation parameters of the model and the English language pattern matching efficiency are analyzed and judged on several important indicators. Because the algorithm designed in this paper has a good effect on the control of error, the error reduction rate has reached 85.6%. [ABSTRACT FROM AUTHOR]

At present, the research on machine translation mainly focuses on English-Chinese translation, while the research on Japanese college students using Japanese Chinese machine translation software is relatively few. In order to solve the above problems in Chinese Japanese bilingual translation, this paper proposes a phrase translation method based on sequence intersection. This method regards sentences as word sequences and aligns the sequence intersection of all source sentences corresponding to the target sentence in the corpus with Chinese and Japanese sentences containing the phrases to be translated. By fully mining the information of sentence alignment bilingual corpus without word alignment resources, we can obtain high-quality phrase translation, syntactic analysis, and dictionary. Then, we focus on the automatic construction of sentence level aligned bilingual corpus and explore the automatic sentence alignment technology of Chinese and Japanese bilinguals. A ten-year alignment model based on combination cues and core extended square matching is proposed. The preprocessing of the computer corpus and the basic construction of the corpus are completed. This paper also puts forward corresponding countermeasures and approaches to the problems encountered in the construction of computer translation. [ABSTRACT FROM AUTHOR]

For a long time, due to the influence of curriculum orientation and examination-oriented education, the learning of music appreciation courses has not been paid much attention. This makes the teaching method in music appreciation teaching single, the classroom effect cannot reach the expected learning goal, and the classroom teaching efficiency becomes low, which urgently needs a new learning method to deal with these problems. Focusing on the digital education background of the new curriculum reform, this paper investigates the teaching nature, teaching methods, teaching design and evaluation, classroom construction, and student ability development of music appreciation. In this paper, the Kalman filter algorithm is used, and the optimal transformation order determined in advance is used to suppress the noise in music and improve the sound quality of music appreciation. If the order of the full-band signal model is selected as 10, and the number of sub-bands is selected as 4, then the order of the sub-band signal model should be at least greater than or equal to 2. In the music appreciation teaching test based on the Kalman filter, it was found that 35.6% of the students believed that the music appreciation course based on the Kalman filter could completely improve the learning efficiency, and 49% of students believe that the teaching of music appreciation class based on the Kalman filter can improve a large part of the learning efficiency. Overall, 85.5% of the students believed that the music appreciation class based on the Kalman filter is of great help in improving the learning efficiency, and the Kalman filter denoising method proposed in this paper has an obvious effect, which is a new attempt to promote the digital development of music appreciation. [ABSTRACT FROM AUTHOR]

This paper provides an in-depth study and analysis of the relevance of adopting the idea of 5G for the optimal configuration of university library layout to the development of students' academic literacy. It also analyzes the benefits of 5G on the digital governance of libraries together with the convenience that the application of 5G can provide to smart libraries and helps to realize the research on the service innovation model of smart libraries under 5G. The survey mainly focuses on four indicators: the development of physical space services in university libraries; facilities and equipment for physical space services; the content and form of physical space services; and it presents the current problems of physical space services, including the overall convergence of physical space services in university libraries. When different libraries are co-constructed and shared, there will be incompatibility problems, and if the library is required to develop its own management system, it will waste resources. The library space layout is arranged by highlighting the elements of the cognitive map to improve the lending efficiency; using a more continuous and transparent interface in terms of color and material to increase the experience of spaciousness; using more wood elements and green to add a natural atmosphere to achieve the purpose of relaxing the readers' heart; and finally increasing the light source in the reading area to complement the illumination in the shaping of the light environment. In terms of spatial functions, more attention is paid to the design of public interaction and leisure spaces so that readers can rejuvenate themselves and devote themselves to the next learning tasks. The interior design is considered in terms of the relationship between arousal theory and task performance, and a moderately complex interface is designed to improve readers' efficiency in the space. Finally, the paper concludes with a summary of what has not been thoroughly researched and an outlook on the future of university library interior design. [ABSTRACT FROM AUTHOR]

This paper presents the suitability and computational features of the cylindrical TLM approach, when it is used as an accurate and efficient alternative for the analysis of the bending effect on performances of the antenna operating in an on-body mode. A design goal was to create the model of the rectangular patch antenna wrapped around the part of a cylinder, which can be used to represent a human body (torso, leg, or arm) and simultaneously to model dielectric properties of muscle tissue. Moreover, the paper illustrates problems in terms of accuracy and limitations when the antenna deformation is modelled by using numerical methods based on the cubic mesh. The advantages of the cylindrical TLM method over the rectangular TLM approach have been emphasized through an analysis of how the bending affects the resonant frequency of the antenna. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the mathematical control of space-based kinetic energy weapons and the evaluation of the damaging effect by partial differential equations. The spectral element discrete format of the optimal control problem is constructed, the a priori error estimate of the control problem solution is proved theoretically, the posteriori error estimator is constructed, and the adaptive solution algorithm is designed. The posteriori error estimator is used as the encryption criterion to guide the local encryption of the grid so that the distribution of the dissection nodes is denser where the function regularity is poor. In the case of a compartment subjected to a shell attack, the effect of different factors on the structural damage of a single compartment under two explosions is investigated by varying the explosive mass, the location of the blast point, and the interval between the two explosions. In this paper, the problem of implosion in the cabin is studied, and the main factors affecting the response of the implosion structure are analyzed using dimensionality. The data of various simulation conditions are counted, and the dimensionless damage number is fitted with the deformation results of the bulkhead under each damage mode. It is still difficult to obtain the acceleration information of the target. Experimental studies were conducted on the composite honeycomb sandwich structure penetrated by fragmentation at different velocities and angles, and the accuracy of the theoretical model and the fragmentation residual formula was verified based on the test results. The analysis found that the antidamage performance of the composite honeycomb sandwich structure is better than the existing honeycomb sandwich body structure, and the energy absorption per unit volume of the structure is 27.2%–84.2% higher than that of the existing structure in the range of different penetration velocities. The average error between the theoretical calculation results and experimental results of the remaining velocity of the broken piece is within 6%, the error with the numerical simulation is about 8%, and the composite honeycomb sandwich structure with the best energy absorption characteristics and structural parameters is obtained. [ABSTRACT FROM AUTHOR]

In order to improve the effect of ethnic music teaching and improve the recommendation accuracy of teaching resources, this paper proposes a personalized recommendation method for ethnic music teaching resources based on video tags. A national music teaching resource library is constructed as the resource source, and the K-nearest neighbor (KNN) algorithm is used to classify the teaching resources in the resource library. According to the results of resource classification, describe the characteristics of teaching resources, so as to recommend suitable teaching resources to corresponding students to meet individual needs. According to the main characteristics of tags, a tagging system is established, and at the same time, the decay of user interest over time is considered, and weights are assigned to user video interest tags in descending order of time, and the total user interest tag vector is calculated. In this paper, the obtained user total interest label vector is normalized. The vector space model is used to represent teaching resources and user interests, so as to realize personalized recommendation of teaching resources. The experimental results show that the accuracy rate of the proposed method is higher than 90%, the recall rate is more than 97%, and the F1 value is larger, and the convergence is better, reflecting the better recommendation effect of the method. [ABSTRACT FROM AUTHOR]

Higher education question and answer system is a current research hotspot in the field of natural language processing and artificial intelligence, which can return accurate answers directly and also allow users to input in natural language, avoiding the need to return large collections of valid and invalid data links for users to filter twice. In recent years, deep learning techniques have made great progress in the field of natural language processing, thus making it possible to apply it in the field of teaching and learning in junior high school. Most of the traditional quiz systems based on traditional retrieval techniques have problems such as insufficient semantic portrayal of text, inability to extract semantic features in context, and poor processing of complex utterances. In order to fill the gap and deficiencies of question and answer systems in junior high school teaching, this paper applies multimodal and adversarial network-related technologies to build a multimodal adversarial network-based question and answer system for higher education. The experimental results show that the principles of the traditional application system and the multimodal adversarial system designed in this paper are similar, but the current application system is relatively more effective. [ABSTRACT FROM AUTHOR]

Combining filters in an ensemble to improve feature selection performance is a growing field in the literature. Current techniques, however, are focused on approaches that suffer from drawbacks such as sensitivity to skewed distribution, among others. To address this gap, this paper investigates the applicability of multiple criteria decision-making in ensemble feature selection. This paper adopts the Evaluation based on Distance from Average Solution (EDAS) method due to its many familiar elements to the feature selection community. An experiment was performed on six datasets and a control group. The paper uses the six datasets as levels of the blocking factor. A negative control group (i.e., no feature selection) was adopted to compare with the proposed algorithm. Results show that the proposed ensemble FS algorithm was able to reduce the dataset without compromising the performance of the classifier. The findings in this study would contribute to the literature in several ways. First, the paper is one of the few works to demonstrate how MCDM can be used in feature selection with promising results. Second, this paper is one of the few works to demonstrate the significance of including datasets as levels of a blocking factor when performing significance testing. Finally, this paper is the first to demonstrate the applicability of EDAS as an ensemble FS algorithm. As such, the findings in this paper could spark the cross-fertilization of feature selection and MCDM. [ABSTRACT FROM AUTHOR]

This paper employs machine learning algorithms to detect tax evasion and analyzes tax data. With the development of commercial businesses, traditional algorithms are not appropriate for solving the tax evasion detection problem. Hence, other algorithms with acceptable speed, precision, analysis, and data decisions must be used. In the case of assets and tax assessment, the integration of machine learning models with meta-heuristic algorithms increases accuracy due to optimal parameters. In this paper, intelligent machine learning algorithms are used to solve tax evasion detection. This research uses an improved particle swarm optimization (IPSO) algorithm to improve the multilayer perceptron neural network by finding the optimal weight and improving support vector machine (SVM) classifiers with optimal parameters. The IPSO-MLP and IPSO-SVM models using the IPSO algorithm are used as new models for tax evasion detection. Our proposed system applies the dataset collected from the general administration of tax affairs of West Azerbaijan province of Iran with 1500 samples for the tax evasion detection problem. The evaluations show that the IPSO-MLP model has a higher accuracy rate than the IPSO-SVM model and logistic regression. Moreover, the IPSO-MLP model has higher accuracy than SVM, Naive Bayes, k-nearest neighbor, C5.0 decision tree, and AdaBoost. The accuracy of IPSO-MLP and IPSO-SVM models is 93.68% and 92.24%, respectively. [ABSTRACT FROM AUTHOR]

This paper is devoted to studying the p-th moment exponential stability for a class of stochastic differential equation (SDE) driven by G-Brownian motion under non-Lipschitz condition. The delays considered in this paper are time-varying delays τ i t 1 ≤ i ≤ 3 . Since the coefficients are non-Lipschitz, the normal enlargement on the coefficients is not available and the Gronwall inequality is not suitable in this case. By Bihari inequality and Itô integral formula, it is pointed out that there exists a constant τ ∗ such that the p-th moment exponential stability holds if the time-varying delays are smaller than τ ∗. [ABSTRACT FROM AUTHOR]

Camellia oil contains a variety of active substances, which have the functions of strengthening the heart and lowering cholesterol, and can prevent a variety of cardiovascular and cerebrovascular diseases caused by vascular sclerosis edible oil. Based on the logistics model, this paper builds a research model on the influencing factors of consumption and purchase intention and analyzes the coastal areas of camellia oil. Using this model, it makes an empirical analysis and research on the differences in the purchase intention and influencing factors of different consumers of camellia oil. Based on the results of empirical analysis, the main research conclusions of this paper are extracted, including the influencing factors of consumers' camellia oil purchase willingness and the differences in the influencing factors of consumers' camellia oil purchase willingness under different income levels, and put forward some corresponding measures and policy suggestions in a targeted manner. Based on the research model of the influencing factors of purchase intention established by logistic algorithm, this paper analyzes the coastal areas of Chashan oil and finds that the factors affecting consumers' purchase intention are positively related to education and personal monthly income and negatively related to gender. "Educational education" is significant at 1% and has a positive correlation with a coefficient of 0.864; personal income is significant at 5% with a coefficient of 0.762; for gender, it is negatively correlated with a coefficient of -0.259, indicating that other variables remain unchanged. Education can promote consumers' willingness to buy camellia oil, and consumers with higher education are more likely to buy camellia oil. In the same regression prediction model, the regression prediction efficiency of this model is better. In general, this shows that the model in this paper has certain superior performance. [ABSTRACT FROM AUTHOR]

With the acceleration of social modernization, the level of public management has also been significantly improved compared with previous times. People are more and more aware that a harmonious public life is the basis for realizing a good personal life. As an important guarantee for a harmonious public life, public management occupies an extremely important position. However, the great changes in the economy and society have brought forth new problems in modern public management, and the tasks and burdens of public management have become heavier and heavier, which has had a huge impact on the effectiveness of social governance. On the basis of studying the factors affecting the development of public management and the problems of management, this paper integrates the new situation of reliability mathematics application, and explores and solves the problems of public management. In the experiment, the effectiveness of the method proposed in this paper is tested from two aspects of management analysis and management effect, and the feasibility of this method is verified by comparing with the traditional management method. The final experimental data show that the innovation degree and coordination degree of management work under the traditional management method are 16.6% and 58.4%, respectively. The innovation degree and coordination degree of management work under the method of this paper reach 36.1% and 78.8%, respectively. It shows that the analysis and realization of the new situation of reliability mathematics application in public management have certain operability. [ABSTRACT FROM AUTHOR]

This paper designs a feature extraction-based physical characteristics and kinematic analysis of collegiate martial arts Sanda athletes by profoundly studying the model of athletes' action feature extraction and constructing physical characteristics and kinematic model of collegiate martial arts Sanda athletes. This paper provides feature extraction category support by describing the data types of clustered objects, calculating behavioral operator distances, identifying multiple behaviors occurring in collegiate martial arts Sanda tactics, and delineating the correct thresholds for operator identification. A comparison experiment is designed to verify that the proposed method can improve the accuracy of feature extraction for athletes' technical and tactical use and is more applicable to the comprehensive assessment of athletes' behaviors. The underlying features of the athletes' motion technique forming image features are analyzed, the image features are regionally divided, the feature sets of different levels of developing actions are obtained, and the image features are allowed to map to the corresponding feature dimension space. The AdaBoost algorithm filters out the feature data of the athletes' action images that contribute most to the intelligent visual analysis. It is used as training samples for training and recognition to complete the extraction of athlete action image features. The action times of the five different phases of side kick up, knee lift, kick strike, and strike: recovery is 0.2986 s, 0.1819 s, 0.1322 s, 0.0708 s, and 0.9986 s, respectively, which reflects that the time of different action phases of athletes with extra body weight is more related to the athletes' weight, strength, flexibility, and coordination. [ABSTRACT FROM AUTHOR]

This paper adopts the method of visual characteristics of the double random matrix to conduct in-depth research and analysis on the design of fractal art patterns. For the practical application needs in fractal graphic design, a method is proposed to automatically extract the core base pattern based on fractal graphic content and generate a four-sided continuous pattern. The method first uses the Canny operator for edge detection to analyze the area of the main pattern. Then, it uses a grayscale cogeneration matrix to extract and analyze the graphic texture features, based on which the best splicing method is selected to splice the extracted pattern, and then, it achieves two splicing methods of flat row and staggered four-sided continuous pattern. The method has good practicality with low complexity and high versatility under the premise of ensuring the beauty of the generated four-sided continuous pattern. It can assist designers to design patterns, improve efficiency, and save design costs. In this paper, we improve the existing image segmentation methods, adopt two segmentation methods, namely quadratic tree segmentation and HV segmentation, propose a new local codebook selection strategy, and study the degree of self-adaptation of different methods to images in terms of segmentation methods and local codebook selection strategies. It makes the network unable to train efficiently on long text prediction problems. Finally, the improved algorithm of this paper is tested on the standard and living image libraries, and the experimental results show that the use of local codebooks makes the image coding speed significantly improved compared with the fixed fractal. In the face of the image to be retrieved, it is only necessary to perform the coding operation to obtain the fractal code to perform similarity matching, which can meet the requirement of real-time retrieval. Applying the improved distance formula, the search accuracy obtained on the test gallery is significantly better than that of the grayscale histogram algorithm. [ABSTRACT FROM AUTHOR]

General education is an important part of university education aiming at cultivating students' comprehensive quality and sense of responsibility. As professional sports colleges and universities, higher sports colleges implement the curriculum reform of general education relatively late, and there is a problem of curriculum system construction in the process of implementing general education. The purpose of this paper is to research and discuss the application of general education management in higher education based on fuzzy cluster analysis in the Internet environment. This paper first discusses the teaching mode in the Internet environment and then analyzes the application of fuzzy cluster analysis in general education. Through fuzzy cluster analysis, the students' understanding of general education before and after learning general education courses is compared, which paves the way for the promotion of general education in colleges and universities. Finally, this paper takes the general education curriculum system of higher physical education colleges as the research object and conducts fuzzy cluster analysis on it. The experimental results show that before studying general courses, 66.7% of the students hope to improve their language communication and expression skills, and 52.8% and 46% of the students hope to improve their practical ability and logical thinking ability. After receiving general education, only 23.5% of students have improved logical thinking ability, and only 13.7% of students have improved language communication and expression skills. The results show that general education does not meet the expectations of students, general education in higher physical education colleges is not effective, and the realization of general education needs to be further improved. To this end, schools can purchase network information resources, cooperate with well-known universities at home and abroad, develop general education courses with a global perspective, train students' spirit of independent thinking, and master correct thinking methods. [ABSTRACT FROM AUTHOR]

The quality of sports performance mainly depends on the scientific degree of sports training. The best monitoring and control of the whole process of sports training is an important guarantee for improving the scientific degree of training. For the scientific monitoring of sports training process, many scholars have carried out research from many aspects. Current research still finds that athletes still have many problems with the protection of their own physical endurance. (1) The monitoring of the sports training process by coaches and athletes is still mainly spontaneous. The monitoring of athletes has not yet formed a system. (2) At present, the monitoring of athletes is mainly carried out from a single discipline or a certain field, and there is no systematic monitoring system. (3) There are many common problems in the monitoring items of the training process in the existing research. Existing studies have relatively little content on specific characteristics. In this paper, a dynamic monitoring system for the training process of elite rock climbers is established, and a training operation mode for rock climbers is established. (1) This paper sorts out the various subsystems of the training system. The monitoring system of sports training process mainly includes target system, personnel organization system, and software and hardware system. In addition, the sports training system proposed in this paper also includes monitoring content system, implementation system, and evaluation system. The sports training process monitoring system also includes a subsystem for monitoring the development of athletic ability of athletes. We take on the training load condition monitoring subsystem; daily physical function state and psychological state monitoring subsystem; and physical and mental health monitoring subsystem. (2) In addition, this paper establishes an operation mode dominated by "scientific research leaders." The model includes three parts: "Administrative Managers, Scientific Research Leaders, and Coaches." (3) Finally, this paper establishes five basic guiding ideologies for the evaluation of training process monitoring results. This paper establishes three reference standards for the evaluation of monitoring indicators. [ABSTRACT FROM AUTHOR]

As a product of digital technology, financial shared services can be deeply integrated into enterprise operation and management, which is significant to accelerate the financial transformation of real enterprises and achieve high-quality development. Based on this, this paper examines whether and how financial shared services empower the real economy, using data from listed companies in China from 2008 to 2017 as a sample. The research results show that financial shared services can significantly improve enterprise value and thus empower the real economy, and the optimization of operational efficiency is an important path for financial shared services to empower the real economy. Further research finds that the empowering effect of financial shared services on the real economy mainly exists in enterprises with many subsidiaries, large scale, and high labor intensity. This paper not only bridges the gap between financial shared services and micro-enterprise related research but also provides references and lessons for how to implement financial shared services in the real economy. [ABSTRACT FROM AUTHOR]

This paper proposes a nonelliptic extended English teaching ability evaluation algorithm based on an adaptive random matrix model. The algorithm models a nonelliptical expansion target as multiple elliptical sub-objectives, and the expansion state of each sub-target is described by an inverse Wishart distribution. The new method is combined to improve the robustness problem caused by the initialization after the algorithm expansion. This research uses the smart teacher education platform to conduct research and analysis on the evaluation of teaching practice ability of intern normal students. From the perspective of data evaluation, we explore the influence of normal students' curriculum training on the teaching practice ability of normal students. By analyzing the correlation between normal students' course grades and practice grades, this paper explores the influence of normal students' prepractice training on normal students' teaching practice ability. This paper explores the influence of the training of normal students' course learning on the teaching practice ability of normal students. The results show that the learning level of normal students' professional courses has a significant impact on the development of normal students' practice performance and teaching practice ability; the impact of normal students' pedagogical course level on normal students' practice performance and teaching practice ability is relatively low. [ABSTRACT FROM AUTHOR]

Actively responding to population aging has become the new national condition of China's aging society in the new era. To do a good job of actively coping with aging in the new era, it is necessary to take multiple measures and work together on the basis of clarifying the major realities and problems faced by China's aging society. From the perspective of big data technology, this paper systematically studies the concerns of China's aging through the method of artificial intelligence. In addition, with two typical aging indicators, three coupled intelligent algorithms are introduced in this paper. The prediction results show that the multi-dimensional support vector machine optimized by the immune algorithm shows good advantages compared with the other two algorithms. The multi-dimensional support vector machine optimized by the immune algorithm, one of the big data, has a good prediction effect on the performance indicators of China's aging, which indirectly illustrates the applicability of big data analysis to the concerns of China's aging. [ABSTRACT FROM AUTHOR]

Data mining algorithms combine expertise in machine algorithm learning, software modeling pattern recognition, statistical analysis principles, database construction, and artificial intelligence. With the rapid development of Internet technology and the common application of cell phones, mobile medical, a new medical method based on this technology, has been spawned, which greatly facilitates multiple aspects of medical services such as doctor diagnosis, patient treatment, disease care, and health management of critically ill patients and also alleviates the imbalance of medical resources. This paper firstly starts from the background of rapid development of information technology and mobile technology, combines the theoretical knowledge of data mining algorithm and mobile medical, as well as previous research reviews, and presents the main research content of this paper: analysis of factors influencing the development of mobile medical innovation based on data mining algorithm. Based on the K-means algorithm in the data mining algorithm and the Apriori algorithm in the association algorithm, this paper analyzes the current situation and problems of mobile medical development in China based on the algorithm model, analyzes the influencing factors of mobile medical innovation development in China based on the algorithm model, and summarizes and concludes the influencing factors of mobile medical innovation development and concludes that there are four categories of mHealth innovation development influencing factors: demand influence, policy orientation, technological innovation, and capital injection. [ABSTRACT FROM AUTHOR]

In today's diversified development of contemporary culture, how Chinese traditional culture and contemporary graphic design can be perfectly integrated into contemporary design and how to reflect their unique cultural symbols in contemporary design is a major issue that designers and researchers must face. This paper takes Chinese cultural symbols as the research object and adopts a multivariate statistical model based on a random matrix. By means of factor analysis and cluster analysis, the number of dimensions of multivariate data is minimized while ensuring minimal information loss to improve the efficiency of the analysis. This paper takes Chinese cultural symbols as the core and adopts a multivariate statistical method based on the random matrix for factor analysis and cluster analysis, in an attempt to establish a complete and scientific evaluation index system, which provides a reliable tool for the study of Chinese cultural symbols. [ABSTRACT FROM AUTHOR]

The steps of generating basic data by the LDA model and calculating text by the weighted algorithm have a good effect on text clustering. In this paper, the LDA topic model is used to effectively improve the accuracy of strategy text clustering. FTZ economics text clustering simulates FTA economics text data and economic data, imports economics and economic figures and word lists, and uses the traditional vector space model for factor representation. After that, the text vectors are independent of each other, ignoring the semantic relationship, which affects the clustering analysis results. A Chinese text clustering algorithm based on semantic clustering is proposed. Based on the principle of cooccurrence and semantic relevance of words, the algorithm uses the collocation vector of feature words to construct semantic clustering; find the document vector with embedded semantic information. Finally, document vectors with embedded semantic information are used. Finally, K vector is used for cluster analysis. The simulation analysis in this paper shows that the economic growth of the free trade zone is the largest under the economics guidance, which can reach 15%. [ABSTRACT FROM AUTHOR]

The prosperity of the economy has made the society develop rapidly, and the traditional education management system can no longer meet the needs of the modern society. Under the background of college enrollment expansion, it has brought new challenges to school management and development. In recent years, the evolution and widespread adoption of virtualization, service-oriented architecture, autonomous, and utility computing have converged to give birth to a new paradigm: cloud computing. Cloud computing allows on-demand delivery of software, hardware, and data as a service. Cloud computing has the characteristics of massive processing and storage capacity, high efficiency, virtualization, and low cost. It has changed the traditional way of accessing software and hardware, providing users with convenient services and computing resources, which has achieved remarkable results in the related fields. Meanwhile, cloud computing plays an important role in education and teaching through its wide application in various fields. Based on this, this paper aims to study the application of distributed real-time cloud computing system in educational administration management in colleges and universities. This paper establishes a university educational administration management platform based on the cloud service computing system, and applies the management technology and distributed storage technology of the cloud computing platform. The system constructed in this paper significantly improves the utilization of CPU and memory. The research results of this paper showed that the response speed of the educational administration management system built through cloud computing has been significantly improved, with an average response time of 1.3s and an error of less than 0.4. [ABSTRACT FROM AUTHOR]

Digital literacy has been increasingly important among workers in the labour market, and it also has been viewed as a micro reflection of the growing of China's digital economy. Internet use is an integral part of workers' digital literacy, which has a significant influence on entrepreneurial decision-making. This paper aims to explore the direct impact of Internet use on entrepreneurship, analyzing the mechanism and group differences of Internet use. It has a practical meaning to China's policies of promoting employment. Based on the data of China Labour Dynamics Survey (CLDS), the paper uses Probit model and 2SLS regression analysis to examine the direct impact of Internet use on entrepreneurship, and to analyze its mechanism by adopting the mediation effect model. The study indicates that Internet use has significantly increased the probability of starting a business, the impact has been stronger on the new generation, rural residents and the groups with work experience. Further analysis shows that Internet use not only increases venture capital, social capital and income, but also helps to shape individual workers' learning ability, which has an indirect effect on promoting entrepreneurship. The above results show the way of promoting workers' willingness of entrepreneurship, which are expanding Internet access, promoting policies of improving workers' Internet skills, focusing on cultivating comprehensive cognitive abilities including risk awareness and learning abilities. [ABSTRACT FROM AUTHOR]

Smart new retail mode has rapidly become the hot shopping mode. In order to develop vigorously in the new retail environment, it is urgent for merchants to set up supply chain network reasonably. This paper constructs a supply chain newsvendor model consisting of 1 wholesaler and N retailers. Analyse how channel conversion rate, negotiation ability of supply chain members, number of retailers, and other factors affect the balance among supply chain members when offline demand, online demand, and cross-channel demand exist simultaneously. Combined with the case data of two fast fashion brands, this paper compares and analyses the profit changes of decentralized supply chain and centralized supply chain under the influence of multiple factors. The study shows that there is no absolute optimal supply chain model and the choice of supply chain model will change with the change of influencing factors. However, no matter in which supply chain mode, improving channel conversion rate and controlling the number of retailers are beneficial. In addition, in decentralized supply chain, balance can be achieved among supply chain members so as to achieve Pareto optimality. Wholesalers should take the initiative to promote the transformation of demand among channels, and should not be too strong in member negotiations. This paper analyses the basic characteristics of the supply chain network under the new retail, and establishes the network optimization strategy that is easy to implement, which can help enterprises to carry out the supply network layout more effectively under the smart new retail mode. [ABSTRACT FROM AUTHOR]

The constant expansion of higher education has led to the continuous increase of the teaching staff and the complexity of the work in the modern era of education, forcing the traditional colleges and universities (it is abbreviated as CU in this article) to participate in trade union activities to open up the road of innovation. What kind of method can effectively and quickly optimize trade union work (TUW) and improve satisfaction has become one of the research topics that has attracted much attention. Aiming at this problem, it has great research significance for the field of CU to participate in trade union activities. With the in-depth research on the work of trade unions in CU, the research on multivariate optimal design in the work of trade unions in CU is gradually carried out, and its advantages are of great significance for solving problems in the work of trade unions in CU. The purpose of this paper is to study the innovation of TUW mode in CU based on multivariate optimization. Through the analysis and research of the multivariate optimization idea, it can be applied to the innovation of the construction of the labor union work model in CU, and the multiobjective linear weighting method is used to solve the objective function, so as to solve the problem of improving the evaluation of labor union work satisfaction in CU. This paper analyzed the TUW in CU (it is abbreviated as TUW in this article) and the multivariate optimization design, conducted an experimental analysis on the improved TUW mode in CU, and used relevant theoretical formulas to explain. The results have shown that the overall evaluation score of the college TUW method proposed in this paper is higher than the traditional college TUW method, and the difference between the two is 18.7%. It can be seen that the university TUW based on mathematical modeling and multivariable optimal design can meet the innovation needs of university TUW, and the satisfaction and work efficiency are greatly improved. [ABSTRACT FROM AUTHOR]

Compared with the modern intelligent landscape design, traditional landscape design is often limited to landscape level architectural design and traditional functional design, thus ignoring the advantages of ecological factors and scientific and technological factors embodied in intelligent landscape design. Therefore, the traditional landscape design often has some disadvantages, such as waste of design resources, serious design energy consumption, poor adaptability of design products, weak practicality, and fragile ecological structure. Based on this, this paper will fully consider the role of climate factors such as solar radiation, temperature, wind conditions, and relative temperature and humidity and air pollutants in landscape design. At the same time, various design factors in landscape design will be quantified and statistically analyzed by using the principle of nonlinear random matrix, so as to further optimize the index distribution of balanced design factors and optimize the landscape design scheme. In the actual principle design, this paper adopts the logic idea of digital garden design, based on the design process of garden logic design, garden design foundation preparation, garden design environment analysis, and digital garden design results display, and establishes a parametric digital garden forest model, so as to realize the digital design and simulation of intelligent garden. In the corresponding practical simulation part, this paper carried out an example verification based on the interior and exterior landscape design of a company and realized the establishment of the parametric analysis model of the case based on parametric and visual-related programmable software, thus completing the simulation design of functional components such as garden road layout optimization design, garden greening planting design, garden landscape visual creation effect design, and garden numerical service design; the experimental results show that compared with the traditional garden design, the garden designed in this paper has increased the degree of garden intelligence by about 30%, reduced the energy consumption by about 20% in the corresponding degree of energy conservation and environmental protection, and increased the degree of garden comfort by about 30%. [ABSTRACT FROM AUTHOR]

Energy-saving driving and regenerative braking energy utilization are two main ways to realize energy-saving optimization of urban rail transit train timetables. On the basis of the more mature energy-saving driving achievements of the predecessors, the absorption utilization rate of regenerative braking energy is improved by adjusting the dwell time of trains in the station so that while a train is braking, other trains in the same electric section are just under traction. In this paper, the overlap time between the traction and braking processes of different trains is used as a measure of the proportion of regenerative braking energy that is absorbed. In order to maximize this overlap time, an energy-saving optimization model of urban rail transit train timetable based on regenerative braking technology was established. To facilitate the solution, the nonlinear constraints are converted to linear at the time of model construction in this paper. In the solution, the spatio-temporal local rolling algorithm and the commercial optimization software ILOG CPLEX are used for the solution. The solution results show that the method in this paper can effectively improve the absorption and utilization of regenerative braking energy. [ABSTRACT FROM AUTHOR]

Society is in urgent need of English talents with high comprehensive English application ability and strong independent learning ability. Task-based language teaching has always been an effective way to improve English talents, but the shortcomings of traditional task-based language teaching in teaching practice are beginning to emerge, such as insufficient classroom time, lack of real-time monitoring, and scientific evaluation mechanisms. Therefore, the traditional task-based language teaching cannot meet the new requirements of society for the cultivation of English talents. Random matrix theory uses the principles of statistical mechanics to model the interaction of complex systems in multiple mathematical domains to improve the teaching effectiveness. Random matrix theory has been widely used in theoretical neuroscience and optimal control. This paper is based on teaching task-based language and analyzing the data from the teaching process with big data so as to give timely feedback and suggestions to teachers. This paper constructs a model of college English teaching based on random matrix theory using a task-based teaching model. Since the teaching model of college English is a typical high-dimensional nonlinear function problem, a network model of deep confidence network is used to determine the implicit relationship between input and output. To verify the effectiveness of the model, this paper conducts a practical study of the constructed model. The study shows that the task-based teaching model of college English based on random matrix theory improves college students' English expertise and English language usage ability and also greatly enhances students' enthusiasm for learning English. [ABSTRACT FROM AUTHOR]

In order to better construct the financial management standard distribution architecture, this paper proposes a financial management target architecture based on the ISM model. By discussing the stability, periodicity, and hierarchy of enterprise financial management objectives, this paper describes the system structure of enterprise financial management objectives by using the ISM model method of system engineering and establishes a five-level hierarchical structure model of financial management objective system. In this paper, the ISM algorithm is improved, two algorithms are proposed and their root mean square error is compared and analyzed. The experimental results show that the root mean square error of the EWISM algorithm and ETISM algorithm is significantly smaller than that of the traditional ism algorithm when the signal-to-noise ratio is 5–20db. Conclusion. By analyzing the architecture of financial management objectives based on the ISM model and improving the ISM algorithm, it can provide a better reference for enterprises to determine financial management objectives. [ABSTRACT FROM AUTHOR]

At present, artificial intelligence algorithms based on deep learning have achieved good results in image classification, biometric recognition, medical diagnosis, and other fields. However, in practice, many times researchers are unable to obtain a large number of samples due to many limitations or high sampling costs. Therefore, image sorting zero-sampling order research algorithms have become the central engine of intelligent processing and a hot spot for current research. Because of the need for the development of deep learning prediction capability, coupled with the emergence of time and technical-level drawbacks, the advantages of zero-sample and small-sample are gradually emerging, so this paper chooses to fuse the learning methods of both for image recognition research. This paper mainly introduces the current situation of zero-sample and small-sample learning and summarizes the learning of zero-sample and small-sample. And the meaning of zero-sample learning and small-sample learning and the classification of the main learning methods are introduced and compared and outlined, respectively. Finally, the methods of zero-sample and small-sample learning are fused, the design is introduced and analyzed, and the future research directions are prospected according to the current research problems. [ABSTRACT FROM AUTHOR]

Music is an important way for people to express and communicate their thoughts and feelings. It not only has the functions of cultivating sentiment, developing intelligence, and promoting personality development but also has an energetic function on the spiritual fitness of pupils. As an important part of quality education in schools, music education has become a pioneer in the implementation of psychological intervention for students. Music education can cultivate sentiment and lively and optimistic mood by emotional images conveyed through various music and has a special role that cannot be replaced by other disciplines. However, the current music education in schools only focuses on the dissemination of music knowledge and there is no research on which type of music can effectively interfere with students' mental health. In order to be able to choose music that is effective for students' mental health intervention in music education, this paper will study the intervention research of music education on students' mental health based on fuzzy computing. This paper extracts the musical features such as average pitch, average pitch intensity, melody direction, pitch stability value, rhythm intensity, and beat, uses fuzzy computing to classify music, determines which types of music can improve students' mental health, and uses experiments to verify the validity of this research. The consequences of the research show that choosing effective music to intervene in students' mental health can greatly improve students' mental health problems. The scores of students' psychological status after the intervention are 0.73 times of those before the intervention. It demonstrates the validity of the study. [ABSTRACT FROM AUTHOR]

As an important factor in the creation and growth of new enterprises, social entrepreneurship and new enterprise performance have attracted more and more attention from scholars in recent years. This research analyzes the network relationship of new companies on the basis of existing entrepreneurial network research. This paper proposes social entrepreneurial capabilities and new company performance based on the SOM neural network algorithm to solve these problems and then establishes entrepreneurial networks, organizational learning, and new business performance models. The method of this paper is to study the SOM neural network algorithm and then establish the entrepreneurial ability and enterprise performance evaluation system. The function of these methods is to put forward the meaning and research of venture capital. It also defines the meaning and research of innovation capabilities based on innovation theory, ensuring the scientific nature of the evaluation indicators, evaluation standards, and evaluation processes of innovative enterprises. In this survey, this paper conducted a field survey in Shanxi Province, China, and analyzed the internal impact of the network of social entrepreneurship and new companies on corporate performance. The survey results show that the value of the correlation β between entrepreneurial orientation and entrepreneurial environment dynamics is 0.167 (P < 0.05). This shows that improving the entrepreneurial environment and enhancing social entrepreneurial capabilities have a positive impact on corporate performance. [ABSTRACT FROM AUTHOR]

With the continuous development of society, people's attention to the body is also increasing. This article explains and analyzes the clinicopathological characteristics of a more serious disease, lymphoma, and the effect of treatment. In the previous studies of this type of disease, the collected data was very limited, and the theory for data analysis was also lacking. The direct result was that the analysis of such disease types was not complete, and it was difficult to obtain statistical significance, which made the clinical guidance weak. In order to improve this problem, this paper applies the sampling theory in statistics to the clinicopathological characteristics of lymphoma and the effect of treatment. Through the scientific analysis of such samples, it is hoped to provide some ideas for clinical treatment. In the sampling theory, this paper selects two methods, Bayesian algorithm and adaptive sampling theory, and introduces the corresponding methods for the correlation analysis of the clinicopathological characteristics and curative effect of lymphoma according to the above two methods. These related processing methods start from a large number of samples and provide sufficient computational support for the final experiments. Experiments show that the score of clinical symptoms of lymphoma is not less than 96%, which means that the patient is in a state of recovery. When the evaluation score is between 63% and 96%, the corresponding treatment has a significant effect. These results show that, in the analysis of the clinicopathological characteristics of lymphoma and related treatment effects, by using the Bayesian algorithm in the sampling theory and the adaptive sampling theory, the data results can well correspond to the clinical effects. [ABSTRACT FROM AUTHOR]

The traditional project management in the construction industry has the uniqueness of exclusive customization, the irreversibility of the project, the immobility of the project address, the long-term nature of the project, and the dynamic nature of the project. Based on these characteristics, the complexity and difficulty of project management in the construction industry are determined. Modern engineering is no longer satisfied with the traditional basic needs, but develops in a green, environmental friendly and efficient way. On the one hand, the project is required to meet good use requirements and meet the most basic functional requirements. On the other hand, the project construction process is required to meet the requirements of green environmental protection and ecological harmony. Therefore, the maintenance cost and operation cost corresponding to modern projects have become the key to the sustainable and healthy development of the engineering construction industry. Based on this, this paper will take the construction industry as an example to fully analyze the current situation and existing problems faced by the current project cost management. Through the research on the full life-cycle project cost management mode of the construction industry, the random matrix weight algorithm is creatively introduced, and the adaptive full life-cycle project cost management extended target tracking algorithm is proposed, which cuts the corresponding full life-cycle project cost management model into multiple submodels, and the corresponding management subobjectives are constructed from the submodel, and the expansion status of the corresponding subobjectives is described and analyzed in detail using the inverse distribution description, so as to accurately estimate the effect and target weight coefficient required by the management subobjectives, and finally realize the efficient, reasonable and scientific operation of the construction industry's whole life-cycle project cost management, so as to maximize economic benefits. An experimental verification is carried out for a specific construction company. The verification results show that the full life-cycle project cost management scheme based on random matrix weight algorithm proposed in this paper has obvious advantages in management efficiency, cost control, process control, and other aspects compared with the traditional management scheme. [ABSTRACT FROM AUTHOR]

Human action recognition is a hotspot in the fields of computer vision and pattern recognition. Human action recognition technology has created huge social value and considerable economic value for the society. Meeting people's needs and understanding people's expressions are the current research focus. Aiming at the problem that the movement cannot be continuously identified and due to a lack of detailed features in the action decomposition pattern recognition in the traditional Wushu routine decomposition process, it is proposed to use Kinect technology to identify the Wushu routine movement decomposition process in the Wushu routine movement decomposition process. This paper analyzes the principle of skeleton tracking and skeleton extraction performed by the Kinect human sensor and uses the Kinect sensor with the Visual Studio 2015 development platform to collect and process the skeleton data of limb movements and defines eight static limb motion samples and four dynamic limbs. The study uses a deep learning neural network algorithm to train and identify the established database of static body movements and uses the same template matching algorithm and K-NN. The recognition effects of the algorithms were compared and analyzed, and it was concluded that the static body motion recognition rates of the three algorithms were all above 90%. In this paper, recognition experiments are carried out on the MSR action 3D database. The influence of different integrated decision-making methods on the recognition results is further discussed and analyzed, and the average method integrated decision-making, which is most suitable for the algorithm model in this paper, is proposed. The results show that the recognition accuracy of the algorithm reaches 98.1%, which proves the feasibility of the preprocessing algorithm. [ABSTRACT FROM AUTHOR]

At present, China's academic circles have made some research results on the hidden dropout of junior middle school students, but they have not formed a relatively mature theory. Based on Durkheim's "social integration" theory, this paper studies hidden dropout from the perspective of class sociology and puts forward the influencing mechanism of "class integration" of junior middle school students' hidden dropout behavior. By class observation, this paper obtains the hidden dropout theoretical model of the interaction between individuals and class organization. [ABSTRACT FROM AUTHOR]

The optimization of business management strategy is a research hotspot in the field of resource processing under the market economy environment. Based on the theory of business process orientation, this paper constructs a reactive power optimization method of random matrix for business management and verifies the correctness and effectiveness of the method through an example. The random matrix construction method of enterprise management network and the feature extraction method that can reflect the operating state of the system are optimized, and the conflict problem in the process of enterprise management resource competition is solved. In the process of OpenDSS/Matlab cosimulation, the business management network state data and random matrix index data are processed by means of statistical modeling and feature extraction, and the random Petri net technology is used to construct a continuous time random matrix. The experimental results show that, combined with the verification of the business management network simulation model of the transformed IEEE-37 node, the traditional optimization method takes 96.63 s, while the business process-oriented scene matching method takes 1.32 s, which shows that the business process-oriented method constructed in this paper takes 96.63 s. The business management random matrix has obvious advantages in optimizing the operation speed, which verifies the accuracy, feasibility, and rapidity of the method and effectively improves the performance of the system. [ABSTRACT FROM AUTHOR]

Based on random finite sets and random matrices, this paper conducts research on ETT methods, focusing on solving key problems such as measurement set division, hybrid reduction, and target shape modeling, and provides theoretical and methodological support for ETT applications in complex environments. Evaluation is an activity to determine value, which is to judge the degree to which the object meets the requirements of the subject, that is, to judge against certain standards. Usually, the evaluation needs to go through a series of qualitative and quantitative combination; that is, subjective materials and objective statistical data are used together to judge and analyze to obtain the final evaluation result. The evaluation of preschool education resource allocation refers to qualitative and quantitative description of the relevant material and financial and human resources invested in the development of preschool education. This paper will study the preschool education resource allocation evaluation system from the aspect of the index system of the preschool education resource allocation evaluation system. The research results show that the average technical efficiency of preschool education resource allocation from 2019 to 2021 will increase steadily, and the combination of input elements in educational resources will be reasonable. In 2021, Spearman's rho and Kendall's tau correlation coefficients between overall technical efficiency and pure technical efficiency are as high as 0.958 and 0.841, which are higher than the correlation coefficients of 0.325 and 0.380 between overall technical efficiency and scale efficiency. [ABSTRACT FROM AUTHOR]

The combination and application of big data and machine learning in the offline consumer market makes the formulation of marketing strategies more scientific. The use of machine learning can make statistics and analysis of users' consumption behavior and classify customers according to users' consumption behavior, so as to realize the personalized promotion of marketing content. In this context, this paper carries out research on enterprise digital precision marketing strategy based on big data. Starting from the actual application, this paper analyzes the current problems faced by consumption data sharing, as well as the characteristic needs of precision marketing for consumer groups, introduces homomorphic encryption technology, completes the structural design, process design, and algorithm design of the scheme in combination with the actual scenario that enterprises need to carry out precision marketing for customers based on consumption data, describes each design link in detail, and verifies the feasibility of the algorithm in the scheme. [ABSTRACT FROM AUTHOR]

China's financial market also faces some outstanding problems, namely, obvious structural imbalance in the size of the financial market, on the one hand, the absolute dominance of the size of the indirect financing market, mainly the bank lending market and on the other hand, the imbalance in the size of the direct financing market and the indirect financing market. At the same time, there is also a structural imbalance of the financial market among regions in China. How to verify the objective existence of such structural imbalance in different regions and measure the difference in the level of financial market development among regions is the focus of this paper. This paper constructs a financial market development level indicator system from three dimensions, equity market, bond market, and lending market, and measures the financial market development level by using inter-provincial panel data from 2001 to 2020 through neural network algorithm, time series model, and support vector machine algorithm, and analyzes the regional heterogeneity of financial market development on this basis. The results show that the overall market level of the financial industry in the eastern coastal region of China is the highest, but the intra-regional differences are also the most obvious; the overall market level of the financial industry in the northeastern region of China is the lowest, and the differences with the eastern region rise significantly; the market level of the financial industry at the provincial level in the central and eastern regions shows an all-time decreasing trend, while the western and northeastern regions of China show a convergence, then divergence, and then convergence trend. Therefore, differentiated financial policies should be implemented according to the stage of regional development in order to enhance the financial development level of each economic region and gradually narrow the regional financial development gap. [ABSTRACT FROM AUTHOR]

With the deepening of economic globalization, the communication between countries is getting closer and closer. The common topic of communication between people of different cultures and environments is English. Since the country has established English as a subject, the importance of English has become one of the main factors for the all-round development of students. In recent years, higher vocational education in China has once again developed towards high quality, all-round diversification, and innovation. According to the continuous reform and innovation and independent innovation, college English has been proved to be an important part of higher vocational education and an important measure to promote the development trend of higher vocational education all over the country. With the progress of science and technology and the development of economy, the level of education in China is still improving. People have put forward stronger teaching standards and regulations for English, a course with strong adaptability and practicality. Part of Chinese translation in college English shows its comprehensive ability and high-level thinking ability. In order to integrate this trend, this paper proposes the formulation and use of college English translation teaching model. The experiment proves that the accuracy of the traditional fuzzy evaluation method in the comment module design of college English translation teaching mode is low, only 82% at most. In order to solve this problem, BP optimization algorithm is introduced. The precision and reliability of BP optimization algorithm are improved. Because of the learning effect of the college English translation teaching mode based on the fuzzy evaluation BP optimization algorithm proposed in the certification paper, the certification is carried out according to the questionnaire and the final evaluation method. The students' satisfaction and favoritism at the assessment level and the final results of their classmates have proved the proposed college English translation teaching model. The article has excellent learning effect. [ABSTRACT FROM AUTHOR]