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In this paper, the random matrix big data analysis model is thoroughly studied and constructed, the ecological model of college students' innovation and entrepreneurship education is analyzed, and the optimization model of college students' Innovation and entrepreneurship education environmental model based on the random matrix big data analysis model is designed. This paper briefly explains the random matrix and its M-P rate theory deduces the idea of feature extraction by the difference of eigenvalue limit spectrum distribution between different nonrandom matrices and random matrices, gives the data matrix representation method of FEMPL and the specific feature composition basis, and describes the steps of FEMPL feature extraction. A performance model for predicting the running time of Hadoop jobs is constructed using a random matrix. In this paper, innovation and entrepreneurship education has been carried out gradually, and the innovation and entrepreneurship education curriculum, platform, and mechanism have been progressively established. However, there is still a gap between the proper level of innovation and entrepreneurship education development. This study takes education ecology as the research perspective, analyzes the ecosystem of typical schools of innovation and entrepreneurship education, summarizes the dimensions and parameters of the invention and entrepreneurship education ecosystem, constructs an ecological model of innovation and entrepreneurship education for college students, and analyzes the problems and causes of the current innovation and entrepreneurship education ecology for college students based on the model, to propose specific strategies to promote the ecological development of innovation and entrepreneurship education for college students. [ABSTRACT FROM AUTHOR]

Skipping training belongs to a competitive event, which can not only lose weight but also improve the physical quality of trainers. At present, there are some problems in skipping training, such as unreasonable plan and unsatisfactory actual training effect. The original rough set method cannot solve the analysis of multivariate data in skipping training, and the ability to evaluate the training effect of skipping is poor. Aiming at the problems existing in the training of skipping rope, this paper puts forward a skipping rope training method based on a chaotic logistics algorithm, aiming at improving the training effect of skipping rope. First, the chaos theory is used to classify the training data. Different data classifications correspond to different training results, which eliminates irrelevant information in the training scheme and reduces the amount of single data analysis. Finally, the data after classification are optimized in a single stage, and the optimal results of different training items are obtained and summarized. After the MATLAB test, the chaotic logistics algorithm is superior to the rough set method in accuracy and convergence speed and meets the actual training needs. Therefore, the chaotic logic analysis method proposed in this paper is suitable for the evaluation of skipping training. [ABSTRACT FROM AUTHOR]

With the continuous progress of society and economy, more and more attention has been paid to the physical quality of students. As an essential support carrier, physical education (PE) class is responsible for the exercise and improvement of the physical quality and fitness of students. In this paper, with physical training as a breakthrough point, the data on training quality are evaluated based on the Apriori algorithm and the factor mining is carried out accordingly. Through the Apriori algorithm, an in-depth analysis is carried out in this paper on the practical teaching status of physical training programs at colleges and universities in the country. From various perspectives such as the development of the existing physical training programs and their functional features, the advantages of physical training programs in teaching at colleges and universities are analyzed. At the same time, suggestions are given with regard to the current teaching model and status quo at colleges and universities to achieve the goal of driving the development of physical training in the country. Finally, the results of the practical case analysis indicate that physical training programs are feasible in the education curriculum at colleges and universities. To some extent, the spirit of physical training programs is similar to the concept of physical education at colleges and universities. There are many types of physical training programs, which can be used as rich resources for physical education at colleges and universities. From the results of the research, it can be seen that the Apriori algorithm can be used to evaluate the training quality of physical education effectively; the mastery levels of physical education skills among male students are generally higher than those among female students, and the difference is relatively significant; the algorithm proposed in this paper can be used to evaluate the data effectively, which is of great significance for supporting the decisions on improving the quality of training. [ABSTRACT FROM AUTHOR]

In some remote areas and even some vocational colleges, the information management of students is still stagnant on paper. Such traditional methods have obviously lagged behind the development and needs of society. The traditional manual management is a waste of both manpower and material resources. In today's rapidly developing information age, it is very necessary to study an efficient vocational education management system. The vocational education management system designed in this paper includes Web server, DB server, college management terminal, and load balancing switch. In this system, CONN.ASP is a common module of all ASP programs in the whole system. It is mainly used to realize the connection between ASP program and database and the calling of common function submodules. In order to reduce the power consumption of the vocational education management system, the Discrete Fourier Transform (DFT) theory is proposed in this paper. This algorithm can effectively save the size of the storage unit and the number and frequency of reading the twiddle factor, so as to achieve the purpose of improving the operation speed. This paper randomly selected the scores of 10 classes and use the system for data processing test. A total of 50% of teachers were particularly satisfied with the tables generated by the discrete Fourier transform-based vocational education management system. They believed that the vocational education management system based on discrete Fourier transform is very fast in data processing, and the processing speed was within 0.1 s. The system designed in this paper can meet various demands in vocational education management and continuously enhance the function of educational administration management. [ABSTRACT FROM AUTHOR]

This paper proposed a hybrid approach for the identification of encrypted traffic based on advanced mathematical modeling and computational intelligence. Network traffic identification is the premise and foundation of improving network management, service quality, and application security. It is also the focus of network behavior analysis, network planning and construction, network anomaly detection, and network traffic model research. With the increase in user and service requirements, many applications use encryption algorithms to encrypt traffic during data transmission. As a result, traditional traffic classification methods classify encrypted traffic on the network, which brings great difficulties and challenges to network monitoring and data mining. In our article, a nonlinear modified DBN method is proposed and applied to encrypted traffic identification. Firstly, based on Deep Belief Networks (DBN), this paper introduces the proposed Eodified Elliott (ME)-DBN model, analyzes the function image, and presents the ME-DBN learning algorithm. Secondly, this article designs an encrypted traffic recognition model based on the ME-DBN model. Feature extraction is carried out by training the ME-DBN model, and finally, classification and recognition are carried out by the classifier. The experimental results on the ISCX VPN-non-VPN database show that the MEDBN method proposed in this article can enhance the classification and recognition rate and has better robustness to encrypt traffic recognition from different software. [ABSTRACT FROM AUTHOR]

As a new type of language expression, interactive translation is widely used in English, and based on the Internet of Things technology, text harmony and glyphs are combined for information exchange. This design adopts B/S mode to realize the relational query of document content. This paper adopts the B/S architecture mode to design, implement, and test its functional modules and related data analysis to provide a friendly interactive English translation system service to the user group. In this paper, the development of the Internet of Things information-assisted translation system mainly focuses on the semantic embedding and correlation analysis based on the Internet of Things as well as the realization of interactive functions. On this basis, an interactive English translation process based on corpus and text data sets is designed and completed. This method can not only effectively solve the problems of large amount of text and high difficulty of expression but also can make the translation more concise and clear to achieve the required expression effect of the original text. At the same time, the system can automatically perform statistical analysis on the translation results to verify the accuracy and completeness of the translation results, providing reference value for subsequent function implementation. In this paper, we mainly study the realization of interactive functions and propose solutions to its existing problems and deficiencies. The experimental results show that the average BLEU of the system in this paper is 0.985, which indicates that the translation quality of the system is good and the interaction with users is improved. [ABSTRACT FROM AUTHOR]

With the development of network information technology, the application of virtual simulation technology in medicine, electromechanical, military, and other fields has changed the traditional field operation mode and achieved good results, but the application of virtual simulation technology in the field of news and communication is very little. This paper takes the construction of virtual simulation news system as an example. Based on the support of 3D modeling technology of Multigen Creator, the functional requirements of virtual simulation news interview system are analyzed, the overall system objectives and architecture are set up, virtual scene database, 3D entity modeling, model optimization, and real-time rendering are established. A virtual simulation system for emergency news interviews is designed and developed, including real-time simulation environment and online operational feedback. This paper discusses the application effect of the system. By means of the questionnaire survey, SPSS23.0 is used to further analyze the feedback data of users. The data show that the expected functions of 3D modeling and real-time simulation of system modules have been realized. The research shows that virtual simulation technology can be successfully applied in the field of news and communication, and can successfully penetrate into the practical dimension of news and communication, change the existing single information interaction mode, and create a new era of real-time simulation. [ABSTRACT FROM AUTHOR]

The working conditions (i.e., gas content, working pressure, and working temperature) of oil in the actual hydraulic system are always in a dynamic process, which will cause the basic properties of oil (i.e., density, absolute viscosity, and effective bulk modulus) to change and further generate changes of the flow characteristics in the straight conjugate crescent gear pump (SCCGP). However, no clear explanation has been given for the variation of the flow characteristics of the SCCGP with the working conditions in previous studies. This paper first expounds the gear pair profile equations used to draw the tooth profile curves and the equation applicable to the calculation of the geometric displacement at the given geometric parameters. Subsequently, the mathematical models of the oil's basic properties are analyzed, and it is explained how the changing working conditions affect the changes in oil properties. After that, the paper designs an orthogonal test scheme and applies it to the study of the flow characteristics of the SCCGP for the first time to optimize its working conditions. Then, the paper performs simulations in commercial CFD numerical software, and the change laws of the internal flow field, flow pulsation, pressure pulsation, volumetric efficiency, and total efficiency of the SCCGP at different working conditions are analyzed in detail. The working conditions for decreasing pulse rate and improving total efficiency are summarized. Finally, for model validation purposes, the paper elucidates the construction process of a high-pressure positive displacement pump test system and carried out experimental research. The simulation and actual measurement results are compared under the same working conditions. The results validate the accuracy of the simulation model and the potential for future analytical studies. [ABSTRACT FROM AUTHOR]

This paper adopts a random matrix approach to model spatial panel data and applies the model to the analysis of regional economic disparity. A panel data clustering method based on variable influence and response structural relationship clustering is proposed to define the structural relationship distance, cluster the panel data from the structural relationship so that the data within the class have the same structural relationship, and give the specific algorithm for determining the classification number in the clustering process based on the structural relationship. The methods of clustering panel data with the presence of linear, nonlinear, and functional relationships are considered separately to aggregate and classify the panel data in terms of the structural relationships of parameters. Using the classification modeling approach proposed in this paper, the empirical analysis is better than the estimation effect in the case of no classification; the empirical test data also show that there is a strong cross-sectional correlation between the economic and financial development data of different regions, and the estimation effect of the model with the correlation treatment is better than the estimation effect of the model without the correlation treatment. The study finds that there is a significant imbalance between financial and economic development, the phenomenon of financial exclusion is serious, and the dynamic influence relationship between financial and economic development in relatively less developed regions is different from that between financial development and economic development in relatively developed regions. In addition to the influencing factors, we should improve the efficiency of investment utilization, give better play to the geographical advantages, keep the industrial structure rationalized, develop the special economy according to the local conditions, and give full play to the spatial effect as well as improve the economic development. [ABSTRACT FROM AUTHOR]

The paper measures the income distribution effects and welfare effects of VAT itself based on Chip2013 household income and expenditure microdata using the Gini coefficient, Suits index, and Theil index from the perspective of annual income and lifetime income, respectively, and the effective VAT rate. Taking provinces as the basic research unit, we use the first-right method, LISA time path, and Spatiotemporal leap to evaluate China's well-being level from 2010 to 2020 and analyze its Spatiotemporal dynamic characteristics. At present, most countries adopt a tax rate model with 1∼3 bands. The current VAT rate structure is "13% + 9% + 6%," which is in line with the international development trend. In the data preprocessing module, the HTTP(S) responses of non-IoT devices are first filtered out and then the text that may contain device information is extracted from the remaining HTTP(S) responses by integrating multidimensional features, and finally, the irrelevant strings in the text are filtered out. At the end of the paper, simulation experiments are designed to verify the function and performance of the identification information extraction system for this heritage connected device, and the results verify that the model identification method in this paper can achieve better identification results compared with existing methods. The results verify that the model recognition method in this paper can achieve better recognition results compared with existing methods. [ABSTRACT FROM AUTHOR]

According to the indicators, the evaluation index system of convergent media communication efficiency is constructed, and its corresponding hierarchical structure model is constructed. According to the nine-level scale in the analytic hierarchy process, the indicators are scored by designing an expert questionnaire with weights of relevant indicators, and a pairwise comparison judgment matrix is constructed from the questionnaire data obtained through the expert consultation questionnaire. This paper uses the YAAHP analytic hierarchy process software to calculate the matrix results and test the final results. If the obtained results are within the consistency test range, it will pass; otherwise it will not pass. The fine-tuning matrix is processed, and the weight vector is calculated to obtain the final indicator weight result. After studying a class of using matrix factorization to enhance the independence of random matrix column vectors, a Gram-Schmidt orthogonalization of random matrices is proposed to minimize the intrinsic correlation of the matrix. After analyzing the reconstruction characteristics of the orthogonal matching pursuit algorithm, a new signal reconstruction algorithm is given by combining the new optimization method with the reconstruction algorithm. Referring to the relevant theories of physics and molecular dynamics, this paper draws on the relevant description of the information fusion theory, introduces the basic concepts of "energy" and "collision," and constructs the energy description model before data object fusion and the collision model when data objects are fused. By combining the practice of the radio mobile client log access and record data resources that have completed situation assessment and state transition, it effectively handles many low-efficiency regional data object processing problems generated in the process of data object fusion and deepens data object fusion. [ABSTRACT FROM AUTHOR]

As an important means of communication, sports public opinion has been paid more and more attention by the country. This article will focus on the emergency events of sports events as the main object and research and analysis of the communication function and law in the public opinion of sports events and analyzes the characteristics of public opinion communication. This paper holds that the media play the "should be" roles in different periods, such as network public opinion monitoring and early warning, public opinion information processing and interpretation, consensus builder highlighting the mainstream, and introspection of public opinion events, and there is a media role cycle in the secondary public opinion period. Spreading law of public opinion in sports events in China: trigger-conduction-outbreak mechanism. At present, the diversified interactive mode of sports events in China makes the communication environment of public opinion in sports events more and more three-dimensional and multidimensional, and it is also convenient for us to make better use of the current regulation means of public opinion in sports events. Sports network public opinion can easily go to "post-truth." In order to strengthen the monitoring of sports public opinion and improve the ability to respond to network public opinion, using interdisciplinary thinking, this paper proposes a network public opinion guidance strategy for sports events. The experiment is aimed at the disadvantage that the trend of sports public opinion analysis and tracking is not clear. The optimization method is proposed, and the accuracy is improved by 87.5%. [ABSTRACT FROM AUTHOR]

Digitizing ethnic villages through VR technology is helpful for putting limited resources into the most urgent protection projects at this stage. In this paper, Basha village in western Hunan of China is selected as the carrier, and VR technology is used to simulate its virtual village scene. In this paper, ArcGIS is used to show the distribution of villages, through SketchUp software, 3D modeling of the buildings in the village is carried out, which is imported into Lumion platform and optimized, and finally the VR scene of Basha village is established. Through professional analysis tools, accurate reduction scale, and rational analysis and decision-making, it can not only open up a new situation for the protection of national history and culture, but also better promote the construction and renewal of new countryside. [ABSTRACT FROM AUTHOR]

The arrival of the national fitness boom has enabled the marathon as a large-scale sports event to attract the attention of a sufficient number of sports enthusiasts in a relatively short period of time. These people attracted by the event will have a series of expenses and expenditures also precisely reflect the prominent role of sports events on social and economic effects. In this paper, the AISAS consumption behavior model is used as the theoretical research model, and the survey data of urban marathon participants is used as the basis for analysis. The methods of generating attention, interest surveys, information search methods, actual consumption items and amounts during the action, and postmatch satisfaction and information sharing willingness and methods have been analyzed and counted in detail, and spss21.0 software is used to analyze the statistics. The results are analyzed. In addition, this paper studies the effect of the subspace weighting algorithm based on the singular value of random matrix on the urban marathon. Using the results of the eigenvalues of the sample covariance matrix in random matrix theory, the energy of each subspace is estimated, and then the subspace weighting matrix is constructed with the estimated energy. Through the calculation of the weights of the indicators, the weights of the three-level indicators of the event quality are ranked in the order of participation experience, cooperative media level, enthusiasm of local residents to watch the competition, registration status of the competition, number of sponsors, quality of sponsors, enthusiasm of local residents to participate in the competition, media value, and number of cooperative media algorithm, in which the marathon economic effect is defined as the estimated angle where the deviation between the estimated value and the true value exceeds 20%, that is, the root mean square error of the estimated angle is greater than 10%. In the first-level indicators, the quality of the event to evaluate the impact of the event itself has been added. The quality of the event is the influence of the urban marathon itself, which originates from the quality of the marathon itself. At the same time, the quality of the urban marathon is also the foundation of its economic, social, and environmental impact on the host site, so the quality of the urban marathon is an important impact indicator. [ABSTRACT FROM AUTHOR]

The Semantic Accessibility Scale (SAS) is one of the criteria for systematically assessing the semantic readability of corpus texts. With the advent of the Internet, English language content has been widely distributed. This constitutes an adequate corpus for corpus research. However, how to assign and evaluate the semantic acceptability of English literary texts with the aid of corpus has become a hot topic of study for academics around the world. In this paper, we propose an analysis method for corpus semantic acceptance based on the Kano model. This method combines the Kano model with corpus semantic acceptance. Initially, the method identifies the initial corpus semantic acceptance demand items using the initial corpus semantic acceptance identification questionnaire. The Kano categories of each requirement item are then identified and filtered based on the second Kano questionnaire. In this paper, we propose a Kano-based method for corpus semantic acceptance requirement analysis and apply Kano theory to corpus semantic acceptance requirement analysis. First, the corpus semantic acceptance requirements are classified into corresponding Kano categories and filtered using a Kano survey; second, the initial weights of the corpus semantic acceptance requirements are determined using the coarse number method; and finally, the initial weights of the requirements are adjusted using the corresponding Kano adjustment coefficients to obtain the final weights of the corpus semantic acceptance requirements. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study and analysis of the reliability of indicators for the evaluation of sports training using the algorithm of random matrices. Random matrix theory is used to obtain the degree of randomness of the data and the overall correlation characteristics of the data through statistical analysis of the energy spectrum and eigenstates of complex systems. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct the subspace weighting matrix. The problem of reliable finite-time stability of the model under several types of uncertain time-lagged bounded transfer probability Markov jump parameter compatibility indicators is also explored. The four basic theoretical issues, namely, value orientation, construction basis, construction principles, and conceptual model of the sports training work evaluation index system, are studied. This paper takes the construction of a sports training evaluation index system as the research object and uses the literature method, expert interview method, questionnaire survey method, data analysis method, Delphi method, and hierarchical analysis method as the main research methods to construct a set of sports training evaluation index system as the research task, to enrich the idea of sports training evaluation work, and to provide a reference for the future government to be able to develop a sports training evaluation index system that is in line with the development of the times. The research method was used to collect the opinions of experts. The subjective opinions given by experts were collected using the assignment method, and the weight coefficients of each index were calculated using the AHP hierarchical analysis method. The result is that the index of basic conditions (0.3591) has the largest weight in the whole system. After that, organization and management (0.1911), work effectiveness (0.1376), staffing (0.103), activity development (0.0828), financial and fund management (0.0562), publicity and reporting (0.0382), and material provision (0.032) are in order. The evaluation results of the index system are consistent with the current actual situation of specialization teaching, indicating that the index system for evaluating the effectiveness of high school sports training has good validity. [ABSTRACT FROM AUTHOR]

Higher education has a major role to play in social progress, technology, and economic development. However, as the number of students enrolled in higher education increases, the quality of graduates is declining and their ability to work is being questioned. Therefore, it is very important to effectively evaluate the quality of teaching and learning. With this in mind, an evaluation approach using data mining based on wireless networks is proposed in this paper. In the educational work of universities, data mining helps to assist universities to improve management efficiency and teaching effectiveness in managing students by mining and analyzing large-scale educational data. In order to improve their teaching management, this paper uses the Apriori method among correlation rule mining methods. The algorithm is improved in terms of time efficiency, and then the improved algorithm is used to mine and analyze the impact of teachers' age, title, educational background, and student performance on teaching quality evaluation. The results of the exploration showed that, through the mining algorithm, it was found that the student's evaluation was proportional to the student's performance. For example, the average grade was excellent, and most evaluation grades were excellent. Its support level was 26% and its confidence level was 62.5%, indicating that more than half of the students met its characteristics. It also showed that the results were real, and the quality of teachers' teaching could be known through the results of students. [ABSTRACT FROM AUTHOR]

Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China's power trading scale, the increasingly fierce market competition, and the instability of new energy output, the credit problems of new energy power generation enterprises in the trading process cannot be ignored. Therefore, improving and perfecting the credit system of new energy power generation enterprises is necessary for building a modern market system. Firstly, the credit indexes of new energy power generation enterprises are constructed from the three dimensions of performance ability, performance behavior, and performance willingness. Then, a credit index evaluation model of new energy power generation enterprises is proposed based on the fuzzy best-worst and improved matter-element extension method. Finally, an empirical study is carried out. The analysis results show that scheduling discipline compliance, historical credit, and participation rate of the market-oriented transaction have a more significant impact on the recognition of new energy power generation enterprises. They should focus on market transactions. The model proposed in this paper can effectively deal with the ambiguity of indexes in the credit evaluation of new energy power companies. Through comparison with other models, the effectiveness of the model proposed in this paper in the credit evaluation of power companies is further verified. Construction provides method support. [ABSTRACT FROM AUTHOR]

Deep learning is an important concept introduced into modern learning science. It is different from the surface learning of mechanically and passively acquiring knowledge and storing individual information but emphasizes learners' active and critical learning. It wants them to understand the full meaning of what they have learned. By establishing a link between existing knowledge and new knowledge, it transfers existing knowledge to a new environment, makes decisions, and solves problems. Deep learning plays an important role in students' learning. Deep learning ability is the key factor affecting the quality of learning and the development of students' academic ability. The quality of in-depth teaching is difficult to guarantee, which requires a complete, comprehensive, and evaluation system to evaluate it. This paper introduces the analytic hierarchy process to weight the indexes in mathematics deep learning and puts forward some suggestions on creating an environment for deep learning. The experimental results show that teachers' teaching accounts for the highest proportion of primary indicators, reaching 67%. Multimedia resources account for the highest proportion of secondary indicators, reaching 73.01%. This paper then puts forward some suggestions for indicators with large weights. [ABSTRACT FROM AUTHOR]

Image semantic segmentation is a visual scene understanding task. The goal is to predict the category label of each pixel in the input image, so as to achieve object segmentation at the pixel level. Semantic segmentation is widely used in automatic driving, robotics, medical image analysis, video surveillance, and other fields. Therefore, improving the effect and accuracy of image semantic segmentation has important theoretical research significance and practical application value. This paper mainly introduces the pyramid scene parsing network PSPNet based on pyramid pooling and proposes a parameter optimization method based on PSPNet model using GPU distributed computing method. Finally, it is compared with other models in the field of semantic segmentation. The experimental results show that the accuracy of the improved PSPNet model in this paper has been significantly improved on Pascal VOC 2012 + 2017 data set. [ABSTRACT FROM AUTHOR]

Track failure at a railway station is a common disruption in the station area caused by abnormal weather or frequent use. This paper focuses on the real-time track reallocation problem to recover the affected track utilization plan and minimize the total train delays and passenger inconveniences. Train platforming operations in busy complex passenger stations are generally conducted according to fixed track utilization rules. In this paper, we presented a mixed-integer linear programming model for train platforming problems with constraints relevant to fixed track utilization rule and objectives of balanced usage of tracks. Furthermore, we proposed an improved genetic simulated annealing algorithm based on improved crossover and selection methods without breaking the fixed track utilization rule constraint. An experiment of Guanzhou East Station with fixed track utilization rules shows the effectiveness of the proposed model and algorithm. The model and algorithm provide efficient approaches for track reallocation problems based on fixed track utilization rules in busy complex passenger stations. [ABSTRACT FROM AUTHOR]

With the rising usage of technology, a tremendous volume of data is being produced in the current scenario. This data contains a lot of personal data and may be given to third parties throughout the data mining process. Individual privacy is extremely difficult for the data owner to protect. Privacy-Preservation in Data Mining (PPDM) offers a solution to this problem. Encryption or anonymization have been recommended to preserve privacy in existing research. But encryption has high computing costs, and anonymization may drastically decrease the utility of data. This paper proposed a privacy-preserving strategy based on dimensionality reduction and feature selection. The proposed strategy is based on dimensionality reduction and feature selection that is difficult to reverse. The objective of this paper is to propose a perturbation-based privacy-preserving technique. Here, random projection and principal component analysis are utilized to alter the data. The main reason for this is that the dimension reduction combined with feature selection would cause the records to be perturbed more efficiently. The hybrid approach picks relevant features, decreases data dimensionality, and reduces training time, resulting in improved classification performance as measured by accuracy, kappa statistics, mean absolute error and other metrics. The proposed technique outperforms all other approaches in terms of classification accuracy increasing from 63.13 percent to 68.34 percent, proving its effectiveness in detecting cardiovascular illness. Even in its reduced form, the approach proposed here ensures that the dataset's classification accuracy is improved. [ABSTRACT FROM AUTHOR]

English is the most widely used language in the world, and at the same time, English translation is becoming increasingly important. However, the traditional English translation model still has some problems, such as poor translation effect, repeated translation, translation solidification, and translation limitations caused by regional language differences in the application process of language communication. Differential equations refer to relational expressions containing unknown functions and their derivatives. Solving differential equations is to find unknown functions. Differential equations are a branch of mathematics developed along with calculus. In order to solve the problem, this paper puts forward the use of differential equation thinking research English translation language propagation dynamic model, the differential equation combines the neural network language model (NNLM) to form the language dynamic propagation using the differential equation and NNLM model algorithm of the dynamic model for the actual translation effect test (the database includes more than 3,000 indexed, abstract journals and newspapers, including nearly 3,000 full-text journals. The database covers topics, such as international business, economics, economic management, finance, accounting, labor and personnel, and banking. It is suitable for professionals in economics, business administration, financial banking, labor, and personnel management, etc.), and analyze the average code length and the first word hit rate of general language model (GLM), NNLM model, and domain model in the dynamic model in different fields. Then its statistics the overall recall rate and accuracy rate of the dynamic model. The results show that the average code length of the NNLM model is relatively shorter at 2.2 bits. The first word hit rate is higher than the other two models. The first word hit rate of the NNLM model is 85% under non-professional phrases, while 90% under professional phrases. The overall accuracy of the English translation of the dynamic model of language propagation in this paper is 86.37%, and the recall rate is 79.67. It shows that the dynamic model of language propagation combined with neural network and differential equation has a good translation effect and is feasible. [ABSTRACT FROM AUTHOR]

With the promotion of volleyball, the technical and tactical level of volleyball is also constantly improving, and the competition in volleyball competition is also extremely fierce. Especially in important games, it can be seen that most of the outstanding volleyball players have the characteristics of both offense and defence. The outside hitter is the main attack point and end point of the team's offense and defence. The outside hitter's offensive and defensive ability plays a crucial role in the final outcome of the game. It can be said that an excellent outside hitter is one of the main indicators to measure whether a team can become a world volleyball team. This paper introduces and analyses the optimal solution of kernel norm minimization and its application in the RPCA model. Through analysis, we know that when the alternating direction method solves the RPCA problem, the most expensive calculation is the optimization of the nuclear norm. Facing the singular value decomposition calculation of large-scale matrices, in order to solve the shortcomings of time-consuming and memory-consuming to solve large-scale matrix calculations, this paper proposes new stochastic singular value decomposition algorithms. The outside hitters use the rubbing, slapping, and hanging techniques to adjust their offense, but they rarely use them in the setter's active organization. Through the research on adjusting the offense, it is found that the outside hitter's advantage in adjusting the offense not only compensates for the impact of the fluctuating first pass and inadequate defence but also resolves the opponent's sharp serve and spike, which increases the morale of the team. It is the most important condition for the women's volleyball team to win the Olympic Games. Overall, the scoring rate of the outside hitter in each offensive position is analysed, which provides important data parameters for the women's volleyball team to distribute the ball. [ABSTRACT FROM AUTHOR]

With the rapid development of high-speed rail in China, addressing the issue of safety assurance during the operation of the train is very important. A very important part of a train's power supply system is the pantograph and catenary system, which consists of a pantograph and a catenary. Failure of the pantograph-catenary system can cause significant damage to the normal operation of the train. The dynamic performance of the pantograph-catheter system must be detected in real time during the operation of the train. This paper is based on the study and analysis of pantograph-catheter dynamic performance parameters and developed a system for real-time detection of pantograph-catheter dynamic performance parameters based on a car visual system. The results are as follows: based on this detection method, the visual error is low and the accuracy is high. The machine-based directional height detection module developed in this paper has a good detection effect and high test accuracy; the arcing detection module designed in this paper can effectively detect the arcing and store the arcing pictures and can display the duration of single arcing and the arcing rate of the section in real-time. The practical application effect is good. The results show that the focal length of the camera lens is 16 mm, and the error of the machine vision system is low. The system designed in this paper may make a great contribution to the operation condition monitoring and fault diagnosis of the pantograph-catenary system of a high-speed train in the future. [ABSTRACT FROM AUTHOR]

College English teaching aims at students with different foundations and characteristics. Also, it is necessary to grasp the mental state of students in time during the teaching process. Based on the bimodal information of facial expressions and speech of classroom students, this paper designs a mental state assessment method based on deep learning. For bimodal emotion recognition of facial expressions and speech, a feature fusion method based on sparse canonical correlation analysis (SCCA) is proposed in this paper. First, the emotional features of the facial expression and speech are extracted, respectively. Then, SCCA is used to fuse the emotional features of the two modalities. Finally, the sparse representation-based classification (SRC) is used as the classifier for emotional prediction. Based on the prediction results, the mental state of different students can be grasped, so as to adjust the teaching strategy in a targeted manner. Experiments are carried out based on public datasets. First, the proposed method achieves the average classification accuracy of 92.4%, which is higher than those from the present methods for comparison. Second, under the condition of noise corruption, the proposed method keeps the superior robustness over the comparison methods. The results show that the proposed bimodal emotion recognition method based on SCCA and SRC can achieve higher recognition rates than some present methods. [ABSTRACT FROM AUTHOR]

The automatic description (AD) of sports videos is a fundamental task for archiving the content of broadcasters, as well as understanding video scenes, and economic management effectiveness visualization techniques are key to the classification of sports videos. In this paper, a freestyle gymnastics video is used as an example to study the automatic video description by observing the set of movements of an athlete in a freestyle gymnastics video to generate the terminology of the movements performed by that athlete. The technique used in this paper to visualize the effectiveness of economic management is the long and short-term memory (LSTM) network model, which is used to learn the mapping relationship between word sequences and video frame sequences. Attention mechanisms (AM) are also introduced to highlight the importance of keyframes that determine freestyle gymnastics movements. The study is carried out by building a dataset of free gymnastics (FG) breakdown movements from professional events and applying a planned sampling method. Experimental results show that the method can improve the accuracy of an automatic free gymnastics video (FGV) description. The proposed method has a wide range of applications in sports analysis and instruction. [ABSTRACT FROM AUTHOR]

The evaluation of college teachers' teaching ability is very important. Currently, the indicators for evaluating the quality of college English teaching are unclear and insufficient. This paper evaluates the quality of university classroom teaching from two aspects: students' learning effect and teachers' teaching work. This paper employs the K-means algorithm to analyze the relationship between the indicators in the evaluation model and teachers' teaching ability, finds out the specific factors that affect teaching activities, and guides the implementation of teachers' teaching work. At the same time, the K-means model is used to evaluate students' learning effect, identify the relationship between the indicators in the model and teachers' teaching ability, and find out the specific factors that affect teachers to guide the implementation of teachers' teaching work. Experiments show that the method proposed in this paper can solve the problem that the evaluation indicators of traditional evaluation methods are not clear and insufficient and can be better applied to teaching evaluation. [ABSTRACT FROM AUTHOR]

The ship-radiated noise (SN) is easily affected by other hydroacoustic objects or complex ocean noise when it spreads through water. In order to reduce the impact from the environment, a denoising method for SN based on optimized variational mode decomposition with snake optimization (SO-VMD) and dual-threshold criteria of correlation coefficient (CC) is proposed in this paper. The first step is to optimize the parameter combination, that is, decomposition number K and penalty factor α, of variational mode decomposition (VMD) by snake optimization (SO) with envelope entropy (EE). Then, the input signal using the optimized results is decomposed and the intrinsic mode functions (IMFs) are obtained. After that, the IMFs are classified into three classes with the dual-threshold criteria of CC, including signal components, signal-noise components, and noise components. Finally, all the signal components and the processed signal-noise components denoised by wavelet threshold (WT) are reconstructed together. Simulations performed in this paper demonstrate that SO is the more appropriate optimization for VMD and the proposed method has the more outstanding performance in denoising different kinds of test signals. In addition, experiments on measured SNs show that the proposed method is effective and accurate in denoising. [ABSTRACT FROM AUTHOR]

Post competency assessment has become an important part of modern recruitment, and teaching is one of the indispensable occupations. Assessing the competence of teachers in teaching positions has become a hot topic. This paper takes English teachers as the research object and uses K-means clustering algorithm to evaluate the competence of teachers. This paper first analyzes the references related to this research and finds suitable materials for this research and then mainly describes in detail the main point of English teaching post competency. Next, this paper uses the formula to illustrate the K-means clustering algorithm used in this paper. Finally, it uses experiments to verify the influencing factors of the four dimensions of English job competency. And the final survey results show that the influencing factors have different significant differences on the four dimensions. And the experiment proves the effectiveness and robustness of the proposed algorithm in evaluating the competence of English teaching positions. In addition, from the perspective of the composition of professional titles, it is consistent with the age structure, accounting for 63.3% of the total. Associate senior researchers are the backbone of colleges and universities. This research provides a theoretical basis and reference for measurement tools for college English teacher recruitment and selection, vocational training, performance evaluation, salary reform, and other related educational practices. [ABSTRACT FROM AUTHOR]

In recent years, my country's economic development has entered a new stage, and economic development has entered a new height. Gross Domestic Product (GDP) is the most important and commonly used measure to evaluate social and economic development. It can comprehensively reflect the operating conditions of the economy. At the 2014 APEC summit, Xi Jinping focused on the characteristics of the new normal of China's economy. The economic growth has undergone major changes, and the economic structure has become more and more complete and has been continuously optimized. Therefore, the driving force of innovation plays an important role in the future social and economic development. The high-tech demonstration zone is the main hub of my country's economic innovation and scientific and technological progress, and the development of the high-tech demonstration zone is particularly important in the development of the innovative economy. By predicting the economic growth, the development structure can be adjusted in time, and the coordinated allocation of resources can be achieved, which is conducive to the stable and rapid development of the economy. Therefore, it is very necessary to predict my country's economic growth. At present, more and more scholars have invested more energy in the establishment of economic forecasting models. Previously, the prediction effect of traditional models was not satisfactory. At present, the field of target camera machine learning has been transferred. The application of the powerful nonlinear fitting ability of the machine learning model can improve the prediction effect. Based on this, this paper studies the use of artificial neural network with improved BP algorithm to predict social and economic development, and analyzes social and economic development trends and changing laws. The results show that the forecasting model constructed in this paper has a good forecasting effect, and the research results can provide a theoretical basis for scientifically formulating macro-control policies. [ABSTRACT FROM AUTHOR]

At present, China is in a period of rapid urbanization, and the concept of green structure is getting more and more attention. Green structures can solve asset utilization and ecological problems brought about by rapid urbanization. Therefore, green structure has become the only way for the development of China's construction industry. This paper aims to study the feasibility analysis of new green building technology measures. The related concepts of green building, the analytic hierarchy process, and the meaning of life cycle cost are proposed. The main assessment bottom lines include engineering, environmental, social, and economic bottom lines. From the application examples of green building technology in the starting area of Guangzhou International Financial City, it can be seen that, based on the green building technology proposed in this paper, it is necessary to sort out specific technologies and practices suitable for the actual application of the project in a specific project. Then, the applicability and incremental cost of each technology can be analyzed, and finally, a green building technology system suitable for specific project applications is formed. The experimental results show that compared with residential buildings, the increase of public buildings is 338 million yuan, and the increase of nonassigned parts is about 9.84 million yuan. [ABSTRACT FROM AUTHOR]

Night lighting is closely related to social and economic development. Inversion of socioeconomic parameters using nighttime light (NTL) remote sensing data is a research hot spot currently. In this paper, a calibration method based on adaptive clustering algorithm for the Suomi National Polar-orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data was proposed to remove background noise in the original imagery. The validity of the calibration method was evaluated through comparing the correlation between the corrected NTL data and the regional economic data. The result indicated that the NTL data obtained by this calibration method have higher correlation with the regional GDP data, and the values of R2 and the root mean square error (RMSE) were 0.8531 and 133.18, respectively. On this basis, the total nighttime light (TNL)-gross domestic product (GDP) regression model obtained from this paper was used to invert the GDP of Liaoning Province from 2012 to 2016. Using the TNL-GDP regression model established in high-quality regions to verify the fraudulent economic statistics of Liaoning Province, it can be proved that NTL data can be a reliable reference for reflecting regional economic development trends. [ABSTRACT FROM AUTHOR]

In order to study the dynamic characteristics of the nonlinear system of the maglev train, a vibration model of the two-degree-of-freedom nonlinear suspension system of the maglev train is established in this paper. Based on the linearized model of the suspension control module, the suspension system is controlled by the state feedback method. Compared with the results of the Runge–Kutta method, the accuracy of the settlement results is verified. This paper analyzes the influence of system parameters and feedback control parameters on the system. The results show that the linear stiffness mainly affects the left-right migration of the resonance peak and the difference between the maximum and minimum of the resonance region of the suspension frame; the nonlinear stiffness mainly affects the slope of the resonance region of the car body and the suspension frame; displacement feedback control parameters can reduce the amplitude of the system, velocity feedback control parameters can make the state change of the suspension frame more smoothly, and acceleration feedback control parameters make the car body and the suspension frame have a strong coupling effect. According to the Hurwitz criterion of Hopf bifurcation, this paper deduces the conditions that the control parameters should satisfy when the equilibrium point is stable, and the instability produces periodic vibration under PID control. Through numerical simulation, it is found that the system has complex dynamic behavior, the results show that the system works under some conditions, and there are multiple stable and unstable limit cycles simultaneously. Therefore, the system will appear alternately between multiperiod and chaotic motion, which will affect the stability of the system. [ABSTRACT FROM AUTHOR]

Manufacturing industry has always occupied this very important proportion in the national economy. However, with the economic problems in the past two years, some enterprises began to have a deficit crisis. In order to fundamentally alleviate and solve the financial problems of enterprises, this paper studies the financial model prediction of enterprises based on principal component analysis and support vector machine. The multiple financial analysis trajectories are constructed by using PCA-SVM model, and the results are compared with those of logistic model, BP neural network model, and single support vector machine. Experiments show that the prediction level of PCA-SVM model is the best, and the accuracy of the second model is not as good as this model. The error rates of the second type are 14.81%, 14.54%, and 7.67%, respectively, which are higher than those of PCA-SVM model. After comparing the models in this paper, it is found that the components of the model should be extracted first, and then the data operation should be carried out. Such calculation trajectory has a high level of accuracy. The research of this paper provides reference value for solving the financial problems of manufacturing enterprises. [ABSTRACT FROM AUTHOR]

For smart city traffic flow prediction in the period of big data and industry 4.0, the prediction accuracy is low, the prediction is difficult, and the prediction effect is different in different geographical locations. This paper proposes a smart city traffic communication forecast based on Industry 4.0 and big data analysis application. Firstly, this paper theoretically explains the application scenario of urban traffic fault text big data and analyzes the characteristics of related problems, especially the fault problems. Secondly, the AC traffic prediction algorithm is studied, and the application analysis of PVHH, IDT, and Ford–Fulkerson algorithms is applied, respectively. Finally, the above three algorithms are used to predict and analyze traffic flow. [ABSTRACT FROM AUTHOR]

The operating environment of mechanical equipment is complex, and it is in high-intensity working conditions for a long time. The condition monitoring and fault diagnosis of equipment are very important. As a kind of precision part commonly used in mechanical equipment, the healthy operation of the rolling bearing is a necessary condition to ensure the reliable operation of the whole equipment. This research takes rolling bearing as the research object and is devoted to mechanical fault diagnosis analysis and system design. This paper studies the structure and working principle of rolling bearing, analyzes the types and locations of rolling bearing faults, and puts forward the overall framework of the fault diagnosis system and the workflow of the fault diagnosis system. This paper also studies the relevant theory of deep learning, proposes a neural network model framework for rolling bearing fault diagnosis, and preprocesses the rolling bearing vibration data. It uses a dropout algorithm and GRU to reduce the model parameter size and reduce the risk of overfitting. [ABSTRACT FROM AUTHOR]

Recently, the same authors provided a switching algorithm for the preferred clock skew estimator appropriate for the precision time protocol (PTP) scenario. The algorithm chooses the one-way delay (OWD) clock skew estimator for the Forward path or the Reverse path or the two-way delay (TWD) clock skew estimator that has the best performance in the mean square error (MSE) perspective. However, the switching algorithm applies only to the Gaussian scenario. In a real system, the packet delay variation (PDV) can be characterized as an fractional Gaussian noise (fGn) process where the Hurst exponent parameter can also have values higher than 0.5. Thus, the Gaussian case-switching algorithm may not apply in a real system where after a small set of PTP measurements, the switching algorithm should be able to switch effectively to the preferred clock skew estimator with the best performance in the MSE perspective. In this paper, the PDV is characterized as an fGn process where the Hurst exponent (H) is in the range of 0.5 ≤ H < 1. We estimate the unknown PDV variances and the Hurst exponent parameters of the Forward and Reverse paths. These estimated parameters are used for switching to the preferred clock skew estimator from the MSE perspective, even in the presence of asymmetry in the PDV or in the Hurst exponent parameters. We also detect and alarm for the unexpected load, cyber-attack, or clock skew deviation that can occur in a real system. [ABSTRACT FROM AUTHOR]

In this paper the role of solid nonlinearity on the fluid–structure interaction (FSI) is studied. First a simple mass-spring-damper system with a third-degree spring is coupled with a linear sloshing system to study the effect of solid nonlinearity. It shows that even both systems show similar behavior in free vibration tests, but nonlinearity caused 50% less on the amplitude of vibration. Then a higher order solid system coupled with a sloshing tuned liquid damper which is modeled by boundary element method (BEM) is examined. Finally, the coupling of a nonlinear solid with the sloshing system calculated at high-resolution method of smoothed particle is analyzed. The results reveal that considering surface nonlinearity in BEM has not improved the prediction significantly and the full NavierStokes calculations is in another phase and amplitude to a step response of the system even after two periods of the system in comparison with linear estimation. As shown the nonlinearity in solid part can cause a big difference in response of coupled system that pointed the necessity of the use of accurate fluid models such as BEM or smoothed particle hydrodynamics method as well as solid system identification before the coupling. [ABSTRACT FROM AUTHOR]

Smart grid, responsible for upgrading traditional power networks by integrating with cutting-edge information and communication networks, forms coupled networks but also pose potential hazards in the face of fault cascade. In coupled networks, fault prediction is of significance because tight interaction between power nodes and communication nodes makes the smart grid more vulnerable. Unfortunately, most existing works of fault prediction are specific to a single network and do not consider the correlation of coupled elements. To address these limitations, in this paper, we highlight the interdependence of networks and define fault correlation. Further, we propose a probabilistic prediction model using collaborative filtering in machine learning. We finally present an online prediction algorithm. We conduct experiments to illustrate the effectiveness of our prediction algorithm with different parameters and give some observations that may give more insight into interdependent networks. [ABSTRACT FROM AUTHOR]

The accuracy of autonomous navigation and obstacle avoidance of unmanned aerial vehicles (UAVs) in complex environments has become one challenging task. In this paper, an autonomous navigation and obstacle avoidance of the UAV (ANOAU) algorithm based on deep reinforcement learning (DRL) has been proposed to achieve accurate path planning in complex environments. In our work, we use an actor–critic-based DRL framework to achieve autonomous UAV control from sensor input to the output of the UAV's action and design a set of reward functions that can be adapted to autonomous navigation and obstacle avoidance for the UAV in the complex environment. Meanwhile, to alleviate the decision-making bias caused by the incomplete observables of the UAV, we use a gate recurrent unit network to enhance the ability to perceive the uncertain environment, enhance the perception representation and improve the accuracy of UAV real-time decision-making. Experimental simulation results verify that the ANOAU algorithm achieves good UAV flight attitude adaptive adjustment in navigation and obstacle avoidance tasks and significantly improves the generalization ability and training efficiency of the UAV navigation controller in a complex environment. [ABSTRACT FROM AUTHOR]

In this article, an adaptive sliding mode fault tolerant control (FTC) is improved in the case of uncertain nonlinear system which is affected by both multiplicative and additive faults in actuator. Especially, when the nonlinear system is modeled by Takagi–Sugeno (T-S) fuzzy system with local nonlinear model. The main contribution of this paper is developing a model of multiplicative faults, which offers a more realistic dynamic evolution of the actuator degradation. The degradation process is modeled by Wiener process and estimated by the maximum likelihood estimation (MLE). Sliding mode observer (SMO) is conceived to realize the additive actuator faults using convex multiobjective optimization. On these bases, the estimated multiplicative and additive actuator faults are used to design the adaptive sliding mode controller (SMC). Finally, the proposed fault-tolerant control scheme is demonstrated by the results of inverted pendulum system simulation. [ABSTRACT FROM AUTHOR]

Analysis of multisource errors accompanying process execution and study of the propagation effects of the corresponding multiprocess manufacturing process are the basic prerequisites for optimizing the generation process and improving the quality of the product. This paper proposes an analytical framework for error propagation effects in multiprocess manufacturing, which completely covers error source analysis, mathematical calculation of error propagation accumulation, and description of error propagation effects, which can provide better support for error traceability, error propagation monitoring, and error dissipation in manufacturing production. First of all, establish the process state model into the features of the manufacturing level, analyze the type of multisource error, and its mode of action when executing the process operation; second, establish the mathematical calculation model of error propagation accumulation based on state space equation is established, so as to realize calculation and analysis of error propagation; and finally, establish the link graph and link matrix of error propagation influence in multiprocess manufacturing process are established to further reveal the scope and path of error propagation influence. In this section of the case study, a plate cover part had been taken as an example, the multisource error in its manufacturing was analyzed, and the propagation and accumulation of these error values are obtained by state space and state transition theory. On this basis, the link graph and its corresponding link matrix were used to explicitly describe the error propagation effects during the multiprocess manufacturing of the example part. The example analysis illustrated the proposed error analysis framework could analyze and calculate the multisource error of multiprocess manufacturing and visually describe the error propagation effects, which showed the effectiveness and feasibility of the proposed method framework. [ABSTRACT FROM AUTHOR]

In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a scheduling technique based on modified particle swarm optimization to combat the issues of excessively long scheduling time and high computation costs associated with scheduling jobs in a cloud environment. The modified PSO is used to allocate the jobs to virtual machines in order to minimize the objective function consisting of cost and makespan. The algorithm relies on biological changes that occur in organisms to regulate premature convergence and improve local search capability. The technique is analyzed and simulated using CloudSim, and the simulation results demonstrate that the proposed approach decreases makespan and cost effectively as compared to standard PSO. [ABSTRACT FROM AUTHOR]

Traction energy consumption (TEC) is a critical part of the total energy consumption in urban rail transit (URT) systems. Energy consumption patterns and abnormal analysis of TEC guarantee the energy-saving URT operation. With the rapid development of urbanization, the current energy consumption is becoming more and more prominent with some inherent drawbacks, such as complex original data, complicated statistical analysis, and abnormal energy consumption. This paper proposes a method for time accrual abnormal analysis of TEC. The system architecture of TEC typical values is presented, composed of three elements: research object, evaluation index, and time scale. The time series prediction algorithm calculates the typical values of the cumulative energy consumption index in each energy consumption mode. For the abnormality in TEC mode, the distance of the string vector is used as the similarity measure. Then, the similarity-based anomaly analysis method is used to judge the pattern abnormality. By comparing the advantages and disadvantages of engineering practice and theoretical research methods, we analyze the applicability of traditional anomaly detection algorithms to perform anomaly analysis of TEC in URT systems. The adopted time accrual abnormal analysis achieves a high fault detection rate, outperforming other models. [ABSTRACT FROM AUTHOR]

In order to study the creep characteristics of frozen soil under different confining pressures, triaxial creep tests of frozen soil under different temperatures are carried out and the creep deformation law of frozen soil under different confining pressures and different freezing temperatures is analyzed. In this paper, the unsteady Abel dashpot is constructed by the unsteady viscosity coefficient. On this basis, the fractional order of the viscosity dashpot is unsteady. Then, a time-dependent creep constitutive model for frozen soil based on the unsteady fractional order is established. The model can better describe the law of accelerated creep deformation of rock. Through the Levenberg–Marquardt algorithm, the parameters of the triaxial creep model are identified to verify the correctness of the creep model. The results show that the model can describe the creep deformation of frozen soil well and overcome the shortcoming that the traditional model cannot describe the law of accelerated deformation. At the same time, the agreement between the model curve and the test curve is also much greater than that between the traditional Nishihara model curve and the test curve. This lays the foundation for the later use of the creep model to simulate the settlement and horizontal displacement of subgrade soil under long-term loads and also provides a theoretical basis for the construction of other roadbed projects. [ABSTRACT FROM AUTHOR]

As one of the most important radio communication scenarios in vertical industry, the high-speed railway (HSR) station is facing the challenge of coverage optimization due to its complex structure. Regarding the wireless network planning and optimization of HSR stations as a part of the customized network, this paper makes an analysis on the 5G-R channel in the HSR station scenario. Channel characteristics, including path loss, power ratio (PR), and angular spread (AS), are extracted on the basis of ray tracing (RT). Multipath components can also be distinguished based on RT. In order to achieve a better performance, the reconfigurable intelligent surface (RIS) technology is adopted to the network deployment. Moreover, machine learning (ML) is used to locate the best direction of the beam. The analysis results show that the received power in RIS-assisted channels is significantly promoted. Our research can provide a deep understanding to the 5G-R channel in station scenario and a well reference for the design and optimization of the customized network. [ABSTRACT FROM AUTHOR]

With the exponential enhancement of manufacturing effectivity and the structural transformation of the software of manufacturing elements induced by using the new spherical of scientific and technological revolution, the sample of comparative gain of manufacturing elements in the worldwide manufacturing enterprise has passed through profound changes, progressively forming a transformation from the industrial chain to the price chain, therefore promoting the transformation of manufacturing mode and aid utilization mode. Based on the applicable theories of wise manufacturing, this paper proposes an clever manufacturing functionality assessment and prediction mannequin primarily based on utilized mathematical modelling, which integrates the contrast facts of agencies into a database, establishes the corresponding time sequence relationship of the comparison data, and establishes a relationship with the corresponding organization attributes, fashions the assessment data, and combines them into a total prediction mannequin through built-in learning. By functionality of migration learning, the prediction model applicable to large statistics is migrated to small information to recognize the distinction of the equal type alternatively special entities. Input the comparison facts of the organization to be evaluated into the prediction mannequin to reap the shrewd manufacturing functionality maturity fee of the enterprise. It realizes the automated comparison of shrewd manufacturing capability, saves human cost, and improves the accuracy of evaluation. It solves the troubles that manufacturing companies have no longer deep hold close and understanding of smart manufacturing, inaccurate identification of their very personal smart manufacturing enhancement stage, and unscientific self-evaluation and diagnosis. [ABSTRACT FROM AUTHOR]

Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near-optimal solutions to test cases. [ABSTRACT FROM AUTHOR]

The new infrastructure is becoming the key support and important material guarantee for a new round of scientific and industrial changes in China. In recent years, the further upgrading of the scale and structure of resident consumption has faced resistance from both the supply and demand sides. By enhancing the consuming environment, enhancing transaction efficiency, and lowering spatial barriers, among other benefits, new infrastructure could encourage improvements in resident consumption level and the optimization of consumption structure. This research empirically investigates the consumption implications of new infrastructure under two standards using panel data from 30 Chinese provinces from 2003 to 2020. The findings demonstrate that new infrastructure significantly affects the expansion and upgrading of consumption. Its influence is quite heterogeneous. Additionally, it is discovered during the mechanism of influence test that improving the industrial structure might have a considerable impact on the consumption effect of new infrastructure. In the context of China's economic transition to high quality, this paper aims to quicken the upgrading of industrial structure and optimize the layout of new infrastructure investment in order to increase consumer returns from infrastructure and support consumption upgrading. [ABSTRACT FROM AUTHOR]