Search

Food quality detection is an important method for ensuring food safety. Efficient quality detection methods can improve the efficiency of food circulation and reduce storage and labor costs. Traditional methods use instrumentation, testing reagents, or manual labor. These methods take a long time to detect, are time-consuming and labor-intensive, and require professionals to operate. Fruit, as a high-value food that provides essential nutrition for human beings, is susceptible to spoilage during packaging, transportation, and sales, so the freshness and safety assurance of fruit are a hot and difficult area of current research. Therefore, for the detection of fruit freshness, this paper proposes an efficient and nondestructive way to detect fruit freshness by using the machine learning algorithm convolutional neural network (CNN). This paper shows that convolutional neural networks have good performance in identifying the freshness of fruits through extensive experimental results and discusses the overfitting of machine learning based on the experimental results. [ABSTRACT FROM AUTHOR]

In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dynamic window algorithm (IDWA) is used to optimize the optimal path considering dynamic threat. Through the algorithm, the UAV path planning with dynamic threat change is realized. Finally, simulation results verify the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

The identification of disruptive technologies is intended to focus on training and incubation in advance and is an important means to accelerate the upgrading of industrial structure and the transformation of developmental mode and seize the commanding heights of future development. Based on summarizing the existing major identification methods of disruptive technologies, this paper concludes the rule that "disruptive technologies are always at the root node of a certain classification in the deeply classified technology development network". It also proposes a new algorithm to use term frequency-inverse document frequency technology and a patented Subject-Action-Object structure to extract technical features, develop networks based on similarity matrix generation technology, and identify subversive technologies based on the depth classification model. Using patent data, it is found that the technology development network generated by the algorithm proposed in this paper can effectively show the trajectory of technology development by fitting the patent citation relationship. Through this algorithm, we have successfully identified technologies that have had disruptive effects in the field and verified the effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

Research on the price prediction of natural gas is of great significance to market participants of all kinds. In order to predict natural gas prices more reliably, this paper introduces a quadratic decomposition technology based on the combination of variational modal decomposition (VMD) and ensemble empirical modal decomposition (EEMD), which decomposes the residual term (Res) after VMD by EEMD; then, a new hybrid model called VMD-EEMD-Res.-LSTM is constructed in combination with the long short-term memory (LSTM) prediction model. The contribution of this new hybrid model is that, unlike existing application research that combines existing decomposition technology with the LSTM model, it does not ignore the important information contained in the residual after the VMD. In order to verify the predictive performance of the proposed new model, this paper uses the data of the spot price of natural gas in the United States to conduct a multistep-ahead empirical comparative analysis. The results show that the new hybrid model constructed in this paper has significant predictive advantages. [ABSTRACT FROM AUTHOR]

With the popularization of the Internet and the widespread application of mobile terminal, travelers are increasingly dependent on traffic information. It is particularly important to construct and develop more accurate discrete traffic state prediction models. Considering that most of the previous studies about traffic state prediction are essentially a prediction of specific parameters, this paper proposes a generalized fractional difference (GFD)-auto regressive moving average (ARMA)-Fisher combined model that can directly predict the discrete traffic state. First, using the original historical traffic flow data as training samples for cluster analysis, we obtain different traffic state classification samples. Then, using the Fisher discriminant method, we develop a discrete traffic state recognition model based on the traffic state classification samples. The model can help us identify the discrete state of the future traffic flow when we input a set of predicted traffic parameters to it. In order to improve the accuracy of the model prediction, this paper creatively proposes to apply the GFD method to the stabilization of original traffic flow data and then develop an ARMA model to predict the traffic parameters processed by GFD (GFD-ARMA). Last, we obtain the predicted discrete traffic state using the calibrated Fisher discrimination model whose inputs are the parameters predicted by the GFD-ARMA. The proposed method is tested using field data from CHangzhou, China. The results suggest that the developed GFD-ARMA-FISHER method shows a higher accuracy for traffic state prediction and is better than other similar methods. [ABSTRACT FROM AUTHOR]

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

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

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

In the investigations of fish swimming, the relationship between fish swimming and the corresponding characteristics of flow field attracts lots of attention. In this paper, the influence of different Strouhal numbers on the flow field characteristics and mechanical characteristics of fish-like body is investigated. It is found that the strength of the anti-Karman vortex and the thrust on the fish-like body are affected by the Strouhal number. When the Strouhal number is small, the thrust coefficient is small but the propulsion efficiency is high. Similarly, when the Strouhal number is large, the thrust coefficient is large and the propelling efficiency is low. In order to better reveal the swimming status of the flexible body of the fish imitation, the effect of the Strouhal number is analyzed in detail in this paper. Besides, the influence of the three parameters in the Strouhal number formula on the flow field characteristics and thrust coefficient of the fish-like body is further investigated by the Taguchi method. The thrust coefficient is positively correlated with the swing amplitude and frequency, which is found to be negatively correlated with the inflow velocity. Swing amplitude and swing frequency are the main factors affecting the thrust coefficient; there is little effect of the inflow velocity on the thrust coefficient. [ABSTRACT FROM AUTHOR]

Chinese Semantic Role Labeling (SRL) is the core technology of semantic understanding. In the field of Chinese information processing, where statistical machine learning is still the mainstream, the traditional labeling methods rely heavily on the parsing degree of syntax and semantics of sentences. Therefore, the labeling precision is limited and cannot meet the current needs. This paper adopts the model based on a bidirectional long short-term memory network combined with the Conditional Random Field (Bi-LSTM-CRF). In the feature processing stage, pooling technology is combined with sampling and selecting multifeature vector groups to improve the performance of the sequence labeling model. Lexical, syntactic, and other multilevel linguistic features are integrated into the training to realize in-depth improvement of the original labeling model. Through several groups of experiments, the precision of model annotation in this paper has been significantly improved combined with linguistic-assisted analysis, which proves that it can optimize the annotation performance of the model by integrating relevant linguistic features into the model based on Bi-LSTM-CRF and sampling and extracting multifeature groups; the evaluation of F increases to 82.18 percent. [ABSTRACT FROM AUTHOR]

The interactive mechanism between college and university entrepreneurship education ecosystem and their students' entrepreneurial performance needs to be further discussed, as college and university students are an important force in entrepreneurship. Since there is a lack of symbiotic mechanism analysis and interactive optimization research using ecological methods, this paper constructs the evaluation index system of entrepreneurship education ecosystem and entrepreneurship performance evaluation index system and uses the entropy weight method to determine the weight of various indicators in the index system more objectively. In this paper, Lotka–Volterra model in ecology is used to deeply study the mechanism between college and university entrepreneurship education ecosystem and entrepreneurship performance. Lotka–Volterra multichoice goal programming (MCGP) model is used to optimize the collaborative relationship between college and university entrepreneurship education ecosystem and entrepreneurship performance. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the research method. The results show that Lotka–Volterra multichoice goal programming (MCGP) method is effective in evaluating the synergy between college and university entrepreneurship education ecosystem and the students' entrepreneurship performance. [ABSTRACT FROM AUTHOR]

Aiming at the operation and maintenance requirements of the fault location of high-temperature superconducting cables, a fault location method of high-temperature superconducting cables based on the improved time-frequency domain reflection method and EEMD noise reduction is proposed. Considering the cross-term interference problem in the traditional time-frequency domain reflection method, this paper introduces the affine transformation to project the time-frequency distribution of the self-term and the cross term and further highlights the characteristic differences between the two through coordinate transformation, and the particle swarm algorithm is employed to solve the optimal stagger angle of the affine transformation. The unscented particle filter is adopted to separate the cross term, and EEMD noise reduction is introduced to solve the signal noise problem. Finally, two software programs, PSCAD and MATLAB, are employed for joint simulation to build a model of high-temperature superconducting cable. The simulation example shows that the proposed method in this paper can eliminate the cross-term interference of the traditional time-frequency domain reflection method, effectively locate the fault of the high-temperature superconducting cable, and improve the positioning accuracy. [ABSTRACT FROM AUTHOR]

In this paper, considering combined excitations of the steady current, surface wave, internal solitary wave as well as, the vessel's surge and heave motions, a time-domain model is established to investigate the lateral dynamic response of marine drilling risers. The finite element and Newmark-β methods are used to solve the nonlinear partial differential equation in the time-domain. The entire numerical solution process is realized by a self-developed program based on MATLAB. The reliability of the numerical solution program is verified by comparing it with published numerical results. The lateral dynamic responses of a real-scale marine drilling riser, usually used in the deep water oil/gas industry in the South China Sea, are studied. The impact level of each factor in the riser's lateral dynamic response is discussed. [ABSTRACT FROM AUTHOR]

In order to further improve the accuracy of electric bus energy consumption estimation and reduce the complexity of using data, the paper proposes a new method for estimating electric bus energy consumption based on a deep learning approach with a data-driven model. The method can estimate the single-trip energy consumption of an electric bus by employing CNN (convolutional neural network) to time series prediction, which takes into account easily accessible trip data of electric buses, including initial SOC (state of charge), average speed, average temperature. First, we need to convert the raw data into a trip dataset by preprocessing the collected real-world trip data of an electric bus. Then, the single trip of the bus from the original station to the terminal station is considered the basic unit for energy consumption estimation, and the trip data are processed in a quasi-time series. Following that, the trip data were modified so that the subsequent convolutional operations more closely matched the interactions between adjacent trips, and a time series prediction method based on CNN was used instead of the regression analysis methods used in traditional data-driven models. Finally, single-trip operation energy consumption estimation of electric buses is achieved with time series prediction based on CNN, and this method is compared and analysed with the LSTM (long-short term memory) time series prediction method and multivariate nonlinear regression prediction methods in traditional data-driven models. The results show that the energy consumption estimation model for electric buses developed in this paper has a higher prediction accuracy, which can improve by 3.68 percent over the traditional multivariate nonlinear regression prediction method and by 1.32 percent over the LSTM time series prediction method. [ABSTRACT FROM AUTHOR]

This paper considers the hybrid flow shop scheduling problem with two types of sequence-dependent setup times, one depending on the order of jobs and the other depending on both the order of jobs and the machines where the jobs are allocated. Three constructive heuristic algorithms based on the NEH algorithm are developed with the object of minimizing the total energy consumption cost. The first algorithm, called EPRA, obtains the order of jobs using the energy consumption cost based on the processing time. The second algorithm, called ESRA, obtains the order of jobs using the energy consumption cost based on the setup time. The third algorithm, called ESPRA, takes advantage of both EPRA and ESRA by constructing fictitious jobs with ESRA and acquiring the sequence of fictitious jobs with EPRA. Due to the drawback of NEH algorithm for the FFS problem proposed in this paper, all three heuristic algorithms use EST rules to allocate the jobs to the machines. Two lower bounds are developed to evaluate the heuristic algorithms proposed in this paper. A computational experiment based on the split-plot design is developed to carry out the factor analysis, algorithm analysis, lower bounds analysis, and CPU running time analysis. The analysis reveals that all main factor interactions except the algorithm are significant; the ESPRA algorithm is generally outstanding among the three algorithms; LB1 is better than LB2; the CPU running time of ESPRA algorithm is significantly reduced compared to those of EPRA and ESST algorithms. [ABSTRACT FROM AUTHOR]

To address the controller workload with the forecast, the capacity of the air traffic management system is effectively enhanced. It should be based on a specific analysis of the controller workload. In the current controller workload studies, there is no clear means to analyze the process of controller workload development propagation. In this paper, we propose a new method for analyzing the factors influencing the controller workload. This method takes into account the influence of various situations in the actual work of controllers and objectively quantifies the complexity of work conditions. A complex network is constructed by treating various factors as nodes and the complexity relationships between these nodes as edges. The complexity network was then tested using the contagion model. The sum of the number of times of infecting other nodes and being infected in the detection result was defined as the infection capacity of the nodes, and the point with the strongest infection capacity was controlled and analyzed. The results show that the point with the strongest infection capacity is the key factor for the development of controller workload generation. In addition, the analysis of the key factors using a backpropagation neural network shows that the prediction of the controller workload can be made by the key factors. It will provide a new effective method to control controller workload and improve air traffic control capability. [ABSTRACT FROM AUTHOR]

The 21st century is in the era of information explosion, the level of information technology is also increasing day by day, and the fields of its application are also wider and wider. As a new classroom teaching method that relies on information technology, multimedia teaching has become an important knowledge transfer method in colleges and universities. As a practical major, physical education (PE) often uses multimedia teaching in teaching. However, in many schools, the advantages of multimedia PE have not been fully exerted, which also makes the quality of multimedia PE not high. For the purpose of providing solutions to the problems existing in the current multimedia PE teaching, based on the characteristics and requirements of multimedia PE classroom teaching, this paper selected teachers and students from a college of PE to conduct interviews and questionnaires. Through the theoretical analysis of quality evaluation and the introduction of research methods, the evaluation indicators and weights of multimedia physical education teaching quality were determined, and the quality evaluation model of multimedia PE teaching in this university was established, and the practical application analysis was carried out. After determining the quality evaluation indicators and weights of multimedia PE, the actual application analysis included the evaluation of teachers with different titles and genders, as well as the evaluation of teachers by students of different grades. According to the assessment results, students' evaluation of teaching quality was not obvious in terms of teachers' gender, but there were differences in professional titles. Among them, associate professors had the highest evaluation score of 82.14, and students of different grades had different evaluations of teachers. The empirical analysis proves that the evaluation index and data analysis model of this paper have research significance. [ABSTRACT FROM AUTHOR]

In order to solve the problems of inconsistent accounting and untimely accounting information, this paper proposes a research method of intelligent optimization of financial sharing path based on big data of accounting. This paper analyzes the current working situation of the financial sharing service center of large- and medium-sized enterprises and expounds the reasons for the blockchain. The author embeds the blockchain technology in the financial sharing service center, with a view to optimizing the design of the current FSSC's functions in terms of the scope of daily business work, the effectiveness of accounting information processing, and the utilization of financial analysis decisions, and finally analyzes the corporate effect of applying the financial sharing architecture based on blockchain technology. The results show that compared with the financial sharing score under the traditional mode, the financial sharing score under the blockchain technology is higher, and the former is 69.675 points, and the latter is 80.6340 points. From this, we can conclude that financial sharing under blockchain technology has significant advantages. This framework realizes the effective allocation of enterprise information resources through finance to achieve real industry finance integration. [ABSTRACT FROM AUTHOR]

This paper carried out an analysis and evaluation research of engineering job demand based on big data technology. By collecting the semistructured or unstructured recruitment information of engineering positions on the recruitment website, text mining technology is used to mine the knowledge model hidden in the market by building a relatively perfect Dictionary of professional skills of engineering positions. Based on the large sample data, a comprehensive, multidimensional and high-precision postdemand characteristic model is constructed. The model can not only interpret the existing recruitment market and elaborate the specific skill needs of different positions but also predict and analyze the model and estimate the required skills in combination with the specific postresponsibility characteristics, which can not only enable candidates to submit resumes reasonably, Moreover, it provides suggestions for colleges and universities or other relevant employment institutions to have an accurate, comprehensive and in-depth insight into market demand and carry out effective talent training for all units. Through the analysis of the experimental results, it is proved that the established engineering postdemand analysis model is consistent with the actual situation, and has certain explanatory significance for the current economic and social recruitment phenomenon, and the model has reference value. [ABSTRACT FROM AUTHOR]

Macroeconomic situation is the overall performance of the economic situation of a country and region. Making accurate forecasts of macroeconomic trends is of great significance for analyzing the success or failure of macroeconomic control policies, evaluating the quality of economic system operation, and correctly formulating future development planning strategies. The macroeconomic system is a nonlinear system, the environment is constantly changing, and additional disturbing factors directly affect the operation of the macroeconomic system, which has a great impact on the forecast results. The historical information required for macroeconomic modeling is unstable, unclear, and incomplete, which makes it very difficult to solve such problems with traditional forecasting methods. In response to the multivariate and nonlinear characteristics of macroeconomic forecasting, this paper proposes the application of artificial neural networks for forecasting. This paper introduces the recurrent neural network into the field of economic forecasting to solve the problems of the traditional BP (back propagation) neural network method. The experimental data are verified and the experimental results prove that the studied scheme based PSO-GRU improve the performance of economic forecasting. [ABSTRACT FROM AUTHOR]

The instability of single-phase grounding faults makes it difficult to track the fault development process accurately. In this paper, a transition conductance tracking method for single-phase grounding fault identification is established to perceive the health state of the distribution network. To solve the situation that the insulation parameters of the neutral ungrounded system are difficult to obtain, a phase-shift method is proposed to measure the insulation parameters accurately. Based on the measurement results of insulation parameters, a transition conductance tracking model is constructed to monitor the changes of transition conductance in real time. Since the transition conductance varies steadily, it can be used as an important parameter to evaluate the development of a single-phase grounding fault. The innovative tracking method is verified by the simulation model, and the error analysis shows that the tracking error of transition conductance is less than 9%. The simulation results demonstrate that the proposed method can track the development of grounding faults effectively and give a quick warning to the operator. The fault identification theory adopted in this paper is not limited by the neutral grounding mode and especially can be applied to situations where the neutral point parameters are not adjusted. [ABSTRACT FROM AUTHOR]

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

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

Animated Graphics Interchange Format (GIF) images have become an important part of network information interaction, and are one of the main characteristics of analyzing social media emotions. At present, most of the research on GIF affection recognition fails to make full use of spatial-temporal characteristics of GIF images, which limits the performance of model recognition to a certain extent. A GIF emotion recognition algorithm based on ResNet-ConvGRU is proposed in this paper. First, GIF data is preprocessed, converting its image sequences to static image format for saving. Then, the spatial features of images and the temporal features of static image sequences are extracted with ResNet and ConvGRU networks, respectively. At last, the animated GIFs data features are synthesized and the seven emotional intensities of GIF data are calculated. The GIFGIF dataset is used to verify the experiment. From the experimental results, the proposed animated GIFs emotion recognition model based on ResNet-ConvGRU, compared with the classical emotion recognition algorithms such as VGGNet-ConvGRU, ResNet3D, CNN-LSTM, and C3D, has a stronger feature extraction ability, and sentiment classification performance. This method provides a finer-grained analysis for the study of public opinion trends and a new idea for affection recognition of GIF data in social media. [ABSTRACT FROM AUTHOR]

Since there is a close relationship between network information security attack events and time complexity, it is necessary to count the degree of correlation between the current connection record and the connection record within a certain period of time before. Only in this way can the relationship between network connection data and network information security attack events be better reflected. In this paper, a rough Fourier fast algorithm based on rough set theory is proposed. Based on the characteristic attributes of the intrusion detection data set with the most value of character attributes as the data division basis, the computer network intrusion anomaly detection data set is intelligently divided into small data sets, so as to carry out attribute reduction. The network intrusion detection rule update experiment adopts the misuse detection method, extracts some samples from the KDD99 data set for training, obtains the computer network intrusion detection rules in the hierarchical decision table, and uses the incremental learning algorithm to update the rules, compared with the intrusion detection rules expressed by the decision table to test the feasibility and effectiveness of the rule update, and compared with the improved RSDB, RE-RFE algorithm, and KNN algorithm to evaluate the effect of the rough Fourier fast detection model applied to the problem of network intrusion anomaly detection. Using the attribute subset reduced by the rough Fourier algorithm to perform classification and modeling of computer network intrusion anomaly detection is significantly reduced, and the average time is reduced by 0.09 seconds, and this paper lays a good foundation for the application of network security intrusion detection algorithm. [ABSTRACT FROM AUTHOR]

A logical and appropriate job advising system for college students is the only way to address the current dire work scenario for recent graduates in a society that is becoming more and more competitive. The process of building new career advice systems for college students is still fraught with many major issues. Existing problems are manifested in the lack of a perfect employment guidance system for college students and a lack of effective ideological and political education, which leads to the inability of college students to establish a correct employment concept. At the same time, the recommendation method used in the current college student employment guidance system is not reasonable enough and only considers students' one-way preference for employment units. Then, the history of one user's employment is likely to lead to unreliable negative samples, which affects the recommendation performance. In addition, the current employment recommendation system cannot fully integrate the service function, management function, education function, and research function of the employment guidance system. In this paper, we address the aforementioned issues and propose an unique career counseling system for college students by combining algorithmic recommendation with ideological and political education. The system adaptively aggregates the preferences and needs of students and employers and fully integrates several functions of the student career competence and career guidance system. The experimental results show that the establishment of a college students' employment guidance system integrating artificial intelligence (AI) and civic education not only has better interpretability compared with the traditional employment guidance system but also has a higher area under curve (AUC). Furthermore, we also discuss the importance of ideological and political education and referral systems in the work of the career guidance system through ablation experiments. The results show that the AUC values of integrating ideological and political education and recommender systems were higher than those of integrating only ideological and political education or recommender systems. Therefore, establishing an employment guidance system for college students that integrates AI and civic education can effectively guide students' employment. At the same time, the college student employment guidance system proposed in this paper can offer employment guidance courses in colleges and universities, help graduates to solve their doubts, and make graduates adjust their psychological state in the process of job hunting. [ABSTRACT FROM AUTHOR]

With the development of green energy, the penetration rate of new energy in the power grid is rising rapidly. Incorporating a large amount of new energy into the power grid will affect the power supply balance of the power grid, and even in severe cases will affect the safe and stable operation of the power grid. The information park has various small and medium data centers. The adjustable load of the data center can be transferred in time and space through the optical fiber and other communication networks, which can enhance the controllability of the power grid to a certain extent and promote the consumption of new energy. This paper proposes a collaborative planning method for information parks that considers the temporal and spatial transfer characteristics of data center loads, builds a load transfer model for data centers and builds a park power grid expansion model with the goal of minimizing the total cost of construction and operation. In this paper, an example of a park is used to verify the calculation example, and it is compared with other schemes to verify the feasibility of the method in this paper. [ABSTRACT FROM AUTHOR]

In this paper, the existing scenic spot recommendation algorithms ignore the implicit trust and trust transmission of users when dealing with user relationships, and the lack of historical browsing behavior data of users in new city scenes leads to an inaccurate recommendation. In this paper, a personalized scenic spot recommendation method combining user trust relationship and tag preference is proposed. Firstly, the trust degree is introduced when the recommendation quality is poor only considering the similarity of users. By mining the implicit trust relationship of users, the problem that the existing research cannot make recommendations when the direct trust is difficult to obtain is solved, and the data sparsity and cold start problems are effectively alleviated. Secondly, in the process of user interest analysis, the relationship between scenic spots and tags is extended to the relationship among users, scenic spots and tags, and users' interest preferences are decomposed into long-term preferences for different scenic spots tags, which effectively alleviates the problem of poor recommendation quality when users' historical tour records are lacking. The personalized tourism recommendation method proposed in this paper effectively integrates many features of social networks and effectively alleviates the problems of data sparseness and feature learning in tourism recommendation based on social networks by using vectorization and deep learning technology. Its research has very important usage scenarios and commercial value in the tourism industry. This model can efficiently mine the association rules between scenic spots in multisource information data. The experimental results show that mining the correlation between the scenic spots selected by tourists can provide effective information for tourism decision-making. [ABSTRACT FROM AUTHOR]

This paper takes the job management of a private university as the research object and analyzes the function, performance, and operating environment of the job management system. We design the overall design scheme of the job management system according to the requirements and carry out the general design, detailed design, and database design of the system. By comparing and analyzing the characteristics of different MPPT algorithms, in view of the shortcomings of the fruit fly optimization algorithm, this paper proposes an improved fruit fly optimization algorithm to realize the maximum power point tracking of photovoltaic systems. The initialization position is improved, and the iterative optimization is carried out by means of variable step size. Compared with the traditional fruit fly optimization algorithm, the improved algorithm has a faster convergence speed, and the optimization process is stable and less oscillating, and the tracking of MPPT is improved. The accuracy can effectively avoid the system falling into local optimum. Based on the understanding of user demand analysis, the problems to be solved by this system are obtained, and solutions to these problems are given. Through the analysis and comparison of various technologies commonly used in the current website development and combined with the characteristics of the problem, the ASP-based Browser/Server (B/S) architecture is used to realize it. This system realizes the digitization of traditional paper work, solves the problems of slow speed, poor real-time performance, difficult inquiries, and difficult statistics in traditional homework management, effectively improves the quality of homework management in private colleges and universities, and has certain effects on improving the quality of school teaching. [ABSTRACT FROM AUTHOR]

Along with the accelerating urbanization process in China, the problem of urban infrastructure layout has become increasingly prominent. The high density of buildings and the extremely unreasonable distribution of infrastructure make the development face great resistance. This paper reveals the problems in the layout of garden infrastructure by studying and analyzing the theoretical foundations of federal learning and distributed learning and provides an in-depth analysis and elaboration of the problem. The paper uses the shape index and landscape index of green infrastructure (green space, arable land, and water bodies), the average width of roads, road network density, and weighted buildings to conduct a comparative study through the differences in ventilation speed and temperature at different layout garden scales. According to the problems existing in the garden layout in the experimental results, corresponding improvement measures are targeted, and the infrastructure layout of the garden is combined with ecology to make the layout within the garden more suitable. [ABSTRACT FROM AUTHOR]

With the sustained and rapid development of the economy, the food consumption structure has changed correspondingly and the market of cold and fresh products has developed rapidly, which puts forward the matching of cold chain logistics. Under the background of the construction of the belt and road and the pilot-free trade zone, the organic integration of the fresh agricultural products market and industry in Hainan is facing opportunities and challenges. In particular, in-depth research on the design and planning of the entire logistics system has not been carried out and the research on the distribution vehicle routing problem is still in the initial stage. This paper combs the relevant policies of cold chain logistics development in Hainan province. Given the imperfect system, the lack of policy guarantee measures, and the lack of regionality of the policy objective system, this paper puts forward some suggestions for improvement. The effective inventory control model of Hainan fresh agricultural products under cold chain logistics is explored by using the inventory management theory and the related theory of system dynamics. The simulation is carried out by using Vensim software, which provides a reference for improving the status of cold chain logistics of fresh agricultural products in Hainan. By saving funds, the profits of cold and fresh products are increased and the market competitiveness is improved. [ABSTRACT FROM AUTHOR]

Contextual representation recommendation directly uses contextual prefiltering technology when processing user contextual data, which is not the integration of context and model in the true sense. To this end, this paper proposes a context-aware recommendation model based on probability matrix factorization. We design a music genre style recognition and generation network. In this network, all the sub-networks of music genres share the explanation layer, which can greatly reduce the learning of model parameters and improve the learning efficiency. Each music genre sub-network analyzes music of different genres, realizing the effect of multitasking simultaneous processing. In this paper, a music style recognition method using a combination of independent recurrent neural network and scattering transform is proposed. The relevant characteristics of traditional audio processing methods are analyzed, and their suitable application scenarios and inapplicability in this task scenario are expounded. Starting from the principle of scattering transform, the superiority and rationality of using scattering transform in this task are explained. This paper proposes a music style recognition method combining two strategies of scattering transform and independent recurrent neural network. In the case that the incremental data set is all labeled, this paper introduces the solution of the convex hull vector, which reduces the training time of the initial sample. According to the error push strategy, an incremental learning algorithm based on convex hull vector and error push strategy is proposed, which can effectively filter historical useful information and at the same time eliminate useless information in new samples. Experiments show that this method improves the accuracy of music style recognition to a certain extent. Music style recognition based on independent recurrent neural network can achieve better performance. [ABSTRACT FROM AUTHOR]

The data of new urban investment bonds in cities from 2011 to 2021 to represent the growth of local government debt is used in this article. This paper studies the relationship between the increase of local government debt and the underinvestment of A-share listed enterprises, which are nonfinancial companies in China. The research finds that the increase in local government debt will crowd out the debt financing of enterprises and then significantly the degree of underinvestment of enterprises, which is represented non-state-owned enterprises, enterprises with high financing constraints, and enterprises with nonlocal debt invested in the industry. As for listed enterprises with relatively low fixed assets, the increase in local debt will further lead to the degree of underinvestment. The above conclusions remain unchanged after a series of robustness tests in this paper. Further research shows that the increase in local government debt will weaken the stimulus effect of loose monetary policy and thus affect the underinvestment of enterprises. This paper expands the research on the factors affecting the underinvestment of enterprises and deepens the research on the consequences of the influence of local government debt on the economic activities of microsubjects. [ABSTRACT FROM AUTHOR]

In recent years, an increasing number of population-based metaheuristic algorithms have been proposed to solve the flexible job-shop scheduling problem (FJSP) according to its practicality and complexity. Most of these algorithms are single-population-based and hence are very susceptible to becoming trapped in local optimal values. Thus, multipopulation methods are widely used to improve these algorithms, resulting in multipopulation algorithms. These multipopulation algorithms have been widely studied in the context of single-layer complex networks recently. However, coupled networks used to control two (or more) algorithms simultaneously to get a better algorithm are always ignored in literature. Therefore, in this paper, using coupled scale-free networks (with different scaling exponents) to control the genetic algorithm (GA) and the invasive weed optimization (IWO) simultaneously, a multipopulation GA/IWO with coupled scale-free networks (MPGAIWO-SF) is proposed to solve FJSP. Then, we study how some parameters (e.g., subpopulation size, subpopulation number, and scaling exponent) of MPGAIWO-SF affect its performance, which is also ignored in literature. The simulation results illustrate that (1) the performance of MPGAIWO-SF is significantly improved compared with that of GA and IWO; (2) as the subpopulation size increases, the performance of MPGAIWO-SF first becomes better and then remains almost unchanged; (3) as the subpopulation number increases, the performance of MPGAIWO-SF first becomes better and then decreases rapidly; and (4) the performance of MPGAIWO-SF becomes worse slightly as the scaling exponent decreases. Finally, solving more FJSP instances, the MPGAIWO-SF with optimized parameters is compared with other related algorithms to verify its effectiveness. [ABSTRACT FROM AUTHOR]

To cope with the challenge of successful edge offloading brought by the mobility of mobile devices in intelligent factories, this paper studies the optimization problem of the edge offloading strategy of mobile devices based on mobility. Considering the decision task flow executed by priority, the unique offloading mode of a single task, the communication range of the edge server, and the delay constraint of the offloading of a single task, appropriate computing resources are selected according to the real-time location of the mobile device to offload the computing task. Based on the edge computing architecture of an intelligent factory, this paper puts forward five different computation offloading methods. From a global perspective, the energy consumption and delay of tasks offloading in local, edge, cloud center, local-edge collaboration, and local-edge-cloud collaboration are considered. In this paper, the algorithm based on the genetic algorithm and particle swarm optimization is used to design and obtain the decision task flow offloading strategy with the lowest energy consumption and delay. Simulation results show that the proposed algorithm can reduce the computation offloading energy consumption and delay of mobile devices. [ABSTRACT FROM AUTHOR]

Occupying the same crucial position as land resources, ecological resources originate from the marine system nourish various aspects of human welfare and the world economy. It is significant to apply efficient decision-making approaches, which assist with the achievements of sustainable development and environmental protection. This paper aims to review decision-making approaches in marine management and present a comprehensive bibliometric study. To achieve this goal, bibliometric analysis is used to analyze the current status and emerging trends in this field, including the cluster network, the collaboration network, and burst detection. The results show that the number of publications in this field has increased rapidly, and it has become an increasing interdisciplinary research subject. Besides, marine conservation is the largest clusterand the USA is the most productive country in marine management research and the marine policy and NOAA are the most productive journal and institution, respectively. Furthermore, "Diversity," "Management," and "Population" are the most influential keywords, and "People" and "Public participation" are the emerging trends in decision-making and marine management. Our work clarifies where we are now in terms of marine management and decision-making and how do they serve each other to achieve sustainable development. More importantly, some deep insights could be given that motivate researchers to learn the connections and collaborations between natural science and social study, which is our effort to deal with the social-ecological obstacle as well. [ABSTRACT FROM AUTHOR]

The postmanagement of an enterprise is to rationally and scientifically arrange the talents who meet the postrequirements and are suitable for the development of the enterprise in their suitable posts through testing, selection, and employment. The job recruitment information published on the recruitment website is the data that best reflects the market demand for data analysis talents. However, as online recruitment information is mostly presented in the form of text, this paper explores the information contained in the recruitment information through text mining technology, which has reference value for data analysis job seekers. Based on text mining and multicriteria decision-making, this paper studies job matching. The technical jobs under text mining are mainly distributed in the Internet industry, and the number of recruits accounts for 66.45%. Other industries have less demand for technical data analysis, all of which are below 15%. However, the market of business data analysis is scattered and extensive, and education still occupies the largest demand provider, accounting for 30.53%. However, real estate and media are closely followed, accounting for 24.28% and 22.34%, respectively. Besides these three industries, compared with technical talents, service industries will also need business data analysis talents more. In the stage of data collection, through the data analysis of the recruitment website, the job recruitment information is crawled, as well as other job information such as job salary, job qualifications, work experience requirements, and the number of recruits. This paper carries out the data collection stage and crawls the job recruitment information through the data analysis of the recruitment website. And, other position information, such as position salary, position qualification, and work experience requirements, as well as the number of recruits. The research of this paper can help the data released by the recruitment website to analyze the recruitment information and quickly reflect the market demand data for data analysis talents. [ABSTRACT FROM AUTHOR]

In order to obtain the ideal high-value product and recognize the contribution of various waste components to the final product, each typical single component of domestic waste was pyrolyzed separately, the volatile was reformed with half coke at 600°C, and the yield and components of the reforming gas and liquid were analyzed. The investigation was followed by the results: after reforming, the yield of the gas is the highest, reaching 66.40 wt.% of plastic air-dry weight, 119.74% higher than before reforming, the highest yield, 37.22 wt.% of kitchen waste air-dry weight, only 2.21% lower than before reforming. At the same time, after plastic pyrolysis volatile reforming, H2 production is the highest, 13.14 mol/kg of air-dry-based plastic, and the largest molar fraction in the reforming gas, 37.33 mol%. Compared with the results of single-component pyrolysis-reforming and multicomponent waste copyrolysis-reforming results, the multicomponent copyrolysis-reforming process promotes hydrolysis deoxidation, electrolyte-water vapor reforming and cracking reaction, chain hydrocarbon looping and aromatic dehydrogenation reaction, hydrogenation gasification reaction and methylation reaction, copyrolysis-reforming H2, CO2 production, and less oxygen content in the liquid. [ABSTRACT FROM AUTHOR]

The optimal solution is output as the result to the Kmeans algorithm as the initial clustering center, and the proposed linear distance model is used to complete the clustering. Combined with the theory of target management, focusing on the job requirements and responsibilities of the counselors, the counselors' performance appraisal objectives were determined, the counselor performance appraisal system was established, and the first-level indicators and the second-level indicators and their weights were determined by using Del Illegal and Analytic Hierarchy Process (AHP). This paper constructs a performance appraisal system for local undergraduate college counselors based on management by objectives, and has carried out a pilot implementation in a college. The behavior anchoring method is used to determine the scoring standards of each index, which solves the problem of inconsistent scoring standards for different assessment subjects in the past. In the assessment results, the assessment results of the three dimensions are independently evaluated by category. This paper has a certain practical significance and reference value for the optimization research of the counselor's performance appraisal scheme under the background of institutional reform in a university. [ABSTRACT FROM AUTHOR]

The high consumption of energy resources but low utilization, low factor allocation, and unreasonable economic structure affect the sustainability of economic development. Based on the energy factor theory from a low-carbon perspective, this paper constructs an impact model of green total point productivity and conducts a detailed analysis of the resource mismatch in the energy industry. Under the premise of efficient resource allocation, the factor input of an enterprise is positively correlated with total point productivity. Second, the model adds environmental constraints, considers the undesired output of carbon dioxide, and considers the input of energy factors, and reduces the undesired output as much as possible while increasing the expected output. The model realizes the update estimation of each component of the stationary part of a class of nonstationary stochastic processes and can use the information at a certain time and its previous time in a cycle to forecast all subsequent times. In the simulation process, in order to make the results more objective and accurate, this paper calculates the index and its decomposition results under two conditions, and uses MaxDEA 6.0 professional software to measure the regional industry total point productivity index. The experimental results show that the effective factor of low-carbon data is 0.91, and the relative error is less than 3.11%, which meets the test threshold and effectively improves the robustness of the model. [ABSTRACT FROM AUTHOR]

It is very important for power grid development research and related technical improvement to obtain the disaster situation of fine-scale distribution network, such as the transportation condition evaluation of distribution network and the wind waterlogging disaster prediction of distribution network. Among them, the wind waterlogging disaster prediction of distribution network is the main one, and the prediction of its disaster degree often determines whether the distribution network can be prevented before and rescued after the disaster. Therefore, in view of the above problems, combined with the actual transmission situation of the distribution network, after collecting the measured disaster data of the distribution network in relevant areas, combined with the multi-source data fusion technology and neural network modeling technology, this paper analyzes the disaster degree indicators of different distribution networks and constructs the relevant fuzzy matrix through the fuzzy theory to evaluate the disaster degree, which is verified by the measured data. This distribution network disaster loss prediction model can effectively implement the disaster loss prediction of distribution network and compare its prediction results with the other two different common models. The comparison results show that the prediction accuracy of the multi-source data fusion prediction model constructed in this paper is more than 0.95 compared with the other two models, while the prediction accuracy of the other two models is not more than 0.9, which proves that the model constructed in this paper has smaller errors. It has the advantages of higher accuracy and faster convergence speed. [ABSTRACT FROM AUTHOR]

In order to meet the intelligent demand of modern financial data analysis, this paper proposes a financial accounting information data analysis system based on the Internet of things. Based on the central reinforcement learning architecture, the model uses multiple execution modules to enhance the computing and generalization ability of the single-agent reinforcement learning algorithm. In the selection of reinforcement learning algorithm, the instantaneous time difference algorithm is introduced. The algorithm can synchronize the experience of the previous iteration state in the learning process and does not depend on the final prediction value, which greatly saves the storage cost. In the establishment of the financial data analysis index system, the paper comprehensively considers the enterprise's operation, development, debt repayment, and other capabilities, ensuring the integrity and rationality of the index system. In order to evaluate the performance of the algorithm, this paper takes the real financial data as the sample and uses BP neural network to conduct a comparative experiment. The experimental results show that the recognition accuracy of the model is better than that of the BP neural network in each experimental scenario, and the recognition accuracy of Experiment 3 is improved by 4.6%. Conclusion. The performance of the distributed reinforcement learning algorithm is better than that of the common back-propagation neural network in the real data set scenario. [ABSTRACT FROM AUTHOR]

Lack of trust, lack of standards, and low efficiency are the three biggest problems in China's trade financing at present. With the development and application of new generation technologies such as big data, cloud computing, artificial intelligence, and blockchain technology, China is in the stage of financial technology 3.0 under the deep integration of finance and technology. In the field of financial technology, the most concerned is the application of blockchain technology in trade finance business. With the successive construction of various blockchain platforms and the acceleration of the internationalization process, the international trade credit risk behind it is also increasing. Among many financial services, trade finance is the most closely integrated field with blockchain technology. In this context, preventing the risks in the business process of international trade enterprises, so as to reduce the cost of financial transactions, improve the effectiveness of financial services, and better serve the real economy is not only the internal development needs of enterprises, but also the national financial strategy needs. In view of the above problems, this paper analyzes the risk factors faced by multinational trading enterprises in the transaction process through the transaction data of some multinational enterprises on mobile phones, and constructs a credit evaluation system of international trading enterprises based on blockchain, in order to enhance the trade risk resistance ability of international trading companies. [ABSTRACT FROM AUTHOR]

As an important part of financial market, stock market price volatility analysis has been the focus of academic and industry attention. Candlestick chart, as the most widely used indicator for evaluating stock market price volatility, has been intensively studied and explored. With the continuous development of computer technology, the stock market analysis method based on candlestick chart is gradually changed from manual to intelligent algorithm. However, how to effectively use stock market graphical indicators to analyze stock market price fluctuations has been pending solution, and deep learning algorithms based on structured data such as deep neural networks (DNN) and recurrent neural networks (RNNs) always have the problems of making it difficult to capture the laws and low generalization ability for stock market graphical indicators data processing. Therefore, this paper proposes a quantification method of stock market candlestick chart based on Hough variation, using the graph structure embedding method to represent candlestick chart features and multiple attention graph neural network for stock market price fluctuation prediction. The experimental results show that the proposed method can interpret the candlestick chart features more accurately and has superiority performance over state-of-the-art deep learning methods, including SVM, CNN, LSTM, and CNN-LSTM. Relative to these algorithms, the proposed method achieves an average performance improvement of 20.51% in terms of accuracy and further achieves at least 26.98% improvement in strategy returns in quantitative investment experiments. [ABSTRACT FROM AUTHOR]

In this paper, the finite difference scheme of the spatiotemporal fractional convection-diffusion equation is established, and its stability and convergence are proved. Furthermore, this discrete technique is extended to solve nonlinear spatiotemporal fractional convection-diffusion equations. By using the Krylov subspace method to solve the discrete system, the numerical solution of the spatiotemporal fractional convection-diffusion equation can be obtained quickly. In this paper, an efficient optimal control algorithm is proposed to solve the free control problem of a class of nonlinear time-delay systems. We obtained the optimal control law of the system through the Bellman optimality principle, obtained the asymptotic stability criterion of the system in the form of LMI under the optimal control input by using the Lyapunov stability theory, and discussed the effect of the delay parameter on the system stability. Using the principle of intelligent neural network approximation function, the evaluation neural network and the execution neural network are used to approximate the optimal performance index function and optimal control input, respectively, the optimal control strategy of the system is obtained, and the convergence of the weight estimation error is proved to be optimal. On the basis of optimal state adjustment, the optimal tracking control problem is further solved. Numerical example results verify the effectiveness of the proposed method in terms of stability analysis, optimal state control, and optimal tracking control for the nonlinear time-delay system proposed in this paper. We calculate the parameters of the conveyor and select a reasonable transmission and sorting mechanism to realize the speed regulation of the driving motor of each mechanism. Through the work of each part, the design scheme of the automatic transmission system is formed, and the reliability, practicability, and economy of the system are guaranteed. [ABSTRACT FROM AUTHOR]

The enterprise financial management cloud platform can conveniently obtain computing resources such as network, storage, and services according to actual needs based on the network. The maintenance of resources can be realized in a general way, and various computers and their storage resources can be effectively integrated and effectively utilized. But internal conflicts within the company, such as asset management issues, can seriously hinder the company's long-term growth. In order to solve the above problems, this paper analyzes the actual needs of the current financial information management personnel of enterprises and the business process of financial management. Combined with the characteristics and requirements of the current enterprise financial management, the integrated fuzzy mathematics cluster analysis was introduced to complete the design and implementation of the financial IMS, and to build a modern, systematic, and scientific enterprise management system. It greatly improves the financial level of the enterprise and the intelligent level of enterprise management, provides objective and strong support for enterprise decision-making through the enterprise's financial management ability, and finally realizes the long-term development of the enterprise. The prediction effect of the samples under the FCM is shown in Table 3. The prediction effect of the training samples of the normal company is 83.33%, and the prediction effect of the training samples of the ST company is 80%. The test sample prediction effect of normal company is 83.33%, the test sample prediction effect of ST company is 80%, and the comprehensive prediction effect under the FCM is 85.28%. [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]

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]

A Visual Simultaneous Localization and Mapping (VSLAM) method is proposed to construct a 3D environment map in the creation of dynamic noise information which leads to significant errors in camera pose estimation and a substantial number of noise points. For this problem, this paper proposed a method based on YOLACT, which combined optical flow and ViBe+ semantic map construction algorithm. First, our approach uses LK optical flow method to estimate the overall motion trajectory of adjacent frames. Then the trajectory data are employed to intercept the relative position of the current frame to the previous frame. Afterward, we combine ViBe+ algorithm to accurately detect and eliminate dynamic noise. Secondly, image semantic segmentation is performed based on YOLACT model. Image feature points are extracted from MAPLAB algorithm, pose estimation of the camera is performed and movement trajectory is recorded to complete a semantic map. Finally, through ablations study with common algorithms, the experimental results show that the proposed algorithm effectively avoids interference of dynamic noise information on VSLAM. Additionally, the constructed semantic map provides higher precision, fewer noise points, and pretty robustness. [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]