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This paper combines the digital twin system modeling method to conduct an in-depth study and analysis of graph-theoretic combinatorial optimization. This paper provides new ideas and approaches for optimal numerical analysis work by studying the digital twin modeling method that integrates digital modeling and graph theory combination, provides theoretical support for safe, stable, and economic operation of the system, proposes a solution for digital twin model based on big data platform, focuses on the nearest neighbor propagation (AP) and graph theory combination, solves the digital twin real-time monitoring data asynchronous, incomplete problem, and applies the algorithm to the digital twin model based on the big data platform for data preprocessing to achieve better results. This paper also presents a web-based digital twin system based on intelligent practical needs, analysis, and comparison of existing models, combined with digital twin technology, detailing the differences and connections between the various levels of numerical analysis and the implementation of this data in various fields, such as user management, equipment health management, product quality management, and workshop 3D navigation and detailed modeling of the digital twin system based on this numerical analysis to realize remote online monitoring, analysis, and management. In this paper, for the numerical analysis process, firstly, the key technologies of modeling and simulation operation control of production line based on digital twin are studied, and the rapid response manufacturing system based on a digital twin is designed and validated. Secondly, a scheduling technology framework for capacity simulation evaluation and optimization is established, and batching optimization, outsourcing decision, and rolling scheduling techniques are thus proposed to form a batching optimization algorithm based on priority rules, which realizes batching processing, outsourcing decision, and rolling scheduling of production orders to optimize equipment utilization and capacity. Finally, digital twin-based modeling is designed, and the validation results demonstrate the system's superior performance in achieving information interaction between physical and virtual production lines, optimization of numerical analysis, and display of results. [ABSTRACT FROM AUTHOR]

In recent years, because the complex system discrete model has brought good results in the application of many research directions such as human management analysis, control system prediction, and animal group prediction, how to combine the complex system discrete model with the prediction of enterprise brand communication effect has become a current research hotspot. Based on this, this paper studies the application of the discrete model based on the complex system in sports club to enterprise brand communication and designs a model of the impact of professional sports club on enterprise brand communication based on the SIR model and PSO algorithm. Firstly, this paper analyzes the research status of the impact on enterprise brand communication at home and abroad and optimizes and improves the impact of professional sports clubs on enterprise brand communication and the shortcomings of big data analysis technology in the current research. The complex system discrete model can accurately analyze the enterprise brand communication and conduct big data mining analysis with known professional sports clubs to achieve accurate calculation. The results show that in the process of analyzing the impact of professional sports clubs and enterprise brand communication, the communication mode design of professional sports clubs based on SIR-PSO algorithm can not only effectively deal with various complex discrete data evaluated by club users but also analyze and evaluate the impact of professional sports clubs on enterprise brand communication. [ABSTRACT FROM AUTHOR]

Logo design is related to the image of a company, unit, product, etc. A good logo can leave a deep impression on people and intuitively understand the content of the product. With the increasing emphasis on intellectual property protection, the design of logos should also pay more attention to the protection of property rights. It can be said that the logo of some big brands is enough to represent the enterprise itself; for example, seeing the red cross as a hospital. This paper aims to propose a logo design process and method under the background of intellectual property big data, so as to make the logo design more procedural and efficient, and to better focus on the protection of property rights. By introducing the relevant knowledge of logo design and trademark registration, it is indicated that trademark search can be used as an auxiliary tool for logo design. Combined with digital media and big data technology, a logo image recognition and classification method is designed so that the trademark registration and screening stage can avoid the infringement design to the greatest extent, so as to put forward the design process and method to improve the innovation ability of logo design. According to the research, the classification accuracy rate of the algorithm proposed in this paper is 91.5%, and the recognition rate of various defects is also above 95%. In addition, in design practice, the stability of design proposals should be monitored through multiple search methods using trademark search tools. If a high similarity is found in the search results, the design direction should be adjusted in time until the final design scheme is determined. [ABSTRACT FROM AUTHOR]

The stock market is a real and continuously evolving extremely complex dynamic system. This paper analyzes the change of stock market efficiency from the perspective of dynamic evolution, and the recursive graph method is used to obtain the dynamic characteristics of stock price time series. For the sharp rise and fall of stock prices, this paper uses the heuristic segmentation algorithm of nonlinear time series mutation detection to study the detection of market dynamics characteristics before the stock market crash. Based on the above research results, this paper studies the dynamic evolution of financial markets and the construction of a complex network of dynamic characteristics between financial markets. The simulation results show that there are typical characteristics of small world network in stock market complex network, the stock market complex network shows stronger synchronization ability, and the transmission range of information between the stock markets in the complex network is significantly expanded. [ABSTRACT FROM AUTHOR]

With the progress of society, the quality requirements of international business enterprises for international business talents have been improved accordingly. So, it is urgent to conduct in-depth research on the teaching model and the improvement of students' practical ability. Taking international business as an example, this paper analyzes the contradiction between the supply and demand of international business technical talents by literature research. Furthermore, the convolution neural network model is used to improve the consistency between the talent cultivation of international business major and the talent demand of enterprises by interviewing teachers and questionnaire survey of students. By studying how to implement the professional training curriculum system construction and enterprise to talented person ability training requirements cohesion, this paper in view of the secondary vocational school of international business in today's society professional training curriculum system was modified and perfected. The ultimate purpose of this paper is to meet the demand of international business for characteristic talents and constantly promote the high-quality development of international business education. [ABSTRACT FROM AUTHOR]

The data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of food quality source tracing systems. Currently, there are many studies on the application of food quality source tracing systems; however, most of them are in the field of food quality databases, and few have concerned about its application in the field of big data. Therefore, to fill in this research gap, this paper aimed to study a dynamic source tracing method for food supply chain quality and safety based on big data. At first, this paper summarized the variables of food supply chain quality and safety, constructed a Petri net model and a Bayesian network model for food quality prediction and source tracing, and realized the prediction of food quality features. Then, this paper applied two data analysis and processing methods—the density-based clustering algorithm and the cosine similarity algorithm—to preliminarily process the collected quality tracing information of each link in the food supply chain and analyzed the influencing factors of food quality. Finally, experimental results proved the effectiveness of the constructed model. Relying on the real-timeliness and authenticity of big data, this paper guarantees the credibility of the traceable information in the tracking process and improves the accuracy through real-time stream processing of the updated data, providing unlimited possibilities for the comprehensive tracking of food sources. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet in modern society has promoted the development of many different network platforms. In the context of big data, many types of multimodal data such as pictures, videos, and texts are generated in the platform. Through the analysis of multimodal data, we can provide better services for users. The traditional big data analysis platform cannot achieve a completely stable state for the analysis of multimodal data. The construction of multimodal intelligent platform can achieve efficient analysis of relevant data, so as to create greater economic benefits for the society. This paper mainly studies the historical development trend of big data multimodal intelligence technology and the data processing method of multimodal intelligence technology applied to network expression classification, including data acquisition, storage, and analysis. Finally, it studied the fusion algorithm between multimodal data and visual design, as well as the classification of network expression and the application result analysis of visual design in big data multimodal intelligence technology. [ABSTRACT FROM AUTHOR]

With the deepening of diversified and professional business philosophy in large enterprises, enterprises play more and more abundant social functions in society. Sense of mission and honor play an important role in the healthy development of enterprises. With the in-depth integration of enterprise management and computer technology, some human resource management problems related to corporate social responsibility often appear in this process. From the traditional human resource management paradigm to people-oriented social responsibility human resource management, "strengthening effectiveness" has become the main research direction of enterprise management in the electronic age. On this basis, through the discrete modeling method of a large amount of data, this paper creatively puts forward the coupling correlation between corporate social responsibility and enterprise human resource management based on grey correlation algorithm. Compared with the management mode based on trapezoidal data analysis and cluster center adopted in the current mainstream enterprise human resource management research, the innovation of this algorithm is to analyze the dynamic data of corporate social responsibility and enterprise human resources and establish a coupling model related to corporate social responsibility. It can not only realize the dynamic tracking of human resource data, but also make full use of the relevant characteristic information of the coupling relationship between enterprise human resource management and corporate social responsibility. According to the dynamic big data such as employee welfare, employee compensation, employee training, employee overtime, and timeliness of transaction processing, this paper analyzes the social problems such as corporate social responsibility evaluation index, employee turnover rate, and enterprise income, so as to provide theoretical support for enterprise business strategy. [ABSTRACT FROM AUTHOR]

Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. When the growth of new customers cannot meet the needs of enterprise development, the enterprise will fall into a survival dilemma. Focusing on the customer churn prediction model, this paper takes the telecom industry in China as the research object, establishes a customer churn prediction model by using a logistic regression algorithm based on the big data of high-value customer operation in the telecom industry, effectively identifies the potential churned customers, and then puts forward targeted win-back strategies according to the empirical research results. This paper analyzes the trends and causes of customer churn through data mining algorithms and gives the answers to such questions as how the customer churn occurs, the influencing factors of customer churn, and how enterprises win back churned customers. The results of this paper can better serve the practice of customer relationship management in the telecom industry and provide a reference for the telecom industry to identify high-risk churned customers in advance, enhance customer loyalty and viscosity, maintain "high-value" customers, and continue to provide customers with "value" and reduce the cost of maintaining customers. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology, the college music big data management system also needs a computer-aided data model for optimization. In order to improve the efficiency of computer-aided teaching of the music big data management system in colleges and universities, this paper analyzes and processes the music database of music software based on the computer-aided Bayesian algorithm and establishes a Bayesian model. Firstly, this paper introduces the principle and function theorem of the Bayesian algorithm. The data is divided into original music and new music, and a college music data management system with search recommendations as the core is established. We iteratively calculate the optimal hidden semantic matrix and we finally select ReLU function as the activation function of this experiment. After the model of the music management system is established, it is applied to a university. The experimental results show that the experiment has reached the expected standard. When the hidden factor dimension is 11, the system model has the best representation of music features. Students' use of the music search and the recommendation system has greatly improved their music literacy and music coverage. [ABSTRACT FROM AUTHOR]

Traditional motion data mining models have some problems, such as poor dynamic data capture effect, low information classification effect rate, poor quantitative representation effect, and so on. Based on this, this paper studies the mining method of dynamic motion data based on neural network, constructs a data mining model based on discrete dynamic modeling technology, and realizes the collection of data information from the aspects of motion characteristics and types combined with multilayer sensors. Neural network algorithm is used for comprehensive analysis to realize multivariate analysis and objective evaluation of all data of dynamic motion process and accurate analysis and evaluation according to different data characteristics of different types of motion data. The results show that the data mining model based on discrete dynamic modeling technology and wearable sensor technology has the advantages of high feasibility, high intelligence, and wide application range. [ABSTRACT FROM AUTHOR]

The application and economic efficiency evaluation mode of traditional composition in the field of modern music cannot meet the needs of various types of music in China, especially Guqin music. Based on this, this paper studies the big data simulation model of Guqin sound synthesis and plucking based on computer composition technology. The computer composition technology uses the discrete dynamic modeling technology of complex system to complete the computer simulation of Guqin sound through the analysis of the correlation between Guqin music data and realizes the storage and analysis of the generated Guqin sound data. In addition, the technology can analyze the data information of composition mode in the piano sound plucking simulation model with the classical Guqin music data stored in the cloud system over the years and then feed it back to relevant professionals for verification. The experimental results show that the Guqin sound simulation model can efficiently compare and analyze the melody and other data of classical Guqin sound with the simulated Guqin sound and can realize secondary data mining. This paper studies the application of computer composition method based on discrete dynamic modeling technology in Guqin sound simulation, which has certain reference significance for improving the cloud data in China's modern music field and the intelligent construction of Guqin sound data cloud storage system. [ABSTRACT FROM AUTHOR]

Currently, long-distance freight transport is shifting towards multimodal transport, the combination of multiple freight transport modes. Multimodal transport enables enterprises with the same logistics function to operate on the same level of the supply chain. Through horizontal cooperation, these enterprises can give play to their advantages, make up their deficiencies, improve service levels, reduce cost input, and thereby enhance market status. Therefore, multimodal transport is an intensive development model that promotes the alliance between giants. The reasonable path design and planning (PDP) and investment and construction mode (ICM) of the multimodal transport network help freight demanders, as well as multimodal freight transport platforms, obtain the maximum profit. Therefore, this paper explores the PDP and ICM of the multimodal transport network based on big data analysis. Firstly, the influencing factors and behavioral features of multimodal transport were deeply examined, drawing on the logit model and the big data on multiple freight services, namely, railway transport, highway transport, waterway transport, and airway transport. After classifying the freights, the authors analyzed the modeling and decision-making of path design and optimization (PDO) for multimodal transport network. The proposed model was proved effective through experiments. This paper theoretically explores the goals, principles, and needs of path selection in the modern transportation industry. In a realistic sense, the research findings help decision-makers optimize their decisions on the multimodal transport network and operate the network at the minimum transport cost. [ABSTRACT FROM AUTHOR]

With the advent of the big data era, information technology and intelligent algorithms are more and more widely used. In the engineering project management industry, big data technology and dynamic modeling have achieved good results. Enterprises attach great importance to the innovation of engineering project management. The establishment of engineering management and quality optimization innovation model can improve the management efficiency of engineering project process and enable enterprises to obtain great advantages in market competition. The use of big data technology can make project management and quality innovation more information-based and intelligent. Based on the above situation, this paper puts forward the research on discrete dynamic modeling of engineering management and optimization innovation mode based on big data intelligent algorithm. It mainly uses big data technology to collect and classify the obtained data information and uses discrete dynamic modeling technology to optimize the management system, focus on the management and innovation of green engineering, use big data dynamic modeling technology to build engineering management model, and improve the balance of project model through intelligent planning algorithm. The experimental results show that the big data intelligent algorithm combined with discrete dynamic modeling technology increases the applicability and feasibility of the system. The accurate data after big data analysis can be used for quality supervision and detection, which not only improves the project quality problems but also improves the management efficiency of the project. [ABSTRACT FROM AUTHOR]

With the research of machine learning technology and big data intelligent processing technology in engineering application becoming more and more mature, people gradually combine machine learning technology and big data intelligent processing technology. Aiming at the problem of innovative employment in colleges and universities, this paper proposes a dynamic decision tree algorithm based on these two technologies and constructs a dynamic model of graduates' behavior. Through the analysis of dynamic decision tree algorithm, a big data analysis system is formed. Finally, simulation experiments verify whether the dynamic model can correctly reflect the behavior of college graduates. The results show that the big data integration system based on big data and dynamic decision tree algorithm has high adaptability. Incremental adaptive optimization of the traditional decision tree model can significantly improve the prediction effect and prediction time of dynamic data and provide theoretical support for the industrialization and social significance of big data technology. The dynamic decision tree algorithm of college employment proposed in this paper has good predictability and provides a certain theoretical reference for college graduates' entrepreneurship. [ABSTRACT FROM AUTHOR]

The arrival of the big data era not only provides corresponding technical support for the development of Educational Networking but also promotes the acceleration of Educational Networking. Therefore, this paper puts forward the research on the impact of big data on the development of education network and constructs a Bayesian knowledge tracking model to collect and analyze the behavior data of teachers and learners in network education. The experimental results show that big data technology provides greater development space for Education Networking. Its market scale has reached 502.47 billion yuan in 2021, and there is a trend of continuous growth. At the same time, the increase in the number of users also makes its teaching content richer and teaching methods more diversified and personalized. And, through the analysis of relevant data, learners and teachers can more comprehensively and truly understand their own level, achieve the purpose of accurate assistance to learners and teachers, and help learners and teachers find their own problems and make targeted adjustments. In addition, the campus intelligent management system based on big data technology can achieve the purpose of multipurpose and information management. [ABSTRACT FROM AUTHOR]

The purpose of this interdisciplinary study is to investigate the applications of modern computational technologies in the field of credit investigation and discuss the related legal governance measurements in order to unblock the bottlenecks in the future development of credit investigation market. By analyzing the computational technologies and algorithms most commonly used in credit data collection and data storage, data transmission and data access, data analysis and processing, data calculation, result output and effect evaluation, this paper summarizes and proposes a unified general process of modern credit investigation, pointing out that in this general process, low data quality, privacy violation, algorithmic bias are the main challenges in the big data era, and countermeasures like data quality control, privacy protection, and algorithm governance need to be to be taken seriously into account in order to further explore the great potential of the credit investigation under the legal framework. [ABSTRACT FROM AUTHOR]

The paper is regarding the fair distribution of several files having different sizes to several storage supports. With the existence of several storage supports and different files, we search for a method that makes an appropriate backup. The appropriate backup guarantees a fair distribution of the big data (files). Fairness is related to the used spaces of storage support distribution. The problem is how to find a fair method that stores all files on the available storage supports, where each file is characterized by its size. We propose in this paper some fairness methods that seek to minimize the gap between used spaces of all storage supports. In this paper, several algorithms are developed to solve the proposed problem, and the experimental study shows the performance of these developed algorithms. [ABSTRACT FROM AUTHOR]

With the improvement of the intelligence level of the power network and the gradual improvement of the information and communication of the power system, the application of power big data to the corporate credit market is not only an important supplement to the national social credit system but also an important strategic goal for the State Grid to expand profitability. This paper constructs a benefit evaluation model for power data credit investigation products based on the utility of the products that financial institutions experience when they purchase and use financial risk control products. Based on the theory of customer value, comprehensively considering the characteristics of power credit reporting products, and taking profit maximization as the corporate goal, the pricing of a single power credit reporting product is studied, and the method is verified using example products. This article gives the pricing strategy of power credit investigation, which can be used as a reference for enterprises to further develop market strategies. [ABSTRACT FROM AUTHOR]

In recent years, with the development of Big Data and dynamic modeling technology, people have gradually deepened the research on the efficient construction of music audio database. Based on this background, this paper first realizes the construction of multilevel music audio database using Big Data technology and collaborative filtering algorithm. Then, through the Big Data analysis technology based on collaborative filtering algorithm, the QRE (quick reaction estimate) model is constructed and the data query system is formed. Finally, experiments are designed to verify whether the music audio database based on collaborative filtering algorithm can correctly retrieve the target music audio data. The experimental results show that compared with the traditional single ∗ input stacked database, the music and audio database constructed by the discrete Big Data dynamic modeling technology based on collaborative filtering algorithm has faster search rate and higher accuracy and can accurately locate the data nodes in the database. This research uses Big Data and collaborative filtering algorithm technology to establish a new multilevel music audio database with discrete dynamic modeling characteristics of complex systems, which can greatly improve the data structure of each level of the database, so as to improve the efficiency and accuracy of retrieving various types of music and audio files in the database. [ABSTRACT FROM AUTHOR]

Based on big data and cloud computing technology, the development process of this system includes hardware cluster deployment of the delivery system, optimization of website delivery strategy, and development of the delivery management background system. The main functions in the following aspects are realized: first, we built the website back-end delivery subsystem and data collection and analysis subsystem to realize the control of website delivery and data collection; second, we designed and developed a process management subsystem for booking, management, and delivery of website resources and developed the contract management and order management subsystems to realize the accurate placement of user portraits on the website; then, the placement data monitoring and effect feedback subsystems, and the data inventory subsystem of the website system were designed and developed. Finally, based on the research of Android and based on the Eclipse platform, this article has completed the construction of the Android 4.0.3 version environment, successfully used the Java development language to develop a website information intelligent analysis and navigation system, and analyzed the various functional modules of the entire system. Experimental results show that the system not only realizes ordinary route and site query functions but also combines map API, integrates big data and cloud computing technology, and realizes congestion avoidance query, time optimal query, population heat map, and real-time viewing. The nearby use of this software's personnel density distribution and other functions provides great convenience for personal travel, which can facilitate the real-time planning of travel plans, and has great practical and practical significance. Through the different levels of testing of various subsystems, the website delivery system meets the functional and nonfunctional requirements proposed by the network and on this basis realizes the use of group wisdom based on Pearson correlation coefficient, Cosine similarity, and Tanimoto coefficient for collaborative filtering website recommendation algorithm. [ABSTRACT FROM AUTHOR]

Smart tourism can provide high-quality and convenient services for different tourists, and tourism itinerary planning system can simplify tourists' tourism preparation. In order to improve the limitation of the recommendation dimension of traditional travel planning system, this paper designs a mixed user interest model on the premise of traditional user interest modeling and combines various attributes of scenic spots to form personalized recommendation of scenic spots. Then, it uses heuristic travel planning cost-effective method to construct the corresponding travel planning system for travel planning. In terms of the accuracy rate of travel planning recommendation, the accuracy rate of multidimensional hybrid travel recommendation algorithm is 0.984, and the missing rate is 0. When the travel cost and travel time are the same and the number of scenic spots is 20–30, the memory occupation of MH algorithm is only 1/2 of that of TM algorithm. The results show that the multidimensional hybrid travel recommendation algorithm can improve the personalized travel planning of users and the travel time efficiency ratio. The results of this study have a certain reference value in improving user satisfaction with the travel planning system and reducing user interaction. [ABSTRACT FROM AUTHOR]

The driving force of high-quality development of regional economy is inseparable from the support of technology. With the support of big data, we need to solve this problem in order to solve the difficulty of large-scale experimental testing and accurately reflect the feasibility growth of data sample changes. This paper proposes a discrete dynamic modeling technology based on big data background to analyze the development and change of regional economy. The reliability AMSAA model is usually used for dynamic discrete modeling. It can be combined with the change data provided by big data to form a dynamic modeling method for reliability growth evaluation. Then, the Bayesian regression method is used to predict the change parameters of the model, and the spatial econometric method is used to analyze the regional economic change. The results show that compared with the traditional methods, the discrete dynamic modeling method is more accurate and can effectively solve the problem of reliable growth under the condition of big data. After introducing the spatial effect measurement model, it can also reflect the main factors of the growth and change of regional economic real output value. In addition to the development of high and new technology, terrain factors, investment, and government support have also had different effects. Therefore, according to the above results, it is proved that the discrete dynamic modeling technology can accurately obtain the experimental data and provide reliable technical support for dynamic data processing. [ABSTRACT FROM AUTHOR]

From the perspective of information, this paper constructs a theoretical model based on information system (IS) success model and information adoption model (IAM), aiming to further disclose the action mechanism of business intelligence (BI) on user information adoption (UIA). Firstly, the BI factors affecting UIA were modeled in reference to the existing literature. Next, a questionnaire survey was carried out to collect valid samples from 423 Chinese enterprises. After that, an empirical analysis on structural equations was conducted on Amos 24.0 and SPSS 26.0. The results show that BI information content quality (ICQ), expected performance (EP), expected ease-of-use (EEOU), and perceived risk (PR) have a direct and significant influence on UIA, and ICQ further significantly influences UIA via mediators such as EP, EEOU, and PR; BI information access quality (IAQ) has a direct yet insignificant influence on UIA, but exerts a significant positive effect on UIA of BI via EP and EEOU. The research provides a new perspective into BI user behaviors in the context of big data, and a good reference for the successfully implementation and use of BI in practice. [ABSTRACT FROM AUTHOR]

Building an information-based rural economic supply chain is an effective way to implement the rural revitalization strategy. The construction of China's agricultural product logistics market is still very backward, mainly including imperfect information system and high cost of intermediate circulation links. How to optimize the supply chain of agricultural products, ensure the normal supply of agricultural products quality and quantity in the market, improve people's living standards, and achieve the purpose of improving benefits is an important topic to be studied today. Aiming at the problems of traditional agricultural products supply chain, the application of Internet of Things technology in supply chain can be effectively solved. Therefore, this paper proposes the research on the environmental effect evaluation of rural economic supply chain based on Internet of Things technology and proposes to build a wireless sensor network for agricultural products for the collection and exchange of agricultural products information. At the same time, RFID (radio-frequency identification) electronic tags integrated with sensors are applied in other links of the supply chain. The experimental results show that the construction of the rural economic supply chain system based on Internet of Things technology can effectively optimize the rural economic supply chain, realize efficient information sharing, improve the quality of agricultural products, reduce the cost of each link of the supply chain, improve the competitiveness of rural supply chain, and promote the good development of rural agricultural products and rural economic supply chain environment. [ABSTRACT FROM AUTHOR]

Academics and practitioners have shown growing interests in big data analytics and artificial intelligence (BDA-AI) in recent years. Despite this, research on the application of BDA-AI for green supply chain collaboration (GSCC) and its influence on environmental performance (EP) is still limited. The current research addresses this gap and extends organizational information processing theory by incorporating BDA-AI and exploring top management commitment (TMC) as a moderator. The current study developed a moderated mediation model based on 402 samples of data from Turkish manufacturing firms. The result revealed that the application of BDA-AI has a positive impact on GSCC and EP. The results also indicated that GSCC has a positive impact on EP. Our findings revealed that GSCC mediated the association between BDA-AI and EP. The results also revealed that TMC moderated the positive relationship between BDA-AI and GSCC, such that the strength of the positive relationship is further intensified at higher levels of TMC. The results also show that TMC moderated the positive relationship between BDA-AI and EP, such that the strength of the positive relationship is dampened at lower levels of TMC; significant findings have not been outlined in the extant literature. The current research will assist supply chain and logistics managers and top management in deploying BDA-AI technology to support GSCC and improve EP. [ABSTRACT FROM AUTHOR]

In order to improve the stability of the cost control of marine fishery culture, a method of controlling the cost of marine fishery culture based on big data analysis algorithm was proposed. We establish the cost analysis model of marine fishery, use the big data correlation analysis method to conduct distributed mining on the cost characteristics of marine fishery, deeply grasp the relevance of data characteristics, use the information fusion method to build the constraint parameter analysis model of aquaculture cost operation, limit the amount of calculation, and use the adaptive neural network weighted training method to adaptively optimize the cost control to avoid falling into local optimization. The objective model of aquaculture cost control is established, and the cost constraint is carried out by the parameter optimization method. The statistical feature quantity of marine fishery cost control is obtained, and the cost control of marine fishery is realized by the feature recombination method. The simulation results show that this method is more stable in the cost control of marine fishery culture and improves the adaptability of the cost control of marine fishery culture. [ABSTRACT FROM AUTHOR]

Music is an art course that is different from traditional subjects. It not only needs to teach basic music knowledge but also needs to show the timbre contained in music itself, the form of music creation, or the effects expressed by music. The teaching effect evaluation model for traditional subjects can no longer be applied to the teaching effect evaluation of music subjects. If the traditional subject teaching effect evaluation model is used to evaluate the music subject teaching effect, it will easily lead to the inaccuracy of the effect evaluation. Because there is a strong subjectivity in the evaluation of the teaching effect of music subjects, this study combines deep neural network technology to study the feasibility and accuracy of the convolutional neural network (CNN) and long short-term memory neural network (LSTM) method in the evaluation of music teaching effects. It mainly reflects the evaluation under the three characteristics of music basic knowledge, music expression effect, and music innovation effect. The research results show that the CNN and LSTM methods can better extract the three features in the evaluation of music teaching effect, and the maximum prediction error is only 2.23%. Hybrid CNN-LSTM with LSTM neural network has higher accuracy in predicting music teaching effect than single neural network technique. The linear correlation coefficients also all exceeded 0.955. [ABSTRACT FROM AUTHOR]

According to the resource-based view, research and development (R&D) and advertising are critical resources firms use to improve their performance. This study aims to clarify the different effects of R&D and advertising on firm performance using distinct criteria—firm value and firm profitability. It also verifies whether the effects of R&D and advertising apply in a volatile environment. We run panel data regression models with a big data sample of manufacturing firms publicly listed on the Korea Composite Stock Price Index over an extended period of 27 years. We find that R&D has more positive effects on firm value than advertising, while advertising has more positive effects on firm profitability than R&D; these relationships are consistent even when volatility is considered. This suggests that firms should mix and match their investments between R&D and advertising resources for improved effectiveness and efficiency, and these resources should be accumulated and exploited consistently regardless of environmental dynamics. [ABSTRACT FROM AUTHOR]

The advent of the era of big data has promoted the reform of sports governance, and intelligent governance is becoming a strategic choice and a new trend to promote the physical fitness development of college students. Based on the profound analysis of the current situation of college students' physical fitness, this study examines the great value of big data for the intelligent governance of physical fitness, and further constructs the model to achieve intelligent governance of college students' physical fitness. The study also provides specific strategies to promote this process. According to the study, although the current situation of college students' physical fitness is still not optimistic, data-driven intelligent governance of physical fitness provides a new solution to a personalized, refined, and diversified way of governance. By creating an intelligent governance concept with data thinking, building an intelligent governance platform supported by data, and by such measures as improving the data literacy of physical education teachers in colleges and universities and upgrading stadiums with intelligent techniques, the study seeks to break through the bottleneck of college students' physical fitness governance and open up a new path to realize the effective governance of college students' physical fitness. [ABSTRACT FROM AUTHOR]

The traditional financial informatics administration system risk assessment method in the evaluation of system risk can deal with less data throughput, and the system risk assessment effect is not good, so the study of the financial informatics administration system of the risk assessment method under the background of big data is a necessity. The method sets logical nodes according to system modules and builds risk estimation models by finding logical relationships between data. Based on the big data background, according to the utility theory and the risk preference function estimation, choose the big data batch calculation and streaming calculation methods to calculate the system programme risk index and financial data faithless risk index. According to the risk residual value, the probability of a system function failure in the main module and submodule of the system is adjusted, and the risk level is defined by the exponential function to realize the system risk estimation under the background of big data. Experimental results show that compared with the two traditional system risk estimation methods, the proposed method has a larger data throughput and a wider range of risk indicators. It can be seen that the proposed method meets the risk estimation requirements of the enterprise financial informatics administration system. [ABSTRACT FROM AUTHOR]

In order to improve the classification accuracy and reduce the classification time of the economic text big data visualization classification algorithm, one based on the Pitton algorithm is proposed. The economic text big data are preprocessed by filtering out useless symbols, word segmentation processing, and removing stop words. According to the processing results, the most relevant features of the economic text big data classification process are selected, including Gini index, information gain, mutual information, etc., and the TF-IDF weighting algorithm is used to weight the economic text data features. Based on feature weighting, using Naive Bayesian algorithm, combining classification probability distribution and text probability distribution, Naive Bayesian classifier is constructed to obtain the optimal classification result through input vector, and the visual classification of economic text big data is completed through Python software programming. The simulation results show that the classification accuracy of the algorithm for the visual classification of economic text big data can reach 100%, and the classification time is less than 5 seconds. It has high accuracy and fast efficiency. [ABSTRACT FROM AUTHOR]

The traditional teaching model of national vocal music in colleges and universities has some problems, such as low quality of teaching, poor diversity of teaching, and low interest of students. Based on this, this study studies the innovation of the teaching model of national vocal music in colleges and universities based on the deep recurrent neural network algorithm and designs the teaching quality evaluation model based on the deep recurrent neural network algorithm. The collection of data and information is realized from the aspects of students' class state, vocal music examination results, classroom interaction, etc., and the comprehensive analysis is carried out using the deep recursive neural network algorithm, to realize the multiple analysis and objective evaluation of the whole process of different teaching modes of national vocal music in music colleges and universities, according to the different characteristics and teaching objectives of the teaching process of national vocal music in music colleges and universities The standard requires accurate analysis and evaluation. The results show that the optimization model based on the deep recurrent neural network algorithm has the advantages of high feasibility, high intelligence, and wide range of applications. The experiments show that the deep recurrent neural network algorithm can analyze the effectiveness of the innovation model, and the fuzzy evaluation method can realize the comprehensive evaluation of the innovation mode, which is conducive to the improvement of students' vocal music learning ability. [ABSTRACT FROM AUTHOR]

Urban landscape design as a contemporary art embodies postmodernist philosophical thinking, aesthetic thinking, and breaking the traditional concept of art, and it is a new way of creating and presenting art. Big data technology characterized by large scale, speed, variety, value, and uncertainty of data is used to achieve urban landscape design. In this article, during the research process, we strive to raise the revelation of the design layer rather than the brand new level of cross-fertilization and interaction between big data-driven discrete dynamic model and urban landscape design; we also reveal how the benefits of promoting urban development and harmonious life are achieved in the interactive expression of the urban landscape after the application of the big data-driven discrete dynamic model, which provides designers and related professionals with more detailed and novel design ideas at the theoretical level and makes the theory of big data-driven discrete dynamic models in landscape design interactive methods more enriched. Finally, this article puts forward its thinking and outlook on the design of the big data-driven discrete dynamic model in the interactivity of urban landscape design, hoping that artists will strengthen its functional and material design elements when creating performance. Moreover, more design means of emerging technologies of modern science and technology should be integrated so that modern urban landscape can achieve ordinary and uncommon benefits and promote the rapid development of the big data-driven discrete dynamic model in urban landscape design development. [ABSTRACT FROM AUTHOR]