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The H∞ control issue for nonlinear Markov networked control systems in the presence of data packet loss and periodic denial-of-service (DoS) attacks is researched in this paper. First, two Bernoulli random variables are used to describe the packet loss between sensor to controller and controller to actuator. Considering the impact of DoS attacks, the probability of packet loss is set to be different during the attack sleeping interval and the attack active interval. Secondly, an observer is constructed on the controller side, and a comprehensive mathematical model including packet loss and DoS attacks is established. The sufficient conditions for the stochastic stability of the system are derived by the Lyapunov theory, and the design method of the controller and the minimum disturbance suppression performance index are also provided. Finally, a numerical example is utilized to reveal the applicability of the approach. [ABSTRACT FROM AUTHOR]

The problem of infinite horizon H∞ control for general delayed nonlinear stochastic Markov jump systems with the infinite jumping parameters is considered in this paper, in which the noise is dependent on the state, control, and external disturbance. The coupled Hamilton–Jacobi inequalities (HJIs)-based sufficient condition is given to ensure the existence of the H∞ controller. As a corollary, infinite horizon H∞ controllers are designed for nonlinear stochastic time-delay systems without jumps by solving a series of coupled HJIs. Besides, the effectiveness of the proposed method is verified by a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, an off-policy model-free algorithm is presented for solving the cooperative optimal output regulation problem for linear discrete-time multi-agent systems. First, an adaptive distributed observer is designed for each follower to estimate the leader's information. Then, a distributed feedback-feedforward controller is developed for each follower to solve the cooperative optimal output regulation problem utilizing the follower's state information and the adaptive distributed observer. Based on the reinforcement learning method, an adaptive algorithm is presented to find the optimal feedback gains via online data collection from system trajectory. By designing a Sylvester map, the solution to the regulator equations is calculated via data collected from the optimal feedback gain design steps, and the feedforward control gain is found. Finally, an off-policy model-free algorithm is proposed to design the distributed feedback-feedforward controller for each follower to solve the cooperative optimal output regulation problem. A numerical example is given to verify the effectiveness of this proposed approach. [ABSTRACT FROM AUTHOR]

Everybody wants to maintain solitariness to some extent or entirely in his dealings with other people during different modes of communication. To retain privacy, researchers materialized distinct image encryption algorithms using chaotic maps. Due to their extraordinary features, most researchers employed multidimensional chaotic maps to barricade clandestine information or digital images from potential invaders. Still, multidimensional chaotic maps have many impediments conferred in the literature review. In this paper, we developed a cryptosystem utilizing multiple chaotic maps to mitigate the shortcoming of multidimensional chaotic maps. A distinctive approach is adopted to sire a key stream using a combination of chaotic maps and create a sequence of random integers linked with the pixels of the plain image to shatter the association between neighboring pixels of a plain image. Finally, diffusion is accomplished using the previously diffused pixels at a decimal level. Security and statistical analysis demonstrate that the presented encryption algorithm is robust against well-known attacks. An ample key space indicates that it is best suited for secure communication. [ABSTRACT FROM AUTHOR]

In this paper, we consider a two-commodity stochastic s , S perishable inventory system of the multiqueue Jackson network at a service facility with reneging and jockeying of the customers. The waiting room capacity of the first queue is M / M / 1 / ∞ and the second queue is M / M / 1 / C 2 , where C 2 < ∞. In this network, different queues have different service rates. Service times are exponentially distributed. Customers join the system at a rate of λ i ; i = 1,2 using a Poisson process. We assumed that some customers maybe impatient due to the long waiting time in the queue and can leave the system without service or may switch from the longest queue to the shortest queue to reduce the waiting time. If the inventory levels of the items from the warehouse i at any time reaches below or reorder level s i , an order Q i = S i − s i > 0 ; i = 1,2 units is placed to bring the inventory level up to S i . The joint probability distribution of the number of customers in the system and also the stock level of the warehouse is determined in the equilibrium case. Several important measures of steady-state system performance are derived. Several examples are presented to demonstrate the reneging and jockeying behavior of the customers for this system. A sensitivity analysis is performed to investigate the effect of changing the parameters on the total expected cost of the system. [ABSTRACT FROM AUTHOR]

This paper integrates the classical resource-constrained project scheduling problem with step deterioration and accordingly presents a solution-based tabu search (SB-TS) algorithm to solve it with challenging computations. First, we choose successor list size as the priority rule to generate the initial solution of the presented methods. Next, the corresponding solution decoding and calculation ways are defined to update the related configuration at each iteration. Compared with the state-of-the-art methods, in terms of the known datasets, the proposed SB-TS finds the optimal solution for each instance in J30 and shows strongly competitive performance in J60, J90, and J120. Then, on newly generated datasets, where the step deterioration is attached to, the comparison experimental data state that the SB-TS with high-quality solution is superior to the move-based tabu search (MB-TS) algorithm. In addition, two key components are investigated to emphasize their attributions to the proposed algorithm. [ABSTRACT FROM AUTHOR]

In big data analytics, Jaccard similarity is a widely used block for scalable similarity computation. It is broadly applied in the Internet of Things (IoT) applications, such as credit system, social networking, epidemic tracking, and so on. However, with the increasing privacy concerns of user's sensitive data for IoT, it is intensively desirable and necessary to investigate privacy-preserving Jaccard similarity computing over two users' datasets. To boost the efficiency and enhance the security, we propose two methods to measure Jaccard similarity over private sets of two users under the assistance of an untrusted cloud server in this paper. Concretely, by leveraging an effective Min-Hash algorithm on encrypted datasets, our protocols output an approximate similarity, which is very close to the exact value, without leaking any additional privacy to the cloud. Our first solution is under a semihonest cloud server, and our enhanced solution introduced the consistency-check mechanism to achieve verifiability in malicious model. For efficiency, the first solution only need about 6 minutes for billion-element sets. Furthermore, as far as we know, the consistency-check mechanism is proposed for the first time to achieve an effective verifiable approximate similarity computation. [ABSTRACT FROM AUTHOR]

High-rise building fires can be characterized by a rapid spreading and are challenging to extinguish. The chimney effect of cable shafts in high-rise buildings will greatly increase the spread speed of fire and smoke, and will also make it harder to put out fires. Therefore, fire-blocking materials or fire protection measures are usually used in cable shafts to slow down or even block the spread of fire. Currently, most of the research focuses on the fire performance of fire protection materials, and there are relatively few studies on the impact of fire-blocking materials on the operation state of cables. Based on the theories of electromagnetism, fluid mechanics, and thermodynamics, the paper establishes a multiphysics simulation model of a double-layer cable shaft. Then, the electromagnetic-fluid-temperature distribution in the cable shaft under rated operating conditions is analyzed, and the influence of the fire-blocking material is discussed. The simulation results show that the firestop material will affect the heat dissipation of the cable, which causes the hot-spot temperature of the cable to slightly increase by 1 to 2°C, and the relative growth rate of its hot-spot temperature rise is close to 6.83%. [ABSTRACT FROM AUTHOR]

The "14th Five-Year" development plan of the construction industry of the Chinese government makes it clear that intelligent buildings are the general direction of future construction development. However, the occurrence of construction accidents in recent years has brought huge casualties. With the rapid development of third-party inspection institutions, their judgment has become an important threshold for the access to smart buildings. The failure of inspection institutions to detect the renewal of leases may lead to the failure of intelligent systems, leading to the failure of construction equipment and causing major safety accidents such as the uncontrolled fall of construction elevators and the fall of tower crane items. As the supervisor of third-party inspection institutions, government functional departments are responsible for the failure of intelligent systems. All of them undertake the important role of intelligent building security. This paper builds an evolutionary game model with government regulatory authorities, third-party testing institutions, and intelligent construction enterprises as the three game parties, based on variables that affect the factors of the players, and discusses the possible scenarios of the stabilization strategy, which draws the following points: (1) the increase of incentives and penalties by the government is conducive to promoting the construction of high-quality intelligent buildings and the normative behavior of the third party without intention to seek rent, but the increase of incentives will not be conducive to the government fulfilling its regulatory responsibilities. (2) The reasonable reward and punishment mechanism set by the government must meet the condition that the sum of rewards and punishments for all parties is greater than their speculative returns so as to ensure the safety of intelligent buildings in the evolving and stable market environment. (3) The accountability of the superior government for the dereliction of duty of the regulatory authorities is of great significance to enhancing the stability of enterprises in building high-quality intelligent buildings. (4) It is also an effective way to avoid the construction of low-quality smart buildings by improving the listed sales income of enterprises and increasing the rent-seeking cost of enterprises. Combined with the improved SIR model, it is calculated that after the occurrence of a construction accident, when the rumor first appears, the rumor will quickly spread out of control without timely management and supervision. The conclusion of the game model provides a great reference value for the government's regulatory behavior. The quality of the government's regulatory behavior is directly related to the future of high-quality development of intelligent construction and also expands the application field of evolutionary game theory and SIR theory. The government should actively manage third-party testing institutions and intelligent building enterprises before construction accidents happen. After the accident, strict management and supervision should be carried out on well-known bloggers before the emergence of rumors so as to reduce the possibility of standardizing individuals' reprinting of rumors and avoid the uncontrollable negative impact in the later period, thus seriously affecting the credibility of government supervision. [ABSTRACT FROM AUTHOR]

The world is having a vast collection of text with abandon of knowledge. However, it is a difficult and time-taking process to manually read and recognize the text written in numerous regional scripts. The task becomes more critical with Gurmukhi script due to complex structure of characters motivated from the challenges in designing an error-free and accurate classification model of Gurmukhi characters. In this paper, the author has customized the convolutional neural network model to classify handwritten Gurmukhi words. Furthermore, dataset has been prepared with 24000 handwritten Gurmukhi word images with 12 classes representing the month's names. The dataset has been collected from 500 users of heterogeneous profession and age group. The dataset has been simulated using the proposed CNN model as well as various pretrained models named as ResNet 50, VGG19, and VGG16 at 100 epochs and 40 batch sizes. The proposed CNN model has obtained the best accuracy value of 0.9973, whereas the ResNet50 model has obtained the accuracy of 0.4015, VGG19 has obtained the accuracy of 0.7758, and the VGG16 model has obtained value accuracy of 0.8056. With the current accuracy rate, noncomplex architectural pattern, and prowess gained through learning using different writing styles, the proposed CNN model will be of great benefit to the researchers working in this area to use it in other ImageNet-based classification problems. [ABSTRACT FROM AUTHOR]

With the development of science and technology, it has had a great impact on the manufacturing industry. The manufacturing industry has higher and higher requirements for industrial manipulators, and the expectations for the quality and function of the industrial manipulators in the manufacturing industry are increasing day by day. At the same time, the requirements for the working accuracy of the manipulators are also getting higher and higher. In this paper, a new method is proposed. An error compensation and calibration algorithm based on parameter separation and integration of the standard DH model (SDH) is used to construct the kinematic model of the manipulator. The six groups of structural parameters of the six-axis manipulator are calibrated once every two groups, thereby eliminating the influence of the singular value of the position of the manipulator. Finally, the feasibility of this scheme is verified by simulation experiments, the accuracy is ensured, and the expected purpose is achieved. Because the method in this paper has high requirements on the posture of the manipulator, it reduces the overall calibration calculation and saves time, which has a certain effect on the improvement of the industrial manipulator in the manufacturing industry. [ABSTRACT FROM AUTHOR]

The development of geographically indicative (GI) agricultural products can effectively promote the development of local rural residents' per capita net income (PCNI) and the gross output value of agriculture, forestry, animal husbandry, and fishery (GOV-AFAHF). This paper takes agricultural products with geographical indications in Anhui Province as the object of study and uses numerical analysis to analyze their application, geographical distribution, and various distribution in detail. By comparing it with the central region, this paper conducts an empirical study on its impact on agricultural economic development, analyzes its related variables and data, draws its effect on farmers' PCNI, and on this basis puts forward some countermeasures. In order to make the agricultural products with GI in Anhui Province develop better, according to the results of comparative analysis and empirical analysis, it puts forward suggestions for regulating the development of management industry associations, implementing the regional branding strategy of GI agricultural products, and increasing the construction of rural infrastructure. In addition, the experiment shows that under the condition of controlling other influencing factors unchanged, for every 1% increase in GI agricultural products, the average increase of PCNI of rural residents is 0.08%, which further confirms the conclusion that GI agricultural products can promote the development of GOV-AFAHF. [ABSTRACT FROM AUTHOR]

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world's leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country's total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists' willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans. [ABSTRACT FROM AUTHOR]

Using big data technology to promote the maturity and application of human analysis is the key to establish and maintain the competitive advantage of the school. Modern teachers must have the quality and ability to adapt to their work, that is, professional ability, in order to improve the teaching quality more pertinently. In order to effectively promote teachers' professional growth, this paper proposes a Chinese teachers' Ability Optimization Model Based on big data technology. Chinese teachers should take the initiative to meet the mission and challenges given by the times, strengthen Chinese teaching ability through the promotion of microability of information technology, explore the changes of learning and teaching style under the environment of information technology, and improve the ability of Chinese teachers. The research on the ability optimization of Chinese teachers based on big data technology in this paper can help schools optimize the training plan system by using the winning power model, that is, the training plan system can be improved from three aspects: determining the training objectives and contents and selecting the training methods. Teachers must improve their abilities through continuous learning, so as to promote education more actively and cultivate more talents for the country. [ABSTRACT FROM AUTHOR]

Random matrix theory is applied in the financial field to study the correlation of the financial correlation coefficient matrix, which is a key factor in network construction. In this paper, the random matrix theory is combined with network construction to study the art financial risk prediction algorithm based on the random matrix. Based on the stochastic matrix theory and the key nodes of the network, the financial network and the "noise" network before and after the "denoising" are analyzed and compared. It is found that the key and important information of the original network is still retained after the "denoising" of the network, and the noise information corresponds to the information represented by the smaller nodes in the original network. Based on the stochastic matrix of artwork financial risk prediction, the topological properties of the financial network before and after denoising are analyzed and compared from the perspectives of minimum spanning tree, model, and community structure. Based on the random matrix theory, this paper discusses the financial correlation coefficient matrix and the statistical properties of the eigenvalues of the random matrix, and on this basis, the existing denoising methods are improved, the correlation coefficient matrix more suitable for constructing the financial network is established, and the art financial risk prediction algorithm is constructed. Then, based on the stochastic matrix theory and the key nodes of the network, the financial network and the noise network before and after denoising are analyzed and compared. It is found that the key and important information of the original network is still retained after denoising the network, and the noise information corresponds to the information represented by the relatively small nodes in the original network. Finally, based on the stochastic matrix of art financial risk prediction, analysis of the financial network topology, such as minimum spanning tree, model, and community structure, found that the improved financial network topology is more obvious and the structure is closer. [ABSTRACT FROM AUTHOR]

According to the constructed rural community residents' health information literacy evaluation system, this paper proposes a rural community residents' health information literacy evaluation method based on intuitionistic fuzzy sets. Based on the intuitionistic fuzzy set, the multistage interactive group evaluation method whose data form is a point set has been expanded, and the intuitionistic fuzzy set that expresses objective things more delicately and truly is used to reflect the evaluator's preference, which can well simulate the human decision-making psychology and improve the applicability of the interactive group evaluation method. How to accurately measure the value information of relevant intuitionistic fuzzy numbers in uncertain environment has become a difficult problem. Therefore, it is very important to excavate important information in intuitionistic fuzzy numbers, deal with intuitionistic fuzzy numbers, and study the theory and method of multicriteria decision-making under fuzzy information. In this paper, we conducted a research on the health information literacy evaluation model of rural community residents based on intuitionistic fuzzy sets and multicriteria, and found that the formation rate of health behaviors of manual workers is relatively low, and the formation rate of healthy behaviors of female, elderly, and highly educated people is relatively high. A high level of education is conducive to improving the level of residents' health knowledge. [ABSTRACT FROM AUTHOR]

Scientific computing visualization is also known as visualization. In an academic sense, visualization is a way of computing. It can convert data into intuitive geometric images, making it easier to understand. Visualization is the use of computer graphics and image processing techniques. It is a technical means to convert piles of seemingly disorganized data into graphics or images through special computer methods. Recording is the process of recording sound signals on a medium. Specifically, it is the process of converting sound into electrical signals through microphones and amplifiers and recording them with different materials and techniques. Piano tuning is the adjustment of the strings so that they reach a certain high pitch. This paper aims to study the analysis and evaluation of multi-tone piano transcriptions based on scientific computing visualization and knowledge recognition algorithms. It expects to improve the quality of piano recording with the support of scientific visualization technology and knowledge recognition algorithm. This paper briefly explains the big data, and on this basis, the visual data mining technology is mentioned, and the current situation in the visual data mining is explained. A matrix calculation of the characteristic matrix of piano timbres was carried out to achieve the resynthesis of the 25th harmony in piano timbres electronically. The results of the trials in this article demonstrate that when the Gaussian number changes to 5 during the recording process, the difference that exists of the acoustic voltage intensity and the noise level phase is clearly diminished. When the Gauss figure is 10, there is an inversion of the value of the sound pressure phase. [ABSTRACT FROM AUTHOR]

Urbanization is the accompaniment of industrialization and modernization, a symbol of the progress of human civilization, and it has always been the focus of social attention. With the transfer of rural population to cities, the secondary and tertiary industries continue to produce agglomeration effects, and the urbanization process is accelerated. The level of urbanization has gradually become an important indicator to measure the degree of regional development, which has led to the exploration of the inner dynamics of urbanization. Finance is the core of modern economy, and urbanization is an important driving force for modern economic and social development. Research on the relationship between financial development and urban scale should also receive widespread attention. On the basis of reviewing and sorting out the related research results of urbanization and financial development, this paper makes an in-depth analysis of urbanization and financial development, respectively, aiming to have a systematic, comprehensive, and objective analysis of the interaction between urbanization and financial development. On this basis, combined with the current situation of our country's financial development and urbanization, the internal relationship between the two is discussed. Based on the mutual information algorithm, this paper selects indicators that are representative of urbanization level, financial development, and economic development. By using the mutual information theory to analyze and rank the social and economic factors related to financial factors, it reveals the most relevant and accurate financial forecast. Then, it also discusses whether there is a cointegration relationship between urbanization and financial development and economic growth in our country and the causal relationship between them and analyzes the reasons on this basis. [ABSTRACT FROM AUTHOR]

As the underground space is relatively closed compared to other buildings, the space is hardly connected to the outside world through entrances and exits, which makes it difficult to ventilate and exchange heat with the outside. In this case, it is easy to cause a large number of casualties when a fire breaks out. When the fire happens smoke will be discharged merely by the mechanical exhaust, and air system should work simultaneously to replenish the oxygen inside. By setting four-group models, the paper analyzes the strategies for smoke extraction and air makeup in the underground construction. The paper points out that when the fire happens in a smoke bay, the neighborhood smoke extraction fan should be opened and the air makeup system should not be located at the top to disturb the smoke layer. By comparing and analyzing the performance of the smoke extraction and air conditioning system, the casualties caused by the high temperature toxic smoke from the fire can be reduced as far as possible. [ABSTRACT FROM AUTHOR]

The rapid development of tourism in China has also caused a lot of energy consumption and serious environmental problems. In the context of sustainable development, low-carbon tourism has become the consensus of management departments, relevant industries, and academia. In environmental-related fields, DEA is considered as an excellent efficiency analysis tool because of its powerful optimization ability. The final variable of DEA method is weight. In order to avoid the influence of human factors with predetermined weights in tourism analysis, this paper studies the evaluation of tourism ecological efficiency based on DEA model. China's overall tourism ecological efficiency showed a fluctuating upward trend, rising from 0.853 in 2018 to 0.906 in 2021. The national average efficiency over the past four years is 0.855, which is at a low level, indicating that the construction of tourism ecology is still in its infancy, and there is still much room for improvement in the future. As the tourism industry system involves many aspects, such as natural ecology, humanities, economy, and so on, it is an extremely complex system. The research on the ecological efficiency of tourism cannot simply rely on the knowledge system and thinking of a single discipline. Therefore, combined with the relevant theories and knowledge of tourism, management, industrial economics, regional economics, industrial ecology, and other disciplines, this paper makes a systematic study on the problems and countermeasures of Qiaozhai. [ABSTRACT FROM AUTHOR]

With breakthroughs in big data, communication networks, artificial intelligence, and other next-generation information technologies, intelligent production methods and digital lifestyles have risen rapidly. Digital economy has become a significant force in promoting regional low-carbon development as a new economic form in the important period of the intersection of digitalization and "carbon peaking and carbon neutrality goals." This paper empirically explores the moderating role and threshold role of human capital in digital economy and regional carbon productivity by systematically measuring digital economy indicators. Research shows that China's digital economy development index is rising annually. However, the development of digital economy varies significantly between different regions. The results of moderating effect showed that human capital accumulation could dramatically strengthen the role of digital economy in promoting regional carbon productivity. The results of the threshold effect showed that human capital plays an important threshold role in the relationship between digital economy and regional carbon productivity. This paper provides a practical reference for exploring power sources, policy design, and path selection of low-carbon development. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles have been widely promoted by virtue of environmental protection and pollution-free advantages. In the future, they are expected to become a mainstream means of transportation. However, as new services and business models become more and more closely related to the distribution network, the traditional AC distribution district has limited ability to respond to the large-scale access of electric vehicles. Therefore, it is becoming increasingly important to improve the ability to respond to electric vehicle access, reduce the negative impact of large-scale electric vehicle access on the grid, and predict and calculate the uncertainty caused by electric vehicle load access. Therefore, this paper proposes the concept of flexible distribution district, constructs the security operational boundary model for flexible distribution district, and uses "security margin percentage" as the measurement mechanism to provide dispatchers with an intuitive system operating status. In addition, this paper also proposes an effective uncertainty quantification method for the problem that it is difficult to measure the changes of bus voltage and line current at network nodes when large-scale electric vehicles are connected to the network. Numerical results demonstrate both the extensibility and the practicality of the proposed security operational boundary. In addition, the proposed uncertainty quantification method can reflect the impact of load changes on the characteristics of the power grid, which is helpful to study the improvement significance of adjusting electric vehicle load changes to distribution network optimization. [ABSTRACT FROM AUTHOR]

With the rise of low-carbon concept and the trend of low-carbon planning, the evaluation of low-carbon planning has been paid more and more attention by the academic and planning circles. Carbon evaluation has become a hot topic after "environmental evaluation" and "green evaluation." In order to promote the important content of energy conservation and sustainable development in the construction of low-carbon cities, this paper constructs and plans the content of low-carbon city planning and construction. On the basis of the effectiveness evaluation index system, this paper discusses the linkage between analytic hierarchy process and fuzzy comprehensive evaluation method and makes a comprehensive quantitative evaluation on the effectiveness of case planning EIA. Combining the advantages of analytic hierarchy process and fuzzy comprehensive evaluation method, the subobjectives and weights of each index are determined by the analytic hierarchy process. The multi-level fuzzy comprehensive evaluation method is used to evaluate the factors that affect the low-carbon effect of urban high-rise residential district planning. This study leads the new trend of future urban construction, and the construction of low-carbon residential areas, as an important part of promoting urban sustainable development, is the core component of future low-carbon urban planning and construction. [ABSTRACT FROM AUTHOR]

In order to solve the practical problem that the massive video data generated by monitoring equipment cannot be processed efficiently temporarily, this paper proposes a framework for face recognition of massive video data based on distributed environment. Combined with the application features of face recognition, this method designs a strategy for fast reading of massive video data and optimizes the feature data obtained by the cloud platform, so as to speed up the retrieval speed of face features. The results are as follows: the compression rate of the proposed compression method is higher than that of the traditional matrix triple and binary methods, which is increased by about 65%; the data optimization method in this paper greatly reduces the amount of feature data, which is 7.08 times less than that in the nonoptimization state. At the same time, the process of face recognition is reduced from 12.6 seconds to 2.73 seconds, and the time of feature decompression is only 0.75 seconds more than the original; the experiment shows that it takes 10180 seconds for the system to process 200 GB pictures with 9 computing nodes, and the total running time of the system is 4737 seconds longer than that of a single node, accounting for about 5.45% of the total time of a single node system. At the same time, the experimental data show that the system is 8.53 times faster than that of a single node with 9 computing nodes. It is proved that this framework has certain research significance in dealing with massive unstructured data. It not only provides theoretical reference value for the research of massive video processing but also makes a contribution to the actual industry. [ABSTRACT FROM AUTHOR]

Digitalization is a long-standing concern in modern industrial engineering. At present, there is no widely recognized standard for the connotation and definition of digital economy, the scope of digital economy is difficult to be defined. This paper conducts an evaluation study on the development level of the digital economy, which mainly contains two parts: first, a comprehensive digital economy; second, a study on the influencing factors. This paper first summarizes the connotation of the digital economy and digital economy evaluation-related research, extracts the influencing factors of digital economy development, and adopts partial least squares (PLS) path model to verify the hypotheses from five dimensions: basic resources, integration development, innovation capacity, social benefits, and network security. It also has an indirect impact on integrated development by influencing innovation ability; innovation capability has a significant positive impact on convergence development; convergence development has a significant positive impact on social benefits; both convergence development and social benefits have a significant negative impact on cybersecurity. Finally, based on the above research results, corresponding countermeasures and suggestions are proposed for the development of the regional digital economy. [ABSTRACT FROM AUTHOR]

In order to effectively solve the problem of relatively large errors in individual positioning strategies in indoor environments, this paper applies the genetic optimization neural network algorithm to indoor location based on multi-source information fusion. The range of the geomagnetic fitness is constrained based on the results obtained by using the wireless WiFi positioning for combination and matching, which can reduce the value of the matching error effectively. Subsequently, the global optimal value of the indoor network is calculated based on the genetic algorithm, which can optimize the initial value and threshold of the neural network after genetic optimization so as to improve the accuracy of the network to the greatest extent possible while accelerating the convergence speed at the same time. After the optimization processing is completed, fusion training can be performed on the coordinates of the actual positions based on the obtained combination positioning situation and the predicted positioning result in the indoor network. Finally, the optimal positioning result can be obtained accordingly. Through the analysis of practical cases, it can be known that the mean square error predicted based on the genetic optimization neural network calculated by using the genetic algorithm can be effectively reduced by 76%, and the accuracy of the fusion positioning can be increased by 48% on average compared with the accuracy of a single positioning strategy. Hence, the method put forward in this paper has effectively improved the positioning accuracy, which suggests that its positioning performance is superior. [ABSTRACT FROM AUTHOR]

In real recommendation systems, implicit feedback data is more common and easier to obtain, and recommendation algorithms based on such data will be more applicable. However, implicit feedback data cannot directly express user preferences. Meanwhile, data sparsity caused by massive data is still an urgent problem to be solved in recommendation system. In response to this phenomenon, this paper proposes a deep collaborative filtering algorithm. In the perspective of implicit feedback, this method uses the advantages of convolutional neural network for effective learning of the nonlinear interaction of users and items and the characteristics of collaborative filtering algorithm for modeling the linear interaction of users and items and combines the two methods for recommendation. Finally, the baseline method is set up and the comparative experiment and parameter adjustment is carried out. The experimental results show that the proposed algorithm has significantly improved the recommendation accuracy on public dataset (Yahoo! Movie). The parameter adjustment results show that, under the condition of uniformly collecting negative feedback data and setting a certain number of convolution layers, the sparser the data is, the better the recommendation performs. As a result, this paper has made some progress in solving the problem of data sparsity and enriching the research of recommendation system. [ABSTRACT FROM AUTHOR]

The conventional college innovation and entrepreneurship education ecosystem is not considered to be designed for low energy consumption, and there is confusion in function switching, which leads to a large value of packet loss rate in system transmission. In this paper, we analyze the construction of college innovation and entrepreneurship education ecosystem and construct the scientific ecosystem of college innovation and entrepreneurship education based on user experience so as to realize college. At the same time, it takes into account the principle of green and low energy consumption of system hardware and software design, in which the hardware part selects S3C6410 RISC microcontroller as the core component to build the structure of educational ecological controller, connects a 16 bit buffer in parallel outside the controller, uses the buffer as the actual conversion port, specifies the function switching bearing interface, uses a protocol conversion chip, and designs the data acquisition circuit structure. The software part builds a model of user experience elements, sets to get an ecological interaction interface under the interoperability between the model and information data, and uses JAVA programming to realize functional modules according to the deviation of educational information shown by users. The experimental results show that the educational ecosystem designed in this paper has high data throughput, high fault tolerance, and low packet loss rate and system energy consumption, which verifies the effectiveness of the system. [ABSTRACT FROM AUTHOR]

Random matrix theory, as a theoretical tool for statistical processing of complex systems and high-dimensional data, has been more and more combined with big data technology and applied to automatic attendance prediction analysis of classroom education. Firstly, the basic definition of the random matrix is introduced. Secondly, the single ring theorem of the random matrix is introduced. Based on this, the random matrix model of data is established. However, few undergraduate or graduate students in physics have been exposed to this theory. By introducing the basic concept of random matrix theory, this paper analyzes the necessity and feasibility of introducing random matrix theory into automatic attendance prediction teaching in classroom education. As a biometrics technology, random matrix theory technology has been widely used in various fields of the identity verification system, and classroom attendance system is an important field of random matrix theory technology application. The traditional random matrix theory class attendance has the problem of slow recognition speed and low accuracy. With the popularity of deep learning, the random matrix theory based on deep learning has gradually replaced the traditional random matrix theory. In this paper, the SSD target detection algorithm, NB algorithm, and RF algorithm based on deep learning are used to improve and optimize the traditional random matrix theory class attendance system, which effectively improves the efficiency and accuracy of random matrix theory attendance. Then, aiming at the low recall rate and recognition rate of random matrix theory in low-pixel images, we try to apply the random matrix theory of single image resolution reconstruction method to face detection and random matrix theory and give a calculation method to deal with outliers and noise. [ABSTRACT FROM AUTHOR]

As a key means to elevate low-carbon energy transformation in China, multi-energy microgrid accelerates the construction of new power systems. In order to scientifically evaluate the benefits of multi-energy microgrids, we proposed a benefit evaluation index system from the dimensions of economy, reliability, low carbonization, and intelligence. Considering the relationship between the evaluation indicators, this paper innovatively proposed a multi-energy microgrid benefit evaluation model based on AHP-VWT-MEEM. In addition, this paper selected different multi-energy microgrid demonstration projects in the China Southern Power Grid Company Limited's region for example analysis. In the example analysis, through the comparison of the comprehensive benefits of the three projects, the comprehensive benefit of the project 3 is the best. On this basis, from the specific configuration plan of Project 3, it can be concluded that the configuration of larger capacity thermal storage can realize the "thermal decoupling" of cogeneration, thereby improving the flexible adjustment capability of the demonstration area, promoting the consumption of renewable energy, and obtaining more considerable comprehensive benefits. At the same time, it is pointed out that the comprehensive benefit evaluation result can be improved by appropriately reducing the investment cost of project 3. [ABSTRACT FROM AUTHOR]

As computer and multimedia technology advances and as the variety of knowledge display forms expands, there are more ways for people to obtain knowledge and information. The best course in musicology is voice performance. A crucial issue is how to select and categorize valuable curriculum materials of varying quality levels. This paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning technology and tests it on the corresponding dataset, beginning with multidimensional association rule mining technology and the premise that multimedia data contain numerous useful characteristics. Experiments demonstrate the classification precision and data integration capacity of the proposed algorithm. Vocal performance is the most engaging course in musicology. To cultivate corresponding talents, we can rely on college and university offline courses and the rapidly developing multimedia technology to train talents online. An important topic is how to efficiently select and classify curriculum materials of varying quality in order to provide them to students with different learning needs. We can achieve the automatic classification of the course content by leveraging the superior learning capabilities of artificial intelligence and machine learning technology. Consequently, this paper implements an automatic classification and integration algorithm for vocal performance learning materials using machine learning-related technology and conducts an experimental test on the corresponding dataset, beginning with multidimensional association rule mining technology and the perspective that multimedia information itself contains a large number of useful characteristics. Experiments demonstrate that the proposed algorithm has a high level of classification accuracy and data integration capability. [ABSTRACT FROM AUTHOR]

Research on English teaching reform and model design is the focus of the current society. It is a novel idea to use artificial intelligence algorithm to design an English teaching platform, which combines the current field of English teaching reform with the field of artificial intelligence network. The current method is to use the sincent algorithm in artificial intelligence to design the model. Its defect is that the single network education learning makes the learners feel boring. In order to solve these problems, this paper proposes an English blended education method based on artificial intelligence, which aims to study the integration of the network teaching platform and traditional education. This paper uses the artificial intelligence sincent algorithm to establish a teaching platform framework and simulate application strategies for the development of blended education. Through the investigation process of blended English teaching, the results show that the proportion of students who are generally satisfied with English teaching has reached 53.67% and the proportion of students who feel generally satisfied has reached 27.83%. The results of this survey indicate that the majority of students approve of this type of English blended education method. [ABSTRACT FROM AUTHOR]

With the rapid development of Internet, new media news has gradually become the most concerned information source of new media people. New media public opinion is a force that cannot be ignored. It needs monitoring and guidance. The research on hot topic discovery and trend analysis can timely find social hot topics and analyze the trend of topics, which is conducive to grasp the trend of public opinion, so as to correctly guide and maintain social stability. At the same time, the emerging industry of new media has sprung up like mushrooms after a rain, with a rapid momentum. With the advent of the era of big data, the development of new media presents characteristics and advantages that are different from traditional media, but we should also note that the era of big data has both advantages and disadvantages for the development of new media. In this paper, LDA and ARIMA models can be used to calculate and analyze the popularity measurement and trend analysis of new media reports under the background of big data mining. The model designed in this paper has a conclusion: the missed detection rate is reduced by 75.4%. Through experiments, it is found that the accuracy of heat topic detection of the model designed in this paper can reach 84.6%. In trend analysis, the first stage of transmission is called the incubation period. Then after a certain critical point, it will come to the outbreak period. The outbreak period lasted for a period of time, entered a period of plateau, and finally came to a subsidence period. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study of the sentiment of social network communication through a deep learning-based natural language processing approach and designs a corresponding model to be applied in the actual social process. Specifically, the network can dynamically select the most important word in the current state according to the information available and achieve the accurate recognition of the dynamically changing important content in a sentence. Based on this, the semantic understanding of the whole sentence is achieved through a continuous cycle of the process. In addition, considering that the semantic representation of natural language is highly dependent on contextual information, the lack of contextual information will lead to the ambiguity and inaccuracy of semantic representation. In this paper, we study the sentiment analysis algorithms in social networks at two levels, unimodal and multimodal, and construct a text sentiment analysis model and a picture-text multimodal sentiment analysis model in social networks, respectively. By comparing the experiments with the existing models on several datasets, the accuracy of the two models exceeded the benchmark models by 4.45% and 5.2%, respectively, which verified the effectiveness of the two models. The feasibility of applying the optimized convolutional neural network recurrent optimization network to social network sentiment analysis is verified by practically applying the optimized convolutional neural network recurrent optimization network to single task and multitask and comparing other existing deep learning classifiers. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor precision, low recall rate, and large recommendation time overhead in the personalized recommendation of sports online video teaching resources, this paper designs a personalized recommendation method for sports online video teaching resources based on multi-user characteristics. The area where the collected sports online video teaching resources are collected is fixed, and the confidence space for data collection is determined. The components of each clustering point are determined by the k-means clustering algorithm, and the data collection is completed continuously iteratively. The similar data in the data segments is removed with the help of cosine similarity, and the video segment data in the sports online video teaching resources with high similarity is removed for further data normalization. According to the multi-user feature analysis, the video teaching resource data that the user is interested in is matched, and the matching weight is calculated. In this paper, a personalized recommendation model of sports online video teaching resources is constructed by the recurrent convolutional neural networks (RCNN) method, and the personalized recommendation of video teaching resources is realized. The experimental results show that the mining accuracy of this method is about 96%, the highest recall rate can reach 98%, and its data recommendation time overhead is less than 1.3 s. This method effectively improves the effect of sports online video teaching resources. [ABSTRACT FROM AUTHOR]

This paper presents an in-depth study of a statistical system using image feature extraction technology for cultural heritage tourism information. Based on the specificity of the tourism industry, it combines the technical architecture, framework of image feature extraction, GIS basic design, database design, and other related theories; realizes visual mapping through relevant tourism statistics in GIS; and uses the spatial analysis capability of ArcGIS software to complete the statistical scheme of cultural heritage tourism information data in the context of Internet big data. In this paper, the image preprocessing process is accelerated by the parallel optimization of the filtering operation and down-sampling operation in the relevant image preprocessing algorithm. The preprocessed images can effectively improve the recognition effect of the image feature extraction and classification stage. Based on cultural heritage tourism information mining results, the design and development of the tourism information statistics platform are carried out. The platform is built and developed using GIS secondary development technology and HTML + CSS front-end development technology API, Echarts, and other image feature components. The platform enables the massive and disorganized tourism text information to be displayed in a way that is easier to understand in both maps and text. This paper's research on critical technologies provides technical support for image feature extraction of tourism statistics, which has specific practical value. Implementing the cultural heritage tourism platform offers a visual operating environment for tourism, enabling cultural heritage tourism businesses to tap into user preferences so that users can effectively access the travel options that interest them, which is conducive to promoting the development of travel recommendation platforms. [ABSTRACT FROM AUTHOR]

In the context of rapid social development, education has also faced many challenges in the market. As an important part of higher education, music courses have important value and effect on the cultivation of students' healthy psychological quality. Correct and effective music teaching can effectively guide students' music theory knowledge and aesthetic awareness and promote students' personal future development. However, the learning of theoretical knowledge in music courses is often abstract and boring. In the actual teaching process, problems such as unsatisfactory teaching effect of teachers and low learning interest and enthusiasm of students are often caused by the single teaching mode. Based on numerical analysis and scientific computing, this paper studies the new ideas of college music teaching mode. Under the guidance of scientific educational theory, numerical analysis and scientific calculation are integrated into the teaching of music curriculum theory, and abstract music theory knowledge is concretized to guide students to learn knowledge in a more intuitive way. In the practice of college music teaching mode based on numerical analysis and scientific computing, the method proposed in this paper can effectively improve students' learning effect and learning enthusiasm. And through the questionnaire survey, it is found that more than 80% of the students are satisfied with this teaching mode, and 62.5% of the students hope to continue to use this teaching mode in the learning process. It is feasible to apply it in practical teaching. [ABSTRACT FROM AUTHOR]

Apart from the three revenue sharing parameters, the other parameters of the revenue sharing contract (RSC) in a low-carbon supply chain cannot be precise values in most cases. As a result, adopting the RSC will cause challenges in the low-carbon supply chain. In this paper, to calculate the value of the revenue distribution parameter, a decision method based on multiple index is put forward. First, we construct an index system of revenue distribution based on the viewpoints of cost, efficiency, and satisfaction. Second, we propose the third-party participation (TPP) mechanism and the group analytic hierarchy process (GAHP) decision-making approach according to the premise that "heteronomy is better than self-discipline." By the use of the case of company "A," we demonstrated the applicability and practicability of the decision-making approach. The main contributions of this paper are twofold. First, we surveyed 22 experts and set up the 43 indexes, while also constructing the analytical hierarchy process matrix and explaining each index in depth. Second, the TPP mechanism and the GAHP method are used to calculate the value of each index and their sensitivity to the decision-making by the software of Yaahp12.0. Overall, this research can help the decision-makers of low-carbon supply chain in determining the ratio of revenue distribution and improving the applicability of revenue sharing contract. [ABSTRACT FROM AUTHOR]

This paper analyzes the viscoelastic fractional constitutive model based on the analytical form of the fractional derivative of the sine (cosine) function. First, the polynomials of the linear model are combined into a fractional derivative term, then the complex modulus can be easily obtained, and the response characteristics of the model can be analyzed. Second, the fractional nonlinear model is expressed in harmonic form, which can easily identify nonlinear parameters. Finally, fractional-order nonlinear models can also be transformed into integer-order nonlinear forms. In this paper, the transformation model is verified by literature test data, which show the correctness and accuracy of the transformation form. [ABSTRACT FROM AUTHOR]

With the continuous growth of the film market and the consumption demand of audiences, the value of film content has become increasingly apparent. Extracting movie content elements is an important step in quantitative analysis of movie content. In this paper, based on text mining technology, deep data analysis of movie reviews is carried out using TF-IDF and machine learning to visualize high-frequency words, and sentiment analysis is performed on reviews to find out the hidden deep information behind the big data of movie reviews. Although the extracted keywords can draw the content and characteristics of the film for us, they cannot establish the correlation with the creative elements of the film and television works. Therefore, in this paper, the extracted keywords are clustered to find the central words of these words. First of all, in order to improve the representativeness of keywords, the Epoch data set and the Batchsize data set are used to conduct experimental analysis on the model in this paper. Through comparative experiments, it is concluded that when Batchsize = 32 and Epoch = 25, the model achieves the optimal classification effect. The analysis shows that when the training times are too small, the model is not fully learned, and when the training times are too many, the model will be overfitted, which will reduce the accuracy of the model, resulting in the opposite effect. The keywords extracted above can draw the content and characteristics of the movie for us. [ABSTRACT FROM AUTHOR]

In recent years, the development of intelligent iteration technology and the use of big data processing technology have set off an upsurge, and the analysis and application of artificial intelligence algorithms have been paid more and more attention. In order to face the challenges of Arab migration and international trade, this paper constructs the basic structure of the Arab migration action imitation model. In this paper, a simulated servo clustering algorithm based on big data and intelligent iteration is used. Then, through the analysis of pseudo servo clustering algorithm, an optimization model is established, and a big data analysis system is formed. This paper focuses on the wide application of big data statistics to solve the construction of Arab immigration and entrepreneurship data system. This paper studies and applies the big data statistics and intelligent iterative algorithm of Arab immigration behavior, focuses on the annual ladder degree of Arab immigrants, and constructs a pseudo servo cluster system based on Intelligent iterative algorithm. Finally, the simulation experiment verifies whether the clustering model can accurately retrieve the behavior of Arab immigrants in China. The era of big data provides good development opportunities for Arab immigrants and international trade, but it also faces severe challenges. The study provides a reference for strengthening the analysis of international trade, and puts forward perfect countermeasures in combination with the actual situation, so as to improve the efficiency of international trade management and promote the better implementation of international trade. [ABSTRACT FROM AUTHOR]

The economies of all the countries are growing with the passage of time. In order to promote the businesses in the global markets, technology managers/technical brokers play a key role such as in the optimal allocation of scientific and technological resources. The industrialization of scientific and technological achievements is also possible due to optimal utilization of resources and a balance between exports and imports. The technology managers should be competent enough to bridge the gaps between the resource requirements and the availability of the resources required to run the businesses smoothly. This paper is using multi-criteria methods for analyzing the qualities in the technical managers which can assist the managers to run the businesses in an efficient manner to compete in the global markets. The interviews and questionnaire are used to collect the primary data. The opinion of experts is also considered. Then, SPSS 22.0 statistical analysis software is used to analyze the reliability and validity of the factors considered in the questionnaire. Then by using statistical techniques, a quantifiable competency model of technology managers is constructed. The multi-criteria AHP (analytical hierarchical process) method is used to compare the competency characteristic indexes of each level in pairs and quantitatively describe their importance of the criteria. It also assigns weights to judge the importance of each competency characteristic index. Since the evaluation of technical managers' competency will be affected by various factors as mentioned in the paper, the fuzzy comprehensive evaluation method is also used to transform the evaluation dimension to accurately evaluate the competency of the technical manager which assists to judge the effectiveness of model evaluation. The proposed method provides theoretical and practical basis for the selection, evaluation, and cultivation of technical managers. It provides accuracy in results by 94% by adopting the statistical techniques. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the logistics industry, the business volume of the road transportation industry is also growing rapidly and its transportation costs are also increasing. Therefore, it is particularly important to control the transportation cost. This paper aims to evaluate and analyze the cost management of road logistics transportation based on the optimal solution of linear programming. This paper introduces the basic concept of highway logistics in general and analyzes its components and characteristics. Road logistics transportation has the characteristics of fast speed, low investment, strong flexibility, and easy to adapt to local conditions and does not require much infrastructure. We then analyzed the general linear programming model, made a brief introduction to its mathematical model, and proposed the construction of the optimization model to explore the transportation model of production and sales balance. Finally, taking W logistics company as a case to analyze and discuss, the influence of the transportation route in transit on the transportation cost is studied. And its structure and the corresponding transportation cost of logistics enterprises are analyzed in detail. The experimental results of this paper show that by comparing the calculation results of the road segment method and the linear programming model, the total freight calculated by the segment method is 5610 yuan, while the total freight calculated by the linear programming method is 5490 yuan. Comparing the piecewise methods, it can be found that the linear programming method is more economical in transportation cost than the piecewise method. Therefore, when logistics companies optimize road transportation costs, it is more feasible and effective to use linear programming methods. [ABSTRACT FROM AUTHOR]

Reliability analysis provides a basis for the anticorrosion design and maintenance strategy of pipelines. This paper introduces a calculation method for corroded pipeline life reliability by step-down-stress testing (SDST) and Weibull distribution. SDST is used to obtain the corrosion rate of N80 steel under the action of an H2S and CO2 environment at four different temperatures. The Arrhenius model is used to establish the conversion model of failure time and then obtain quasi-samples with parameters. The quasi-samples are used to estimate the parameters of the Weibull distribution, and finally, we can obtain the reliability function of the corroded pipeline. The life reliability curve shows that the pipeline life decreases with the increase of temperature, and when the operation temperature is 363 K, the average life is 10.09 years, which is far less than the designed life. The life reliability of the pipeline decreases with increasing time, when the service life of a pipe with 90% reliability is approximately 7.4 years, and with 50% reliability, it will increase to approximately 10.2 years. With increasing temperature, the average life of the pipeline declines, and at the same temperature, the higher the reliability is, the lower the average life of the pipeline, which provides a reference for the rational use and maintenance decisions of N80 steel pipes. The life reliability of a pipeline for which the operating time is shorter has a smaller temperature effect than that the operating time is longer. This proves that the effect of temperature on life reliability accumulates. This paper strives to provide a scientific basis for the safety management of oil and gas pipelines. [ABSTRACT FROM AUTHOR]

The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism. [ABSTRACT FROM AUTHOR]

SPOCS is a brand-new teaching concept, which focuses on reflecting the dominant position of students. The application of SPOCS guidance mode in international trade practice teaching will help stimulate students' interest in knowledge, improve classroom-teaching effect, and improve classroom teaching quality. In order to fully mobilize students' subjective initiative and creativity, we should cultivate students into new talents with strong comprehensive quality. Let students study actively and autonomously under the correct guidance of teachers. From the perspective of "1 + x" certificate system, this paper introduces the necessity of developing application-oriented undergraduate education. This paper expounds the requirements of the "1 + x" certificate system for the classroom teaching of applied economics and analyzes the problems existing in the classroom teaching. This paper puts forward the reconstruction of efficient classroom teaching mode, makes great efforts to deepen the cooperation between industry and education in classroom teaching, and realizes the goal of "1 + x" certificate system to cultivate talents. A three-stage tutoring mode of "knowledge transfer before class," "knowledge internalization in class," and "evaluation and reflection after class" is designed. Practice has proved that the combination of microclassroom and flipped classroom is an effective strategy to improve the effect of learning knowledge. It provides some references and suggestions for front-line teachers to implement flipped classroom teaching in the future. [ABSTRACT FROM AUTHOR]

With breakthroughs in big data, communication networks, artificial intelligence, and other next-generation information technologies, intelligent production methods and digital lifestyles have risen rapidly. Digital economy has become a significant force in promoting regional low-carbon development as a new economic form in the important period of the intersection of digitalization and "carbon peaking and carbon neutrality goals." This paper empirically explores the moderating role and threshold role of human capital in digital economy and regional carbon productivity by systematically measuring digital economy indicators. Research shows that China's digital economy development index is rising annually. However, the development of digital economy varies significantly between different regions. The results of moderating effect showed that human capital accumulation could dramatically strengthen the role of digital economy in promoting regional carbon productivity. The results of the threshold effect showed that human capital plays an important threshold role in the relationship between digital economy and regional carbon productivity. This paper provides a practical reference for exploring power sources, policy design, and path selection of low-carbon development. [ABSTRACT FROM AUTHOR]

With the advancement of science and technology as well as the continual improvement of big data analysis technology, the accuracy of traditional data information classification has declined, making it impossible to assess English ability effectively. A competency evaluation model for college English teaching vacancies is built using this information and the big data architecture. The ability of the big data information model is evaluated and feature information of ability constraints is extracted using the predefined constraint parameter index analysis model. Simultaneously, the K-means clustering algorithm is used to cluster and integrate a series of index parameters of English ability using big data, and the English teaching resource allocation plan is completed in accordance with this, allowing for the scientific evaluation of English teaching ability. The results of the studies show that the clustering method utilized in the context of big data can aid in the evaluation of English competence. In the experiment, four test cycles of English teaching skills were set up, and the effectiveness of the English evaluation techniques described in this paper and two classical cluster evaluation methods were compared and tested. The research shows that using the method described in this paper to evaluate English teaching skills can significantly improve the full utilization of data. [ABSTRACT FROM AUTHOR]

This paper explores the general decision mechanism of stock price synchronicity through in-depth research on the two main viewpoints of information efficiency view and irrational behaviour view. On this basis, we explore the effect of China's economic policy uncertainty gradually on the synchronisation of stock prices and provide a reasonable explanation for it. Based on the China Economic Policy Uncertainty Index developed by Baker et al., this paper takes China's A-share listed firms from 2010 to 2019 as the primary sample and tests the effect of China's economic policy uncertainty on the synchronicity of stock prices by constructing a comprehensive mediation effect test procedure. The fundamental effect and its internal mechanism of action have been supplemented and verified from investor sentiment. Strengthening the research on economic policy uncertainty and stock price synchronisation may deeply explore the internal mechanism of the listed firms' stock price changes, help in improving the information production function and information transmission function of the securities market, help in improving the resource allocation capacity of the entire capital market, and then promote the long-term healthy development of China's capital market. The main findings of this paper are as follows: (1) in China's stock market, which is driven by noise, the increase of economic policy uncertainty will significantly reduce the synchronisation of stock prices; (2) increased uncertainty about China's economic policy will fuel the investor sentiment and reduce stock price synchronicity, with investor sentiment playing an intermediary role. [ABSTRACT FROM AUTHOR]

With the globalization of the contemporary social economy and its ever-increasing influence, it is particularly important to master a common language in the world at this stage English. For the subjects who have the opportunity to learn English, their listening, reading, writing, and other abilities are relatively good, but the speaking ability is relatively lacking. Therefore, the self-study of spoken English is particularly important for the improvement of the practical application ability of English, and an efficient self-study system will also play a better role in the self-study process. The efficiency of a system depends on the maximum and minimum magnitudes that the system can carry. Therefore, for the self-learning system of spoken English in this paper, it is necessary to calculate the maximum and minimum carrying capacity. Therefore, it is necessary to deal with the limit thinking of the maximum and minimum carrying capacity of the system. The evaluation based on limit theory in this paper is a kind of evaluation of the maximum and minimum carrying capacity of the English oral self-learning system by using the convergence and dispersion characteristics of limit thinking. In order to evaluate the performance of the oral English self-learning system, this paper implements the dynamic evaluator of the oral English self-learning system, and constructs a hardware system for the self-learning oral English. This paper also uses the hidden Markov model in the self-learning system and the application of the central limit theory. The corresponding experimental results show that the mean value of the spoken English test data is 25.1337, and the standard deviation is 2.01385, which is in line with the normal distribution. It shows that the evaluation based on the limit theory can make the operation of the spoken English self-learning system stable. [ABSTRACT FROM AUTHOR]