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This paper presents a comprehensive study and analysis of the intelligent grouping of papers in adult higher education using a random matrix approach. Using the results of random matrix theory on the eigenvalues of the sample covariance matrix, the energy of each subspace is estimated, and the estimated energy is then used to construct a subspace weighting matrix. The statistical properties of the sample covariance matrix eigenvectors are analyzed using the first-order perturbation approximation, and then, asymptotic results from random matrix theory on the projection of the sample covariance matrix signal subspace to the real signal parametrization are used to obtain the weighting matrix based on the random matrix eigenvectors. Dynamic adjustment according to the fitness of individuals in the population is performed to ensure population diversity, while the combination of the small habitat technique can avoid the algorithm from falling into early convergence. The algorithm introduces chaos theory to optimize the population initialization process and uses the dynamic traversal randomness of chaos to select individuals in the population so that the initial population is close to the desired target solution. The design of the fitness function in the genetic algorithm generally maps the objective function of the problem to the fitness function. A good fitness function can directly reflect the quality of the individuals in the group. Based on the in-depth study of the basic attributes of the test questions and the principles of test paper evaluation, the mathematical model and objective function of intelligent paper grouping are determined by the difficulty, knowledge points, and cognitive level of the test questions as the main constraints, and NCAGA is applied to the intelligent paper grouping method, which better completes the intelligent paper grouping session for the computer system architecture course. In the process of designing the intelligent grouping algorithm, for the situations of premature convergence and convergence to locally optimal solutions that easily occur in the traditional genetic algorithm, this paper adopts the approach of adaptive adjustment of crossover probability and variation probability to improve the algorithm and achieves satisfactory results. Based on extensive business research, this paper completes the requirement analysis of the online practice system based on the intelligent grouping of papers and presents the functional design and database design of the key functional modules in the system in detail. Finally, this paper conducts functional tests on the system and analyses the test results. [ABSTRACT FROM AUTHOR]

To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of the Internet industry, the publicity effect of paperbark walnuts has been substantially increased, and the consumption and demand of paperbark walnuts have been rising, and the output of paperbark walnuts in China has also increased year by year. This paper takes the panel data of the top three prefecture-level cities in each GDP ranking of five provinces in northwest China from 2012 to 2021 as the sample and combines the DEA model with Malmquist to evaluate and analyze the logistics efficiency of paper walnuts in northwest China. It is found that the overall logistics efficiency of paperback walnuts in Northwest China has been improved in recent years, but there are still areas that need to be improved, such as the factor distribution is not reasonable enough; the logistics efficiency of paperback walnuts in Northwest China from 2012 to 2021 is in a fluctuating state; the logistics efficiency of paperback walnuts in Shaanxi Province is better than the other provinces and is in the leading position; the improvement of technological progress has a key role in the total factor productivity improvement. As a result, this paper puts forward five specific suggestions based on the research results to help accelerate the improvement of paper walnut logistics efficiency in Northwest China. [ABSTRACT FROM AUTHOR]

Distributed multitarget tracking (MTT) is suitable for sensors with limited field of view (FoV). Generalized covariance intersection (GCI) fusion is used to solve the MTT problem based on label probability hypothesis density (PHD) filtering in this paper. Because the traditional GCI fusion only has good fusion performance for the targets in the intersection of each sensor's FoV, and the targets outside the intersection range would be lost, this paper redivides the Gaussian components according to the FoV and distinguishes the Gaussian components of the targets inside and outside the intersection. GCI fusion is sensitive to label inconsistency between different sensors. For label fusion in the intersection region, the best match of labels is found by minimizing label inconsistency index, and then GCI fusion is performed. Finally, the feasibility and effectiveness of the proposed fusion method are verified by simulation, and its robustness is proved. The proposed method is obviously superior to local sensor and traditional GCI algorithm. [ABSTRACT FROM AUTHOR]

In order to improve the effect of Chinese dance training, this paper combines digital feature recognition technology to correct and analyze Chinese dance training movements and constructs an intelligent auxiliary training system. In order to solve the travel time problem at the computationally complex interface and ensure its computational accuracy, a local adaptive triangulation technique is used in the fast-advance algorithm. Moreover, this paper designs the function of the system according to the user's needs, transforms the design concept into a figurative visual representation through the interactive prototype according to the function, carries out the visual design of the interface according to the interactive prototype, and uses the interactive technology to realize the development of the system. From the test analysis results, it can be seen that the Chinese dance training action correction system based on the digital feature recognition technology proposed in this paper has a good effect and can effectively promote the improvement of the Chinese dance training effect. [ABSTRACT FROM AUTHOR]

Software development effort and cost estimation (SDECE) is one of the most important tasks in the field of software engineering. A large number of research papers have been published on this topic in the last five decades. Investigating research trends using a systematic literature review when such a large number of research papers are published is a very tedious and time-consuming task. Therefore, in this research paper, we propose a generic automated text mining framework to investigate research trends by analyzing the title, author's keywords, and abstract of the research papers. The proposed framework is used to investigate research trends by analyzing the title, keywords, and abstract of select 1015 research papers published on SDECE in the last five decades. We have identified the most popular SDECE techniques in each decade to understand how SDECE has evolved in the past five decades. It is found that artificial neural network, fuzzy logic, regression, analogy-based approach, and COCOMO methods are the most used techniques for SDECE followed by optimization, use case point, machine learning, and function point analysis. The NASA and ISBSG are the most used dataset for SDECE. The MMRE, MRE, and PRED are the most used accuracy measures for SDECE. Results of the proposed framework are validated by comparing it with the outcome of the previously published review work and we found that the results are consistent. We have also carried out a detailed bibliometric analysis and metareview of the review and survey papers published on SDECE. This research study is significant for the development of new models for cost and effort estimations. [ABSTRACT FROM AUTHOR]

The nature of construction and formulation of the multi-objective functions in the flexible job shop (FJS) provides great insights about the study of the technique and the mechanism in scheduling helping to prioritize efforts for optimizing the manufacturing execution performance, which draw the focus of the researchers at home and abroad. From the perspective of the model, the solution method and the objective function, this paper reviews and analyzes the related academic papers and conferences articles during past few years. As a result, the construction forms of the multi-objective function and the scheduling scenarios show strongly positive correlation with the Makespan, the delivery tardiness, the energy consumption or the cost. Consequently, facing with the characteristic in a general way, the detailed construction analysis on the function for the flexible job shop scheduling problem (FJSP) is critical reviewed, and a total 6 types with 49 kinds classification are discussed. Obviously, the main goals are to summarize, analyze, discuss, and synthesize the existing achievements, the current research status, and the ongoing studies on the FJSP functions construction, and additionally to give useful insight into future research. Combined with the development trend of the intelligent manufacturing and the operations research, arguing for the formulation of the multi-obj. function, this review is expected to contribute to the future research of FJSP. [ABSTRACT FROM AUTHOR]

A security model of multimedia education resource fusion based on multistage integration of multimedia educational resources is constructed in this paper in the context of smart education. First, the teaching model of multimedia education resource fusion is analyzed and the IoT communication evolution model and functional architecture model are constructed accordingly, based on which the realization path of IoT intelligent sensing, intelligent management, emotional computing, device sharing, and vision simulation functions for smart education are discussed. The security model uses a decentralized cryptographic data security sharing method based on blockchain for protection and supports blockchain to record user attributes to ensure the confidentiality and integrity of data. The experimental and analytical results indicate that the scheme can effectively reduce the computational overhead of smart devices while ensuring the security of data sharing. Moreover, the proposed model in the paper achieves better performance than other methods in terms of security strength, encryption, and decryption time. [ABSTRACT FROM AUTHOR]

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

The common perception of people about recycling involves reusage of aluminum cans, glass bottles, and newspapers, whereas recycling of water resources is a most important aspect nowadays. Water recycling is known to reuse cleaned wastewater for beneficial applications including agricultural and landscape irrigation, industrial activities, and replenishing a groundwater using the latest technologies. The current methods have some flaws in the evaluations of water resources' recycling systems such as large mean square error, time complexity, and low-evaluation efficiency; therefore, this paper proposes an online evaluation method for the recycling process of water resources in urban landscaping. The health model of water resources recycling in urban landscaping has been analyzed using fuzzy-based approach. Second, the evaluation index system of water resources' recycling is also analyzed using the ecological water-level analysis results, water resource quality, water resource abundance, and water resource utilization rate. Then, the extension of analytic hierarchy process (AHP) is utilized to calculate the weight of water recycling evaluation index. Then a fuzzy-based comprehensive evaluation method is used to find the online evaluation model of the water recycling. Eventually, the evaluation of the effect of water recycling in urban landscaping is performed and analysis is made for decision-making. The results prove that the proposed AHP and fuzzy method has a low mean square error and high accuracy. [ABSTRACT FROM AUTHOR]

With the increasing number of college students, the difficulty and workload of ideological and political education are also increasing. For the calculation and analysis of big data, it is most suitable to use data mining algorithm for calculation and research. In order to help college students' ideological and political education to develop and innovate, this paper establishes a new model of college students' ideological and political education combined with particle swarm optimization algorithm to help carry out college students' ideological and political education. The particle swarm optimization algorithm is tested. Therefore, this paper uses data mining algorithm to calculate and analyze the research in this paper. The algorithm is tested and analyzed. Through the test of data mining algorithm, the calculation accuracy of the algorithm is proved, and the feasibility of this paper is proved. This paper studies the ideological and political education by using data mining algorithm. The computational efficiency of the optimized data mining algorithm is twice that of the traditional algorithm, and the optimized algorithm has also been significantly improved. In general, the data mining algorithm optimized in this paper is reasonable, which can improve the calculation accuracy and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

This study was aimed at developing an automatic exam system as an intelligent and high-quality higher math examination solution for Department of Computer Engineering at Pai Chai University (PCU) based on S/W Engineering. As a research and development (R&D) project, the study used the rational unified process (RUP) method for software development. RUP describes how to effectively use commercial and reliable methods to develop and deploy software system. It is a heavyweight process; therefore, it is particularly suitable for large teams to develop large projects. This paper describes the RUP process of this R&D project what we named it as Automatic Higher Mathematically Exam System (AHMES). AHMES provides a new way to automatically generate exam questions. In the study, our team and the requesting department collected the requirements and selected methods and tools. Then, our team designed and implemented the framework of the system, completed the development of some main functions, tested and summarized the system, and planned the future work. [ABSTRACT FROM AUTHOR]

In a democratic regime, voting is crucial to making collective decisions. Unfortunately, although this activity has great significance and value, little effort has been made to improve the way we vote. Paper ballots are still the most used method, although this method is relatively simple, brings many inconveniences, and represents a contradiction to the modern world and its advances. This paper mostly focuses on a review study of blockchain-based voting systems. It aims at identifying the strategies and the guidelines as well as provides a comprehensive end-to-end electronic voting system based on blockchain, with the help of cryptographic techniques such as zero-knowledge proofs to improve privacy. The novelty of this paper is that we tackle the limitations of electronic voting systems found in the literature, including cost, identity management, and scalability problems. Our purpose is to provide key elements for organizations on how to design their proper electronic voting system based on blockchain technology. [ABSTRACT FROM AUTHOR]

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

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

China, situated in the circum-Pacific seismic belt, experiences frequent seismic activity and faces diverse geological conditions, making structural stability of paramount importance, especially under seismic conditions. The majority of current earthquake generation methods do not consider the nonstationary nature of earthquakes. This paper introduces a spectral representation-random function model for generating nonstationary earthquakes, effectively simulating stochastic seismic ground motion. Furthermore, traditional slope stability analysis methods are deterministic and incapable of providing probabilistic assessments of slope instability. Therefore, this paper proposes a unified framework for static and dynamic structural reliability analysis based on the direct probability integration method, quantifying the impact of stochastic seismic ground motion on the dynamic reliability of slope stability. Finally, the proposed methods are applied to an excavation slope in Nanjing, using sliding displacement and safety factors as evaluation criteria to study the reliability of the slope under the influence of stochastic seismic events. [ABSTRACT FROM AUTHOR]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

Finally, the last paper presents a design-task-oriented model assignment framework that involves model value selection, multiobjective model establishment, and multiobjective optimization algorithm. We hope that the papers presented in this Special Issue will be useful and stimulating for further developments and applications of multicriteria and multiobjective models and that we again have been able to highlight the extensive range of contexts over which these methods can be used. This is the Fourth Special Issue (SI) dealing with "Building Mathematical Models for Multicriteria and Multiobjective Applications.". [Extracted from the article]

This paper presents a control strategy for a photovoltaic (PV)-based distributed generator (DG) to enhance the frequency regulation of a microgrid. A microgrid is a small-scale power system that integrates distributed energy sources and interconnected loads, which can operate independently or in parallel with the main grid. However, the high integration of DGs poses stability problems, particularly the frequency regulation under contingencies in the microgrid. For example, the shutdown of one generating unit or the reduction of system inertia owing to the high integration of converter-based DGs will increase the risk of instability of the microgrid and the main grid. Therefore, maintaining and increasing the frequency regulation capability is crucial in microgrid operation. This paper focuses on developing a controller for PV DG based on virtual inertia control combined with the conventional control method of a converter in order to regulate the microgrid frequency under contingency. The proposed control strategy for a PV-based DG is then verified through simulation of the 14-bus microgrid model using MATLAB/Simulink, showing regulation in frequency under island mode operation of the microgrid. In addition, comparisons between the original and proposed control strategy microgrid model are then carried out. [ABSTRACT FROM AUTHOR]

To study the effect of nano-SiO2 doping content on the properties of chopped fiber composite meta-nano-aramid insulating paper (IP) for transformer bushings, the laboratory-prepared chopped fiber composite meta-nano-aramid IP with different nano-SiO2 doping content is analyzed to assess its impact on the environment. The study has been carried out by considering crystallographic properties and morphology of chopped fiber composite meta-nano-aramid fiber, the influence-mechanism of nano-SiO2 doping content on the mechanical properties, the thermal stability and insulating properties of aramid paper. The experimental results show that the appropriate graft modification of nano-SiO2 enhances the thermal stability and mechanical properties of the meta-aramid (MA) insulating paper. It also shows lesser carbon emissions. When the results are compared with SiO2 without grafted silane coupling agent, SiO2 with a surface graft ratio of 6%, the former method (SiO2 without grafted silane coupling agent) shows a greater increase in the glass transition temperature of the MAIP. At the same time, it shows weakened molecular weight in terms of MSD. The proposed method shows more prominence in terms of the chain kinematic ability, the resistance to deformation, and shearing in terms of mechanical properties. When SiO2 with the graft ratio of 12% is observed, it does not show any improvement, and also the effects on thermal stability and mechanical properties of IP are not significant which is similar to grafting. After hot pressing at 245°C, 30 MPa for 5 min, the breakdown strength, and volume resistivity can reach as high as 37 kV/mm and 1.34 × 1015 Ω·cm respectively. The proposed method shows a significant environmental impact in terms of less emission of CO and CO2. [ABSTRACT FROM AUTHOR]

Cultural symbol generation has always been a challenging task to achieve symbols that can represent national culture and promote people's identification with Chinese culture. In this paper, we combine generative adversarial network (GAN) to propose a symbolic generation model of Chinese national cultural identity based on visual images. First, combining pattern search regular terms with generator cross-loss functions based on GAN generative adversarial networks to improve the pattern collapse phenomenon of generative adversarial networks. Second, the normal convolutional layer of the generator in the network structure is replaced with a deep-space separable convolution to improve the real-time performance of the model by reducing the model parameters. Through extensive testing on real datasets, the results show that the model in this paper can generate higher performance ethnic culture symbols while maintaining better temporal performance, which has some practical application value. [ABSTRACT FROM AUTHOR]

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

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

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

Diabetes is one of the alarming issues in today's era. It is a chronic disease that may cause many health-related problems. It is a group of syndrome that results in too much sugar in the blood. Diabetes's chronic hyperglycemia has been linked to long-term damage, organ breakdown, and organ failure, notably in the eyes, kidneys, nerves, heart, and veins. Machine learning has quickly advanced, and it is now used in many facets of medical health. The goal of this research is to create a model with the highest level of accuracy that can predict a patient's chance of developing diabetes. This paper proposes a novel architecture for predicting diabetes patients using the K-means clustering technique and support vector machine (SVM). The features extracted from K-means are then classified using an SVM classifier. A publicly available dataset, namely, the Pima Indians Diabetes Database, is tested using this approach. Accuracy of 98.7% is noted on the used dataset. On this dataset, the combined method performs better than the conventional SVM-based classification. This paper also compared the accuracy, precision, recall, and F1-score of the different machine learning techniques for classifying diabetes patients. [ABSTRACT FROM AUTHOR]

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

Garbage is an underutilized resource, and garbage classification is one of the effective ways to make full use of these resources. In order to realize the automation of garbage classification, some deep learning models are used for garbage images recognition. A novel garbage image recognition model Garbage Classification Net (GCNet) based on transfer learning and model fusion is proposed in this paper. After extracting garbage image features, EfficientNetv2, Vision Transformer, and DenseNet, respectively, are combined to construct the neural network model of GCNet. Data augmentation is used to expand the dataset and 41,650 garbage images are contained in the new dataset. Compared with other models through experiments, the results show that the proposed model has good convergence, high recall rate and accuracy, and short recognition time. [ABSTRACT FROM AUTHOR]

The minimum spanning tree (MST) is widely used in planning the most economical network. The algorithm for finding the MST of a connected graph is essential. In this paper, a new algorithm for finding MST is proposed based on a deduced theorem of MST. The algorithm first sorts the edges in the graph according to their weight, from large to small. Next, on the premise of ensuring the connectivity of the graph, it deletes the edges in this order until the number of edges of the graph is equal to the number of vertexes minus 1. Finally, the remaining graph is an MST. This algorithm is equivalent to an inverse Kruskal algorithm. For sparse graphs, the proposed algorithm has higher efficiency than the Kruskal algorithm. Experiments and analysis verify the effectiveness of the algorithm proposed in this paper. The algorithm provides a better choice for finding the MST of the sparse graph. [ABSTRACT FROM AUTHOR]

The development of 5G (fifth-generation wireless systems) determines the future direction of technology and economy and has received extensive public attention. Studying the changing rules of public attention to 5G can provide an important guiding significance for the sustainable development of 5G. This paper takes Baidu Index as the measurement index of 5G public attention and analyzes the spatial and temporal evolution characteristics and influencing factors of public attention to 5G from 2011 to 2021 by using the elasticity coefficient, Gini coefficient, geographical concentration index, and panel data model. The results of the study show the following. (1) The public concern to 5G is generally on the rise, but the heat has declined in the past two years. (2) The public's 5G attention shows a seasonal effect, with the highest attention in March and June. (3) The spatial difference of 5G public attention is obvious. The eastern region has a high degree of attention, the internal differences between the eastern and western regions are obvious, and the central region is relatively balanced. (4) The factors such as local economic level, education level, Internet development, and media attention have significantly affected the public focus on 5G. Also, some suggestions are made for the sustainable development of 5G and the planning of 6G (sixth-generation wireless systems). [ABSTRACT FROM AUTHOR]

The methods of sports event management are the life cycle management method, which can be divided into four stages: start-up planning, plan preparation, real-time control, and followup evaluation. The administrative measures management law includes administrative orders, instructions, regulations, and systems of administrative organizations at all levels. The system management law has four characteristics: mandatory, authoritative, stable, and preventive. The current management mode of sports events is mainly that the government is deeply involved in the operation of the entire event, and relevant personnel are selected from various government departments to form an organizing committee, and the resources related to the event are controlled and regulated by the government. However, in our country, there is currently no unified sports event management standard. Therefore, this paper proposes the application and evaluation of sports event management method based on recurrent neural network (RNN). The comments of sports events are extracted from the network, classified with an RNN, and finally, an improvement plan is obtained through evaluation. The main work of this paper is as follows: (1) the development status of sports event management at home and abroad and the application of RNN are introduced, and RNN is used in the evaluation and classification of sports event management. (2) We propose a sentiment classification model GCNN-GRU that fuses local feature extraction. Aiming at the defect that the basic model is easy to lose key phrase information, a CNN with a gating mechanism is used to extract and filter local features. The classification experiment results show that the proposed GCNN-GRU has the best classification effect on the Chinese sentiment dataset. [ABSTRACT FROM AUTHOR]

Image enhancement and reconstruction are the basic processing steps of many real vision systems. Their purpose is to improve the visual quality of images and provide reliable information for subsequent visual decision-making. In this paper, convolution neural network, residual neural network, and generative countermeasure network are studied. A rain fog image enhancement generative countermeasure network model structure including a scalable auxiliary generation network is proposed. The objective loss function is defined, and the periodic consistency loss and periodic perceptual consistency loss analysis are introduced. The core problem of image layering is discussed, and a layering solution framework with a deep expansion structure is proposed. This method realizes multitasking through adaptive feature learning, which has a good theoretical guarantee. This paper can not only bring a pleasant visual experience to viewers but also help to improve the performance of computer vision applications. Through image enhancement technology, the quality of low illumination image can be effectively improved, so that the image has better definition, richer texture details, and lower image noise. [ABSTRACT FROM AUTHOR]

Health education competency is essential for nurses to engage in clinical nursing practice. Nurses with a high level of health education competency have a promising future. However, at the present stage, health education of nurses suffers from insufficient effective communication skills, poor skillfulness, and lack of health education knowledge. The above problems seriously affect the improvement of nurses' health education level and restrict the development of health education in health industry. Therefore, it is very important to construct a health education competency model on how to improve health education. This paper, by taking nurses as the research object, constructed a health education competency model for nurses using the fuzzy comprehensive evaluation method and fuzzy cluster analysis method. In this paper, we first applied the fuzzy cluster analysis method to classify the relevant factors affecting nurses' health education competency. Then, we investigated 10 experts from four tertiary general hospitals in Heilongjiang Province of China and asked them to rate nurses' health education competency through the fuzzy comprehensive evaluation method. According to the results of this research, nurses' knowledge and experience, nurse-patient communication competency, and health education implementation competency are the key factors affecting nurses' health education competency. But the influence from overall quality is relatively minimal. Among the indicators of knowledge and experience, nurses' health education knowledge is the most important indicator because it can provide objective basis and expand ideas for the hospital health education management and nursing personnel training. [ABSTRACT FROM AUTHOR]

This paper adopts the fuzzy set qualitative comparative analysis method to carry out an empirical analysis on Chinese latecomer enterprises and examines the key factors and realization paths that affect the technology path transformation of latecomer enterprises. This paper finds that there are configuration characteristics among the influencing factors of the technological path transformation of latecomer enterprises. The paper reveals the causal complexity mechanism of multiple factors that jointly affect technology path transformation. The key factors that affect the technological path transformation of latecomer enterprises include organizational vigilance, strategic transformation capability, network connections breadth, resource governance depth, dynamics of the technological environment, and the scale of social demand for technology. The above factors not only emphasize that latecomer enterprises should focus on new technologies rather than the market but also reveal that strategic transformation capability and resource governance depth are the keys for enterprises to achieve technology path transformation. In addition, it is pointed out that latecomer enterprises should pay attention to the "window of opportunity" brought about by the scale of social demand for technology. The transition from technology path dependence to technology path transformation of latecomer enterprises is a process in which the key factor becomes stronger. [ABSTRACT FROM AUTHOR]

With the rapid development of modern science technology and Internet technology, the establishment of a unified and standardized bibliographic summary database to realize the exchange and resource sharing of ancient Chinese medicine bibliography, is the inevitable trend of ancient Chinese medicine bibliography digital service. Firstly, this paper formulates the bibliographic metadata specification of traditional Chinese medicine ancient books (TCMAB), extracts each cataloging file into an XML document in line with the bibliographic metadata specification of TCMAB. Secondly, this paper realizes the unified description of ancient book resources in the database system of TCMAB, uses the native XML database eXist to store and manage the XML documents of all traditional Chinese medicine ancient book resources, and integrates the multimedia data with XML data. Finally, genetic algorithm and BP neural network are used for knowledge mining and discovery, the overall model design of TCMAB bibliographic abstracts database system is proposed based on the construction process of knowledge map. The system platform adopts B/S mode, eXist database management system, PowerSSP streaming media and video server for audio video processing. [ABSTRACT FROM AUTHOR]

Analyzing monitoring data to recognize structural anomalies is a typical intelligent application of structural safety monitoring, which is of great significance to hydraulic engineering operational management. Many regression modeling methods have been developed to describe the complex statistical relationships between engineering safety monitoring points, which in turn can be used to recognize abnormal data. However, existing studies are devoted to introducing the correlation between adjacent response points to improve prediction accuracy, ignoring the detrimental effects on anomaly recognition, especially the pseudo-regression problem. In this paper, an anomaly recognition method is proposed from the perspective of causal inference to realize the best exploitation of various types of monitoring information in model construction, including four steps of constructing causal graph, regression modeling, model interpretation, and anomaly recognition. In regression modeling stage, two deconfounding machine learning models, two-stage boosted regression trees and copula debiased boosted regression trees, are proposed for recovering the causal effects of correlated response points. The validation was carried out with Shanmen River culvert monitoring data, and experiment results showed that the proposed method in this paper has better anomaly recognition compared to existing regression modeling methods, as shown by lower false alarm rates and lower averaged missing alarm rates under different structural anomaly scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a model predictive speed control strategy for a surface-mounted permanent magnet synchronous motor by applying Laguerre functions. The model predictive controller (MPC) incorporates an integrator. A quadratic programming procedure is applied to solve the constrained optimization problem online. The paper also provides a solution for stability. The performance efficiency of the proposed scheme is validated by comparing the results with the performance of an optimal linear quadratic regulator, conventional state-space model predictive control, and a simple MPC algorithm with integral action. Extensive simulation results confirm the efficacy of the proposed scheme, showing that it achieves good steady-state performance while maintaining a fast dynamic response. [ABSTRACT FROM AUTHOR]

Vehicle antilock systems play a very important role in the stability and reliability during vehicle braking. Due to the complexity of the braking process, antilock braking system (ABS) usually face the problems such as nonlinearity, time-varying, and uncertain parameter modeling. Thus, aiming at the parameter model uncertainty problem of ABS, an adaptive neural network sliding mode controller (ADRBF-SMC) is designed in this paper. On this basis, establishing the quarter-vehicle model and the seven-degree-of-freedom vehicle model, and treating the difference between the two models as a kind of disturbance, carrying out vehicle braking performance simulation experiments to analyze the variation of braking performance parameters such as vehicle and wheel speeds, slip ratio, braking distance, braking torque, under the three cases of adaptive neural network sliding mode controller, traditional sliding mode controller, and no control. Simulation results show that the adaptive neural network sliding mode controller (ADRBF-SMC) proposed in this paper can play an effective control role in both vehicle dynamics models. In addition, the control method proposed in this paper has stronger anti-interference capability and higher robustness compared with the sliding mode controller (SMC). [ABSTRACT FROM AUTHOR]

Localization is an important method for autonomous indoor robots to recognize their positions. Generally, the navigation of a mobile robot is conducted using a camera, Lidar, and global positioning system. However, for an indoor environment, GPS is unavailable. Therefore, a, state-trajectory tracking method is utilized based on a Lidar map. This paper presents the path following of an autonomous indoor mobile robot, that is, a shuttle robot, using a state-flow method via a robot operating system network. MATLAB and Linux high-level computers and an inertial measurement unit sensor are used to obtain the Cartesian coordinate information of a bicycle-type mobile robot. The path following problem can be solved in the state-flow block by setting appropriate time and linear and angular velocity variables. After the predetermined time, the linear and angular velocities are set based on the length of the path and radius of the quarter-circle of the left and right turns in the state-flow block, path planning, which can execute the work effectively, is established using the state-flow algorithm. The state-flow block produces time-series data that are sent to Linux system, which facilitates real-time mobile platform path following scenario. Several cases within the path-following problem of the mobile robot were considered, depending on the linear and angular velocity settings: the mobile robot moved forward and backward, turned in the right and left directions on the circular path. The effectiveness of the method was demonstrated using the desktop-based indoor mobile robot control results. Thus, the paper focuses on the application of the state-flow algorithm to the shuttle robot specifically in the narrow indoor environment. [ABSTRACT FROM AUTHOR]

In seismic data processing, data recovery including reconstruction of the missing trace and removal of noise from the recorded data are the key steps in improving the signal-to-noise ratio (SNR). The reconstruction of seismic data and removal of noise becomes a sparse optimization problem that can be solved by using sparse regularization. Sparse regularization is a key tool in the solution of inverse problems. They are used to introduce prior knowledge and make the approximation of ill-posed inverses feasible. It deals with ill-posedness by replacing an ill-posed inverse problem with a well-posed problem that has a solution close to the true solution. In the last 2 decades, interest has shifted from linear toward nonlinear regularization methods even for linear inverse problems. In inverse problems, regularizations serve as stabilizing the solution of ill-posed inverse problems and give a solution that adequately fits measurements without producing unjustifiably complex artifacts. In this paper, we present a novel sparse regularization based on a tensor-based dictionary method for inverse problems (seismic data interpolation and denoising). This regularization avoids the vectorization step for sparse representation of seismic data during the reconstruction process. The key step in sparsifying signals is the choice of sparsity-promoting dictionary learning. The learning-based approach can adaptively sparsify datasets but has high computational complexity and involves no prior-constraint pattern information for the dataset. Many existing dictionary learning methods would be computationally infeasible for the high dimensional seismic data processing. These methods also destroy the essential information as well as it reduces the discriminability and expressibility of the signal, since they deal with vectorization. In this paper, the orthogonal tensor dictionary learning that learns a dictionary from the input data by employing orthogonality and separability is proposed as sparse regularization for the inverse problems. The performance of the proposed method was validated in seismic data interpolation and denoising individually as well as simultaneously. Numerical examples of synthetic and real seismic datasets demonstrate the validity of the proposed method. The SNR of the recovered data confirms that the proposed method is the most effective method than K-singular value decomposition and orthogonal dictionary learning methods. [ABSTRACT FROM AUTHOR]

This research paper presents a new iterative method (NIM) for obtaining the solution to the potential Kadomtsev–Petviashvili (PKP) equation. NIM is a promising approach to solving complex mathematical problems, and its effectiveness and efficiency are highlighted through its application to the PKP equation. The results obtained through the use of NIM are compared to the exact solutions of the PKP equation, and it is found that the NIM approach provides results that are in close agreement with the exact solutions. This demonstrates the utility and accuracy of NIM and makes it a valuable tool for solving similar mathematical problems in the future. Furthermore, the lack of discretization in the NIM approach makes it a more convenient method for solving the PKP equation compared to traditional approaches that require discretization. Overall, the findings of this research paper suggest that NIM is a highly effective and convenient method for obtaining approximate analytical solutions to complex mathematical problems, such as the 2 + 1 -dimensional PKP equation. [ABSTRACT FROM AUTHOR]

Object tracking is a hot issue in vision technology research; it is used in scenarios like intelligent monitoring, autonomous driving, and robot visual perception. With the rapid development of sports, tracking of targets in complex sports scenes represented by basketball and football has gradually attracted attention. The target tracking algorithm based on machine learning (ML) has been gradually proposed. With the powerful feature extraction for convolutional neural network (CNN), it greatly improves accuracy and has better robustness in the face of complex sports scenes. However, the tracking algorithm based on deep learning has many network layers and parameters, which makes training and update speed of model slower. In this regard, taking basketball as an example, this paper designs a low-parameter deep learning-based complex sports target tracking algorithm, which greatly reduces the size of the model while ensuring the tracking accuracy. Aiming at the problem of large number of parameters and large model of deep learning tracking algorithm, this work proposes a network structure model with asymmetric convolution module. The asymmetric convolution module includes two convolutional layers, the compression layer and the asymmetric layer. To improve accuracy, this work designs a new triplet loss. Compared with original logistic loss, triplet loss function can fully utilize the latent relationship between the inputs, so that the network model can obtain higher tracking accuracy. Finally, this paper proposes a low-parameter deep learning-based target tracking algorithm combining asymmetric convolution and triple loss function. Comprehensive and systematic experiments demonstrate the effectiveness of this work in tracking complex sports objects. [ABSTRACT FROM AUTHOR]

Heterogeneous multicore processor systems, as one of the highlights of multicore processor systems, are widely loved by people for their high efficiency and low cost, and they have also become the most commonly used processor systems in embedded systems. In the process of research on heterogeneous multicore processor systems, system task scheduling is particularly important. A good task scheduling algorithm can give full play to system performance. In this paper, the intelligent approximation algorithm is applied to the task scheduling problem of heterogeneous multicore processor system, and the heterogeneous multicore processor system is obtained as a highlight in the multicore processor system. Relying on the characteristics of high efficiency and low cost, it is widely loved by people, and at the same time, it has become the most commonly used processor system in embedded systems. In the process of research on heterogeneous multicore processor systems, the system task scheduling problem is particularly important. A good task scheduling algorithm can give full play to system performance. Some commonly used heuristic task scheduling algorithms are insufficient in solving such problems. This paper combines the granularity-based wavefront parallel decoding algorithm and the fast fusion loop filter algorithm to apply pipeline parallel technology between pixels. Decoding reconstruction module and fast loop filter module realize the fusion of multilevel parallel decoding. Based on the multicore platform, a dynamic multiparallel scheduling algorithm is designed to realize two-way video real-time parallel high-speed decoding, which improves the core resource utilization rate and decoding execution efficiency of the multicore processing platform. This paper also designs an indicator collector, a read-write hit collector, and a cache block priority determiner to implement a dynamic generation strategy with low hardware overhead. Multimodal teaching has certain feasibility and effectiveness and can have a positive impact on the English reading motivation and English reading comprehension ability of university students. Multimodal teaching improves students' English reading comprehension ability, deepens students' understanding and memory of words, and broadens the scope of knowledge, which has a significant promoting effect. The research results show that multimodality can be applied to college English teaching, and it can achieve better results than traditional teaching methods. By comparing the test results before and after the test, there are obvious differences between the experimental class and the control class. Whether it is the paired sample T-test of the experimental class or the independent sample T-test of two teaching methods in two classes, it proves that the scores under the multimodal English teaching mode are higher than the traditional teaching mode. Multimodal classrooms provide students with many opportunities to participate in classroom activities and form a competitive learning atmosphere. This competitive learning atmosphere has become a driving force to promote student learning. Multimodal teaching methods help to cultivate students' independent and cooperative learning. In a multimodal classroom, many activities require cooperation and discussion among students. When they encounter difficulties, they can help each other, discuss with each other, and cooperate to complete tasks. Therefore, the multimodal teaching method will stimulate students' interest in learning, give full play to students' initiative, and improve students' English ability. [ABSTRACT FROM AUTHOR]

This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]

At present, China's equipment manufacturing industry and producer service industry are gradually ushered in the leapfrog high-quality development of the rising period. It is particularly important how Shanxi, as the vanguard of the transformation and development, positively responds to the national call to break the shackles of the current integrated development of equipment manufacturing and producer services, for Shanxi Province and even the country's future industrial integration, transformation, and upgrading to accumulate "Shanxi Experience." Therefore, this paper measures and analyzes the integration level of equipment manufacturing and production services between 2012 and 2017 using the input-output method. Then, by constructingthe model of the factors influencing the integration of Shanxi's equipment manufacturing industry and producer service industry, selecting the relevant indicators and bringing in the relevant data from 2009 to 2019, by using the principal component regression analysis method, and thus clarifies the main factors that influence the convergence of the equipment manufacturing industry and production service industry in Shanxi. Finally, this paper has provided targeted countermeasures and suggestions to further promote the integration of the two industries in Shanxi Province. [ABSTRACT FROM AUTHOR]

In the context of the continuous promotion of China's big data development strategy, this paper quantitively analyses China's existing national-level big data policies from the perspective of policy instruments and coword networks, discusses the rationality of existing policies, explores ways to improve policies, and provides a reference for the innovation of China's big data policies. This paper carries out a quantitative textual analysis of China's national big data policy from the perspective of policy instruments using word frequency analysis to obtain a keyword coword matrix and visualization analysis tools to obtain a coword network. This paper further studies the network node characteristics and structure using social network analysis methods, including degree centrality, clustering analysis, and multidimensional scale analysis, to identify the policy structure and characteristics. Improving big data policy requires improvements in policy instruments on the supply side, resolving existing policy gaps, and strengthening coordination with other policies. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to solve the problem of big data and small samples caused by the high manual annotation cost of a military corpus. The deep learning algorithm of entity extraction in the military field was organically combined with the method of bootstrapping loop iteration to complete a study on the application of intelligent corpus annotation of military field entities. With the experimental research showing that using a small number of military field entity corpus annotations for RoBERTa pretraining word vectors and BiLSTM-CRF models and based on the bootstrapping algorithm idea to complete 3 rounds of loop iterations and 10 rounds of cross-validation joint-voting model iterations, the best entity extraction model evaluation F value reached up to 91.5%. Finally, the 60M intelligent corpus annotation application testing was completed using the best model of iteration of this round, with a total of 178,177 sentences of military field corpus intelligently labeled, the number of entities that should be labeled reaching 417,734. Therefore, this is an efficient way of construction and evaluation of intelligent corpus annotation model in the military entity extraction field. The findings of this paper provide an effective way of how to complete the labeled corpus. The research serves as a first step for future research, for example, the construction of knowledge graphs and military intelligent Q&A. [ABSTRACT FROM AUTHOR]

An important prerequisite for promoting sustainable development and value chain climbing of textile enterprises is to achieve a win-win situation between environmental protection and export technical complexity. Thus, based on Chinese enterprises' microdata, this paper investigates the impact of cleaner production standards implementation on the export technical complexity of Chinese textile enterprises using a double difference method. The results show that the implementation of cleaner production standards significantly increases the export technical complexity of Chinese textile enterprises. The results of the mechanism test indicate that cleaner production standards increase export technical complexity by enhancing capital and labor inputs of textile enterprises, as well as by promoting enterprise innovation and increasing productivity. Heterogeneity analysis shows that cleaner production standards have a significant role in increasing the export technical complexity of textile enterprises in the eastern region and larger scale; it is not significant in increasing the export technical complexity of textile enterprises in the central and western regions and smaller scale; in terms of enterprise ownership attributes, it has the greatest role in increasing the export technical complexity of state-owned textile enterprises, followed by foreign and private textile enterprises. The results of this paper show that cleaner production standards, as an environmental regulation policy, can achieve a win-win situation for environmental protection and export technical complexity increase. [ABSTRACT FROM AUTHOR]