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Abstract Let E be a Q‐isogeny class of elliptic curves defined over Q. The isogeny graph associated to E is a graph which has a vertex for each elliptic curve in the Q‐isogeny class E, and an edge for each cyclic Q‐isogeny of prime degree between elliptic curves in the isogeny class, with the degree recorded as a label of the edge. In this paper, we define an isogeny‐torsion graph to be an isogeny graph where, in addition, we label each vertex with the abstract group structure of the torsion subgroup over Q of the corresponding elliptic curve. Then, the main result of the paper is a classification of all the possible isogeny‐torsion graphs that occur for Q‐isogeny classes of elliptic curves defined over the rationals.

Abstract The construction introduced by Gross, Hacking and Keel in (Several Complex Variables (Springer, New York, NY, 1976))allows one to construct a formal mirror family to a pair (S,D) where S is a smooth rational projective surface and D a certain type of Weil divisor supporting an ample or anti‐ample class. In that paper, they proved two convergence results when the intersection matrix of D is not negative semi‐definite and when the matrix is negative definite. In the original version of that paper, they claimed that if the intersection matrix were negative semi‐definite, then family extends over an analytic neighbourhood of the origin but gave an incorrect proof. In this paper, we correct this error. We reduce the construction of the mirror to such a surface to calculating certain log Gromov–Witten invariants. We then relate these invariants to the invariants of a new space where we can find explicit formulae for the invariants. From this we deduce analytic convergence of the mirror family, at least when the original surface has an I4 fibre.

This paper aims to investigate new coincidence point theorems for self-maps under an extended rational contraction. Our approach uses the p-adic distance in partially ordered ultrametric spaces. Furthermore, to illustrate our major discoveries, we give a comprehensive numerical analysis. This study expands on prior research in partially ordered ultrametric spaces to provide a more comprehensive understanding of such spaces using rational contraction.

This paper focuses on solving the challenge of observer-based exponential control (O-BC) regarding conformable fuzzy polynomial models with time delay. In this work, polynomial matrices with unmeasurable states are considered to enhance the practicality of the model in the design problem. The proposed approach guarantees the existence of the polynomial controller and observer gains by satisfying sufficient conditions based on the sum-of-squares (S-O-S) approach. The stability conditions of the addressed system are solved using MATLAB SOSTOOLS, and the effectiveness of the proposed control scheme is evaluated through the presentation of simulation results from a numerical example and a mass-spring-damper system.

This paper introduces a new probability distribution called the mixture symmetric gamma (MSG) distribution, which is defined as a mixture of two symmetric gamma distributions. Its statistical properties and applications are explored. We first examine its mathematical properties, including the possible shapes of the corresponding probability density function, as well as the moments, and the moment-generating function. We then look at parameter estimation using various frequentist and Bayesian methods, such as moment estimation, maximum likelihood method, least-squares method, and Bayesian approaches. In addition, the prediction of future observations under the MSG model is extensively covered, considering both frequentist and Bayesian perspectives, including median prediction, best unbiased prediction, and Bayesian prediction. A comprehensive simulation study is conducted to evaluate the performance of the proposed estimation and prediction techniques. Finally, the practical applicability of the MSG model is demonstrated through the analysis of four real-world datasets. It is shown to outperform several well-known competing models in terms of goodness-of-fit. The results highlight the inherent simplicity, efficiency, robustness, and intuitive interpretability of the MSG distribution, making it a compelling choice for modeling data with a symmetric pattern, with potential applications in diverse domains.

In this paper, a delayed predator–prey model with a square root functional response is structured and analyzed. Through a discussion of the time delay and an analysis of the characteristic equations, the local stability of the boundary equilibrium and the positive equilibrium and the existence of Hopf bifurcation are investigated. On this basis, the critical value of the Hopf bifurcation is derived. According to the central manifold theorem and normal form theory, the nature of the Hopf bifurcation is obtained. Finally, by conducting numerical simulations, it is observed that incorporating a time delay can influence the stability of the predator and prey populations, causing periodic oscillations in the number of two populations.

This paper aims to find generalizations of inequalities that hold for unified integral operators by applying strongly exponentially α,ℏ−m−p-convex functions. These inequalities generate results for several fractional integral operators and simultaneously hold for convex, strongly convex, and exponentially convex functions. The particular cases of presented inequalities are directly connected with many recently published results.

The purpose of this paper is to introduce q-Post–Widder operators based on a new parameter and study their approximation properties. The moments and central moments are investigated. And some local approximation properties of these operators by means of modulus of continuity and Peetre’s K-functional are presented. Furthermore, the rate of convergence for these operators is obtained. Weighted approximation and the quantitative q-Voronovskaja type theorem are discussed. Finally, numerical illustrative examples have been given to show the convergence of these newly defined operators.

Grey systems theory can be used to predict the evolution of a system with insufficient information. Unfortunately, the most used version of the grey model (GM), namely, GM(1,1), works best when the system series have an increasing exponential rate. In any other case, the GM(1,1) produces inaccurate predictions. In this paper, we examine the mathematical formulation of the conventional GM(1,1) in order to propose a new GM that addresses its shortcomings through a new time response function. Examples are presented to demonstrate the flexibility and accuracy of the new model when implemented with series of various natures. Comparisons with other intelligent GM(1,1) show that the proposed model performs better than the reference models.

In the operation of airlines, the most important link is determining the route and scheduling of aircraft. The key to this problem is to input the flight segment and aircraft type and finally determine all flight segments for each aircraft. In this paper, we focus on finding feasible, robust scheduling for various uncertainties in the flight process. This paper presents a new robust integer mathematical model based on game theory that considers daily aircraft routing. Then, in order to find the suboptimal solution to a large-scale integer programming problem in a limited amount of time, a heuristic algorithm integrating a column generation algorithm and variable domain search is introduced. In addition, we use the data of a Chinese airline to verify, and the experimental results show that the model proposed by us is more robust than the model in general.

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

Oil is a highly demanded worldwide product as oil prices can have a major economic and social impact. There are many factors affecting oil prices, but little is known about which factor mostly influenced oil prices. To understand what caused the fluctuation of oil prices, the investigation leading up to the identification of the factors must be constructed. Thus, this paper attempts to explore the factors that might have contributed to oil prices and analyze them using the fuzzy preference ranking organization method for enrichment evaluation (PROMETHEE) method. In particular, the objective of this study is to rank the influential factors affecting oil prices with the consideration of a few criteria. The fuzzy PROMETHEE model comprises four criteria and five factors as the input information. A group of four experts are invited to provide an evaluation on scales of 1 to 5 of the importance of factors with respect to criteria. In computation, the evaluations are treated as triangular fuzzy numbers in which the uncertainty in data is dealt with amicably. This approach combines the advantages of transformation equation and nonarithmetic mean of the defuzzification method that transforms triangular fuzzy numbers into crisp numbers. The results indicate that “supply and demand” is the most influential factor in determining oil prices. Comparative analysis and future research directions are also discussed at the end of this paper.

Linear and quadratic discriminant analysis are two fundamental classification methods used in statistical learning. Moments (MM), maximum likelihood (ML), minimum volume ellipsoids (MVE), and t-distribution methods are used to estimate the parameter of independent variables on the multivariate normal distribution in order to classify binary dependent variables. The MM and ML methods are popular and effective methods that approximate the distribution parameter and use observed data. However, the MVE and t-distribution methods focus on the resampling algorithm, a reliable tool for high resistance. This paper starts by explaining the concepts of linear and quadratic discriminant analysis and then presents the four other methods used to create the decision boundary. Our simulation study generated the independent variables by setting the coefficient correlation via multivariate normal distribution or multicollinearity, often through basic logistic regression used to construct the binary dependent variable. For application to Pima Indian diabetic dataset, we expressed the classification of diabetes as the dependent variable and used a dataset of eight independent variables. This paper aimed to determine the highest average percentage of accuracy. Our results showed that the MM and ML methods successfully used large independent variables for linear discriminant analysis (LDA). However, the t-distribution method of quadratic discriminant analysis (QDA) performed better when using small independent variables.

In order to define the positioning of the urban brand image, design the urban brand image, integrate and optimize the communication channels, improve the public participation awareness, and enhance the core competitiveness of the city. In this paper, a personalized recommendation search engine based on big data identifies keywords input by urban users. And give more accurate results based on some relevant information that can be extracted. This paper analyzes how to make better use of big data for tourism destination brand image management, and the existing shortcomings, and puts forward relevant suggestions. The industries related to cultural creative design and tourism elements constitute an intertwined cultural and tourism industry chain, and data technology plays an important role in the cultural and tourism industry chain. Through the development of tourism, tourists will produce comprehensive and diversified consumption in the city. Based on the analysis of big data, it can provide strong decision support for the government and industry managers, and realize the image design and communication of the urban brand identification system. Through the big data platform, establish the brand management strategy, improve the communication content of the city’s brand image, and timely feedback the opinions and suggestions of tourists on the tourism destination, so as to adjust the communication strategy of the tourism image according to the feedback information of tourists. The results show that the big data personalized recommendation system can achieve ideal results in urban brand value and urban tourism related factors.

In today’s big data context, the composition of virtual networks is becoming increasingly complex, so it is neither easy nor difficult to strengthen the control of network security issues. This paper studies the important content of network big data mining-assisted laboratory management. On the basis of discussing the connotation of laboratory safety, combined with management practice, this paper puts forward some suggestions on laboratory safety management. This paper puts forward some views and thoughts on some problems existing in the management and experimental teaching of chemistry laboratory. By judging the stability of instruments and equipment, we know whether the calibration status of instruments and equipment has changed during this period, so as to ensure the sustainable use of instruments and equipment and the accuracy and traceability of laboratory test results. The use of laboratory management system can greatly improve the utilization rate of laboratory equipment, promote students’ autonomous learning, and promote the standardization of laboratory construction. Improve the network monitoring method of big data mining technology and the application mechanism of big data network monitoring, in one type of laboratory. Teachers and students in the laboratory can reasonably arrange their own time according to their own interests to guide and operate experiments. Through continuous operation and practice, students’ practical ability and experimental knowledge level should be improved as much as possible.

The study of farmers’ migration choice behavior can reflect people’s needs for the future living environment from the side and has practical and important guiding significance for adjusting and improving the focus of future development. In order to make a detailed analysis of the prediction of migrant workers’ behavior in cities, this paper studies the internal mechanism of the change of commuters’ commuting patterns under the background of urban suburbanization. Using Bayesian network, this paper establishes a model of commuting mode transfer of urban immigrants, taking personal and family attributes as control variables. The influence of factors such as migration attributes and changes in building environment perception before and after migration is analyzed. Based on the survey data, a Bayesian network of farmers’ land use behavior is established by using Bayesian network method to investigate the choice of farmers’ migration direction. In the research process of migration direction, the research structure shows that 60.1% of farmers want to stay in the countryside, rather than migrate through Bayesian network algorithm. The results of regression analysis by establishing a multivariate logistic model show that there is a strong willingness to choose small towns and large cities. However, the willingness to choose not to relocate to local villages is relatively weak.

This paper analyzes the deficiencies of human resource allocation in the tourism industry by investigating the human resource allocation in the tourism industry, puts forward corresponding improvement measures and suggestions, and strives to provide certain guidance and helpful effects for the construction of tourism resource informatization. In this paper, a modified BP neural network model is proposed by introducing random perturbation terms on the hidden layer in the BP neural network algorithm, and the weight matrix connecting the input values is added with the random perturbation matrix to obtain a new weight matrix so that the convergence effect of the improved BP neural network algorithm is improved. Then, to address the problem that the initial weights of the long and short-term memory neural network and gated BP unit neural network have a large impact on the convergence speed and prediction accuracy of the algorithm after the initial weight selection is determined, this paper introduces the random perturbation term into the gate structure of the long and short-term memory neural network and gated BP unit neural network and proposes and connects an improved long and short-term memory neural network and gated BP unit neural network. The weight matrix of the input values is added with the random perturbation matrix to obtain the new weight matrix so that the convergence effect of the improved long and short-term memory neural network algorithm and the gated BP unit neural network algorithm is improved. Constructing the human resource allocation model of the tourism industry and proposing coping strategies and countermeasures and taking the human resource allocation system of the tourism industry as the core, the human resource allocation model of the tourism industry is established by combining the network image crisis life cycle system of tourism scenic spots and the network public opinion dissemination model. From the perspective of managers, the human resource allocation management policy and management procedures of the tourism industry are proposed. Using the quantifiable and disenable characteristics of online text information, the response strategy of online monitoring and propaganda and offline management and enhancement is proposed, and innovative countermeasures to the human resource allocation of the tourism industry are proposed in three categories: network originated, reality coexisting, and reality originated. Through this paper, we propose a new approach to human resource allocation management and development in the tourism industry and improve the efficiency of human resource allocation in the tourism industry.

Game theory has become an important tool to study the competition between oligopolistic enterprises. After combing the existing literature, it is found that there is no research combining two-stage game and nonlinear dynamics to analyze the competition between enterprises for advertising. Therefore, this paper establishes a two-stage game model to discuss the effect of the degree of firms’ advertising input on their profits. And the complexity of the system is analyzed using nonlinear dynamics. This paper analyzes and studies the dynamic game for two types of application network models: data transmission model and transportation network model. Under the time-gap ALOHA protocol, the noncooperative behavior of the insiders in the dynamic data transmission stochastic game is examined as well as the cooperative behavior. In this paper, the existence of Nash equilibrium and its solution algorithm are proved in the noncooperative case, and the “subgame consistency” of the cooperative solution (Shapley value) is discussed in the cooperative case, and the cooperative solution satisfying the subgame consistency is obtained by constructing the “allocation compensation procedure.” The cooperative solution is obtained by constructing the “allocation compensation procedure” to satisfy the subgame consistency. In this paper, we propose to classify the packets transmitted by the source nodes, and by changing the strategy of the source nodes at the states with different kinds of packets, we find that the equilibrium payment of the insider increases in the noncooperative game with the addition of the “wait” strategy. In the transportation dynamic network model, the problem of passenger flow distribution and the selection of service parameters of transportation companies are also studied, and a two-stage game theoretical model is proposed to solve the equilibrium price and optimal parameters under Wardrop’s criterion.

How to efficiently guide passengers and ensure the order of airport operation is an urgent transport problem for airport management. Based on the analysis of the factors that affect the driver’s decision-making, this paper deeply explores the collaborative association of the core factors, such as the number of flight arrivals in different periods and the average seeking distance of taxis. Firstly, according to the GPS data of taxis, the paper uses clustering algorithm to get the average passenger-seeking time from the airport and makes matching interaction between the number of flights based on time distribution and the average passenger-carrying capacity of vehicles in the parking garage, so as to build a decision-making model based on the number of taxis N; secondly, it takes passenger safety and traffic order as the priority and uses M/M/S queuing model to integrate the two factors. Taking the maintenance cost and passenger evacuation time as constraints, the judgment condition of minimum cost Zmin and the optimal number of boarding points Sm are solved. Finally, taking the flight and taxi data of Shanghai Hongqiao Airport as an example, the driver’s decision-making standard is simulated, and the accuracy of the model is verified by the deviation rate. It can provide decision-making support for taxi management of urban transportation hub and rapid evacuation of airport passengers, so as to realize the collaborative optimization of airport flight arrival and taxi carrying order.

A fully integrated Chen chaotic oscillation system using operational amplifiers (OAs) and multipliers is designed and verified in this paper. Unlike the conventional breadboard-based Chen chaotic system using off-the-shelf discrete components, the fully integrated Chen chaotic oscillation circuit presented in this paper is realized using GlobalFoundries’ 0.18 μm CMOS 1P6M process, and all the circuit components are integrated in a chip. The fully integrated Chen chaotic oscillation system is verified using Cadence IC Design Tools, and the post-layout simulation results indicate that the presented integrated Chen chaotic oscillation system only consumes 148 mW from ± 2.5 V supply voltage, and its chip area is 6.15 mm2.

This paper uses data mining technology to mathematically model the training movements of tennis players, establish a three-dimensional data information database of athletes utilizing depth imaging, analyze the data with data mining algorithms, and derive the results after comparative evaluation and analysis with a database of movement characteristics of tennis dribblers. This paper uses video observation and mathematical modeling to construct a tennis player training action evaluation model, which provides a reference basis for tennis players to improve and enhance their tactical level; it can also provide a reference for the development of sports training special theory of tennis projects and enrich the tactical diagnosis method of tennis matches. To improve the accuracy of 3D human pose estimation, this paper adopts a 3D skeleton point extraction method based on RGBD images; for the action alignment problem, this paper uses a dynamic time warping (DTW) algorithm; for the similarity measure, this paper gives a Pearson correlation coefficient method based on the joint point features of human parts. This paper aims to conduct a systematic theoretical analysis of tennis players’ training movements based on theories and methods such as system science theory and social network analysis. On this basis, the characteristics of tennis training technology development are analyzed from a combination of qualitative and quantitative perspectives, while the development of tennis player training is explored based on tracking observations of tennis player movement training, and finally, the attack and service characteristics of tennis training are analyzed to better provide some reference for the sustainable development of tennis.

This paper explores the effect of the Double-pillar regulation on promoting enterprise innovation and its influencing mechanism. Based on the panel data of A-share listed nonfinancial enterprises in China from 2010 to 2019, this paper empirically examines the driving effect of the Double-pillar regulation on enterprise innovation and its influencing mechanism. The empirical results demonstrate that the Double-pillar regulation can promote enterprise innovation, and there is significant regional heterogeneity. The Double-pillar regulation has a stronger driving effect on the innovation of state-owned enterprises. The Double-pillar regulation can effectively reduce the financialization of enterprises, thus boosting enterprise innovation. The degree of financing constraint and the improvement of enterprise risk-taking level will enhance the driving effect of the double-pillar regulation on enterprise innovation. The research of this paper promotes the understanding of the effect, mechanism, and regional differences of enterprise innovation under the Double-pillar regulation. Meanwhile, it also examines the necessity of building the Double-pillar regulation framework.

Track and field is a competitive sport with a long history, and it is one of the earliest sports that started scientific training. It is also the main source of many classic theories and methods of modern competitive training, and most of them are still in use today. The purpose of this paper is to study how to analyze and discuss the content system of physical fitness training for track and field athletes and some evaluation criteria of indicators based on artificial neural network. It also describes the BP neural network. This paper puts forward the problem of training content system and index evaluation standard, which is based on artificial neural network. Therefore, the concept and related algorithms are elaborated, and the case design and analysis are carried out. The experimental results show that the physical training method is mainly based on the transformation training method. The goal is to improve the athlete’s training motivation, interest, and adaptability. The training method is relatively simple in design. According to the distribution of muscles, there are more core and trunk training methods, accounting for 23.64% of physical training.

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

The study of convex functions is an interesting area of research due to its huge applications in pure and applied mathematics special in optimization theory. The aim of this paper is to introduce and study a more generalized class of convex functions. We established Schur (S), Hermite-Hadamard (HH), and Fejér (F) type inequalities for introduced class of convex functions. The results presented in this paper extend and generalize many existing results of the literature.

If the driving behavior of an autonomous vehicle is similar to that of a skilled driver, the human driver can extricate himself from fatigue operation and the comfort of passengers can also be guaranteed. Therefore, this paper studies the human-like lane-changing model of an autonomous vehicle. The lane-changing characteristic data of skilled drivers are collected and analyzed through a real vehicle test. Then, comparing the MPC-based driver model with the steering wheel angle of human drivers, we found that the MPC-based model could hardly reflect the maneuvering characteristics of human drivers, so we proposed a driver model with steering wheel angle continuity for human drivers. This paper uses four neural network models to compare the prediction on the test set, then uses different input types to compare the prediction accuracy of the model, and finally verifies the generalization ability of the model on the verification set. These three test results show that the prediction results of the human-like lane-changing driving model based on Bi-LSTM are closest to the real steering wheel angle sequence of skilled drivers. The test results demonstrate that the Bi-LSTM-based human-like lane-changing driving model achieves 9.8% RMSE and 6.8% MAE, which improves 10.8% RMSE and 10.3% MAE over LSTM. The model can generate the steering wheel angle sequence in the process of lane changing like a human, so as to realize the human simulation control of an autonomous vehicle for lane-changing conditions.

Polya’s plausible reasoning methods are crucial not only in discovery of mathematics results, modeling methods, and data processing methods but also in many practical problems’ solving. This paper exemplifies how to use Polya’s plausible reasoning methods to generalize the popularized notion of 2-connectedness of graphs to a more universal notion of the connected degree of fuzzifying matroids. We introduce the connectedness of fuzzifying matroids, which is generalized from 2-connectedness of graphs, connectedness of matroids, and 2-connectedness of fuzzy graphs. Moreover, the connected degree of fuzzifying matroids is presented by considering the fuzziness degree of connectedness. It is proved that a fuzzifying matroid M is connected, which is equivalent to its connectedness degree ConM is the biggest (i.e., ConM=1). This, together with other properties of the connected degree of fuzzifying matroids, demonstrates the rationality of the proposed notion. Finally, we describe the concepts of this paper through some examples.

In this paper, by the use of a new fixed point theorem and the Green function of BVPs, the existence of at least one positive solution for the third-order boundary value problem with the integral boundary conditions is considered,where there is a nonnegative continuous function. Finally, an example which to illustrate the main conclusions of this paper is given.

Based on the close relationship between real estate and capital financing, this paper specifically discusses the impact of interest rate changes in China on real estate market transactions. Housing price regulation has been a hot and focused topic of economics research. It is important to explore the effect of interest rate on real estate market transactions, for the interest rate is an important tool for the government to carry out macrocontrol of the real estate market and affects residents’ consumption choices of goods. Currently, most scholars believe that interest rate changes have a significant impact on house prices, but their studies are often based on empirical analysis of macro data, making it difficult to exclude the effects of economic differences, geographic characteristics, and other factors. Also, some scholars believe that it is difficult for interest rates to have a long-term impact on real estate prices. This paper applies the experimental economics method to simulate real estate transactions in the real market and control the irrelevant variables to study the movements of housing prices, transaction volumes, consumer transaction intentions and bubble indices under different deposit and lending rates. The results show that: (1) The level of deposit interest rate has a significant inhibitory effect on housing price and a significant promoting effect on real estate market transaction volume and traders’ house-buying behavior. The higher the level of deposit interest rate is, the better the degree of regulating house prices deviating from the theoretical price is and the better the effect of inhibiting market bubbles. (2) The level of lending rate has a significant inhibiting effect on the volume of real estate transactions and a significant promoting effect on the house-buying behavior of traders, but its effect on housing prices and market bubbles is not significant. The higher the level of lending rate is, the better the degree of regulating housing prices deviating from the theoretical price is. (3) The effect of deposit interest rate and lending rate policies on the house selling behavior of market traders is not significant.

Affected by the fluctuation of the market and economic environment during the epidemic period, the capital pressure of companies has increased sharply, which increases the possibility of risk transmission in the same industry and poses new challenges to the operation and financing of companies. From the perspective of preventive motivation of corporate cash holding, we creatively use the industry working capital shortfall as the explanatory variable to construct an Extended Cash Holding Model and a Cash Holding Value Regression Model. Taking the panel data of A-share listed companies in Shanghai and Shenzhen as samples, this paper uses the Classical Linear Regression Model and Fixed Effects Regression Model to study the relationship between industry working capital shortfall and cash holdings in the same industry, as well as the relationship between industry working capital shortfall and cash holding value. The empirical results show that industry working capital shortfall has an important impact on the cash holding level within the same industry, and the cash holding level is significantly and positively correlated with industry working capital shortfall. Moreover, this study also reveals that the industry working capital shortfall has a dual impact on cash holdings. Specifically, the higher the risk of industry working capital shortfall, the lower the value of company cash holdings. The conclusions of this paper not only extend the research on cash holding but also provide support and reference for companies to optimize the cash holding value.

In the present paper, we defined lacunary sequence spaces of fractional difference operator of order α,β over n-normed spaces via Musielak-Orlicz function M=Ik. Our aim in this paper is to study some topological properties and inclusion relation between the spaces I−NαβA,M,u,θ,Δγ,·,⋯,·0, I−NαβA,M,u,θ,Δγ,·,⋯,·, and I−NαβA,M,u,θ,Δγ,·,⋯,·∞.

The fractional integral inequalities are crucial to deal applied problems. The present paper deals with the generalize midpoint type inequalities for a certain class of convex functions, namely, MT-convex functions in the setting of weighted fractional integral. It can be observed from the remarks given in the paper that our results or more generalized than existing results of literature and many of them can be obtained immediately from our results.

In recent years, commercial banks and rural financial institutions in and below the county area have developed rapidly, and the number of outlets has increased rapidly. But for the vast rural areas of the country, the number of outlets is still limited. At present, there are still a large number of areas with zero financial coverage in rural the country. The development of the financial industry in a region not only affects the level of local economic growth but also has a good effect on improving the income level of local residents and changing the way of local economic development. To better develop rural finance and county economy, this paper constructs an artificial neural network model and FA model to predict the rural finance industry, and four indicators are selected to test the performance measurement of the model. The hit rates of the five models are 54.65%, 53.75%, 59.78%, 65.1%, and 73.08%, respectively. The model proposed in this paper has the highest hit rate and can predict rural finance more accurately as well, allowing for clearer changes in county economic and industrial growth.

The world economy, since the outbreak of the coronavirus epidemic, has undergone profound changes. Especially since the coronavirus became the norm, how to achieve rapid economic revitalization has become a problem that countries have to face. In order to analyze how the government should promote the enterprises’ economic revitalization and effectively control pollution under the normalized epidemic situation, this paper analyzes the economic revitalization and pollution control problems faced by enterprises under the normalized epidemic situation by using the evolutionary game method. Through the analysis of the evolutionary game model, we draw the following conclusions: (1) Discovered by comparing two different incentives mechanism and penalties mechanism, the dynamic incentives mechanism, and penalties mechanism has a better effect on the process of enterprises’ economic revitalization, and it also can reduce the discharge of enterprises’ pollutants. (2) In terms of discharge reduction effect, penalties have a better effect than incentives. Compared with incentives, in the process of the economic revitalization of enterprises, in order to reduce pollution discharge, the government can adopt dynamic penalties strategies. This paper analyzes what the government should do when enterprises face the problems of economic revitalization and pollution control. This study can not only provide suggestions for the government in the process of governance but also provide countermeasures for the economic revitalization of enterprises.

To revitalize talents and give full play to the maximum utility of HR (human resources), it is not enough to accumulate talents alone. HR must be effectively allocated to realize the matching of people and posts. Competency is a personal characteristic of an organization that distinguishes its performance level in a specific job and organizational environment. In order to solve the problem that job seekers’ job-seeking ability is difficult to match the job requirements, this paper combines neural network with traditional HRM (human resource management) algorithm based on ability perception and designs a depth model of accurate matching of people and posts in HR field, which can improve the quality of data training of traditional algorithm. The results show that compared with other algorithms, the F1 value of the proposed algorithm is obviously improved, and the proposed algorithm performs best, with the F1 value of 0.829. In this paper, the method of global network plus local network is used, which can effectively improve the hidden features of data and then improve the matching degree and recommendation accuracy of the algorithm.

This paper is concerned with a well-known epidemiological concept to measure the spread of infectious disease, that is, the basic reproduction number. This paper has two major objectives. The first is to examine Bayesian sensitivity and consistency of this measure for the case of Markov epidemic models. The second is to assess the Martingale method by comparing its performance to that of Markov Chain Monte Carlo (MCMC) methods in terms of estimating this parameter and the infection and removal rate parameters given only removal data. We specifically consider the Markovian SIR (Susceptible-Infective-Removed) epidemic model routinely employed in the literature with exponentially distributed infectious periods. For illustration, numerical simulation studies are performed. Abakaliki smallpox data are examined as a real data application.

Innovation in the business model (BM) may be irreversible and therefore entails more significant risk and ambiguity compared to product, service, or process innovation. This paper focused on exploring the role of organizational learning in mediating the relationship between business model design and innovation performance using hierarchical hybridized linear regression analysis (HHLRA). Furthermore, this paper shows that organizational learning is based on variables that mediate the relationship between training and performance and improve performance through its positive effect on organizational learning. Furthermore, the study examines the multi-dimensional and contingent progressive impact on the corporate performance of organizational training through innovative conjunctive roles. However, this study determines that both organizational learning capabilities (OLC) and organization innovativeness (OI) have a positive effect on organizational performance (OP). Besides, organizational innovativeness mediates the relationship between OLC and OI. Finally, the findings suggest that managers should improve organizational learning capabilities and improve innovation performance to enhance positive entrepreneurial orientation performance connections.

Art design education in China has nearly 30 years of development history. Due to China’s vast territory, a large population, regional economic development, and social resources are extremely unbalanced, resulting in the uneven development of art design education in various regions. Art design is not only a practical and artistic comprehensive discipline, it also spans more majors. It contains almost all aspects of urban and rural construction, beautification, so its scientific and effective design scheme indirectly affects the future development process of urban and rural economy. First, this paper analyzes the impact of urban and rural economic development on art design teaching. The research results show that it has a great influence and a wide range of influence on art design teaching, including enhancing the naturalness of art discipline, promoting the creativity of art design discipline, and promoting the historicity of art discipline. Second, this paper also analyzes the role of art design in promoting regional economic development. For example, we should make full use of regional cultural differences, expand regional historical elements, pay attention to art design talents, and ensure the supply of funds.

Focusing on the basic research subject of “seeking effective ways to enhance knowledge workers’ creativity,” this paper has surveyed a total of 208 employees from 11 Chinese high-tech enterprises. Based on review and analysis of relevant literature, this paper has constructed a moderating effect model including mediating factors relying on the social cognitive theory and tested the model by hierarchical multiple regression. The results show that there is a significant positive correlation between knowledge sharing motivation and creativity of members in a virtual organization. Knowledge sharing behavior plays a partial mediating role between knowledge sharing motivation and creativity, and self-efficacy moderates the complete mediating effect of knowledge sharing behavior on the relationship between knowledge sharing motivation and creativity. These conclusions enrich the theory of the relationship between knowledge sharing motivation and creativity, and enable enterprises to understand the importance of employees’ knowledge sharing motivation and the ways to stimulate employees’ creativity by activating their knowledge sharing behavior.

In this paper, we discussed some saturated classes of ℋ-commutative semigroups, left (right) regular semigroups, medial semigroups, and paramedial semigroups. The results of this paper significantly extend the long standing result about normal bands that normal bands were saturated and, thus, significantly broaden the class of saturated semigroups.

At present, the economic development of the world’s major economies is showing a positive and positive state. Driven by the development of related industries, the development of the financial field is also changing with each passing day. Various activities in the financial industry are in full swing, and the forecasts of related prospects are also full of uncertainties. Summarizing the laws of financial activities through technical means and making accurate predictions of future trends and trends is a hot research direction that relevant researchers pay attention to. Accurate financial forecasts can provide reference for financial activities and decision-making to a certain extent, promote the steady development of the market, and improve the conversion rate of financial profits. As an algorithm model that can simulate the biological visual system, the convolutional neural network can predict the numerical trend of the next period of time based on known data. Therefore, this paper integrates the support vector machine with the established model by establishing a convolutional neural network model and applies the prediction model to the prediction of financial time series data. The experimental results show that the model proposed in this paper can more accurately predict the trend of the stock index.

Tax forecast has an important impact on financial budget and tax plan. The amount of tax data is greatly increased, the difficulty of tax forecast is improved, and the accuracy is always difficult to keep up with the development demand. Analyze the application of optimized support vector machine model in tax prediction system. Based on the simple analysis of the research situation of tax prediction and the research status of data mining algorithm in tax data classification and prediction, this paper constructs a tax prediction model. Aiming at the problem of too many influencing factors of tax prediction, this paper puts forward the use of principal component analysis to extract the main factors, reduce the dimension of tax data, and reduce the difficulty of analysis. Support vector machine is used to realize tax prediction, aiming at the problem of parameter optimization proposed to optimize the parameters. Finally, the prediction accuracy is evaluated by comparing the error between tax prediction value and real value. The results show that the algorithm used in this paper can optimize the parameters of support vector machine. The tax prediction results show that the predicted value is similar to the real value curve. After grid search optimization, the introduction of principal component analysis reduces the redundancy and improves the prediction accuracy.

In this paper, we study the oscillatory properties of the solutions of a class of fourth-order p-Laplacian differential equations with middle term. The new oscillation criteria obtained by using the theory of comparison with first- and second-order differential equations and a refinement of the Riccati transformations. The results in this paper improve and generalize the corresponding results in the literatures. Three examples are provided to illustrate our results.

The definition of uniformly nonsquareness in Banach spaces is extended to F-normed spaces. Most of the results from this paper concern (uniformly) nonsquareness in the sense of James or in the sense of Schäffer in Orlicz spaces equipped with the Mazur-Orlicz F-norm. It is well known that uniform nonsquareness in the sense of Schäffer and in the sense of James are equivalent in Banach spaces. In this paper, we found that uniform nonsquareness in the sense of James and in the sense of Schäffer are not equivalent for F-normed spaces. Criteria for Orlicz spaces equipped with the Mazur-Orlicz F-norm to be nonsquare and uniformly nonsquare in the sense of James or in the sense of Schäffer are given.

The number of conjugate classes of derangements of order n is the same as the number hn of the restricted partitions with every portion greater than 1. It is also equal to the number of isotopy classes of 2×n Latin rectangles. Sometimes the exact value is necessary, while sometimes we need the approximation value. In this paper, a recursion formula of hn will be obtained and also will some elementary approximation formulae with high accuracy for hn be presented. Although we may obtain the value of hn in some computer algebra system, it is still meaningful to find an efficient way to calculate the approximate value, especially in engineering, since most people are familiar with neither programming nor CAS software. This paper is mainly for the readers who need a simple and practical formula to obtain the approximate value (without writing a program) with more accuracy, such as to compute the value in a pocket science calculator without programming function. Some methods used here can also be applied to find the fitting functions for some types of data obtained in experiments.

The development of information technology has led to the rapid development of modern book packaging design. Book packaging design is different from painting. It is a kind of design process that combines artistry and practicality and has double characteristics. With the continuous progress of society, people’s requirements for book packaging design have become higher and higher, and modern book packaging design has become an important topic in the field of art design. To this end, this paper introduces the machine learning algorithms used in this paper, including the AdaBoost algorithm and the SVR algorithm. Specifically, it includes the principles and specific implementation steps for AdaBoost classification algorithm and SVR regression algorithm, as well as evaluation indexes of AdaBoost classification and SVR regression analysis. Realization of physical books reflects artistry, creativity, professionalism, popularity, and vitality of books in the packaging and design of books in the electronic information era. The stimulation effect of this paper starts from packaging design, inspection mechanism, and brand psychology to get the superior design in modern book packaging design.

The vulnerability assessment indicator system (VAIS), including the tourism economic sensitivity and respondence, is modified and established in this paper. According to the collected data, during 2014–2018 of the 31 provinces of China, of the tourism economy sensitivity and respondence, the improved comprehensive evaluation projection pursuit clustering (PPC) model is established, and the vulnerability indexes of the 31 provinces are calculated, thus expanding the tourism economic vulnerability assessment methods. Our empirical results show that, during the period of 2014 to 2018, the sensitivity, the respondence, and the vulnerability indexes are unbalanced overall. The tourism economy sensitivity and the respondence show that the spatiotemporal distribution characteristics are high in the east and low in the west. On the contrary, as for the vulnerability, the spatiotemporal distribution characteristics are low in the east and high in the west. Among the 40 indicators, the ratio of industrial solid waste utilized (%), urbanization rate, and the density of grade highway and railway network (km/km2) have the greatest impact on the respondence, while the proportion of the population affected by natural disasters, the diversification index of industrial structure, and the number of traffic accident casualties have the most significant impact on the sensitivity, which are the indicators that have the greatest impact on vulnerability. Therefore, in order to effectively reduce sensitivity, improve respondence, and thus reduce the vulnerability index of the tourism economy, the provinces should first improve the above-mentioned evaluation indicators with the largest weights. Our research results in this paper enrich the theory of sustainable development of the tourism industry and derive managerial and policy insights for further achieving the high-quality development of the tourism economy.

In order to promote the development of sports industry teaching and accurately evaluate the teaching reform of sports industry, this paper constructs an evaluation model based on the teaching reform of sports industry. In addition, the k-means method is used to classify the teaching effect and simplify the data collection process, so as to improve the accuracy of teaching reform evaluation. The sample data come from the data released by the sports department and the government from 2017 to 2020, as well as the actual survey. The 26 evaluation indexes were determined by expert survey, questionnaire interview, and relevant domestic literature. In addition, the Euclidean distance in K-means method is used to calculate the weight of each index, and the results of the evaluation model are analyzed. The results show that the evaluation accuracy of the sports industry teaching reform model proposed in this paper is 98.4% and the error is 1.3%. The evaluation result is better than the previous ant colony model and is suitable for the evaluation of sports teaching reform.

HRMS is a very critical tool for companies. The recruitment text contains rich information that can provide strong information support for the company’s recruitment work and also improve the efficiency of job seekers in finding job opportunities. To this end, for the problem of multilabel text classification of recruitment information, this paper provides two algorithms for multilayer classification based on supported SVM. First, the same learning subclass method is used for text sorting subclass acquisition, and then, the class of the text is determined. Second, the hemispherical support SVM is used to find the smallest hypersphere in the feature space that contains the most text of that class and segment the text of that class from other texts. For the text to be classified, the distance from it to the center of each hypersphere is used to determine the class of the text. Experimental results on recruitment data demonstrate that the algorithm in this paper has a high check-all rate, check-accuracy rate, and F1. And, the relationship between HRM activities and corporate performance is discussed.

This paper is motivated by some papers treating the fractional derivatives. We introduce a new definition of fractional derivative which obeys classical properties including linearity, product rule, quotient rule, power rule, chain rule, Rolle’s theorem, and the mean value theorem. The definition Dαft=limh⟶0ft+heα−1t−ft/h, for all t>0, and α∈0,1. If α=0, this definition coincides to the classical definition of the first order of the function f.