To improve the accuracy of unmanned underwater vehicle (UUV) state estimation of non-cooperative targets, an axial attention-based target state estimation method is proposed in this paper. The state estimation mechanism of the UUV non-cooperative target based on sonar observation is analyzed. The non-Markov state-space model of the problem is transformed into a first-order Markov state-space model with memory, and a recursive filtering model is constructed. Aiming at the unreliability of forward-looking sonar observation and the unpredictability of target motion, a multi-step prediction network based on transformer is proposed to describe the complex motion process of non-cooperative target relative to sonar under nonlinear observation. Aiming at the instability of observation and the unpredictability of posterior distribution, based on the Monte Carlo approximate inference principle, the multi-step prediction network is used to map the particles in the target measurement state space to the target prediction state space, and a non-cooperative target state estimation algorithm based on the axial attention is constructed. The simulation results show that the adaptability and robustness of the proposed method to uncertain inputs.

The development of rural revitalization industry is the basis for stimulating the vitality of rural areas and the premise for solving all problems in rural areas. Based on the development status of the “new community factory” in the Qinba Mountains of Shaanxi Province, this paper identifies the important participants such as community factories, local governments and leading enterprises, and constructs a tripartite evolutionary game model considering factors such as cost, subsidy, investment and income, and then discusses how each subject makes strategic choices in the process of rural revitalization industry development, and conducts sensitivity analysis and numerical simulation. The results show that, on the one hand, the evolutionary stabilization strategy is affected by the local government subsidy, and the appropriate subsidy is conducive to the joint participation of the three parties. The investment of leading enterprises in production enterprises should be within a relatively reasonable range, and at the same time, it is necessary to increase the investment of all parties in society to the local government; On the other hand, for community factories and leading enterprises, reducing costs and increasing profits will be more conducive to promoting the development of rural revitalization industries such as “new community factory”.

In this paper, we utilize the Fourier neural operator (FNO) for the first time to investigate the derivative nonlinear Schrödinger (DNLS) equation and fractional derivative nonlinear Schrödinger (fDNLS) equation. For the DNLS equation, we successfully establish the mappings between the initial conditions of the equation and their respective solutions. The transition process of the soliton to the $M$-type wave is studied, and the periodic solution is also obtained. Simultaneously, the FNO learning method is employed to investigate the transformation process of the periodical rogue wave. Moreover, we focus on learning the mapping between the fractional order exponential space and the soliton in the fDNLS equation. By comparing the data-driven solution with the exact solution, the powerful approximation capability of the FNO network is highlighted. Finally, we discuss the effects of the full-connected layer $P$ and the activation function on the characterization ability of the network.

Algorithmic trading has emerged in the A-share market in recent years, and its impact on capital market pricing efficiency has received wide attention across industry and academia. This paper focus on companies listed on the SZE Growth Enterprises Market and SSE STAR Market, aiming to explore the influence of algorithmic trading on the information content of stock prices before the release of quarterly earnings announcements. Firstly, by considering the trading system features and investor structure characteristics of the A-share market, this paper constructs algorithmic trading indicators tailored to the A-share market. Based on these indicators, empirical tests reveal that algorithmic trading reduces the information content of stock prices before earnings announcements, indicating that the liquidity demand strategy of algorithmic trading plays a dominant role. Further mechanism analysis shows that the negative impact of algorithmic trading on stock price information content stems from increased transaction costs for slower investors and reduced large orders from informed traders. Lastly, the research finds that while algorithmic trading exhibits a crowding-out effect on informed traders, it also mitigates abnormal stock price fluctuations caused by noise trading. This study deepens our understanding of the economic consequences of algorithmic trading at the market level and provides insights for regulators to improve related policies concerning algorithmic trading.

With the ongoing emergence of global infectious disease virus variants and the increasingly severe biological security situation, the accuracy and efficiency of infectious disease detection have become crucial components in monitoring virus spread and protecting public health. However, the reliability of detection is often challenged by factors such as technical limitations and operational standardization. This paper examines the accuracy of large-scale detection of infectious diseases. Taking the COVID-19 epidemic as an example, we calculated the false-negative predictive values of “single test” and “pooled test” based on Bayesian statistical modeling, and proposed optimal testing strategies for different areas and sensitivities. It includes the selection of “single testing” and “pooled testing” strategies, the optimization of testing population and frequency, and adjustments to the intervals of regular testing. Furthermore, we computed the corresponding testing accuracy and optimal interval times for gender-separated testing schemes in low-risk areas. Finally, combined with the calculation results of this study, we formulated policy recommendations concerning infectious disease detection, aimed at providing a scientific foundation and policy support for effective disease prevention and control.

Since the proposal of the capital asset pricing model (CAPM), the risk-return relationship has always been a key issue in academic research. However, the current debate on the effectiveness of the CAPM model is still ongoing, and the conclusions are not unified. Based on existing studies, this paper comprehensively considers the differences in trading mechanisms, trading characteristics, and investor types between the intraday and overnight periods, innovatively introduces a time heterogeneity perspective to decompose the daily trading period into two parts, and further studies the risk-return relationship in China. Specifically, we use the portfolio-sort way and Fama-MacBeth regressions to test the relationship between systematic risk (proxied by the beta coefficient) and stock returns and find positive correlations from the daily and intraday betas. This positive relationship does not exist in overnight periods and may even display a high-risk but low-return phenomenon. The previous studies based on the daily data mainly display findings from the intraday trading period but fail to reveal the role of overnight trading, and this paper tries to supply them. In addition, the positive relationship is stronger in stocks with small market capitalization, poor liquidity, and high idiosyncratic risk, and investor sentiment and arbitrage limitation also play an essential role. Our results are robust under the adjustment of different factor models, alternative beta measurements, and various subsamples. This paper is of great importance for investors to further understand the CAPM model, understand the heterogeneous performances at three periods, and improve the risk-return evaluation framework. Our paper also helps regulators revise the related policies and pricing mechanisms and achieve effective measurement of systemic risk in China's A stock market.

Insufficient aggregate demand is the prominent contradiction facing the current economic operation, we must restore and expand consumption in a priority position. This paper empirically investigates the impact of the housing provident fund system on household consumption using data from three editions of the China Household Finance Survey in 2015, 2017, and 2019. The empirical results show that household contributions and withdrawals to the housing provident fund significantly increase household consumption levels and improve household consumption structure. Robustness tests show that the above conclusions remain robust after replacing the way the core variables are defined, replacing the instrumental variables, and relaxing the exclusivity constraints on the instrumental variables. The Heterogeneity analysis shows that the housing provident fund system has a greater impact on the consumption of housed, low-income, as well as young and middle-aged households. Further research finds that contributing to the housing fund reduces households' precautionary saving incentives, withdrawing from the housing fund increases households' disposable income and eases liquidity constraints, thereby boosting household consumption. This paper provides micro-level evidence that housing funds promote household consumption and improve household consumption structure, which can provide important references for relevant policy formulation.

Promoting enterprises to accelerate digital transformation is of great significance to enhance the core competitiveness of enterprises, empower the upgrading of traditional industries, generate new forms of business, as well as drive China's digital economy to become better and stronger. From the perspective of enterprises, this paper analyzes the antecedents of enterprises' digital transformation, constructs related indexes based on the text analysis method, proposes a two-factor theoretical model of manager characteristics and dynamic capabilities, and uses the structural equation model based on partial least squares estimation (PLS-SEM). The empirical results show that manager characteristics such as entrepreneurship, digital evangelist and coordinator, as well as corporate dynamic capabilities such as sensing, learning, integrating and coordinating, have a significantly positive role in promoting the tendency and output of digital transformation of enterprises. In addition, manager characteristics can significantly improve the level of enterprises' dynamic capabilities, and the effect of manager characteristics on enterprises' dynamic capabilities and digital transformation is moderated by managers' perception of policy uncertainty. In addition, the above effects are heterogeneous between state-owned and private enterprises, enterprises in the eastern, central and western regions, as well as enterprises in provincial and non-provincial capitals. This paper fills the research gap on the antecedents of digital transformation, and provide a feasible practical path for enterprises to cultivate managers in the digital era and improve their dynamic capabilities.

The “Triple Reform” policy is a significant initiative aimed at addressing the challenges of renewable energy consumption and promoting the high-quality development of new energy in the context of increasing renewable energy penetration. This study focuses on Shandong Province, which has a strong reliance on traditional coal-fired power, and employs a static Computable General Equilibrium (CGE) model to link renewable energy with the “Triple Reform” policy. The study simulates the impact on the economic and environmental aspects of Shandong Province under different shares of renewable energy electricity consumption, coupled with the simultaneous implementation of the “Triple Reform” policy. The research investigates the underlying reasons for the slow progress in the implementation of the “Triple Reform” policy for coal-fired power units and, based on this, transforms the compensation issue into a cost problem. Cost estimation for the implementation of the “Triple Reform” policy in Shandong Province is carried out. The results indicate that the implementation of the “Triple Reform” plan will have certain negative impacts on the regional economy but can also bring significant environmental benefits. In the scenario with 60% coal-fired power and 40% renewable energy electricity (F60C40), the cost of implementing the “Triple Reform” policy in Shandong Province is estimated to be 4 billion yuan. The policy recommendations of this study emphasize the need for vigorous development of renewable energy in China, urging coal-fired power units to actively implement the “Triple Reform” policy. It also suggests timely introduction of reasonable policy support or economic incentives to leverage the stabilizing role of coal-fired power.

The overuse and misuse of antibiotics has become a major problem for public health. People become resistant to antibiotics and because of this the anticipated therapeutic effect is never reached. In-hospital infections are often aggravated and large amounts of money are spent for treating complications in the patients' condition. In this paper a nonlinear optimal (H-infinity) control method is developed for the dynamic model of bacterial infections exhibiting resistance to antibiotics. First, differential flatness properties are proven for the associated state-space model. Next, the state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instance around a time-varying operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. For the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed. To compute the controller's gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. The proposed method achieves stabilization and remedy for the bacterial infection under moderate use of antibiotics.

The safety of vehicle travel relies on good stability performance, making vehicle motion control a vital technology in vehicles. This paper focuses on investigating the impact of roll control on vehicle performance, particularly in terms of avoiding rollover and ensuring lateral stability. By introducing a feedback roll moment, the roll motion can be effectively controlled. The paper considers two roll reference generators: A static one aimed at zero roll, and a dynamic one based on the vehicle's lateral acceleration. The static roll reference generator enhances stability by employing a fixed reference, particularly beneficial during routine driving conditions. In contrast, the dynamic roll reference generator continually adapts the roll angle reference in response to real-time vehicle dynamics and driving conditions. These proposed reference generators can be paired with varying suspension systems — Static reference could be achieved using semi-active suspensions, while the dynamic one is integrated into advanced active suspension systems, offering heightened adaptability and performance. To address the roll control objectives, this paper proposes a novel sum of squares (SOS) integral polynomial tracking control. The proposed controller satisfies control bounds and considers control constraints during the design phase. The effectiveness and robustness of the proposed control scheme are evaluated through numerical simulations using a full vehicle nonlinear model in Matlab/Simulink. The results of these simulations are compared to super-twisting sliding mode and Lyapunov-based controllers.

In this paper, the authors address the attitude regulation problem of uncertain flexible spacecraft with unknown control directions and input disturbances. The major challenges of the problem include the concurrence of the unknown actuation sign and the unknown parameters in both the plant and the external disturbances, along with the impact of vibrations from flexible appendages. To overcome these challenges, the authors transform the conventional mathematical model of a flexible spacecraft to a multivariable strict-feedback normal form and adopt a systematic approach within the framework of nonlinear output regulation. To solve the attitude regulation and disturbance rejection problem, the authors introduce a nonlinear internal model candidate to convert the problem into a stabilization problem for an augmented system. Then, a Nussbaum function-based stabilizer is designed to handle unknown control directions and complete the design. Simulation results are provided to show the effectiveness of the proposed controller.

This paper studies the stabilization problem of heterogeneous multi-agent systems composed of the first-order and second-order dynamic agents in a signed directed graph. By utilizing the knowledge of Laplacian matrix and graph theory, corresponding protocols are designed for the second-order and first-order dynamic agents, respectively. Based on the layering theory proposed in this paper, independent strongly connected components (SBiSCC) of structural equilibrium are utilized to design control parameters. The necessary and sufficient conditions for achieving stabilization of the first-order and second-order heterogeneous systems in communication topology are given. Finally, the paper provides several simulation verification theoretical results.

This paper studies centralized state estimation problems for multi-agent systems with random packet losses. The random variables satisfying Bernoulli distribution are employed to depict the phenomena of packet losses during measurement data transmission from sensors to the data processing center. First, an optimal centralized state filter in the linear minimum variance (LMV) sense is designed in the case that the fusion center knows whether data packets are lost or not at each time, where the filtering gain requires computing in real time. To reduce the online computational burden, a suboptimal filter dependent on probabilities of packet losses is also designed and its steady-state property is analyzed. Then, an optimal centralized state filter in the LMV sense is designed in the case that the fusion center does not know whether data packets are lost or not at each time, and its steady-state property is analyzed. Finally, the estimation accuracy and computational burden of the proposed three filtering algorithms are analyzed. A simulation example verifies the effectiveness of the algorithms.

This paper uses the textual vectorization method to digitize the text of earnings conference call of Chinese listed firms, and then analyzes whether managers choose the tone strategically and how investors respond to it. It is the first time that the management tone is refined into market, industry and corporate component. We find that managers strategically arrange their tones for their own interests at earnings conference call; and the net positive market tone moves the stock price up in the long window of the event. Furthermore, investors can gain from analyzing the corporate tone of firms with high investor attention during normal market and industry situations. This paper shows that the text messages disclosure on earnings conference call can provide a valuable information, and also provides a new perspective for management tone analysis. In addition, under the highly dependence of semantics on context within Chinese cultural background, this paper provides new empirical evidence for such hot issues as investors' information acquisition and comprehension.

China's high-quality development and carbon peaking and carbon neutrality goals both require an overall consideration to economic benefits and environmental cost. Transnational investment promotes the reconstruction of global industrial and supply chains, which also leads to dispute of environmental responsibilities under the accounting of economic benefits based on the ownership principle and the accounting of carbon emission based on the territorial principle. In this paper, we employed an inter-country inter-industry input-output database that distinguishes the activities of multinational enterprises (MNEs) and introduced counterfactual analysis and scenario analysis to evaluate impact of structural change in GVC on China's gross national income (GNI) and CO$_2$ emissions. There was significant industrial shift toward China from 2005 to 2016, boosting China's GNI and CO$_2$ emissions by 15.23% and 20.50% respectively compared to 2016 levels. For the future shift, the scenario analysis shows that compared with the relocation of GVC led by developed economies, the relocation led by China would yield lower negative impact on China's GNI when reducing same amount of China's CO$_2$ emissions. The negative impact on GNI and CO$_2$ emissions varies by sector initiating the relocation and by economy undertaking the relocation. Our analysis provides policy implications for China's future GVC relocation and high-quality development.

Our country is in the crucial process of industrial structure reformation, and expanding the new infrastructure investment, which has a feature of digital technology application, has a practical significance in pushing the upgrading industrial structure. This article picks China's 2004-2021 provincial panel data as samples to empirically prove the spatial effect of new infrastructure investment on industrial structure upgrading. Empirical results illustrate that the new infrastructure investment will accelerate the upgrading of the province's industrial structure, and have a positive spatial spillover effect. New infrastructure investment will inhibit the coordinated transformation of industrial structure, but this negative impact is not contagious among regions; The impact of new infrastructure investment on the upgrading of industrial structure is heterogeneous among different infrastructure types and eastern, central, and western regions. The above conclusions have certain policy implications for China to grasp the direction of new infrastructure investment, improve the efficiency of resource allocation, and accelerate the pace of industrial structure optimization and upgrading.

The high-quality transformation of the manufacturing industry structure is the key to further stimulate the potential of energy conservation and emission reduction. Taking growth, employment, energy saving and carbon reduction as multiple objectives, and the input-output equation and the goal of carbon peaking as the main constraint conditions, in order to accurately predict the adjustment trend of manufacturing structure, this paper constructs a multi-objective optimization model of manufacturing structure, and uses the dual population coevolution framework to solve it, drawing the following conclusions: Under the premise of carbon peaking, the adjustment of the manufacturing structure can effectively promote the balance, trade-off and coordination among the objectives. As their proportions continue to increase, the communication equipment, computers and other electronic equipment manufacturing, and the instrumentation and other manufacturing will ultimately become the two categories of industries with the largest proportion in the manufacturing industry. The low-carbon and high-quality development of manufacturing industry cannot simply rely on the reduction of energy-intensive production capacity, and its key is to achieve profound low-carbon transformation or structural optimization of raw materials, productive processes, and products.

This study investigates the impact of two types of FinTech developed and utilized by banks and non-bank financial institutions on fraudulent behavior in China's A-share listed companies. Based on panel data from 2011 to 2020, the research findings suggest that both types of FinTech can suppress corporate fraud by enhancing internal control levels and external monitoring levels. Heterogeneity analysis indicates that the inhibitory effects of both FinTech types are more pronounced in companies with higher levels of digital transformation and lower levels of information disclosure. Additionally, due to differences in operating conditions, strategies, and objectives of FinTech developers, the inhibitory effect of bank FinTech is significant across all firms, whereas the effect of non-bank FinTech is only significant in high-risk firms. When distinguishing types of corporate fraud, both FinTech types significantly inhibit fraudulent activities related to information disclosure, fund utilization, and other categories. Further analysis reveals a complex interaction between the application effects of bank FinTech and non-bank FinTech. Specifically, the inhibitory effect of bank (non-bank) FinTech is significant when the development of other FinTech is high (low). By simultaneously incorporating both types of FinTech and their interaction terms, significant synergistic inhibitory effects are observed in fund misuse and other types of fraud. Finally, the results indicate that the synergistic development of both types of FinTech may introduce potential risks. In summary, this paper, by identifying the impact of FinTech development on corporate fraudulent behaviors, highlights the common characteristics and individual differences of different types of FinTech, emphasizes potential future cooperation opportunities between bank and non-bank FinTech, and points out potential risks in the development of FinTech.

Entrepreneurship is one of the key means to ease the pressure on social employment, and it is also a long-term driving force to ensure medium-high economic growth. The in-depth development of digital inclusive finance has stimulated the vitality of entrepreneurship, but whether it can effectively improve the quality of entrepreneurship is still a topic worth exploring. Using data from the China Household Finance Survey (CHFS), we find that digital inclusive finance has a positive impact on improving the performance of household entrepreneurship. Further analysis shows that optimizing the external entrepreneurial environment such as regional credit environment, regional innovation level, and market integration, is an important way for households to improve their entrepreneurial performance. In addition, based on the differences in the characteristics of entrepreneurial subjects and regional characteristics, the paper finds that the impact of digital inclusive finance on entrepreneurial performance is more significant in groups with medium and high financial literacy, long-distance groups, and groups in more developed areas. This paper has certain reference significance for further promoting the development of digital inclusive finance and better improving the quality of entrepreneurial development.

In the context of the development of financial technology, we start with the complex characteristics of financial big data and elaborate on the importance of transfer learning of using multi-source data information to assist target tasks. We explain the significance of transfer learning technology in dealing with data heterogeneity from the perspective of multi-source data, and summarize the relevant concepts and methods of transfer learning technology, including data-driven and model-based transfer learning methods. In addition, this paper proposes the unified framework of transfer learning method based on generalized moment estimation (GMM), gives the effective algorithm of transfer learning, and applies the proposed method to the application of transfer learning in risk value (VaR) and risk measure based on expected quantile (expectile) under multi-source data. Then, we simulate two scenarios where samples are of insufficient or imbalanced sample sizes, respectively, in the application to personal bank credit evaluation, with tests of the prediction accuracy of three transfer learning methods, and analysis of the importance of filtering resource domain information. Finally, we described more application scenarios and development prospects of transfer learning in the financial field.

A fuzzy adaptive control method based on command filtering technology is proposed for a stochastic system of flexible joint manipulators with dead-zone input, which achieves tracking control of the system output on the expected trajectory. Firstly, command filtering technology is used to solve the problem of "explosion of complexity" inherent in the traditional backstepping method, and error compensation mechanism is introduced to eliminate the influence of filtering errors on the system control precision. Then, a fuzzy logic system is utilized to deal with uncertainties and stochastic disturbances in the system, which overcomes the influence of stochastic disturbances and improves the control effect of the system. Finally, considering the system with dead-zone input, the control signal is constructed by backstepping control method, which conquers the adverse impact of dead-zone input on system performance. In the stability analysis, the effectiveness of the control strategy studied in the stochastic system of flexible joint manipulators with dead-zone input is proved, and it is verified by Matlab simulation.

In order to investigate the impact of significant economic or political events on the dependence of financial markets, we construct the factorial hidden Markov Copula model (FHM-Copula) that allows the coefficients of dependence to follow a regime-switching process in high-dimensional state space. The FHM-Copula model is able to capture external shocks of varying magnitude, direction, duration, and short or long-term from significant events to the dependence. In the empirical study, we analyze the dynamic dependence between the stock markets of China and other BRICS countries by adopting the FHM-Copula approach. Our findings indicate that the FHM-Copula model can effectively identify the external shocks caused by significant events such as the subprime crisis, the European debt crisis, the Chinese stock market crash, China's taking over the BRICS presidency and the COVID-19 epidemic on the dependence between the stock markets of China and other BRICS countries. Our works not only provide a theoretical analysis framework based on the information shock perspective for the study of dynamic dependence among financial variables, but also provide a reference for investors and government regulators in investment decisions and risk management.

In recent years, due to the superior performance of semiparametric joint elicitable risk models in joint statistical modeling and prediction of value at risk (VaR) and expected shortfall (ES), they have attracted widespread attention in the field of financial measurement. This paper first studies the statistical properties and risk prediction performance of the model under the framework of the asymmetric Laplace distribution. Unlike the existing semiparametric joint elicitable risk models, this model jointly models VaR and ES by assuming the conditional distribution of asset returns follows the asymmetric Laplace distribution based on VaR and ES, taking into account the typical asymmetric characteristic of financial markets, and regarding VaR and ES as dynamic structures composed of conditional standard deviation process of returns containing the asymmetric feature and a parameter to be estimated. Based on the structure of the model, we discuss the quasi-maximum likelihood estimation method and establish the consistency and asymptotic normality theorems for the estimator under certain regular conditions. Subsequently, numerical simulation results considering various conditions confirm the finite sample properties of the estimator and the effectiveness on predicting one-step ahead risks. Finally, empirical results show that the proposed model performs best in predicting multi-step ahead VaR and ES.

Industrial policy has always played an important role in promoting industrial structure transformation and high-quality economic development. Based on the Five-Year Plan of the province level local governments and the micro-data of Chinese industrial enterprises, this paper constructs a staggered DID identification strategy to empirically analyze the impact of local key industrial policies on firms' TFP. The study found that local key industrial policies can significantly improve the TFP of enterprises through policy effects (financial subsidies, tax breaks, low-interest loans) and competitive effects. Further analysis shows that the way local key industrial policies formulated and implemented will have an important impact on the effect of industrial policies. The impact of local key industrial policies formulated combining with the regional comparative advantage, or implemented dispersedly is better. This study provides Chinese empirical evidence for the impact of industrial policies on firms' productivity, which can provide useful reference for the government to formulate and implement industrial policies and promote high-quality economic development.

This paper is based on panel data from multiple countries and applies a dual threshold variables panel model to analyze the differential effects of material, human, and technological inputs growth on economic growth in different stages of the economic environment. We have found that there are significant dual threshold effects of inflation and economic growth in the impact mechanism of factor inputs on economic growth. When the economy falls into the range of high inflation and low growth rate, measures need to be taken to prevent it from entering a recession phase. During the post-pandemic era, global economic growth rates have generally declined, so controlling excessive inflation has become one of the important measures to stabilize the economy and regulate the markets. Furthermore, the dual threshold effects exhibit heterogeneity based on the level of national development. Developing countries have relatively high dual thresholds, and economic growth still relies primarily on human capital input. In these countries, the range of high inflation and low growth rate is most unfavorable for their sustained economic development. However, the double thresholds in developed countries is generally lower, and the economic growth model in the double high range poses high risks for developed countries. Therefore, controlling inflation is currently a paramount concern for developed nations.

The constraints of financing are widely regarded as a key factor limiting firms' innovation. However, whether easing these constraints always promotes innovation is still a matter of debate. Given that the Chinese financial system relies primarily on bank credit, this paper mainly examines whether credit expansion can significantly promote firms' innovation and analyzes its mechanism. Using the implementation of the "removal of the deposit interest rate limit" policy as an exogenous shock of credit expansion, this paper constructs a difference-in-differences (DID) model for empirical testing. The analysis shows that the bank deposit competition effect brought by the policy implementation significantly increases the loan size obtained by private enterprises relative to non-private enterprises. However, there is no significant change in innovation input and output by enterprises after obtaining additional credit resources. The results remain unchanged in samples with greater innovation demand, such as high-tech enterprises and emerging strategic enterprises, and are insensitive to the degree of intellectual property protection in different regions and the loan term structure obtained by enterprises. Further analysis find that enterprises allocate more financial assets, especially short-term financial assets, after credit expansion, indicating that a lack of subjective willingness for innovation is more likely to be the reason why credit expansion does not bring about new firms' innovation. This article reveals that promoting innovation cannot rely solely on credit expansion, but should be combined with innovation-driven strategies, improved innovation environment, and reduced short-sighted behavior of enterprises to promote innovation by enhancing relative returns on innovation.

In this paper, the edge controllability of multi-agent systems under matrix weights is studied by using the transformation of topological graphs from point graphs to line graphs, where dynamics occur on the edges. Firstly, from the perspective of graph theory, a quantitative analysis is conducted on the incidence matrix of a line graph, and the relationship between the rank of the incidence matrix of the line graph and the number of connected components of the line graph are given. Furthermore, we find that there is a certain relationship between the algebraic multiplicity of zero eigenvalues of the Laplacian matrix of the line graph and the number of connected components of the line graph. Secondly, under the leader follower structure model, two conditions that need to be satisfied when the multi-agent system is edge controllable are obtained. In addition, according to the definition of canonical transformation, the balanced symbolic line graph of matrix weight structure is transformed into an unsigned line graph without negative edges. The results show that the controllability of the line graph before and after transformation is equivalent. Finally, the relationship between the controllability of line graphs and point graphs is analyzed, and it is found that when the point graph is structurally imbalanced, the controllability of line graphs is equivalent to that of point graphs.

In complex system, resource allocation and internal interaction of the system are key factors that need to be paid attention to when studying the innovation efficiency of the system, but in previous related researche, scholars did not take the mutual feedback between divisions inside the system into account. Based on what we mentioned, this paper has expanded the previous model and constructed a three-stage network DEA model with shared resources, incorporating the interaction effect between the internal divisions of the system. We prove the necessary and sufficient conditions of effective system and define effectiveness of the system and each stage. We measure the knowledge innovation efficiency of 54 Chinese universities and divide the knowledge innovation system into the knowledge production stage and the knowledge application stage. Compared with the distinction of different methods (GP1: Focusing on the first stage; GP3: Focusing on the third stage), the results show that there is no DMU with systemic efficiency; Compared with GP3, GP1 will underestimate the efficiency of knowledge application and reduce the level of distinction between DMUs at the efficiency of knowledge production. This paper also analyzes various sample universities in accordance with different characteristics and puts forward relevant policies and measures.

Cylindrical algebraic decomposition is a basic tool in semi-algebraic system solving and real quantifier elimination. In the actual solving process, the choice of a variable ordering may have a significant impact on the efficiency of cylindrical algebraic decomposition. At present, the existing heuristic or machine learning ordering selection methods are basically based on the implicit assumption that the support set of a polynomial system is the determinant for affecting the variable orderings. In this paper, we first test this hypothesis by designing an experiment with the support set fixed but the coefficients varying. The experimentation shows that the support set is indeed an important factor, though not the only factor, determining the optimal variable ordering. Aiming at selecting the optimal ordering for computing cylindrical algebraic decompositions for systems with the same support set but different coefficients, this paper designs an ordering selection scheme via reinforcement learning. The experimentation on four variables shows that this scheme can surpass the accuracy limit of existing methods on selecting the optimal variable ordering that rely solely on the support set. In addition, experiments on systems owning up to 20 trillion of possible orderings show that the scheme is much more efficient than traditional heuristic methods. In contrast to the existing supervised learning methods for selecting the variable ordering of a few variables, this reinforcement learning scheme overcomes the difficulty of obtaining high-quality labeled data when the number of variables increases, which may lead to the combinatorial explosion of the number of variable orderings.

Component design (“digitalization”), performance optimization (“optimization”), and process simulation (“simulation”) are three critical modules in the 3D printing process. “Digitalization” refers to the transformation of design drawings or pre-processed physical objects into editable digital components through means such as images, videos, and scanning processes. “Optimization” involves the application of constraints from physical fields such as mechanics and thermodynamics to enhance the performance of digital components. “Simulation” entails the digital simulation and twin modeling of physicochemical changes during the manufacturing process, based on the optimized components, to emulate real-world physical conditions. This research aims to introduce integrated modeling and algorithmic studies in design, optimization, and simulation within the phase field framework. In the “digitalization” module, we will present three-dimensional reconstruction models, repair models, and lightweight support structure design models that correspond to common data types in computer-aided design. In the “optimization” module, we will introduce a series of multi-scale, multi-physical field, and multi-material coupled topology optimization problems and their corresponding solutions. In the “simulation” module, we will discuss macroscopic (phase transition) to microscopic (grain boundary) scale coupling theories and the physical field couplings such as laser-thermal-flow-solid, in addition to simulating methods for processes like Fused Deposition Modeling (FDM) and Selective Laser Melting (SLM), integrating 3D printing parameters and techniques. The integrated research approach to design, optimization, and simulation based on the phase field model framework aims to shorten the product development cycle, enhance design efficiency, and provide a theoretical basis and algorithmic support for quality traceability and root cause analysis of 3D printed components.

Green development involves a wide range and covers a large range, so the difference in interest demands makes the government, enterprises and financial institutions unable to reach an effective consensus in the game. This study constructs a tripartite evolutionary game model based on green preference perspective, and analyzes the relationships among green regulation, green transition, and green bond investment. The study found that the green preference of government, enterprises and financial institutions has different effects on green development. Enterprise green preference plays a decisive role in green transformation. Firstly, when the green preference of the enterprise is large, even if the government does not carry out green regulation or financial institutions do not invest in green bonds, the enterprise will still carry out green transformation production. However, when the green preference of other participants is not large enough, the phenomenon that the government makes green regulatory decisions but has no policy effect will occur. Secondly, when the size of the green preference of enterprises is in a specific range, while the green preference of financial institutions and the government is large, there are two possibilities: The simultaneous success or failure of green transition and green bond issuance. Finally, the main conclusions of this paper are verified by numerical simulation.

This study constructs a Chinese provincial-world multi-regional nested input-output table to estimate China's embodied carbon emissions in exports to the European Union (EU) at the provincial scale, and furthers investigates the driving factors of embodied carbon emissions changes using structural decomposition analysis. The results show that China embodied carbon emissions in exports to the EU in 2017 were 249 Mt$\mathrm{CO}_2$, equivalent to 2.45$\%$ of China's carbon emissions. The provincial distribution of embodied carbon emissions varies significantly, with seven provinces——Jiangsu, Guangdong, Shandong, Hebei, Zhejiang, Liaoning, and Inner Mongolia which all exported more than 10 Mt$\mathrm{CO}_2$——together accounting for 56$\%$ of China's total embodied carbon emissions in exports to the EU. The four industries directly related to the EU's carbon border adjustment mechanism——basic metals, metal products, non-metallic mineral products, and chemical industry——exported 21.44 Mt$\mathrm{CO}_2$ to the EU, accounting for 9$\%$ of China's total. From 2012 to 2017, China's embodied carbon emissions in exports to the EU decreased by 16.62 Mt$\mathrm{CO}_2$, with 12 provinces increasing their embodied carbon emissions and 18 provinces realizing a decrease. The production structure effect or the direct carbon intensity effect is the primary driving factor of embodied carbon emissions changes in most provinces. The results of this study could provide some references for China to manage embodied carbon emissions and address the EU's carbon border adjustment in the future.

"The adjustment of coal-based electric energy transportation" plays a significant role in enhancing the ecological environment and reducing coal consumption in recipient areas. However, it faces challenges in practice. This study examines the "Structure adjustment of coal electric energy transport" from the "Sanxi Region" to the Beijing-Tianjin-Hebei region based on real-world experiences. By employing a multi-regional CGE model, this paper quantitatively analyzes the environmental, economic, and social impacts of this adjustment on both regions. The research reveals that the primary issue with the current transition is the imbalance of interests between the sending and receiving areas. Specifically, while the Beijing-Tianjin-Hebei region benefits from improved air quality, the "Sanxi Region" suffers from negative effects on both the atmosphere and economy. Considering China's 14th Five-Year Plan environmental protection goals, this paper suggests an optimal annual growth range for coal-based electric energy transportation from 2021 to 2025 of [14\%, 27\%]. Additionally, it proposes an optimized tax rate range for joint air pollution control and an economic compensation plan. This research offers a solution path and reference for overcoming challenges in the transformation of coal-based electric energy transportation and contributes to achieving ecological objectives in the Beijing-Tianjin-Hebei region.

In the process of coal mine drilling, controlling the rotary speed is important as it determines the efficiency and safety of drilling. In this paper, a linear extended state observer (LESO) based backstepping controller for rotary speed is proposed, which can overcome the impact of changes in coal seam hardness on rotary speed. Firstly, the influence of coal seam hardness on the drilling rig's rotary system is considered for the first time, which is reflected in the numerical variation of load torque, and a dynamic model for the design of rotary speed controller is established. Then an LESO is designed to observe the load torque, and feedforward compensation is carried out to overcome the influence of coal seam hardness. Based on the model of the compensated system, a backstepping method is used to design a controller to achieve tracking control of the rotary speed. Finally, the effectiveness of the controller designed in this paper is demonstrated through simulation and field experiments, the steady-state error of the rotary speed in field is 1 r/min, and the overshoot is reduced to 5.8$%$. This greatly improves the stability and security, which is exactly what the drilling process requires.

This paper studies the periodic zero-dynamics attacks (ZDAs) in multi-agent systems without velocity measurements under directed graph. Specifically, two types of attack modes are addressed, i.e., infinite number and finite number of zero-dynamics attacks. For the former case, the authors show that the consensus of the considered system cannot be guaranteed. For the latter case, the dynamic evolution of the agents is investigated and it is found that only attacking the rooted agents can destroy the consensus. Then, a sufficient condition which quantifies whether or not the consensus value is destroyed is given, revealing the relationship among parameters of system model, filter and attack signal. Finally, simulations are carried out to verify the effectiveness of the theoretical findings.

This paper focuses on the state estimate for a class of systems with both process noise and measurement noise under binary-valued observations, in which the Gaussian assumption on the predicted density of the state is not required. A recursive projected filter algorithm with time-varying thresholds is constructed to estimate the state under binary-valued observations. The time-varying thresholds are designed as the prediction value of the measurement, which can provide more information about the system state. The convergence property is established with some suitable stability, boundedness and observability conditions. In particular, the estimation error between state and estimate is proved to be asymptotically bounded in the mean-square sense, whose upper bound is related to the variance of process noise. Finally, the theoretical results are demonstrated via numerical examples of first-order and high-order systems.

In the digital economy, the emergence of digital currencies has attracted considerable attention from both investors and researchers. However, their high volatility characteristics present new challenges in investment decision-making and risk assessment. To capture the characteristics comprehensively, this paper proposes a novel approach for constructing confidence regions for interval-valued variables based on the exponentially decay weighted bootstrap. The coverage area of the confidence regions and tail quantiles provide new indicators for assessing the volatility and tail risks in the market. Empirical results using Bitcoin as a case study demonstrate the proposed approach outperforms other traditional point-based methods such as exponential weighted moving average in measuring the uncertainty and intraday price volatility. Furthermore, the derived tail quantiles exhibit superior predictive performance for tail risk compared to Value-at-risk methods and the exponential weighted moving average, as evidenced by various tests. The proposed methodology not only contributes a new statistical tool for analyzing digital currency volatility but also provides novel perspectives for extreme risk management in financial markets.

With the rise of social media, its impact on the financial transparency of publicly listed companies has received increasing attention. This study investigates how social media, particularly posting activity on East Money's stock message boards, affects the financial fraud behavior of listed companies. Utilizing data from East Money's stock message boards and a bivariate probit regression model, the study finds that the number of posts on the message boards is inversely related to the probability of fraud occurrence and positively related to the probability of fraud detection. This finding indicates that social media may play a dual role in both deterring financial fraud and uncovering it. To address endogeneity issues, the study employs an instrumental variable approach. Additionally, based on the "fraud triangle" theory, the paper proposes and validates two mechanisms through which message board posting activity reduces the likelihood of financial fraud: By decreasing potential opportunities for fraud and increasing the difficulty of rationalizing fraud. Heterogeneity analysis reveals that negative posts and posts by senior users are more effective in curbing financial fraud. This research not only enhances the understanding of how social media can function in corporate governance but also provides insights for regulatory authorities on leveraging social media for financial supervision.

With the rising labor costs and increasing resource and environmental constraints in China, coupled with geopolitical conflicts, related industries or production processes are shifting to emerging economies such as Southeast Asia, South Asia, and Mexico. Among these, India's development potential has garnered significant attention, and the "China-to-India industrial relocation model" in the global industrial chain poses a greater impact and threat to China. This paper constructs a pre-quantitative model to measure the impact of industrial relocation on the home country. It designs three scenarios—Ultra-long-term, medium-to-long-term, and short-to-medium-term—And uses counterfactual analysis to assess the impact of India's absorption of China's industrial relocation on China's GDP and employment under different scenarios. The research results indicate that the relocation of industries from China to India will generate significant socio-economic shocks. In the ultra-long-term, this industrial transfer could lead to a 15.6% reduction in China's GDP, a 16.8% decrease in the overall income of the workforce, and a reduction in the number of employed people by 110 million. The impacts are also substantial in the medium-to-long-term and short-to-medium-term scenarios. By sectors, the relocation of low and medium-low R&D intensity manufacturing sectors has a significant impact on the Chinese economy in both the short-to-medium and medium-to-long term perspectives. The relocation of high R&D intensity manufacturing sectors, represented by the computer industry, also causes considerable negative effects on the Chinese economy in the ultra-long-term perspective. This quantitative analysis helps anticipate the economic impact of future changes in industrial layout on China's economy and facilitates the development of preemptive strategies. Based on the medium-to-long-term international economic outlook and the characteristics of domestic regional and industrial economic development, we propose three policy recommendations to provide scientific reference for decision-making by relevant government departments.