Large language models (LLMs) have powerful natural language processing capabilities. In this paper, we systematically review the recent literature in this field and highlight the new research opportunities that LLMs bring to text analysis in economics and finance. First, we introduce GPT and BERT, the two most representative LLMs, as well as a number of LLMs developed specifically for economic and financial applications. Additionally, we also elaborate on the fundamental principles behind applying LLMs for text data analysis. Second, we summarize the applications of LLMs in economic and financial text analysis from two perspectives. On the one hand, we highlight the significant advantages of LLMs in traditional text analysis scenarios, such as calculating text similarity, extracting text vectors for prediction, text data identification and classification, building domain-specific dictionaries, topic modeling and analysis, and text sentiment analysis. On the other hand, LLMs have strong human alignment capabilities, thus opening up entirely new application scenarios, i.e., acting as economic agents that simulate humans in generating beliefs or expectations about texts and making economic decisions. Finally, we summarize the limitations and existing research gaps that LLMs face in pioneering new paradigms of economic and financial text analysis research, and discuss potential new research topics that may arise from these issues.

As a result of the transition of atmospheric environmental governance from territorial administration to joint management, regional joint prevention and control has become a crucial air pollution control measure. However, joint prevention and control cannot exist wholly without territorial governance, and how to coordinate joint prevention and control with territorial governance has become the key to air pollution control. This paper explores the policy relevance and impact of territorial governance from the perspective of policy governance, using 12166 air pollution prevention and control policy texts issued by Chinese local governments from 2000 to 2018, and combining unsupervised learning and spatial econometric models. Research has found that local prevention and control policies mainly focus on supervision and regulation, including emergency management of heavily polluted weather, total pollutant emission control, project control and dust control, and mobile pollution source control, but each has its own emphasis on specific prevention and control; And the higher the correlation between regional policies, the more similar their pollution emissions, energy consumption, and industrial development are. The results indicate that pollution emissions and some influencing factors, such as the spatial spillover effect of environmental regulations, are also caused by similar policy prevention and control systems. The prospective policy relevance in territorial governance can serve as the foundation for regional joint governance, and promote regional environmental collaborative governance by further integrating regions with high policy relevance. This study provides a new explanation for the spatial dispersal and transmission of air pollution, and a feasible direction for regional joint prevention and control.

Corporate continuous green innovation activities are an important driver for promoting green and sustainable economic development. This paper uses a sample of A-share listed companies in China's capital market from 2009 to 2022 to empirically examine the influence of local government environmental protection concern on corporate green sustainable innovation levels. This study finds that local government environmental protection concern can enhance corporate green sustainable innovation levels, with stronger concern leading to higher levels of corporate green sustainable innovation. By distinguishing between types of corporate green sustainable innovation activities, the study finds that local government environmental protection concern promotes both upstream green sustainable innovation levels (source control) and downstream green sustainable innovation levels (end-of-pipe treatment), with a more pronounced impact on upstream green sustainable innovation levels. The analysis of the influencing mechanisms indicates that local government environmental protection concern improves corporate green sustainable innovation levels by increasing environmental resource compensation and strengthening managerial environmental awareness. Further heterogeneity analysis reveals that the positive impact of local government environmental protection concern on corporate green sustainable innovation levels is more pronounced in samples that CEOs with green experience, firms with stable institutional investors, heavily polluting industries, and cities with key environmental protection. The economic consequence test shows that local government environmental concern is beneficial for enhancing corporate environmental protection performance by improving corporate environmental outcomes, increasing corporate environmental advantages, and reducing corporate pollutant emissions and environmental governance costs. This paper uses the level of corporate green sustainable innovation as an entry point to explore the microeconomic consequences of local government environmental protection concern, providing theoretical references for promoting the current transition to green and sustainable economic development.

The accuracy of stock return prediction has a critical impact on investment decisions. The advent of deep learning models has markedly improved the accuracy of return forecasts. However, stock market sequences are often observed with anomalies that can distort key statistical measures, obscure the true trends of the data, and diminish the predictive capabilities of deep learning models. In extreme cases, these anomalies can result in erroneous investment decisions. Based on the presence of anomalies and the learning dynamics of gradient descent algorithms, this paper introduces a novel loss function, the threshold distance weighted loss (TDW), which is designed to mitigate the susceptibility of the model to outliers by assigning variable weights to data samples. The TDW loss function has been tested through simulation studies and empirical analysis. These evaluations have confirmed the improved predictive accuracy and robustness of the method, highlighting its potential to deliver consistent positive returns to investment portfolios and to bolster informed financial investment decisions.

Blockchain technology changes the current financing channel of firms. It would help firms to solve the financing difficulties. This article considers a bank financing model to analyze the firm's optimal production strategy and investment of blockchain technology when the market demand is stochastic. The article also discusses the different decisions in three types of firms (the firm that initial capital to invest in the blockchain is relatively sufficient, the firm that initial capital to invest in the blockchain is insufficient, and the firm with no blockchain investment). In our model, we find that the firm's profit, production, and blockchain investment decision would be affected by initial capital, the bank interest rate, and the bank's interest rate discount coefficient of the blockchain investment. The article finds that with the difference in the level of investment efficiency and the level of profitability of the company, blockchain investment has an adverse impact. Besides, the stimulated market demand generated by blockchain investment can reduce the risk of firms' loan default. The article finds that blockchain investment can create huge value for firms and reduce actual financing costs. Moreover, the article identifies the different impacts of market risk on firm decisions. This work gives managerial insights into firms' financing and production strategy when investing in blockchain technology. The paper also finds that the discounts of interest rates and blockchain investment interest rates formulated by banks would play a guiding role in firms' production.

In the process of low-carbon transition, enterprises require substantial financial support for related investments. Therefore, the effectiveness of carbon pricing policies depends on a well-functioning financial market. However, in reality, financial markets face various frictions that hinder the flow of capital, leading to inefficient allocation of resources. These frictions may affect corporate investment behavior, thereby weakening the implementation effects of carbon pricing policies. This paper, focusing on the issue of financing constraints, constructs an environmental-dynamic stochastic general equilibrium (E-DSGE) model incorporating a financing collateral constraint mechanism to analyze the impact of financing constraints on the effectiveness of carbon pricing policies and explores corresponding policy responses. The results show that: 1) From the perspective of environmental benefits, financing constraints weaken the “emission reduction effect” of carbon pricing policies, suppress corporate low-carbon investments, and reduce corporate emission intensity; 2) From the perspective of economic costs, financing constraints amplify the cost impact of carbon pricing on enterprises, restrict output growth, and increase the overall economic cost of the low-carbon transition; 3) Introducing carbon asset-backed loans as a complementary measure to carbon pricing policies can effectively mitigate the negative impact of financing constraints on carbon pricing policies; 4) Numerical simulation shows that financing constraints increase the proportion of carbon pricing-related costs in enterprises' total production costs from an average of 15.31% to 19.47% annually, while reducing the annual average scale of low-carbon investments by approximately 37%. Furthermore, providing more carbon asset-backed loans to high-emission enterprises can significantly enhance policy benefits. The conclusions of this paper are of great significance for improving mechanisms for green and low-carbon development and establishing a systematic climate policy framework.

With the rapid development of the Internet, people have become accustomed to sharing hobbies, obtaining information and discussing common hot topics on the Internet, studying the law of public opinion communication in multi-layer social networks is beneficial to public opinion analysis and public opinion governance. Based on the traditional SEIR epidemic model, this paper considers the influence of node importance on propagation probability, and introduces dynamic parameters, and constructs a single-layer network public opinion propagation model. At the same time, considering the impact of different time steps and degree correlations on public opinion propagation, a multi-layer network cross propagation public opinion propagation model is proposed. In this paper, theoretical verification and experimental analysis are carried out on various communication performances and laws of multi-layer network public opinion communication model. Experiments show that time step and degree correlation have a significant impact on public opinion communication. Finally, some public opinion governance mechanisms and public opinion response measures are put forward, which can help the government and relevant administrative departments to improve the efficiency of public opinion management, ensure rapid response in public opinion events, and reduce potential negative effects, and this is of great significance.

During the 14th Five Year Plan period, the reform of green fiscal and tax policies was proposed to strengthen the regulatory and guiding role of incentive tax policies, such as value-added tax. This paper is based on a quasi natural experiment of China's tax system reform from business tax to value-added tax. By constructing a multi-time point double difference model, we analyze panel data of 1805 A-share listed companies and examined the impact mechanism of tax policy incentives on the green innovation of enterprises. The research finds that the replacement of business tax with value-added tax significantly enhances green innovation in enterprises, mainly reflected in substantive innovation rather than strategic innovation. Research on the mechanism of action indicates that the reform from business tax to value-added tax indirectly promotes green innovation in enterprises through the effects of division of labor and the tax burden. Heterogeneity studies have shown that the incentive effect of replacing business tax with value-added tax on green innovation is more prominent in non-state-owned enterprises and manufacturing enterprises. This paper is an important supplement to the research on existing tax policy reform and micro-enterprise behavior, providing an important basis for promoting green development through financial and tax policy reform in the future.

With the increasing complexity of road traffic and the development of intelligent transportation, safe driving has received extensive attentions. This paper aims to give a series of algorithms regarding safe driving of distributed vehicle formation in dynamical environments, and verify the effectiveness of the algorithms. First, by introducing the distributed formation protocol of multi-agent systems, the formation model of vehicles on the road is provided. Then, this paper proposes an improved artificial potential field algorithm by adding the velocity parameter into the attractive and repulsive force functions in the potential field, which solves the obstacle avoidance and overtaking problem of vehicle formation in dynamical lane environments. Meanwhile, based on the escape behavior of fish swarming, the model of vehicle following and lane changing are proposed, and the model solves the problem of vehicle formation emergency avoidance under distributed information. Finally, simulations on emergency evasion of vehicles based on distributed information interactions are conducted in dynamical traffic environments, which verify the effectiveness of the proposed algorithms.

The international economic circulation affects China's economic growth through the production process of the product and the market demand of the product. In terms of production process, the mutual substitution of imported intermediate products and domestic intermediate products affects economic growth; in terms of market demand, foreign demand for China intermediate and final products affects China economic growth. Based on the structural decomposition analysis method, this paper decomposes the change of the Leontief inverse matrix into technology level change and import substitution, and decomposes the final demand change into domestic final demand change and export change, so as to measure the impact of international economic circulation on China economic growth from two aspects. The results show that: 1) Import substitution is an important channel for the international cycle to affect China economic growth, and it shows periodic characteristics. From 2000 to 2005, imported intermediate products replaced domestic intermediate products, which had a negative impact on economic growth; From 2005 to 2014, domestic intermediate goods substituted imported intermediate goods, and China gradually took control of more intermediate goods production processes. From 2015 to 2021, the share of imported intermediate goods again increased. 2) Compared to domestic final demand, the contribution of exports to China's economic growth has been continuously decreasing, and China's dependence on the final demand of international circulation has been gradually declining.

Promoting carbon emission reduction of urban residents is of great significance for mitigating climate problems. Based on the panel data of 288 cities above prefecture level in China from 2009 to 2019, this paper calculated the living carbon emissions of urban residents, and combined population and economic characteristics to cluster cities into four types for analysis, and analyzed the influencing factors of living carbon emissions of urban residents. And BP neural network and scenario analysis were used to predict the carbon reduction potential of various urban residents. The results show that: 1) The total carbon emission of urban residents in China is increasing year by year, and the proportion of carbon emission from electricity is the highest, and the growth rate of carbon emission from heating is the highest. 2) Urbanization level, per capita disposable income, energy structure and total population size all have positive effects on the carbon emissions of urban residents, while energy intensity and consumption tendency of urban residents have negative effects, and the influencing factors of carbon emissions of various cities have certain differences. 3) All kinds of cities have great carbon reduction potential in residents' life, and there are great differences. The carbon reduction potential of the second type of cities is significantly higher than that of other cities. The first type of cities has the lowest carbon reduction potential overall. The change degree of carbon reduction potential of the third and fourth types of cities is similar, showing a trend of first increasing and then decreasing. All localities may formulate and implement carbon reduction measures for residents according to local conditions.

Improving firm ESG performance is an important measure to achieve sustainable economic development. This study takes the implementation of the “Broadband China” strategy released in 2013 as a quasi-natural experiment and uses differences-in-differences method to evaluate the impact of digital infrastructure on firm ESG performance. The study finds that digital infrastructure has a significant promoting effect on firm ESG performance. The mechanism analysis shows that the impact of digital infrastructure on firm ESG performance is mainly achieved through pathways such as increasing government environmental concerns, incentivizing firms to fulfill social responsibilities, and improving firm information transparency. Heterogeneity analysis reveals that the promoting effect of digital infrastructure on firm ESG performance is more significant in large-scale firms, firms with high customer concentration, high-polluting industries, and firms with strong green innovation capabilities. This study evaluates the practical role of digital infrastructure from a sustainable development perspective, providing new empirical evidence for understanding the influencing factors of firm ESG performance and offering policy recommendations for strengthening digital infrastructure construction and promoting economic green transformation.

With the escalating impact of climate change leading to more frequent extreme precipitation events, effectively mitigating the risks and economic losses associated with this uncertainty is increasingly critical. This paper introduces a novel “Markov-Gumbel” theoretical model to measure precipitation index, which, in conjunction with risk-neutral theory, forms the basis for a new pricing model for precipitation index derivatives. We apply this model using daily precipitation data from regions such as Zhengzhou City in Henan Province and Xuzhou City in Jiangsu Province, China, to validate and analyze its efficacy. Research indicates that this new method for measuring the precipitation index offers enhanced flexibility and better captures seasonal variations. Moreover, the proposed pricing model for precipitation derivatives, grounded in risk-neutral valuation methods, results in more focused and stable pricing outcomes, significantly improving pricing accuracy. The findings of this research not only provide an innovative framework for the measurement and application of precipitation index derivatives but also offer valuable theoretical and practical insights for effectively hedging against risks associated with precipitation uncertainty.

Analysts' past performance and their employment status in the securities industry jointly affect the information disclosure quality of listed companies tracked and analyzed. This article uses the analyst-company dataset from 2011 to 2021, and constructs an analyst network reputation through 16 relevant indicators at the individual analyst and securities firm levels, and explores its impact on the information efficiency of the stock market. The study found that analyst network reputation increases competitive information, widens opinion divergence, and divides investors' attention, thereby reducing the information efficiency of the stock market. In particular, analyst recommendations may contain invalid information. When investors face numerous information with limited attention, trading activity decreases, stock liquidity declines, and market information efficiency is weakened. In addition, media coverage has a diminishing effect on analyst network reputation, and market investor sentiment has an amplifying effect on analyst network reputation, which reduces information efficiency.

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.