With the development and applications of the Smart Court System (SCS) in China, the reliability and accuracy of legal artificial intelligence have become focal points in recent years. Notably, criminal sentencing prediction, a significant component of the SCS, has also garnered widespread attention. According to the Chinese criminal law, actual sentencing data exhibits a saturated property due to statutory penalty ranges, but this mechanism has been ignored by most existing studies. Given this, the authors propose a sentencing prediction model that combines judicial sentencing mechanisms including saturated outputs and floating boundaries with neural networks. Building on the saturated structure of our model, a more effective adaptive prediction algorithm will be constructed based on the fusion of several key ideas and techniques that include the utilization of the $L_1$ loss together with the corresponding gradient update strategy, a data pre-processing method based on large language model to extract semantically complex sentencing elements using prior legal knowledge, the choice of appropriate initial conditions for the learning algorithm and the construction of a double-hidden-layer network structure. An empirical study on the crime of disguising or concealing proceeds of crime demonstrates that our method can achieve superior sentencing prediction accuracy and significantly outperform common baseline methods.

Rosenblatt and Rosenblatt-Volterra processes are two families of stochastic processes that are described by double Wiener-Itô integrals with singular kernels. The Rosenblatt processes have exponential singular kernels and the Rosenblatt-Volterra processes have singular Volterra kernels for the Wiener-Itô integrals. Empirical evidence shows that for many control systems the assumption of Gaussian noise is not appropriate so Rosenblatt and Rosenblatt-Volterra processes are some generalizations of Gaussian processes that can provide natural alternatives to Gaussian probability laws. Furthermore, the results for Rosenblatt and Rosenblatt-Volterra processes are tractable for some applications. These results can be compared to prediction for Gaussian processes and Gauss-Volterra processes.

Ever since Brockett and Clark (1980), Brockett (1981) and Mitter (1980) introduced the estimation algebra method, it becomes a powerful tool to classify finite-dimensional filtering systems. In this paper, the authors investigate estimation algebra on state dimension $n$ and linear rank $n-1$, especially the case of $n=4$. Mitter conjecture is always a key question on classification of estimation algebra. A weak form of Mitter conjecture states that observation functions in finite dimensional filters are affine functions. In this paper, the authors shall focus on the weak form of Mitter conjecture. In the first part, it will be shown that partially constant structure of $\varOmega$ is a sufficient condition for weak form Mitter conjecture to be true. In the second part, the authors shall prove partially constant structure of $\varOmega$ for $n = 4$ which implies the weak form Mitter conjecture for this case.

In the context of the new era, the fundamental way to promote high-quality economic and social development is to improve urban development efficiency, and digital economy plays an important driving role in the process. This paper constructs a theoretical analytical framework for digital economy-driven urban development efficiency improvement, and empirically tests the impact of digital economy on urban development efficiency and spatial spillover effects using a spatial and temporal double-fixed spatial Durbin model. This paper finds that: Firstly, digital economy significantly contributes to urban development efficiency in the region and surrounding areas, and the finding still holds through a series of robustness tests. Secondly, digital economy contributes to urban development efficiency by enhancing social, economic and ecological benefits, but the enhancement is limited by the reduction of land benefits, while industrial integration, technological advancement, and urban-rural integration play an important role in its mechanism. Thirdly, the effect of digital economy in driving the improvement of urban development efficiency shows a non-linear trend of "downward and then upward" and spatial spillover characteristics. Fourthly, there is city-level heterogeneity and geographic-area heterogeneity in the impact of the digital economy on urban development efficiency, which means that the role of digital economy in driving urban development efficiency is more pronounced in cities with high administrative levels and large populations, as well as in the eastern and northern regions. The above findings imply that at present, China should take urban development efficiency as an important target to consider for the high-quality economic development, and take the development of digital economy as the main driving force to improve urban development efficiency.

To systematically explore the impact of regional synergistic development on urban sprawl, this paper firstly constructs a theoretical framework of regional synergistic development on urban sprawl; Subsequently, the fixed effects model, threshold effects model, spatial measurement model and spatial threshold model were used to reveal the effects and non-linear mechanisms of regional synergistic development on urban sprawl, as well as the spatial threshold effects and spatial spillover boundaries of regional synergistic development on urban sprawl at different stages. The results show that: 1) Regional synergistic development has an inhibitory effect on urban sprawl. On this basis, the threshold effect indicates that the relationship between the two has a non-linear characteristic of "first promoting, then inhibiting, and then strengthening the inhibitory effect", and is constrained by the thresholds of population mobility, industrial development, environmental concerns and transportation construction. 2) The increase in the level of regional synergistic development of the local region will exacerbate the phenomenon of urban sprawl in the neighboring regions, which has the obvious characteristic of "beggar-thy-neighbor", but the boundary of the spatial effect of the attenuation is only 280 km. 3) As the level of regional synergistic development increases, its inhibitory effect on local urban sprawl will continue to increase, while its facilitating effect on urban sprawl in neighboring areas will continue to decrease. 4) The spatial spillover effect of regional synergistic development on urban sprawl at different stages shows a wavy spatial distance decay characteristic, and the radiation boundary shrinks as the level of regional synergistic development increases.

As a new financial service, supply chain finance plays an important role in improving financing efficiency and reducing transaction costs for enterprises. Behind the huge benefits there are often frequent incidents of pseudo supply chain finance, and "supply chain security" is gradually elevated to the level of the macro national security system. This paper uses the data of A-share non-financial listed companies in Shanghai and Shenzhen Stock markets from 2007 to 2021, and we find that supply chain finance can significantly reduce firms' risk-taking, while this negative relationship is more obvious in non-state-owned enterprises and small enterprises. Further analysis shows that supply chain finance will enhance the resilience of the industrial chain and supply chain by improving the company's operating efficiency, alleviating underinvestment, stabilizing supply chain relations to reduce the risk-taking level. In addition, enterprises with good bank-enterprise relationship, higher industry competition and higher risk preference of management can enhance the reducing effects to a greater extent. Under the background of high environmental uncertainty faced by enterprises at present, this paper provides feasible ideas for enterprises to carry out supply chain finance to reduce production and operation risks and financial risks, and then maintain the security of industrial chain and supply chain.

Salary incentives are an important means for enterprises to stimulate employees' work passion, creative potential and improve corporate performance. On the one hand, the internal pay gap has a positive motivating effect and promotes the effort of employees. On the other hand, it can also lead to a sense of unfairness and cause some employees to feel “flat”. This paper constructs a mathematical model including core executives, non-core executives, and ordinary employees to analyze the impact of internal salary gaps on corporate performance, and conducts empirical research using data from privately-owned listed companies in Shanghai and Shenzhen from 2008 to 2020. Both theoretical and empirical results show that the relationship between pay gap within management, executive-employee pay gap, the degree of compensation incentives of non-core executives and corporate performance all show an inverted U shape. Further empirical research shows that non-core executive-employee pay gap has the strongest effect on corporate performance, while core executive-employee pay gap has the smallest effect on corporate performance. This research shows that non-core executive-employee pay gap is the most important compensation relationship within the company and core executive-employee pay gap is of least importance. In addition, in the salary incentive design of private enterprises, the “constraint” of operating profit is greater than the “constraint” of operating income, which reflects that private enterprises pay attention to seizing the key points.

To quantify the impact of the stability of power supply on the development of the digital economy, a system dynamics model is constructed, including variables such as power supply installed capacity and the scale of the digital economy in the three industries. The model explores the mechanism of how the stability of power supply affects the development of the digital economy in scenarios such as power outages and electricity rationing. The results show that the scale of China's digital economy will maintain a high growth trend during the “14th Five-Year Plan” period and is expected to exceed 70 trillion yuan by 2025. The scale of the digital economy will decrease with the reduction of power supply. If the average daily electricity generation time or the number of working days per year decreases by 1%, 5%, and 10%, the digital economy scale will decrease by an average of 9.28%, 14.24%, and 20.43% respectively. By promoting technological innovation to improve the value-added coefficients of various industries, the impact of power outages or generator failures on the development of the digital economy can be reduced. Finally, policy recommendations are proposed to enhance power supply stability in China.

The anomaly detection has been a widely concerned topic of great application value and research importance for a long time. There are many machine learning algorithms dealing with the anomaly detection problem without clear statistical guarantee on the degree of false discovery. We propose a general framework for anomaly detection based on conformal inference that enables online false discovery rate control and does not rely on any model or distribution assumptions. The proposed procedure can incorporate different machine learning algorithms and online multiple hypothesis testing algorithms, thus providing a flexible and versatile approach for anomaly detection. We verify the effectiveness of the proposed procedure on simulated data and apply it to Server Machine Dataset to detect anomalies.

The Fast Multipole Method (FMM) is a highly efficient numerical algorithm for handling large-scale multi-particle systems, playing an important role in fields such as molecular dynamics, astrodynamics, acoustics, and electromagnetics. This paper first reviews the history of the Fast Multipole Method, then taking Helmholtz and Maxwell equations as examples, introduces the data structures, mathematical principles, implementation steps, and complexity analysis of the FMM based on kernel analytical expansion in two-dimensional and three-dimensional cases, and describes corresponding adaptive version of FMM. Finally, numerical experiments on multi-particle simulations in two-dimensional and three-dimensional spaces are given on the MATLAB platform.

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.

Let $q\in(0,\infty]$ and $\varphi$ be a Musielak-Orlicz function with uniformly lower type $p_{\varphi}^-\in(0,\infty)$ and uniformly upper type $p_{\varphi}^+\in(0,\infty)$. In this article, the authors establish various real-variable characterizations of the Musielak-Orlicz-Lorentz Hardy space $H^{\varphi,q}(\mathbb{R}^n)$, respectively, in terms of various maximal functions, finite atoms, and various Littlewood-Paley functions. As applications, the authors obtain the dual space of $H^{\varphi,q}(\mathbb{R}^n)$ and the summability of Fourier transforms from $H^{\varphi,q}(\mathbb{R}^n)$ to the Musielak-Orlicz-Lorentz space $L^{\varphi,q}(\mathbb{R}^n)$ when $q\in(0,\infty)$ or from the Musielak-Orlicz Hardy space $H^{{\varphi}}({\mathbb{R}^n})$ to $L^{\varphi,\infty}(\mathbb{R}^n)$ in the critical case. These results are new when $q\in(0,\infty)$ and also essentially improve the existing corresponding results (if any) in the case $q=\infty$ via removing the original assumption that $\varphi$ is concave. To overcome the essential obstacles caused by both that $\varphi$ may not be concave and that the boundedness of the powered Hardy-Littlewood maximal operator on associated spaces of Musielak-Orlicz spaces is still unknown, the authors make full use of the obtained atomic characterization of $H^{\varphi,q}(\mathbb{R}^n)$, the corresponding results related to weighted Lebesgue spaces, and the subtle relation between Musielak-Orlicz spaces and weighted Lebesgue spaces.

In this paper, we establish a weighted maximal $L_2$ estimate of operator-valued Bochner-Riesz means. The proof is based on noncommutative square function estimates and a sharp weighted noncommutative Hardy-Littlewood maximal inequality.

Let Ω be a domain of $(\mathbb{R}^n)$ with n ≥ 2 and p(·) be a local Lipschitz funcion in Ω with 1 < p(x) < ∞ in Ω. We build up an interior quantitative second order Sobolev regularity for the normalized p(·)-Laplace equation -Δ_p(·)^Nu = 0 in Ω as well as the corresponding inhomogeneous equation -Δ_p(·)^Nu=f in Ω with f ∈ C⁰(Ω). In particular, given any viscosity solution u to -Δ_p(·)^Nu= 0 in Ω, we prove the following:
(i) in dimension $n=2$, for any subdomain $U \Subset \Omega$ and any $\beta \geq 0$, one has $|D u|^\beta D u \in L_{\text {loc }}^{2+\delta}(U)$ with a quantitative upper bound, and moreover, the map $\left(x_1, x_2\right) \rightarrow|D u|^\beta\left(u_{x_1},-u_{x_2}\right)$ is quasiregular in $U$ in the sense that
$\left|D\left[|D u|^\beta D u\right]\right|^2 \leq-C \operatorname{det} D\left[|D u|^\beta D u\right] \quad$ a.e. in $U$.
(ii) in dimension $n \geq 3$, for any subdomain $U \Subset \Omega$ with $\inf _U p(x)>1$ and $\sup _U p(x)<3+\frac{2}{n-2}$, one has $D^2 u \in L_{\text {loc }}^{2+\delta}(U)$ with a quantitative upper bound, and also with a pointwise upper bound
$\left|D^2 u\right|^2 \leq-C$ $\sum\limits_{1 \le i < j \le n} {} $ $\left[u_{x_i x_j} u_{x_j x_i}-u_{x_i x_i} u_{x_j x_j}\right]$ a.e. in $U$.
Here constants $\delta>0$ and $C \geq 1$ are independent of $u$. These extend the related results obtaind by Adamowicz-Hästö [Mappings of finite distortion and PDE with nonstandard growth. Int. Math. Res. Not. IMRN, 10, 1940-1965 (2010)] when $n=2$ and $\beta=0$.

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.