Multidimensional system reduction is often converted to the equivalence between multivariate polynomial matrices and their Smith forms. This paper focuses on the unimodular equivalence of a class of multivariate polynomial matrices to their Smith forms. A criterion for the unimodular equivalence of such matrices and their Smith forms is proposed, with the result extended to non-square and rank-deficient cases. Finally, a general algorithm for reducing such matrices to their Smith forms is presented and an example is provided to illustrate the algorithm.

This paper investigates the output regulation problem for uncertain variable-coefficient heat equations with matched boundary disturbances. The thermal system is governed by an uncertain parabolic partial differential equation (PDE) with mixed boundary conditions, characterized by unknown spatially varying diffusion coefficients and subject to smooth uncertain boundary disturbances that may potentially be unbounded in magnitude. We propose a robust control strategy that synergistically combines linear feedback design with the Twisting second-order sliding mode control algorithm, properly reformulated and integrated within an infinite-dimensional framework. The well-posedness of the closed-loop system, formulated as a differential inclusion, is established through maximal monotone operator theory. Furthermore, by employing a nonstandard Lyapunov functional and under the condition that the controller tuning parameters satisfy certain fundamental inequalities, we prove the global asymptotic stability of the closed-loop system in an appropriate Sobolev space. Numerical simulations in the experimental section validate the effectiveness of the proposed controller design.

The rational allocation of basic education resources is essential to achieving educational equity and improving educational quality, while the enhancement of human capital is a core driving force for building a strong education system. Based on annual provincial panel data from 2000 to 2022, this paper employs fixed-effects models and difference-in-differences (DID) models to examine the effects of the accessibility, quality, and balanced allocation of basic education resources on human capital accumulation. The results show that the accessibility of basic education resources has a long-term positive effect on the stock of human capital, with the most significant effect observed at the junior high school stage. The effects of the quality of basic education resources on human capital quality vary across educational stages. The mechanism analysis indicates that high-quality education resources further enhance regional human capital quality by attracting inflows of highly educated individuals. The balanced allocation of basic education resources also plays a positive role in improving the structure of human capital, especially at the junior high school stage. In addition, the DID analysis further confirms the positive effect of policies promoting the balanced development of education on human capital accumulation.

The gradient method is one of the simplest and most fundamental computational methods for solving optimization problems. Since all gradient methods find the next iterate along the steepest descent direction, the difference among various gradient methods lies in the choice of step size. The BB step size is one of the most renowned choices for step size in gradient methods. In this paper, by interpreting the BB step as a step size based on onedimensional subspace approximation, we construct new step sizes based on two-dimensional and three-dimensional subspace approximations. These new step sizes possess favorable theoretical properties and are expected to be developed into effective numerical methods.

This paper mainly discusses the long-time behavior of solutions to a class of degenerate heat-beam coupled partial differential dynamical systems. The model consists of two parts: One part is described by a degenerate heat equation, and the other part is described by the Timoshenko beam equation. These two parts are coupled together at a common boundary through certain transmission conditions. Based on the frequency domain analysis method, this paper provides a refined estimate of the polynomial stability of solutions to this type of coupled system under smooth initial conditions and determines an explicit relationship between the decay rate of the system and the degree of heat degeneracy.

This study explores in depth the impact mechanism of state-owned institutional investors' shareholding on corporate investment efficiency, especially in the context of mixed ownership reform. Combining theoretical analysis and empirical testing, it reveals the key role of state-owned institutional investors in improving corporate investment efficiency. Research has shown that an increase in the shareholding ratio of state-owned institutional investors has a significant promoting effect on improving the investment efficiency of enterprises. In addition, factors such as corporate governance level, enterprise size, industry competition level, and cash flow volatility also have a direct impact on the investment efficiency of enterprises. Further research has found that digital transformation and green innovation of enterprises play an important intermediary role between the shareholding of state-owned institutional investors and the efficiency of enterprise investment. State owned institutional investors accelerate the process of digital transformation and green innovation by providing financial support, strategic guidance, and resource allocation. Digital transformation can improve information processing capabilities and optimize resource allocation efficiency, while green innovation can indirectly enhance the investment efficiency of enterprises by improving resource utilization efficiency and enhancing market competitiveness, enabling state-owned institutional investors to hold shares can indirectly improve the investment efficiency of enterprises. This article not only enriches the relevant research on the economic consequences of state-owned institutional investors, but also provides a new theoretical perspective for understanding the mechanism of improving investment efficiency of enterprises. At the same time, it provides important theoretical basis and policy inspiration for deepening the reform of state-owned enterprises, optimizing the layout of state-owned capital, and enhancing the overall competitiveness of enterprises in the new era.

The rapid expansion of shared mobility services has introduced innovative solutions for urban transportation. Ride-hailing platforms, facilitated through user-friendly smartphone applications, seamlessly connect individual preferences with immediate vehicle availability. In carpooling services, passengers can share a ride in the same vehicle, setting their respective destinations as waypoints to increase vehicle utilization. This study proposes a carpooling and dispatching model for ride-hailing services based on a self-attention reinforcement learning network. Initially, a carpooling travel topology network is constructed, considering factors like passenger pick-up and drop-off times and locations. An on-demand algorithm is designed to identify ride-hailing orders suitable for carpooling. Subsequently, a self-attention reinforcement learning network is employed for order dispatching optimization. Through the implementation of policy gradient techniques for learning and training, the integration of masking methods ensures the efficacy of order dispatching. Leveraging the strengths of “offline training & online decision-making”, the proposed strategy tackles the challenges of enhancing the real-time responsiveness of large-scale ride-hailing dispatching services. Finally, the real-world case study is conducted based on ride-hailing orders in Beijing, China. Results underscore the efficiency of the order dispatching algorithm in achieving near-optimal route selections while reaching a real-time demand response. Although carpooling slightly increases passenger waiting times, it significantly boosts ride-hailing operational efficiency, alleviates traffic congestion, and mitigates environmental pollutants.

This paper investigates the adaptive tracking control problem for Auto-Regressive Moving Average (ARMA) systems with quantized observations, explicitly focusing on reference signals composed of non-periodic sequences. The authors propose an adaptive tracking control scheme integrating an adaptive controller with a stochastic approximation-type estimation algorithm. Different from the control scheme for Finite Impulse Response (FIR) systems, the estimation part not only estimates the unknown system parameters but also the unknown system outputs. Next, based on the certainty equivalent principle, the adaptive controller is designed using the above two estimates instead of the actual parameters and system outputs. To tackle the inherent coupling between the two estimates, the authors introduce a novel approach that combines the Lyapunov function method with a backward-shifted polynomial method featuring time-varying coefficients. This approach assists in establishing the mean square convergence of the estimates with a convergence rate of $O\left(\frac{1}{k}\right)$ under suitable conditions of the step size coefficient. Additionally, this paper shows that the designed adaptive control law can achieve asymptotically optimal tracking of non-periodic reference signals in the mean square sense. Finally, a numerical simulation is presented to validate the theoretical results obtained in this paper.

In this paper, the authors address the problem of almost sure polynomial stabilization for a class of highly nonlinear stochastic systems via sampled-data feedback. The considered systems fall within a general framework that includes two key features: (a) Continuous-time irreducible Markov chain- the authors introduce a continuous-time irreducible Markov chain to describe systems that can undergo sudden alterations in their parameters and structures. This flexibility allows us to model real-world scenarios more accurately; (b) Diffusion and drift coefficients with polynomial growth - unlike existing literature that primarily focuses on systems with bounded delays, the authors investigate the stabilization conditions for highly nonlinear stochastic systems with pantograph delay, an unbounded delay. Specifically, the authors analyze systems where the diffusion and drift coefficients satisfy a polynomial growth condition. To achieve the proposed goal, the authors employ $M$-matrix theory and Lyapunov functions as basic tools. The main results establish that the system can attain almost sure polynomial stabilization through a subtly and innovatively designed sampled-data feedback. The authors validate the theoretical findings with numerical simulations, demonstrating the effectiveness of the proposed approach. This work contributes to the understanding of stabilization in highly nonlinear stochastic systems, particularly those with unbounded delays, and broadens the practical applicability of stochastic modeling.

To explore the impact of various groups and methods on rumor propagation, the authors propose a 'Double-Refutation $(DR)$ and Double-Blocking $(DB)$' rumor control strategy. This strategy combines external refutation via media reports, internal refutation by counteracting individuals, and both continuous and impulse blocking methods. By leveraging multi-synergy and aiming to minimize control costs, the authors propose stochastic optimal hybrid control strategies for rumor containment. Additionally, to enhance the response speed of the control strategy, the authors introduce an ensemble learning algorithm as a substitute for theoretical solutions. Numerical simulations demonstrate that the trained ensemble learning control algorithm can quickly identify sub-optimal control strategies for rumor spreading, with costs only 4.1% higher than those of the optimal control theory.

In this paper, the controllability of signed multi-agent networks based on the consensus protocol is studied from the perspective of topological structure. Firstly, based on the eigenvectors of Laplacian matrix and the leader-follower structure, the necessary and sufficient algebraic condition for the controllability of undirected signed topologies is obtained. According to the condition, for the composite topologies obtained by connecting two sub-topologies, two methods are proposed to construct controllable composite topologies by connecting the controllable sub-topologies. In addition, based on uncontrollable undirected signed topologies, the same sign double controllability destructive nodes (SSDCDN) and inverse sign double controllability destructive nodes (ISDCDN) are defined for the first time. By analyzing the characteristics of these nodes, the necessary and sufficient condition for the controllability on multi-leader undirected signed topological graphs is obtained. Finally, on the basis of the existing results, the design methods of two special types of uncontrollable signed sub-topologies connected with controllable signed sub-topologies to form controllable composite signed topologies are proposed.

China's policy implementation and technological evolution of the energy industry exert a substantial global influence in confronting climate challenges. Given the notable regional differences in China's economic development and resource endowments, there are significant distinctions in the distribution structures and evolutionary trajectories of energy industry technologies among various regions. This study constructs the global-China multi-region integrated assessment model, namely WITCH-China, aiming to explore the optimal evolutionary paths of energy industry technology development and carbon emissions for the selected 30 regions in China under the global temperature control scenarios. And the technological optimization evolution of energy industry is comprehensively evaluated. The findings reveal that: 1) Under policy scenarios, the consumption of coal and oil show a significant downward trend, wind and solar energy experiences a sharp increase, and the proportion of non-fossil energy consumption will exceed fossil energy for the first time in 2050. 2) Carbon emissions gradually decline with the decrease in fossil energy consumption, but due to the improvement of energy efficiency and the popularization of clean energy technology, the decline of carbon emissions gradually slows down. 3) There are obvious differences in the trend of energy consumption structure in different regions, and clean resource-rich areas, such as Qinghai, Xinjiang, Inner Mongolia, Gansu, etc., have greater development potential in clean energy, and the clean substitution potential of wind power and solar energy is the most significant. Finally, the study puts forward relevant policy suggestions for the development of China's energy industry.

This paper proposes a class of spatial varying-coefficient autoregressive models with autocorrelated errors. The proposed framework simultaneously incorporates spatial correlation in the response variable and spatial autocorrelation in the error term within the spatial varying-coefficient setting, thereby jointly capturing both heterogeneity and dependency structures in spatial data to better reflect their complex characteristics. To overcome the endogeneity issue of the model, an effective three-stage estimation method is proposed that integrates local linear estimation, generalized method of moments (GMM), and profile least squares estimation methods, and the asymptotic properties of the estimators are derived. The Monte Carlo simulation results indicate that the estimation method for the studied model demonstrates good efficacy under finite samples. Empirical analysis based on the Boston housing price data further shows that this model significantly enhances the explanatory power of spatial economic phenomena.

This paper applies the Cheng projection to the support vector machine (SVM) in handling missing data. In the process of handling missing data, each sample with missing values is replaced by its Cheng projection in the original space. Additionally, two classification algorithms for handling linearly separable and nonlinearly separable datasets with missing data are presented. For linearly separable datasets with missing data, Cheng kernel function is introduced, and an SVM classification algorithm that improves the linear kernel function to the Cheng kernel function is proposed. For nonlinearly separable datasets, a generalized Gaussian Radial Basis Function kernel is introduced and an SVM classification algorithm for handling missing data is given. For both algorithms, two comparative experiments are conducted to demonstrate their effectiveness.

Both actuator faults and time delays degrade the performance of control systems. Although fault-tolerant mechanisms are commonly used in advanced control systems, no results are available in investigating the adaptive tracking problem of stochastic nonlinear time-delay systems in the presence of Markovian jump actuator faults. After establishing some mathematical fundamentals for stochastic differential delayed equations with multi-Markovian switching, this issue is tackled in this article, by proposing a novel adaptive backstepping fault-tolerant controller. Uncertainties caused by random actuator faults, unknown time-varying delays, the Wiener noise of unknown covariance as well as the unknown plant parameters are handled skillfully in a unified stochastic framework. By constructing a suitable Lyapunov-Krasovskii functional, it is proved that all closed-loop signals are bounded in probability, and the tracking error can converge into an arbitrarily small residual set in the sense of mean quartic value. In addition, the range of reference signals is greatly enlarged by comparison with the conventional backstepping controller. Two simulation examples are presented to illustrate the proposed theoretical findings.

In recent years, artificial cilia have attracted widespread research interest due to their enormous application prospects in the fields of medicine and environmental therapy. Deformation is a key issue to consider in the design and preparation of artificial cilia, however the corresponding mathematical analysis is still lacking. This paper introduces a multi-agent model for the magnetic artificial cilium, where each agent denoting a bead is influenced by the external magnetic field and neighboring agents. Then, the authors provide the existence and uniqueness of the solution to the proposed model, and give a stability condition for avoiding magnetic chain breakage and collisions between adjacent magnetic beads. To our best knowledge, it is the first mathematical result on the stability of magnetic bead chain. Finally, simulations are conducted to verify the proposed theoretical result.

This paper presents a primal-dual prediction-correction (PD-PC) method for solving linearly constrained time-varying convex optimization problems, which frequently arise in control, signal processing, and online learning applications. The proposed method establishes a novel integration of primal-dual gradient dynamics with a discrete-time prediction-correction structure, specifically designed for problems with time-dependent linear constraints. A tunable memory parameter is introduced in the prediction phase to perform linear extrapolation using past iterates, enabling a flexible trade-off between the amount of historical information stored and the computational cost of correction. In the correction phase, primal and dual variables are updated via gradient descent-ascent iterations, thus maintaining the computational efficiency of a first-order method without requiring Hessian or high-order derivative computations. Theoretical analysis shows that the method achieves $\mathcal{O}(h^2)$ asymptotic tracking accuracy for both primal and dual variables, matching the state-of-the-art performance among first-order methods even in unconstrained settings. Numerical experiments on problems with both time-invariant and time-varying constraints validate the theoretical findings and demonstrate the method's effectiveness.

The cooperative output regulation problem for unknown linear multi-agent systems has been studied by both policy-iteration method and value-iteration method via distributed internal model approach. However, the original results were limited to single-input single-output linear multi-agent systems under the assumption that the communication digraph is acyclic. Recently, the authors have extended the existing result to multi-input multi-output linear multi-agent systems over a general static and connected digraph by a more efficient value-iteration method. Since the policy-iteration method is simpler and has a much faster convergence rate than the value-iteration method, in this paper, the authors further apply the policy-iteration method to the cooperative output regulation problem of unknown multi-input multi-output multi-agent systems over a general static and connected digraph. Compared with the existing policy-iteration method, the proposed policy-iteration approach not only drastically reduces the computational cost, but also significantly weakens the solvability conditions. Moreover, by introducing a virtual exosystem, the proposed policy-iteration approach eliminates the need for employing a distributed observer. As a result, the data collection can start at any time, and the computing cost for each agent is also reduced.

Using ESG rating data of Sino-Securities Index Information Service from 2009 to 2020, this paper examines the impact of listed companies' ESG rating on the level of stock price, financial and operational risk in the next year. The study finds that better ESG rating has a significant inhibitory effect on all three risk levels of enterprises in the next year. Specifically, for the risk of stock price crash, ESG rating higher than the benchmark level, as a strong market signal, has a more significant reduction in the risk level of stock price crash. The trading volume of individual stocks, which reflects the attention of investors, has an intermediary effect on ESG to reduce the risk of enterprise stock price crash. ESG of large-scale enterprises that occupy an important position in the market and attract more attention from investors has a stronger inhibitory effect on the risk of stock price crash; In addition, the negative relationship between ESG and the risk of stock price crash is more significant after the implementation of the “Environmental Protection Law”. For financial risk, ESG has a marginal diminishing effect on reducing corporate financial risk, and the improvement of ESG rating from low to medium can improve the level of corporate financial risk. At the same time, enterprises' voluntary disclosure of non-financial information could strengthen the inhibitory effect of ESG on financial risks. For operational risk, ESG rating has a marginal diminishing effect on reducing operational risk; At the same time, the nature of equity has a moderating effect on the reduction of operating risks by ESG rating. Compared with private enterprises, ESG has a stronger inhibition effect on the operation risk of state-owned enterprises. Finally, the sub-sample heterogeneity test results based on the length of enterprise life in this paper show that the inhibitory effect of ESG rating on risk is stronger for enterprises with a long establishment age, but weaker for enterprises with a short establishment age.

Based on investor behavior data from the Eastmoney Guba, this paper employs the effective transfer entropy method to investigate whether the mediating role of stock forums in the stock market better represents investor attention or sentiment. The findings reveal that: 1) Investor attention in stock forums conveys more valuable information flows to the stock market, providing a more accurate representation of investor behavior; 2) The net comment volume, as a key indicator of investor attention, exhibits strong information flow correlations with the stock market, significantly influencing stock volatility, with its effect extending up to one month into the future; 3) Across posts with different sentiments (positive, neutral, and negative), investor attention consistently transmits information flows that predict stock volatility, while investor sentiment lacks significant incremental value in transmitting information flows to the market. This study further underscores the mediating role of stock forums in capturing investor attention, particularly highlighting the unique predictive value of net comment volume, offering investors a novel perspective on analyzing online stock forum data. Future research should focus on integrating more comprehensive datasets and conducting in-depth explorations to enhance the accuracy of market forecasts and the scientific rigor of investment decision-making.

Focusing on the supply side of vehicle charging service, this study proposes two differential game models for charging pile operation decision-making, with the government, operator, and third-party platform (hereinafter referred to as the platform) as the participants. The two differential game models are decentralized (i.e., operators and platforms aim to maximize their own interests) and centralized (i.e., operators and platforms aim to maximize the overall interests of both). The results show that under the equilibrium state with fixed revenue distribution ratios between operator and platform, compared with the decentralized decision-making mode, the centralized decision-making mode can improve the efforts of operator and platform, service quality and social benefit. When the platform is relatively weak and has a lower share of revenue, adopting the centralized decision-making mode can achieve a Pareto improvement in the revenues of both operator and platform; conversely, when the platform is relatively strong and has a higher share of revenue, adopting the decentralized decision-making mode can increase the revenues of both operator and platform. This indicates that as platform develops from weak to strong, the decision-making mode of the charging service market may shift from centralized to decentralized. At this time, the proportion of government policy support will increase, and social benefit and service quality may decrease.

In this paper, the stability and robustness of a recurrent neural network (RNN) controller with saturation function and ReLU function as activation function are analyzed for first-order linear uncertain systems. The necessary conditions for the closed-loop system to converge to the non-zero target and the suffcient conditions for exponential stability are provided. The quantitative relationships between the recurrent neural network controller’s parameters and the robustness of the initial state value, the target value and the unknown parameter of the plants are analyzed. The analysis results show that the RNN controllers with ReLU function as the activation function have stronger robustness.

The registration-based system represents a pivotal reform in China’s capital market development. The inquiry system reform aims to transfer pricing authority more substantially to market participants and enhance IPO pricing effciency. Consequently, systematic research evaluating IPO pricing effciency and the effects of inquiry system reforms under the registration-based framework have attracted considerable scholarly attention. This paper employs a bilateral stochastic frontier model to measure IPO pricing effciency across 2365 listed companies in China’s A-share market from 2016 to 2023, and empirically verifies the systematic impact of inquiry system reforms on IPO pricing under the registration-based system. Research findings indicate that during 2016–2023, underpricing effects dominated overpricing effects in A-share market initial offerings, with overall pricing 7.06% below reasonable levels, exhibiting distinct characteristics across different boards, years, ownership structures, and break-even status. The inquiry system reform generally elevated initial offering prices, primarily driven by the distinctive characteristics of the STAR Market, while other boards demonstrated declining trends. During the registration-based system expansion phase, significant competitive dynamics emerged between the STAR Market and ChiNext Board, while reform effects on the main board remained limited. Furthermore, the study identifies two critical transmission pathways explaining these impacts: The number of inquiry institutions and the effectiveness of price quotations. Based on these conclusions, this paper proposes targeted recommendations for regulatory authorities to guide future inquiry system reforms.

Sparsity is a crucial assumption in high-dimensional modeling, as only a small subset of variables typically exert significant influence on the response in high-dimensional regression analysis. Based on varying coeffcient models, this paper proposes a varying sparse coeffcient mixed-effects quantile regression (VSCMEQ) model for longitudinal data, which incorporates variable selection. In this model, the coeffcient functions are estimated using B-splines, and penalties are imposed on both random and fixed effects to investigate the influence of relevant important factors, including varying effects and constant effects. Finally, the proposed method is applied to the Primary Biliary Cirrhosis (PBC) dataset to analyze disease progression, identifying the influence of significant factors on disease progression (biomarkers) at different quantiles.

This paper considers the supply interruption of a supply chain composed by two suppliers and one retailer, where one supplier is an integrated supply and marketing supplier and the other supplier is a pure supplier, and the retailer makes replenishment from the latter supplier and makes and emergency order from the former when the supply is interrupted. For this system, we mainly investigate the retail price decision and the emergency replenishment from the supply and retailing integrated supplier in the case of a supply interruption with a random end time from the second supplier. Based on maximizing the benefits of each member in the supply chain, we establish an optimization model, and its solution is obtained via a theoretical analysis which gives the optimal decision for each member of the supply chain. Some numerical experiments are made which give the impact analysis of main parameters on the optimal decision of each member of the supply chain and their benefits for the supply interruption period.

Trend filtering is a widely used method for extracting long-term trends and eliminating short-term noise from time series data. In order to accurately capture the global change pattern and local fluctuation of the potential trend, this paper proposes the generalized trend filtering model with composite $\ell_0$ constraint (L0CTF) based on the primitive function representing sparsity, and the optimality theory is analyzed. However, solving the L0CTF model is a challenging task because of the combinatorial property and indivisibility of the composite $\ell_0$ function. Therefore, based on the properties of composite $\ell_0$ function, this paper reformulates the L0CTF model as a mixed integer programming problem with special ordered sets of type 1 and analyzes its equivalence with L0CTF in the sense of global optimal solution. Finally, experimental results on simulated and real data sets show that the proposed method is superior to some mainstream trend filtering methods in extracting potential trends.

In engineering practice, exact failure times of individual components are generally not available. In contrast, only the number of component failures and the system’s cumulative operating time are known, which leads to the aggregate lifetime data. Inference of lifetime distributions based on the aggregate lifetime data is of great challenge. This paper proposes a moment-based point estimation method, and uses the bias-corrected Bootstrap method to construct confidence intervals for quantities of interest. The maximum likelihood method needs the likelihood function of the aggregate lifetime data, however, it is only applicable to a few distributions that have the closure property with respect to the operation of convolution. Differently, the proposed method does not utilize the likelihood function, thus it applies to more distributions. Finally, both the simulation study and the real data analysis are performed for demonstration and illustration.

This paper aims to investigate some recent progress on inverse source problems of timeharmonic wave equations and establish the stability in general cases. For scattering models of deterministic and stochastic wave equations, we show the methodology to obtain stability and summarize existing theoretical and numerical results.

In order to generate structured representations of conflict events, and identify the logical relations of events, the key events and event evolution patterns, this paper proposes a conflict event ontology models and an automated construction framework of event-centric conflict knowledge graph. Graph neural network incorporating attention mechanism and dependent syntactic analysis are leveraged to solve long-range dependencies, and RoBERTa-based fine-tuning is employed to extract the implicit event relations. The results show that both algorithms of event and event relation detection in this paper outperform the comparing algorithms. Additionally, the MPCCN algorithm identifies key events and critical paths that influence the development of conflict events. The event-centric conflict knowledge graph not only can be used to identify the key events and evolution paths of conflict events, but also provides multi-level analysis of conflict events, and improves the comprehensiveness and scientificity of decision-making.

The efficient and accurate estimation of sensitivity distribution parameters and quantiles are crucial for the design and evaluation of the reliability of pyrotechnic products. The approach developed in this paper employs a Bayesian framework to establish a semiparametric generalized linear model for sensitivity data, using the Hamiltonian Monte Carlo algorithm for posterior inference. Within this framework, the deviance information criterion and the logarithm of the pseudo-marginal likelihood are used in a data-driven manner to select the optimal model. Extensive simulation comparisons demonstrate that the proposed method can accurately estimate sensitivity distribution parameters in the case of small sample sizes. Finally, the new method is applied to two real datasets, validating its effectiveness. The new method provides an alternative and complementary modeling tool for the analysis of sensitivity data.

Duadic codes are an important class of cyclic codes. It is interesting to construct duadic codes whose minimum distance has the square-root lower bound. In this paper, we propose two construction methods of odd-like duadic codes whose minimum distance has the square-root lower bound. Two classes of odd-like duadic codes with the square-root lower bound on the minimum distance are obtained.

The authors extend the marginal coordinate test for predictor contribution (Cook, 2004) to the case with multivariate responses. Instead of explicitly specifying the link functions between the responses and the predictors, an asymptotic test is proposed under the normality assumption of the predictors as well as an asymmetry assumption about the unknown regression mean function. When these assumptions are violated, the asymptotic test with elliptical trimming and clustering is still valid with desirable numerical performances.

This paper examines whether the parametric regression model is correctly specified for both source and target data and whether the regression pattern in the source domain aligns with that of the target domain. This evaluation is a critical prerequisite for applying model-based transfer learning methods under covariate shift assumptions. Traditional regression model checks and two-sample regression tests are insufficient to address this issue. To overcome these limitations, the authors propose a novel adaptive-to-regression test statistic that is asymptotically distribution-free. Under the null hypothesis, the test follows a chi-square weak limit, preserving the significance level and enabling critical value determination without resampling techniques. Additionally, the authors systematically analyze the test’s power performance, highlighting its sensitivity to different sub-local alternatives that deviate from the null hypothesis. Numerical studies, including simulations, assess finite-sample performance, and a real-world data example is provided for illustration.

Hydraulic actuators are widely used in industrial control systems, where precise displacement control is critical to system performance. Traditional physical modeling methods struggle to accurately capture the nonlinear and time-series characteristics of hydraulic actuators, limiting their application in complex environments. This paper proposes a displacement modeling method for hydraulic actuators based on a long short-term memory (LSTM) neural network. By collecting time-series data of voltage input and displacement output, an LSTM network is employed to characterize the dynamic behavior of hydraulic actuators. The LSTM network effectively captures long-term dependencies in the data, adapting to the nonlinear time-series properties of hydraulic systems. During model training, the mean squared error is used as the optimization objective, and the effectiveness of the model is validated through experiments. The experimental results demonstrate that, compared to traditional methods, the LSTM network achieves lower prediction errors on the validation set, exhibiting stronger modeling capabilities and higher accuracy.

The national independent innovation demonstration zone (NIIDZ), as an important engine leading innovative development, takes institutional and policy reforms as a starting point to radiate and drive the coordinated development of surrounding regions. The gradual improvement of the high-speed rail (HSR) network has opened up a new pattern for the “ual circulation” and expanded the scope of the NIIDZ's innovation spillover effects. Based on data of HSR city pairs from 2008 to 2019 in China, this paper examines the impacts and mechanisms of the improvement in innovation levels of ordinary cities after the opening of HSR connected to NIIDZs by applying a staggered DID model. The empirical results are as follows. Firstly, the opening of HSR connected to NIDDZs significantly improves the innovation levels of ordinary cities. Secondly, the innovation spillover effects are more pronounced for cities in the eastern region, cities with a better innovation environment, and large-scale cities. Thirdly, the innovation spillover effects are realized by utilizing innovation endowment, government-guided innovation and demonstration driving effects. This paper provides empirical evidence and policy insights for innovation-driven development in the context of HSR network. It optimizes the spatial allocation of innovation resources and accelerates the development of new quality productive forces, achieving high-quality economic development.

Multi-source data are commonly encountered nowadays. The analysis of multi-source data is important for unleashing the data potential and realizing data value. However, many multi-source data still exist in the form of “ata silos”. Interconnection between data remains extremely challenging. Meanwhile, the data security issue is a significant concern, making it crucial to achieve secure development of multi-source data while protecting data privacy. To address these challenges, we propose a privacy-protected paradigm for multi-source data analysis. This method is based on the federated learning framework, enabling different data sources to collaborate on data analysis tasks without exposing their raw data. Meanwhile, to further prevent malicious attacks on data, we incorporate differential privacy into federated learning by adding noise to the transmitted data to protect individual-level information. Finally, we demonstrates the practical application of the proposed paradigm using the example of predicting violation risks of enterprises. By combining data from various departments, the prediction accuracy can be well enhanced.

In this paper, disturbance attenuation is considered for linear systems with partially modeled disturbance. The disturbance signal is composed of known signals and uncertain parameters that leads to some difficulties for solving the disturbance rejection problem. To overcome this issue, the original system is reformulated as a linear parameter-varying (LPV) system by absorbing the unknown parameters in disturbance. Then an adaptive state-disturbance-feedback controller relying on a dictionary of state-feedback gains and disturbance-feedback gains is designed to estimate the uncertain parameters in the LPV system. Moreover, the presence of multiple variables in the sufficient condition given to reject the external disturbance of the LPV system also brings challenges. To tackle this problem, the quadratic separation technology is applied into the sufficient condition, and the original unsolvable condition can be successfully transferred into a solvable one. Furthermore, by adding the known part of the disturbance signal into the feedback loop, more information of the whole system can be utilized. Meanwhile, the asymptotical stability of the closed-loop system can be achieved and the $H_\infty$ performance index of the closed-loop system is verified to be smaller. Numerical simulations are given to illustrate the merits of the proposed approach.

The authors consider a two-period joint inventory and pricing decision problem for a retailer facing strategic customers with behavioral preferences such as reference dependence, loss aversion and risk preferences. The authors develop and analyze a model that accounts for customers' these behavioral preferences as well as value depreciation on the product, and makes predictions on the retailer's optimal decisions. Moreover, the authors demonstrate how the presence of these behavioral preferences and primary parameters will leverage the retailer's optimal decisions. It is revealed that strategic customers' loss aversion behavior could benefit the retailer from pushing up the regular price, the stocking quantity and hence the expected profit. However, customer's value depreciation on the product will drive down these aspects. To alleviate the negative effect of the strategic customers' behavioral preferences, the authors suggest the retailer applying inventory commitment strategy and price guarantee policy, which could increase the retailer's profit beyond the rational expectation equilibrium level in some situations.