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, we 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.

This paper focuses on the McKean-Vlasov system's stochastic optimal control problem with Markov regime-switching. To this end, we establish a new Itô's formula using the linear derivative on the Wasserstein space. This formula enables us to derive the Hamilton-Jacobi-Bellman equation and verification theorems for McKean-Vlasov optimal controls with regime-switching using dynamic programming. As concrete applications, we first study the McKean-Vlasov stochastic linear quadratic optimal control problem of the Markov regime-switching system, where all the coefficients can depend on the jump that switches among a finite number of states. Then, we represent the optimal control by four highly coupled Riccati equations. Besides, we revisit a continuous-time Markowitz mean-variance portfolio selection model (incomplete market) for a market consisting of one bank account and multiple stocks, in which the bank interest rate, the appreciation and volatility rates of the stocks are Markov-modulated. The mean-variance efficient portfolios can be derived explicitly in closed forms based on solutions of four Riccati equations.

Numerous complex nonlinear systems in the real-world constantly suffer from random noises, which contribute to the enormous challenge of system control. Additionally, unknown powers in nonlinear systems always lead to the inapplicability of many reported control methods. This article investigates the control issue of stochastic systems which contain complicated nonlinearities and unknown system powers. With the newly constructed Lyapunov function, as well as the control algorithm presented in this work, we successfully obtain a controller so that the closed-loop system is semiglobally finite-time stable in probability (SGFSP). Besides, the system output can trackthe reference signal fast. The presented method significantly enlarges the range of application for nonlinear systems. The presented strategy is successfully applied to the liquid-level system.

This paper utilizes the internal model principle based method to analyze the effects of time delays on robust bipartite output regulation. In the considered system setting both input and communication delays are presented, unfortunately failing conventional protocols and closed-loop analysis tools. For such a challenge, a distributed protocol equipped with a dynamical compensator subject to communication delay is proposed without employing the information of the exosystem's output, and a regulator equation with a matrix exponential function is utilized to characterize the dynamics of the closed-loop delayed system. Further, the protocol gains are shown to be determined in terms of the solution of a parametric algebraic Riccati equation, and sufficient conditions are obtained to describe robust bipartite output regulation with time delays. A simulation example illustrates the effectiveness of the proposed approach.

In this article, the nonfragile control issue is explored for semi-Markov jump singularly perturbed systems (SMJSPSs) utilizing the semi-Markov kernel method. When studying the considered systems under denial of service attacks, some virtual points are inserted to obtain stability analysis conditions for SMJSPSs. The mean-square exponential stability (MSES) criterion is inferred. Following these, a nonfragile controller is proposed as a way to assure MSES and $\mathscr{\mathcal{H}}_{\infty}$ performance in the considered systems. Lastly, a numerical simulation and an inverted pendulum model are presented to confirm the theoretical results' reliability.

In this paper, the authors give quantum algorithms for two fundamental computation problems: Solving polynomial systems over finite fields and optimization where the arguments of the objective function and constraints take values from a finite field or a bounded interval of integers. The quantum algorithms can solve these problems with any given success probability and have polynomial runtime complexities in the size of the input, the degree of the inequality constraints, and the condition number of the associated matrices of the problem. So, the authors achieve exponential speedup for these problems when their condition numbers are small. As applications, quantum algorithms are given to three basic computational problems in cryptography: The short integer solution problem, the shortest vector problem, the polynomial system with noise problem, and cryptanalysis for the lattice-based NTRU cryptosystem. It is shown that these problems and NTRU can against quantum computer attacks only if their condition numbers are large, so the condition number could be used as a new criterion for lattice-based post-quantum cryptosystems.

We investigate the local integrability and linearizability of a family of three-dimensional polynomial systems with the matrix of the linear approximation having the eigenvalues $1, \zeta, \zeta^2 $, where $\zeta$ is a primitive cubic root of unity. We establish a criterion for the convergence of the Poincaré-Dulac normal form of the systems and examine the relationship between the normal form and integrability. Additionally, we introduce an efficient algorithm to determine the necessary conditions for the integrability of the systems. This algorithm is then applied to a quadratic subfamily of the systems to analyze its integrability and linearizability. Our findings offer insights into the integrability properties of three-dimensional polynomial systems.

Let $\beta$ be an integer and satisfy $0\leq\beta\leq5$. In this paper, we prove that the partition polynomial $$\prod_{k=1}^{n}[1+(2+\beta)q^k+q^{2k}]$$ is symmetric and unimodal for $n\geq1$.

The factorial design within a conditional model is utilized when the effects of one factor in a factorial experiment hold greater significance under each fixed level of another factor. This paper investigates the generalized minimum aberration $(N,s^p)$-design, where each factor is $s$-level, with $s$ being any prime or prime power. Via utilizing the method of complementary designs, we explore the design with a pair of conditional and conditioning factors. The proposed approach applies not only to regular designs but also to nonregular designs. Additionally, the findings can be extrapolated to encompass designs under the two pairs conditional model. The findings presented in this paper not only strengthen but also generalize the existing knowledge in this field.

Visual-based defect detection efficiently monitors the health and quality of construction and industrial products. However, current defect detection methods often improve detection accuracy at the cost of lower inference speeds or more parameters, struggle with complex data representation, emphasize target features while neglecting environmental information importance, and utilize convolutional or max pooling operations for downsampling, leading to more feature loss. To address these issues, this work presents a low complexity, accurate defect detection network augmented by environmental information-assisted and flexible activation functions to enhance the neural network performance on complex data representation. Environmental information-assisted module is designed for defect detection tasks to assist in accurately locating and predicting defects. Moreover, this work restructure features post-downsampling to mitigate feature loss and design a simple feature module called deep-global fusion that integrates deep and global features to enhance detection performance. Extensive experiments validate the superiority of our detection network. The deployment of our network on edge computing devices confirms its competitive advantage in portability and reliability.

For $k$-valued (control) networks, two types of (control) invariant subspaces are proposed, namely, the state-invariant and dual-invariant subspaces, which are subspaces of the state space and dual space, respectively. Algorithms are presented to check whether a dual subspace is dual-(control) invariant{, and to construct state feedback controls}. The bearing space of $k$-valued (control) networks is introduced. Using the structure of the bearing space, the universal invariant subspace is introduced, which is independent of the dynamics of particular networks. Finally, the relationship between the state-invariant subspaces and the dual-invariant subspaces of a network is investigated. A duality property shows that if a dual subspace is invariant, then its perpendicular state subspace is also invariant, and vice versa.

This paper proposes a trajectory generation method utilizing the Bézier curve for mobile robots and employs it in the cooperative transportation task. In contrast to the approach of predefining spatial features and then allocating time accordingly, the proposed method expresses the spatial characteristics through duration on the basis of ensuring the continuity of dynamics, and then concurrent planning of spatial and temporal characteristics, thereby leading to a trade-off between time efficiency and optimizing spatial features. Furthermore, the proposed method is combined with formation control and applied to a cooperative transportation task. The effectiveness and practicability of the proposed trajectory generation method are verified through both simulation and experiment.

The Proportional-Integral-Derivative (PID) control has enjoyed significant success and widespread adoption in aviation, automotive, robotics, and various other domains. However, to align with the current trend of networked control systems and optimize communication resource utilization, we introduce an extended PID (EPID) control framework that leverages an event-triggered mechanism. This controller is designed for single-input single-output (SISO) high-order affine nonlinear systems, overcoming the limitation of traditional PID control, which typically guarantees stability only for second-order systems. Leveraging the open unbounded parameter manifold and event-triggered conditions of the controller parameters, we prove through the Lyapunov method that our proposed controller achieves uniformly ultimately bounded stability and guarantees the absence of the Zeno phenomenon in the event-triggered EPID (ET-EPID) system. The efficacy of the ET-EPID control system approach is exhibited through simulation of a third-order system as well as practical experiment conducted on a second-order direct current motor.

This paper investigates the prescribed-time leader-follower consensus problem of second-order multi-agent systems (MASs) with stochastic switching directed topologies. For unknown external disturbances, a prescribed-time disturbance observer is designed to accurately estimate disturbances within a prescribed time, and a fuzzy logic system (FLS) is used to approximate the unknown nonlinear functions, which reduces the conservativeness of the control scheme. The information exchange between agents is modeled using a Bernoulli random process due to various factors influencing the communication layer. Meanwhile, a prescribed-time distributed sliding mode control (SMC) scheme is designed, ensuring that the convergence time of the MASs is independent from the initial values and other parameters of the system. Finally, the effectiveness of the proposed method is verified by a real single link robot arm system.

With advances in network control systems, new applications such as the analysis of opinion dynamics have emerged. Using mathematical tools, various dynamic models have been developed to study how people change opinions in social settings. One such model is the DeGroot-Friedkin model, a two-stage approach for sequential discussion of multiple issues. For each issue, the classical DeGroot model, closely related to the consensus problem, is used to analyze opinion dynamics. This model incorporates the theory of self-appraisal to study the evolution of an individual's self-esteem, where individuals evaluate their contributions to the issues being discussed. However, the question "How does the social network affect one's self-esteem?" remains unaddressed. In this paper, we propose a new self-esteem evolution model that examines the effects of social relationships on individuals' self-esteem by considering changes in opinion as an indicator of the social network's impact. We also introduce a convergence criterion for the new model and provide analytical proofs of the results.

This paper investigates the strong structural controllability (SSC) of multi-agent systems (MASs) defined over Laplacian dynamics on directed graphs. The agents that are divided into leaders and followers are connected based on the consensus law and only leaders are manipulated by the external control input directly. In contrast to existing work, the topology of MAS contains uncertain interconnection edges between agents. The interconnection graph has a zero/nonzero/arbitrary structure, to handle the parameters uncertainty problem in MASs. Under this framework, we propose a color-changing rule based on the zero-forcing set (ZFS). A graph-theoretic sufficient condition of SSC is proved. Next, we investigate the leader selection problem to ensure the SSC of MASs. A greedy algorithm based on ZFS is introduced. In addition, we figure out that the redundant property of edges in MASs can help us decide the leader selection problem. A new heuristic algorithm of polynomial complexity is developed to select minimum leaders of the multi-agent system. Finally, we support our analysis with numerical results on various simulations.

This paper concerns the decentralized event-based $H_{\infty}$ filter design problem for networked dynamic system (NDS). A more practical situation is studied, in which the communication between subsystems is affected by uncertainties and only local sampled measurement output is available for each filter in the developed filter scheme. Firstly, an event-triggered mechanism is introduced for each subsystem to process the sampled output in order to reduce the communication load. Secondly, on the basis of the well-posedness, the augmented filtering error system composed of the original NDS and the filter is modeled as a time-delay system of high dimension. After that, by employing the Lyapunov functional approach and space construction method, novel computationally attractive sufficient conditions are derived to check the well-posedness, asymptotic stability and $H_{\infty}$ performance of the augmented filtering error system. Thirdly, a co-design method of the filter and event-trigger matrices is obtained by using Finsler lemma and slack matrix approach. Finally, a numerical example is provided to demonstrate the effectiveness of the derived design approach.

This paper deals with the distributed solving problem of a specific class of linear algebraic equations (LAEs) with block Toeplitz structures. To reduce the communication burden and achieve computation efficiency, a distributed iterative algorithm from the communication-efficient perspective is proposed by incorporating the specific structure of the coefficient matrix tied to any given LAE over a multi-agent network. Each agent possesses a state vector of size smaller than the dimensions of unknown variables related to the LAE and receives information from its neighbors. It is shown that the presented distributed iterative algorithm can solve the specific class of LAEs without requiring any initialization conditions, irrespective of whether it admits a unique solution or multiple solutions. Moreover, an equivalent relation is established between the problem of solving LAEs and the tracking problem of iterative learning control (ILC) systems. The proposed distributed iterative algorithm is leveraged to obtain the distributed control law for ILC systems to realize the tracking objective. Theoretical guarantees are provided for our developed solution results of LAEs, and the effectiveness of them is also verified through simulation examples.

This paper investigates the output tracking control problem of heterogeneous linear multi-agent systems with a novel dynamic event-triggered control strategy. In contrast to existing observer methods, the learning algorithm is first developed and applied to the observer such that the each observer corresponding to each follower can provide an optimal estimation of the leader's state by optimizing a specified cost function. Then, a controller consisting of the observer's state and the agent's state is designed and learned by a data-based off-policy learning algorithm to achieve the optimal output tracking control. Under the learned gain matrix, to reduce the communication burden for each agent, a model-free dynamic event-triggered control strategy for each agent is developed to realize the optimal event-triggered output tracking control without depending on any prior knowledge. Rigorous analysis shows that our proposed algorithms not only ensure the model-free output tracking control while saving the limited bandwidth, but also exclude Zeno behavior. Finally, a numerical example is provided to verify the theoretical analysis.

In this paper, an adaptive Unmanned Ground Vehicle swarm (UGVS) control for crossing corridors or gaps with self-organized formation is proposed to overcome the limitations of obstacle scenes. In traditional methods, obstacles are typically arranged in a regular and simplistic manner, often relying on global information for path planning, which makes it difficult to deal with more complex scenes. To tackle this problem, we propose a new control strategy to achieve efficient tracking and obstacle avoidance of UGVS in corridors or gaps environments. Firstly, a constraint model that considers the performance of tracking, obstacle avoidance, and formation maintenance is constructed, which is more suitable for obstacle scenes of complex corridors or gaps. Secondly, a new control framework based on the barrier Lyapunov function (BLF) is proposed to achieve tracking and obstacle avoidance of UGVS through constraint control. Meanwhile, a control strategy of UGVS for crossing corridors or gaps is designed, which avoids pre-training for specific obstacle scenes and cumbersome path planning for each individual. At last, the stability analysis is provided by the designed Lyapunov function. Simulation results of three different scenes verify the effectiveness of the proposed method.

Driven partly by cyber-security issues there is growing interest in modelling epidemics on networks. However it turns out that there are some gaps in the formulation of these network models that have precluded a proper study of stochastic fluctuations. this being necessary to get a fuller understanding of the stochastic dynamics. We revisit some popular models, rectify their shortcomings and then develop an asymptotic second order averaging analysis of their behaviour.

This paper investigates the output containment control problem of unknown heterogeneous non-minimum phase linear multi-agent systems over directed communication graphs. The dynamics of each follower are allowed to be unknown. A novel distributed adaptive pole placement control strategy is developed to address the output containment control problem of the concerned multi-agent system. It is shown that the proposed distributed adaptive control strategy guarantees the boundedness of all the signals in the resulting closed-loop system and the convergence of the followers' outputs to a convex hull spanned by the leaders' outputs. The efficacy of the proposed control strategy is demonstrated by two simulation examples.

This article reviews the mean field social (MFS) optimal control problem for multi-agent dynamic systems and the mean-field-type (MFT) optimal control problem for single-agent dynamic systems within the linear quadratic (LQ) framework. For the MFS control problem, this review discusses the existing conclusions on optimization in dynamic systems affected by both additive and multiplicative noises. In exploring MFT optimization, we first revisit researches associated with single-player systems constrained by these dynamics. We then extend our review to scenarios that include multiple players engaged in Nash games, Stackelberg games, and cooperative Pareto games. Finally, the paper concludes by emphasizing future research on intelligent algorithms for mean field optimization, particularly using reinforcement learning method to design strategies for models with unknown parameters.

Direct modeling in CAD offers a promising avenue for model modification through direct interaction. However, a critical impediment to the advancement of direct modeling technology is the inconsistency between modified geometry and unaltered constraints. While several methods have been proposed to address this challenge, they often fail to provide effective solutions, particularly in contexts demanding high model precision. In this paper, we introduce a novel approach by integrating tolerance analysis into the constraint update system, followed by the proposal of a constraint update scheme utilizing the extreme value tolerance model and the probability tolerance model. This innovative tolerance-based scheme adeptly resolves the inconsistency problem prevalent in direct modeling while satisfying the requisites of high-precision modeling. A comparative analysis against established methodologies is conducted to demonstrate the advantages of our proposed approach.

Contingent free shipping can improve purchase motivation and increase sales, and is an important promotional means for e-commerce platforms. Among them, it is particularly common for e-commerce platforms to provide free shipping coupons to paid members. How much cost does it take to provide free shipping coupons and how much revenue does it bring, causing confusion to ecommerce platforms. We build pricing decision models based on consumers’ shopping frequency and their sensitivity to shipping costs when e-commerce platforms offering single, multiple and unlimited number of free shipping coupon services. Through the analysis, we find that high-sensitivity consumers dislike paying for shipping and prefer to purchase free shipping coupons. We also find that when adopting multiple free shipping coupons strategy, the optimal number of free shipping coupons to offer is associated with the frequency of consumers’ small order purchases. However, if unlimited number of free shipping coupons is offered, online retailers need to increase membership fees to make more profit. This study brings practical insights for e-commerce platforms as well as online retailers to determine reasonable pricing under different free shipping coupon service strategies and provide consumers with a more convenient shopping experience.

Riemman metric tensor (Rmt) plays a significant role in deducing basic formulas and equations arising in differential geometry and (pseudo-)Riemannian manifolds. It is a fundamental and challenging problem to determine the equivalence of indexed differential Rmt polynomials. This paper solves the problem by extending Gröbner basis theory and the previous work on the computational theory for indexed differentials. L-expansion of an indexed differential Rmt polynomial is defined. Then a decomposed form of the Gröbner basis of defining syzygies of the polynomial ring is presented, based on a partition of elementary indexed monomials. Meanwhile, the upper bound of the dummy index numbers of sim-monomials of the elements in each disjoint elementary indexed monomial subset is found. Finally, a DST-fundamental restricted ring is constructed, and the canonical form of a polynomial is confirmed to be the normal form with respect to the Gröbner basis in the DST-fundamental restricted ring.

This paper tackles the problem of constructing Bézout matrices for Newton polynomials in a basis-preserving approach that operates directly with the given Newton basis, thus avoiding the need for transformation from Newton basis to monomial basis. This approach significantly reduces the computational cost and also mitigates numerical instability caused by basis transformation. For this purpose, we investigate the internal structure of Bézout matrices in Newton basis and design a basis-preserving algorithm that generates the Bézout matrix in the specified basis used to formulate the input polynomials. Furthermore, we show an application of the proposed algorithm on constructing confederate resultant matrices for Newton polynomials. Experimental results demonstrate that the proposed methods perform superior to the basis-transformation-based ones.

Leveraging the principal-agent theory, this study incorporates reputational capital into a continuous-time framework for analyzing venture capital exit decisions. We explore how the principal-agent relationships influence the decision to take capital public by synthesizing optimal incentive alignment and IPO timing within a unified model. Our results uncover an intriguing aspect of reputational capital: despite entrepreneurial efforts to augment this capital, its growth is not linear but tends to reach the highest level before the IPO event. Furthermore, the accumulation of reputational capital expedites the capital exit process and enhances the returns from such exits. Subsequently, as the exit timeline extends, the proportion of exit returns allocated to entrepreneurs escalates to a full share, imposing a constrained decision window for investors on exit timing. Our findings suggest that the timing for maximizing investor returns at an IPO is later than the timing that maximizes company revenue. Moreover, excessive IPO costs further delay this exit decision for investors. These findings offer fresh insights into the languishing IPO market observed over the last two decades.

Extreme risk plays an important role in financial supervision and financial investment, which can cause substantial loss in the financial market. To better manage the severe risks resulting from extreme events, we propose a novel fixed-$k$ autoregressive conditional Fréchet ($k$-AcF) model. The proposed model incorporates the $k$-dimensional extremal distribution and an observation-driven evolution scheme for the key parameters, which accommodates well with the time-varying tail behavior of financial data. Compared to the existing dynamic methods under the extreme value theory framework that focus solely on maximum observations, the $k$-AcF model employs the largest $k$ observations, which enhances the utilization of tail information and obtains a more accurate extreme risk estimation. Furthermore, this paper uses the maximum likelihood estimators to conduct the model estimation and investigates their statistical properties. Simulation studies validate the reliability of the estimators and confirm the theoretical properties of $k$-AcF. Empirical applications to the constituent stocks of two major stock indices in the U.S. demonstrate that the $k$-AcF model accurately captures the clustering and dynamics of extreme risk in the stock market. Moreover, the results show that our model is more responsive and sensitive to a financial crisis than the benchmark model considering only the maximum observations.

Using a sample of Chinese listed firms for the period 2006-2021, this paper constructs dynamic stock networks annually using symbolization and mutual information methods, and investigates the impact of stock network centrality on one-year-ahead stock price crash risk with the help of the bad news hoarding mechanism. We find robust evidence that firms with higher centrality are less likely to experience stock price crashes in the future. An examination of underlying mechanisms suggests that being at the center of the stock network enhances firms’ investment efficiency and managers’ cost of engaging in earnings management, thereby reducing the likelihood of such firms forming and hoarding bad news, and hence their crash risk. Further analysis reveals that the mitigating effect of network centrality on stock price crashes is more salient for firms with weaker external monitoring and less conservative accounting policies. Overall, this paper sheds light on a novel benefit of being at the center of the stock network, namely that central firms are less prone to crash risk, which provides practical insights for risk-management applications related to asset pricing and tail events.

This paper presents a new proportional-integral-derivative control approach for positive switched systems based on a positive proportional-integral observer. First, a positive proportional-integral observer is constructed. By using the observer state, the proportional-integral-derivative control and the corresponding integral part are designed, respectively. Using $1$-norm inequality, two dynamic event-triggering conditions are established for the proportional-integral observer and proportional-integral-derivative controller, respectively. A dynamic event-triggered proportional-integral observer-based dynamic event-triggered proportional-integral-derivative controller is proposed by combining the sample state and the integral of the weighted sample output estimation error. Under the designed event-triggering conditions, an interval system with upper and lower bounds is introduced. The positivity and $\ell_1$-gain stability are achieved by realizing the according properties of the lower and upper bound systems in terms of multiple copositive Lyapunov function, respectively. All gain matrices are designed by a matrix decomposition approach and the corresponding conditions are solved by linear programming. Finally, two examples are provided to illustrate the validity of the results.

In this paper, fully actuated system approaches are utilized to synthesize feedback linearizable nonlinear systems. First, a simple transformation is introduced to convert feedback linearizable nonlinear systems into fully actuated system models for both single-input and multi-input scenarios. Once the fully actuated system model is established, a nonlinear state feedback controller can be derived to achieve a constant linear closed-loop system with an assignable eigenstructure. All degrees of freedom present in the closed-loop system can be leveraged to enhance overall system performance. In comparison to feedback linearization, the proposed method places greater emphasis on the control variables, making it more convenient to address control challenges in dynamic systems. Finally, two numerical examples are presented to illustrate the design procedure for both single-input and multi-input cases, and to validate the effectiveness of the proposed approach.

The allocation of heterogeneous battlefield resources is crucial in Command and Control (C2). Balancing multiple competing objectives under complex constraints so as to provide decision-makers with diverse feasible candidate decision schemes remains an urgent challenge. Based on these requirements, a constrained multi-objective multi-stage weapon-target assignment (CMOMWTA) model is established in this paper. To solve this problem, three constraint-feature-guided multi-objective evolutionary algorithms (CFG-MOEAs) are proposed under three typical multi-objective evolutionary frameworks (i.e., NSGA-II, NSGA-III, and MOEA/D) to obtain various high-quality candidate decision schemes. Firstly, a constraint-feature-guided reproduction strategy incorporating crossover, mutation, and repair is developed to handle complex constraints. It extracts common row and column features from different linear constraints to generate the feasible offspring population. Then, a variable-length integer encoding method is adopted to concisely denote the decision schemes. Moreover, a hybrid initialization method incorporating both heuristic methods and random sampling is designed to better guide the population. Systemic experiments are conducted on three CFG-MOEAs to verify their effectiveness. The superior algorithm CFG-NSGA-II among three CFG-MOEAs is compared with two state-of-the-art CMOMWTA algorithms, and extensive experimental results demonstrate the effectiveness and superiority of CFG-NSGA-II.

This paper investigates a security problem of simultaneously addressing two types of attacks: eavesdropping and infiltration. We model the target system as a discrete-event system (DES) with subsets of concealable events and protectable events, in order to make our methodology applicable to various practical systems and employ two existing works of DES security: degree of opacity and state protection. Specifically, we consider that all protectable events are observable, and some observable events are concealable. In addition, protectable events cannot be protected once they are concealed. Given such a constraint, our goal is to figure out which events to conceal and which transitions to protect so that the prescribed requirements of degree of opacity and state protection are satisfied. In this work we decide which events to conceal as all transitions of a given event label are concealed or not concealed. Our problem formulation also requires a solution to only involve absolutely necessary protectable events in order for the system to avoid superfluous protection costs. We first examine a general version of our security problem with an intuitive algorithm to compute acceptable solutions, and then present a special version which results in a reduced computation time compared to the general version.

In this paper, we study the problem of distributed Nash equilibrium seeking of $N$-player games with high-order integrator dynamics subject to disturbances generated by an uncertain exosystem. Similar problems have been studied for disturbances with an exactly known exosystem. Compared with the existing results of high-order integrator dynamics, which can only handle sinusoidal disturbances with known frequencies, this paper aims to handle multi-tone disturbances with unknown frequencies by introducing an adaptive control technique to estimate the unknown frequencies. Technically, when the exosystem is known, the disturbance can be dealt with by the Luenburger observer. In contrast, the Luenburger observer cannot deal with an uncertain exosystem. We have combined the internal model design and some adaptive control technique to solve our problem. Further, we also establish the sufficient condition to guarantee the convergence of the estimated unknown frequencies to the actual values of these frequencies. Two examples are given to verify the proposed algorithm.

In clinical and observational research, multivariate recurrent event data are commonplace, as people may encounter several kinds of repeating occurrences. While event times are consistently recorded, there are instances where the corresponding event types might not be observed. For multivariate recurrent event data, we introduce a time-varying coefficients additive rate model which takes into consideration scenarios in which event types are missing at random (MAR). We employ an inverse probability-weighted estimating equation to derive inferences for the time-independent and time-dependent effects, with proofs for the estimators' asymptotic behavior. Moreover, we offer statistical tests to assess the temporal variation of covariate effects. The estimators' performance is evaluated by simulation experiments, and we apply this method to a platelet transfusion reactions dataset.

In the context of global value chain restructuring, developing countries involved in processing trade face severe challenges in industrial upgrading. The development of transportation can accelerate the flow of knowledge resources and promote regional technology upgrading. This study aims to investigate whether the development of air capacity can alleviate the regional demand for technologically sophisticated trade, incorporate the factor of international air capacity into the influencing factors of product technology sophistication, explore the relationship between regional transportation development and the technical level of trade products from the perspective of enterprises, so as to fill the gap in relevant research. By combining OAG databases with customs data, the study matches the import and export data of 1302 listed companies from 2010 to 2016, along with corresponding regional air transport data. The empirical results demonstrate that increasing air capacity reduces the regional demand for trade technology sophistication. Moreover, internal factors such as enterprise R&D expenditure and capital-intensive negatively moderate the influence of air capacity growth on the trade technology sophistication. Additionally, external institutional factors, such as government effectiveness and the business environment across various countries negatively regulate the impact of the air capacity expansion on trade technology sophistication.

Cohesive players are a generalization of necessary players, where a player being a cohesive player means that his absence would result in the coalition's worth being equal to the sum of the individual worths of its members. This paper proposes axioms on cohesive players to characterize the positively weighted Shapley values. We first suggest (weak) differential invariance for cohesive players and equal surplus of cohesive players to axiomatize the Shapley value. Then, using weak versions of these two axioms, we provide axiomatizations of the class of positively weighted Shapley values.

This paper analyses problems in the price of anarchy (PoA) for a mixed traffic network with stochastic demands. We focus on two types of users with distinct path-selection principles, self-interested users (SU) and altruistic users (AU). A variational inequality model for the SU-AU mixed traffic equilibrium assignment with stochastic demands is proposed. We develop an upper bound formulation for monomial link travel cost functions by the nonlinear programming approach and analyze the log-normal demand distribution. Additionally, an extended study on the PoA with road pricing for this mixed traffic network is presented and justified. Emphasis is placed on scenarios where road pricing is not included in the total system cost and the other where road pricing is included. The numerical results illustrate that the upper bounds on the PoA are contingent on the degree of the link travel cost function, as well as the maximum/minimum altruism coefficients for both non-road pricing and road pricing implementation. Notably, these bounds are also related to the maximum variation coefficient of the demand and the highest degree of the link travel cost function when the travel demand follows the log-normal distribution.