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 focuses on the McKean-Vlasov system's stochastic optimal control problem with Markov regime-switching. To this end, the authors 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, the authors 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, the authors represent the optimal control by four highly coupled Riccati equations. Besides, the authors 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.

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 the 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.

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, the authors 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, the authors 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.

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.

In this paper, a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor. By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task, the shared features between multi-sensor data can be captured, and the gap between channel data features will be reduced. Meanwhile, in order to model fault features in the downstream task, the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy. Finally, experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset (SMT-SA dataset) show that the proposed method performs favorably against other STATE-of-the-art methods.

In this article, the authors investigate and derive adaptive strategies for the pursuit-evasion problem where both players lack knowledge of the opponent’s cost function parameters, which has rarely been investigated in the existing literature. To address this challenge, the authors consider a basic information structure that assumes that the evader will use an adaptive learning algorithm to estimate the unknown parameters and update its adaptive strategy piecewise, whereas the pursuer will adopt a strategy based on the opponent’s choices at each time instant. By employing methods of diminishing excitation and random switching, the authors establish certain excitation conditions for signals of the closed-loop game system to ensure the strong consistency of the parameter estimates. Moreover, the authors demonstrate that the adaptive game system can asymptotically reach the Nash equilibrium, which is the same equilibrium achieved in pursuit-evasion games when all game parameters are known.

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 presented, which is independent of the dynamics of particular networks. Finally, the relation 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.

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.

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.

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

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

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

Feature correspondence is a crucial aspect of various computer vision and robot vision tasks. Unlike traditional optimization-based matching techniques, researchers have recently adopted a learning-based approach for matching, but these methods face challenges in dealing with outlier features. This paper presents an outlier robust feature correspondence method that employs a pruned attentional graph neural network and a matching layer to address the outlier issue. Additionally, the authors introduce a modified cross-entropy matching loss to handle the outlier problem. As a result, the proposed method significantly enhances the performance of learning-based matching algorithms in the presence of outlier features. Benchmark experiments confirm the effectiveness of the proposed approach.

In this paper, the authors consider the stabilization and blow up of the wave equation with infinite memory, logarithmic nonlinearity and acoustic boundary conditions. The authors discuss the existence of global solutions for the initial energy less than the depth of the potential well and investigate the energy decay estimates by introducing a Lyapunov function. Moreover, the authors establish the finite time blow up results of solutions and give the blow up time with upper bounded initial energy.

In this paper, the problem of identifying autoregressive-moving-average systems under random threshold binary-valued output measurements is considered. With the help of stochastic approximation algorithms with expanding truncations, the authors give the recursive estimates for the parameters of both the linear system and the binary sensor. Under reasonable conditions, all constructed estimates are proved to be convergent to the true values with probability one, and the convergence rates are also established. A simulation example is provided to justify the theoretical results.

The scheduling problem in surgery is difficult because, in addition of the planning of the operating rooms which are the most expensive resources in hospitals, each surgery requires a combination of human and material resources. In this paper, the authors address a surgery scheduling problem which arises in operated health care facility. Moreover, the authors consider simultaneously materiel and human resources. This problem is a three-stages flow shop scheduling environment. The first stage (ward) contains a limited number of resources of the same type (beds); The second stage contains different resources with limited capacity (operating rooms, surgeons, nurses, anesthesiologists) and the third stage contains a limited number of recovery beds. There is also a limited number of transporters (porters) between the ward and the other stages. The objective of the problem is to minimize the completion time of the last patient (makespan). The authors formulate this NP-Hard problem in a mixed integer programming model and conduct computational experiments to evaluate the performance of the proposed model.

Curve interpolation with B-spline is widely used in various areas. This problem is classic and recently raised in application scenario with new requirements such as path planning following the tangential vector field under certified error in CNC machining. This paper proposes an algorithm framework to solve Hausdorff distance certified cubic B-spline interpolation problem with or without tangential direction constraints. The algorithm has two stages: The first stage is to find the initial cubic B-spine fitting curve which satisfies the Hausdorff distance constraint; the second stage is to set up and solve the optimization models with certain constraints. Especially, the sufficient conditions of the global Hausdorff distance control for any error bound are discussed, which can be expressed as a series of linear and quadratic constraints. A simple numerical algorithm to compute the Hausdorff distance between a polyline and its B-spline interpolation curve is proposed to reduce our computation. Experimental results are presented to show the advantages of the proposed algorithms.

The authors present a novel deep learning method for computing eigenvalues of the fractional Schrödinger operator. The proposed approach combines a newly developed loss function with an innovative neural network architecture that incorporates prior knowledge of the problem. These improvements enable the proposed method to handle both high-dimensional problems and problems posed on irregular bounded domains. The authors successfully compute up to the first 30 eigenvalues for various fractional Schrödinger operators. As an application, the authors share a conjecture to the fractional order isospectral problem that has not yet been studied.

In this paper, the authors propose Neumann series neural operator (NSNO) to learn the solution operator of Helmholtz equation from inhomogeneity coefficients and source terms to solutions. Helmholtz equation is a crucial partial differential equation (PDE) with applications in various scientific and engineering fields. However, efficient solver of Helmholtz equation is still a big challenge especially in the case of high wavenumber. Recently, deep learning has shown great potential in solving PDEs especially in learning solution operators. Inspired by Neumann series in Helmholtz equation, the authors design a novel network architecture in which U-Net is embedded inside to capture the multiscale feature. Extensive experiments show that the proposed NSNO significantly outperforms the state-ofthe-art FNO with at least 60% lower relative L²-error, especially in the large wavenumber case, and has 50% lower computational cost and less data requirement. Moreover, NSNO can be used as the surrogate model in inverse scattering problems. Numerical tests show that NSNO is able to give comparable results with traditional finite difference forward solver while the computational cost is reduced tremendously.

This paper focuses on the disturbance suppression issue of hidden semi-Markov jump systems leveraging composite control. The system consists of a semi-Markov layer and an observed mode sequence layer, and it is subject to a matched disturbance generated by an exogenous system and a mismatched disturbance that is norm bounded. The proposal is to design a composite controller based on a disturbance observer to counteract and attenuate the disturbances effectively. By constructing a special Lyapunov function comparison point, the exponential stability is analyzed with the stability criterion in the form of linear matrix inequality is established. Two simulation examples are provided to demonstrate the practical merits of the composite controller relative to the single H_∞ control.

This paper investigates the networked evolutionary games (NEGs) with profile-dependent delays, including modeling and stability analysis. Profile-dependent delay, which varies with the game profiles, slows the information transmission between participants. Firstly, the dynamics model is proposed for the profile-dependent delayed NEG, then the algebraic formulation is established using the algebraic state space approach. Secondly, the dynamic behavior of the game is discussed, involving general stability and evolutionarily stable profile analysis. Necessary and sufficient criteria are derived using the matrices, which can be easily verified by mathematical software. Finally, a numerical example is carried out to demonstrate the validity of the theoretical results.

The cross-dimensional dynamical systems have received increasing research attention in recent years. This paper characterizes the structure features of the cross-dimensional vector space. Specifically, it is proved that the completion of cross-dimensional vector space is an infinite-dimensional separable Hilbert space. Hence, it means that one can isometrically and linearly embed the cross-dimensional vector space into the $\ell^{2}$, which is known as the space of square summable sequences. This result will be helpful in the modeling and analyzing the dynamics of cross-dimensional dynamical systems.

This paper studies the cluster consensus of multi-agent systems (MASs) with objective optimization on directed and detail balanced networks, in which the global optimization objective function is a linear combination of local objective functions of all agents. Firstly, a directed and detail balanced network is constructed that depends on the weights of the global objective function, and two kinds of novel continuous-time optimization algorithms are proposed based on time-invariant and timevarying objective functions. Secondly, by using fixed-time stability theory and convex optimization theory, some sufficient conditions are obtained to ensure that all agents’ states reach cluster consensus within a fixed-time, and asymptotically converge to the optimal solution of the global objective function. Finally, two examples are presented to show the efficacy of the theoretical results.

The tracking problem of uncertain nonstrict-feedback nonlinear systems (UNFNS) is examined to develop a novel adaptive neural control scheme to ensure fixed-time convergence. In particular, the challenge associated with the unknown nonlinear function can be overcome through neural network (NN) based estimation. Therefore, an NN-based adaptive fixed-time control scheme is established with only one parameter, using the property of the basis function vector to address the algebraic loop problem. Furthermore, the singularity problem can be solved by incorporating a smooth switching function. A rigorous theoretical analysis is performed to demonstrate that the output signal can track the reference signal within a fixed time and that the signals in the control systems are bounded. Finally, numerical simulations are performed to validate the feasibility of the proposed methodology.

This paper studies the output consensus problem of heterogeneous linear stochastic multiagent systems with multiplicative noises in system parameters and measurements, where the system noise in each agent is allowed to be different. By employing stochastic output regulation theory and the stochastic Lyapunov function approach, a composite controller embedded with stochastic output regulator equations (SOREs) and a stochastic dynamic compensator is designed to achieve the meansquare output consensus of the multi-agent systems. To implement the consensus algorithm, a sufficient condition for feasible solutions of the SOREs is first established in terms of Lyapunov and Selvester equations. Then the time-varying SOREs are approximated by the Euler-Maruyama method combined with an a-posteriori partial estimation of the increments of the Brownian motion. A numerical example illustrates the theoretical results.

This paper studies the formation control problem for the second-order heterogeneous nonlinear multi-agent systems (MASs) with switching topology and quantized control inputs. Compared with formation control under the fixed topology, under the switching topology inherent nonlinear dynamics of the agent and the connectivity change of the communication topology are considered. Moreover, to avoid the chattering phenomenon caused by unknown input disturbances, the hysteretic quantizers are incorporated to quantize the input signals. By using the Lyapunov stability theory and leader-follower formation approach, the proposed formation control scheme ensures that all signals of the MASs are semi-globally uniformly ultimately bounded (SGUUB). Finally, the efficiency of the theoretical results is proved by a simulation example.

In this paper, a new stochastic analysis tool on semi-global stability is constructed, for nonlinear systems disturbed by stochastic processes with strongly bounded in probability. The definition of semi-global noise to state practical stability in probability and its Lyapunov criterion for random systems are presented. As a major application of stability, the semi-global practical tracking of random nonlinear systems based on dynamic surface control technique is considered. The trajectory tracking of manipulator robot driven by direct current motors is carried out in simulation to illustrate the effectiveness and feasibility of the control scheme.

A cooperative game theoretical approach is taken to production and transportation coordinated scheduling problems of two-machine flow-shop (TFS-PTCS problems) with an interstage transporter. The authors assume that there is an initial scheduling order for processing jobs on the machines. The cooperative sequencing game models associated with TFS-PTCS problems are established with jobs as players and the maximal cost savings of a coalition as its value. The properties of cooperative games under two different types of admissible rearrangements are analysed. For TFS-PTCS problems with identical processing time, it is proved that, the corresponding games are σ₀-component additive and convex under one admissible rearrangement. The Shapley value gives a core allocation, and is provided in a computable form. Under the other admissible rearrangement, the games neither need to be σ₀-component additive nor convex, and an allocation rule of modified Shapley value is designed. The properties of the cooperative games are analysed by a counterexample for general problems.

In this paper, a theoretical model is developed on the basis of systems theory, which structures the livelihood system of low-income households in a European country characterized by a semi-peripheral economy. Based on the proposed model, the complex system of network connections and formal and informal financial transactions, which households use in their daily lives to cover their expenses, becomes graspable. The proposed theoretical model is analyzed through simulations based on agent-based modelling (ABM) centred on empirical network data. Through the simulations, the author explores the mechanisms of the market and asks what formal and informal credit transactions determine its operation, how these factors shape the local social structure and how resilient the market is to crises. The results show that this dynamic, complex risk-sharing system has an inherent logic and it can mitigate the small liquidity shocks but it is not resistant to bigger financial shocks or overconsumptions.

This paper adopts the DID approach to investigate the trade destruction effects and trade deflection effects of the US additional tariffs. The authors find that the US additional tariffs significantly reduce China’s exports of tariffed products to the US (i.e., trade destruction effect), especially intermediate and labor-intensive products. On the other hand, they significantly increase China’s exports of tariffed products to the third market (i.e., trade deflection effect). For the $50 billion list, both the trade destruction effect and trade deflection effect are concentrated on processing exports, and the US additional tariffs significantly increase China’s exports of tariffed products to ASEAN, Japan and Australia. For the $200 billion list, the US additional tariffs boost China’s exports of chemicals, textiles, wood, metal products, furniture and other products to the EU, Australia, Japan, South Africa and Hong Kong, China. Furthermore, most trade deflections are not realized by lowering export prices, indicating that the trade deflections could compensate for the profit losses caused by the US additional tariffs to a certain extent. The proposed results suggest that searching for substitutions of export markets and a more open trade policy is an important way to avoid profit losses and reduce risks for enterprises and economies suffering from trade frictions.

Influenced by the global economy, politics, energy and other factors, the price of carbon market fluctuates sharply. It is of great practical significance to explore a suitable measurement method of extreme risk of carbon market. Considering that the return series of carbon market has the characteristics of leptokurtosis, fat tail, skewness and multifractal, and there maybe many extreme risk values in the carbon market, this paper introduces the Skewed-$t$ distribution which can describe the characteristics of leptokurtosis, fat tail and skewness of return series into MSM model which can describe multifractal characteristic of return series to model volatility of carbon market. On the basis, based on the extreme value theory, this paper constructs Skewed-$t$-MSM-EVT model to measure extreme risk of carbon market. This paper chooses EUA market as the object to study extreme risk of carbon market, and draws the following conclusions: Skewed-$t$-MSM-EVT model has significantly higher prediction accuracy for carbon market's VaR than MSM-EVT models under other distributions (including normal distribution, $t$ distribution, GED distribution); Skewed-$t$-MSM-EVT model is superior to traditional Skewed-$t$-FIGARCH-EVT and Skewed-$t$-GARCH-EVT models in predicting carbon market's VaR. This research has important practical significance for accurately grasping the risk of carbon market and promoting energy conservation and emission reduction.

This paper studies the multi-period mean-variance (MV) asset-liability portfolio management problem (MVAL), in which the portfolio is constructed by risky assets and liability. It is worth mentioning that the impact of general correlation is considered, i.e., the random returns of risky assets and the liability are not only statistically correlated to each other but also correlated to themselves in different time periods. Such a model with a general correlation structure extends the classical multiperiod MVAL models with assumption of independent returns. The authors derive the explicit portfolio policy and the MV efficient frontier for this problem. Moreover, a numerical example is presented to illustrate the efficiency of the proposed solution scheme.

The authors consider an M/M/1 queue with two types of customers, where customers are classified into two categories according to their psychological feelings when facing uncertainty about queue information. In the unobservable queue, experienced customers could accurately calculate their expected utilities, while first-time customers are loss-averse and the psychological feelings could incur additional gain-loss utilities. By defining customers’ willingness to pay, the authors derive the equilibrium joining-balking behaviors for each type of customer and obtain the service provider’s optimal pricing decision. The authors also classify the implications of the obtained results.

Xinjiang’s agriculture is a typical irrigated agriculture for its agriculture water consumption accounts for 96% of the total water use. As a typical resource-deficient area, the key to Xinjiang’s agricultural development is saving water. This paper takes the high-efficient water-saving irrigation technology of 41 regions along the Tarim River from 2002 to 2013 as the research object, adopts spatial stochastic frontier model to measure the space efficiency of high-efficient water-saving irrigation technology, and analyzes the effect of water-saving irrigation technology on agricultural development. Results show that the water-saving irrigation technology has a spatial effect, if neglecting it, the error of missing variables will occur, and the average loss will be 6.98 percentage points. The spatial correlation effect promotes the improvement of the efficiency of water-saving irrigation technology. The spatial heterogeneity leads to the spatial imbalance of the efficiency of water-saving irrigation technology. The promotion of agricultural water-saving irrigation technology can increase production and the efficiency of agricultural development. Due to the technical heterogeneity of different types of water-saving irrigation technology, the contribution to the development of agriculture is also different. The study finds that water-saving irrigation technology of drip irrigation in the Tarim River contributes more to agricultural development.