In this article, attitude tracking control with arbitrary convergence time for rigid spacecraft is considered. First, a novel time-varying sliding function is proposed to achieve free-will arbitrary time convergence when the system states reside on the sliding surface. With such a sliding function, an attitude tracking controller is designed to guarantee that the states of the closed-loop system converge to the sliding surface within a predetermined time in the presence of external disturbances. The free-will arbitrary time convergences of the closed-loop system and sliding function are illustrated by numerical simulations.

In this paper, we consider the inverse problem for the Moore-GibsonThompson equation with a memory term and variable diffusivity, which introduce a sort of delay in the dynamics, producing nonlocal effects in time. The Hölder stability of simultaneously determining the spatially varying viscosity coefficient and the source term is obtained by means of the key pointwise Carleman estimate for the Moore-Gibson-Thompson equation. For the sake of generality in mathematical tools, the analysis of this paper is discussed within the framework of Riemannian geometry.

In various fields such as medical science and finance, it is not uncommon that the data are heavy-tailed and/or not fully observed, calling for robust inference methods that can deal with the outliers and incompleteness efficiently. In this paper, we propose a rank score test for quantile regression with fixed censored responses, based on the standard quantile regression in an informative subset which is computationally efficient and robust. We further select the informative subset by the multiply robust propensity scores, and then derive the asymptotic properties of the proposed test statistic under both the null and local alternatives. Moreover, we conduct extensive simulations to verify the validity of the proposed test, and apply it to a human immunodeficiency virus data set to identify the important predictors for the conditional quantiles of the censored viral load.

The problem of nonconvex and nonsmooth optimization (NNO) has been extensively studied in the machine learning community, leading to the development of numerous fast and convergent numerical algorithms. Existing algorithms typically employ unified iteration schemes and require explicit solutions to subproblems for ensuring convergence. However, these inflexible iteration schemes overlook task-specific details and may encounter difficulties in providing explicit solutions to subproblems. In contrast, there is evidence suggesting that practical applications can benefit from approximately solving subproblems; however, many existing works fail to establish the theoretical validity of such approximations. In this paper, we propose a hybrid inexact proximal alternating method (hiPAM), which addresses a general NNO problem with coupled terms while overcoming all aforementioned challenges. Our hiPAM algorithm offers a flexible yet highly efficient approach by seamlessly integrating any efficient methods for approximate subproblem solving that cater to specificities. Additionally, we have devised a simple yet implementable stopping criterion that generates a Cauchy sequence and ultimately converges to a critical point of the original NNO problem. Our numerical experiments using both simulated and real data have demonstrated that hiPAM represents an exceedingly efficient and robust approach to NNO problems.

In many applications involving data streams, the sequences of data arise from highly dynamic and often unstable real-life processes, rendering untenable the standard assumption that current and future data come from the same distribution. In response, new methodologies, such as dynamic online learning, have been proposed in order to account for the nonstationary features in the data-generating process. Motivated by the stability and statistical efficiency of the notable stochastic approximation method, average stochastic gradient descent (ASGD) in time-invariant systems, we propose an exponentially weighted moving average (EWMA)-based stochastic gradient descent (SGD) which accommodates the dynamic structure by introducing a forgetting factor and replacing the simple averaging step in ASGD with an EWMA step. Provided that the dynamic drift is Lipschitz continuous, the mean squared tracking error rate of the proposed method achieves the optimal rate in the nonparametric statistical paradigm. Our framework also allows us to derive the dynamic regret bound and asymptotic normality with a path variation constraint in a natural manner. Numerical analysis has been conducted to verify the performance of our proposed method. In particular, our method is much more robust to the selection of learning rates compared with the ordinary SGD method.

We consider a robust optimal reinsurance and investment problem in a risk model with two dependent classes of insurance business for an Ambiguity-Averse insurer (AAI). The insurer aims to minimize the goal-reaching probability that the value of the wealth process reaches a low barrier before a high goal. Using the stochastic control approach based on the Hamilton-Jacobi-Bellman (HJB) equation, we derive the robust optimal reinsurance and investment strategies, as well as the corresponding value function. We conclude that the robust optimal investment-reinsurance strategy coincides with the one without model ambiguity, but the value function differs. As a consequence, ignoring model uncertainty leads to significant value function loss for the AAI. Besides, it is worth noting that if the insurer has only one business, the sum of the degenerated value function and the one of Luo et al.^[1] is equal to 1 both for ambiguity and ambiguity-neutral. Finally, numerical examples are given to illustrate our results.

In this paper, we consider distributed convex optimization over hierarchical networks. We exploit the hierarchical architecture to design specialized distributed algorithms so that the complexity can be reduced compared with that of non-hierarchically distributed algorithms. To this end, we use local agents to process local functions in the same manner as other distributed algorithms that take advantage of multiple agents' computing resources. Moreover, we use pseudocenters to directly integrate lower-level agents' computation results in each iteration step and then share the outcomes through the higher-level network formed by pseudocenters. We prove that the complexity of the proposed algorithm exponentially decreases with respect to the total number of pseudocenters. To support the proposed decomposition-composition method for agents and pseudocenters, we develop a class of operators. These operators are generalizations of the widely-used subgradient based operator and the proximal operator and can be used in distributed convex optimization. Additionally, these operators are closed with respect to the addition and composition operations; thus, they are suitable to guide hierarchically distributed design and analysis. Furthermore, these operators make the algorithm flexible since agents with different local functions can adopt suitable operators to simplify their calculations. Finally, numerical examples also illustrate the effectiveness of our method.

This paper investigates optimal control for two kinds of multi-agent systems with the sampled-data based communication protocols. Necessary and sufficient conditions on data-sampling controllability are obtained for sampled-data based multi-agent systems, which indicate that sampling periods affect the controllability of double-integrator systems, whereas the controllability of single-integrator systems is decided by the interconnection topology. To obtain the optimal control inputs under different objectives, two concepts of $2$-norm-optimal control and infinity-norm-optimal control are proposed. For both kinds of systems, generalised inverse matrices of controllability test matrices are utilised to derive the 2-norm-optimal controls, and the infinity-norm-optimal controls are equivalently transformed to be the optimal solutions of some specific linear programming problems via matrix vectorization. Some illustrative examples are provided for the main results in this paper.

The Russia-Ukraine Conflict (RUC) has emerged as a significant international flashpoint, with the resulting evolution of global diplomatic relations becoming an issue of considerable importance. To analyse the evolving diplomatic strategies of countries involved in the RUC, we apply the theories and methodologies of complex network games. First, we incorporate overflow payoff theory from economic and trade cooperation networks to summarise the spillover income phenomenon in diplomatic games and to facilitate modelling. Drawing on the classic Fermi rule in network game theory, we introduce belief and same-strategy factors to propose an evolutionary game rule driven by interests, beliefs, and neighbouring strategies. Using numerical simulations, we examine the factors that affect the evolutionarily stable strategies of various nations. We also conduct comparative experiments to validate the scientific credibility of the method proposed in this paper.

In cloud control systems, generating an efficient and economical workflow scheduling strategy for deadline-constrained workflow applications, especially in uncertain multi-workflow dynamic scheduling processes, is a crucial challenge. To optimize the total cost of workflow scheduling, we propose a cost-driven heuristic scheduling algorithm F-MWSA which consists of two phases: fuzzy deadline distribution and fuzzy task scheduling. In the fuzzy deadline distribution phase, a new workflow deadline distribution strategy with fuzziness is designed to obtain the sub-deadline constraint of each task. The fuzzy task scheduling phase focuses on a cost-effective strategy to assign tasks to cloud resources, reducing multi-workflow scheduling costs. Performance evaluations on five real-world workflows demonstrate that the proposed F-MWSA outperforms the baseline policy in terms of total cost, success ratio, resource utilization, and makespan.

This paper investigates the problem of cooperative localization (CL) for a multi-robot system (MRS) under dynamic measurement topology, which involves a group of robots collectively estimating their poses with respect to a common reference frame using ego-motion measurements and robot-to-robot relative measurements. We provide a theoretical analysis of the time-varying unobservable subspace and propose a consistent cooperative localization algorithm. First, we introduce the relative measurement graph (RMG) to represent the relative pose measurements obtained by the multi-robot system (MRS) at each instant. Then, we derive the local observability matrix over a time interval. An equivalent relationship is established between the observability properties of the local observability matrix and the spectral matrices of the RMG. Moreover, we present a method for constructing the unobservable subspace based on the RMG under different topology conditions. Based on this analysis, we design a consistent cooperative localization algorithm that satisfies the constraints of the time-varying unobservable subspace. An analytical optimal solution is derived for the constrained optimization problem. Monte Carlo numerical simulations are conducted to demonstrate the consistency and accuracy of the proposed method.

Optical solitons play an important role in long-distance, high-capacity communications. To enhance the precision of soliton dynamics modeling, we combine incremental learning techniques with physics-informed neural network. The novel model employs a process of knowledge distillation and fine-tuning to continually integrate fresh physical information into the neural network. This iterative approach leads to a constant improvement in the network's ability to extract features. We conducted experiments on three solitons, and the new method significantly reduces the error compared to the general physics-informed neural network. The modeling approach put forward in this research is anticipated to contribute to the advancement of all-optical computing research and facilitate the development of novel fiber optic communication systems.

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.

Simultaneously finding active predictors and controlling the false discovery rate (FDR) for high-dimensional survival data is an important but challenging statistical problem. In this paper, we propose a novel variable selection procedure with error rate control for the high-dimensional Cox model. By adopting a data-splitting strategy, we construct a series of symmetric statistics and then utilize the symmetry property to derive a data-driven threshold to achieve error rate control. We establish finite-sample and asymptotic FDR control results under some mild conditions. Simulation results as well as a real data application show our proposed approach successfully controls FDR and is often more powerful than the competing approaches.

The mobile loading arm (MLA) system is one of the most important mechanical equipments in the petrochemical industry consisting of rigid pipelines and rotating elbows. This paper researches the problems of dynamical modelling and tracking control. At first, a MLA dynamic model is built by using Euler-Lagrange function. And then, an adaptive third-order fixed time sliding mode (FDTSM) tracking controller with chattering free is represented, which enables the mobile loading arm (MLA) system to dock with the vehicle tank mouth. In this paper, a new double-layer third-order fixed time sliding mode controller is first proposed to address the tracking problem in model-based MLA systems with known parameters. Moreover, considering the presence of modeling uncertainty and external disturbances, a nerual network adaptive FDTSM controller is designed to ensure that the close-loop system state tracking error converges to a bounded region around zero within a fixed time. Further, the stability of the close-loop system is proved by using the Lyapunov stability theorem. Simulation results illustrate the validaty of the controller.

Interval-censored failure time data arise frequently in periodical follow-up studies including clinical trials and epidemiological surveys. In addition, some covariates may be subject to measurement errors due to the instrumental contamination, biological variation or other reasons. For the analysis of interval-censored data with mis-measured covariates, the existing methods either assume a parametric model or rely on the availability of replicated surrogate measurements for the error-prone covariate, which both have obvious limitations. To overcome these shortcomings, we propose a simulation-extrapolation estimation procedure under a general class of transformation models. The resulting estimators are shown to be consistent and asymptotically normal. The numerical results obtained from a simulation study indicate that the proposed method performs reasonably well in practice. In particular, the proposed method can reduce the estimation bias given by the naive method that does not take measurement errors into account. Finally, the proposed method is applied to a real data set on hypobaric decompression sickness.

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, we 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 discusses the uncooperative target tracking control problem for the unmanned aerial vehicle (UAV) under the performance constraint and scaled relative velocity constraint, in which the states of the uncooperative target can only be estimated through a vision sensor. Considering the limited detection range, a prescribed performance function is designed to ensure the transient and steady-state performances of the tracking system.Meanwhile, the scaled relative velocity constraint in the dynamic phase is taken into account, and a time-varying nonlinear transformation is used to solve the constraint problem, which not only overcomes the feasibility condition but also fails to violate the constraint boundaries. Finally, the practically prescribed-time stability technique is incorporated into the controller design procedure to guarantee that all signals within the closed-loop system are bounded. It is proven that the UAV can follow the uncooperative target at the desired relative position within a prescribed time, thereby improving the applicability of the vision-based tracking approach. Simulation results have been presented to prove the validity of the proposed control strategy.

In this paper, we design a reinforcement learning algorithm to solve the adaptive linear-quadratic stochastic $n$-players non-zero sum differential game with completely unknown dynamics. For each player, a critical network is used to estimate the Q-function, and an actor network is used to estimate the control input. A model-free online Q-learning algorithm is obtained for solving this kind of problems. It is proved that under some mild conditions the proposed policies converge to a Nash equilibrium. A simulation with five players is given to verify the effectiveness of the algorithm.

In this paper, we investigate the optimal per-claim reinsurance problem under the continuoustime framework to minimize the insurer’s ruin probability based on the Lundberg exponent. Considering reinsurance participants’ diversified risk preferences, we assume that the reinsurance premium is calculated by a combined premium principle, including the expected value premium principle and upper moment premium principle. Then, we derive the insurer’s optimal reinsurance strategy satisfying the principle of indemnity and the incentive compatibility condition in an infinite reinsurance space based on the point-wise optimization approach. Besides, our work emphasizes the optimality and admissibility of the combination of the excess of loss reinsurance and its dual form when a piecewise reinsurance premium principle is considered. As a special case, the optimal reinsurance strategy under the expected value premium principle reduces to the classic result. Furthermore, the numerical analyses are provided to illustrate the effects of the main parameters on the maximal Lundberg exponent and the optimal reinsurance strategy.

Government-sponsored venture capital (GVC) has been used to support financially constrained start-ups as an important policy tool in China. Typically, to motivate the social capital to invest in start-ups, GVC provides subsidies for them to bridge the funding gap in the early-stage venture capital market. However, the effect and mechanism of GVC affecting social capital investment have not been clearly studied. In this paper, we not only develop a game model to analyze this issue in theory, but conduct an empirical study by analyzing the 14741 records matched by propensity score matching (PSM) of Chinese venture capital market data from 2011 to 2021. Our findings are as the follows. Firstly, the subsidies offered by GVC will simultaneously increase the returns and risks of investments in start-ups of social capital with more volatile incentive effect of GVC. The incentive effect of GVC is only effective when the returns resulting from the subsidies outweigh the risks they introduce. In the context of the Chinese venture capital market, the incentive effect of GVC is effective. Secondly, the incentive effect of GVC is more pronounced in high-tech industries, which can be attributed to the signaling effect facilitated by GVC. In this context, the subsidy mainly helps social capital to bear the costs associated with screening potential investments. Thirdly, the incentive effect of GVC is more significant in underdeveloped venture capital markets, which can be explained by the “virtuous cycle” effect, in which GVC plays a pioneering role in establishing a more robust early-stage market trading system. By examining these three points, this study contributes to a better understanding of how GVC can effectively guide social capital investment, especially in the Chinese landscape.

In this paper, we aim to study Kronecker canonical form theory for T-type digraphs, which can be used to construct trees by tensor product with some directed paths. Firstly, we show that some bicyclic digraphs and multicyclic digraphs are T-type digraphs. Secondly, we provide a characterization for T-type digraphs by their Kronecker canonical form. Moreover, we present an algorithm for computing the Kronecker canonical form, which can be used to determine whether or not a digraph is a T-type digraph. Lastly, for a class of T-type digraphs, we show that their incidence matrix pair can be transformed into Kronecker canonical form using unimodular matrices. We also present an algorithm related to finding such unimodular matrices.

Characteristic pairs consist of lexicographical Gröbner bases and the minimal triangular sets, called W-characteristic sets, contained in them, and they are good representations of multivariate polynomial ideals in terms of Gröbner bases and triangular sets simultaneously. In this paper, we study how to decompose a polynomial set of arbitrary dimensions into characteristic pairs with simple W-characteristic sets, and two algorithms are proposed over fields of characteristic zero and over finite fields respectively. Both of the algorithms rely on the concept of strong regular characteristic divisors, and the one for fields of characteristic zero also uses Lazard Lemma to test whether an ideal is radical. Experimental results are presented to illustrate the effectiveness of the proposed algorithms.

In this paper, we study a class of general mean-field BDSDEs whose coefficients satisfy some stochastic conditions. Specifically, we prove the existence and uniqueness theorem of solution under stochastic Lipschitz condition and obtain the related comparison theorem. Besides, we further relax the conditions and deduce the existence theorem of solutions under stochastic linear growth and continuous conditions, and we also prove the associated comparison theorem. Finally, an asset pricing problem is discussed, which demonstrates the application of the general mean-field BDSDEs in finance.

Continuous state nonhomogeneous Markov chains are widely used to model the performance of random variables continuously varied over time in many fields such as population biology. Existing works mainly focus on their strong law of large numbers. There is little work developed on their limit theorems. To this end, this paper investigates the limiting properties of continuous state nonhomogeneous Markov chains, and establishes limit theorems for multivariate functions of continuous state nonhomogeneous Markov chains, including the strong law of lager numbers, the central limit theorem and almost sure central limit theorem under some mild conditions, which are some basic theoretical properties for statistical inference and predictions of continuous-time-varying random variables.

This paper investigates region tracking and perimeter surveillance of second-order multi-agent systems, where all agents move within a star-shaped set. First, by coordination transformations, the region tracking problem is converted from the star-shaped sets to a circular region. We employ communication and collaboration to complete region tracking and perimeter surveillance tasks, and then revert back to the star-shaped set by using inverse transformations. Second, we propose a distributed control strategy based on attractive and interaction potential functions, under which all agents can quickly track a given circular region and move around the perimeter. Finally, we validate the effectiveness and performance advantages of the proposed method through simulation experiments.

The paper discusses the regression analysis of current status data, which is common in various fields such as tumorigenic research and demographic studies. Analyzing this type of data poses a significant challenge and has recently gained considerable interest. Furthermore we consider an even more difficult scenario where, apart from censoring, one also faces left-truncation and informative censoring, meaning that there is a potential correlation between the examination time and the failure time of interest. We propose a sieve maximum likelihood estimation (MLE) method and in the proposed method for inference, a copula-based procedure is applied to depict the informative censoring. Also we utilise the splines to estimate the unknown nonparametric functions in the model, and the asymptotic properties of the proposed estimator are established. The simulation results indicate that the developed approach is effective in practice, and it has been successfully applied a set of real data.

We find by applying MacMahon’s partition analysis that all magic labellings of the cube are of eight types, each generated by six basic elements. A combinatorial proof of this fact is given. The number of magic labellings of the cube is thus reobtained as a polynomial in the magic sum of degree 5. Then we enumerate magic distinct labellings, the number of which turns out to be a quasipolynomial of period 720720. We also find the symmetry group can be used to significantly simplify the computation.

In this paper, the flocking behavior of a Cucker-Smale model with a leader and noise is studied in a finite time. We present a Cucker-Smale system with two nonlinear controls for a complex network with stochastic synchronization in probability. Based on the finite-time stability theory of stochastic differential equations, the sufficient conditions for the flocking of stochastic systems in a finite time are obtained by using the Lyapunov function method. Finally, the numerical simulation of the particle system is carried out for the leader and noise, and the correctness of the results is verified.

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 a 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 ±1r/min, and the overshoot is reduced to 5.8 %. This greatly improves the stability and security, which is exactly what the drilling process requires.

The stability margin is a vital indicator for assessing the safety level of aircraft control systems. It should maintain sufficient stability margin to ensure safety during flight, especially in the process of large maneuver operations. The stability margin is generally quantified by the Bode diagram, which strictly depends on the system parameters and the open-loop transfer function. However, due to the uncertain flight environments, transmission delays of sensors and mode switchings, etc., there exist large parameter and structure uncertainties in the aircraft control systems, which makes it difficult to precisely configure the stability margin to the desired value by the usual control methods. To address this problem, an indirect adaptive control strategy is proposed in this paper, where an adaptive PI control law with the capability of self-configuration of stability margin is developed. The developed control law not only achieves stable time-varying command tracking in the time domain, but also is able to automatically configure the phase margin and gain margin in the frequency domain. Finally, the simulation of the one-degree-of-freedom roll rate control model of the air vehicle verifies the validity of the proposed control method.

In this paper, the approximate controllability of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions is investigated in the sense of integral solution in Hilbert spaces. Some sufficient and necessary conditions are obtained. Firstly, the existence and uniqueness of integral solutions of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions are considered by GE-evolution operator theory and Sadovskii’s fixed point theorem, the existence and uniqueness theorem of solutions is given. Secondly, the approximate controllability of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions is studied in the sense of integral solution. The criterion for approximate controllability is provided. The obtained results have important theoretical and practical value for the study of controllability of semilinear integrodifferential degenerate Sobolev equations with nonlocal conditions.

We extend the shell and kernel reductions for hyperexponential functions over the field of rational functions to a monomial extension. Both of the reductions are incorporated into one algorithm. As an application, we present an additive decomposition in rationally hyperexponential towers. The decomposition yields an alternative algorithm for computing elementary integrals over such towers. The alternative can find some elementary integrals that are unevaluated by the integrators in the latest versions of maple and mathematica.

The minimal basis of a univariate polynomial matrix $M(s)\in K[s]^{m\times n}$ is a basis of the syzygies of the polynomial matrix $M(s)$ with lowest possible degree, where $K[s]$ is the univariate polynomial ring over the field of $K$. It provides an efficient tool to compute the moving planes and moving quadratics of a rational parametric surface, which are employed to implicitize the parametric surface as a powerful implicitization method. In this paper, we develop two improved algorithms for computing the minimal bases of polynomial matrices. The algorithms are based on efficient methods to reduce the degrees of a set of univariate polynomial vectors. It is shown that the computational complexities of the two algorithms are $\mathcal{O}(m^{2}n^{3}d^2+d^2n^5-(2mn^4d^2-\frac{1}{6}m^3nd))$, and $\mathcal{O}(m^2nd^2+(n-m)n^3d^2+\frac{m^2n^2d^2}{n-m})$ respectively, where $m,n$ are the sizes of the polynomial matrix $M(s)$ and $d$ is the degree of each entry of the matrix. The new algorithms are faster than the state-of-the-art methods by experimental examples. Some properties about the degree of the minimal basis are also provided.

This paper primarily focuses on solving the Heilbronn problem of convex polygons, which involves minimizing the area of a convex polygon $P_1P_2\cdots P_n$ while satisfying the condition that the areas of all triangles formed by consecutive vertices are equal to $\frac{1}{2}$. The problem is reformulated as a polynomial optimization problem with a bilinear objective function and bilinear constraints. A new method is presented to verify the upper and lower bounds for the optimization problem. The upper bound is obtained by the affine regular decagon. Then Bilinear Matrix Inequalities (BMI) theory and the branch-and-bound technique are used to verify the lower bound of the problem. The paper concludes by proving that the lower bound for the area minimization problem of a convex polygon with 10 vertices is 13.076548. The relative error compared to the global optimum is $0.104\%$.

This study proposes a novel interpretable tourism demand forecasting framework that considers the impact of the COVID-19 pandemic by using multi-source heterogeneous data, namely, historical tourism volume, newly confirmed cases in tourist origins and destinations, and search engine data. This study introduces newly confirmed cases in tourist origins and tourist destinations to forecast tourism demand and proposes a new two-stage decomposition method called ensemble empirical mode decomposition-variational mode decomposition to deal with the tourist arrival sequence. To solve the problem of insufficient interpretability of existing tourism demand forecasting, this study also proposes a novel interpretable tourism demand forecasting model called JADE-TFT, which utilizes an adaptive differential evolution algorithm with external archiving (JADE) to intelligently and efficiently optimize the hyperparameters of temporal fusion transformers (TFT). The validity of the proposed prediction framework is verified by actual cases based on Hainan and Macau tourism data sets. The interpretable experimental results show that newly confirmed cases in tourist origins and tourist destinations can better reflect tourists’ concerns about travel in the post-pandemic era, and the two-stage decomposition method can effectively identify the inflection point of tourism prediction, thereby increasing the prediction accuracy of tourism demand.

In this paper, we consider a sparse parameter estimation problem in continuous-time linear stochastic regression models using sampling data. Based on the compressed sensing (CS) method, we propose a compressed least squares (LS) algorithm to deal with the challenges of parameter sparsity. At each sampling time instant, the proposed compressed LS algorithm first compresses the original high-dimensional regressor using a sensing matrix and obtains a low-dimensional LS estimate for the compressed unknown parameter. Then, the original high-dimensional sparse unknown parameter is recovered by a reconstruction method. By introducing a compressed excitation assumption and employing stochastic Lyapunov function and martingale estimate methods, we establish the performance analysis of the compressed LS algorithm under the condition on the sampling time interval without using independence or stationarity conditions on the system signals. At last, a simulation example is provided to verify our theoretical results by comparing the standard and the compressed LS algorithms for estimating a high-dimensional sparse unknown parameter.

This paper studies the finite-time fuzzy adaptive output feedback resilient control problem for nonlinear cyber-physical systems (CPSs) with sensor attacks and actuator faults. Fuzzy logic systems (FLSs) are used to approximate the unknown nonlinear functions, and a fuzzy state observer is constructed to estimate the unmeasured states. By combining the Nussbaum function with the backstepping control design technique, a fuzzy adaptive resilient control scheme is designed to successfully address the effects of sensor attacks and actuator faults. It is proved that the controlled system is semi-global practical finite-time stability (SGPFS), and the tracking error converges to a small neighborhood of the origin in a finite time interval. Finally, the simulation and comparison results further demonstrate the effectiveness of the designed control method.

Under the assumption that asset prices follow a mixed gamma process, this paper first shows that return series can be presented as a difference of two gamma processes and then proposes a realized probability index for return direction forecasting. The underlying distribution of this new index is analyzed and found to be beta-distributed. Both theoretical and empirical results show that this new index is more efficient than the traditional binary index.

This paper investigates the moment selection and parameter estimation problem of high-dimensional unconditional moment conditions. First, we propose a Fantope projection and selection (FPS) approach to distinguish the informative and uninformative moments in high-dimensional unconditional moment conditions. Second, for the selected unconditional moment conditions, we present a generalized empirical likelihood (GEL) approach to estimate unknown parameters. The proposed method is computationally feasible, and can efficiently avoid the well-known ill-posed problem of GEL approach in the analysis of high-dimensional unconditional moment conditions. Under some regularity conditions, we show the consistency of the selected moment conditions, the consistency and asymptotic normality of the proposed GEL estimator. Two simulation studies are conducted to investigate the finite sample performance of the proposed methodologies. The proposed method is illustrated by a real example.