This article presents a sliding mode control (SMC) strategy to address the finite-time consensus problem of multiagent systems (MASs) under denial-of-service (DoS) attacks. Agents exchange information over network channels that are vulnerable to stochastic DoS attacks, which may disrupt communication and change the network topology. To capture these stochastic variations, a Markov jump model is employed to describe the switching of communication topologies. By introducing a disagreement vector, the consensus problem of the MAS within a finite-time interval is transformed into the stochastic finite-time boundedness (SFTB) problem of the disagreement error dynamic system. A feasible SMC law is developed to drive the disagreement error dynamic system onto a specified sliding surface within a finite time. Furthermore, a partitioning policy is used to ensure the SFTB of the system during both the reaching phase and the sliding phase. A reduced-order approach is used to resolve potential uncontrollability in the system, and sufficient conditions are established to ensure the SFTB of the disagreement error dynamic system under the proposed SMC strategy. Finally, a multiaircraft system example is provided to demonstrate the correctness and effectiveness of the proposed approach.

This article investigates the distributed aggregation optimization problem in multiagent systems (MASs), with a particular focus on addressing the aggregation effect commonly encountered in modern engineering and technological applications. In such scenarios, the local objective function of an agent depends not only on its own decision variables but also interacts with the decision variables of other agents, resulting in complex coupling relationships. To solve these challenges while ensuring that the optimization variables satisfy the coupled inequality constraints, this article introduces a novel framework called distributed aggregative parameter projection (DAPP). Specifically, the proposed distributed protocol is based on an improved parameter projection, including two direction updates, which minimizes the cost function and keeps the search direction obeying the inequality at each iteration. In addition, the linear convergence performance of the proposed scheme over the undirected and connected graph is ensured by rigorous theoretical proof with mild assumptions. Finally, simulation results demonstrate the superior performance of DAPP with smaller global function and faster convergence speed in comparison to the existing method.

Environmental degradation significantly affects coating materials' service life, making accurate prediction under complex conditions crucial for material design and maintenance. This study applies the Feature Token Transformer (FTTransformer) to predict coating degradation using a dataset of 32 experimental units from NIST SPHERE accelerated aging tests under different temperature-humidity combinations. The FTTransformer's specialized feature tokenization mechanism uniformly handles heterogeneous tabular data containing numerical features (time, log-time) and categorical features (temperature, humidity, unit ID), distinguishing it from standard Transformer architectures designed for sequential data. Through rigorous group-wise data splitting that ensures test units are completely unseen during training, the model achieves exceptional performance with$R^{2}=0.965$and$\mathbf{R M S E}=\mathbf{0. 0 1 2 7}$on the original scale, significantly outperforming traditional methods and vanilla Transformer. The model successfully captures physically consistent degradation patterns including monotonic temperature acceleration following Arrhenius-like behavior and nonmonotonic humidity effects where mid-range conditions (25-50% RH) produce peak degradation while extreme conditions show reduced impact. These findings align with competing degradation mechanisms in polymeric coatings and demonstrate the model's capability for data-driven lifetime assessment and predictive maintenance applications.

This paper presents a robust$H_{\infty}$performance analysis method for an uncertain linear parameter-varying (LPV) system to achieve less conservative disturbance rejections. Specifically, by adopting Lyapunov function, a feedback controller is designed based on linear matrix inequalities (LMIs) framework to eliminate the influences of unknown uncertainties for continuous-time and discrete-time LPV systems. Meanwhile, a convex optimization technique is explored for solving optimization problems of bilinear matrix inequalities (BMIs), in which several sufficient conditions guaranteeing system stability are relaxed. Furthermore, compared with some existing approaches, the superior performances of the presented scheme are demonstrated by rigorous theoretical analysis and numerical simulations on a practical truck-trailer system.Note to Practitioners—This paper is motivated by the problem of minimal control domain range (also called conservativeness) in robust$H_{\infty}$control approaches for LPV systems. Generally, LMI conditions are accompanied by a certain level of conservativeness for the robust stability of LPV systems. However, excessive conservatism makes the proposed control strategy impossible to implement in practical engineering. Considering this gap, we formulate a lemma to reduce the conservativeness in robust stability conditions while dealing with the effects of system uncertainty. Strict theoretical proofs and comparative simulation experiments are also conducted to verify the effectiveness of the proposed robust control scheme in continuous and discrete LPV systems.

In this paper, a novel sliding mode control (SMC) method is designed to investigate the finite-time consensus tracking (FTCT) problem of the second-order leader-following multi-agent systems (MASs) with imprecise communication topology of each agent. First, a T-S fuzzy model is introduced to characterize the inexact communication topology of the leader-following MASs. Moreover, a fuzzy SMC law is designed to ensure the reachability of the constructed sliding mode surface (SMS). Meanwhile, in light of the partitioning strategy, sufficient conditions for FTCT of the leader-following MASs are established. It is worth mentioning that the distributed SMC method proposed in this paper is based on the unknown sliding gain, which can greatly reduce the conservatism. Finally, the simulation study on a group of single-link robots and a numerical simulation demonstrate the feasibility of proposed control method. Note to Practitioners—This paper aims to ensure the transient performance and good robustness of second-order multi-agent systems. Generally, the classic nonlinear control method, sliding mode control (SMC), is accompanied by a certain level of conservativeness due to its fixed sliding gain matrix. Moreover, excessive conservatism may prevent the proposed control strategy from being implemented in practical engineering. To address this issue, we formulate an improved finite-time SMC framework to reduce the conservatiness. We also designed an improved algorithm based on genetic algorithm (GA) and linear matrix inequalities (LMIs) to solve the difficulties brought by the new control algorithm, thereby facilitating its practical implementation and enhancing the overall performance of the second-order multi-agent systems.

Contemporary advancements in large language models (LLMs), both proprietary and open-source, have demonstrated capabilities surpassing human performance across diverse general domains. Nevertheless, their efficacy in specialized professional fields, particularly medicine, remains suboptimal within the open-source ecosystem. To address these limitations, we introduce Domain Adaptative Medicine-LLM (DAMe-LLM). Our approach encompasses continued pre-training, supervised fine-tuning (SFT), and reinforcement learning from human feedback (RLHF) methodologies. In the development of DAMe-LLM, we have curated an extensive Chinese and English medical dataset for continued pre-training, complemented by a high-fidelity SFT dataset encompassing a broad spectrum of medical specialties. Our contribution provides the scientific community with valuable resources for further investigation and development in the domain of medicine artificial intelligence.

This article focuses on solving a distributed convex optimization problem of multiagent systems with multiple inequality constraints. Considering communications between agents are prone to failures and not synchronized among themselves in some cases, a novel asynchronous adaptive step sizes-DIGing algorithm is proposed through integrating the projection operators and logic-andframework. In specific, a bilaterally adjustable adaptive step size mechanism is introduced to automatically abandon the irrational evolutionary route which relieves the limitations of traditional DIGing algorithm. By adopting the operator theory, the almost sure convergence of the proposed algorithm under asynchronous communication is proved. Finally, the theoretical and simulation results for the plantwide optimization problem in the ethylene production process illustrate the effectiveness of the proposed algorithm.

This article presents a distributed optimization framework in order to solve the plant-wide energy-saving problem of an ethylene plant. First, the ethylene production process is abstracted into a distributed network, and then, a new distributed consensus algorithm is proposed, which is called adaptive step-size-based distributed proximal consensus algorithm (ASS-DPCA). This algorithm can dynamically adjust the step size and automatically abandon the irrational evolutionary route while eliminating the dependence of optimization algorithms on model gradient information. Moreover, the designed algorithm is able to converge to an optimal solution for any convex cost functions and approach to a convex constraint set of agents over an undirected connected graph. Finally, the results of numerical simulation and industrial experiments show that the algorithm can reduce the total energy consumption of an ethylene plant with less computing time and assured consensus.

This paper is concerned with the sliding mode control (SMC) via finite-time stabilization (FTS) for a class of conic-type nonlinear semi-Markovian jumping systems (SMJSs). Comparing with the classical Markovian jumping systems, the transition rates of SMJSs are related to the random sojourn-time g. Based on this, a suitable SMC law for driving the state trajectories to the designed sliding surface within a finite-time interval is given. Then, the FTS over reaching phase and sliding motion phase is further proved to guarantee the FTS of the whole SMJSs. Finally, the effectiveness of the proposed method is demonstrated by a time-delayed Chua's circuit simulation.

This article study the asynchronous control problem for a class of discrete-time Markovian jumping Lur’e systems (MJLSs) over the finite-time interval. The partial accessibility of system modes with respect to the designed controller is described by a hidden Markov model (HMM). The asynchronous control law consists of two parts, i.e., the states and the nonlinearities involved in the dynamics of the controlled system. By selecting the appropriate Lyapunov functional and applying the modified sector condition, the finite-time stabilization conditions under the control constraints are derived. Finally, the effectiveness of the designed method is verified by an illustrative simulation.