Given the critical role of quorum sensing (QS) in the pathogenicity of Pseudomonas aeruginosa, inhibiting QS is considered a promising alternative to traditional antibiotics for treating P. aeruginosa infections. The QS system of P. aeruginosa comprises two acyl-homoserine lactone (AHL) circuits, LasI/R and RhlI/R, along with a third system, the PQS system. However, due to the frequent clinical isolation of lasR mutants and the avirulent nature of rhlR mutants, targeting both the LasI/R and RhlI/R systems represent a more viable therapeutic strategy. YXL-13, an AHL analog, has been identified as a QS inhibitor, though its precise molecular target remains unclear. This study demonstrates that YXL-13 effectively inhibits the QS response in P. aeruginosa QS mutants (ΔlasR, ΔrhlR, and ΔpqsR) as well as in clinically isolated strains (PA1, PA2, and PA3), while also providing protection to C. elegans against infection. The related evidences, including qPCR analysis, QS reporter assays, isothermal titration calorimetry (ITC), molecular docking, and molecular dynamics (MD) simulations, indicate that YXL-13 targets both LasR and RhlR. In summary, YXL-13 exhibits potent inhibitory effects on the QS response of various P. aeruginosa strains.

This paper investigates the problem of attack synthesis in the supervisory control of discrete event systems using ALTER model. The ALTER model is capable of modeling a wide range of sensor attacks, including deletions, insertions, replacements, and all-out attacks by describing possible attacks through the corresponding attack languages. We study the synthesis of sensor and actuator attacks, focusing on two key objectives: stealthiness and safety compromise. Necessary and sufficient conditions for the existence of such attacks are provided, along with methods to synthesize attacks when these conditions are met. The results advance the understanding of cyber attacks in discrete event systems and provides tools to ensure cyber security in supervisory control.

As the penetration of inverter-based resources (IBRs) increases in microgrids, they are increasingly expected to play a greater role in load frequency control (LFC). Model predictive control (MPC) is attractive for LFC because it incorporates system dynamics and operational constraints. However, most MPC-based LFC formulations rely on fixed reserve headroom based on forecasted renewable availability or storage systems. Under short-term renewable intermittency, IBR headroom is stochastic and time-varying, causing optimal control commands to exceed the physically deliverable regulation capability and cause stochastic saturation. This control-actuator mismatch degrades LFC performance. Accordingly, this study develops two headroom-aware strategies for PV-dominated microgrids. First, stochastic headroom constrained MPC (SHCMPC) incorporates headroom predictions through time-varying input constraints to enforce control feasibility. Second, stochastic adaptive MPC (SAMPC) embeds headroom awareness into the MPC objective function by adaptively penalizing control actions based on predicted headroom, reducing reliance on units with limited headroom without hard time-varying constraints. Simulation results show that stochastic saturation degrades conventional MPC-based LFC, particularly under tight reserve margins. Both strategies improve regulation performance. SHCMPC eliminates saturation events, while SAMPC achieves substantial saturation mitigation with lower computational effort, offering a computationally efficient alternative for real-time LFC under stochastic renewable availability.

We investigate deterministic and nonblocking supervisory control of discrete event systems under cyber-attacks using the ALTER (Attack Language for Transition-basEd Replacement) model. While prior works consider supervisory control that achieves either the large (upper bound) language or small (lower bound) language separately, deterministic supervisory control achieves both large language and small language at the same time to ensure that the language generated by the supervised system is unique and deterministic. We introduce two new concepts of CA-D-controllability and CA-D-observability and prove that they are necessary and sufficient for the existence of a deterministic supervisor. For nonblocking supervisory control, the objective is to ensure that the supervised system can always reach marked states under any attack scenario. We prove that relative closure, CA-D-controllability, and CA-D-observability together are necessary and sufficient for the existence of a nonblocking supervisor. We further develop methods to verify CA-D-controllability and CA-D-observability. We also illustrate our results using a robotic system example.

Anomaly detection is critical for ensuring the reliability of industrial cyber-physical systems. Identifying anomalies based on data distribution is regarded as a promising approach. However, inherent noise in data collection and complex dependencies within the underlying structure can lead to class ambiguity. This ambiguity obscures the boundary between normal data and anomalies, thereby degrading the accuracy of distribution modeling. To address this issue, we shift the distribution modeling from the data space to a latent space to mitigate ambiguity and then propose a label free anomaly detection network, named ALDM. In ALDM, a contrastive-based methods is designed to facilitate the construction of a latent space, where the margin between normal data and anomalies has been expanded. Anomalies are then discerned through embeddings using a flow-based process. Recognizing the importance of distance metrics in contrastive-based methods, we propose an adaptive approach to obtain the optimal distance metric during network training instead of presetting a fixed formula. Furthermore, to accommodate anomalies of varying durations, we propose another event-wise performance index for evaluation. Extensive evaluations on three widely used benchmarks and a newly constructed dataset demonstrate that ALDM achieves state-of-the-art detection performance across both conventional metrics and our proposed index. Note to Practitioners-Accurately detecting faults (anomalies) in industrial systems, such as factories or power grids, is vital to prevent costly downtime. However, noisy data and complex interactions make it challenging. Our solution, ALDM, tackles this by learning a clearer representation of system's data where normal and abnormal patterns are easier to distinguish. ALDM employs an adaptive projection to aid in anomaly detection, which minimizes expert tuning and enhances uptime. This approach improves detection accuracy, leading to enhanced system reliability and fewer unplanned stoppages. Although ALDM requires sufficient training data for reliable detection, its label-free methodology provides a practical solution for more robust monitoring in industrial systems.

Fuzzy Discrete Event Systems (FDES) extend traditional Discrete Event Systems (DES) by incorporating fuzzy logic to handle uncertainties and vagueness in system states and events. While supervisory control of FDES has been studied before, existing approaches often assume crisp distinctions between safe and unsafe states and do not consider target states. In this paper, we substantially extend the existing results in the literature by developing three new frameworks of supervisory control. First, we introduce a threshold-based safe-state supervisory control framework where fuzzy states are classified as partially safe or unsafe based on a user-defined threshold. Second, we propose a target-seeking supervisory control framework. The framework allows supervisors to select control actions that ensure the supervised system can always reach some target fuzzy states. Third, we propose a threshold-based target-seeking supervisory control framework, where target fuzzy states are subject to user-specified thresholds. For all these frameworks, we derive necessary and sufficient conditions for existence of the supervisors and develop algorithms for calculating them online. The proposed methods enhance the flexibility and applicability of FDES supervisory control, allowing complete or partial fulfillment of control targets while ensuring threshold-based safety and target seeking. This is particularly important in real-world scenarios such as medical treatment planning, where the goal is to maximize treatment effectiveness while minimizing adverse side effects.

For modular discrete-event systems (DES) with shared uncontrollable and/or unobservable events, the modular closed-loop systems depend on the chosen decomposition of a specification, which is not the case if all shared events are controllable and/or observable. In this paper, our approach is based on maximal conditional decompositions of global specification languages. Such maximal decompositions then lead to less restrictive modular synthesis compared to the use of minimal (projection-based) decompositions. Another application of maximal decompositions is the construction of larger coobservable sublanguages in decentralized supervisory control compared to previous approaches.

Constrained Markov decision processes (CMDPs) are widely used for sequential decision-making under safety or resource limits. Fundamental limitations of existing CMDPs include binary state representations and an all-or-nothing criterion for safety determination. These limitations restrict the applicability of CMDPs in domains such as healthcare, where system states (e.g., patient conditions) are inherently uncertain or ambiguous, and safety requirements (e.g., side effects) exist along a continuum and must be individualized at each decision point. Motivated by these challenges and building on our previously developed stochastic fuzzy discrete event systems (SFDESs) theory, we develop a fuzzy CMDP framework that integrates fuzzy state representations with membership-based, per-decision safety constraints. For each subject, user-specified lower and upper membership thresholds define a safe fuzzy system state set, from which corresponding safe action sets are derived. An ideal fuzzy-state CMDP would require policy synthesis over a continuous membership space and is therefore computationally intractable. To address this, we introduce a projection that preserves the stochastic semantics and CMDP structure, in which safe fuzzy system states are projected onto binary states, yielding a tractable reduced binary CMDP representation. We show that Bellman recursion and dynamic programming remain directly applicable in the reduced CMDP, while membership-based safety constraints continue to govern policy admissibility. The projection ensures that any optimal policy in the reduced CMDP satisfies the fuzzy state safety constraints. The resulting framework unifies fuzzy state evolution and constrained stochastic optimization, enabling graded, individualized, and state-dependent safety specifications that are not expressible within conventional CMDPs based on binary or probabilistic constraints. A numerical example illustrates the framework. The fuzzy CMDP framework reduces to an SFDES-based Markov decision process (MDP) framework when no safety constraints are imposed. Together, these frameworks provide a mathematically rigorous and tractable basis for individual-specific sequential decision-making, with particular relevance in applications where intersubject variability plays a critical role, such as personalized healthcare.

This paper investigates the problem of information control in networked multi-user systems, where agents such as robots, sensors, and software entities interact via a communication network to achieve individual or shared goals. Information control involves deciding which state estimates to share or broadcast, balancing cooperation among friends and privacy from adversaries. Since each user has only partial knowledge of the system, efficient protocols for sharing relevant data to balance privacy, security, and transparency is needed. This study models multi-user systems as discrete-event systems where agents need to distinguish certain state pairs in order to perform their tasks. We systematically study and solve critical problems to address the key aspects of information control: determining the necessity of shared information, minimizing communication for security, and maximizing public information release when required. A framework that addresses private communications, public broadcasting, and adversarial dynamics, offering strategies to meet both security and transparency requirements is introduced. Solutions and algorithms are proposed to solve these problems.

This paper studies design algorithms of output feedback controllers for Markovian randomly switched linear systems (RSLSs) with unobservable and uncontrollable subsystems. The design method must accommodate unobservable and uncontrollable subsystems that prevent controllers from achieving stability individually, and organize them into an integrated algorithm for stabilizing the overall system. Using the characteristics of Markov chains, the almost-sure stability of the design algorithms is analyzed and established. Combination of state estimation under unobservable subsystems and state feedback under uncontrollable subsystems introduces technical difficulties in state decomposition, observer design, controller design, and especially interactions among unobservable and uncontrollable subspaces under different subsystems. In particular, since the controllable sub-states and the observable sub-states are different at each subsystem, the controller may use the estimated states from an open-loop estimator in the current time interval, creating a technical complication and challenge in this study. Extending our previous results, this paper develops algorithms that simultaneously estimate and control the system, and proves convergence of state estimates and stability of the overall closed-loop stochastic systems. Numerical simulations demonstrate the effectiveness of the algorithms and main properties. A case study is also presented by using networked battery systems.

Contingencies can alter power-system dynamics and introduce prediction mismatch in model predictive control (MPC)-based load frequency control (LFC). Although such events may be detected or cleared by protection systems, the corresponding post-contingency dynamic model may not be available to the MPC controller on the LFC time scale. This paper proposes a contingency detection-integrated MPC (CDI-MPC) framework that combines disturbance-aware contingency detection with predictive frequency regulation. Contingencies are modeled as stochastic discrete events of a stochastic hybrid system (SHS), and a disturbance-aware residual formulation is developed to jointly identify the active mode and estimate unknown disturbances. The detected mode is then used to update the MPC prediction model, reducing contingency-induced prediction mismatch under changing operating conditions. Simulation results demonstrate accurate contingency detection and substantial improvements in closed-loop LFC performance under multiple contingency scenarios and unknown disturbances.

Recognizing complex events revealed by raw data is an increasingly crucial task that serves as one of the foundations for system monitoring and decision making. Our goal is to accurately recognize the occurred complex events, that is, uniquely determine the occurred complex event sequence from the raw data. We abstract the outputs of data sources as a set of atomic events, and then, use an automaton to describe all atomic event sequences that can be generated by the given system. We represent a complex event as a set of atomic event sequences. For a given atomic event sequence and a complex event to be recognized, we introduce the notion of "partition" to stand for a possible single complex event sequence. By constructing an augmented automaton that includes all possible partitions, we derive a necessary and sufficient condition for the complex event recognition problem to be solvable. We then find an algorithm to check the condition. When the complex event recognition problem is solvable, any occurred complex event can be determined accurately and promptly online with existing methods like the Aho-Corasick algorithm.

Networks have now been widely used in everyday life. Many engineering systems are networked systems that can be modeled as discrete event systems (DES). To control such systems, supervisory control of networked DES has been developed. Because of nondeterminism, the behavior of a controlled/supervised system is described by the upper bound (large) language and the lower bound (small) language. Therefore, three types of controls are needed: 1) control for the large language to ensure safety, 2) deterministic control (when the large and small languages are equal) to ensure nonblocking, and 3) control for the small language to ensure minimum requirements. Network observability, DL-observability, and network S-observability are needed to characterize the existences of three types of controls, respectively. In this article, algorithm to check network observability is developed for systems with observation losses. DL-observability is then defined. Algorithm to check DL-observability is also developed for systems with observation losses. The algorithm is of polynomial complexity. Finally, the relationship among three types of controls are investigated and proved.

Modern power systems (MPSs), including microgrids (MGs), are increasingly incorporating multiple renewable energy sources (RESs) such as wind and solar power, as well as battery storage and controllable loads. While environmentally beneficial, these sources pose challenges for control and management due to their intermittent and stochastic nature, especially in maintaining frequency stability with multiple interconnected generators of varying capacities. Traditional droop control methods are effective in systems with generators that are dispatchable and have fixed generation capacities, but they fall short when applied to systems with RESs, where generation capacities are dynamic and affected by unpredictable environmental conditions. To address these challenges, this paper introduces a novel stochastic adaptive droop control (SADC) method for load frequency control (LFC). The proposed method adapts droop coefficients in real time, based on the measured stochastic data of power generation capacities, enabling more effective frequency regulation in systems with variable and intermittent power generation. Unlike traditional adaptive control methods, which assume constant or slowly-varying system parameters, this approach accounts for stochastic processes by modeling them as Markov chains, enabling robust performance under highly dynamic and unpredictable conditions. The key contributions of this work include the development of real-time droop coefficient adaptation algorithms, derivation of their stability and convergence properties, and the demonstration of the advantages of the method through simulations. Case studies highlight the improved performance of frequency regulation, particularly in addressing the impact of stochastic weather conditions and the benefits of reducing dependence on battery reserves in dealing with intermittency of RESs. This paper provides a comprehensive analysis of the theoretical foundations of the method, as well as practical implementation insights for future power systems with high penetration of RESs.

To address the challenges of excessive feature parameter redundancy and insufficient scene correlation in terahertz (THz) channel scenario recognition, a recognition algorithm integrating the minimal redundancy maximal relevance (mRMR) criterion with genetic algorithm (GA) optimization was constructed based on feature selection theory and evolutionary computation principles. The crossover and mutation operations of channel characteristics were executed by the genetic algorithm (GA), and the optimal feature parameters with high scenario relevance were selected using the minimum redundancy maximum relevance (mRMR) criterion. These parameters were then inputed into a backpropagation neural network model. To validate the method, a dataset containing 12 channel features was constructed with 1 745 groups of terahertz channel simulation data collected from indoor scenarios, and the model was trained and rigorously validated based on this dataset. The results demonstrate that the proposed algorithm improves accuracy and efficiency by 8% and 38.8%, respectively, and outperforms traditional algorithms in terms of convergence and transfer generalization capabilities.

In process industries, dynamic uncertainties necessitate that experienced operators adjust process parameters. This paper tries to mine the decision knowledge of operators and proposes an artificial knowledge-based (AKB) decision approach for process parameter optimization. The methodology comprises three functionally interdependent stages: Data preprocessing, quality prediction, and AKB decision modeling. Data preprocessing includes outlier processing which adopts a sliding-window-based iForest method to detect the outlier caused by batch changeover and data alignment which aligns delayed quality indicators with process variables based on a dynamic-window-based distance correlation. Quality prediction uses temporal convolutional networks with feature processing and temporal attention mechanisms (FP-TCN-TA) to reconstruct the operators' realtime quality assessment references. AKB decision modeling combines one-dimensional convolutional neural network (1D-CNN) for local parameter feature extraction within finite time steps and multilayer perceptron (MLP) for nonlinear adjustment mapping. It emulates operators' decision logic. A case study using real-world operating data collected from the tire tread extrusion line demonstrates the approach's capability to replicate operator decisions for process parameter optimization. The effectiveness of each methodological component is also confirmed by experiments.

Modern power systems (MPSs) face significant challenges due to the high penetration of renewable energy sources (RESs) and new types of loads such as electric vehicles (EVs). Traditional load frequency control (LFC) methods struggle with the intermittent, stochastic nature of RESs, the near-zero inertia of power-electronics-based generators, and the mobility of controllable loads and battery systems. This paper introduces a novel resilient distributed frequency regulation method to address these issues. The proposed method employs a state space model to represent the dynamic behavior of participating power sources while accounting for stochastic switching processes to model structural and parameter variations caused by disruptions such as generator connection/disconnection, communication interruptions, and physical faults. By integrating these dynamic and stochastic components, the method treats power grids as a comprehensive stochastic hybrid system. Our method enhances conventional frequency control by incorporating local stability control, neighborhood control decoupling, and coordination feed-back. Theoretical analyses establish the stability, convergence, and resilience of the proposed method, and its effectiveness is validated through case studies.

This paper presents a general framework for joint opacity of discrete-event systems under partial observation. It discusses a class of state-estimate-intersection-based (SEI-based) intrusions that existing opacity conditions cannot prevent. The paper provides a procedure to verify the opacity of a system against such SEI-based intrusions. The results are formally verified by Isabelle/HOL. (c) 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

A rapid and efficient method for synthesizing 1-[(3-aryl-5-isoxazolyl)methyl]-2-aryl-1H-benzimidazole was developed using 3-aryl-5-bromomethyl isoxazole and 2-substituted benzimidazoles as raw materials, which could be expanded to a wide range of benzimidazoles in moderate to excellent yields. Halo and hetero functional groups as well as alkyl groups were tolerated in this transformation. The antimycobacterial activity of all synthesized compounds were tested using rifampicin as a positive control. Some compounds exhibited moderate to good tuberculostatic activities against Mycobacteria smegmatis MC2155 with MIC values ranging from 64.00 to 128.00 µg/mL, providing lead compound for the subsequent development of anti-tuberculosis drugs.

This letter proposes a new framework to capture detectability property in stochastic discrete-event systems. A new notion, name Partition-based Detectability or P-Detectability, is proposed based on partitions of the system state space, rather than the state space itself. In other words, the proposed P-Detectability focuses on the system capability to detect certain state group from other state groups, while ignoring the ambiguity between individual states within the same state group. As a consequence, the proposed P-Detectability allows users to define customized public and cover to ignore irrelevant ambiguity. Compared to existing notions such as A-Detectability and A-Diagnosabiltiy, the proposed notion is shown to be more general. A necessary and sufficient condition to verify P-Detectability, together with a testing algorithm, are developed.