This paper addresses the problem of asynchronous finite-time H 2 /H ∞ control for discrete-time Markovian jump systems with parameter uncertainties. The asynchronous behaviors between the system modes and controller modes are fully taken into account. A novel definition of robust asynchronous finite-time boundedness is proposed and a hidden Markovian model is used to characterize the controller-system mode mismatch. Sufficient conditions for the existence of a state feedback controller are derived via the Lyapunov-Krasovskii functional and linear matrix inequality approach. The designed controller simultaneously achieves finite-time boundedness, H ∞ disturbance attenuation and H 2 cost minimization. To illustrate the theoretical results, a practical pest population control application is given.

Accurate cell image segmentation is fundamental to disease research, drug discovery, and quantitative clinical decision-making. Yet many U-shaped architectures still struggle when cells present indistinct boundaries, dense spatial arrangements, and pronounced scale variability. Under these conditions, fine structural details are often lost, and feature interactions between shallow and deep layers remain insufficient, which limits the reliability of downstream analysis. To mitigate these issues, this study introduces the Dual-Encoder Bi-Level Attention U-KAN (DEBi-UKAN), a coordinated segmentation framework for complex cell images. The proposed model employs a dual-encoder design in which a convolutional branch emphasizes local textures and boundary cues, while a MaxViT branch models long-range semantic context. Their complementary representations improve the characterization of complex cellular morphology, particularly in regions with weak contrast or severe crowding. Building on this representation, a tokenized KAN component enhances nonlinear function approximation and strengthens deep feature modeling in the high-level layers. On this basis, a feature fusion module is constructed. It combines direction-aware attention, residual convolutional refinement, and multibranch channel recalibration to enhance cross-scale feature alignment, reduce information loss during downsampling, and preserve both edge and interior details. At the bottleneck, bi-level routing attention is used to selectively emphasize informative regions and strengthen long-range semantic dependencies. This design promotes more coherent contour delineation and improves robustness under challenging imaging conditions. Extensive quantitative evaluations show that DEBi-UKAN achieves consistently higher scores than several representative segmentation baselines across multiple metrics, with clear gains in scenarios characterized by low contrast and densely packed cells. These results indicate that DEBi-UKAN provides a stable and accurate framework for high-throughput cell image analysis and offers a practical methodological basis for intelligent cell imaging applications.

This paper mainly addresses the finite-time mixed H2/H infinity control issue of stochastic T-S fuzzy networked control systems with semi-Markov switching by constructing a dynamic output feedback controller (DOFC). Firstly, a novel criterion of finite-time stability (FTS) is defined for stochastic systems, and by employing linear matrix inequality (LMI) and the free-weighting matrix methods, sufficient conditions for the system to satisfy this definition are given. Then, a new Lyapunov-Krasovskii functional (LKF) associated with the sojourn time is constructed for semi-Markov jump systems (semi-MJSs), this function encompasses the entire Markov switching process and augments the practical applicability of the results. Furthermore, considering the transition rates (TRs) matrix of semi-MJSs is time-varying, we suppose that the TRs matrix belongs to a bounded yet unfixed polytope to overcome the computational challenges induced by time-varying TRs. Next, in order to reduce communication burden of stochastic networked semi-MJSs and save network resources, the event-triggered mechanism (ETM) and the quantized output strategy are adopted simultaneously. Finally, a solution algorithm is proposed based on the obtained conditions, and the feasibility of this work is verified through the practical application of a mass-spring-damper mechanical system (MSDMS) and a comparative example.

This paper investigates the problem of finite-time H_2/H_∞ static output feedback (SOF) control for discrete-time stochastic Markovian jump systems (MJSs) subject to parametric uncertainties. Initially, by constructing a Lyapunov–Krasovskii functional and fully accounting for the characteristics of MJSs, new sufficient conditions for the existence of a robust finite-time H_2/H_∞ controller for closed-loop system (C-LS) are derived. Subsequently, a SOF finite-time H_2/H_∞ controller is designed using a novel linear matrix inequality approach. This controller not only ensures robust H_2/H_∞ finite-time boundedness but also aims to minimize the upper bound of the H_2 performance index for the C-LS. Three case studies–two numerical simulations and a pest population control application–are provided to validate the effectiveness and practicality of the proposed approach.

Slow-fast stochastic differential equations (SDEs) are extensively utilized in the mathematical modeling of dynamical systems exhibiting multiple time scales. Due to a larger sampling interval tailored to the slow time scale, it is challenging to discover the slow-fast SDEs from data, particularly when internal or external noise is present. This paper proposes a data-driven modeling method to learn the slow-fast SDEs from observation data. The proposed method first introduces a data binning strategy to preprocess the data, which are measured in different time scales, while the Kramers-Moyal expansion theory is leveraged to construct the time sequence describing the drift and diffusion terms in SDEs. Then, a large candidate library is created to represent the slow-fast dynamics of SDEs and a sparse Bayesian learning algorithm is proposed to solve the resulting sparse regression problem of inferring the active feature terms in the drift and diffusion terms and their corresponding parameters from data. Finally, several numerical experiments are provided to illustrate the effectiveness and feasibility of the proposed method for inferring slow-fast SDEs from data. As a consequence, it is observed that the data binning facilitates mitigating the imbalance between the fast component and the slow one.

The finite-time boundedness (FTB) of closed-loop systems with $H_{2}$ gain constraints for discrete-time linear timevarying random Markov Jump Systems (MJSs) is studied in this paper. First, finite-time bounded criteria for linear stochastic systems and the definition of $H_{2}$ gain are given. Secondly, sufficient conditions for a time-bounded closed-loop system to satisfy the $H_{2}$ gain constraint are obtained by using the Lyapunov functional and Linear matrix inequality (LMI) and designing state feedback controllers that satisfy the conditions. In conclusion, a numerical example is presented to demonstrate the feasibility of the proposed method.

In recent years, research on knowledge graphs (KGs) has exploded due to their capability of effective organization and representation of massive heterogeneous data. However, existing KGs are often incomplete and contain incorrect triples, which easily incurs a negative impact on the performance of downstream tasks. Besides, few works have been done on the construction of ship KGs. For the former, the mainstream solution is link prediction, also known as KG completion. To this end, we propose a novel method of KG embeddings (KGE) for link prediction, called QuatGAT, combining the graph attention mechanism with quaternion embeddings. Specifically, the multihead attention mechanism is employed first to obtain entity embeddings by capturing features of both entities and relations of the neighborhood. Then, to represent relations more sufficiently, we use quaternion embeddings to explicitly represent relations as well as entities. Experimental results on benchmark datasets FB15k and FB15k-237 demonstrate the superiority of QuatGAT over existing state-of-the-art methods. Moreover, in terms of ship KG construction, we also built a multimodal ship KG named MSKG. Likewise, experimental results on this dataset verify the effectiveness of QuatGAT.

This paper concentrates on the finite-time H∞ control problem for a type of stochastic discrete-time Markovian jump systems, characterized by time-delay and partly unknown transition probabilities. Initially, a stochastic finite-time (SFT) H∞ state feedback controller and an SFT H∞ observer-based state feedback controller are constructed to realize the closed-loop control of systems. Then, based on the Lyapunov–Krasovskii functional (LKF) method, some sufficient conditions are established to guarantee that closed-loop systems (CLSs) satisfy SFT boundedness and SFT H∞ boundedness. Furthermore, the controller gains are obtained with the use of the linear matrix inequality (LMI) approach. In the end, numerical examples reveal the reasonableness and effectiveness of the proposed designing schemes.

The robust finite-time H 2 / H infinity control problems for linear discrete-time stochastic Markovian jump systems (MJSs) with external disturbance are investigated. Firstly, the definitions of robust finite-time boundedness, and robust finite-time H 2 / H infinity boundedness are presented. Subsequently, by using the stochastic Lyapunov-Krasovskii functional method and linear matrix inequality technique, sufficient conditions for the existence of a robust finite-time H 2 / H infinity controller are provided for stochastic MJSs. A state feedback controller is designed. Furthermore, the finite-time guaranteed cost bounds are given. More specifically, the designed controller not only ensures the finite-time H infinity bounded but also makes the H 2 cost function less than the bound given by the performance index of the systems. Finally, two examples are given to illustrate the validity of the proposed approach.

The adaptive fuzzy backstepping control problem is studied for Itô-type nonlinear switched systems subject to unknown hysteresis input. Compared with existing works, the unknown hysteresis and stochastic disturbances are considered in the pure-feedback switched systems. The mean value theorem tackles the non-affine functions. The backstepping technique introduces an auxiliary virtual controller. In addition, the Nussbaum function is employed to solve the difficulty caused by the unknown hysteresis under arbitrary switching. Based on a fuzzy logic system and backstepping technique, a new adaptive control proposal is obtained, which ensures that the system states satisfy semiglobally uniformly ultimately bounded (SGUUB) in probability and that the tracking error converges to a region of the origin. Finally, we provide two examples to show the validity of the presented scheme.

This paper investigates the event-triggered finite-time state and output feedback H_2/H_∞ control problem for discrete-time stochastic mean-field systems (MFSs). Firstly, for a class of MFSs with external interference signals and stochastic white noise, the H_2 and H_∞ performance functions are given, which contain some mathematical expectation terms. In order to reduce communication burden and network congestion, a novel event-triggered mechanism (ETM) is constructed, which has a minimum triggering interval to effectively avoid Zeno behavior. Next, by constructing two novel Lyapunov–Krasovskii functionals (LKFs), some sufficient conditions for stochastic MFSs to satisfy stochastic finite-time (SFT) H_2/H_∞ boundedness are established, and the minimum H_2 performance index upper bound of the system is found. Then, the control gains for the two designed controllers and the event-triggered strategy parameters are obtained by using the linear matrix inequality (LMI) technique. Finally, the effectiveness of the proposed approach is validated through the performance of the designed controllers in a multi-particle system with 2000 interacting particles and a stochastic mean-field system.

A design proposal of finite -time H-infinity controller for stochastic mean -field systems (MFSs), which are described by a It & ocirc; type differential equation, is presented in this paper. Firstly, based on H-infinity control theory and finite -time control theory, three sufficient conditions, which ensure the MFS satisfies stochastic H-infinity finite -time state feedback stabilization, are gained. Secondly, by decoupling correlation matrix inequalities, the state feedback controller (SFC) gains are obtained. Finally, the simulation results demonstrate that the large system of interacting particles can be approximated by MFS under certain conditions, and the controller design schemes proposed are feasible.

In this essay, the finite-time annular domain $H_{\infty}$ control issue is studied for discrete-time Markov jump systems (DTMJSs) with time-delay, using state feedback controller. Firstly, by employing Lyapunov-Krasovskii function and linear matrix inequality (LMI) technique, a sufficient condition is obtained to ensure that the closed-loop system is finite-time annular domain bounded (FTADB) and meets a certain disturbance attenuation index. Furthermore, the state feedback controller gains are presented via LMIs. Lastly, the feasibility of the designed method is confirmed via a numerical illustration.

This article presents an annular finite-time H_∞ filtering approach for continuous-time mean-field stochastic systems (MFSSs). Our attention is focused on obtaining a set of stability criteria for analyzing the H_∞ performance and the annular finite-time boundedness of the filtering error system. Sufficient conditions in the form of linear matrix inequality (LMI) are established to guarantee the existence of the designed filter. Then, the filter gains are derived through a convex optimization problem. Through a simulation example, the validity of the obtained results is demonstrated.

In this article, the observer-based finite-time H_∞ control (OFTHC) issue is considered for a category of nonlinear discrete-time Markovian jump systems (DMJSs) with multiplicative noises and time-delay. The nonlinear object is described via linear Takagi Sugeno (T–S) fuzzy model (TSFM). State feedback is an effective method to realize system control, which requires all states to be measurable. In the presence of the system state is not fully measurable, an observer-based state feedback controller (OSFC) represented by TSFM is designed via the parallel distributed compensation (PDC) method. By applying Lyapunov functional and linear matrix inequality (LMI) methods, sufficient criteria are given, which guarantee the resulting closed-loop systems (CLSs) finite-time boundedness (FTB) with a specified H_∞ attenuation level. Finally, numerical simulations are employed to prove the feasibility of the developed techniques.

Emergency evacuation is a problem of great concern at home and abroad. Aiming at the emergency evacuation problem of personnel, this paper uses MATLAB, Pathfinder and other software to establish an evacuation model based on cellular automata to simulate the different dynamic behaviors that the crowd may exhibit during evacuation under different situations and parameters. In this paper, firstly, several factors affecting the anxiety of the crowd are determined, and three key factors affecting the degree of anxiety of the personnel are screened out by the analytic hierarchy process: the distance from the safe exit, the number of personnel and the emotional infection. Then, a personnel evacuation model based on cellular automata is established, and the influence of the degree of anxiety on the length of evacuation time is analyzed under the influence of each key factor. Finally, by changing the key parameters, the experimental results are affected. Finally, the key parameters that cause the difference in evacuation effect are analyzed.

This article presents a framework for annular finite‐time H∞$$ {H}_{\infty } $$ filtering problem for discrete‐time mean‐field stochastic systems with state‐ and disturbance‐dependent noise. Both static and dynamic event‐triggered mechanisms are introduced, which are related to two different thresholds and internal variables, respectively. Based on mean‐field theory and the Lyapunov functional method, two groups of sufficient conditions are derived to ensure the filtering error systems have annular finite‐time boundedness under an H∞$$ {H}_{\infty } $$ performance level. Then, the desired filter gains can be obtained by solving two convex optimization problems. Finally, two numerical examples are given to show the validity of the proposed results.

This paper deals with the problems of finite-time boundedness (FTB) and H∞ FTB for time-delay Markovian jump systems with a partially unknown transition rate. First of all, sufficient conditions are provided, ensuring the FTB and H∞ FTB of systems given by linear matrix inequalities (LMIs). A new type of partially delay-dependent controller (PDDC) is designed so that the resulting closed-loop systems are finite-time bounded and satisfy a given H∞ disturbance attenuation level. The PDDC contains both non-time-delay and time-delay states, though not happening at the same time, which is related to the probability distribution of the Bernoulli variable. Furthermore, the PDDC is extended to two other cases; one does not contain the Bernoulli variable, and the other experiences a disordering phenomenon. Finally, three numerical examples are used to show the effectiveness of the proposed approaches.

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

Identifying cancer driver genes is an important task in cancer research. Numerous methods that identify these driver genes have been proposed, most of which identify driver genes in entire patient cohorts. However, research has shown that cancers in different patients may be caused by different driver genes. Therefore, if we pay more attention to the individual level to identify personalized driver genes, patients can receive precise treatment and achieve better treatment results. Among the methods for identifying personalized cancer drivers, most of these methods only identify the coding driver genes, however, non-coding cancer drivers are also crucial for the initialization and development of cancer. Therefore, we develop an approach to identify coding drivers and non-coding drivers for individual patients named PerVote. In PerVote, the personalized network is firstly constructed based on expression data of mRNAs and miRNAs, then identifies cancer drivers by a voting approach in the network. To verify the performance of our method, we use five cancer datasets of TCGA and compare it with the state-of-the-art methods. The results show that PerVote outperforms other methods. Our method also predicts and prioritizes miRNA drivers, most of which are confirmed by OncomiR to be related to tumorigenesis. Therefore, PerVote can bring further help to the personalized treatment of cancer patients and is an effective method for the identification of cancer drivers.