In this article, a novel hybrid event-triggered (ET) control framework is constructed based on the adaptive critic technique, aiming to address the optimal tracking issue of discrete-time nonlinear networked control systems. First, an augmented plant is created by combining the system state with the reference trajectory, transforming the optimal tracking control design into the optimal regulation problem of the reconstructed nonlinear error system. Subsequently, to conserve communication network resources and ensure the stability of the error system, a hybrid ET mechanism is developed to determine a constant interval for event silence. This approach not only alleviates the limited network bandwidth but also eliminates the need for continuous evaluation of triggering conditions, as seen in traditional event-based methods. Regarding algorithm implementation, the model, critic, and action networks are established to execute the online adaptive critic algorithm, which allows the tracking control policy to be adjusted in real-time to reach the optimal level. Finally, an experimental plant with nonlinear characteristics is presented to illustrate the overall performance of the proposed online tracking control method with the hybrid ET mechanism.

Whole-body PET tumor segmentation remains challenging due to limited training data and substantial tumor heterogeneity, which impact the segmentation accuracy and the clinical utility. Tumor distribution information is usually contained in patient medical records and routinely utilized in medical image interpretation, which can also be used to improve the segmentation accuracy. This study introduces a novel 3D PET/MR tumor segmentation framework which integrates tumor distribution priors extracted from medical records. The proposed Tumor Localization Priors(TLP) are generated based on medical records using Large Language Models (LLMs) and organ localization based on MRI. Furthermore, the Region-Aware Fusion Module (RAFM) is designed to fuse TLP and encoded PET information through attention within the Anatomically-Consistent Multitask Model(ACMM). Moreover, Anatomical Consistency Loss(AC Loss) is introduced, integrating tumor localization and its anatomical distribution to enhance segmentation performance. Our method achieves an 9.30% Dice improvement over the baseline nnU-Net v2, with particularly notable 23.06% gains in precision while maintaining high recall (+6.19%). Clinical evaluations confirm superior detection of both primary and metastatic lesions, alongside reduced physiological uptake artifacts.

In this article, a novel online self-triggered transmission control (STTC) framework is constructed based on the critic learning technique, which aims at tackling the optimal regulation issue of discrete-time nonlinear systems. On the premise of ensuring the system stability, a self-sampling function is designed only related to the sampling state, so that the next triggering moment can be determined. This not only effectively reduces the computational burden, but also avoids continuous judgment for the triggering condition similar to traditional event-based methods. Furthermore, the developed control method can be found to possess excellent triggering performance through theoretical analysis. Then, the model, critic, and action networks are established to execute the online critic learning algorithm, which make the control policy is adjusted in real-time to the optimal level. Finally, an experimental plant with nonlinear characteristics is given to illustrate the overall performance of the proposed online STTC method.

This article presents two new event-triggered control (ETC) schemes based on the online critic learning technique, which aims at tackling the optimal regulation problem of discrete-time constrained nonlinear systems with the disturbance input. First, a novel stability criterion condition is designed to obtain an initial admissible policy pair by using an offline iterative method under the time-triggered control framework. Then, starting from the stability of the constrained system, a nonperiodic ETC method and a periodic ETC method are developed by adopting an online learning algorithm. In addition, four kinds of neural networks are constructed for the implementation of the event-based online$H_{\infty }$optimal control strategy. Finally, two experimental examples with physical backgrounds are provided to illustrate the effectiveness and superiority of the developed schemes.

Parallelization is widely employed to improve the exploration ability of controllers. However, it is rare to provide a lightweight scheme for reducing homogeneous policies with theoretical guarantees. This article is concerned with a novel parallel scheme for solving optimal control problems. In brief, we design a novel global indicator that inherits the theoretical guarantees of a class of iterative reinforcement learning algorithms. By generating a tentative function, the global indicator can guide and communicate with parallel controllers to accelerate the learning process. Using two typical exploration policies, the novel parallel scheme can rapidly compress the neighborhood of the optimal cost function. Besides, two parallel algorithms based on value iteration and Q-learning are established to improve the data efficiency through different extensions. Finally, two benchmark problems are presented to demonstrate the learning effectiveness of the novel parallel scheme.

BACKGROUND: The experimental verification of a drug discovery process is expensive and time-consuming. Therefore, efficiently and effectively identifying drug-target interactions (DTIs) has been the focus of research. At present, many machine learning algorithms are used for predicting DTIs. The key idea is to train the classifier using an existing DTI to predict a new or unknown DTI. However, there are various challenges, such as class imbalance and the parameter optimization of many classifiers, that need to be solved before an optimal DTI model is developed. METHODS: In this study, we propose a framework called SSELM-neg for DTI prediction, in which we use a screening approach to choose high-quality negative samples and a spherical search approach to optimize the parameters of the extreme learning machine. RESULTS: The results demonstrated that the proposed technique outperformed other state-of-the-art methods in 10-fold cross-validation experiments in terms of the area under the receiver operating characteristic curve (0.986, 0.993, 0.988, and 0.969) and AUPR (0.982, 0.991, 0.982, and 0.946) for the enzyme dataset, G-protein coupled receptor dataset, ion channel dataset, and nuclear receptor dataset, respectively. CONCLUSION: The screening approach produced high-quality negative samples with the same number of positive samples, which solved the class imbalance problem. We optimized an extreme learning machine using a spherical search approach to identify DTIs. Therefore, our models performed better than other state-of-the-art methods.

In this article, an event-based near-optimal tracking control algorithm is developed for a class of nonaffine systems. First, in order to gain the tracking control strategy, the costate function is established through the iterative dual heuristic dynamic programming (DHP) algorithm. Then, the event-based control method is employed to improve the utilization efficiency of resources and ensure that the closed-loop system has an excellent control performance. Meanwhile, the input-to-state stability (ISS) is proven for the event-based tracking plant. In addition, three kinds of neural networks are used in the event-based DHP algorithm, which aims to identify the nonaffine nonlinear system, estimate the costate function, and approximate the tracking control law. Finally, a numerical experimental simulation is conducted to verify the effectiveness of the proposed scheme. Moreover, in order to further validate the feasibility, the algorithm is applied to the wastewater treatment plant to effectively control the concentrations of dissolved oxygen and nitrate nitrogen.

In this article, through adaptive critic, a dual event-triggered (DET) constrained control scheme is established for discrete-time nonlinear zero-sum games. The neural networks are trained from the dual heuristic dynamic programming technique to obtain the approximate optimal policy pair. Two corresponding independent triggering conditions are constructed for the control input and the disturbance to improve the utilization efficiency and ensure the independence between them. In addition, in order to overcome the challenge caused by the actuator saturation, we constrain the control input to a bounded range. Meanwhile, the asymptotically stability is proved for the DET control system. Finally, experimental simulations are conducted to verify the effectiveness of the proposed algorithm.

The existing, semisupervised, spectral clustering approaches have two major drawbacks, i.e., either they cannot cope with multiple categories of supervision or they sometimes exhibit unstable effectiveness. To address these issues, two normalized affinity and penalty jointly constrained spectral clustering frameworks as well as their corresponding algorithms, referred to as type-I affinity and penalty jointly constrained spectral clustering (TI-APJCSC) and type-II affinity and penalty jointly constrained spectral clustering (TII-APJCSC), respectively, are proposed in this paper. TI refers to type-I and TII to type-II. The significance of this paper is fourfold. First, benefiting from the distinctive affinity and penalty jointly constrained strategies, both TI-APJCSC and TII-APJCSC are substantially more effective than the existing methods. Second, both TI-APJCSC and TII-APJCSC are fully compatible with the three well-known categories of supervision, i.e., class labels, pairwise constraints, and grouping information. Third, owing to the delicate framework normalization, both TI-APJCSC and TII-APJCSC are quite flexible. With a simple tradeoff factor varying in the small fixed interval (0, 1], they can self-adapt to any semisupervised scenario. Finally, both TI-APJCSC and TII-APJCSC demonstrate strong robustness, not only to the number of pairwise constraints but also to the parameter for affinity measurement. As such, the novel TI-APJCSC and TII-APJCSC algorithms are very practical for medium- and small-scale semisupervised data sets. The experimental studies thoroughly evaluated and demonstrated these advantages on both synthetic and real-life semisupervised data sets.

The classical maximum entropy clustering (MEC) algorithm usually cannot achieve satisfactory results in the situations where the data is insufficient, incomplete, or distorted. To address this problem, inspired by transfer learning, the specific cluster prototypes and fuzzy memberships jointly leveraged (CPM-JL) framework for cross-domain MEC (CDMEC) is firstly devised in this paper, and then the corresponding algorithm referred to as CPM-JL-CDMEC and the dedicated validity index named fuzzy memberships-based cross-domain difference measurement (FM-CDDM) are concurrently proposed. In general, the contributions of this paper are fourfold: 1) benefiting from the delicate CPM-JL framework, CPM-JL-CDMEC features high-clustering effectiveness and robustness even in some complex data situations; 2) the reliability of FM-CDDM has been demonstrated to be close to well-established external criteria, e.g., normalized mutual information and rand index, and it does not require additional label information. Hence, using FM-CDDM as a dedicated validity index significantly enhances the applicability of CPM-JL-CDMEC under realistic scenarios; 3) the performance of CPM-JL-CDMEC is generally better than, at least equal to, that of MEC because CPM-JL-CDMEC can degenerate into the standard MEC algorithm after adopting the proper parameters, and which avoids the issue of negative transfer; and 4) in order to maximize privacy protection, CPM-JL-CDMEC employs the known cluster prototypes and their associated fuzzy memberships rather than the raw data in the source domain as prior knowledge. The experimental studies thoroughly evaluated and demonstrated these advantages on both synthetic and real-life transfer datasets.