publicly available.High Dynamic Range (HDR) imaging aims to reconstruct scenes with a wide range of luminance by fusing multi-exposure Low Dynamic Range (LDR) images. In dynamic scenes with pronounced foreground motion or camera jitter, especially under challenging conditions including extremely low or high luminance, widespread saturation, and substantial motion, existing approaches often encounter ghosting artifacts, spatial misalignment, and degradation of fine structural details. Traditional techniques based on handcrafted priors struggle to generalize to complex motion patterns, while most deep learning-based methods operate exclusively in the spatial domain, limiting their ability to capture global contextual cues and restore high-frequency structures that are better represented in the frequency domain. To address these challenges, we introduce a Dual-Domain Parallel Fusion Network with Prompt Refinement (DDPF-PR), which jointly leverages spatial and frequency-domain features for enhanced HDR reconstruction. Specifically, the proposed framework consists of a Bi-Domain Interaction Module(BDIM), which integrates spatial features for local detail and frequency features for global structure to suppress ghosting artifacts caused by motion. In addition, a Prompt Refinement Module(PRM) is designed to recover fine details in degraded regions such as saturated or misaligned areas by adaptively generating structural cues. Extensive experiments demonstrate that DDPF-PR consistently outperforms state-of-the-art methods across multiple benchmarks in both qualitative and quantitative evaluations. The code will be made publicly available.

The forwarding behavior of social media users within social circles facilitates intensive discussions of specific social events in cyberspace, significantly contributing to the dissemination and development of public opinions. Existing models for calculating the popularity of public opinion (PPO) overlook the effects of forwarding behavior. This article addresses this gap with two primary objectives: 1) by developing a calculation model for PPO that integrates the forwarding dynamics within social networks; and 2) by establishing a predictive model that is applied to the temporal evolution of forwarding circles, thus enabling a time-series prediction for PPO. The approach commenced by determining the information entropy based on the structural attributes of forwarding circles. Then, we assess the similarity between information entropy production and the Baidu search index to validate the calculation model’s accuracy. Building on this foundation, public opinion data centered around 30 social events with a total sample size of 15.567 million blogs were collected for modeling. Finally, we design a deep learning algorithm to predict the PPO trend. The results demonstrate that the information entropy of forwarding circles accurately represents PPO, and the proposed predictive model can capture the time-series evolution trend of PPO on social media. These findings offer valuable insights into public opinion analysis and present a robust method for academics and social media practitioners.

In this article, the optimal control for a nonlinear affine system under aperiodic Denial-of-service (DoS) attacks is investigated. To solve the Hamilton–Jacobi–Bellman (HJB) equation, an adaptive dynamic programming (ADP) algorithm based on a single critic network is developed. The proposed regulative learning rate strategy outperforms traditional fixed-rate gradient descent approaches found in existing works. With the objective of minimizing the performance index, the optimal value function and the optimal controller are derived from the approximate solution of the HJB equation. To alleviate the resource demand and enhance the flexibility of the threshold function, an adaptive event-triggered (AET) scheme integrating the idea of sampling control and event-triggered strategy is applied to the optimal control initially. Compared with the static event-triggered strategy, the AET method contains increasing engineering value. A piecewise Lyapunov function is constructed based on optimal value function, estimated error introduced by neural network (NN) weight, and the classic Lyapunov-Krasovskii function. Thus, uniform ultimate boundedness for tracking error is proven theoretically. Moreover, the maximum tolerable strength of cyberattacks is provided from the stability analysis. The simulation results exhibit the designed approach’s reliability.

Abstract This paper is focused on the consensus problem of multi-agent systems via uncertain pinning control under switching topologies. The stochastic disturbances and randomly occurring nonlinearities are proposed to describe more realistic systems. The communication topology is modeled by a directed graph and it is divided into two cases, the consensus problem is discussed in these two cases. In addition, there exist some uncertain pinning connections between the followers and leader due to switching topologies, the distributed control protocol is designed to satisfy the follower asymptotically converge to the leader. By constructing suitable multiple Lyapunov functions and utilizing tools of M -matrix theory, some sufficient consensus criteria are deduced to reach this goal. Finally, two examples are given to verify the correctness of the proposed method.

Mild cognitive impairment (MCI) is the early stage of Alzheimer's disease, which is associated with abnormal brain proteins, the recognition of MCI being a challenging task. Recent studies have shown that the performance of MCI identification can be improved by combining protein features captured in Positron Emission Computed Tomography(PET). Nevertheless, there are still great challenges in extracting effective features from the vast amount of information. Most brain networks only considered the unilateral features of nodes or edges, ignored the interactions between them. In response to this problem, our study proposed to combine the quadruple Siamese network and GCN with self-attention pooling(QS-SAGCN) for MCI identification. In detail, we constructed the multiple protein features network(MPN) and higher-order MPN(MPHN) by PET images to promote the MCI identification. Furthermore, a pooling operation with self-attention mechanism was incorporated into GCN(SAGCN), which considered the node characteristics and topology in the graph network to facilitate the acquisition of robust biomarkers, simultaneously. Additionally we combined quadruple Siamese network with SAGCN as classification framework to improve the identification accuracy. Our proposed MCI identification method was evaluated on 230 subjects (including 117 MCI subjects, 113 normal control subjects) with both 18F-AV-1451 PET and 18F-AV-45 PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Experimental results showed that 1) QS-SAGCN enhanced the ability of feature identification, laying the foundation for obtaining more effective biomarkers for MCI patients; 2) The MCI identification accuracy (93.5%) was obtained by combining QS-SAGCN and higher-order network, indicating that the framework had advantages in mental disorders recognition. Finally, through comparison, the accuracy of our proposed MCI recognition method was superior to some of the existing state-of-the-art methods. Overall, the MCI identification method in this study was effective and promising to assist in the diagnosis of MCI in future clinical practice.

In this article, we consider multiparty synchronization (MS) for coupled memristive neural networks (CMNNs) with a time delay. Some Takagi–Sugeno typeif–then fuzzy rules are also introduced into the CMNNs. An event-triggered controller (ETC) is designed to achieve the MS in finite time, which avoids continuous control signals. Along with Lyapunov theory, differential inclusion theory, and inequalities, some criteria can be obtained to achieve the finite-time MS (FTMS), and the setting time (ST) of the FTMS can be calculated. By jointly considering ST, control inputs, error networks, and synchronization conditions, an optimization model is provided to get an optimal ETC (OETC). Further, the particle swarm optimization algorithm is utilized to solve the optimization model. Thus, it gives a method to choose control gains and parameters of an event-triggered function. Finally, two examples are given to verify the theoretical results. Especially, two comparative experiments are proposed to demonstrate that the OETC can save more control energy and reduce the number of triggered times.

This article studies the memristive neural networks with multiple time delays (MNNsMTDs). The topology of networks is signed, which contains both cooperative and competitive relationships. Two controllers without time delays are designed to achieve finite-time bipartite synchronization (FTBS) and practical FTBS (PFTBS) of MNNsMTDs. A novel controller with a saturation function rather than a sign function is proposed to avoid chattering. Along with the Lyapunov function method, some mathematical techniques, and scaling inequalities, some sufficient conditions for FTBS and PFTBS of MNNsMTDs are attained. Besides, this article also concerns fixed-time bipartite synchronization (FXBS) and practical FXBS (PFXBS) of MNNsMTDs. An optimization model is designed to obtain some optimal control parameters. An algorithm based on particle swarm optimization (PSO) is provided to solve this model. Some numerical examples are included to demonstrate the correctness and applicability of the approaches.

This paper is concerned with the sampled-data state estimation problem for neural networks of neutral-type with time-varying delays. A new state estimator was designed based on the sampled measurements. The sufficient condition for the existence of state estimator is derived by using the Lyapunov functional method. A numerical example is given to show the effectiveness of the proposed estimator.

In this paper, the finite-time control problem for Markov systems with partly known transition probabilities and polytopic uncertainties is investigated. The main result provided is a sufficient conditions for finite-time stabilization via state feedback controller, and a simpler case without controller is also considered, based on switched quadratic Lyapunov function approach. All conditions are shown in the form of LMIs. An illustrative example is presented to demonstrate the result.

In this paper, the delayed projection neural network for a class of solving convex programming problem is proposed. The existence of solution and global exponential stability of the proposed network are proved, which can guarantee to converge at an exact optimal solution of the convex programming problems. Several examples are given to show the effectiveness of the proposed network.

In this letter, the delayed projection neural network for solving convex quadratic programming problems is proposed. The neural network is proved to be globally exponentially stable and can converge to an optimal solution of the optimization problem. Three examples show the effectiveness of the proposed network.