Compared to the traditional activated sludge process, the membrane bioreactor (MBR) has several advantages such as the production of high-quality effluent, generation of low excess sludge, smaller footprint requirements, and ease of automatic control of processes. The MBR has a broader prospect of its applications in wastewater treatment and reuse. However, membrane fouling is the biggest obstacle for its wider application. This paper reviews the techniques available to predict fouling in MBR, discusses the problems associated with predicting fouling status using artificial neural networks and mathematical models, summarizes the current state of fouling prediction techniques, and looks into the trends in their development.

This paper focuses on dynamic output feedback control of Cyber-Physical Systems (CPS) under Denial-of-Service (DoS) attacks. Firstly, by analyzing the different effects of DoS attacks on the sensor-to-controller (S-C) channel and the controller-to-actuator(C-A) channel respectively, a switching system modeling method for the dynamic output feedback CPS is presented. Considering the bandwidth of the two channels is different and the energy limitation of the attacker, it is fair to assume that an attacker can only jam one communication channel at each time step and the maximum number of consecutive DoS attacks is bounded. Secondly, according to a packet-based transmission scheme, a nested switching model considering both the spatial variability and the temporal persistence of DoS attacks is constructed on the basis of the switching system. Then, the exponential stability condition of the dynamic output feedback CPS is obtained, and a controller design method based on linear matrix inequality is proposed. Finally, a simulation example is given to verify the effectiveness of the proposed method.

In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time, considering the disadvantage of the back propagation (BP) network in predicting MBR membrane flux, such as the local minimum value and poor generalization ability of the model, this article introduces tent chaotic mapping in the standard sparrow search algorithm (SSA), which improves the uniformity of population distribution and the searching ability of the algorithm (used to optimize the key parameters of the BP network). The tent sparrow search algorithm back propagation network (Tent-SSA-BP) membrane fouling prediction model is established to achieve accurate prediction of membrane flux; compared to the BP, genetic algorithm back propagation network (GA-BP), particle swarm optimization back propagation network (PSO-BP), sparrow search algorithm extreme learning machine(SSA-ELM), sparrow search algorithm back propagation network (SSA-BP), and Tent particle swarm optimization back propagation network (Tent–PSO-BP) models, it has unique advantages. Compared with the BP model before improvement, the improved soft sensing model reduces MAPE by 96.76%, RMSE by 99.78% and MAE by 95.61%. The prediction accuracy of the algorithm proposed in this article reaches 97.4%, which is much higher than the 48.52% of BP. It is also higher than other prediction models, and the prediction accuracy has been greatly improved, which has some engineering reference value.

Accurate fault diagnosis in analog circuits faces significant challenges owing to the inherent complexity of fault data patterns and the limited feature representation capabilities of conventional methodologies. Addressing the limitations of current convolutional neural networks (CNN) in handling heterogeneous fault characteristics, this study presents an efficient channel attention-enhanced multi-input CNN framework (ECA-MI-CNN) with dual-domain feature fusion, demonstrating three key innovations. First, the proposed framework addresses multi-domain feature extraction through parallel CNN branches specifically designed for processing time-domain and frequency-domain features, effectively preserving their distinct characteristic information. Second, the incorporation of an efficient channel attention (ECA) module between convolutional layers enables adaptive feature response recalibration, significantly enhancing discriminative feature learning while maintaining computational efficiency. Third, a hierarchical fusion strategy systematically integrates time-frequency domain features through concatenation and fully connected layer transformations prior to classification. Comprehensive simulation experiments conducted on Butterworth low-pass filters and two-stage quad op-amp dual second-order low-pass filters demonstrate the framework’s superior diagnostic capabilities. Real-world validation on Butterworth low-pass filters further reveals substantial performance advantages over existing methods, establishing an effective solution for complex fault pattern recognition in electronic systems.

Onboard computation of a fuel-optimal steering law is a pivotal technology for future lunar and planetary campaigns with pinpoint landings. This paper proposes a method of downrange estimation based on improved powered explicit guidance (PEG) focusing on the existing throttleable engine. The main contribution includes two aspects. First, the conventional PEG algorithm is improved to enforce prediction accuracy. The prediction of lander cutoff state can be used to estimate downrange position, and thus, the thrust switching time is corrected to recalculate optimal steering law. Second, in consideration of the thrust identification error and navigation uncertainty, the thrust switching condition triggered by the state of the lander instead of time is established. The thrust is switched depending on estimated downrange feedback, and thus, a pinpoint landing with near-optimal fuel is achieved. The effectiveness of the proposed algorithm are demonstrated through Monte Carlo simulations in the presence of thrust deviation and navigation error.

In view of the synchronized uniform equidistant sampling method that is widely used in network control systems (NCSs), multirate sampling control for NCSs is presented by considering the natural characteristics of NCSs. Two kinds of typical structures of network multirate control systems (NMCSs) are presented and the multirate sampling mechanisms are formulated. Then, fundamental issues and prospective directions in NMCSs are preliminarily discussed. The contents involve modeling, sampling pattern selection, control strategies, scheduling algorithms, and codesign of NMCSs. The purpose of this paper is to arouse more attention of researchers in related subjects, so that a complete theory for NCSs can be established.