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Time-sensitive networking (TSN) is a kind of network communication technology applied in fields such as industrial internet and intelligent transportation, capable of meeting the application requirements for precise time synchronization and low-latency deterministic forwarding. In TSN, cyclic queuing and forwarding (CQF) is a traffic shaping mechanism that has been extensively discussed in the recent literature, which allows the delay of time-triggered (TT) flow to be definite and easily calculable. In this paper, two algorithms are designed to tackle the start time planning issue with the CQF mechanism, namely the flow–path–offset joint scheduling (FPOJS) algorithm and congestion-aware scheduling algorithm, to improve the scheduling success ratio of TT flows. The FPOJS algorithm, which adopts a novel scheduling object—a combination of flow, path, and offset—implements scheduling in descending order of a well-designed priority that considers the resource capacity and resource requirements of ports. The congestion-aware scheduling algorithm identifies and optimizes congested ports during scheduling and substantially improves the scheduling success ratio by dynamically configuring port resources. The experimental results demonstrate that the FPOJS algorithm achieves a 39% improvement in the scheduling success ratio over the naive algorithm, 13% over the Tabu-ITP algorithm, and 10% over the MSS algorithm. Moreover, the algorithm exhibits a higher scheduling success ratio under large-scale TSN. [ABSTRACT FROM AUTHOR]

Summary: Grid‐forming (GFM) inverters offer stable frequency support for microgrid systems, even in the absence of synchronous generators. However, the GFM inverters have low inertia and vulnerability to system uncertainties and external disturbances. The conventional dual‐loop proportional integral (PI) control strategy, while widely used for its simplicity and robustness, suffers from poor dynamic performance. Motivated by this, this paper presents an observer‐based residual‐driven dynamic compensation (RDDC) strategy based on the coprime factorization technic and the Youla parameterization theory to achieve the primary control of the GFM inverter. The observer‐based RDDC strategy comprises four components: a PI controller for tracking control, a linear quadratic regulator (LQR) controller for dynamic adjustment, a residual generator based on the Kalman filter for state estimation and residual generation, and a residual compensation controller designed using model matching theory and solved through linear matrix inequality (LMI) methods for disturbance suppression. Simulation and experiment results consistently demonstrate that the observer‐based RDDC strategy ensures system robustness, enhances the dynamic and steady‐state performance of the GFM inverter system, and strengthens the ability of the GFM inverter to suppress disturbances. [ABSTRACT FROM AUTHOR]

Low-current grounding systems are the main grounding method used in power distribution networks and belong to non-direct grounding systems. The most common fault in this type of system is a single-phase grounding fault, which may cause electrical fires and endanger personal safety. Due to the difficulty of troubleshooting, the selection of fault lines in low-current grounding systems has always been an important research topic in power system relay protection. This study proposes a new approach for fault identification of power lines based on the Euler transformation and deep learning. Firstly, the current signals of the distribution network are rapidly Fourier-transformed to obtain their frequencies for constructing reference signals. Then, the current signals are combined with the reference signals and transformed into images using Euler transformation in the complex plane. The images are then classified using a residual network model. The convolutional neural network in the model can automatically extract fault feature vectors, thus achieving the identification of faulty lines. The simulation was conducted based on the existing model, and extensive data training and testing were performed. The experimental results show that this method has good stability, fast convergence speed, and high accuracy. This technology can effectively accomplish fault identification in power distribution networks. [ABSTRACT FROM AUTHOR]

Peptide and protein postmodification have gained significant attention due to their extensive impact on biomolecule engineering and drug discovery, of which cysteine‐specific modification strategies are prominent due to their inherent nucleophilicity and low abundance. Herein, the study introduces a novel approach utilizing multifunctional 5‐substituted 1,2,3‐triazine derivatives to achieve multifaceted bioconjugation targeting cysteine‐containing peptides and proteins. On the one hand, this represents an inaugural instance of employing 1,2,3‐triazine in biomolecular‐specific modification within a physiological solution. On the other hand, as a powerful combination of precision modification and biorthogonality, this strategy allows for the one‐pot dual‐orthogonal functionalization of biomolecules utilizing the aldehyde group generated simultaneously. 1,2,3‐Triazine derivatives with diverse functional groups allow conjugation to peptides or proteins, while bi‐triazines enable peptide cyclization and dimerization. The examination of the stability of bi‐triazines revealed their potential for reversible peptide modification. This work establishes a comprehensive platform for identifying cysteine‐selective modifications, providing new avenues for peptide‐based drug development, protein bioconjugation, and chemical biology research. [ABSTRACT FROM AUTHOR]

A control strategy using variable speed limit (VSL) is a proven solution to reduce freeway collision risks and improve safety. However, the heterogeneity of human drivers restricts the effectiveness of traditional VSL controls, which may be made up by recent advanced technologies of connected and automated vehicles (CAVs). This study aims to propose a CAV-based VSL control system to address the limitations caused by human drivers on VSL control's effectiveness. First, the heterogeneity of human drivers is analyzed, and its impact on the safety performance of VSL is examined. Specifically, a microscopic simulation platform is established, and two vehicle dynamic models developed for CAVs and human-driven vehicles (HDVs) are incorporated into the simulation platform. Based on a widely utilized surrogate safety measurement, time-to-collision, its derivative metrics are applied to evaluate collision risks, and the total travel time is used to assess operational efficiency. Extensive simulations are conducted to examine the performance of the proposed CAV-VSL system. The results indicate the following: (1) the heterogeneity of human drivers negatively affects the performance of the VSL; (2) the performance of the proposed control system in a mixed flow can be improved by advanced wireless communication technology; (3) CAVs are able to implement the VSL control strategy effectively resulting in the proactive reduction of the heterogeneity., Author(s): Ye Li [1]; Yuntao Shi [1]; Jaeyoung Lee [1]; Chen Yuan [1,2]; Baojie Wang (corresponding author) [3] 1. Introduction Freeway bottlenecks have attracted considerable attention from transportation researchers since [...]

The perimeter control method is an effective way to alleviate traffic congestion that is based on Macroscopic Fundamental Diagrams (MFDs). However, the strategy may lead to congestion when it ignores the uncertainty of MFDs. To address this problem, this paper presents a risk‐averse perimeter control method. First, the urban traffic network system with uncertainty is modeled using a neural network and scenario tree. Then, this research quantifies the congestion risk caused by uncertainty using an average value‐at‐risk. The next step sees the design of a risk‐averse model predictive control (MPC) controller that takes the multi‐stage risk as the optimization objective and improves robustness by interpolating between the conventional stochastic and worst‐case MPC formulations. Finally, this paper analyzes the risk‐sensitive stability of an urban traffic network system and gives a solvable form of risk‐averse optimal control for this system. Finally, two simulations are conducted to verify the presented method's validity and superiority for an urban traffic network system with uncertainties. The simulation results show that the risk‐averse perimeter control method presented by this paper is superior because it reduces the total travel time by 12.98% compared to Stochastic MPC, by 15.96% compared to bang‐bang control, and by 14.54% compared to proportional‐integral. This paper designs a risk‐averse model predictive controller to track the urban traffic network system with uncertainty. This method significantly alleviates congestion and reduces the travel time of the traffic system. [ABSTRACT FROM AUTHOR]