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This article investigates the consensus problem of networked multiagent systems (NMASs) operating over communication networks with multiple channels. Due to limited network resources and denial-of-service (DoS) attacks, control signals transmitted through each channel may experience transmission delay and packet loss. In contrast to previous studies, we propose a predictor-like control law that incorporates the currently available state and some past control information. This design enables us to derive a necessary and sufficient condition that links the mean-square consensus to the mean-square stability of the constructed error model. Moreover, we provide a threshold under a global DoS attack that guarantees the mean-square consensus of the NMAS under consideration, which is dependent on the topology and subsystem parameters. Finally, we present a simulation example that demonstrates the effectiveness of the proposed approach and verifies the theoretical results.

This brief addresses the mean square consensus problem in a multi-agent system (MAS) operating over a directed graph that encounters non-identical transmission delays and non-identical fading channels. Notably, the presence of non-identical delays poses challenges to directly modeling the error within the MAS. To circumvent this issue, we introduce a novel approach following discretization, where we establish predefined controller update intervals equal to the maximum delay, yielding an equivalent delay-free MAS. Then, we derive an error model whose mean square stabilization problem is ultimately equivalent to the mean square consensus issue of the original MAS, utilizing edge dynamics. Based on this framework, we present a sufficient condition for achieving consensus within the MAS. Finally, a numerical simulation example is demonstrated to verify our proposed results.

In this article, we investigate the problem of security control for T-S fuzzy Markov jump systems (FMJSs) under actuator faults and deception attacks while introducing a cooperative event-triggered mechanism (CETM). In order to enhance the efficiency of communication resources, we develop the CETM in the forward channel, which operates concurrently on sensor-to-observer and observer-to-controller channels utilizing a united event generator. Additionally, we design an attack-compensating controller to eliminate the impact of nonlinear malicious injection information generated by deceptive attacks on the system, where the compensation signal is generated by approximating the attack signal using radial basis function neural network technology. Furthermore, using the Lyapunov function, sufficient conditions for ensuring that T-S FMJSs are mean square exponential ultimate bounded are derived. Finally, the effectiveness of our proposed approach is demonstrated through a simulation example.

GENeralized-Ensemble SImulation System (GENESIS) is a molecular dynamics (MD) software developed to simulate the conformational dynamics of a single biomolecule, as well as molecular interactions in large biomolecular assemblies and between multiple biomolecules in cellular environments. To achieve the latter purpose, the earlier versions of GENESIS emphasized high performance in atomistic MD simulations on massively parallel supercomputers, with or without graphics processing units (GPUs). Here, we implemented multiscale MD simulations that include atomistic, coarse-grained, and hybrid quantum mechanics/molecular mechanics (QM/MM) calculations. They demonstrate high performance and are integrated with enhanced conformational sampling algorithms and free-energy calculations without using external programs except for the QM programs. In this article, we review new functions, molecular models, and other essential features in GENESIS version 2.1 and discuss ongoing developments for future releases.

Smoke taint in wine has become a critical issue in the wine industry due to its significant negative impact on wine quality. Data-driven approaches including univariate analysis and predictive modeling are applied to a data set containing concentrations of 20 VOCs in 48 grape samples and 56 corresponding wine samples with a taster-evaluated smoke taint index. The resulting models for predicting the smoke taint index of wines are highly predictive when using as inputs VOC concentrations after log conversion in both grapes and wines (Pearson Correlation Coefficient PCC = 0.82; R2= 0.68) and less so when only grape VOCs are used (Pearson Correlation Coefficient PCC = 0.76; R2= 0.56), and the classification models also show the capacity for detecting smoke-tainted wines using both wine and grape VOC concentrations (Recall = 0.76; Precision = 0.92; F1 = 0.82) or using only grape VOC concentrations (Recall = 0.74; Precision = 0.92; F1 = 0.80). The performance of the predictive model shows the possibility of predicting the smoke taint index of the wine and grape samples before fermentation. The corresponding code of data analysis and predictive modeling of smoke taint in wine is available in the Github repository (https://github.com/IBPA/smoke_taint_prediction).

As a quasi-layered ferrimagnetic material, Mn3Si2Te6nanoflakes exhibit magnetoresistance behavior that is fundamentally different from their bulk crystal counterparts. They offer three key properties crucial for spintronics. First, at least 106times faster response compared to that exhibited by bulk crystals has been observed in current-controlled resistance and magnetoresistance. Second, ultralow current density is required for resistance modulation (∼5 A/cm2). Third, electrically gate-tunable magnetoresistance has been realized. Theoretical calculations reveal that the unique magnetoresistance behavior in the Mn3Si2Te6nanoflakes arises from a magnetic field induced band gap shift across the Fermi level. The rapid current induced resistance variation is attributed to spin–orbit torque, an intrinsically ultrafast process (∼nanoseconds). This study suggests promising avenues for spintronic applications. In addition, it highlights Mn3Si2Te6nanoflakes as a suitable platform for investigating the intriguing physics underlying chiral orbital moments, magnetic field induced band variation, and spin torque.

Graph Neural Network (GNN) has emerged as a predominant tool for graph data analysis. Despite their proliferation, the low-quality labels of many real-world graphs will undermine their performance dramatically. Existing studies on learning neural networks with noisy labels mainly focus on independent data and thus cannot fully exploit the structural information of graph data. Currently, there are few studies of robustness to noisy labels for graph-structured data even if this problem is commonly seen in real-world settings. To remedy this deficiency, we propose GNN Cleaner which utilizes structural information of graph data to combat noisy labels. More specifically, a pseudo label is computed from the neighboring labels for each node in the training set via a modified version of label propagation. Additionally, a novel method is developed to learn to correct the labels adaptively and dynamically. Extensive experiments show that GNN Cleaner can train GNNs robustly and correct both the synthetic and real-world noisy labels even if the noise is severe. Moreover, GNN Cleaner is model-agnostic and can be combined with various GNNs to improve their robustness against label noise.

This brief focuses on the problem of adaptive output feedback tracking control for a specific class of nonlinear time-delay systems that are subject to uncertainties in the sensor measurements, external disturbances, and unknown parameters. The proposed control approach is based on the dynamic gain scaling technique, which involves constructing a state observer and an adaptive controller to mitigate the effects of sensor uncertainties. The use of a single dynamic gain simplifies the implementation of the controller. By selecting an appropriate Lyapunov-Krasovskii (L-K) functional, the time-delay terms can be eliminated, and it is possible to prove that all the closed-loop states are bounded. The proposed control scheme ensures that the real tracking error will converge to zero, thereby achieving the desired tracking performance.

In this article, we mainly study an integrated networked control system operating over multipath routing networks, where the delivered control signal in each path experiences both transmission delay and packet loss. These imperfections significantly compromise system performance and may even cause instability. For such a system, we derive, for the first time, the necessary and sufficient stabilization condition utilizing a delay-dependent algebraic Riccati equation. Different from many previous works, one significant contribution is that our control policy is designed as the feedback of an extended state that contains the current available state and relevant past control information. Furthermore, we introduce an existence and uniqueness theorem for the maximum packet loss rate in symmetric systems. Notably, we derive its closed-form expression specifically in the scalar case.

Deep generative models have unlocked another profound realm of human creativity. By capturing and generalizing patterns within data, we have entered the epoch of all-encompassing Artificial Intelligence for General Creativity (AIGC). Notably, diffusion models, recognized as one of the paramount generative models, materialize human ideation into tangible instances across diverse domains, encompassing imagery, text, speech, biology, and healthcare. To provide advanced and comprehensive insights into diffusion, this survey comprehensively elucidates its developmental trajectory and future directions from three distinct angles: the fundamental formulation of diffusion, algorithmic enhancements, and the manifold applications of diffusion. Each layer is meticulously explored to offer a profound comprehension of its evolution. Structured and summarized approaches are presented here.

Graph neural networks (GNNs) have been playing important roles in various graph-related tasks. However, most existing GNNs are based on the assumption of homophily, so they cannot be directly generalized to heterophily settings where connected nodes may have different features and class labels. Moreover, real-world graphs often arise from highly entangled latent factors, but the existing GNNs tend to ignore this and simply denote the heterogeneous relations between nodes as binary-valued homogeneous edges. In this article, we propose a novel relation-based frequency adaptive GNN (RFA-GNN) to handle both heterophily and heterogeneity in a unified framework. RFA-GNN first decomposes an input graph into multiple relation graphs, each representing a latent relation. More importantly, we provide detailed theoretical analysis from the perspective of spectral signal processing. Based on this, we propose a relation-based frequency adaptive mechanism that adaptively picks up signals of different frequencies in each corresponding relation space in the message-passing process. Extensive experiments on synthetic and real-world datasets show qualitatively and quantitatively that RFA-GNN yields truly encouraging results for both the heterophily and heterogeneity settings. Codes are publicly available at: https://github.com/LirongWu/RFA-GNN.

Although face recognition payment system has not yet been implemented in Malaysia, the technology is currently in use in other sectors in Malaysia. The main objective of this research is to better understand Malaysian customers' intention to use the facial recognition payment system. The six independent variables included in this study are perceived ease of use, perceived usefulness, attitude, facilitating conditions, personal innovativeness, and security and the dependent variable is intention to use. The target respondents for this study are targeted on the general public between the ages of 18 until 60 years old. The data collected were keyed into the Statistical Package for Social Sciences (SPSS) and further analysed by using Smart PLS software. Our analysis result revealed that perceived ease of use, perceived usefulness, attitude, and security have a substantial relationship with consumer intention to use. In conclusion, this study is useful for both developers and researchers.

Branded applications have lately piqued the attention of a growing number of businesses looking to connect with and meet the needs of their target customers. This study aims to examine the factors influencing consumers' continuance intention towards the mobile branded apps. The participants were recruited over an online survey questionnaire using a cross-sectional study. It was found that perceived usefulness, perceived ease of use, and trust influence the continuance intention towards the mobile branded apps. Surprisingly, perceived enjoyment and information quality do not significantly affect the continuance intention towards the mobile branded apps. The results of the importance-performance matrix analysis (IPMA) demonstrated that both the importance and performance of perceived usefulness played a significant role in consumer continuance intention for mobile branded apps. This study adds to the existing body of knowledge on mobile applications by presenting thorough practical consequences that highlight the continued usage of social mobile applications.

This paper studies the mixed H2/H∞control for Takagi–Sugeno (T–S) fuzzy Markovian jump systems (MJSs) subject to random delays and multiple uncertain transition probabilities. In contrast to existing research, this study presents uncertainty parameters, external disturbance, random delays, and uncertain transition probabilities simultaneously in a unified T–S fuzzy model. Specifically, this study examines multiple Markov chains with partially unknown transition probabilities. These complex imperfections have a substantial adverse impact on system performance and the associated challenge of mixed H2/H∞control remains unresolved. Our innovative contributions are described as follows. The proposed approach utilizes free-weighting matrix technique and Lyapunov–Krasovskii functional to get the H2/H∞controller, which ensures that the stochastic T–S fuzzy systems exhibit stochastic stability and comply with the H∞performance index.

Two-dimensional (2D) ferromagnets with high Curie temperatures provide a rich platform for exploring the exotic phenomena of 2D magnetism and the potential of spintronic devices. As a prototypical 2D ferromagnet, Fe5-xGeTe2has recently been reported to possess a high Curie temperature with Tc∼ 310 K, making it a promising candidate for advancing 2D nanoelectromechanical systems. However, due to its intricate magnetic ground state and magnetic domains, a thorough study of the transport behavior related to its lattice and domain structures is still lacking. Here, we report a nonreciprocal antisymmetric magnetoresistance in Fe5-xGeTe2nanoflakes observed under an external magnetic field between 85–120 K. Through a detailed examination of its temperature, field orientation, and sample thickness dependence, we trace its origin to an additional electric field induced by the domain structure. This differs from the previously reported antisymmetric magnetoresistance due to thickness inhomogeneity. Notably, at lower temperatures, we observed an unconventional Hall effect (UHE), which can be attributed to the Dzyaloshinskii-Moriya interaction (DMI) resulting from the non-coplanar magnetic moment structure. The pronounced influence of sample thickness on magneto-transport properties underscores the competition between magnetic anisotropy and DMI in Fe5-xGeTe2flakes with varying thicknesses. Our findings provide a deeper understanding of the magneto-transport behavior of the exotic magnetic structure in 2D ferromagnetic materials, which may benefit future spintronic device applications.

In this article, we study sampled-data networked control systems (NCSs) that operate over communication networks with multipath channels. Due to limited bandwidth power, the delivery of observed state signals from the sensor to the controller may suffer packet loss possibility, which leads to intermittent observation. An unreliable multipath routing network is responsible for the transmission of control signals from the controller to the actuator, subject to random transmission delays. These communication problems lead to degradation in system performance, which may even cause instability. By utilizing an idle-time-based scheduling policy with a predefined deadline constraint on the actuator side, we transform the affiliated integrated system into a stochastic model with constant input delay. For such an implementation, we propose a set of Riccati-type stabilization conditions, which are necessary and sufficient. It is remarkable that the stabilizing control policy is designed based on the feedback of the optimal estimate with minimum mean square error. Moreover, for the scalar system, we study the uniqueness and existence property of the maximum sampling period, and derive its close form representation in terms of system parameters, service capability, and packet dropout rate.

In this brief, a novel reduced-size 180° hybrid is presented to implement a ${U}$ -band subharmonic mixer (SHM) via a 0.15- $\mu \text{m}$ GaAs pHEMT process. The isolations between $\Sigma $ -port and $\Delta $ -port of the proposed 180° hybrid are significantly enhanced by two structures: one is a compact multiconductor edge-coupled line balun with artificial transmission line and the other is an in-phase power divider composed of ${\lambda }_{\mathrm{ LO}}$ /4 coupled line. As a proof-of-principle demonstration, a single balanced SHM with an antiparallel diode pair configuration is designed and fabricated, which exhibits a conversion gain of −12.25 dB ± 1.55 dB within the operating RF frequency of 40 to 60 GHz under 14 dBm LO pumping power. The improved isolations of LO-to-RF and 2LO-to-RF are 33 to 47 dB and 44 to 57 dB, respectively. The measured results surpass previously reported performance of SHMs.

We focus on the problem of asymptotically mean-square stabilization in discrete-time stochastic systems that exhibit plant uncertainty, multiple input delays, and multiplicative noises. Our innovative contributions are described as follows. First, we employ a reduction method to transform the original model into a delay-free auxiliary system, and establish an equivalent proposition for stabilization based on this reformulation. On the basis of the reformulated model, we propose two stabilization criteria for the uncertainty-free case, including both Lyapunov-type and Riccati-type criteria. More generally, we extend the stabilization result to the uncertain model, and propose a necessary and sufficient stabilization criterion utilizing matrix homogeneous polynomials. Finally, we explore the existence and uniqueness of a delay margin under certain structural restrictions, and provide a closed-form representation of this margin.

There are many constraints for a multirobot system to perform a region coverage search task in an unknown environment. To address this, we propose a novel multirobot distributed collaborative region coverage search algorithm based on Glasius bio-inspired neural network (GBNN). First, we develop an environmental information updating model to represent the dynamic search environment. This model converts the environmental information detected by the robot into dynamic neural activity landscape of GBNN. Second, we introduce the distributed model predictive control method in search path planning to improve search efficiency. In addition, we propose a distributed collaborative decision-making mechanism among the robots to produce several dynamic search subteams. Within each subteam, collaborative decisions are made among the robot members to optimize the solution and obtain the next movement path of each robot. Finally, we conduct experiments in three aspects to verify the effectiveness of the proposed method. Compared with three algorithms in this field, the experimental results demonstrate that the proposed algorithm exhibits good performance in a multirobot region coverage search task.

In recent years, the role of mitochondrial dynamics in neurodegenerative diseases has becoming increasingly important. More and more evidences have shown that in pathological conditions, abnormal mitochondrial divisions, especially Drp1-Fis1-mediated divisions, play an important role in the occurrence and development of Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis, Huntington’s disease, glaucoma, and other neurodegenerative diseases. This review highlights several new mechanisms of physiological fission of mitochondria and the difference/connection of physiological/pathological mitochondrial fission. In addition, we described the relationship between abnormal mitochondrial dynamics and neurodegenerative diseases in detail and emphatically summarized its detection indicators in basic experiments, trying to provide references for further mechanism exploration and therapeutic targets.

Biomolecular condensation is involved in various cellular processes; therefore, regulation of condensation is crucial to prevent deleterious protein aggregation and maintain a stable cellular environment. Recently, a class of highly charged proteins, known as heat-resistant obscure (Hero) proteins, was shown to protect other client proteins from pathological aggregation. However, the molecular mechanisms by which Hero proteins protect other proteins from aggregation remain unknown. In this study, we performed multiscale molecular dynamics (MD) simulations of Hero11, a Hero protein, and the C-terminal low-complexity domain (LCD) of the transactive response DNA-binding protein 43 (TDP-43), a client protein of Hero11, under various conditions to examine their interactions with each other. We found that Hero11 permeates into the condensate formed by the LCD of TDP-43 (TDP-43-LCD) and induces changes in conformation, intermolecular interactions, and dynamics of TDP-43-LCD. We also examined possible Hero11 structures in atomistic and coarse-grained MD simulations and found that Hero11 with a higher fraction of disordered region tends to assemble on the surface of the condensates. Based on the simulation results, we have proposed three possible mechanisms for Hero11’s regulatory function: (i) In the dense phase, TDP-43-LCD reduces contact with each other and shows faster diffusion and decondensation due to the repulsive Hero11–Hero11 interactions. (ii) In the dilute phase, the saturation concentration of TDP-43-LCD is increased, and its conformation is relatively more extended and variant, induced by the attractive Hero11–TDP-43-LCD interactions. (iii) Hero11 on the surface of small TDP-43-LCD condensates can contribute to avoiding their fusion due to repulsive interactions. The proposed mechanisms provide new insights into the regulation of biomolecular condensation in cells under various conditions.

Deep learning on graphs has recently achieved remarkable success on a variety of tasks, while such success relies heavily on the massive and carefully labeled data. However, precise annotations are generally very expensive and time-consuming. To address this problem, self-supervised learning (SSL) is emerging as a new paradigm for extracting informative knowledge through well-designed pretext tasks without relying on manual labels. In this survey, we extend the concept of SSL, which first emerged in the fields of computer vision and natural language processing, to present a timely and comprehensive review of existing SSL techniques for graph data. Specifically, we divide existing graph SSL methods into three categories: contrastive, generative, and predictive. More importantly, unlike other surveys that only provide a high-level description of published research, we present an additional mathematical summary of existing works in a unified framework. Furthermore, to facilitate methodological development and empirical comparisons, we also summarize the commonly used datasets, evaluation metrics, downstream tasks, open-source implementations, and experimental study of various algorithms. Finally, we discuss the technical challenges and potential future directions for improving graph self-supervised learning. Latest advances in graph SSL are summarized in a GitHub repository https://github.com/LirongWu/awesome-graph-self-supervised-learning.

Additively manufactured (AM) metal alloys normally exhibit very diverse grain size distributions. The size measured by electron backscatter diffraction (EBSD) is generally smaller than that from optical microscope (OM) due to better resolution, but proper scan setup must be chosen to reveal the existing fine grains. In this work, we compared these two widely applied techniques and investigated the effects of different parameters on the resulting average diameter d. Regarding the discrepancies of parameters in the literatures, a standard EBSD setup is suggested, so that >99% of the total grain area should be detected. The grain distribution characteristics for AM fabricated alloys were also illustrated. A simply modified Hall–Petch equation is proposed by considering the grain size distributions using grains covering >99% of the measured area. It gives remarkable agreement between predicted strength and experimental values for almost all the AM alloy systems we tested, which possess very diverse grain size distributions. This revised model can extend to other AM alloys with heterogeneous microstructures and is very practical for engineering approach.

Manipulating the exchange bias (EB) effect using an electronic gate is a significant goal in spintronics. The emergence of van der Waals (vdW) magnetic heterostructures has provided improved means to study interlayer magnetic coupling, but to date, these heterostructures have not exhibited electrical gate-controlled EB effects. Here, we report electrically controllable EB effects in a vdW heterostructure, FePS3-Fe5GeTe2. By applying a solid protonic gate, the EB effects were repeatably electrically tuned. The EB field reaches up to 23% of the coercivity and the blocking temperature ranges from 30 to 60 K under various gate-voltages. The proton intercalations not only tune the average magnetic exchange coupling but also change the antiferromagnetic configurations in the FePS3layer. These result in a dramatic modulation of the total interface exchange coupling and the resultant EB effects. The study is a significant step toward vdW heterostructure-based magnetic logic for future low-energy electronics.

ABSTRACTThe knowledge base on various forms of structuring students’ learning by ability grouping is more robust than that on teachers’ instructional practices being implemented within these groupings. The present study examines if student-reported mathematics teachers’ instructional practices vary among schools with different degrees of implementing between-class ability grouping and if between-class ability grouping moderates the relationship between these practices and students’ mathematics achievement. International data from 281,591 fifteen-year-old students and 11,765 principals were analysed. One-way ANOVA showed that students perceived greater implementation of teachers’ instructional practices in schools with more between-class ability grouping. However, hierarchical linear modelling (HLM) results showed that between-class ability grouping did not moderate the relationship between teachers’ instructional practices and students’ mathematics achievement. These results imply that it is more important to examine the relationship between teachers’ instructional practices and students’ achievement in homogenous ability classes vis-à-vis that between ability grouping and students’ achievement per se.