H∞ Controller Design for Networked Systems With Two-Channel Packet Dropouts and FDI Attacks.

In this article, the stochastic analysis and H∞ controller design problems of networked systems with packet dropouts and false data injection attacks are investigated. Different from the existing literature, we focus on the linear networked systems with external disturbances and both sensor-controller channel and controller-actuator channel are studied. First, we present a discrete-time modeling framework that leads to a stochastic closed-loop system with randomly varying parameters. To facilitate the analysis and H∞ control of resulting discrete-time stochastic closed-loop system, an equivalent yet analyzable stochastic augmented model is further constructed by matrix exponential computation. Based on this model, a stability condition is derived in the form of linear matrix inequality (LMI) with the aid of a reduced-order confluent Vandermonde matrix, Kronecker product operation, and law of total expectation. Specifically, the dimension of the LMI obtained in this article does not increase as the upper bound of consecutive packet dropouts does, which is also different from the existing literature. Subsequently, a desired H∞ controller is obtained such that the original discrete-time stochastic closed-loop system is exponentially mean-square stable with a prescribed H∞ performance. Finally, a numerical example and a direct current motor system are exploited to substantiate the effectiveness and practicability of the designed strategy.

Discovery of Novel (+)-Nootkatone-Based Amine Derivatives as Potential Insecticide Candidates.

To discover novel natural product-based insecticides, a series of (+)-nootkatone-based amine derivatives 3a-t were prepared and evaluated for their insecticidal activities against Mythimna separata Walker, Myzus persicae Sulzer, and Plutella xylostella Linnaeus. Insecticidal assays showed that most of the title (+)-nootkatone derivatives exhibited stronger insecticidal activities against three insect pests than the precursor (+)-nootkatone after the introduction of amine groups on the parent (+)-nootkatone. Compounds 3a, 3d, 3h, 3m, 3n, 3p, and 3r displayed more promising growth inhibitory (GI) effect against M. separata than the commercially available botanical insecticide toosendanin. Compound 3o exhibited the most potent aphicidal activity with an LD50 value of 0.011 µg/larvae, which was 2.09-fold higher than the positive control rotenone. Additionally, compounds 3g and 3n showed more promising larvicidal activity against P. xylostella with LC50 values of 260 and 230 mg/L, respectively, superior to that of rotenone (460 mg/L). Moreover, derivatives 3g and 3n exhibited better control efficacy toward P. xylostella than rotenone under greenhouse conditions. Preliminary mechanistic studies revealed that derivative 3n could inhibit the activity of glutathione S-transferase (GST) in P. xylostella and thus exerted larvicidal activity, and molecular docking further demonstrated that 3n could interact well with some amino acid residues of GST. Finally, the toxicity assay suggested that derivatives 3g and 3n were relatively less toxic to nontarget organisms. These findings will provide insights into the development of (+)-nootkatone derivatives as green pesticides.

Dispelling the Fake: Social Bot Detection Based on Edge Confidence Evaluation.

Social bot detection is essential for maintaining the safety and integrity of online social networks (OSNs). Graph neural networks (GNNs) have emerged as a promising solution. Mainstream GNN-based social bot detection methods learn rich user representations by recursively performing message passing along user-user interaction edges, where users are treated as nodes and their relationships as edges. However, these methods face challenges when detecting advanced bots interacting with genuine accounts. Interaction with real accounts results in the graph structure containing camouflaged and unreliable edges. These unreliable edges interfere with the differentiation between bot and human representations, and the iterative graph encoding process amplifies this unreliability. In this article, we propose a social Bot detection method based on Edge Confidence Evaluation (BECE). Our model incorporates an edge confidence evaluation module that assesses the reliability of the edges and identifies the unreliable edges. Specifically, we design features for edges based on the representation of user nodes and introduce parameterized Gaussian distributions to map the edge embeddings into a latent semantic space. We optimize these embeddings by minimizing Kullback-Leibler (KL) divergence from the standard distribution and evaluate their confidence based on edge representation. Experimental results on three real-world datasets demonstrate that BECE is effective and superior in social bot detection. Additionally, experimental results on six widely used GNN architectures demonstrate that our proposed edge confidence evaluation module can be used as a plug-in to improve detection performance.

Novel natural osthole-inspired amphiphiles as membrane targeting antibacterials against methicillin-resistant Staphylococcus aureus (MRSA).

Methicillin-resistant Staphylococcus aureus (MRSA) is a widespread pathogen causing clinical infections and is multi-resistant to many antibiotics, making it urgent need to develop novel antibacterials to combat MRSA. Herein, we designed and prepared a series of novel osthole amphiphiles 6a-6ad by mimicking the structures and function of antimicrobial peptides (AMPs). Antibacterial assays showed that osthole amphiphile 6aa strongly inhibited S. aureus and 10 clinical MRSA isolates with MIC values of 1-2 µg/mL, comparable to that of the commercial antibiotic vancomycin. Additionally, 6aa had the advantages of rapid bacteria killing without readily developing drug resistance, low toxicity, good membrane selectivity, and good plasma stability. Mechanistic studies indicated that 6aa possesses good membrane-targeting ability to bind to phosphatidylglycerol (PG) on the bacterial cell membranes, thereby disrupting the cell membranes and causing an increase in intracellular ROS as well as leakage of proteins and DNA, and accelerating bacterial death. Notably, in vivo activity results revealed that 6aa exhibits strong anti-MRSA efficacy than vancomycin as well as a substantial reduction in MRSA-induced proinflammatory cytokines, including TNF-α and IL-6. Given the impressive in vitro and in vivo anti-MRSA efficacy of 6aa, which makes it a potential candidate against MRSA infections.

Modeling the Uncertainty of Information Propagation for Rumor Detection: A Neuro-Fuzzy Approach.

Automatic rumor detection is critical for maintaining a healthy social media environment. The mainstream methods generally learn rich features from information cascades by modeling the cascade as a tree or graph structure where edges are built based on interactions between a tweet and retweets. Some psychology studies have empirically shown that users' various subjective factors always cause the uncertainty of interactions such as differences among interactive behavior activation thresholds or semantic relevancy. However, previous works model interactions by employing a simple fully connected layer on fixed edge weights in the graph and cannot reasonably describe this inherent uncertainty of complex interactions. In this article, inspired by the fuzzy theory, we propose a novel neuro-fuzzy method, fuzzy graph convolutional networks (FGCNs), to sufficiently understand uncertain interactions in the information cascade in a fuzzy perspective. Specifically, a new strategy of graph construction is first designed to convert each information cascade into a heterogeneous graph structure with the consideration of explicit interactive behaviors between a tweet and its retweet, as well as implicit interactive behaviors among retweets, enriching more structural clues in the graph. Then, we improve graph convolutional networks by incorporating edge fuzzification (EF) modules. The EFs adapt edge weights according to predefined membership to enhance message passing in the graph. The proposed model can provide a stronger relational inductive bias for expressing uncertain interactions and capture more discriminative and robust structural features for rumor detection. Extensive experiments demonstrate the effectiveness and superiority of FGCN on both rumor detection and early rumor detection.

Resilient control design for networked DC microgrids under time-constrained DoS attacks.

This paper studies the resilient current controller design for the networked DC microgrid system with multiple constant power loads (CPLs) under a new type of time-constrained denial-of-service (DoS) attack. Different from the existing DoS attack models, which are often characterized by DoS frequency and DoS duration, this paper only considers the duration characteristics of the sporadic/aperiodic DoS attacks, and proposes a new type of time-constrained DoS attack model. Under the effects of such DoS attacks, a switching state feedback control law is constructed and a switching-like DC microgrid system model is then established. Furthermore, based on an attack-parameter-dependent time-varying Lyapunov function (TVLF) method, the exponential stability criterion of the resulting DC microgrid system under aperiodic DoS attacks is derived, and a new resilient controller design method is proposed. Finally, simulation studies are given to verify the effectiveness and merits of the proposed resilient control design scheme on achieving the desired control performance and attack resilience.

USV Formation and Path-Following Control via Deep Reinforcement Learning With Random Braking.

This article addresses the problem of path following for underactuated unmanned surface vessels (USVs) formation via a modified deep reinforcement learning with random braking (DRLRB). A formation control model based on deep reinforcement learning (DRL) is constructed to urge USVs to form a preset formation. Specifically, an efficient reward function is designed from the perspective of velocity and error distance of each USV related to the given formation, and then a novel random braking mechanism is formulated to prevent the training of the decision-making network from falling into the local optimum and failing to achieve the training objectives. Following that, a virtual leader-based path-following guidance system is developed for the USV formation problem. Wherein, with the aid of DRLRB, our proposed system can adjust formation automatically and flexibly even when some USVs deviate from the formation. Simulation verifies the effectiveness and superiority of our formation and path-following control strategy.

HiAM: A Hierarchical Attention based Model for knowledge graph multi-hop reasoning.

Learning to reason in large-scale knowledge graphs has attracted much attention from research communities recently. This paper targets a practical task of multi-hop reasoning in knowledge graphs, which can be applied in various downstream tasks such as question answering, and recommender systems. A key challenge in multi-hop reasoning is to synthesize structural information (e.g., paths) in knowledge graphs to perform deeper reasoning. Existing methods usually focus on connection paths between each entity pair. However, these methods ignore predecessor paths before connection paths and regard entities and relations within every single path as equally important. With our observations, predecessor paths before connection paths can provide more accurate semantic representations. Furthermore, entities and relations in a single path contribute variously to the right answers. To this end, we propose a novel model HiAM (Hierarchical Attention based Model) for knowledge graph multi-hop reasoning. HiAM makes use of predecessor paths to provide more accurate semantics for entities and explores the effects of different granularities. Firstly, we extract predecessor paths of head entities and connection paths between each entity pair. Then, a hierarchical attention mechanism is designed to capture the information of different granularities, including entity/relation-level and path-level features. Finally, multi-granularity features are fused together to predict the right answers. We go one step further to select the most significant path as the explanation for predicted answers. Comprehensive experimental results demonstrate that our method achieves competitive performance compared with the baselines on three benchmark datasets.

Co-Design of Dynamic Event-Triggered Communication Scheme and Resilient Observer-Based Control Under Aperiodic DoS Attacks.

This article is concerned with the problem of observer-based dynamic event-triggered control for a networked control system (NCS) under a class of power-constrained denial-of-service (DoS) attacks that aim at impeding the network communication from time to time. First, by carefully modeling such DoS attacks as aperiodic pulse-width-modulated (PWM) jamming signals, a switching observer, adapting to the DoS attacks, is delicately constructed to deal with the unavailability of full-state information. Second, to economize the limited bandwidth resources, a dynamic event-triggered communication scheme is designed under the aperiodic DoS jamming attacks, whose duration and frequency are assumed to be restricted. Third, a switching system model with artificial state delay is formulated, which characterizes the effects of the aperiodic DoS attacks and event-triggered communication scheme in a unified framework. Then, the asymptotic stability analysis and controller/observer synthesis conditions of the resulting switching system are obtained by using a piecewise Lyapunov-Krasovskii functional approach. Furthermore, a co-design method of the dynamic triggering parameters, controller, and observer gains is presented. Finally, an example is employed to verify the effectiveness of the obtained results.

Observer-Based Event-Triggered Control for Networked Linear Systems Subject to Denial-of-Service Attacks.

This paper is concerned with the observer-based event-triggered control for a continuous networked linear system subject to denial-of-service (DoS) attacks, where the attacks are launched periodically to block the data transmission in control channels. First, a new observer state-based resilient event-triggering scheme is developed in the presence of DoS attacks. Second, a novel event-based switched system model is established by considering the effect of the event-triggering scheme and DoS attacks simultaneously. By virtue of this new model combined with a piecewise Lyapunov-Krasovskii functional method, the sufficient conditions are derived to guarantee exponential stability of the resulting switched system. It is shown that the proposed results can establish a quantitative relationship among the launching/sleeping periods of the attacks, the event-triggering parameters, the sampling period, and the exponential decay rate. Third, criteria for designing a desired observer-based event-triggered controller are provided and expressed in terms of a set of linear matrix inequalities. Finally, an offshore structure model is presented to illustrate the efficiency of the developed control method.

Resilient Event-Triggered Controller Synthesis of Networked Control Systems Under Periodic DoS Jamming Attacks.

In this paper, the event-based controller synthesis problem for networked control systems under the resilient event-triggering communication scheme (RETCS) and periodic denial-of-service (DoS) jamming attacks is studied. First, a new periodic RETCS is designed under the assumption that the DoS attacks imposed by power-constrained pulsewidth-modulated jammers are partially identified, that is, the period of the jammer and a uniform lower bound on the jammer's sleeping periods are known. Second, a new state error-dependent switched system model is constructed, including the impacts of the RETCS and DoS attacks. According to this new model, the exponential stability criteria are derived by using the piecewise Lyapunov functional. In these criteria, the relationship among DoS parameters, the triggering parameters, the sampling period, and the decay rate is quantitatively characterized. Then, a criterion is also proposed to obtain the explicit expressions of the triggering parameter and event-based state feedback controller gain simultaneously. Finally, the obtained theoretical results are verified by a satellite yaw-angles control system.

Stabilization of Neural-Network-Based Control Systems via Event-Triggered Control With Nonperiodic Sampled Data.

This paper focuses on a problem of event-triggered stabilization for a class of nonuniformly sampled neural-network-based control systems (NNBCSs). First, a new event-triggered data transmission mechanism is designed based on the nonperiodic sampled data. Different from the previous works, the proposed triggering scheme enables the NNBCSs design to enjoy the advantages of both nonuniform and event-triggered sampling schemes. Second, under the nonperiodic event-triggered data transmission scheme, the nonperiodic sampled-data three-layer fully connected feedforward neural-network (TLFCFFNN)-based event-triggered controller is constructed, and the resulting closed-loop TLFCFFNN-based event-triggered control system is modeled as a state delay system based on time-delay system modeling approach. Then, the stability criteria for the closed-loop system is formulated using Lyapunov-Krasovskii functional approach. Third, the sufficient conditions for the codesign of the TLFCFFNN-based controller and triggering parameters are given in terms of solvability of matrix inequalities to guarantee the asymptotical stability of the closed-loop system and an upper bound on the given cost function while reducing the updates of the controller. Finally, three numerical examples are provided to illustrate the effectiveness and benefits of the proposed results.

Distributed Optimal Consensus Control for Multiagent Systems With Input Delay.

This paper addresses the problem of distributed optimal consensus control for a continuous-time heterogeneous linear multiagent system subject to time varying input delays. First, by discretization and model transformation, the continuous-time input-delayed system is converted into a discrete-time delay-free system. Two delicate performance index functions are defined for these two systems. It is shown that the performance index functions are equivalent and the optimal consensus control problem of the input-delayed system can be cast into that of the delay-free system. Second, by virtue of the Hamilton-Jacobi-Bellman (HJB) equations, an optimal control policy for each agent is designed based on the delay-free system and a novel value iteration algorithm is proposed to learn the solutions to the HJB equations online. The proposed adaptive dynamic programming algorithm is implemented on the basis of a critic-action neural network (NN) structure. Third, it is proved that local consensus errors of the two systems and weight estimation errors of the critic-action NNs are uniformly ultimately bounded while the approximated control policies converge to their target values. Finally, two simulation examples are presented to illustrate the effectiveness of the developed method.

Emerging Avenues for Utilization of Exotic Germplasm.

Breeders have been successful in increasing crop performance by exploiting genetic diversity over time. However, the reported annual yield increases are not sufficient in view of rapid human population growth and global environmental changes. Exotic germplasm possesses high levels of genetic diversity for valuable traits. However, only a small fraction of naturally occurring genetic diversity is utilized. Moreover, the yield gap between elite and exotic germplasm widens, which increases the effort needed to use exotic germplasm and to identify beneficial alleles and for their introgression. The advent of high-throughput genotyping and phenotyping technologies together with emerging biotechnologies provide new opportunities to explore exotic genetic variation. This review will summarize potential challenges for utilization of exotic germplasm and provide solutions.

Brassinosteroid and gibberellin control of seedling traits in maize (Zea mays L.).

In this study, we established two doubled haploid (DH) libraries with a total of 207 DH lines. We applied BR and GA inhibitors to all DH lines at seedling stage and measured seedling BR and GA inhibitor responses. Moreover, we evaluated field traits for each DH line (untreated). We conducted genome-wide association studies (GWAS) with 62,049 genome wide SNPs to explore the genetic control of seedling traits by BR and GA. In addition, we correlate seedling stage hormone inhibitor response with field traits. Large variation for BR and GA inhibitor response and field traits was observed across these DH lines. Seedling stage BR and GA inhibitor response was significantly correlate with yield and flowering time. Using three different GWAS approaches to balance false positive/negatives, multiple SNPs were discovered to be significantly associated with BR/GA inhibitor responses with some localized within gene models. SNPs from gene model GRMZM2G013391 were associated with GA inhibitor response across all three GWAS models. This gene is expressed in roots and shoots and was shown to regulate GA signaling. These results show that BRs and GAs have a great impact for controlling seedling growth. Gene models from GWAS results could be targets for seeding traits improvement.

Gibberellins Promote Brassinosteroids Action and Both Increase Heterosis for Plant Height in Maize (Zea mays L.).

Brassinosteroids (BRs) and Gibberellins (GAs) are two classes of plant hormones affecting plant height (PHT). Thus, manipulation of BR and GA levels or signaling enables optimization of crop grain and biomass yields. We established backcross (BC) families, selected for increased PHT, in two elite maize inbred backgrounds. Various exotic accessions used in the germplasm enhancement in maize project served as donors. BC1-derived doubled haploid lines in the same two elite maize inbred backgrounds established without selection for plant height were included for comparison. We conducted genome-wide association studies to explore the genetic control of PHT by BR and GA. In addition, we used BR and GA inhibitors to compare the relationship between PHT, BR, and GA in inbred lines and heterozygotes from a physiological and biological perspective. A total of 73 genomic loci were discovered to be associated with PHT, with seven co-localized with GA, and two co-localized with BR candidate genes. PHT determined in field trials was significantly correlated with seedling stage BR and GA inhibitor responses. However, this observation was only true for maize heterozygotes, not for inbred lines. Path analysis results suggest that heterozygosity increases GA levels, which in turn promote BR levels. Thus, at least part of heterosis for PHT in maize can be explained by increased GA and BR levels, and seedling stage hormone inhibitor response is promising to predict heterosis for PHT.

Quantitative Trait Locus Analysis for Deep-Sowing Germination Ability in the Maize IBM Syn10 DH Population.

Deep-sowing is an effective measure to ensure seeds absorbing water from deep soil layer and emerging normally in arid and semiarid regions. However, existing varieties demonstrate poor germination ability in deep soil layer and some key quantitative trait loci (QTL) or genes related to deep-sowing germination ability remain to be identified and analyzed. In this study, a high-resolution genetic map based on 280 lines of the intermated B73 × Mo17 (IBM) Syn10 doubled haploid (DH) population which comprised 6618 bin markers was used for the QTL analysis of deep-sowing germination related traits. The results showed significant differences in germination related traits under deep-sowing condition (12.5 cm) and standard-germination condition (2 cm) between two parental lines. In total, 8, 11, 13, 15, and 18 QTL for germination rate, seedling length, mesocotyl length, plumule length, and coleoptile length were detected for the two sowing conditions, respectively. These QTL explained 2.51-7.8% of the phenotypic variance with LOD scores ranging from 2.52 to 7.13. Additionally, 32 overlapping QTL formed 11 QTL clusters on all chromosomes except for chromosome 8, indicating the minor effect genes have a pleiotropic role in regulating various traits. Furthermore, we identified six candidate genes related to deep-sowing germination ability, which were co-located in the cluster regions. The results provide a basis for molecular marker assisted breeding and functional study in deep-sowing germination ability of maize.

[An epidemiological analysis on malaria in Linzhi district of Tibet in 1986-2004].

OBJECTIVE: To investigate the epidemiological characters of malaria in Linzhi district of Tibet. METHODS: A retrospective analysis on the epidemiology of malaria was carried out using the data on malaria situation in Linzhi district of Tibet in 1986-2004, referring to the distribution of season, population and region. RESULTS: The accumulative number of malaria cases in the period of 1986-2004 was 2459. The annual incidence of malaria in the district was reduced from 2.44 per ten thousand in 1986 to 1.03 per ten thousand in 2004, declined by 57.8% in 17 years. 99.3% of the cases were reported from Motuo County which was a typical high endemic area of malaria. The peak of prevalence occurred in June-October and 66.7% of the total cases were in the age group of 15-59 years old. 81.0% of the cases were farmers and 90.0% were Menba nationality. CONCLUSION: Motuo County has been the major area of malaria endemic in Linzhi district of Tibet. Most malaria cases in other counties are imported from Motuo.

Getting the 'MOST' out of crop improvement.

Unraveling the function of genes affecting agronomic traits is accelerating due to progress in DNA sequencing and other high-throughput genomic approaches. Characterized genes can be exploited by plant breeders by using either marker-aided selection (MAS) or transgenic procedures. Here, we propose a third 'outlet', 'molecular strengthening' (MOST), as alternative option for exploiting detailed molecular understanding of trait expression, which is comparable to the pharmaceutical treatment of human diseases. MOST treatments can be used to enhance yield stability. Alternatively, they can be used to control traits temporally, such as flowering time to facilitate crosses for plant breeders. We also discuss the essence for developing MOST treatments, their prospects, and limitations.