Asynchronous control of networked periodic piecewise linear systems under time-varying transmission delay.

This paper studies the stability of networked periodic piecewise linear systems (NPPLSs) of which communication channels are subject to a time-varying transmission delay. Under data-sampling, the NPPLS is modeled as an asynchronous controlled periodic piecewise linear system with time-varying input delay, where the interval of asynchronous control in each subsystem is uncertain but bounded. Aimed at obtaining less conservative stability and synthesis results when tackling the uncertain switching, the dwell time of each subsystem is divided into three subintervals according to the asynchronous control interval and the maximum delay assumption. In this way, it allows the construction of piecewise Lyapunov functionals for one subsystem, the piecewise Lyapunov functionals capture different dynamic characteristics in each subinterval. Using this approach, general stability conditions of NPPLSs are derived. Then, by constructing Lyapunov functionals as a set of delay-dependent time-varying functionals, tractable exponential stability conditions of NPPLSs under transmission delay are developed using a scaling technique. Due to the coupling of the decision variables in the conditions, an iterative algorithm is proposed to solve the periodic controller gains. A numerical example is provided to illustrate the merits of the obtained controllers.

Balancing Fuzzy Control Performance and Reachable-Set-Dependent Event-Triggered Communication.

This work investigates the control design and reachable set estimation problem for T-S fuzzy systems under bounded external disturbances. By designing a fuzzy controller, the reachable set of the closed-loop fuzzy system can be bounded by an ellipsoid that is minimized via two optimization algorithms. When the fuzzy control scheme is implemented under an event-triggered framework, the limited information availability may degrade desired control performance due to only the triggered state being available for the fuzzy controller. To alleviate this problem, a novel reachable-set-dependent event-triggering condition is proposed such that satisfactory control performance can still be ensured by the above fuzzy controller. Meanwhile, the bounding ellipsoid of the reachable set is explicitly given, and a relationship is established between control performance and communication cost under the event-triggered scheme. In addition, under the condition that the reachable set remains within a specified safe area, the communication rate can be reduced while still achieving optimal control performance. The proposed strategies are verified via a simulation example.

Output Reachable Set-Based Leader-Following Consensus of Positive Agents Over Switching Networks.

This work addresses the output reachable set-based leader-following consensus problem, focusing on a group of positive agents over directed dwell-time switching networks. Two types of non-negative disturbances, namely, 1) L1 -norm bounded disturbances and 2) L∞,1 -norm bounded disturbances are studied. Meanwhile, a class of directed dwell-time switching networks for modeling the communication protocol of positive agents is investigated. To deal with the presence of disturbances, an output-feedback control protocol is developed to achieve a robust consensus with positivity preserved based on the output reachable set. By exploiting the positive characteristics, switched linear copositive Lyapunov functions are adopted to establish output reachable set-based consensus conditions. These conditions can facilitate the control protocol design by solving a bilinear programming problem, and also generate hyperpyramidal regions to confine the output consensus error. A particle swarm optimization-based (PSO-based) algorithm is applied to compute the controller gains and optimize the volume of the hyperpyramids. The proposed methods are verified by the presented numerical case studies.

Multimodal Analgesia and Opioid-Free Anesthesia in Spinal Surgery: A Literature Review.

PURPOSES: To determine if opioid-free anesthesia, opioid-sparing anesthesia, or multimodal analgesia improved outcomes in patients undergoing spinal fusion. DESIGN: A literature review was performed by searching PubMed, CINAHL, Embase, Web of Science, and Cochrane Library. METHODS: MeSH terms included "opioid free" AND "spine surgery," with alternative terms used including: regional anesthesia, multimodal analgesia, opioid-free anesthesia, enhanced recovery after surgery (ERAS), spinal surgery, spinal fusion, ACDF, cervical fusion, lumbar fusion, etc. Seven studies were deemed appropriate for inclusion with a combined sample size of n = 2,102. FINDINGS: All of the seven included articles evaluated total opioid administration and found a reduction in total opioid administered in the research groups versus control groups. Six of the seven included articles evaluated postoperative pain scores with mixed results. Various additional benefits of opioid-free, opioid-sparing, or multimodal analgesia included: decreased hospital length of stay (LOS), decreased post-anesthesia care unit (PACU) LOS, decreased post-operative nausea and vomiting (PONV), and decreased post-operative opioid use through 30 days. CONCLUSIONS: For patients undergoing spine surgery, opioid-free, opioid-sparing, and multimodal analgesia will be less likely to experience the adverse effects of opioid analgesics and ultimately lead to better patient outcomes and reduced hospital stays.

Decentralized H2 Control for Discrete-Time Networked Systems With Positivity Constraint.

In this brief, we study the decentralized H2 state-feedback control problem for networked discrete-time systems with positivity constraint. This problem (for a single positive system), raised recently in the area of positive systems theory, is known to be challenging due to its inherent nonconvexity. In contrast to most works, which only provide sufficient synthesis conditions for a single positive system, we study this problem within a primal-dual scheme, in which necessary and sufficient synthesis conditions are proposed for networked positive systems. Based on the equivalent conditions, we develop a primal-dual iterative algorithm for solution, which helps prevent from converging to a local minimum. In the simulation, two illustrative examples are employed for verification of our proposed results.

Differentially Private Average Consensus for Networks With Positive Agents.

This research paper addresses the problem of achieving differentially private average consensus for multiagent systems (MASs) consisting of positive agents. A novel randomized mechanism is introduced that employs nondecaying positive multiplicative truncated Gaussian noises to maintain the positivity and randomness of the state information over time. A time-varying controller is developed to achieve mean-square positive average consensus, and convergence accuracy is evaluated. The proposed mechanism is shown to preserve (ϵ,δ) -differential privacy of MASs, and the privacy budget is derived. Numerical examples are provided to illustrate the effectiveness of the proposed controller and privacy mechanism.

Soft Robot Proprioception Using Unified Soft Body Encoding and Recurrent Neural Network.

Compared with rigid robots, soft robots are inherently compliant and have advantages in the tasks requiring flexibility and safety. But sensing the high dimensional body deformation of soft robots is a challenge. Encasing soft strain sensors into the internal body of soft robots is the most popular solution to address this challenge. But most of them usually suffer from problems like nonlinearity, hysteresis, and fabrication complexity. To endow the soft robots with body movement awareness, this work presents a bioinspired architecture by taking cues from human proprioception system. Differing from the popular usage of smart material-based sensors embedded in soft actuators, we created a synthetic analog to the human muscle system, using paralleled soft pneumatic chambers to serve as receptors for sensing body deformation. We proposed to build the system with redundant receptors and explored deep learning tools for generating the kinematic model. Based on the proposed methodology, we demonstrated the design of three degrees of freedom continuum joint and how its kinematic model was learned from the unified pressure information of the actuators and receptors. In addition, we investigated the response of the soft system to receptor failures and presented both hardware and software level solutions for achieving graceful degradation. This approach offers an alternative to enable soft robots with proprioception capability, which will be useful for closed-loop control and interaction with environment.

Secure Estimation With Privacy Protection.

In this article, we focus on the state estimation problems for a system with protecting user privacy. Regarding whether the user has conducted a sensitive action in the system as a kind of privacy, we propose a privacy-preserving mechanism (PPM) to prevent its action results from being disclosed or inferred. For such a system with the PPM, we first obtain the optimal estimator (OE). Subject to the inoperability of the OE in practice, we turn to designing a computationally efficient suboptimal estimator (SE) as an alternative. Then, we prove that this SE can remain stable while satisfying the user's requirements on both privacy protection and estimation performance. By solving a privacy-preserving optimization problem, a set of guidelines is established to customize a tradeoff between privacy and performance according to the user's demand. Finally, illustrated examples are used to illustrate the main theoretical results.

Positive Consensus of Fractional-Order Multiagent Systems Over Directed Graphs.

This article investigates the positive consensus problem of a special kind of interconnected positive systems over directed graphs. They are composed of multiple fractional-order continuous-time positive linear systems. Unlike most existing works in the literature, we study this problem for the first time, in which the communication topology of agents is described by a directed graph containing a spanning tree. This is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains, which renders the positivity analysis fairly challenging. Based on the existing results in spectral graph theory, fractional-order systems (FOSs) theory, and positive systems theory, we derive several necessary and/or sufficient conditions on the positive consensus of fractional-order multiagent systems (PCFMAS). It is shown that the protocol, which is designed for a specific graph, can solve the positive consensus problem of agents over an additional set of directed graphs. Finally, a comprehensive comparison study of different approaches is carried out, which shows that the proposed approaches have advantages over the existing ones.

Consensus of Linear Multivariable Discrete-Time Multiagent Systems: Differential Privacy Perspective.

Differential privacy, which has been widely applied in industries, is a privacy mechanism effective in preventing malicious entities from breaching the privacy of an individual participant. It is usually achieved by adding random variables in the data. This article investigates a class of multivariable discrete-time multiagent systems with ϵ -differential privacy preserved. A novel information-masking mechanism is proposed, in which the information of each state transmitted to different neighbors is obscured by adding independent random noises. Then, the mean-square consensus conditions, and the upper bound and lower bound of the convergence rate are obtained. Moreover, the conditions for the convergence rate reaching its upper bound are established. The results can be applied to the average mean-square consensus. In addition, a necessary and sufficient condition is presented under which agents can preserve the dynamics of agents ϵ -differentially private at any time instant.

Nonnegative Consensus Tracking of Networked Systems With Convergence Rate Optimization.

This article investigates the nonnegative consensus tracking problem for networked systems with a distributed static output-feedback (SOF) control protocol. The distributed SOF controller design for networked systems presents a more challenging issue compared with the distributed state-feedback controller design. The agents are described by multi-input multi-output (MIMO) positive dynamic systems which may contain uncertain parameters, and the interconnection among the followers is modeled using an undirected connected communication graph. By employing positive systems theory, a series of necessary and sufficient conditions governing the consensus of the nominal, as well as uncertain, networked positive systems, is developed. Semidefinite programming consensus design approaches are proposed for the convergence rate optimization of MIMO agents. In addition, by exploiting the positivity characteristic of the systems, a linear-programming-based design approach is also proposed for the convergence rate optimization of single-input multi-output (SIMO) agents. The proposed approaches and the corresponding theoretical results are validated by case studies.

Event-Triggered Output Feedback Synchronization of Master-Slave Neural Networks Under Deception Attacks.

The problem of event-triggered synchronization of master-slave neural networks is investigated in this article. It is assumed that both communication channels from the sensor to controller and from controller to actuator are subject to stochastic deception attacks modeled by two independent Markov processes. Two discrete event-triggered mechanisms are introduced for both channels to reduce the number of data transmission through the communication channels. To comply with practical point of view, static output feedback is utilized. By employing the Lyapunov-Krasovskii functional method, some sufficient conditions on the synchronization of master-slave neural networks are derived in terms of linear matrix inequalities, which make it easy to design suitable output feedback controllers. Finally, a numerical example is presented to show the effectiveness of the proposed method.

Further Improvements on Non-Negative Edge Consensus of Networked Systems.

In this article, the non-negative edge consensus problem is addressed for positive networked systems with undirected graphs using state-feedback protocols. In contrast to existing results, the major contributions of this work included: 1) significantly improved criteria of consequentiality and non-negativity, therefore leading to a linear programming approach and 2) necessary and sufficient criteria giving rise to a semidefinite programming approach. Specifically, an improved upper bound is given for the maximum eigenvalue of the Laplacian matrix and the (out-) in-degree of the degree matrix, and an improved consensuability and non-negativevity condition is obtained. The sufficient condition presented only requires the number of edges of a nodal network without the connection topology. Also, with the introduction of slack matrix variables, two equivalent conditions of consensuability and non-negativevity are obtained. In the conditions, the system matrices, controller gain, as well as Lyapunov matrices are separated, which is helpful for parameterization. Based on the results, a semidefinite programming algorithm for the controller is readily developed. Finally, a comprehensive analytical and numerical comparison of three illustrative examples is conducted to show that the proposed results are less conservative than the existing work.

Consensus of Positive Networked Systems on Directed Graphs.

This article addresses the distributed consensus problem for identical continuous-time positive linear systems with state-feedback control. Existing works of such a problem mainly focus on the case where the networked communication topologies are of either undirected and incomplete graphs or strongly connected directed graphs. On the other hand, in this work, the communication topologies of the networked system are described by directed graphs each containing a spanning tree, which is a more general and new scenario due to the interplay between the eigenvalues of the Laplacian matrix and the controller gains. Specifically, the problem involves complex eigenvalues, the Hurwitzness of complex matrices, and positivity constraints, which make analysis difficult in the Laplacian matrix. First, a necessary and sufficient condition for the consensus analysis of directed networked systems with positivity constraints is given, by using positive systems theory and graph theory. Unlike the general Riccati design methods that involve solving an algebraic Riccati equation (ARE), a condition represented by an algebraic Riccati inequality (ARI) is obtained for the existence of a solution. Subsequently, an equivalent condition, which corresponds to the consensus design condition, is derived, and a semidefinite programming algorithm is developed. It is shown that, when a protocol is solved by the algorithm for the networked system on a specific communication graph, there exists a set of graphs such that the positive consensus problem can be solved as well.

Spatial distribution and accumulation profiles of volatile methylsiloxanes in Tokyo Bay, Japan: Mass loadings and historical trends.

We investigated mass loading and the spatial distribution of volatile methylsiloxanes (VMSs) including four cyclic VMSs (D3-D6; cVMSs, the number refers to the number of SiO bonds) and three linear VMSs (L3-L5; lVMSs) in Tokyo Bay, Japan, which is one of the most industrialized, urbanized, and populated areas in the world. Based on the VMS concentrations determined in eight main inflow rivers to the bay, the mass loading of VMSs via inflow rivers and sewage treatment plants located in Tokyo Bay was estimated at 2500 kg/y for total VMSs. Elevated mass loadings of VMSs were found in three of the rivers, inflowing to the inner west of Tokyo Bay. The distribution and deposition characteristics of VMSs were observed depending on the estuarine condition. Estuarine sediments were found to be efficient and effective traps for VMSs and the salting-out effect is one possible mechanism to explain this phenomenon. The overall profiles of D4, D5, and D6 in surface water and sediment were observed across Tokyo Bay; elevated concentrations were identified in the inner west bay with dispersed low concentrations in the outer bay, except for one hotspot of D4 in the sediment, indicating a major emission route of VMSs via inflow rivers. Additionally, the historical pollution profiles of VMSs in Tokyo Bay were reconstructed based on the VMS concentrations determined in a dated sediment core. VMSs were identified throughout the upper 40 cm of the sediment core (representing the mid 1980s); the profiles correspond with the historical use of VMSs in wash-off personal care-products. The noted decreasing trend of D4 might be a reflection of the early 2000s replacement of D4 with D5 in such products. The elevated VMS concentrations in the estuarine sediment raise concerns about the impact on the aquatic environment.

A Multimodal Hydrogel Soft-Robotic Sensor for Multi-Functional Perception.

Soft robots, with their unique and outstanding capabilities of environmental conformation, natural sealing against elements, as well as being insensitive to magnetic/electrical effects, are ideal candidates for extreme environment applications. However, sensing for soft robots in such harsh conditions would still be challenging, especially under large temperature change and complex, large deformations. Existing soft sensing approaches using liquid-metal medium compromise between large deformation and environmental robustness, limiting their real-world applicability. In this work, we propose a multimodal solid-state soft sensor using hydrogel and silicone. By exploiting the conductance and transparency of hydrogel, we could deploy both optical and resistive sensing in one sensing component. This novel combination enables us to benefit from the in-situ measurement discrepancies between the optical and electrical signal, to extract multifunctional measurements. Following this approach, prototype solid-state soft sensors were designed and fabricated, a dedicated neural network was built to extract the sensory information. Stretching and twisting were measured using the same sensor even at large deformations. In addition, exploiting the distinctive responses against temperature change, we could estimate environmental temperatures simultaneously. Results are promising for the proposed solid-state multimodal approach of soft sensors for multifunctional perception under extreme conditions.

Occurrence and Trophodynamics of Marine Lipophilic Phycotoxins in a Subtropical Marine Food Web.

Marine lipophilic phycotoxins (MLPs) are produced by toxigenic microalgae and cause foodborne illnesses. However, there is little information on the trophic transfer potential of MLPs in marine food webs. In this study, various food web components including 17 species of mollusks, crustaceans, and fishes were collected for an analysis of 17 representative MLPs, including azaspiracids (AZAs), brevetoxins (BTXs), gymnodimine (GYM), spirolides (SPXs), okadaic acid (OA), dinophysistoxins (DTXs), pectenotoxins (PTXs), yessotoxins (YTXs), and ciguatoxins (CTXs). Among the 17 target MLPs, 12, namely, AZAs1-3, BTX3, GYM, SPX1, OA, DTXs1-2, PTX2, YTX, and the YTX derivative homoYTX, were detected, and the total MLP concentrations ranged from 0.316 to 20.3 ng g-1 wet weight (ww). The mean total MLP concentrations generally decreased as follows: mollusks (8.54 ng g-1, ww) > crustaceans (1.38 ng g-1, ww) > fishes (0.914 ng g-1, ww). OA, DTXs, and YTXs were the predominant MLPs accumulated in the studied biota. Trophic dilution of the total MLPs was observed with a trophic magnification factor of 0.109. The studied MLPs might not pose health risks to residents who consume contaminated seafood; however, their potential risks to the ecosystem can be a cause for concern.

Antagonistic interaction between perfluorobutanesulfonate and probiotic on lipid and glucose metabolisms in the liver of zebrafish.

Perfluorobutanesulfonate (PFBS) and probiotic bacteria can interact to induce hepatic hypertrophy. However, the molecular events occurring in the hypertrophic liver are still unknown. Therefore, we performed this follow-up study using adult zebrafish that were exposed for 40 days to 0 and 10 µg/L PFBS, with or without dietary supplementation of probiotic Lactobacillus rhamnosus. After PFBS or/and probiotic exposures, proteome perturbation, histological pathogenesis and glucose metabolism were investigated in the livers. Proteomic analysis showed potent intervention of PFBS or/and probiotic with hepatic functions. PFBS single exposure caused marked disturbances in lipid metabolisms, which may underlie the severe vacuolization in male liver. The addition of probiotic alleviated the lipid metabolic disorders of PFBS. Furthermore, probiotic supplementation enhanced ATP energy production using glucose in mitochondrial respiratory chain of male fish. However, PFBS alone caused remarkable increase in blood glucose level (by 2.5-fold relative to the control), underlining the onset of hyperglycemia symptom. In contrast, the liver of male fish from the coexposure group functioned appropriately, which immediately increased insulin levels by 2.2-fold to reduce the glucose accumulation in blood. In female liver, PFBS alone significantly decreased the blood glucagon concentration by 2.9-fold. The deficiency of glucagon hormone consequently contributed to the accumulation of glycogen (3.2-fold) therein. Vigorous antagonistic interaction between PFBS and probiotic was noted with respect to glucose metabolism, which restored ATP, glucose, glycogen and glucagon to the control levels. Overall, the present study finds that probiotic L. rhamnosus is efficient to mitigate the metabolic disorders of PFBS on lipid and glucose, highlighting the potential values of probiotic bacteria to protect the aquatic ecosystem.

Target, Nontarget, and Suspect Screening and Temporal Trends of Per- and Polyfluoroalkyl Substances in Marine Mammals from the South China Sea.

Per- and polyfluoroalkyl substances (PFASs) have been manufactured and widely used for over 60 years. Currently, there are thousands of marketed PFASs, but only dozens of them are routinely monitored. This work involved target, nontarget, and suspect screening of PFASs in the liver of Indo-Pacific humpback dolphin (Sousa chinensis) and finless porpoise (Neophocaena phocaenoides), two resident marine mammals in the South China Sea, stranded between 2012 and 2018. Among the 21 target PFASs, perfluorooctane sulfonate and 6:2 chlorinated polyfluoroalkyl ether sulfonate (6:2 Cl-PFESA) predominated in the samples, accounting for 46 and 30% of the total PFASs, respectively. Significantly higher total target PFAS concentrations (p < 0.05) were found in dolphin liver samples [3.23 × 103 ± 2.63 × 103 ng/g dry weight (dw)] than in porpoise liver samples (2.63 × 103 ± 1.10 × 103 ng/g dw). Significant increasing temporal trends (p < 0.05) were found in the concentrations of two emerging PFASs, perfluoroethylcyclohexane sulfonate and 2,3,3,3-tetrafluoro-2-propanoate in porpoises, indicating increasing pollution by these emerging PFASs. Forty-four PFASs from 9 classes were additionally identified by nontarget and suspect screening, among which 15 compounds were reported for the first time in marine mammals. A primary risk assessment showed that the emerging PFAS 6:2 Cl-PFESA could have possible adverse effects in terms of reproductive injury potential on most of the investigated cetaceans.