Boosting Communication Efficiency in Federated Learning for Multiagent-Based Multimicrogrid Energy Management.

Privacy of user is becoming increasingly significant in constructing efficient multiagent energy management systems for multimicrogrid (MMG). As an emerging privacy-protection method, federated learning (FL) has been used to prevent data breaches in the MMG-related field. However, with the ever-growing participants, the underlying communication burden existing in FL is evident. Besides, since the neural network layers collectively determine an agent's performance, the possible difference in layer convergence speeds would cause the inconsistency problem, that is, the FL may degrade the convergence rate of those fast-convergent layers, which weakens the overall performance of the agent. To address these issues, a communication-efficient FL (CEFL) algorithm is proposed in this study. Considering the cooperative relationship among layers, a layer evaluation (LE) mechanism is developed in CEFL to evaluate layer contribution through the Shapley value (SV), a profit distribution approach for coalitions. In this way, only partial layers with the highest contributions are selected to be uploaded to the server. In addition, instead of average parameters aggregation, a communication-efficient parameter aggregation method is proposed in CEFL to update the parameters of the global model (GM), in which an aggregation model (AM) is developed to receive parameters for aggregation. The performance of the proposed CEFL is verified by the numerical analysis of MMGs with 3-8 MGs participating. Furthermore, experiments investigate the influence of the hyperparameter in the CEFL and also demonstrate performance improvements, compared with the other four state-of-the-art algorithms.

A Memristive Spiking Neural Network Circuit for Bio-inspired Navigation Based on Spatial Cognitive Mechanisms.

Cognitive navigation, a high-level and crucial function for organisms' survival in nature, enables autonomous exploration and navigation within the environment. However, most existing works for bio-inspired navigation are implemented with non-neuromorphic computing. This work proposes a bio-inspired memristive spiking neural network (SNN) circuit for goal-oriented navigation, capable of online decision-making through reward-based learning. The circuit comprises three primary modules. The place cell module encodes the agent's spatial position in real-time through Poisson spiking; the action cell module determines the direction of subsequent movement; and the reward-based learning module provides a bio-inspired learning method adaptive to delayed and sparse rewards. To facilitate practical application, the entire SNN is quantized and deployed on a real memristive hardware platform, achieving about a 21× reduction in energy consumption compared to a typical digital acceleration system in the forward computing phase. This work offers an implementation idea of neuromorphic solution for robotic navigation application in low-power scenarios.

Unspecific peroxygenase enabled formation of azoxy compounds.

Enzymes are making a significant impact on chemical synthesis. However, the range of chemical products achievable through biocatalysis is still limited compared to the vast array of products possible with organic synthesis. For instance, azoxy products have rarely been synthesized using enzyme catalysts. In this study, we discovered that fungal unspecific peroxygenases are promising catalysts for synthesizing azoxy products from simple aniline starting materials. The catalytic features (up to 48,450 turnovers and a turnover frequency of 6.7 s-1) and substrate transformations (up to 99% conversion with 98% chemoselectivity) highlight the synthetic potential. We propose a mechanism where peroxygenase-derived hydroxylamine and nitroso compounds spontaneously (non-enzymatically) form the desired azoxy products. This work expands the reactivity repertoire of biocatalytic transformations in the underexplored field of azoxy compound formation reactions.

Mittag-Leffler stability and application of delayed fractional-order competitive neural networks.

In the article, the Mittag-Leffler stability and application of delayed fractional-order competitive neural networks (FOCNNs) are developed. By virtue of the operator pair, the conditions of the coexistence of equilibrium points (EPs) are discussed and analyzed for delayed FOCNNs, in which the derived conditions of coexistence improve the existing results. In particular, these conditions are simplified in FOCNNs with stepped activations. Furthermore, the Mittag-Leffler stability of delayed FOCNNs is established by using the principle of comparison, which enriches the methodologies of fractional-order neural networks. The results on the obtained stability can be used to design the horizontal line detection of images, which improves the practicability of image detection results. Two simulations are displayed to validate the superiority of the obtained results.

Driving Cognitive Alertness Detecting Using Evoked Multimodal Physiological Signals Based on Uncertain Self-Supervised Learning.

Multimodal physiological signals play a pivotal role in drivers' perception of work stress. However, the scarcity of labels and the multitude of modalities render the utilization of physiological signals for driving cognitive alertness detection challenging. We thus propose a multimodal physiological signal detection model based on self-supervised learning. First, in order to mine the intrinsic information of data and enable data to highlight effective information, we introduce a multiscale entropy (MSE) evoked attention mechanism. Secondly, the multimodal patches undergo processing through a novel cascaded attention mechanism. This attention mechanism is rooted in patch-level interactions within each modality, progressively integrating and interacting with other modalities in a cascading manner, thereby mitigating computational complexity. Moreover, a multimodal uncertainty-aware module is devised to effectively cope with intricate variations in the data. This module enhances its generalization ability through the incorporation of uncertain resampling. Experiments were conducted on the DriveDB dataset and the CogPilot dataset with both the linear probing and the fine-tuning evaluation protocols. Experimental results in subject-dependent setting show that our model significantly outperforms previous competitive baselines. In the linear probing evaluation, our model achieves on average 6.26%, 6.64%, and 7.75% improvements in Accuracy (Acc), Recall (Rec), and F1 Score. It also outperforms other models by 7.96% in Acc, 9.13% in Rec, and 9.2% in F1 using the fine-tuning evaluation. Furthermore, our model also demonstrates robust performance in subject-independent setting.

A Bionic Localization Memristive Circuit Based on Spatial Cognitive Mechanisms of Hippocampus and Entorhinal Cortex.

In this article, a bionic localization memristive circuit is proposed, which mainly consists of head direction cell module, grid cell module, place cell module and decoding module. This work modifies the two-dimensional Continuous Attractor Network (CAN) model of grid cells into two one-dimensional models in X and Y directions. The head direction cell module utilizes memristors to integrate angular velocity and represents the real orientation of an agent. The grid cell module uses memristors to sense linear velocity and orientation signals, which are both self-motion cues, and encodes the position in space by firing in a periodic mode. The place cell module receives the grid cell module's output and fires in a specific position. The decoding module decodes the angle or place information and transfers the neuron state to a 'one-hot' code. This proposed circuit completes the localizing task in space and realizes in-memory computing due to the use of memristors, which can shorten the execution time. The functions mentioned above are implemented in LTSPICE. The simulation results show that the proposed circuit can realize path integration and localization. Moreover, it is shown that the proposed circuit has good robustness and low area overhead. This work provides a possible application idea in a prospective robot platform to help the robot localize and build maps.

Prescribed-Time Network-Based Deployment of Nonlinear Multiagent Systems: A Discrete-Space PDE Method.

This article attempts to design the prescribed-time time-varying deployment schemes for first-order and second-order nonlinear multiagent systems (MASs). We assume that all agents can obtain the information of their current and final relative positions with their neighbors, and the final absolute velocities (as well as their current and final relative velocities, the final absolute accelerations for the second-order MASs) through a communication network, whereas two boundary agents are able to obtain their current and final absolute positions (as well as their current and final absolute velocities for the second-order MASs). The neighbor relationship of all agents is described by a spatial variable and two static-feedback controllers are introduced, which can be expressed as a second-order space difference of the spatial variable. Then, the deployment of MASs can be transformed into the stabilization of discrete-space partial differential equation (PDE) systems. Three virtual agents are introduced to constitute the Dirchlet and Neumann boundary conditions. Several algebraic inequality criteria are derived to guarantee that the prescribed-time time-varying deployment can be achieved within a prescribed time under the Dirchlet and mixed boundary conditions. Unlike the published results, our results are derived based on the discrete-space PDE systems instead of continuous-space PDE systems, which is consistent with the discrete spatial distribution of agents. Finally, two numerical examples are given to illustrate the effectiveness of our results.

The Prognostic Value of Serum Calcium Levels in Elderly Dilated Cardiomyopathy Patients.

Background: It is unclear whether serum calcium on admission is associated with clinical outcomes in dilated cardiomyopathy (DCM). In this study, we conducted a retrospective study spanning a decade to investigate the prognostic value of baseline calcium in elderly patients with DCM. Methods: A total of 1,089 consecutive elderly patients (age ≥60 years) diagnosed with DCM were retrospectively enrolled from January 2010 to December 2019. Univariate and multivariate analyses were performed to investigate the association of serum calcium with their clinical outcomes. Results: In this study, the average age of the subjects was 68.36 ± 6.31 years. Receiver operating characteristic (ROC) curve analysis showed that serum calcium level had a great sensitivity and specificity for predicting in-hospital death, with an AUC of 0.732. Kaplan-Meier survival analysis showed that patients with a serum calcium >8.62 mg/dL had a better prognosis than those with a serum calcium ≤8.62 mg/dL (log-rank χ2 40.84, p < 0.001). After adjusting for several common risk factors, a serum calcium ≤8.62 mg/dL was related to a higher risk of long-term mortality (HR: 1.449; 95% CI: 1.115~1.882; p = 0.005). Conclusions: Serum calcium level could be served as a simple and affordable tool to evaluate patients' prognosis in DCM.

Exponential Boundary Control for 2-D Spatial Distributed Parameter Systems Under Boundary Collocated and Planar Linear Measurements.

For a class of 2-D spatial distributed parameter systems (DPSs) with space-dependent diffusivity, this article aims to achieve exponential realization of their desired profiles. To reduce the number of required sensors and actuators, a planar output feedback boundary control strategy is proposed with combining two nonfull-domain measurement methods, boundary collocated measurement and planar linear measurement, in which only two boundaries of the considered 2-D spatial DPSs are controlled and a little output information is measured. Moreover, by employing the Poincaré-Wirtinger inequality and variable substitution dexterously, the final exponential convergence criteria of the error system can be obtained with method of "Diverse treatment for same term." Finally, we provide a general numerical example and an application example in 2-D heat conduction systems to illustrate the effectiveness and practicability of the proposed measurement and control schemes.

Pinning Control of Multiplex Dynamical Networks Using Spectral Graph Theory.

Pinning control has been attracting wide attention for the study of various complex networks for decades. This article explores grounded theory on the pinning synchronization of the emerging multiplex dynamical networks. The multiplex dynamical networks under study can describe many real-world scenarios, in which different layers have distinct individual dynamics of node. In this work, we build the bridge between multiplex structures and network dynamics by using the Lyapunov stability theory and the spectral graph theory. Furthermore, by analyzing spectral properties of the grounded super-Laplacian matrices, we set up several graph-based synchronization criteria for multiplex networks via pinning control. In addition, we overcome the difficulties induced by distinct node dynamics in different layers, and find that interlayer coupling strengths promote intralayer synchronization of multiplex networks. Finally, a collection of numerical simulations verifies the effectiveness of theoretical results.

An Accelerated Approach on Adaptive Gradient Neural Network for Solving Time-Dependent Linear Equations: A State-Triggered Perspective.

To improve the acceleration performance, a hybrid state-triggered discretization (HSTD) is proposed for the adaptive gradient neural network (AGNN) for solving time-dependent linear equations (TDLEs). Unlike the existing approaches that use an activation function or a time-varying coefficient for acceleration, the proposed HSTD is uniquely designed from a control theory perspective. It comprises two essential components: adaptive sampling interval state-triggered discretization (ASISTD) and adaptive coefficient state-triggered discretization (ACSTD). The former addresses the gap in acceleration methods related to the variable sampling period, while the latter considers the underlying evolutionary dynamics of the Lyapunov function to determine coefficients greedily. Finally, compared with commonly used discretization methods, the acceleration performance and computational advantages of the proposed HSTD are substantiated by the numerical simulations and applications to robotics.

Quasi-synchronization for variable-order fractional complex dynamical networks with hybrid delay-dependent impulses.

This paper focuses on addressing the problem of quasi-synchronization in heterogeneous variable-order fractional complex dynamical networks (VFCDNs) with hybrid delay-dependent impulses. Firstly, a mathematics model of VFCDNs with short memory is established under multi-weighted networks and mismatched parameters, which is more diverse and practical. Secondly, under the framework of variable-order fractional derivative, a novel fractional differential inequality has been proposed to handle the issue of quasi-synchronization with hybrid delay-dependent impulses. Additionally, the quasi-synchronization criterion for VFCDNs is developed using differential inclusion theory and Lyapunov method. Finally, the practicality and feasibility of this theoretical analysis are demonstrated through numerical examples.

Convergence-Rate-Based Event-Triggered Mechanisms for Quasi-Synchronization of Delayed Nonlinear Systems on Time Scales.

Most of the existing event-triggered mechanisms (ETMs) were designed according to the difference between the quadratic form of measurement errors and the quadratic form of sampling states (or real-time states). In order to reduce the amount of data transmission and develop ETMs for continuous-time and discrete-time delayed nonlinear systems (NSs) simultaneously, this article investigates quasi-synchronization (QS) of NSs on time scales based on a novel ETM, which is designed according to the convergence rate instead of measurement errors of the addressed systems. First, a novel ETM is designed under known nonlinear dynamics, and it is demonstrated that QS with given convergence rate and error level can be achieved under matrix inequality criteria. Second, if the nonlinear functions are unknown, we adapt our ETM to handle this special case. Not only QS but also complete synchronization with given convergence rate can be achieved under the ETMs. If the constructed Lyapunov functions passes through 0, the designed ETM will keep it at the origin. In this case, finite-time synchronization is achieved. Third, under the designed ETMs, it is proved that Zeno behavior can be excluded. At last, four numerical simulations are presented to demonstrate the feasibility and the advantage of the designed ETMs in this article.

Memristive Bionic Memory Circuit Implementation and Its Application in Multisensory Mutual Associative Learning Networks.

Memory is vital and indispensable for organisms and brain-inspired intelligence to gain complete sensation and cognition of the environment. In this work, a memristive bionic memory circuit inspired by human memory model is proposed, which includes 1) receptor and sensory neuron (SN), 2) short-term memory (STM) module, and 3) long-term memory (LTM) module. By leveraging the in-memory computing characteristic of memristors, various functions such as sensation, learning, forgetting, recall, consolidation, reconsolidation, retrieval, and reset are realized. Besides, a multisensory mutual associative learning network is constructed with several bionic memory units to memorize and associate sensory information of different modalities bidirectionally. Except for association establishment, enhancement, and extinction, we also mimicked multisensory integration to manifest the synthetic process of information from different sensory channels. According to the simulation results in PSPICE, the proposed circuit performs high robustness, low area overhead, and low power consumption. Combining associative memory with human memory model, this work provides a possible idea for further research in associative learning networks.

Quasi-Synchronization of Fractional Multiweighted Coupled Neural Networks via Aperiodic Intermittent Control.

This article investigates the quasi-synchronization for fractional multiweighted coupled neural networks (FMCNNs) with discontinuous activation functions and mismatched parameters. First, under the generalized Caputo fractional-order derivative operator, a novel piecewise fractional differential inequality is established to study the convergence of fractional systems, which significantly extends some related published results. Subsequently, by exploiting the new inequality and Lyapunov stability theory, some sufficient quasi-synchronization conditions of FMCNNs are presented by aperiodic intermittent control. Meanwhile, the exponential convergence rate and synchronization error's bound are given explicitly. Finally, the validity of theoretical analysis is confirmed by numerical examples and simulations.

Characteristics of the gut microbiome and metabolic profile in elderly patients with sarcopenia.

Introduction: There is growing evidence of research indicating that the gut microbiota is involved in the development of sarcopenia. Nevertheless, there exists a notable deficiency in comprehension concerning the connection between irregularities in the intestinal microbiome and metabolic processes in older individuals suffering from sarcopenia. Methods: To analyze fecal samples obtained from a cohort of 30 older patients diagnosed with sarcopenia as well as 30 older patients without sarcopenia, this study employed 16S rDNA sequencing and liquid chromatography-mass spectrometry (LC-MS)-based non-targeted metabolomics profiling techniques. Results: As a result, we found that 29 genera and 172 metabolites were significantly altered in the sarcopenic patients. Among them, Blautia, Lachnospiraceae_unclassified, and Subdoligranulum were the bacteria with a potential diagnostic value for sarcopenia diagnosis. Correlation analysis between clinical indices and these gut bacteria suggested that the IL-6 level was negatively correlated with Blautia. Function prediction analysis demonstrated that 17 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways differ significantly between sarcopenic and non-sarcopenic patients. The primary classes of metabolites identified in the study included lipids and lipid-like molecules, organic acids and derivatives, and organoheterocyclic compounds. KEGG enrichment analysis showed that purine metabolism, arginine and proline metabolism, alanine, aspartate, and glutamate metabolism, butanoate metabolism, and histidine metabolism may contribute to the development of sarcopenia. The correlation study on gut microbiota and metabolites found that Lachnospiraceae_unclassified was positively associated with seven metabolites that were more abundant in the non-sarcopenia group and negatively correlated with three metabolites that were more abundant in the sarcopenia group. In addition, Subdoligranulum was positively correlated with seven metabolites that were lacking in sarcopenia and negatively correlated with two metabolites that were enriching in sarcopenia. Moreover, Blautia was positively associated with xanthosine. Discussion: We conducted a study on the intestinal microbiota and metabolic profile of elderly individuals with sarcopenia, offering a comprehensive analysis of the overall ecosystem. Through this investigation, we were able to validate existing research on the gut-muscle axis and further investigate potential pathogenic processes and treatment options for sarcopenia.

Adaptive Drive-Response Synchronization of Timescale-Type Neural Networks With Unbounded Time-Varying Delays.

In recent years, adaptive drive-response synchronization (DRS) of two continuous-time delayed neural networks (NNs) has been investigated extensively. For two timescale-type NNs (TNNs), how to develop adaptive synchronization control schemes and demonstrate rigorously is still an open problem. This article concentrates on adaptive control design for synchronization of TNNs with unbounded time-varying delays. First, timescale-type Barbalat lemma and novel timescale-type inequality techniques are first proposed, which provides us practical methods to investigate timescale-type nonlinear systems. Second, using timescale-type calculus, novel timescale-type inequality, and timescale-type Barbalat lemma, we demonstrate that global asymptotic synchronization can be achieved via adaptive control under algebraic and matrix inequality criteria even if the time-varying delays are unbounded and nondifferentiable. Adaptive DRS is discussed for TNNs, which implies our control schemes are suitable for continuous-time NNs, their discrete-time counterparts, and any combination of them. Finally, numerical examples on TNNs and timescale-type chaotic Ikeda-like oscillator with unbounded time-varying delays are carried out to verify the adaptive control schemes.

Multiple Mittag-Leffler Stability of Almost Periodic Solutions for Fractional-Order Delayed Neural Networks: Distributed Optimization Approach.

This article proposes new theoretical results on the multiple Mittag-Leffler stability of almost periodic solutions (APOs) for fractional-order delayed neural networks (FDNNs) with nonlinear and nonmonotonic activation functions. Profited from the superior geometrical construction of activation function, the considered FDNNs have multiple APOs with local Mittag-Leffler stability under given algebraic inequality conditions. To solve the algebraic inequality conditions, especially in high-dimensional cases, a distributed optimization (DOP) model and a corresponding neurodynamic solving approach are employed. The conclusions in this article generalize the multiple stability of integer-or fractional-order NNs. Besides, the consideration of the DOP approach can ameliorate the excessive consumption of computational resources when utilizing the LMI toolbox to deal with high-dimensional complex NNs. Finally, a simulation example is presented to confirm the accuracy of the theoretical conclusions obtained, and an experimental example of associative memories is shown.

Finite/fixed-time synchronization of inertial memristive neural networks by interval matrix method for secure communication.

This paper investigates the finite/fixed-time synchronization problem of delayed inertial memristive neural networks (DIMNNs) using interval matrix-based methods within a unified control framework. By employing set-valued mapping and differential inclusion theory, two distinct methods are applied to handle the switching behavior of memristor parameters: the maximum absolute value method and the interval matrix method. Based on these different approaches, two control strategies are proposed to select appropriate control parameters, enabling the system to achieve finite and fixed-time synchronization, respectively. Additionally, the resulting theoretical criteria differ based on the chosen control strategy, with one expressed in algebraic form and the other in the form of linear matrix inequalities (LMIs). Numerical simulations demonstrate that the interval matrix method outperforms the maximum absolute value method in terms of handling memristor parameter switching, achieving faster finite/fixed-time synchronization. Furthermore, the theoretical results are extended to the field of image encryption, where the response system is utilized for decryption and expanding the keyspace.

Synthesis of Imidazo[1,2-a]pyridine-Fused 1,3-Benzodiazepine Derivatives with Anticancer Activity via a One-Pot Cascade GBB-3CR/Pd(II)-Catalyzed Azide-Isocyanide Coupling/Cyclization Process.

A new one-pot synthesis of imidazo[1,2-a]pyridine-fused 1,3-benzodiazepine derivatives via a sequential GBB-3CR/Pd(II)-catalyzed azide-isocyanide coupling/cyclization process was developed. The Groebke-Blackburn-Bienaymé three-component reactions (GBB-3CR) of 2-aminopyridine, 2-azidobenzaldehydes, and isocyanides in the presence of a catalytic amount of p-toluenesulfonic acid gave azide intermediates without separation. The reaction was followed by using another molecule of isocyanides to produce imidazo[1,2-a]pyridine-fused 1,3-benzodiazepine derivatives in good yields by the Pd(II)-catalyzed azide-isocyanide coupling/cyclization reaction. The synthetic approach produces novel nitrogen-fused polycyclic heterocycles under mild reaction conditions. The preliminary biological evaluation demonstrated that compound 6a inhibited glioma cells efficiently, suggesting potentially broad applications of the approach for synthesis and medicinal chemistry.