Effects of 3-nitrooxypropanol (3-NOP, Bovaer®10) and whole cottonseed on milk production and enteric methane emissions from dairy cows under Swiss management conditions

The objective of this study was to determine the potential effect and interaction of 3- nitrooxypropanol (3-NOP; Bovaer®) and whole cottonseed (WCS) on lactational performance, and enteric methane (CH4) emission of dairy cows. A total of 16 multiparous cows, including 8 Holstein Friesian (HF) and 8 Brown Swiss (BS) [224 ± 36 d in milk, 26 ± 3.7 kg milk yield], were used in a split-plot design, where the main plot was the breed of cows. Within each subplot, cows were randomly assigned to a treatment sequence in a replicated 4 × 4 Latin Square design with 2 × 2 factorial arrangements of treatments with 4, 24-d periods. The experimental treatments were: 1) Control (basal TMR), 2) 3-NOP (60 mg/kg TMR DM), 3) WCS (5% TMR DM), and 4) 3-NOP + WCS. The treatment diets were balanced for ether extract, crude protein, and NDF contents (4%, 16%, and 43% of TMR DM, respectively). The basal diets were fed twice daily at 0800 and 1800 h. Dry matter intake (DMI) and milk yield were measured daily, and enteric gas emissions were measured (using the GreenFeed system) during the last 3 d of each 24-d experimental period when animals were housed in tie stalls. There was no difference in DMI on treatment level, whereas the WCS treatment increased ECM yield and milk fat yield. There was no interaction of 3-NOP and WCS for any of the enteric gas emission parameters, but 3-NOP decreased CH4 production (g/d), CH4 yield (g/kg DMI), and CH4 intensity (g/kg ECM) by 13, 14 and 13%, respectively. Further, an unexpected interaction of breed by 3-NOP was observed for different enteric CH4 emission metrics: HF cows had a greater CH4 mitigation effect compared with BS cows for CH4 production (g/d; 18 vs. 8%), CH4 intensity (g/kg MY; 19% vs. 3%) and CH4 intensity (g/kg ECM; 19 vs. 4%). Hydrogen production was increased by 2.85 folds in HF and 1.53 folds in BS cows receiving 3-NOP. Further, there was a 3-NOP ' Time interaction for both breeds. In BS cows, 3-NOP tended to reduce CH4 production by 18% at around 4 h after morning feeding but no effect was observed at other time points. In HF cows, the greatest mitigation effect of 3-NOP (29.6%) was observed immediately after morning feeding and it persisted at around 23% to 26% for 10 h until the second feed provision, and 3 h thereafter, in the evening. In conclusion, supplementing 3-NOP at 60 mg/kg DM to a high fiber diet resulted in 18 to 19% reduction in enteric CH4 emission in Swiss Holstein Friesian cows. The lower response to 3-NOP by BS cows was unexpected and has not been observed in other studies. These results should be interpreted with caution due to low number of cows per breed. Lastly, supplementing WCS at 5% of DM improved ECM and milk fat yield but did not enhance CH4 inhibition effect of 3-NOP of dairy cows.

Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management

The utilization of large-scale distributed renewable energy (RE) promotes the development of the multimicrogrid (MMG), which raises the need of developing an effective energy management method to minimize economic costs and keep self energy sufficiency. The multiagent deep reinforcement learning (MADRL) has been widely used for the energy management problem because of its real-time scheduling ability. However, its training requires massive energy operation data of microgrids (MGs), while gathering these data from different MGs would threaten their privacy and data security. Therefore, this article tackles this practical yet challenging issue by proposing a federated MADRL (F-MADRL) algorithm via the physics-informed reward. In this algorithm, the federated learning (FL) mechanism is introduced to train the F-MADRL algorithm, thus ensures the privacy and the security of data. In addition, a decentralized MMG model is built, and the energy of each participated MG is managed by an agent, which aims to minimize economic costs and keep self energy sufficiency according to the physics-informed reward. At first, MGs individually execute the self-training based on local energy operation data to train their local agent models. Then, these local models are periodically uploaded to a server and their parameters are aggregated to build a global agent, which will be broadcasted to MGs and replace their local agents. In this way, the experience of each MG agent can be shared and the energy operation data are not explicitly transmitted, thus protecting the privacy and ensuring data security. Finally, experiments are conducted on Oak Ridge National Laboratory distributed energy control communication laboratory MG (ORNL-MG) test system, and the comparisons are carried out to verify the effectiveness of introducing the FL mechanism and the outperformance of our proposed F-MADRL.

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.

Experimental Limits on Solar Reflected Dark Matter with a New Approach on Accelerated-Dark-Matter-Electron Analysis in Semiconductors

Recently a dark matter-electron (DM-electron) paradigm has drawn much attention. Models beyond the standard halo model describing DM accelerated by high energy celestial bodies are under intense examination as well. In this Letter, a velocity components analysis (VCA) method dedicated to swift analysis of accelerated DM-electron interactions via semiconductor detectors is proposed and the first HPGe detector-based accelerated DM-electron analysis is realized. Utilizing the method, the first germanium based constraint on sub-GeV solar reflected DM-electron interaction is presented with the 205.4  kg·day dataset from the CDEX-10 experiment. In the heavy mediator scenario, our result excels in the mass range of 5-15  keV/c^{2}, achieving a 3 orders of magnitude improvement comparing with previous semiconductor experiments. In the light mediator scenario, the strongest laboratory constraint for DM lighter than 0.1  MeV/c^{2} is presented. The result proves the feasibility and demonstrates the vast potential of the VCA technique in future accelerated DM-electron analyses with semiconductor detectors.

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.

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.

Modified Nested Saturated Control for Uncertain Multiple Integrators With High-Order Nonlinear Perturbation

This article studies the global asymptotic stability problem of multiple integrators subject to uncertain parameters and high-order nonlinear perturbation. By introducing a modified saturation function with positive tunable parameters, three types of modified nested saturated controllers with different parameter assignments are proposed to handle the uncertain integrators with high-order nonlinear perturbation. The specific mechanism of the modified nested saturated controllers with different parameter assignments, in which control parameters appear before states, saturation functions, or saturation levels, is comprehensively analyzed. With the help of a modified saturation function, the global convergence domain of each state can be kept invariable in the saturation reduction analysis. Meanwhile, a large saturation level is obtained by adjusting positive tunable parameters. Combining with derivatives calculation and M -matrix-based comparison theory, proposed saturated control schemes are utilized to handle the considered system. Finally, an active magnetic bearing (AMB) system is revisited as an uncertain mechanical system and used to verify the feasibility of the proposed control designs.

Exhalomics as a noninvasive method for assessing rumen fermentation in dairy cows: Can exhaled-breath metabolomics replace rumen sampling?

Previously, we used secondary electrospray ionization-mass spectrometry (SESI-MS) to investigate the diurnal patterns and signal intensities of exhaled (EX) volatile fatty acids (VFA) of dairy cows. The current study aimed to validate the potential of an exhalomics approach for evaluating rumen fermentation. The experiment was conducted in a switchback design, with 3 periods of 9 d each, including 7 d for adaptation and 2 d for sampling. Four rumen-cannulated original Swiss Brown (Braunvieh) cows were randomly assigned to 1 of 2 diet sequences (ABA or BAB): (A) low starch (LS; 6.31% starch on a dry matter basis) and (B) high starch (HS; 16.2% starch on a dry matter basis). Feeding was once per day at 0830 h. Exhalome (with the GreenFeed System), and rumen samples were collected 8 times to represent every 3 h of a day, and EX-VFA and ruminal (RM)-VFA were analyzed using SESI-MS and HPLC, respectively. Furthermore, the VFA concentration in the gas phase (HR-VFA) was predicted based on RM-VFA and Henry's Law (HR) constants. No interactions were identified between the types of diets (HS vs. LS) and the measurement methods on daily average VFA profiles (RM vs. EX or HR vs. EX), suggesting a consistent performance among the methods. Additionally, when the 3-h interval VFA data from HS and LS diets were analyzed separately, no interactions were observed between methods and time of day, indicating that the relative daily pattern of VFA molar proportions was similar regardless of the VFA measurement method used. The results revealed that the levels of acetate sharply increased immediately after feeding, trailed by an increase in the acetate:propionate ratio and a steady increase for propionate (2 h after feeding the HS diet, 4 h for LS), and butyrate. This change was more pronounced for the HS diet than the LS diet. However, there was no overall diet effect on the VFA molar proportions, although the measurement methods affected the molar proportions. Furthermore, we observed a strong positive correlation between the levels of RM and EX acetate for both diets (HS: r = 0.84; LS: r = 0.85), RM and EX propionate (r = 0.74), and RM and EX acetate:propionate ratio (r = 0.80). Both EX-VFA and RM-VFA exhibited similar responses to feeding and dietary treatments, suggesting that EX-VFA could serve as a useful proxy for characterizing RM-VFA molar proportions to evaluate rumen fermentation. Similar relationships were observed between RM-VFA and HR-VFA. In conclusion, this study underscores the potential of exhalomics as a reliable approach for assessing rumen fermentation. Moving forward, research should further explore the depth of exhalomics in ruminant studies to provide a comprehensive insight into rumen fermentation metabolites, especially across diverse dietary conditions.

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.

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.

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.

[A real-world study on the features of postpartum hepatitis flares in pregnant women with chronic HBV infection]

Objective: To analyze the clinical features of postpartum hepatitis flares in pregnant women with hepatitis B virus (HBV) infection. Methods: A retrospective study was conducted. Patients who met the enrollment criteria were included. Liver function and HBV virology tests were collected from pregnant women with chronic HBV infection at delivery, 6, 24, 36, and 48 weeks after delivery through the hospital information and test system. Additionally, antiviral therapy types and drug withdrawal times were collected. Statistical analysis was performed on all the resulting data. Results: A total of 533 pregnant women who met the inclusion criteria were included, with all patients aged (29.5±3.7) years old. A total of 408 cases received antiviral drugs during pregnancy to interrupt mother-to-child transmission. There was no significant difference in the levels of alanine aminotransferase (ALT, z = -1.981, P = 0.048), aspartate aminotransferase (AST, z = -3.956, P < 0.001), HBV load (z = -15.292, P < 0.001), and HBeAg (z = -4.77, P < 0.001) at delivery in patients who received medication and those who did not. All patients ALT, AST, total bilirubin, direct bilirubin, and albumin showed an upward trend within six weeks after delivery. A total of 231 cases developed hepatitis within 48 weeks after delivery. Among them, 173 cases first showed ALT abnormalities within six weeks postpartum. Conclusion: Hepatitis flare incidence peaked six weeks after delivery or six weeks after drug withdrawal in pregnant women with chronic HBV infection.

Exponential Stabilization of Semi-Markov Reaction-Diffusion Memristive NNs via Event-Based Spatially Pointwise-Piecewise Switching Control

This article considers both the semi-Markov jumping phenomenon and spatial distribution characteristics when investigating the exponential stabilization of memristive neural networks (MNNs). The introduction of the semi-Markov jumping parameters relaxes the restriction on the sojourn time of Markovian MNNs. To increase the operability while ensuring control effect, a novel event-based spatially pointwise-piecewise switching control scheme is presented under a unified spatial division criterion, in which the pointwise and piecewise control can switch according to the preset event condition for the applicability to different control requirements. Moreover, by constructing a semi-Markov Lyapunov functional and utilizing the properties of the considered cumulative distribution function, the final exponential stabilization criterion and two related corollaries are obtained. Finally, simulation results illustrate the effectiveness and superiority of the proposed control strategy.

[Serum hepatitis B virus pregenomic RNA profiles in patients with chronic hepatitis B on long-term antiviral therapy]

Objective: To explore the clinical changes in levels of the new clinical marker serum hepatitis B virus (HBV) pregenomic RNA (pgRNA) in patients with chronic hepatitis B (CHB) with long-term antiviral therapy. Methods: 100 CHB cases who were initially treated with nucleos(t)ide analogues (NAs) at Peking University First Hospital were included. The levels of alanine aminotransferase (ALT), HBV DNA, hepatitis B e-antigen (HBeAg), and hepatitis B surface antigen (HBsAg) during the follow-up period were measured. The TaqMan-based real-time quantitative PCR method was used to detect serum HBV pgRNA levels. The independent sample t-test and Mann-Whitney U test were used to compare continuous variables between groups, while Pearson's χ (2) test and Fisher's exact test were used to compare categorical variables. Results: HBV pgRNA levels decreased significantly in patients who developed virological responses at 48 weeks (n = 54) during subsequent treatment compared to those who did not (n = 46). The HBV pgRNA level was lower in HBeAg-positive patients than in HBeAg-negative patients (P < 0.05 or P < 0.01). Patients with higher HBV DNA and HBeAg-positivity levels at baseline had a higher HBV pgRNA level following antiviral therapy. There was no statistically significant difference in HBV pgRNA levels in patients with different HBV pgRNA levels at baseline after antiviral therapy. There was no correlation between serum HBV pgRNA and HBsAg at baseline, but there was a correlation after long-term antiviral therapy, while there was a weak correlation between HBV pgRNA and HBsAg at the fifth and ninth years of antiviral therapy (r = 0.262, P = 0.031; r = 0.288, P = 0.008). Conclusion: HBV pgRNA levels were higher with higher HBV activity in CHB patients with long-term antiviral therapy.

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.

The association between periodontitis and hepatitis virus infection: a cross-sectional study utilizing data from the NHANES database (2003-2018)

OBJECTIVES

Periodontitis and hepatitis virus infection significantly impact individuals' well-being and are prevalent public health concerns globally. Given the current scarcity of large-scale cross-sectional epidemiological studies, this study seeks to enrich the evidence base by examining the link between these two conditions.

STUDY DESIGN AND METHODS

A cross-sectional study was conducted using data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2003-2018. A multivariate logistic regression analysis was performed to assess the association between periodontitis and hepatitis virus infection, adjusting for the potential confounding factors. Subsequently, a stratified analysis was conducted to explore the relationship between periodontitis and hepatitis virus infection based on age, gender, race, marital status, alcohol consumption, smoking status, and the presence of chronic diseases.

RESULTS

In this study, which included 5755 participants, there was a positive association between hepatitis virus infection and periodontitis (odds ratio [OR]: 2.609 [95% confidence interval (CI): 1.513, 4.499]). Furthermore, a significant association was observed between moderate periodontitis and hepatitis virus infection (OR: 2.136 [95% CI: 1.194, 3.822]), and this association was even stronger for severe periodontitis (OR: 3.583 [95% CI: 1.779, 7.217]). Importantly, this positive association between hepatitis virus infection and periodontitis was consistent across different subgroups.

CONCLUSIONS

This study presents evidence of a significant association between periodontitis and hepatitis virus infection. These findings highlight the crucial importance of integrating periodontal health and liver health considerations into public health interventions. Further research is necessary to elucidate the underlying mechanisms and develop targeted interventions for effectively managing periodontitis and hepatitis virus infection.

The Framework and Memristive Circuit Design for Multisensory Mutual Associative Memory Networks

In this work, we propose a multisensory mutual associative memory networks framework and memristive circuit to mimic the ability of the biological brain to make associations of information received simultaneously. The circuit inspired by neural mechanisms of associative memory cells mainly consists of three modules: 1) the storage neurons module, which encodes external multimodal information into the firing rate of spikes; 2) the synapse module, which uses the nonvolatility memristor to achieve weight adjustment and associative learning; and 3) the retrieval neuron module, which feeds the retrieval signal output from each sensory pathway to other sensory pathways, so that achieve mutual association and retrieval between multiple modalities. Different from other one-to-one or many-to-one unidirectional associative memory work, this circuit achieves bidirectional association from multiple modalities to multiple modalities. In addition, we simulate the acquisition, extinction, recovery, transmission, and consolidation properties of associative memory. The circuit is applied to cross-modal association of image and audio recognition results, and episodic memory is simulated, where multiple images in a scene are intramodal associated. With power and area analysis, the circuit is validated as hardware-friendly. Further research to extend this work into large-scale associative memory networks, combined with visual-auditory-tactile-gustatory sensory sensors, is promising for application in intelligent robotic platforms to facilitate the development of neuromorphic systems and brain-like intelligence.

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.

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.

Sampled-Data Output Feedback Control for Nonlinear Uncertain Systems Using Predictor-Based Continuous-Discrete Observer

In this article, we investigate the problem of sampled-data robust output feedback control for a class of nonlinear uncertain systems with time-varying disturbance and measurement delay based on continuous-discrete observer. An augmented system that includes the nonlinear uncertain system and disturbance model is first found, and by using the delayed sampled-data output, we then propose a novel predictor-based continuous-discrete observer to estimate the unknown state and disturbance information. After that, in order to attenuate the undesirable influences of nonlinear uncertainties and disturbance, a sampled-data robust output feedback controller is developed based on disturbance/uncertainty estimation and attenuation technique. It shows that under the proposed control method, the states of overall hybrid nonlinear system can converge to a bounded region centered at the origin. The main benefit of the proposed control method is that in the presence of measurement delay, the influences of time-varying disturbance and nonlinear uncertainties can be effectively attenuated with the help of feedback domination method and prediction technique. Finally, the effectiveness of the proposed control method is demonstrated via the simulation results of a numerical example and a practical example.

Pyridostigmine ameliorates pristane-induced arthritis symptoms in Dark Agouti rats

OBJECTIVE

Rheumatoid arthritis (RA) is a chronic inflammatory disorder. Pyridostigmine (PYR), an acetylcholinesterase (AChE) inhibitor, has been shown to reduce inflammation and oxidative stress in several animal models for inflammation-associated conditions. The present study aimed to investigate the effects of PYR on pristane-induced (PIA) in Dark Agouti (DA) rats.

METHOD

DA rats were intradermally infused with pristane to establish the PIA model, which was treated with PYR (10 mg/kg/day) for 27 days. The effects of PYR on synovial inflammation, oxidative stress, and gut microbiota were evaluated by determining arthritis scores, H&E staining, quantitative polymerase chain reaction, and biochemical assays, as well as 16S rDNA sequencing.

RESULTS

Pristane induced arthritis, with swollen paws and body weight loss, increased arthritis scores, synovium hyperplasia, and bone or cartilage erosion. The expression of pro-inflammatory cytokines in synovium was higher in the PIA group than in the control group. PIA rats also displayed elevated levels of malondialdehyde, nitric oxide, superoxide dismutase, and catalase in plasma. Moreover, sequencing results showed that the richness, diversity, and composition of the gut microbiota dramatically changed in PIA rats. PYR abolished pristane-induced inflammation and oxidative stress, and corrected the gut microbiota dysbiosis.

CONCLUSION

The results of this study support the protective role of PYR in PIA in DA rats, associated with the attenuation of inflammation and correction of gut microbiota dysbiosis. These findings open new perspectives for pharmacological interventions in animal models of RA.

The influence of residual plural scattering after deconvolution in electron magnetic chiral dichroism

This work investigated the existence and influence of residual plural scattering in electron magnetic chiral dichroism (EMCD) spectra. A series of low-loss, conventional core-loss, and q-resolved core-loss spectra at Fe-L2,3 edges were detected from areas of different thicknesses in a plane-view sample of Fe/MgO (001) thin film. It reveals by comparison that there remains noticeable plural scattering in q-resolved spectra acquired at two particular chiral positions after deconvolution, and the residual scattering is more significant in thicker areas than thinner ones. Accordingly, the orbital-to-spin moment ratio extracted from EMCD spectra, which is the difference between the two q-resolved spectra after deconvolution, would be in principle increased with increasing sample thickness. The randomly fluctuated moment ratios displayed in our experiments are greatly attributed to a slight and irregular variation of local diffraction conditions due to the bending effect and imperfect epitaxy in detected areas. We suggest EMCD spectra should be acquired from sufficiently thin samples to minimize the plural scattering effect in originally detected spectra before any deconvolution. In addition, great care should be taken for slight misorientation and imperfect epitaxy when performing EMCD investigation on epitaxial thin films using a nano beam.

Event-Triggered Impulsive Quasisynchronization of Coupled Dynamical Networks With Proportional Delay

The quasisynchronization of nonidentically coupled dynamical networks (NCDNs) with proportional delay is achieved by the event-triggered mechanism (ETM). Heterogeneity and proportional delay greatly increase the difficulty on synchronization of NCDNs. As an unbounded delay in the coupling term, proportional delay is dealt with by the comparison principle, constructing parameter equations, and contradiction method. Moreover, different impulsive effects based on ETM are taken into account to reduce the burden of communication, and the quasisynchronization criteria for NCDNs are derived by the impulsive comparison principle and extended variable parameter formula. The synchronization errors and the exponential convergence rates under different impulsive effects are obtained. It is proven that the proposed ETM can avoid Zeno behavior. Finally, examples show the effectiveness of the proposed control scheme.

Periodic Event-Triggered Control for Networked Control Systems With External Disturbance and Input and Output Delays

This article investigates the problem of periodic event-triggered output-feedback control for networked control systems in the presence of external disturbance and input and output delays. With the aid of the prediction technique, we first develop the predictor-based-extended state observer to reconstruct the system information, including the unknown state and disturbance. The periodic event-triggered output-feedback control law is then designed via the disturbance/uncertainty estimation and attenuation (DUEA) method, such that the communication times can be remarkably reduced and, at the same time, the disturbance rejection ability can be effectively enhanced. Under the predictor-based event-triggered control method, the influence of the time delays is effectively attenuated, and the effect of external disturbance is considerably attenuated due to the prediction technique and the DUEA method. By using the small-gain arguments, this article gives some sufficient stability conditions for the overall control system, and the explicit computations of sampling/updating period and time delays are presented as well. Finally, we employ a practical example and show some comparative simulation results to demonstrate the advantages of the predictor-based event-triggered control method proposed in this article.

STING-Dependent Sensing of Self-DNA Driving Pyroptosis Contributes to Radiation-Induced Lung Injury

PURPOSE/OBJECTIVE(S)

Radiation therapy (RT) is indispensable for managing thoracic carcinomas. However, its application is limited by radiation-induced lung injury (RILI), one of the most common and fatal complications of thoracic RT. Nonetheless, the exact molecular mechanisms of RILI remain poorly understood.

MATERIALS/METHODS

To elucidate the underlying mechanisms, various knockout (KO) mouse strains were subjected to 16 Gy whole-thoracic RT. RILI was assessed by qRT-PCR, ELISA, histology, western blot, immunohistochemistry, and CT examination. To perform further mechanistic studies on the signaling cascade during the RILI process, pulldown, CHIP, and rescue assays were conducted.

RESULTS

We found that the cGAS-STING pathway was significantly upregulated after irradiation exposure in both the mouse models and clinical lung tissues. Knocking down either cGAS or STING led to attenuated inflammation and fibrosis in mouse lung tissues. NLRP3 is hardwired to the upstream DNA-sensing cGAS-STING pathway to trigger of the inflammasome and amplification of the inflammatory response. STING deficiency suppressed the expressions of the NLRP3 inflammasome and pyroptosis-pertinent components containing IL-1β, IL-18, and cleaved caspase-1. Mechanistically, interferon regulatory factor 3, the essential transcription factor downstream of cGAS-STING, promoted the pyroptosis by transcriptionally activating NLRP3. Moreover, we found that RT triggered the release of self-dsDNA in the bronchoalveolar space, which is essential for the activation of cGAS-STING and the downstream NLRP3-mediated pyroptosis. Of note, Pulmozyme, an old drug for the management of cystic fibrosis, was revealed to have the potential to mitigate RILI by degrading extracellular dsDNA and then inhibiting the cGAS-STING-NLRP3 signaling pathway.

CONCLUSION

These results delineated the crucial function of cGAS-STING as a key mediator of RILI, and described a mechanism of pyroptosis linking cGAS-STING activation with the amplification of initial RILI. These findings indicate that the dsDNA-cGAS-STING-NLRP3 axis might be potentially amenable to therapeutic targeting for RILI.

Serum albumin and prognosis in elderly patients with nonischemic dilated cardiomyopathy

AIMS

Hypoalbuminemia was extensively used to diagnose malnutrition in older adults. Malnutrition was associated with mortality in elderly patients with cardiovascular diseases. The relationship between hypoalbuminemia and clinical outcomes in elderly patients with nonischemic dilated cardiomyopathy (NIDCM) remains unknown.

METHODS

A total of 1058 consecutive patients with NIDCM (age ≥60 years) were retrospectively enrolled from January 2010 to December 2019. Univariate and multivariate analyses were performed to assess the association of hypoalbuminemia with clinical outcomes.

RESULTS

Patients with hypoalbuminemia were older (69.29 ± 6.67 vs. 67.61 ± 5.90 years, P  < 0.001) and had higher prevalence of in-hospital and long-term death than those without (6.9 vs. 1.7%, 50.7 vs. 35.2%, P  < 0.001). Logistic regression analysis showed that hypoalbuminemia was significantly related to in-hospital death [odds ratio (OR): 4.334, 95% confidence interval (CI): 2.185-8.597, P  < 0.001]. Kaplan-Meier survival analysis showed that patients with hypoalbuminemia had worse prognosis than those with nonhypoalbuminemia (log-rank χ2 28.96, P  < 0.001). After adjusting for age, serum creatinine, HDL-C, AST/ALT hypoalbuminemia, LVEF and diabetes, hypoalbuminemia remained an independent predictor for long-term death (hazard ratio 1.322, 95% CI 0.046-1.670, P  = 0.019).

CONCLUSION

Hypoalbuminemia was associated with increased risk of in-hospital and long-term mortality in elderly patients with NIDCM.

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.

Optimal Cisplatin Cycles in Locally Advanced Cervical Carcinoma Patients Treated with Concurrent Chemoradiotherapy

PURPOSE/OBJECTIVE(S)

To analyze the effect of cisplatin cycles on the clinical outcomes of patients with locally advanced cervical cancer (LACC) treated with concurrent chemoradiotherapy (CCRT).

MATERIALS/METHODS

This study included 749 patients with LACC treated with CCRT between January 2011 and December 2015. A receiver operating characteristic (ROC) curve was used to analyze the optimal cut-off of cisplatin cycles in predicting clinical outcomes. Clinicopathological features of the patients were compared using the Chi-square test. Prognosis was assessed using log-rank tests and Cox proportional hazard models. Toxicities were compared among different cisplatin cycle groups.

RESULTS

Based on the ROC curve, the optimal cut-off of the cisplatin cycles was 4.5 (sensitivity, 64.3%; specificity, 54.3%). The 3-year overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival for patients with low-cycles (cisplatin cycles < 5) and high-cycles (≥ 5) were 81.5% and 89.0% (P < 0.001), 73.4% and 80.1% (P = 0.024), 83.0% and 90.8% (P = 0.005), and 84.9% and 86.8% (P = 0.271), respectively. In multivariate analysis, cisplatin cycles were an independent prognostic factor for overall survival. In the subgroup analysis of high-cycle patients, patients who received over five cisplatin cycles had similar overall, disease-free, loco-regional relapse-free, and distant metastasis-free survival to patients treated with five cycles. Acute and late toxicities were not different between the two groups.

CONCLUSION

Cisplatin cycles were associated with overall, disease-free, and loco-regional relapse-free survival in LACC patients who received CCRT. Five cycles appeared to be the optimal number of cisplatin cycles during CCRT.

Adaptive Synchronization of Reaction-Diffusion Neural Networks With Nondifferentiable Delay via State Coupling and Spatial Coupling

In this article, master-slave synchronization of reaction-diffusion neural networks (RDNNs) with nondifferentiable delay is investigated via the adaptive control method. First, centralized and decentralized adaptive controllers with state coupling are designed, respectively, and a new analytical method by discussing the size of adaptive gain is proposed to prove the convergence of the adaptively controlled error system with general delay. Then, spatial coupling with adaptive gains depending on the diffusion information of the state is first proposed to achieve the master-slave synchronization of delayed RDNNs, while this coupling structure was regarded as a negative effect in most of the existing works. Finally, numerical examples are given to show the effectiveness of the proposed adaptive controllers. In comparison with the existing adaptive controllers, the proposed adaptive controllers in this article are still effective even if the network parameters are unknown and the delay is nonsmooth, and thus have a wider range of applications.

[Research advances of sepsis biomarkers]

Sepsis is a life-threatening condition for patients. Biomarkers can be used for the diagnosis, treatment, and prognostic assessment of sepsis. In recent years, new biomarkers for sepsis have been discovered, and more than 250 biomarkers have been identified so far. The complexity of the sepsis process and the increased sensitivity of various detection techniques will lead to the emergence of new biomarkers. However, there is still a lack of specific diagnostic biomarkers and effective therapeutic approaches for sepsis in clinical practice. Therefore, the search for reliable biomarkers and the evaluation of the role of biomarkers in sepsis will undoubtedly aid in clinical decision-making. This article reviews the advances on research of sepsis biomarkers in order to improve understanding of current biomarkers of sepsis, and provide reference for the application of biomarkers in clinical diagnosis, treatment, and prognosis of sepsis.

Early strategy vs. late initiation of renal replacement therapy in adult patients with acute kidney injury: an updated systematic review and meta-analysis of randomized controlled trials

OBJECTIVE

The optimal time to start renal replacement therapy (RRT) for acute kidney injury (AKI) remains controversial. We aim to compare the effects of early vs. delayed RRT initiation on clinical outcomes in adult patients with AKI.

MATERIALS AND METHODS

PubMed, Embase, Cochrane Library, Web of Science, Chinese Biomedical Literature Database, ClinicalTrials.gov, and the International Clinical Trial registry platform were systematically searched from inception to 7 August 2022. The review included randomized clinical trials (RCTs) comparing early and delayed initiation of RRT in AKI patients. The selected primary outcomes were short-term and long-term mortality. Secondary outcomes included RRT dependency, intensive care unit (ICU) length of stay, hospital length of stay, mechanical ventilator-free days, vasoactive agents-free days, RRT-free days, and adverse events.

RESULTS

Overall, 15 RCTs, including 5,625 patients, were analyzed. Early RRT showed no survival benefit when compared to the delayed therapy (28-or 30-day mortality: RR, 1.01, 95% CI: 0.94-1.08, p = 0.87; 60-day mortality: RR, 0.87, 95% CI: 0.71-1.06, p = 0.16; 90-day mortality: RR, 1.00, 95% CI: 0.88-1.13, p = 0.97; in-hospital mortality: RR, 1.05, 95% CI: 0.88-1.24, p = 0.58; ICU mortality: RR, 1.00, 95% CI: 0.91-1.10, p = 0.98). The delayed RRT did not lead to a higher risk of RRT dependency, ICU, or hospital length of stay than the early RRT. Similarly, early initiation of RRT did not lead to longer ventilator-free, vasoactive agent-free, and RRT-free days. However, early RRT initiation was associated with more adverse events.

CONCLUSIONS

Our study suggested that early RRT initiation was not associated with survival benefits or better clinical outcomes and increased the risk of RRT-associated adverse events. Current evidence does not support the use of early RRT for AKI patients without urgent indications.

Muricauda okinawensis sp. Nov. and Muricauda yonaguniensis sp. Nov., Two Marine Bacteria Isolated from the Sediment Core near Hydrothermal Fields of Southern Okinawa Trough

Two strains, 81s02^T and 334s03^T, were isolated from the sediment core near the hydrothermal field of southern Okinawa Trough. The cells of both strains were observed to be rod-shaped, non-gliding, Gram-staining negative, yellow-pigmented, facultatively anaerobic, catalase and oxidase positive, and showing optimum growth at 30 °C and pH 7.5. The strains 81s02^T and 334s03^T were able to tolerate up to 10% and 9% (w/v) NaCl concentration, respectively. Based on phylogenomic analysis, the average nucleotide identity (ANI) and the digital DNA-DNA hybridization (dDDH) values between the two strains and the nearest phylogenetic neighbors of the genus Muricauda were in range of 78.0-86.3% and 21.5-33.9%, respectively. The strains 81s02^T and 334s03^T shared 98.1% 16S rRNA gene sequence similarity to each other but were identified as two distinct species based on 81.4-81.5% ANIb, 85.5-85.6% ANIm and 25.4% dDDH values calculated using whole genome sequences. The strains 81s02^T and 334s03^T shared the highest 16S rRNA gene sequence similarity to M. lutimaris SMK-108^T (98.7%) and M. aurea BC31-1-A7^T (98.8%), respectively. The major fatty acid of strains 81s02^T and 334s03^T were identified similarly as iso-C_15:0, iso-C_17:0 3-OH and iso-C_15:1 G, and the major polar lipids of the both strains consisted of phosphatidylethanolamine and two unidentified lipids. The strains contained MK-6 as their predominant menaquinone. The genomic G+C contents of strains 81s02^T and 334s03^T were determined to be 41.6 and 41.9 mol%, respectively. Based on the phylogenetic and phenotypic characteristics, both strains are considered to represent two novel species of the genus Muricauda, and the names Muricauda okinawensis sp. nov. and Muricauda yonaguniensis sp. nov. are proposed for strains 81s02^T (=KCTC 92889^T = MCCC 1K08502^T) and 334s03^T (=KCTC 92890^T = MCCC 1K08503^T).

Finite-Time Synchronization of Neural Networks With Infinite Discrete Time-Varying Delays and Discontinuous Activations

This article investigates finite-time synchronization of neural networks (NNs) with infinite discrete time-varying delays and discontinuous activations (DDNNs). By virtue of theory of differential inclusions, comparison strategies, and inequality techniques, finite-time synchronization of the underlying DDNNs can be developed via a discontinuous state feedback control law, and the synchronous settling time can be estimated. The delayed state feedback controller and finite-time stability theorem are not employed during the analysis. As a special case, finite-time synchronization of NNs with bounded delays and discontinuous activations is given. Finally, two examples are provided to illustrate the validity of the theories.

Adaptive Dynamic Event-Triggered Fault-Tolerant Consensus for Nonlinear Multiagent Systems With Directed/Undirected Networks

The fault-tolerant consensus of nonlinear multiagent systems (MASs) is studied by using the dynamic event-triggered control and the adaptive control techniques. First, a general dynamic adaptive event-triggered mechanism (DAETM) is proposed, which promotes and improves many existing dynamic event-triggered mechanism and static event-triggered mechanism. On this basis, a new distributed dynamic adaptive event-triggered fault-tolerant controller (DDAETFTC) is designed. Then, two simple and clear criteria are derived, respectively, to ensure consensus can be reached asymptotically for nonlinear MASs with directed networks and with undirected networks under the new DDAETFTC. The obtained results also can apply to linear MASs. Furthermore, it is proven that there is no agent to show the Zeno behavior in MASs under the new DAETM. Finally, an example is given to simulate the obtained results.

Semiglobal Robust Consensus of General Linear MASs Subject to Input Saturation and Additive Perturbations

This article considers the robust consensus problem of the general linear multiagent system (MAS) subject to both heterogeneous additive stable disturbances and input saturation. Distributed low gain feedback-based dynamic output feedback control protocols are proposed, which do not need the controller interaction. Algebraic Riccati equation and unified H_∞ controller design method are employed to design the output feedback control protocols. It is established that under the assumption that each agent is asymptotically null controllable with bounded controls, semiglobal robust consensus can always be reached under the proposed controller. Furthermore, the design method specialized for leader-following consensus is addressed, under the assumption that the Laplacian matrix is diagonalizable, one can design the control protocol only with the number of follower agents. Finally, several simulations are provided to show the effectiveness of our results.

Synchronization of Delayed Complex Networks on Time Scales via Aperiodically Intermittent Control Using Matrix-Based Convex Combination Method

This article reconsiders synchronization problem of linear complex networks with time-varying delay on time scales. For different types of time scales, aperiodically intermittent control scheme is established by using a matrix-based convex combination method, which has great potential in reducing control consumption and saving communication bandwidth. By employing a common Lyapunov function, aperiodically intermittent controllers are utilized successfully to achieve synchronization of linear delayed complex networks on special time scales onto an isolated node. Next, by constructing a special Lyapunov function with time-varying coefficients, sufficient criteria that consist of two linear matrix inequalities are demonstrated to make linear delayed complex networks on general time scales synchronized onto an isolated system with an exponential convergence rate given in advance. Due to delayed complex networks in this article defined on time scales, the proposed control schemes are applicable to continuous-time networks, their discrete-time forms, and any combination of them. Four numerical examples are offered to highlight the effectiveness and superiority of the proposed aperiodically intermittent control schemes at last.

EEG-Based Brain-Computer Interfaces Are Vulnerable to Backdoor Attacks

Research and development of electroencephalogram (EEG) based brain-computer interfaces (BCIs) have advanced rapidly, partly due to deeper understanding of the brain and wide adoption of sophisticated machine learning approaches for decoding the EEG signals. However, recent studies have shown that machine learning algorithms are vulnerable to adversarial attacks. This paper proposes to use narrow period pulse for poisoning attack of EEG-based BCIs, which makes adversarial attacks much easier to implement. One can create dangerous backdoors in the machine learning model by injecting poisoning samples into the training set. Test samples with the backdoor key will then be classified into the target class specified by the attacker. What most distinguishes our approach from previous ones is that the backdoor key does not need to be synchronized with the EEG trials, making it very easy to implement. The effectiveness and robustness of the backdoor attack approach is demonstrated, highlighting a critical security concern for EEG-based BCIs and calling for urgent attention to address it.

Master-Slave Synchronization of Neural Networks With Unbounded Delays via Adaptive Method

Master-slave synchronization of two delayed neural networks with adaptive controller has been studied in recent years; however, the existing delays in network models are bounded or unbounded with some derivative constraints. For more general delay without these restrictions, how to design proper adaptive controller and prove rigorously the convergence of error system is still a challenging problem. This article gives a positive answer for this problem. By means of the stability result of unbounded delayed system and some analytical techniques, we prove that the traditional centralized adaptive algorithms can achieve global asymptotical synchronization even if the network delays are unbounded without any derivative constraints. To describe the convergence speed of the synchronization error, adaptive designs depending on a flexible ω -type function are also provided to control the synchronization error, which can lead exponential synchronization, polynomial synchronization, and logarithmically synchronization. Numerical examples on delayed neural networks and chaotic Ikeda-like oscillator are presented to verify the adaptive designs, and we find that in the case of unbounded delay, the intervention of ω -type function can promote the realization of synchronization but may destroy the convergence of control gain, and this however will not happen in the case of bounded delay.

Neural Network-Based Fixed-Time Tracking and Containment Control of Second-Order Heterogeneous Nonlinear Multiagent Systems

This study concentrates on the fixed-time tracking consensus and containment control of second-order heterogeneous nonlinear multiagent systems (MASs) with and without measurable velocity under directed topology. By defining a time-varying scaling function and approximating the unknown nonlinear dynamics with radial basis function neural networks (RBFNNs), a novel distributed protocol for solving the fixed-time tracking consensus and containment control problems of second-order heterogeneous nonlinear MASs with full states available is proposed based on a nonsingular sliding-mode control method constructed by designing a prescribed-time convergent sliding surface. For the scenario of immeasurable velocity, a fixed-time convergent states' observer is designed to reveal the velocity information when the unknown linearity is bounded. Subsequently, a distributed fixed-time consensus protocol based on observed velocity information is proposed for the extended results. Ultimately, the acquired results are verified by three simulation examples.

On Pinning Linear and Adaptive Synchronization of Multiple Fractional-Order Neural Networks With Unbounded Time-Varying Delays

In this article, the synchronization of multiple fractional-order neural networks with unbounded time-varying delays (FNNUDs) is investigated. By introducing a pinning linear control, sufficient conditions are provided for achieving the synchronization of multiple FNNUDs via an extended Halanay inequality. Moreover, a new effective adaptive control which applies to the fractional differential equations with unbounded time-varying delays is designed, under which sufficient criteria are presented to ensure the synchronization of multiple FNNUDs. The introduced control in this article is also workable in traditional integer-order neural networks. Finally, the validity of obtained results is demonstrated by a numerical example.

CRNet: A Fast Continual Learning Framework With Random Theory

Artificial neural networks are prone to suffer from catastrophic forgetting. Networks trained on something new tend to rapidly forget what was learned previously, a common phenomenon within connectionist models. In this work, we propose an effective and efficient continual learning framework using random theory, together with Bayes' rule, to equip a single model with the ability to learn streaming data. The core idea of our framework is to preserve the performance of old tasks by guiding output weights to stay in a region of low error while encountering new tasks. In contrast to the existing continual learning approaches, our main contributions concern (1) closed-formed solutions with detailed theoretical analysis; (2) training continual learners by one-pass observation of samples; (3) remarkable advantages in terms of easy implementation, efficient parameters, fast convergence, and strong task-order robustness. Comprehensive experiments under popular image classification benchmarks, FashionMNIST, CIFAR-100, and ImageNet, demonstrate that our methods predominately outperform the extensive state-of-the-art methods on training speed while maintaining superior accuracy and the number of parameters, in the class incremental learning scenario. Code is available at https://github.com/toil2sweet/CRNet.

Global synchronization of complex-valued neural networks with unbounded time-varying delays

This paper investigates global synchronization of complex-valued neural networks (CVNNs) with unbounded time-varying delays. By applying analytical method and inequality techniques, an algebraic criterion is established to ensure global synchronization of the CVNNs via a devised feedback controller, which generalizes some existing outcomes. Finally, two numerical simulations and one application in image encryption are provided to verify the effectiveness of the theoretical results.

Modeling the charge collection efficiency in the Li-diffused inactive layer of P-type high purity germanium detector

A model of the Li-diffused inactive layer in P-type high purity germanium detectors is built to describe the transportation of charge carriers and calculate the charge collection efficiency therein. The model is applied to calculate charge collection efficiency of a P-type point-contact germanium detector used in rare event physics experiments and validated in another P-type semi-planar germanium detector. The calculated charge collection efficiency curves are well consistent with measurements for both detectors. Effects of the Li doping processes on the charge collection efficiency are discussed based on the model. This model can be easily extended to other P-type germanium detectors, for instance, the P-type broad-energy Ge detector, and the P-type inverted-coaxial point-contact detector.

An Overview of the Stability Analysis of Recurrent Neural Networks With Multiple Equilibria

The stability analysis of recurrent neural networks (RNNs) with multiple equilibria has received extensive interest since it is a prerequisite for successful applications of RNNs. With the increasing theoretical results on this topic, it is desirable to review the results for a systematical understanding of the state of the art. This article provides an overview of the stability results of RNNs with multiple equilibria including complete stability and multistability. First, preliminaries on the complete stability and multistability analysis of RNNs are introduced. Second, the complete stability results of RNNs are summarized. Third, the multistability results of various RNNs are reviewed in detail. Finally, future directions in these interesting topics are suggested.

Multistability of Dynamic Memristor Delayed Cellular Neural Networks With Application to Associative Memories

Recently, dynamic memristor (DM)-cellular neural networks (CNNs) have received widespread attention due to their advantage of low power consumption. The previous works showed that DM-CNNs have at most 318 equilibrium points (EPs) with n=16 cells. Since time delay is unavoidable during the process of information transmission, the goal of this article is to research the multistability of DM-CNNs with time delay, and, meanwhile, to increase the storage capacity of DM-delay (D)CNNs. Depending on the different constitutive relations of memristors, two cases of the multistability for DM-DCNNs are discussed. After determining the constitutive relations, the number of EPs of DM-DCNNs is increased to 3ⁿ with n cells by means of the appropriate state-space decomposition and the Brouwer's fixed point theorem. Furthermore, the enlarged attraction domains of EPs can be obtained, and 2ⁿ of these EPs are locally exponentially stable in two cases. Compared with standard CNNs, the dynamic behavior of DM-DCNNs shows an outstanding merit. That is, the value of voltage and current approach to zero when the system becomes stable, and the memristor provides a nonvolatile memory to store the computation results. Finally, two numerical simulations are presented to illustrate the effectiveness of the theoretical results, and the applications of associative memories are shown at the end of this article.

Effect of surface gallium termination on the formation and emission energy of an InGaAs wetting layer during the growth of InGaAs quantum dots by droplet epitaxy

Self-assembled quantum dots (QDs) based on III-V semiconductors have excellent properties for applications in quantum optics. However, the presence of a 2D wetting layer (WL) which forms during the Stranski-Krastanov growth of QDs can limit their performance. Here, we investigate WL formation during QD growth by the droplet epitaxy technique. We use a combination of photoluminescence excitation spectroscopy, lifetime measurements, and transmission electron microscopy to identify the presence of an InGaAs WL in these droplet epitaxy QDs, even in the absence of distinguishable WL luminescence. We observe that increasing the amount of Ga deposited on a GaAs (100) surface prior to the growth of InGaAs QDs leads to a significant reduction in the emission wavelength of the WL to the point where it can no longer be distinguished from the GaAs acceptor peak emission in photoluminescence measurements. However increasing the amount of Ga deposited does not suppress the formation of a WL under the growth conditions used here.

Chemoenzymatic Synthesis of Sterically Hindered Biaryls by Suzuki Coupling and Vanadium Chloroperoxidase Catalyzed Halogenations

Halogenated biaryls are vital structural skeletons in bioactive products. In this study, an effective chemoenzymatic halogenation by vanadium-dependent chloroperoxidase from Camponotus inaequalis (CiVCPO) enabled the transformation of freely rotating biaryl bonds to sterically hindered axis. The yields were up to 84 % for the tribrominated biaryl products and up to 65 % when isolated. Furthermore, a one-pot, two-step chemoenzymatic strategy by incorporating transition metal catalyzed Suzuki coupling and the chemoenzymatic halogenation in aqueous phase were described. This strategy demonstrates a simplified one-pot reaction sequence with organometallic and biocatalytic procedures under economical and environmentally beneficial conditions that may inspire further research on synthesis of sterically hindered biaryls.

Quasisynchronization of Delayed Neural Networks With Discontinuous Activation Functions on Time Scales via Event-Triggered Control

Almost all event-triggered control (ETC) strategies were designed for discrete-time or continuous-time systems. In order to unify these existing theoretical results of ETC and develop ETC strategies for nonlinear systems, whose state variables evolve steadily at one time and change intermittently at another time, this article investigates quasisynchronization of delayed neural networks (NNs) on time scales with discontinuous activation functions via ETC approaches. First, the existence of the Filippov solutions is proved for discontinuous NNs with finite discontinuities. Second, two static event-triggered conditions and two dynamic event-triggered conditions are established to avoid continuous communication between the master-slave systems under algebraic/matrix inequality criteria. Third, under static/dynamic event-triggered conditions, a positive lower bound of event-triggered intervals is demonstrated to be greater than a positive number for each event-based controller, which shows that the Zeno behavior will not occur. Finally, two numerical simulations are carried out to show the effectiveness of the presented theoretical results in this article.