Distributed Adaptive Cooperative Tracking of Uncertain Nonlinear Fractional-order Multi-agent Systems

In this paper, the leader-following tracking problem of fractional-order multi-agent systems is addressed. The dynamics of each agent may be heterogeneous and has unknown nonlinearities. By assumptions that the interaction topology is undirected and connected and the unknown nonlinear uncertain dynamics can be parameterized by a neural network, an adaptive learning law is proposed to deal with unknown nonlinear dynamics, based on which a kind of cooperative tracking protocols are constructed. The feedback gain matrix is obtained to solve an algebraic Riccati equation. To construct the fully distributed cooperative tracking protocols, the adaptive law is also adopted to adjust the coupling weight. With the developed control laws,we can prove that all signals in the closed-loop systems are guaranteed to be uniformly ultimately bounded. Finally, a simple simulation example is provided to illustrate the established result.

Spatial-Temporal Event Detection Method with Multivariate Water Quality Data

With the rapid development of the society,water contamination events cause great loss if the accidents happen in the water supply system.A large number of sensor nodes of water quality are deployed in the water supply network to detect and warn the contamination events to prevent pollution from speading.If all of sensor nodes detect and transmit the water quality data when the contamination occurs,it results in the heavy communication overhead.To reduce the communication overhead,the Connected Dominated Set construction algorithm-Rule K,is adopted to select a part fo sensor nodes.Moreover,in order to improve the detection accuracy,a Spatial-Temporal Abnormal Event Detection Algorithm with Multivariate water quality data(M-STAEDA)was proposed.In M-STAEDA,first,Back Propagation neural network models are adopted to analyze the multiple water quality parameters and calculate the possible outliers.Then,M-STAEDA algorithm determines the potential contamination events through Bayesian sequential analysis to estimate the probability of a contamination event.Third,it can make decision based on the multiple event probabilities fusion.Finally,a spatial correlation model is applied to determine the spatial-temporal contamination event in the water supply networks.The experimental results indicate that the proposed M-STAEDA algorithm can obtain more accuracy with BP neural network model and improve the rate of detection and the false alarm rate,compared with the temporal event detection of Single Variate Temporal Abnormal Event Detection Algorithm(M-STAEDA).

The specificity of ten non-digestible carbohydrates to enhance butyrate-producing bacteria and butyrate production in vitro fermentation

Butyrate and butyrate-producing bacteria are important indicators of gut microbial metabolism in human health.Ten non-digestible carbohydrates(NDCs),including inulin,fructooligosaccharide(FOS),oatsβ-glucans(OGS),oatsβ-glucan oligosaccharides(OGOS),Astragalus polysaccharides(APS),Astragalus oligosaccharides(AOS),xanthan gum oligosaccharides(XGOS),gellan gum oligosaccharides(GGOS),curdlan oligosaccharides(COS),and pullulan oligosaccharides(POS)were used to investigate NDC specifi city in modulating butyrate-producing bacteria and butyrate production in 48 h in vitro fermentation studies in combination with fecal inocula from 7 healthy donors and 11 patients with type 2 diabetes(T2D).We observed that the amount of these ten NDCs utilized depended on NDC structure and inter-individual gut microbial differences.XGOS and GGOS fermentations signifi cantly increased butyrate-producing bacteria(especially f_Lachnospiraceae)and butyric acid production.Furthermore,XGOS and GGOS fermentations showed a better ability to consistently modulate gut microbiota composition and metabolic properties between individuals of healthy donors or T2D patients when compared to inulin,FOS,APS,AOS,OGS,OGOS,COS and POS fermentation.This research indicated that xanthan gum and gellan gum oligosaccharides have strong specifi city to enhance butyrate-producing bacteria and butyrate production.

Memory-augmented adaptive flocking control for multi-agent systems subject to uncertain external disturbances

This paper presents a novel flocking algorithm based on a memory-enhanced disturbance observer.To compensate for external disturbances,a filtered regressor for the double integrator model subject to external disturbances is designed to extract the disturbance information.With the filtered regressor method,the algorithm has the advantage of eliminating the need for acceleration information,thus reducing the sensor requirements in applications.Using the information obtained from the filtered regressor,a batch of stored data is used to design an adaptive disturbance observer,ensuring that the estimated values of the parameters of the disturbance system equation and the initial value converge to their actual values.The result is that the flocking algorithm can compensate for external disturbances and drive agents to achieve the desired collective behavior,including virtual leader tracking,inter-distance keeping,and collision avoidance.Numerical simulations verify the effectiveness of the algorithm proposed in the present study.

The Impact of a Health Forecasting Service on the Visits and Costs in Outpatient and Emergency Departments for COPD Patients--Shanghai Municipality,China,October 2019–April 2020

What is already known on this topic?The morbidity and mortality of chronic obstructive pulmonary disease(COPD)is associated with adverse weather and air pollution.However,COPD patients are not able to be alerted in advance of high risk environments.What is added by this report?This prospective controlled trial conducted in Pudong New Area of Shanghai from October 2019 to April 2020 provided evidence of COPD risk forecasting service on the reductions in visits and costs of COPD patients in outpatient and emergency departments in China for the first time.What are the implications for public health practice?This study suggests that COPD risk forecasting service could be integrated into existing COPD management in public health to improve the health and economic outcomes.

利用低温水浴鉴定水稻苗期耐寒性

高效准确鉴定苗期耐寒性是水稻(Oryza sativa)耐寒研究的前提。基于流动水浴温度均一这一特性,建立了一种恒温水浴鉴定水稻幼苗耐寒性方法。该方法中环境温度设定为20°C,水浴温度设定为4°C。根据对2个水稻亚种不同材料的处理结果,总结出几种常见品种的低温处理时间与存活率参考值,并对操作过程中的一些注意事项进行了说明。