Stability analysis and stabilization of wireless networked control systems based on deadband control scheduling

The stability analysis and stabilization problems of the wireless networked control systems(WNCSs) with signal transmission deadbands were considered. The deadbands were respectively set up at the sensor to the controller and the controller to the actor sides in the WNCS, which were used to reduce data transmission, furthermore, to decrease the network collision and node energy consumption. Under the consideration of time-varying delays and signal transmission deadbands, the model for the WNCS was presented. A novel Lyapunov functional which took full advantages of the network factors was exploited. Meanwhile, new stability analysis and stabilization conditions for the WNCS were proposed, which described the relationship of the delay bounds, the transmission deadband bounds and the system stability. Two examples were used to demonstrate the effectiveness of the proposed methods. The results show that the proposed approach can guarantee asymptotical stability of the system and reduce the data transmission effectively.

A Novel Hybrid Clustering Based Transmission Protocol for Wireless Body Area Networks

Wireless sensor networks are a collection of intelligent sensor devices that are connected to one another and have the capability to exchange information packets amongst themselves.In recent years,this field of research has become increasingly popular due to the host of useful applications it can potentially serve.A deep analysis of the concepts associated with this domain reveals that the two main problems that are to be tackled here are throughput enhancement and network security improvement.The present article takes on one of these two issues namely the throughput enhancement.For the purpose of improving network productivity,a hybrid clustering based packet propagation protocol has been proposed.The protocol makes use of not only clustering mechanisms of machine learning but also utilizes the traditional forwarding function approach to arrive at an optimum model.The result of the simulation is a novel transmission protocol which significantly enhances network productivity and increases throughput value.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

Stability of the Konjac Glucomannan Topological Chain Based on Quantum Spin Model

In this paper we investigated the stability of konjac glucomnnan（KGM） chain hydrogen networks based on the quantum spin model. Dissipative particle dynamics method was applied in the structure simulation of KGM. The results reveled that acetyl residues of KGM were bonded with water molecules in aqueous solutions. Increasing the hydrogen bond formation decreases the energy in acetyl system. The expect-valuation of the thermal state with respect to the Hamiltonian is negative. Hence, the total energy of konjac glucomnnan chain with the acetyl groups decreases, which indicates the increasing stability of konjac glucomnnan chain. Our approach could provide a new insight into the investigation on the stability of konjac glucomnnan chain.

Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor-actuator networks

A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.

Thermo-mechanical coupling analysis of APSE using submodels and neural networks

The ,Aspoe Pillar Stability Experiment （APSE） is an in situ experiment for investigating the spalling mechanism under mechanical and thermal loading conditions in a crystalline rock. In this study, the thermo-mechanical behaviors in the APSE were investigated with three models： （1） a Full model with rough meshes for calculating the influence of tunnel excavation; （2） a Submodel with fine meshes for predicting the thermo-mechanical behavior in the pillar during the borehole drilling, heating, and cool- ing phases; and （3） a Thin model for modeling the effect of slot cutting for de-stressing around the pillar. In order to import the stresses calculated from the Full model to the Submodel and to define the complex thermal boundary conditions, artificial neural networks （NNs） were utilized. From this study, it was pos- sible to conclude that the stepwise approach with the application of NNs was useful for predicting the complex response of the pillar under severe thermo-mechanical loading conditions.

Detection of social group instability among captive rhesus macaques using joint network modeling

Social stability in group-living animals is an emergent property which arises from the interaction amongst multiple behavioral networks. However, pinpointing when a social group is at risk of collapse is difficult. We used a joint network model- ing approach to examine the interdependencies between two behavioral networks, aggression and status signaling, from four sta- ble and three unstable groups of rhesus macaques in order to identify characteristic patterns of network interdependence in stable groups that are readily distinguishable from unstable groups. Our results showed that the most prominent source of aggres- sion-status network interdependence in stable social groups came from more frequent dyads than expected with opposite direc- tion status-aggression （i.e. A threatens B and B signals acceptance of subordinate status）. In contrast, unstable groups showed a decrease in opposite direction aggression-status dyads （but remained higher than expected） as well as more frequent than ex- pected dyads with bidirectional aggression. These results demonstrate that not only was the stable joint relationship between ag- gression and status networks readily distinguishable from unstable time points, social instability manifested in at least two differ- ent ways. In sum, our joint modeling approach may prove useful in quantifying and monitoring the complex social dynamics of any wild or captive social system, as all social systems are composed of multiple interconnected networks [Current Zoology 61 （1）： 70-84, 2015].

Combined organic-inorganic fertilization builds higher stability of soil and root microbial networks than exclusive mineral or organic fertilization

Plant health and performance are highly dependent on the root microbiome.The impact of agricultural management on the soil microbiome has been studied extensively.However,a comprehensive understanding of how soil types and fertilization regimes affect both soil and root microbiome is still lacking,such as how fertilization regimes affect the root microbiome's stability,and whether it follows the same patterns as the soil microbiome.In this study,we carried out a longterm experiment to see how different soil types,plant varieties,and fertilizer regimens affected the soil and root bacterial communities.Our results revealed higher stability of microbial networks under combined organic-inorganic fertilization than those relied solely on inorganic or organic fertilization.The root microbiome variation was predominantly caused by total nitrogen,while the soil microbiome variation was primarily caused by pH and soil organic matter.Bacteroidetes and Firmicutes were major drivers when the soil was amended with organic fertilizer,but Actinobacteria was found to be enriched in the soil when the soil was treated with inorganic fertilizer.Our findings demonstrate how the soil and root microbiome respond to diverse fertilizing regimes,and hence contribute to a better understanding of smart fertilizer as a strategy for sustainable agriculture.

DISCRETE BIDIRECTIONAL ASSOCIATIVE MEMORY WITH LEARNING FUNCTION

In this paper we propose a new discrete bidirectional associative memory (DBAM) which is derived from our previous continuous linear bidirectional associative memory (LBAM). The DBAM performs bidirectionally the optimal associative mapping proposed by Kohonen. Like LBAM and NBAM proposed by one of the present authors,the present BAM ensures the guaranteed recall of all stored patterns,and possesses far higher capacity compared with other existing BAMs,and like NBAM, has the strong ability to suppress the noise occurring in the output patterns and therefore reduce largely the spurious patterns. The derivation of DBAM is given and the stability of DBAM is proved. We also derive a learning algorithm for DBAM,which has iterative form and make the network learn new patterns easily. Compared with NBAM the present BAM can be easily implemented by software.

Environment drives the co-occurrence of bacteria and microeukaryotes in a typical subtropical bay

The co-occurrence of bacteria and microeukaryote species is a ubiquitous ecological phenomenon,but there is limited cross-domain research in aquatic environments.We conducted a network statistical analysis and visualization of microbial cross-domain co-occurrence patterns based on DNA sampling of a typical subtropical bay during four seasons,using high-throughput sequencing of both 18S rRNA and 16S rRNA genes.First,we found obvious relationships between network stability and network complexity indices.For example,increased cooperation and modularity were found to weaken the stability of cross-domain networks.Secondly,we found that bacterial operational taxonomic units(OTUs)were the most important contributors to network complexity and stability as they occupied more nodes,constituted more keystone OTUs,built more connections,more importantly,ignoring bacteria led to greater variation in network robustness.Gammaproteobacteria,Alphaproteobacteria,Bacteroidetes,and Actinobacteria were the most ecologically important groups.Finally,we found that the environmental drivers most associated with cross-domain networks varied across seasons(in detail,the network in January was primarily constrained by temperature and salinity,the network in April was primarily constrained by depth and temperature,the network in July was mainly affected by depth,temperature,and salinity,depth was the most important factor affecting the network in October)and that environmental influence was stronger on bacteria than on microeukaryotes.

A necessary and sufficient stability criterion for networked predictive control systems.

Stability of a networked predictive control system subject to network-induced delay and data dropout is investigated in this study. By modeling the closed-loop system as a switched system with an upper-triangular structure, a necessary and sufficient stability criterion is developed. From the criterion, it also can be seen that separation principle holds for networked predictive control systems. A numerical example is provided to confirm the validity and effectiveness of the obtained results.

Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

This paper is concerned with fractional-order bidirectional associative memory(BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag–Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.

SCALE-TYPE STABILITY FOR NEURAL NETWORKS WITH UNBOUNDED TIME-VARYING DELAYS

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.

Stability-aware Energy Efficient Clustering Protocol in WSN using Opposition-based Elephant Herding Optimisation

The main intent of this paper is to implement the stability-aware energy-efficient clustering protocol in WSN.This paper plans to derive a multi-objective function with the constraints like energy,distance,delay,stability period,and intents to attain the objective by developing a new well-performing meta-heuristic algorithm called Opposition-based Elephant Herding Optimisa-tion(O-EHO).The objective function diminishes the energy consumption of sensor nodes by optimum selection of cluster heads that leads to maintain the energy balance between the nor-mal nodes.In this way,there is a remarkable enhancement in the performance parameters such as throughput,stability period,and network lifetime.It is proved that the network lifetime is enhanced by the stability period and thus it is considered as the most significant parameter.The experimental analysis proves the competitive performance of the proposed model over other heuristic methods.

Temperature stability of magnetic field for periodic permanent-magnet focusing system

In this study, finite element analysis based on an Ansoft Maxwell software was used to reveal the temperature stability of a magnet ring and the equivalent structural periodic permanent-magnet（PPM） focusing system. It is found that with the temperature increasing, the decrease rate of magnetic induction peak（Bz）maxof single magnet ring is greater than that of remanence Brof magnet in the range from room temperature to 200 °C, however,the PPM focusing system do have the same temperature characteristics of permanent-magnet materials. It indicates that the magnetic temperature properties of the PPM system can be effectively controlled by adjusting the temperature properties of the magnets. Moreover, the higher permeability of the magnets indicates the less Hcb, giving rise to lower magnetic induction peak （Bz）′max： Finally, it should be noted that the magnetic orientation deviation angle θ（／15°） of permanent magnets has little effect on the focusing magnetic field of the PPM system at different temperatures and the temperature stability. The obtained results are beneficial to the design and selection of permanent magnets for PPM focusing system.

Influence of time delay on weighted pseudo-almost periodic dynamics in SICNNs

Purpose–The purpose of this paper is to investigate the weighted pseudo-almost periodic solutions of shunting inhibitory cellular neural networks(SICNNs)with time-varying delays and distributed delays.Design/methodology/approach–The principle of weighted pseudo-almost periodic functions and some new mathematical analysis skills are applied.Findings–A set of sufficient criteria which guarantee the existence and exponential stability of the weighted pseudo-almost periodic solutions of the considered SICNNs are established.Originality/value–The derived results of this paper are new and complement some earlier works.The innovation of this paper concludes two points:a new sufficient criteria guaranteeing the existence and exponential stability of the weighted pseudo-almost periodic solutions of SICNNs are established;and the ideas of this paper can be applied to investigate some other similar neural networks.