Non-Negative Adaptive Mechanism-Based Sliding Mode Control for Parallel Manipulators with Uncertainties

In this paper,a non-negative adaptive mechanism based on an adaptive nonsingular fast terminal sliding mode control strategy is proposed to have finite time and high-speed trajectory tracking for parallel manipulators with the existence of unknown bounded complex uncertainties and external disturbances.The proposed approach is a hybrid scheme of the online non-negative adaptive mechanism,tracking differentiator,and nonsingular fast terminal sliding mode control(NFTSMC).Based on the online non-negative adaptive mechanism,the proposed control can remove the assumption that the uncertainties and disturbances must be bounded for the NFTSMC controllers.The proposed controller has several advantages such as simple structure,easy implementation,rapid response,chattering-free,high precision,robustness,singularity avoidance,and finite-time convergence.Since all control parameters are online updated via tracking differentiator and non-negative adaptive law,the tracking control performance at high-speed motions can be better in real-time requirement and disturbance rejection ability.Finally,simulation results validate the effectiveness of the proposed method.

Comparative Transcriptome Analysis Reveals Different Mechanisms of Adaptation to Environment among Three Species of Saussurea DC.

Saussurea medusa,Saussurea hypsipeta and Saussurea obvallata are typical alpine snowline plants growing in the Qinghai-Tibet plateau.They are characterized by a specialized morphology.S.medusa and S.hypsipeta have very dense trichomes on whole plant,whereas S.obvallata has transparent bracts covered inflorescences.The different forms reflect their adaptation to cold environments.To investigate the different mechanisms of adaptation of these species to cold temperatures,transcriptome sequencing was performed in three species of Saussurea DC.A total of 116394137237 and 113879 Unigenes were identified from S.medusa,S.hypsipeta and S.obvallata,respectively.Of these,55909(48.03%),65519(47.74%)and 51889(45.56%)Unigenes were matched in public databases.GO analysis identified that most of annotated Unigenes in the three species of plants were related to cellular,metabolic,and single−organism processes,and binding and catalytic activities.The differential expression of 37 genes related to environmental adaptation were discovered by pairwise comparisons.Of these,two candidate genes(Interaptin-like and CSLB3)related to trichome development were identified only in S.medusa and S.hypsipeta,which was consistent with their distinct morphology.Our data can provide a valuable resource for the further studies on the adaptive mechanisms of molecular and functional ecology in Saussurea DC.

Molecular Mechanism of Salinity Stress and Biotechnological Strategies for Engineering Salt Tolerance in Plants

Molecular mechanisms of plant responses to salinity stress and the physiological consequences of altered gene expression are reviewed. Extensive use of comparisons with halophytic plants and glycophytos provide a paradigm for many responses to salinity exhibited by stress sensitive plants. Osmolyte biosynthesis, water flux control, and membrane transport of ions are important for maintenance and re-establishment of homeostasis. Transgenic plant and mutant analyses in Arabidopsis improve the understanding of stress responses and elements of stress signal transduction pathways. The genomic DNA sequences and cell-specific transcript expression data, combined with determinant identification based on molecular genetics, will provide the infrastructure for functional physiological dissection of salt tolerance determinants in plants. Protein interaction analysis, genetic activation and suppression screens will lead inevitably to an understanding of the interrelationships of the multiple signaling systems that control stress-adaptive responses and provide more opportunity to engineer salt tolerance in plants.

Research Progress on the Invasion Mechanism of Erigeron annuus

Erigeron annuus(L.)Pers.,an alien species,is widely distributed in most areas of China.It was included in the third batch of invasive alien species list by the Ministry of Ecology and Environment of China,and it is a first-level malignant invasive plant.Due to its great reproducibility with a mass of seeds,power adaptability to the external environment and strong allelopathy,it has highly invasive and adaptability,and has affected the development of the agriculture and forestry,the diversity of local species and the ecosystem.In this paper,we summarized the current invasion situation,invasion and adaptation mechanism of E.annuus,and prospected further research on E.annuus invasion mechanism,hoping to provide a reference for realizing its effective management.

Distributed model predictive control based on adaptive sampling mechanism

In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

Cognition Based Adaptive Control Mechanism

According to the basic functions and objectives of Cognitive Radio (CR) systems, the cognition-based adaptive control mechanism is the generalization of the research contents and approaches of cognitive radio systems. Therefore, the mechanism is described by a cognition loop, which contains the following parts: environment, inner structure of intelligent systems, observation and action.

Metagenomic analysis reveals hidden links between gut microbes and habitat adaptation among cave and surface dwelling Sinocyclocheilus species

Intestinal microbes are closely related to vital host functions such as digestion and nutrient absorption,which play important roles in enhancing host adaptability.As a natural“laboratory”,caves provide an outstanding model for understanding the significance of gut microbes and feeding habits in the habitat adaptability of hosts.However,research on the relationship between gut microbes,feeding habits,and the adaptability of troglobites remains insufficient.In this study,we compared the characteristics of the intestinal microbes of Sinocyclocheilus cavefish and surface fish and further established the relationship between intestinal and habitat microbes.Furthermore,we conducted environmental DNA(eDNA)(metabarcoding)analysis of environmental samples to clarify the composition of potential food resources in the habitats of the Sinocyclocheilus cavefish and surface fish.Results showed that the structure of the Sinocyclocheilus gut microbes was more related to ecological type(habitat type)than phylogenetic relationships.While horizontal transfer of habitat microbes was a source of gut microbes,hosts also showed strong selection for inherent microbes as dominant microorganisms.Differences in the composition and structure of gut microbes,especially dominant microbes,may enhance the adaptability of the two Sinocyclocheilus fish types from the perspectives of food intake,nutrient utilization,and harmful substance metabolism,suggesting that food resources,predation patterns,intestinal flora,digestive and absorptive capacity,and feeding habits and preferences are linked to habitat adaptability.These results should facilitate our understanding of the significance of fish gut microbes to habitat adaptation and provide a new perspective for studying the adaptive mechanisms of cavefish.

Research on Adaptive TSSA-HKRVM Model for Regression Prediction of Crane Load Spectrum

For the randomness of crane working load leading to the decrease of load spectrum prediction accuracy with time,an adaptive TSSA-HKRVM model for crane load spectrum regression prediction is proposed.The heterogeneous kernel relevance vector machine model(HKRVM)with comprehensive expression ability is established using the complementary advantages of various kernel functions.The combination strategy consisting of refraction reverse learning,golden sine,and Cauchy mutation+logistic chaotic perturbation is introduced to form a multi-strategy improved sparrow algorithm(TSSA),thus optimizing the relevant parameters of HKRVM.The adaptive updatingmechanismof the heterogeneous kernel RVMmodel under themulti-strategy improved sparrow algorithm(TSSA-HKMRVM)is defined by the sliding window design theory.Based on the sample data of the measured load spectrum,the trained adaptive TSSA-HKRVMmodel is employed to complete the prediction of the crane equivalent load spectrum.Applying this method toQD20/10 t×43m×12mgeneral bridge crane,the results show that:compared with other prediction models,although the complexity of the adaptive TSSA-HKRVMmodel is relatively high,the prediction accuracy of the load spectrum under long periods has been effectively improved,and the completeness of the load information during thewhole life cycle is relatively higher,with better applicability.

Recursive filtering of multi-rate cyber-physical systems with unknown inputs under adaptive event-triggered mechanisms

Cyber-physical systems(CPSs)take on the characteristics of both multiple rates of information collection and processing and the dependency on information exchanges.The purpose of this paper is to develop a joint recursive filtering scheme that estimates both unknown inputs and system states for multi-rate CPSs with unknown inputs.In cyberspace,the information transmission between the local joint filter and the sensors is governed by an adaptive event-triggered strategy.Furthermore,the desired parameters of joint filters are determined by a set of algebraic matrix equations in a recursive way,and a sufficient condition verifying the boundedness of filtering error covariance is found by resorting to some algebraic operation.A state fusion estimation scheme that uses local state estimation is proposed based on the covariance intersection(CI)based fusion conception.Lastly,an illustrative example demonstrates the effectiveness of the proposed adaptive event-triggered recursive filtering algorithm.

Adaptive Memory Event-Triggered Observer-Based Control for Nonlinear Multi-Agent Systems Under DoS Attacks

This paper investigates the event-triggered security consensus problem for nonlinear multi-agent systems(MASs)under denial-of-service(Do S)attacks over an undirected graph.A novel adaptive memory observer-based anti-disturbance control scheme is presented to improve the observer accuracy by adding a buffer for the system output measurements.Meanwhile,this control scheme can also provide more reasonable control signals when Do S attacks occur.To save network resources,an adaptive memory event-triggered mechanism(AMETM)is also proposed and Zeno behavior is excluded.It is worth mentioning that the AMETM's updates do not require global information.Then,the observer and controller gains are obtained by using the linear matrix inequality(LMI)technique.Finally,simulation examples show the effectiveness of the proposed control scheme.

Controlling chaos using Takagi-Sugeno fuzzy model and adaptive adjustment

In this paper, an approach to the control of continuous-time chaotic systems is proposed using the Takagi-Sugeno （TS） fuzzy model and adaptive adjustment. Sufficient conditions are derived to guarantee chaos control from Lyapunov stability theory. The proposed approach offers a systematic design procedure for stabilizing a large class of chaotic systems in the literature about chaos research. The simulation results on Rossler＇s system verify the effectiveness of the proposed methods.

Changes of proteins in the Antarctic ice microalga Chlamydomonas sp. cultured under UV-B radiation stress

Antarctic ice microalga Chlamydomonas sp. can thrive undisturbed under high UV radiation in the Antarctic ice layer. However, it is unknown that the initial adaptation mechanisms in protein level occurring in response to high UV radiation. Global-expression profiling of proteins in response to stress was analyzed by two-dimensional electrophoresis （2-DE） and image analysis. In the 2-DE analysis, protein preparation is the key step. Three different protein extract methods were compared, and the results showed that the trichloroacetic acid （TCA）-acetone fractional precipitation method was the fittest one. At the same time, the proteins in Chlamydomonas sp. were compared in 2-DE way, and the synthesis of seven protein spots was found disappeared and 18 decreased after exposed to UV-B radiation. In addition, 14 protein spots were enhanced or induced, among which two new peptides （20 and 21 kDa） appeared whose isoelectric point （pI） was 7.05 and 4.60 respectively. These changed proteins might act as key role in the acclimation of Antarctic ice microalga to UV-B radiation

An Adaptive Single Neural Control for Variable Step-Size P&O MPPT of Marine Current Turbine System

Marine current energy has been increasingly used because of its predictable higher power potential.Owing to the external disturbances of various flow velocity and the high nonlinear effects on the marine current turbine(MCT)system,the nonlinear controllers which rely on precise mathematical models show poor performance under a high level of parameters’uncertainties.This paper proposes an adaptive single neural control(ASNC)strategy for variable step-size perturb and observe(P&O)maximum power point tracking(MPPT)control.Firstly,to automatically update the neuron weights of SNC for the nonlinear systems,an adaptive mechanism is proposed to adaptively adjust the weighting and learning coefficients.Secondly,aiming to generate the exact reference speed for ASNC to extract the maximum power,a variable step-size law based on speed increment is designed to strike a balance between tracking speed and accuracy of P&O MPPT.The robust stability of the MCT control system is guaranteed by the Lyapunov theorem.Comparative simulation results show that this strategy has favorable adaptive performance under variable velocity conditions,and the MCT system operates at maximum power point steadily.

A Framework of Information Service Platform in E-Government

For exchanging and sharing information and services in e-government, a framework of information service platform (ISP) is presented. The multi-layer architecture of the ISP is introduced. They are. user's requirement layer, business service layer, business process layer, business function layer and data layer. The lower layers are based on the architecture of web services and the upper layers are related to the institutional and organizational issues. In order to deal with the variation of user's requirements and changing environment, the matching and mapping processes with adaptive mechanism are illustrated.

Perception and Recognition of Vocalization in Anuran Mate Choice

Vocal communications in frogs and toads have been highly diversified and become a hot topic in the fields of herpetology,ecology,and behavioral neuroscience.The present short review summarized several interesting phenomena of vocal communication found mainly in anurans that might contribute to the individual identification of mates or rivals,including call matching,aggressive signaling,acoustic complexity,signal exaggeration,the first note effect and left hemisphere dominance.Investigations on the perception and recognition of vocal communications will facilitate our comprehension of the adaptive mechanisms and evolutionary paths of anuran signaling systems.We proposed here that comparative studies on acoustic signal structures,codes of sender status,and auditory neural responses based on phylogenetic relationships across species can highlight further the evolutionary trajectory in anurans.

Human Faces Detection and Tracking for Crowd Management in Hajj and Umrah

Hajj and Umrah are two main religious duties for Muslims.To help faithfuls to perform their religious duties comfortably in overcrowded areas,a crowd management system is a must to control the entering and exiting for each place.Since the number of people is very high,an intelligent crowd management system can be developed to reduce human effort and accelerate the management process.In this work,we propose a crowd management process based on detecting,tracking,and counting human faces using Artificial Intelligence techniques.Human detection and counting will be performed to calculate the number of existing visitors and face detection and tracking will be used to identify all the humans for security purposes.The proposed crowd management system is composed form three main parts which are:(1)detecting human faces,(2)assigning each detected face with a numerical identifier,(3)storing the identity of each face in a database for further identification and tracking.The main contribution of this work focuses on the detection and tracking model which is based on an improved object detection model.The improved Yolo v4 was used for face detection and tracking.It has been very effective in detecting small objects in highresolution images.The novelty contained in thismethod was the integration of the adaptive attention mechanism to improve the performance of the model for the desired task.Channel wise attention mechanism was applied to the output layers while both channel wise and spatial attention was integrated in the building blocks.The main idea from the adaptive attention mechanisms is to make themodel focus more on the target and ignore false positive proposals.We demonstrated the efficiency of the proposed method through expensive experimentation on a publicly available dataset.The wider faces dataset was used for the train and the evaluation of the proposed detection and tracking model.The proposed model has achieved good results with 91.2%of mAP and a processing speed of 18 FPS on the Nvidia GTX 960 GPU.

Transcriptional responses to starvation of pathogenic Vibrio harveyi strain DY1

Vibrio harveyi is a pathogen of various aquatic organisms that has been recently associated with massive mortality episodes in the aquaculture industry.Recurrent outbreaks of vibriosis are closely correlated with the capacity of this bacterial species to survive long-term starvation conditions.To study the regulation mechanism of gene expression at the transcriptional level in V.harveyi under starvation conditions,the transcriptomic response profiles were determined of the Portunus trituberculatus pathogen V.harveyi strain DY1 under normal conditions and after four weeks of starvation.A total of 4679 and 4661 genes were expressed in the non-starved and starved cells,respectively.The significantly differentially expressed genes(DEGs)between non-starved and starved groups were identified,in which 255 genes were up-regulated and 411 genes were down-regulated.GO analysis and KEGG enrichment analysis were used to analyze the DEGs and revealed the involvement of these DEGs in many pathways,including ABC transporters,flagellum assembly,and fatty acid metabolism.Several DEGs were randomly selected and their expression levels were confirmed by quantitative real-time PCR(qRT-PCR).This is the first comprehensive transcriptomic analysis of starvation ef fects in V.harveyi.Our findings will facilitate future study on stress adaptation and survival mechanisms of V.harveyi.

Physiological Response of the Resurrection Fern Subjected to Environmental Stress as Shown by Plant Cell Membrane Integrity

Resurrection fern has a unique ability to maintain cell wall integrity when the plant cell is desiccated. It uses proteins such as late embryogenesis proteins and heat shock proteins to maintain their cellular functions. The purpose of this experiment is to determine the effects of environmental stressors on the physiological response of the resurrection fern (Pleopeltis polypodioides). The physiological response of resurrection fern plants was subjected to various temperatures (-50°C, 0°C, 25°C, and 50°C) for 7 days. Results indicated that there was a significant difference between hydrated and desiccated ferns based on the temperature. Additionally, electrolyte leakage measurements confirmed cell damage following exposure to temperature extremes of -50°C and 50°C.

Aeroengine Fault Diagnosis Method Based on Optimized Supervised Kohonen Network

To diagnose the aeroengine faults accurately,the supervised Kohonen(S-Kohonen)network is proposed for fault diagnosis.Via adding the output layer behind competitive layer,the network was modified from the unsupervised structure to the supervised structure.Meanwhile,the hybrid particle swarm optimization(H-PSO)was used to optimize the connection weights,after using adaptive inheritance mode(AIM)based on the elite strategy,and adaptive detecting response mechanism(ADRM),HPSO could guide the particles adaptively jumping out of the local solution space,and ensure obtaining the global optimal solution with higher probability.So the optimized S-Kohonen network could overcome the problems of non-identifiability for recognizing the unknown samples,and the non-uniqueness for classification results existing in traditional Kohonen(T-Kohonen)network.The comparison study on the GE90 engine borescope image texture feature recognition is carried out,the research results show that:the optimized S-Kohonen network has a strong ability of practical application in the classification fault diagnosis;the classification accuracy is higher than the common neural network model.

Input-Output Finite-Time Guaranteed Cost Control for Time-Varying Systems Based on an Aperiodic Adaptive Event-Triggered Mechanism

This paper is concerned with the problem of input-output finite-time guaranteed cost control for a kind of time-varying systems(TVSs).To reduce the transmission burden,an aperiodic-sampling-based event-triggered mechanism is proposed with an adaptive law.And a time-varying Lyapunov functional involving some time-dependent piecewise matrices is designed.Input-output finite-time stability(IO-FTS)conditions are presented for the closed-loop system.By resorting to properties of the matrix polynomial,input-output finite-time stabilization criterions are further derived by recursive linear matrix inequalities.And the sampled-data static output feedback controller can be obtained.In addition,the corresponding optimization problem about minimum values of both the guaranteed cost bound and system output norm are established.Finally,a spring-mass-damper system illustrates the effectiveness and superiority.