Dynamics of a Nonautonomous Plant Disease Model with General Nonlinear Incidence Rate and Time-Varying Impulse

In this paper, for controlling the spread of plant diseases, a nonautonomous SEIS (Susceptible → Exposed → Infectious → Susceptible) epidemic model with a general nonlinear incidence rate and time-varying impulsive control strategy is proposed and investigated. This novel model could result in an objective criterion on how to control plant disease transmission by replanting of healthy plants and removal of infected plants. Using the method of small amplitude perturbation, the sufficient conditions under which guarantee the globally attractive of the disease-free periodic solution and the permanence of the disease are obtained, that is, the disease dies out if R12>1.

Two-dimensional particle-in-cell modeling of blow-off impulse by X-ray irradiation

Space objects such as spacecraft or missiles may be exposed to intense X-rays in outer space,leading to severe damage.The reinforcement of these objects to reduce the damage caused by X-ray irradiation is a significant concern.The blow-off impulse(BOI)is a crucial physical quantity for investigating material damage induced by X-ray irradiation.However,the accurate calculation of BOI is challenging,particularly for large deformations of materials with complex configurations.In this study,we develop a novel two-dimensional particle-in-cell code,Xablation2D,to calculate BOIs under far-field X-ray irradiation.This significantly reduces the dependence of the numerical simulation on the grid shape.The reliability of this code is verified by simulation results from open-source codes,and the calculated BOIs are consistent with the experimental and analytical results.

Dynamic propagation velocity of a positive streamer in a 3 m air gap under lightning impulse voltage

Streamers represent an important stage in the initiation of gap discharge. In this work, we used an eight-frame intensified charge-coupled device camera to capture the streamer development process when a lightning impulse voltage of 95%–100% U50% was applied in a 3 m rod–plate gap and the streamer velocity was analyzed. Analysis of the observations shows that streamer velocity can be defined by three stages: rapid velocity decline(stage 1), rapid velocity rise(stage 2)and slow velocity decline(stage 3). The effects of electrode shape, applied voltage and gap breakdown or withstanding on streamer velocity were analyzed. The electrode with a larger radius of curvature will result in a higher initial velocity, and a higher voltage amplitude will cause the streamer to propagate faster at stage 3. Gap withstanding or breakdown has no obvious effect on streamer velocity. In addition, the experimental results are compared with previous results and the statistical characteristics of the primary streamer discharge are discussed.

ON DE FINETTI'S OPTIMAL IMPULSE DIVIDEND CONTROL PROBLEM UNDER CHAPTER 11 BANKRUPTCY

Motivated by recent advances made in the study of dividend control and risk management problems involving the U.S.bankruptcy code,in this paper we follow[44]to revisit the De Finetti dividend control problem under the reorganization process and the regulator's intervention documented in U.S.Chapter 11 bankruptcy.We do this by further accommodating the fixed transaction costs on dividends to imitate the real-world procedure of dividend payments.Incorporating the fixed transaction costs transforms the targeting optimal dividend problem into an impulse control problem rather than a singular control problem,and hence computations and proofs that are distinct from[44]are needed.To account for the financial stress that is due to the more subtle concept of Chapter 11 bankruptcy,the surplus process after dividends is driven by a piece-wise spectrally negative Lévy process with endogenous regime switching.Some explicit expressions of the expected net present values under a double barrier dividend strategy,new to the literature,are established in terms of scale functions.With the help of these expressions,we are able to characterize the optimal strategy among the set of admissible double barrier dividend strategies.When the tail of the Lévy measure is log-convex,this optimal double barrier dividend strategy is then verified as the optimal dividend strategy,solving our optimal impulse control problem.

Direction finding of bistatic MIMO radar in strong impulse noise

For bistatic multiple-input multiple-output(MIMO)radar,this paper presents a robust and direction finding method in strong impulse noise environment.By means of a new lower order covariance,the method is effective in suppressing impulse noise and achieving superior direction finding performance using the maximum likelihood(ML)estimation method.A quantum equilibrium optimizer algorithm(QEOA)is devised to resolve the corresponding objective function for efficient and accurate direc-tion finding.The results of simulation reveal the capability of the presented method in success rate and root mean square error over existing direction-finding methods in different application situations,e.g.,locating coherent signal sources with very few snapshots in strong impulse noise.Other than that,the Cramér-Rao bound(CRB)under impulse noise environment has been drawn to test the capability of the presented method.

基于Impulse 7000DP对除颤监护仪的质控方法研究

除颤监护仪是用于临床抢救的医疗设备,凭借其疗效确切、作用快、操作简便等优势,广泛应用于各级医疗单位中。该仪器是临床上经常使用的高风险医疗设备,其释放能量的准确性直接影响到其救治效果,因此必须定期进行检测。本文按照JJF 1149—2014《心脏除颤器校准规范》中针对除颤监护仪的校准方法,使用除颤器/经皮起搏器分析仪Impulse 7000DP对除颤监护仪的质控方法展开研究。

Saccades of video head impulse test in Meniere's disease and Vestibular Migraine: What can we learn from?

Background:Saccades are often observed on video head impulse tests(vHIT)in patients with Meniere's Disease(MD)and Vestibular Migraine(VM).However,their saccadic features are not fully described.Objective:This study aims to identify the saccades characteristics of MD and VM.Methods:75 VM patients and 103 definite unilateral MD patients were enrolled in this study.First raw saccades were exported and analyzed.The VM patients were divided into left and right based on their ears,while the MD patients were separated into affected and unaffected subgroups based on their audiograms and symptoms.Results:The MD patients have more saccades on the affected side(85%vs.69%),and saccade velocity is more consistent than the contralateral side(shown by the coefficient of variation).The saccades occurrence rates on both sides are similar in VM(77%vs.76%),as are other saccadic parameters.The MD patients have more significant inter-aural differences than the VM patients,manifested in higher velocity(p-value 0.000),earlier arriving(p-value 0.010),and more time-domain gathered(p-value 0.003)on the affected side.Conclusions:Bilateral saccades are commonly observed in MD and VM.In contrast to MD,saccades on VM are subtle,scattered,and late-arrived.Furthermore,the MD patients showed inconsistent saccadic distribution with more velocity-uniform saccades on the affected side.

Acoustic radiation force impulse predicts long-term outcomes in a large-scale cohort:High liver cancer,low comorbidity in hepatitis B virus

BACKGROUND Acoustic radiation force impulse(ARFI)is used to measure liver fibrosis and predict outcomes.The performance of elastography in assessment of fibrosis is poorer in hepatitis B virus(HBV)than in other etiologies of chronic liver disease.AIM To evaluate the performance of ARFI in long-term outcome prediction among different etiologies of chronic liver disease.METHODS Consecutive patients who received an ARFI study between 2011 and 2018 were enrolled.After excluding dual infection,alcoholism,autoimmune hepatitis,and others with incomplete data,this retrospective cohort were divided into hepatitis B(HBV,n=1064),hepatitis C(HCV,n=507),and non-HBV,non-HCV(NBNC,n=391)groups.The indexed cases were linked to cancer registration(1987-2020)and national mortality databases.The differences in morbidity and mortality among the groups were analyzed.RESULTS At the enrollment,the HBV group showed more males(77.5%),a higher prevalence of prediagnosed hepatocellular carcinoma(HCC),and a lower prevalence of comorbidities than the other groups(P<0.001).The HCV group was older and had a lower platelet count and higher ARFI score than the other groups(P<0.001).The NBNC group showed a higher body mass index and platelet count,a higher prevalence of pre-diagnosed non-HCC cancers(P<0.001),especially breast cancer,and a lower prevalence of cirrhosis.Male gender,ARFI score,and HBV were independent predictors of HCC.The 5-year risk of HCC was 5.9%and 9.8%for those ARFI-graded with severe fibrosis and cirrhosis.ARFI alone had an area under the receiver operating characteristic curve(AUROC)of 0.742 for prediction of HCC in 5 years.AUROC increased to 0.828 after adding etiology,gender,age,and platelet score.No difference was found in mortality rate among the groups.CONCLUSION The HBV group showed a higher prevalence of HCC but lower comorbidity that made mortality similar among the groups.Those patients with ARFI-graded severe fibrosis or cirrhosis should receive regular surveillance.

Global exponential stability of Cohen-Grossberg neural networks with time-varying delays and impulses

In this paper, the Cohen-Grossberg neural networks with time-varying delays and impulses are considered. New sufficient conditions for the existence and global exponential stability of a unique equilibrium point are established by using the fixed point theorem and Lyapunov functional. An example is given to demonstrate the effectiveness of our results.

Sliding Mode Control for Recurrent Neural Networks With Time-Varying Delays and Impulsive Effects

Dear Editor,This letter studies the problem of sliding mode control(SMC)design for recurrent neural networks(RNNs)with impulsive disturbances and time-varying transmission delays.To this end,an appropriate integral sliding surface function and SMC law are adopted for use under impulsive disturbances and time-varying delays.

Proper Understanding of the Nerve Impulses and the Action Potential

Neurologists define the transmission of nerve impulses across the membranes of the neural cells as a result of difference in the concentration of ions while they measured an electric potential, called as an action potential, which allows the propagation of such nerve impulses as electrical signals. Such measurements should guide them to a logical explanation of the nerve impulses as electric charges driven by the measured action potential. However, such logical conclusion, or explanation, is ignored due to a wrong definition of the flow of electric charges as a flow of electrons that cannot pass through neural networks. According to recent studies, electric charges are properly defined as electromagnetic (EM) waves whose energy is expressed as the product of its propagating electric potential times their entropy flow which is adhered to the flow of such energy. Such definition matches the logical conclusion of the nerve impulses as electric charges, as previously explained, and defines the entropy of the neural network, measured by Ammeters, in Watt or Joule/Volt. The measured entropy represents a neurodiagnostic property of the neural networks that measures its capacity to allow the flow of energy per unit action potential. Theoretical verification of the innovative definition of nerve impulses is presented by following an advanced entropy approach. A proper review of the machine records of the stimulating electric charges, used in the diagnosis of the neural networks, and the stimulated nerve impulses or stimulated responses, represents practical verifications of the innovative definitions of the electric charges and the nerve impulses. Comparing the functioning of the thermoelectric generators and the brain neurons, such neurons are defined as thermoelectric generators of the electric nerve impulses and their propagating, or action, potential.

Method of SLAS’s ground track manipulation based on tangential impulse thrust

Satellites with altitudes below 400 km are called super low altitude satellites(SLAS),often used to achieve responsive imaging tasks.Therefore,it is important for the manipulation of its ground track.Aiming at the problem of ground track manipulation of SLAS,a control method based on tangential impulse thrust is proposed.First,the equation of the longitude difference between SLAS and the target point on the target latitude is derived based on Gauss’s variational equations.On this basis,the influence of the tangential impulse thrust on the ground track’s longitude is derived.Finally,the method for ground track manipulation of SLAS under the tangential impulse thrust is proposed.The simulation results verify the effective-ness of the method,after manipulation,the satellite can visit the target point and revisit it for multiple days.

Analysis and Design of Time-Delay Impulsive Systems Subject to Actuator Saturation

This paper investigates the exponential stability and performance analysis of nonlinear time-delay impulsive systems subject to actuator saturation. When continuous dynamics is unstable, under some conditions, it is shown that the system can be stabilized by a class of saturated delayed-impulses regardless of the length of input delays. Conversely, when the system is originally stable, it is shown that under some conditions, the system is robust with respect to sufficient small delayed-impulses. Moreover, the design problem of the controller with the goal of obtaining a maximized estimate of the domain of attraction is formulated via a convex optimization problem. Three examples are provided to demonstrate the validity of the main results.

PCA-LSTM:An Impulsive Ground-Shaking Identification Method Based on Combined Deep Learning

Near-fault impulsive ground-shaking is highly destructive to engineering structures,so its accurate identification ground-shaking is a top priority in the engineering field.However,due to the lack of a comprehensive consideration of the ground-shaking characteristics in traditional methods,the generalization and accuracy of the identification process are low.To address these problems,an impulsive ground-shaking identification method combined with deep learning named PCA-LSTM is proposed.Firstly,ground-shaking characteristics were analyzed and groundshaking the data was annotated using Baker’smethod.Secondly,the Principal Component Analysis(PCA)method was used to extract the most relevant features related to impulsive ground-shaking.Thirdly,a Long Short-Term Memory network(LSTM)was constructed,and the extracted features were used as the input for training.Finally,the identification results for the Artificial Neural Network(ANN),Convolutional Neural Network(CNN),LSTM,and PCA-LSTMmodels were compared and analyzed.The experimental results showed that the proposed method improved the accuracy of pulsed ground-shaking identification by>8.358%and identification speed by>26.168%,compared to other benchmark models ground-shaking.

Lyapunov Conditions for Finite-Time Input-to-State Stability of Impulsive Switched Systems

Dear Editor,This letter studies finite-time input-to-state stability(FTISS)for impulsive switched systems.A set of Lyapunov-based conditions are established for guaranteeing FTISS property.When constituent modes governing continuous dynamics are FTISS and discrete dynamics involving impulses are destabilizing,the FTISS can be retained if impulsive-switching signals satisfy an average dwell-time(ADT)condition.

Modeling Near-Field Impulsive Waves Generated by Deformable Landslide Using the HBP Model Based on the SPH Method

Landslide-generated impulsive waves(LGWs)in reservoir areas can seriously threaten waterway safety as well as hu-man life and properties around the two side slopes.The risk reduction and mitigation of such a hazard require the accurate prediction of near-field wave characteristics,such as wave amplitude and run-up.However,near-field LGW involves complicated fluid-solid interactions.Furthermore,the wave characteristics are closely related to various aspects,including the geometry and physical features of the slide,river,and body of water.However,the empirical or analytical methods used for rough estimation cannot derive accurate results,especially for deformable landslides,due to their significant geometry changes during the sliding process.In this study,the near-field waves generated by deformable landslides were simulated by smoothed particle hydrodynamics(SPH)based on multi-phase flow.The deformable landslides were generalized as a kind of viscous flow by adopting the Herschel-Bulkley-Papanastasiou(HBP)-based nonNewtonian rheology model.The HBP model is capable of producing deformable landslide dynamics even though the high-speed sliding process is involved.In this study,an idealized experiment case originating from Lituya LGW and a practical case of Gongjiafang LGW were reproduced for verification and demonstration.The simulation results of both cases show satisfactory consistency with the experiment/investigation data in terms of landslide movement and near-field impulsive wave characteristics,thus indicating the applicability and accuracy of the proposed method.Finally,the effects of the HBP model’s rheological parameters on the landslide dynamics and near-field wave characteristics are discussed,providing a parameter calibration method along with sug-gestions for further applications.

Complementary-Label Adversarial Domain Adaptation Fault Diagnosis Network under Time-Varying Rotational Speed and Weakly-Supervised Conditions

Recent research in cross-domain intelligence fault diagnosis of machinery still has some problems,such as relatively ideal speed conditions and sample conditions.In engineering practice,the rotational speed of the machine is often transient and time-varying,which makes the sample annotation increasingly expensive.Meanwhile,the number of samples collected from different health states is often unbalanced.To deal with the above challenges,a complementary-label(CL)adversarial domain adaptation fault diagnosis network(CLADAN)is proposed under time-varying rotational speed and weakly-supervised conditions.In the weakly supervised learning condition,machine prior information is used for sample annotation via cost-friendly complementary label learning.A diagnosticmodel learning strategywith discretized category probabilities is designed to avoidmulti-peak distribution of prediction results.In adversarial training process,we developed virtual adversarial regularization(VAR)strategy,which further enhances the robustness of the model by adding adversarial perturbations in the target domain.Comparative experiments on two case studies validated the superior performance of the proposed method.

On the Application of Mixed Models of Probability and Convex Set for Time-Variant Reliability Analysis

In time-variant reliability problems,there are a lot of uncertain variables from different sources.Therefore,it is important to consider these uncertainties in engineering.In addition,time-variant reliability problems typically involve a complexmultilevel nested optimization problem,which can result in an enormous amount of computation.To this end,this paper studies the time-variant reliability evaluation of structures with stochastic and bounded uncertainties using a mixed probability and convex set model.In this method,the stochastic process of a limit-state function with mixed uncertain parameters is first discretized and then converted into a timeindependent reliability problem.Further,to solve the double nested optimization problem in hybrid reliability calculation,an efficient iterative scheme is designed in standard uncertainty space to determine the most probable point(MPP).The limit state function is linearized at these points,and an innovative random variable is defined to solve the equivalent static reliability analysis model.The effectiveness of the proposed method is verified by two benchmark numerical examples and a practical engineering problem.

Finite-time Prescribed Performance Time-Varying Formation Control for Second-Order Multi-Agent Systems With Non-Strict Feedback Based on a Neural Network Observer

This paper studies the problem of time-varying formation control with finite-time prescribed performance for nonstrict feedback second-order multi-agent systems with unmeasured states and unknown nonlinearities.To eliminate nonlinearities,neural networks are applied to approximate the inherent dynamics of the system.In addition,due to the limitations of the actual working conditions,each follower agent can only obtain the locally measurable partial state information of the leader agent.To address this problem,a neural network state observer based on the leader state information is designed.Then,a finite-time prescribed performance adaptive output feedback control strategy is proposed by restricting the sliding mode surface to a prescribed region,which ensures that the closed-loop system has practical finite-time stability and that formation errors of the multi-agent systems converge to the prescribed performance bound in finite time.Finally,a numerical simulation is provided to demonstrate the practicality and effectiveness of the developed algorithm.

Nuclear magnetic resonance experiments on the time-varying law of oil viscosity and wettability in high-multiple waterflooding sandstone cores

A simulated oil viscosity prediction model is established according to the relationship between simulated oil viscosity and geometric mean value of T2spectrum,and the time-varying law of simulated oil viscosity in porous media is quantitatively characterized by nuclear magnetic resonance(NMR)experiments of high multiple waterflooding.A new NMR wettability index formula is derived based on NMR relaxation theory to quantitatively characterize the time-varying law of rock wettability during waterflooding combined with high-multiple waterflooding experiment in sandstone cores.The remaining oil viscosity in the core is positively correlated with the displacing water multiple.The remaining oil viscosity increases rapidly when the displacing water multiple is low,and increases slowly when the displacing water multiple is high.The variation of remaining oil viscosity is related to the reservoir heterogeneity.The stronger the reservoir homogeneity,the higher the content of heavy components in the remaining oil and the higher the viscosity.The reservoir wettability changes after water injection:the oil-wet reservoir changes into water-wet reservoir,while the water-wet reservoir becomes more hydrophilic;the degree of change enhances with the increase of displacing water multiple.There is a high correlation between the time-varying oil viscosity and the time-varying wettability,and the change of oil viscosity cannot be ignored.The NMR wettability index calculated by considering the change of oil viscosity is more consistent with the tested Amott(spontaneous imbibition)wettability index,which agrees more with the time-varying law of reservoir wettability.