Nonparametric probabilistic seismic demand model and fragility analysis of subway stations using deep learning techniques

A novel and computationally efficient method for developing a nonparametric probabilistic seismic demand model(PSDM)is pro-posed to conduct the fragility analysis of subway stations accurately and efficiently.The probability density evolution method(PDEM)is used to calculate the evolutionary probability density function of demand measure(DM)without resort to any assumptions of the dis-tribution pattern of DM.To reduce the computational cost of a large amount of nonlinear time history analyses(NLTHAs)in the PDEM,the one-dimensional convolutional neural network(1D-CNN)is used as a surrogate model to predict the time history of struc-tural seismic responses in a data-driven fashion.The proposed nonparametric PSDM is adopted to conduct the fragility analysis of a two-story and three-span subway station,and the results are compared with those from two existing parametric PSDMs,i.e.,two-parameter lognormal distribution model and probabilistic neural network(PNN)-based PSDM.The results show that the PDEM-based PSDM has the best performance in describing the probability distribution of seismic responses of underground structures.Differ-ent from the fragility curves,the time-dependent fragility surface of the subway station shows how the exceedance probability of damage state changes over time.It can be used to estimate the escape time and thus the number of casualties in an earthquake,which are impor-tant indexes when conducting the resilience-based seismic evaluation.

Event-triggered dynamic output-feedback control for a class of Lipschitz nonlinear systems

This paper investigates the problem of dynamic output-feedback control for a class of Lipschitz nonlinear systems.First,a continuous-time controller is constructed and sufficient conditions for stability of the nonlinear systems are presented.Then,a novel event-triggered mechanism is proposed for the Lipschitz nonlinear systems in which new event-triggered conditions are introduced.Consequently,a closed-loop hybrid system is obtained using the event-triggered control strategy.Sufficient conditions for stability of the closed-loop system are established in the framework of hybrid systems.In addition,an upper bound of a minimum inter-event interval is provided to avoid the Zeno phenomenon.Finally,numerical examples of a neural network system and a genetic regulatory network system are provided to verify the theoretical results and to show the superiority of the proposed method.

Recent progress in polar metabolite quantification in plants using liquid chromatography-mass spectrometry

Metabolite analysis or metabolomics is an important component of systems biology in the post-genomic era.Although separate liquid chromatography（LC） methods for quantification of the major classes of polar metabolites of plants have been available for decades,a single method that enables simultaneous determination of hundreds of polar metabolites is possible only with gas chromatography-mass spectrometry（GC-MS） techniques.The rapid expansion of new LC stationary phases in the market and the ready access of mass spectrometry in many laboratories provides an excellent opportunity for developing LC-MS based methods for multitarget quantification of polar metabolites.Although various LC-MS methods have been developed over the last 10 years with the aim to quantify one or more classes of polar compounds in different matrices,currently there is no consensus LC-MS method that is widely used in plant metabolomics studies.The most promising methods applicable to plant metabolite analysis will be reviewed in this paper and the major problems encountered highlighted.The aim of this review is to provide plant scientists,with limited to moderate experience in analytical chemistry,with up-to-date and simplified information regarding the current status of polar metabolite analysis using LC-MS techniques.

Leaf Epidermal Cells： A Trap for Lipophilic Xenobiotics

Plant surfaces are covered by a layer of cuticle, which functions as a natural barrier to protect plants from mechanical damage, desiccation, and microbial invasion. Results presented in this report show that the epicuticular wax and the cuticle of plant leaves also play an important role in resisting xenobiotic invasion. Although the epicuticular wax is impermeable to hydrophilic xenobiotics, the cuticle not only restricts the penetration of hydrophilic compounds into leaf cells, but also traps lipophilic ones. The role of the epidermal cells of plant leaves in resisting xenobiotic invasion has been neglected until now. The present study shows, for the first time, that the epidermal cells may reduce or retard the transport of lipophilic xenobiotics into the internal tissues through vacuolar sequestration. Although the guard cells appear to be an easy point of entry for xenobiotics, only a very small proportion of xenobiotics present on the leaf surface actually moves into leaf tissues via the guard cells .