Influence of Se and Te substitutions at Tl-site of Tl(Ba,Sr)CaCu2O7 superconductor on the AC susceptibility and electrical properties

The Sr and Ba bearing Tl-1212 phase, Tl(Ba,Sr)CaCu₂O₇ is an interesting superconductor. The Sr only bearing TlSr₂CaCu₂O₇ is not easily prepared in the superconducting form. The Ba only bearing TlBa₂CaCu₂O₇ on the other hand does not show improvement in the transition temperature with elemental substitution. In this work the influence of multivalent Se (non-metal) and Te (metalloid) substitutions at the Tl-site of Tl_1-xM_x(Ba,Sr)CaCu₂O₇ (M = Se or Te) superconductors for x = 0–0.6 was studied. The samples were prepared via the conventional solid-state reaction method. XRD patterns showed a single Tl-1212 phase for x = 0 and 0.1 Se substituted samples. The critical current density at the peak temperature, T_p of the imaginary (χ”) part of the AC susceptibility (χ = χ’ +χ”), J_c-inter(T_p) for all samples was between 15 and 21 A cm⁻². The highest superconducting transition temperature was shown by the x = 0.3 Se-substituted sample (T_c-onset = 104 K, T_c-zero = 89 K, T_cχ’ = 104 K and T_p = 80 K). Te suppressed the superconductivity of Tl-1212 phase. The order of highest transition temperatures are as follows: Tl_1-xTe_x(Ba,Sr)CaCu₂O₇<Tl(Ba,Sr)CaCu₂O₇<Tl_1-xSe_x(Ba,Sr)CaCu₂O₇. This work showed that Se was better than Te in improving the transition temperature and flux pinning of the Tl-1212 phase. The roles of ionic radius of Se and Te on the superconductivity of Tl(Ba,Sr)CaCu₂O₇ are discussed in this paper.     

Maximum likelihood-based adaptive differential evolution identification algorithm for multivariable systems in the state-space form

Parameter estimation plays an important role in the field of system control. This article is concerned with the parameter estimation methods for multivariable systems in the state-space form. For the sake of solving the identification complexity caused by a large number of parameters in multivariable systems, we decompose the original multivariable system into some subsystems containing fewer parameters and study identification algorithms to estimate the parameters of each subsystem. By taking the maximum likelihood criterion function as the fitness function of the differential evolution algorithm, we present a maximum likelihood-based differential evolution (ML-DE) algorithm for parameter estimation. To improve the parameter estimation accuracy, we introduce the adaptive mutation factor and the adaptive crossover factor into the ML-DE algorithm and propose a maximum likelihood-based adaptive differential evolution algorithm. The simulation study indicates the efficiency of the proposed algorithms.     

Control theory for stochastic distributed parameter systems,an engineering perspective

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field.     

A Global Maximum Error Controller Based Nonlinearity Aware TPWL Method for Reducing Nonlinear Systems

Abstract

Model order reduction is a common practice to reduce large order systems so that their simulation and control become easy. Nonlinearity aware trajectory piecewise linear is a variation of trajectory piecewise linearization technique of order reduction that is used to reduce nonlinear systems. With this scheme, the reduced approximation of the system is generated by weighted sum of the linearized and reduced sub-models obtained at certain linearization points on the system trajectory. This scheme uses dynamically inspired weight assignment that makes the approximation nonlinearity aware. Just as weight assignment, the process of linearization points selection is also important for generating faithful approximations. This article uses a global maximum error controller based linearization points selection scheme according to which a state is chosen as a linearization point if the error between a current reduced model and the full order nonlinear system reaches a maximum value. A combination that not only selects linearization points based on an error controller but also assigns dynamic inspired weights is shown in this article. The proposed scheme generates approximations with higher accuracies. This is demonstrated by applying the proposed method to some benchmark nonlinear circuits including RC ladder network and inverter chain circuit and comparing the results with the conventional schemes.     

Numerical Investigation of the Capture of Polydisperse Particles by Charged Droplets

The capture of particles by charged droplets was simulated by considering the electrostatic interactions of droplet-droplet and droplet-particle. The results indicate that the electrostatic repulsion between droplets leads to a dynamic accumulation mode of particles. However, the droplet spacing has an insignificant effect on the capture efficiency when the electrostatic deposition predominates. The increase of droplet charge remarkably improves the capture efficiency, in which the capture of fine particles accounts for the largest proportion. Compared to the droplet charge, the droplet size shows a limited improvement in the capture efficiency. Reducing the droplet velocity prolongs the capture time instead of enhancing the capture capacity per unit time, thereby improving capture efficiency.     

Maximum Data Generation Rate Routing Protocol Based on Data Flow Controlling Technology for Rechargeable Wireless Sensor Networks

For rechargeable wireless sensor networks, limited energy storage capacity, dynamic energy supply, low and dynamic duty cycles cause that it is unpractical to maintain a fixed routing path for packets delivery permanently from a source to destination in a distributed scenario. Therefore, before data delivery, a sensor has to update its waking schedule continuously and share them to its neighbors, which lead to high energy expenditure for reestablishing path links frequently and low efficiency of energy utilization for collecting packets. In this work, we propose the maximum data generation rate routing protocol based on data flow controlling technology. For a sensor, it does not share its waking schedule to its neighbors and cache any waking schedules of other sensors. Hence, the energy consumption for time synchronization, location information and waking schedule shared will be reduced significantly. The saving energy can be used for improving data collection rate. Simulation shows our scheme is efficient to improve packets generation rate in rechargeable wireless sensor networks.     

Estimation of the Stress-Strength Reliability for Exponentiated Pareto Distribution Using Median and Ranked Set Sampling Methods

In reliability analysis, the stress-strength model is often used to describe the life of a component which has a random strength (X) and is subjected to a random stress (Y). In this paper, we considered the problem of estimating the reliability R=P [Y<X] when the distributions of both stress and strength are independent and follow exponentiated Pareto distribution. The maximum likelihood estimator of the stress strength reliability is calculated under simple random sample, ranked set sampling and median ranked set sampling methods. Four different reliability estimators under median ranked set sampling are derived. Two estimators are obtained when both strength and stress have an odd or an even set size. The two other estimators are obtained when the strength has an odd size and the stress has an even set size and vice versa. The performances of the suggested estimators are compared with their competitors under simple random sample via a simulation study. The simulation study revealed that the stress strength reliability estimates based on ranked set sampling and median ranked set sampling are more efficient than their competitors via simple random sample. In general, the stress strength reliability estimates based on median ranked set sampling are smaller than the corresponding estimates under ranked set sampling and simple random sample methods.     

基于云概率密度分布估计的大坝监测数据分析

大坝运行监测易受自然环境和监测条件影响，存在时间和空间上的变异性，监测数据具有不确定性。以云理论的随机性和不确定性分析方法为基础，并与空间数据辐射思想相结合，建立了云滴概率密度分布估计模型，然后导出云概率密度分布函数，依据样本监测数据推求母体空间数据的分布特征，并设计了基于逆向云算法云变换的计算程序。分析陆浑水库1979~1999年测压管监测数据和位移变形数据的云概率密度分布特征和云数字特征，得出了20 a来大坝的数据分布特征和运行状态。监测数据分析结果表明，云概率密度分布估计不仅能有效合理地分析大坝的运行状态，而且能够依据云数字特征来判断监测状态和监测环境的异常变化。      

In situ measurements of human cough aerosol hygroscopicity

The airborne dynamics of respiratory droplets, and the transmission routes of pathogens embedded within them, are governed primarily by the diameter of the particles. These particles are composed of the fluid which lines the respiratory tract, and is primarily mucins and salts, which will interact with the atmosphere and evaporate to reach an equilibrium diameter. Measuring organic volume fraction (OVF) of cough aerosol has proved challenging due to large variability and low material volume produced after coughing. Here, the diametric hygroscopic growth factors (GF) of the cough aerosol produced by healthy participants were measured in situ using a rotating aerosol suspension chamber and a humidification tandem differential mobility analyser. Using hygroscopicity models, it was estimated that the average OVF in the evaporated cough aerosol was 0.88 ± 0.07 and the average GF at 90% relative humidity (RH) was 1.31 ± 0.03. To reach equilibrium in dry air the droplets will reduce in diameter by a factor of approximately 2.8 with an evaporation factor of 0.36 ± 0.05. Hysteresis was observed in cough aerosol at RH = ∼35% and RH = ∼65% for efflorescence and deliquescence, respectively, and may depend on the OVF. The same behaviour and GF were observed in nebulized bovine bronchoalveolar lavage fluid.     

An iterative method for suboptimal control of linear time-delayed systems

This article presents a new approach for solving the Optimal Control Problem (OCP) of linear time-delay systems with a quadratic cost functional. The proposed method can also be used for designing optimal control time-delay systems with disturbance. In this study, the Variational Iteration Method (VIM) is employed to convert the original Time-Delay Optimal Control Problem (TDOCP) into a sequence of nonhomogeneous linear two-point boundary value problems (TPBVPs). The optimal control law obtained consists of an accurate linear feedback term and a nonlinear compensation term which is the limit of an adjoint vector sequence. The feedback term is determined by solving Riccati matrix differential equation. By using the finite-step iteration of a nonlinear compensation sequence, we can obtain a suboptimal control law. Finally, Illustrative examples are included to demonstrate the validity and applicability of the technique.