Inhomogeneous states with checkerboard order in the t-J Model

We study inhomogeneous states in the t-J model using an unrestricted Gutzwiller approximation. We find that $pa\times pa$ checkerboard order, where $p$ is a doping dependent number, emerges from Fermi surface instabilities of both the staggered flux phase and the Fermi liquid state with realistic band parameters. In both cases, the checkerboard order develops at wave vectors $(\pm 2\pi/pa,0)$, $(0,\pm2\pi/pa)$ that are tied to the peaks of the wave-vector dependent susceptibility, and is of the Lomer-Rice-Scott type. The properties of such periodic, inhomogeneous states are discussed in connection to the checkerboard patterns observed by STM in underdoped cuprates.Comment: Published Versio

Analysis of Thermal Environment in a Hospital Operating Room

AbstractThis paper presents a computational fluid dynamics (CFD) study for thermal comfort in a hospital operating room. The research aims to analyze indoor thermal comfort using the predicted mean vote (PMV) model which has been presented by ISO7730. The room model includes a patient lying on an operating table with a surgical staff of six members standing around under surgical lights. The airflow is supplied to the room from the ceiling diffuser and exhausted through low-level side walls on both sides. Solutions of distribution of airflow velocity, temperature, relative humidity and so on are presented and discussed. The PMV and PPD are calculated for assessing thermal comfort based on TCM model. The simulation results show that the values of PMV and PPD in some parts of human body are not within the standard acceptable range defined by ISO, but its comfortableness satisfies China national standard GB/T18049 request. It is found that TCM model is a more comprehensive model for thermal comfort analysis

Weighted Sum Synchronization of Memristive Coupled Neural Networks

Funding Information: This work is supported by the National Natural Science Foundation of China (No. 61971185) and the Open Fund Project of Key Laboratory in Hunan Universities (No. 18K010). Publisher Copyright: © 2020 Elsevier B.V.It is well known that weighted sum of node states plays an essential role in function implementation of neural networks. Therefore, this paper proposes a new weighted sum synchronization model for memristive neural networks. Unlike the existing synchronization models of memristive neural networks which control each network node to reach synchronization, the proposed model treats the networks as dynamic entireties by weighted sum of node states and makes the entireties instead of each node reach expected synchronization. In this paper, weighted sum complete synchronization and quasi-synchronization are both investigated by designing feedback controller and aperiodically intermittent controller, respectively. Meanwhile, a flexible control scheme is designed for the proposed model by utilizing some switching parameters and can improve anti-interference ability of control system. By applying Lyapunov method and some differential inequalities, some effective criteria are derived to ensure the synchronizations of memristive neural networks. Moreover, the error level of the quasi-synchronization is given. Finally, numerical simulation examples are used to certify the effectiveness of the derived results.Peer reviewe

Robust Multimode Function Synchronization of Memristive Neural Networks with Parameter Perturbations and Time-Varying Delays

Publisher Copyright: IEEE Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Currently, some works on studying complete synchronization of dynamical systems are usually restricted to its two special cases: 1) power-rate synchronization and 2) exponential synchronization. Therefore, how to give a generalization of these types of complete synchronization by the mathematical expression is an open question that needs to be urgently solved. To begin with, this article proposes multimode function synchronization by the mathematical expression for the first time, which is a generalization of exponential synchronization, power-rate synchronization, logarithmical synchronization, and so on. Moreover, two adaptive controllers are designed to achieve robust multimode function synchronization of memristive neural networks (MNNs) with mismatched parameters and uncertain parameters. Each adaptive controller includes function r(t) and update gain σ. By choosing different types of r(t), multiple types of complete synchronization, including power-rate synchronization and exponential synchronization can be obtained. And update gain σ can be used to adjust the speed of synchronization. Therefore, our results enlarge and strengthen the existing results. Two examples are put forward to verify the validity of our results.Peer reviewedFinal Accepted Versio