Manipulating thermal conductivity through substrate coupling

We report a new approach to the thermal conductivity manipulation -- substrate coupling. Generally, the phonon scattering with substrates can decrease the thermal conductivity, as observed in recent experiments. However, we find that at certain regions, the coupling to substrates can increase the thermal conductivity due to a reduction of anharmonic phonon scattering induced by shift of the phonon band to the low wave vector. In this way, the thermal conductivity can be efficiently manipulated via coupling to different substrates, without changing or destroying the material structures. This idea is demonstrated by calculating the thermal conductivity of modified double-walled carbon nanotubes and also by the ice nanotubes coupled within carbon nanotubes.Comment: 5 figure

Global Convergence of Online Identification for Mixed Linear Regression

Mixed linear regression (MLR) is a powerful model for characterizing nonlinear relationships by utilizing a mixture of linear regression sub-models. The identification of MLR is a fundamental problem, where most of the existing results focus on offline algorithms, rely on independent and identically distributed (i.i.d) data assumptions, and provide local convergence results only. This paper investigates the online identification and data clustering problems for two basic classes of MLRs, by introducing two corresponding new online identification algorithms based on the expectation-maximization (EM) principle. It is shown that both algorithms will converge globally without resorting to the traditional i.i.d data assumptions. The main challenge in our investigation lies in the fact that the gradient of the maximum likelihood function does not have a unique zero, and a key step in our analysis is to establish the stability of the corresponding differential equation in order to apply the celebrated Ljung's ODE method. It is also shown that the within-cluster error and the probability that the new data is categorized into the correct cluster are asymptotically the same as those in the case of known parameters. Finally, numerical simulations are provided to verify the effectiveness of our online algorithms

High-order stochastic integration schemes for the Rosenbluth-Trubnikov collision operator in particle simulations

In this study, we consider a numerical implementation of the nonlinear Rosenbluth-Trubnikov collision operator for particle simulations in plasma physics in the framework of the finite element method (FEM). The relevant particle evolution equations are formulated as stochastic differential equations, both in the Stratonovich and Itô forms, and are then solved with advanced high-order stochastic numerical schemes. Due to its formulation as a stochastic differential equation, both the drift and diffusion components of the collision operator are treated on an equal footing. Our investigation focuses on assessing the accuracy of these schemes. Previous studies on this subject have used the Euler-Maruyama scheme, which, although popular, is of low order, and requires small time steps to achieve satisfactory accuracy. In this work, we compare the performance of the Euler-Maruyama method to other high-order stochastic methods known in the stochastic differential equations literature. Our study reveals advantageous features of these high-order schemes, such as better accuracy and improved conservation properties of the numerical solution. The main test case used in the numerical experiments is the thermalization of isotropic and anisotropic particle distributions

Deep learning fusion of RGB and depth images for pedestrian detection

In this paper, we propose an effective method based on the Faster-RCNN structureto combine RGB and depth images for pedestrian detection. During the training stage,we generate a semantic segmentation map from the depth image and use it to refine theconvolutional features extracted from the RGB images. In addition, we acquire moreaccurate region proposals by exploring the perspective projection with the help of depthinformation. Experimental results demonstrate that our proposed method achieves thestate-of-the-art RGBD pedestrian detection performance on KITTI [12] datas

Full ff and δf\delta f gyrokinetic particle simulations of Alfv\'en waves and energetic particle physics

In this work, we focus on the development of the particle-in-cell scheme and the application to the studies of Alfv\'en waves and energetic particle physics in tokamak plasmas. The $\delta f$ and full $f$ schemes are formulated on the same footing adopting mixed variables and the pullback scheme for electromagnetic problems. The TRIMEG-GKX code [Lu et al. J. Comput. Phys. 440 (2021) 110384] has been upgraded using cubic spline finite elements and full $f$ and $\delta f$ schemes. The EP-driven TAE has been simulated for the ITPA-TAE case featured by a small electron skin depth $\sim 1.18\times10^{-3}\;{\rm m}$, which is a challenging parameter regime of electromagnetic simulations, especially for the full $f$ model. The simulation results using the $\delta f$ scheme are in good agreement with previous work. Excellent performance of the mixed variable/pullback scheme has been observed for both full $f$ and $\delta f$ schemes. Simulations with mixed full $f$ EPs and $\delta f$ electrons and thermal ions demonstrate the good features of this novel scheme in mitigating the noise level. The full $f$ scheme is a natural choice for EP physics studies which allows a large variation of EP profiles and distributions in velocity space, providing a powerful tool for kinetic studies using realistic experimental distributions related to intermittent and transient plasma activities.Comment: 27 pages, 8 figure

Design of Decision-Making System of Emergency Logistics Information System Based on Data Mining

ABSTRACT: Emergency logistics system is mainl

Enhanced electrochemical properties of LiFePO4 by Mo-substitution and graphitic carbon-coating via a facile and fast microwave-assisted solid-state reaction

A composite cathode material for lithium ion battery applications, Mo-doped LiFePO4/C, is obtained through a facile and fast microwave-assisted synthesis method. Rietveld analysis of LiFePO4-based structural models using synchrotron X-ray diffraction data shows that Mo-ions substitute onto the Fe sites and displace Fe-ions to the Li sites. Supervalent Mo6+ doping can act to introduce Li ion vacancies due to the charge compensation effect and therefore facilitate lithium ion diffusion during charging/discharging. Transmission electron microscope images demonstrate that the pure and doped LiFePO4 nanoparticles were uniformly covered by an approximately 5 nm thin layer of graphitic carbon. Amorphous carbon on the graphitic carbon-coated pure and doped LiFePO4 particles forms a three-dimensional (3D) conductive carbon network, effectively improving the conductivity of these materials. The combined effects of Mo-doping and the 3D carbon network dramatically enhance the electrochemical performance of these LiFePO4 cathodes. In particular, Mo-doped LiFePO4/C delivers a reversible capacity of 162 mA h g(-1) at a current of 0.5 C and shows enhanced capacity retention compared to that of undoped LiFePO4/C. Moreover, the electrode exhibits excellent rate capability, with an associated high discharge capacity and good electrochemical reversibility