Numerical study on the aerodynamic noise characteristics of CRH2 high-speed trains

The aerodynamic noise of high-speed trains not only causes interior noise pollution and reduces the comfort of passengers, but also seriously affects the normal life of residents. With the increase of running speed of trains, aerodynamic noises will be more than wheel-rail noises and become the main noise source of high-speed trains. This paper established a computational model for the aerodynamic noise of a CRH2 high-speed train with 3-train formation including 3 train bodies and 6 bogies, adopted the detached eddy simulation (DES) to conduct numerical simulation for the flow field around the high-speed train, applied Ffowcs Williams-Hawkings acoustic model to conduct unsteady computation for the aerodynamic noise of high-speed trains, and analyzed the far-field aerodynamic noise characteristics of high-speed trains. Studied results showed: The main energy of the complete train was mainly within the range of 613 Hz-2500 Hz when the high-speed train ran at the speed of 350 km/h. In the whole frequency domain, it was a broadband noise. Regarding the longitudinal observation point which was 25 m away from the center line of track and 6m away from the nose tip of head train, the sound pressure level of total noises reached the maximum value 88.9 dBA. The maximum sound pressure level of the noise observation point which was 7.5 m away from the center line of track was around the first bogie of head train. Various components made different contributions to the aerodynamic noise of the complete train, and the order was head train, mid train, bogie system (6 bogies) and tail train. The first bogie of head train made the greatest contribution to bogie system and was the main aerodynamic noise source of the complete train

DCMD: Distance-based Classification Using Mixture Distributions on Microbiome Data

Current advances in next generation sequencing techniques have allowed researchers to conduct comprehensive research on microbiome and human diseases, with recent studies identifying associations between human microbiome and health outcomes for a number of chronic conditions. However, microbiome data structure, characterized by sparsity and skewness, presents challenges to building effective classifiers. To address this, we present an innovative approach for distance-based classification using mixture distributions (DCMD). The method aims to improve classification performance when using microbiome community data, where the predictors are composed of sparse and heterogeneous count data. This approach models the inherent uncertainty in sparse counts by estimating a mixture distribution for the sample data, and representing each observation as a distribution, conditional on observed counts and the estimated mixture, which are then used as inputs for distance-based classification. The method is implemented into a k-means and k-nearest neighbours framework and we identify two distance metrics that produce optimal results. The performance of the model is assessed using simulations and applied to a human microbiome study, with results compared against a number of existing machine learning and distance-based approaches. The proposed method is competitive when compared to the machine learning approaches and showed a clear improvement over commonly used distance-based classifiers. The range of applicability and robustness make the proposed method a viable alternative for classification using sparse microbiome count data.Comment: 27 pages, 3 figure

Systematic study of elliptic flow parameter in the relativistic nuclear collisions at RHIC and LHC energies

We employed the new issue of a parton and hadron cascade model PACIAE 2.1 to systematically investigate the charged particle elliptic flow parameter $v_2$ in the relativistic nuclear collisions at RHIC and LHC energies. With randomly sampling the transverse momentum $x$ and $y$ components of the particles generated in string fragmentation on the circumference of an ellipse instead of circle originally, the calculated charged particle $v_2(\eta)$ and $v_2(p_T)$ fairly reproduce the corresponding experimental data in the Au+Au/Pb+Pb collisions at $\sqrt{s_{NN}}$=0.2/2.76 TeV. In addition, the charged particle $v_2(\eta)$ and $v_2(p_T)$ in the p+p collisions at $\sqrt s$=7 TeV as well as in the p+Au/p+Pb collisions at $\sqrt{s_{NN}}$=0.2/5.02 TeV are predicted.Comment: 7 pages, 5 figure

RAR-PINN algorithm for the data-driven vector-soliton solutions and parameter discovery of coupled nonlinear equations

This work aims to provide an effective deep learning framework to predict the vector-soliton solutions of the coupled nonlinear equations and their interactions. The method we propose here is a physics-informed neural network (PINN) combining with the residual-based adaptive refinement (RAR-PINN) algorithm. Different from the traditional PINN algorithm which takes points randomly, the RAR-PINN algorithm uses an adaptive point-fetching approach to improve the training efficiency for the solutions with steep gradients. A series of experiment comparisons between the RAR-PINN and traditional PINN algorithms are implemented to a coupled generalized nonlinear Schr\"{o}dinger (CGNLS) equation as an example. The results indicate that the RAR-PINN algorithm has faster convergence rate and better approximation ability, especially in modeling the shape-changing vector-soliton interactions in the coupled systems. Finally, the RAR-PINN method is applied to perform the data-driven discovery of the CGNLS equation, which shows the dispersion and nonlinear coefficients can be well approximated

A PD Law Based Fuzzy Logic Control Strategy For Simultaneous Control Of Indoor Temperature And Humidity Using A Variable Speed Direct Expansion Air Conditioner

In small to medium scale buildings located in the subtropics, such as Hong Kong, direct expansion air conditioning (DX A/C) systems are widely applied. This is because, as compared to chilled water based central air conditioning systems, DX A/C systems are compact, flexible for multi-room services, energy efficient and cost less to maintain and operate. However, traditionally, a DX A/C system is equipped with a single-speed compressor and supply air fan, and employs ON / OFF control strategy to maintain indoor air temperature only, leaving the indoor moisture content (or relative humidity) uncontrolled. With the introduction of variable speed technology, the speeds of compressor and supply air fan can be varied continuously so as to realize the simultaneous control of the indoor temperature and humidity. In this paper, the development of a novel control strategy based on PD law and fuzzy logic is reported. The compressor speed was adjusted directly according to the indoor air moisture content and supply air fan speed according to the indoor air temperature, respectively, to realize the simultaneous control of indoor air temperature and humidity. Controllability tests for the novel control strategy were carried out and the test results suggested that, although two control loops for temperature and humidity were significantly coupled, the simultaneous control of indoor temperature and humidity was achieved with respect to control accuracy and sensitivity

Trace amounts of copper induce neurotoxicity in the cholesterol-fed mice through apoptosis

AbstractEvidence has been gathered to suggest that trace amounts of copper induce neurotoxicity by interaction with elevated cholesterol in diet. Copper treatment alone showed no significant learning and memory impairments in behavioral tasks. However, copper-induced neurotoxicity was significantly increased in mice given elevated-cholesterol diet. Trace amounts of copper decreased the activity of SOD and increased the level of malondialdehyde (MDA) in the brain of cholesterol-fed mouse. Copper also caused an increase in amyloid precursor protein (APP) mRNA level and the activation of caspase-3 in the brain of cholesterol-fed mice. The apoptosis-induced nuclear DNA fragmentation was detected in the brain of those mice by terminal deoxynucleotidyl transferase (TdT)-mediated dUTP nick-end-labeling staining. These findings suggest that trace amounts of copper induce neurotoxicity in cholesterol-fed mice through apoptosis caused by oxidative stress