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Numerical Investigations of the Dynamical Behaviors and Instabilities for the Gierer-Meinhardt System

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Communications in Computational Physics 2008年第2期3卷 406-426页

作者： Zhonghua qiao Center for Research in Scientific Computation North Carolina StateUniversityRaleighNorth Carolina27695-8205USA.

This work is concerned with the numerical simulations on the Gierer-Meinhardt activator-inhibitor models. We consider the case when the inhibitor timeconstant τ is non-zero. In this case, oscillations and pulse splitting are observed *** experiments are carried out to investigate the dynamical behaviorsand instabilities of the spike patterns. The numerical schemes used are based upon anefficient moving mesh finite element method which distributes more grid points nearthe localized spike regions.

关键词： Gierer-Meinhardtmodel moving finite element method pulse splitting spikes.

Underground location algorithm based on random forest and environmental factor compensation

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International Journal of Coal Science & Technology 2021年第5期8卷 1108-1117页

作者： Xin qiao Fei Chang School of Electronic Engineering Chaohu UniversityChaohu 238000AnhuiChina Huishang Futures Co.LTD Hefei 230061AnhuiChina

Aiming at the poor location accuracy caused by the harsh and complex underground environment,long strip roadway,limited wireless transmission and sparse anchor nodes,an underground location algorithm based on random forest and compensation for environmental factors was ***,the underground wireless access point(AP)network model and tunnel environment were analyzed,and the fingerprint location algorithm was *** then the Received Signal Strength(RSS)was analyzed by Kalman Filter algorithm in the offline sampling and real-time positioning ***,the target speed constraint condition was introduced to reduce the error caused by environmental *** experimental results show that the proposed algorithm solves the problem of insufficient location accuracy and large fluctuation affected by environment when the anchor nodes are *** the same time,the average location accuracy reaches three meters,which can satisfy the application of underground rescue,activity track playback,disaster monitoring and *** has high application value in complex underground environment.

关键词： Underground Coal Mine Random Forest Kalman Filter Compensation Algorithm

PhotoNs-GPU:A GPU accelerated cosmological simulation code

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Research in Astronomy and Astrophysics 2021年第11期21卷 98-104页

作者： qiao Wang Chen Meng Key Laboratory for Computational Astrophysics National Astronomical ObservatoriesChinese Academy of SciencesBeijing 100101China School of Astronomy and Space Science University of Chinese Academy of SciencesBeijing 100049China

We present a GPU-accelerated cosmological simulation code,PhotoNs-GPU,based on an algorithm of Particle Mesh Fast Multipole Method(PM-FMM),and focus on the GPU utilization and optimization.A proper interpolated method for truncated gravity is introduced to speed up the special functions in *** verify the GPU code in mixed precision and different levels of the interpolated method on GPU.A run with single precision is roughly two times faster than double precision for current practical cosmological *** it could induce an unbiased small noise in power *** with the CPU version of PhotoNs and Gadget-2,the efficiency of the new code is significantly *** all the optimizations on the memory access,kernel functions and concurrency management,the peak performance of our test runs achieves 48%of the theoretical speed and the average performance approaches to~35%on GPU.

关键词： methods:numerical cosmology:theory large-scale structure of universe

Analysis of the penalty version of the Arlequin framework for the prediction of structural responses with large deformations

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2011年第7期12卷 552-560页

作者： Hua qiao Wei-qiu CHEN Department of Engineering Mechanics Zhejiang University Hangzhou 310027 China

The Arlequin framework proposed by Ben Dhia in 1998 is a flexible and robust method for conducting global/local analysis of structures and materials.A penalty version of the Arlequin framework for the study of structural problems involving large deformation is considered *** implementation of the penalty-based Arlequin framework into ABAQUS is then explored and the corresponding Arlequin user element subroutine is *** nonlinear simulations of a cantilever beam and a shallow arch are conducted and the choice of the coupling operator with an appropriate penalty parameter is *** numerical results justify the feasibility of the proposed method,ensuring its further application to more complicated problems involving geometric or material nonlinearities.

关键词： Global/Local analysis Geometric nonlinear analysis Penalty-based Arlequin method User defined element

Self-organized Criticality Behavior in Bulk Metallic Glasses

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Journal of Iron and Steel Research International 2016年第1期23卷 7-13页

作者： Jun-wei qiao Zhong WANG Laboratory of Applied Physics and Mechanics of Advanced Materials College of Materials Science and EngineeringTaiyuan University of Technology

Serrated flows are known as repeated yielding of bulk metallic glasses（BMGs）during plastic deformation under different loading conditions,which are associated with the operation of shear *** to the statistics of some parameters,the shear avalanches can display a self-organized critical state,suggesting a large ductility of *** emergence of the self-organized criticality（SOC）behavior in different BMGs is due to the temperature,strain rate,and chemical *** SOC behavior is accompanied with the following phenomena：the interactions occur in the shear bands;the incubation time is longer than the relaxation time;the time interval is lacking of typical time scale;and the spatial or temporal parameters should display apower-law distribution.

关键词： self-organized criticality behavior bulk metallic glass serrated flow power-law distribution

Modeling method and preliminary model of Asteroid Toutatis from Chang'E-2 optical images

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Acta Mechanica Sinica 2014年第3期30卷 310-315页

作者： Xiang-Yu Li Dong qiao School of Aerospace Engineering Beijing Institute of Technology Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education

Shape modeling is fundamental to the analysis of dynamic environment and motion around asteroid. Chang＇E- 2 successfully made a flyby of Asteroid 4179 Toutatis and obtained plenty of high-resolution images during the mis- sion. In this paper, the modeling method and preliminary model of Asteroid Toutatis are discussed. First, the optical images obtained by Chang＇E-2 are analyzed. Terrain and silhouette features in images are described. Then, the modeling method based on previous radar model and preliminary information from optical images is proposed. A preliminary polyhedron model of Asteroid Toutatis is established. Finally, the spherical harmonic coefficients of Asteroid Toutatis based on the polyhedron model are obtained. Some parameters of model are analyzed and compared. Although the model proposed in this paper is only a preliminary model, this work offers a valuable reference for future high-resolution models.

关键词： Chang'E-2 Asteroid Toutatis Shape modeling.Polyhedron method

Involvement of T-complex protein 1-ring complex/chaperonin containing T-complex protein 1(TRiC/CCT) in retrograde axonal transport through tau phosphorylation

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Neural Regeneration Research 2019年第4期14卷 588-590页

作者： Xu-qiao Chen Department of Neurosciences University of California San Diego

The cytosolic chaperonin T-complex protein 1-ring complex(TRiC)or chaperonin containing T-complex protein 1(CCT)is essential in de novo folding of approximately 10%of the eukaryotic,newly translated polypeptides as well as misfolded *** is a close link between the TRiC/CCT cytosolic chaperonin and neurodegenerative diseases(Lopez et al.,2015).A lot of research suggests that CCT plays neuroprotective roles in neurodegenerative diseases including Huntington’s disease(Lopez et al.,2015).Either overexpression of a single or all eight subunits(CCT1-8)or treatment of the substrate-binding apical domain of yeast CCT1(ApiCCT1)prevented mutant Huntingtin aggregation and improved cellular and neuronal functions(Zhao et al.,2016).Importantly,our recent study has demonstrated that both CCT and ApiCCT could reduce mutant Huntingtin level and enhance both anterograde and retrograde axonal transport of brain-derived neurotrophic *** results led to restoration of the trophic status of striatal neurons from a bacterial artificial chromosome transgenic mouse model of Huntington’s disease(Zhao et al.,2016).Axonal transport is regulated by many factors including microtubule-associated protein tau,which promotes tubulin polymerization and stabilizes *** interaction between tau and microtubules plays a vital role in the pathogenesis of multiple neurodegenerative diseases(Wang and Mandelkow,2016).Interestingly,tau phosphorylation is also observed in brains of several Huntington’s disease mouse models and Huntington’s disease patients(Gratuze et al.,2016).In a recent study,we explored if CCT subunit has any effect on axonal transport in a tau-dependent pathway(Chen et al.,2018b).We focused on the retrograde axonal transport of brain-derived neurotrophic factor,as neurotrophic factor-mediated signaling in the form of signaling endosome is essential in both the developing and the mature nervous system and dysregulation of trafficking of neurotrophic fact

关键词： T-complex protein 1-ring complex(TRiC) chaperonin containing T-complex protein 1(CCT) Involvemen

GPS: graph contrastive learning via multi-scale augmented views from adversarial pooling

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Science China(Information Sciences) 2025年第1期 145-158页

作者： Wei JU Yiyang GU Zhengyang MAO Ziyue qiao Yifang QIN Xiao LUO Hui XIONG Ming ZHANG School of Computer Science National Key Laboratory for Multimedia Information ProcessingPeking University Artificial Intelligence Thrust The Hong Kong University of Science and Technology Department of Computer Science University of California

Self-supervised graph representation learning has recently shown considerable promise in a range of fields, including bioinformatics and social networks. A large number of graph contrastive learning approaches have shown promising performance for representation learning on graphs, which train models by maximizing agreement between original graphs and their augmented views(i.e., positive views). Unfortunately, these methods usually involve pre-defined augmentation strategies based on the knowledge of human experts. Moreover, these strategies may fail to generate challenging positive views to provide sufficient supervision signals. In this paper, we present a novel approach named graph pooling contrast(GPS) to address these *** by the fact that graph pooling can adaptively coarsen the graph with the removal of redundancy, we rethink graph pooling and leverage it to automatically generate multi-scale positive views with varying emphasis on providing challenging positives and preserving semantics, i.e., strongly-augmented view and weakly-augmented view. Then, we incorporate both views into a joint contrastive learning framework with similarity learning and consistency learning, where our pooling module is adversarially trained with respect to the encoder for adversarial robustness. Experiments on twelve datasets on both graph classification and transfer learning tasks verify the superiority of the proposed method over its counterparts.

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Renewable Scenario Generation Using Controllable Generative Adversarial Networks with Transparent Latent Space

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CSEE Journal of Power and Energy Systems 2021年第1期7卷 66-77页

作者： Ji qiao Tianjiao Pu Xinying Wang the China Electric Power Research Institute Haidian DistrictBeijing 100192China

With the growing penetration of renewable energysources in power systems, it becomes increasingly important tocharacterize their inherent variability and uncertainty. Scenariogeneration is a key approach to provide a series of possible powerscenarios in the future for the system planner and operator tomake decisions. In this paper, a data-driven method is presentedfor renewable scenario generation using stable and controllablegenerative adversarial networks with transparent latent space(ctrl-GANs). The machine learning based algorithm can capturethe nonlinear and dynamic renewable patterns without the needfor modeling assumptions and complicated sampling *** orthogonal regularization and spectral normalization areadopted to improve the training stabilization of the GAN *** control the generation process, a relationship is built betweenfeatures of the generated scenarios and latent vectors on themanifold. Moreover, several new metrics for GANs are used toevaluate the quality of the scenarios. The proposed approachis applied to generate realistic time series data of wind andphotovoltaic power. The results demonstrate that our methodhas a better performance on numerical stabilization and is ableto control the generation process with latent space.

关键词： Renewable scenario generation deep learning generative adversarial networks neural network transparent latent spacer