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Macroscopic finite-difference scheme based on the mesoscopic regularized lattice-Boltzmann method

Physical Review E 2024年第2期109卷 025301-025301页

作者： Xi Liu Ying Chen Zhenhua Chai Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074 China

In this paper, we develop a macroscopic finite-difference scheme from the mesoscopic regularized lattice Boltzmann (RLB) method to solve the Navier-Stokes equations (NSEs) and convection-diffusion equation (CDE). Unlike the commonly used RLB method based on the evolution of a set of distribution functions, this macroscopic finite-difference scheme is constructed based on the hydrodynamic variables of NSEs (density, momentum, and strain rate tensor) or macroscopic variables of CDE (concentration and flux), and thus shares low memory requirement and high computational efficiency. Based on an accuracy analysis, it is shown that, the same as the mesoscopic RLB method, the macroscopic finite-difference scheme also has a second-order accuracy in space. In addition, we would like to point out that compared with the RLB method and its equivalent macroscopic numerical scheme, the present macroscopic finite-difference scheme is much simpler and more efficient since it is only a two-level system with macroscopic variables. Finally, we perform some simulations of several benchmark problems, and find that the numerical results are not only in agreement with analytical solutions, but also consistent with the theoretical analysis.

关键词： Complex fluids Lattice-Boltzmann methods Navier-Stokes equation

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Auto-ejection of liquid from a nozzle

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Physical Review E 2024年第4期109卷 045302-045302页

作者： Fang Shan Zhenhua Chai Baochang Shi School of Mathematics and Statistics Huazhong University of Science and Technology Wuhan 430074 China Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Wuhan 430074 China Institute of Interdisciplinary Research for Mathematics and Applied Science Huazhong University of Science and Technology Wuhan 430074 China

Auto-ejection of liquid is an important process in engineering applications, and is also very complicated since it involves interface moving, deforming, and jet breaking up. In this work, a theoretical velocity of meniscus at nozzle exit is first derived, which can be used to analyze the critical condition for auto-ejection of liquid. Then a consistent and conservative axisymmetric lattice Boltzmann (LB) method is proposed to study the auto-ejection process of liquid jet from a nozzle. We test the LB model by conducting some simulations, and find that the numerical results agree well with the theoretical and experimental data. We further consider the effects of contraction ratio, length ratio, contact angle, and nozzle structure on the auto-ejection, and observe some distinct phenomena during the ejection process, including the deformation of meniscus, capillary necking, and droplet pinch off. Finally, the results reported in the present work may play an instructive role on the design of droplet ejectors and the understanding of jetting dynamics in microgravity environment.

关键词： Lattice-Boltzmann methods

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Forte: An Interactive Visual Analytic Tool for Trust-Augmented Net Load Forecasting

Forte: An Interactive Visual Analytic Tool for Trust-Augment...

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Innovative Smart Grid Technologies (ISGT)

作者： Kaustav Bhattacharjee Soumya Kundu Indrasis Chakraborty Aritra Dasgupta Department of Data Science New Jersey Institute of Technology USA Optimization and Control Group Pacific Northwest National Laboratory USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory USA

Accurate net load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.

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Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods

Classification of executive functioning performance post-lon...

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IEEE India Conference (INDICON)

作者： Akash K Rao Vishnu K Menon Shashank Uttrani Ayushman Dixit Dipanshu Verma Varun Dutt Applied Cognitive Science Laboratory Indian Institute of Technology Mandi Mandi India School of Computing and Electrical Engineering Indian Institute of Technology Mandi Mandi India

Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration.

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The C-CNN model: Do we really need multiplicative synapses in convolutional neural networks? 14

The C-CNN model: Do we really need multiplicative synapses i...

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14th International Conference on Communications, COMM 2022

作者： Degaru, Radu Mirica, Adrian-Dumitru Dogaru, Ioana Natural Computing Laboratory University 'Politehnica' of Bucharest Dept. of Applied Electronics and Information Eng. Bucharest Romania Doctoral School of Electronics Telecommunications and Information Technology University 'Politehnica' of Bucharest Bucharest Romania

ISBN: (纸本)9781665494854

Comparative synapses are proposed and investigated in the context of convolutional neural networks as replacements for the traditional, multiplier-based synapses. A comparative synapse is an operator inspired from the min() operator used in fuzzy-logic a replacement for product to implement AND function. Its implementation complexity is linear in the number of bits unlike multipliers, requiring quadratic complexity. In effect, using a typical resolution of 8 bits the use of comparative synapse would reduce 8 times the number of hardware resources allocated for the operator. A C-CNN model was constructed to support comparative synapses and their update and error propagation rules. GPU acceleration of the C-CNN model was achieved using CUPY. The model was trained with several widely known image recognition datasets including MNIST, CIFAR and USPS. It turns out that functional performance (accuracy) is not dramatically affected in C-CNN against a similar traditional CNN model with multiplicative operators, thus opening an interesting implementation perspective, particularly for the TinyML and HW-oriented solutions with significant reduction in energy, silicon area and costs. The approach is scalable to more sophisticated CNN models providing adequate optimized operators adapted to this new synaptic model. © 2022 IEEE.

关键词： Convolutional neural networks

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Automatic recognition of depression based on audio and video:A review

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World Journal of Psychiatry 2024年第2期14卷 225-233页

作者： Meng-Meng Han Xing-Yun Li Xin-Yu Yi Yun-Shao Zheng Wei-Li Xia Ya-Fei Liu Qing-Xiang Wang Shandong Mental Health Center Shandong UniversityJinan 250014Shandong ProvinceChina Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and TechnologyQilu University of Technology(Shandong Academy of Sciences)Jinan 250353Shandong ProvinceChina Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250353Shandong ProvinceChina Department of Ward Two Shandong Mental Health CenterShandong UniversityJinan 250014Shandong ProvinceChina

Depression is a common mental health *** current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression ***-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively *** physicians usually require extensive training and experience to capture changes in these *** in deep learning technology have provided technical support for capturing non-biological *** researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression *** article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

关键词： Depression recognition Deep learning Automatic depression estimation System Audio processing Image processing Feature fusion Future development

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Phase Diagram and Physical Properties Anisotropy of Strontianite

SSRN

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SSRN 2024年

作者： Liu, Zi-Jiang Ju, Jia-Qi Dou, Xilong Jin, Neng-Zhi Zhang, Cai-Rong Sun, Xiao-Wei Zhang, Lei School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Cloud Computing of Gansu Province Gansu Computing Center Lanzhou730030 China Department of Applied Physics Lanzhou University of Technology Lanzhou730050 China

The physical properties and anisotropy of carbonate at high pressure are the basis of understanding the deep carbon cycle and storage. Here, the phase diagram of strontianite (SrCO3) was constructed by first-principles calculations and its physical properties and anisotropy at high pressure were investigated. The results show that SrCO3 only has the Pnma and Pmmn phases, and there is no R3m phase. SrCO3 polymorphs have large elastic anisotropy. Compared with CaCO3 polymorphs, SrCO3 polymorphs have lower wave velocity and smaller seismic anisotropy. The linear elastic compressibility combined with the volumetric thermal expansion coefficient was used to obtain the linear thermal expansion coefficient of SrCO3-Pmmn. © 2024, The Authors. All rights reserved.

关键词： Strontium compounds

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Study on the Structure, Elasticity, and Thermal Conductivity of Orthocarbonate Sr2CO4

SSRN

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SSRN 2024年

作者： Liu, Zi-Jiang Ju, Jia-Qi Dou, Xi-Long Jin, Neng-Zhi Zhang, Cai-Rong Wang, Xiao-Yun Wu, Liang School of Mathematics and Physics Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Cloud Computing of Gansu Province Gansu Computing Center Lanzhou730030 China Department of Applied Physics Lanzhou University of Technology Lanzhou730050 China

Orthocarbonate Sr2CO4 is a recently discovered Sr-carbonate that plays a crucial role in understanding the global long-term carbon cycle. In this work, the structure, equation of state, elasticity, and thermal conductivity of Sr2CO4 were studied by first-principles calculations. The results show that the surfaces of the SrO9 and SrO11 polyhedrons, which make up Sr2CO4, are composed of triangles and quadrangles. By comparison with other alkaline earth metal orthocarbonates, SrCO3 polymorphs and the Preliminary Reference Earth Model (PREM), Sr2CO4 is found to be a carbonate with high density, low wave velocity, low anisotropy and low thermal conductivity in the entire Earth's mantle. This study may help to understand the high-pressure behavior of alkaline earth metal orthocarbonates in the Earth's interior. © 2024, The Authors. All rights reserved.

关键词： Elasticity

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Multifunctional silicene/CeO_(2)heterojunctions:Desirable electronic material and promising water-splitting photocatalyst

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Chinese Chemical Letters 2022年第8期33卷 3947-3950页

作者： Liang Xu Jian Zeng Quan Li Xin Luo Tong Chen Jingjing Liu Ling-Ling Wang Energy Materials Computing Center School of Energy and Mechanical EngineeringJiangxi University of Science and TechnologyNanchang 330013China Department of Applied Physics School of ScienceEast China Jiaotong UniversityNanchang 330013China Key Laboratory for Micro-Nano Optoelectronic Devices of Ministry of Education School of Physics and ElectronicsHunan UniversityChangsha 410082China

The first-principles calculations demonstrate that covalently bonded(cb)heterojunction and van der Waals(vd W)heterojunction can coexist in silicene/CeO_(2) heterojunctions,due to the different stacking ***,the cb heterojunction with band gap of 1.97 e V,forms a type-II heterojunction,exhibits good redox performance and has high-effective optical absorption spectra,thus it is a promising photocatalyst for overall water ***,for the vd W heterojunction,the Dirac cone of silicene is well kept on CeO_(2) semiconducting substrate,with a considerable energy gap of 0.43 e V,which can be an ideal material in building silicene-based electronic *** results may open a new gateway in both of nanoelectronic device and energy conversion for silicene/Ce O2 nanocomposites.

关键词： Silicene CeO_(2) Photocatalyst Interfacial interaction Dirac cone

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The Exploration of Neural Collapse under Imbalanced Data

arXiv

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arXiv 2024年

作者： Liu, Haixia School of Mathematics and Statistics Institute of Interdisciplinary Research for Mathematics and Applied Science Hubei Key Laboratory of Engineering Modeling and Scientific Computing Huazhong University of Science and Technology Hubei Wuhan China

Neural collapse, a newly identified characteristic, describes a property of solutions during model training. In this paper, we explore neural collapse in the context of imbalanced data. We consider the L-extended unconstrained feature model with a bias term and provide a theoretical analysis of global minimizer. Our findings include: (1) Features within the same class converge to their class mean, similar to both the balanced case and the imbalanced case without bias. (2) The geometric structure is mainly on the left orthonormal transformation of the product of L linear classifiers and the right transformation of the class-mean matrix. (3) Some rows of the left orthonormal transformation of the product of L linear classifiers collapse to zeros and others are orthogonal, which relies on the singular values of Ŷ = (IK − 1/Nn1TK)D, where K is class size, n is the vector of sample size for each class, D is the diagonal matrix whose diagonal entries are given by √n. Similar results are for the columns of the right orthonormal transformation of the product of class-mean matrix and D. (4) The i-th row of the left orthonormal transformation of the product of L linear classifiers aligns with the i-th column of the right orthonormal transformation of the product of class-mean matrix and D. (5) We provide the estimation of singular values about Ŷ . Our numerical experiments support these theoretical *** Codes ***, *** Copyright © 2024, The Authors. All rights reserved.

关键词： Linear transformations

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