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Stable Grid Refinement and Singular Source Discretization for Seismic Wave Simulations

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Communications in Computational Physics 2010年第10期8卷 1074-1110页

作者： N.Anders Petersson Bjorn Sjogreen center for applied scientific computing L-422Lawrence Livermore National LaboratoryPO Box 808LivermoreCA 94551.

An energy conserving discretization of the elastic wave equation in second order formulation is developed for a composite grid,consisting of a set of structured rectangular component grids with hanging nodes on the grid refinement *** developed summation-by-parts properties are generalized to devise a stable second order accurate coupling of the solution across mesh refinement *** discretization of singular source terms of point force and point moment tensor type are also *** on enforcing discrete moment conditions that mimic properties of the Dirac distribution and its gradient,previous single grid formulas are generalized to work in the vicinity of grid refinement *** source discretization formulas are shown to give second order accuracy in the solution,with the error being essentially independent of the distance between the source and the grid refinement *** numerical examples are given to illustrate the properties of the proposed method.

关键词： Elastic wave equation mesh refinement stability summation by parts singular source term

Memristive feature and mechanism induced by laser-doping in defect-free 2D semiconductor materials

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Journal of Semiconductors 2024年第7期45卷 85-91页

作者： Xiaoshan Du Shu Wang Qiaoxuan Zhang Shengyao Chen Fengyou Yang Zhenzhou Liu Zhengwei Fan Lijun Ma Lei Wang Lena Du Zhongchang Wang Cong Wang Bing Chen Qian Liu College of Electronic and Information Engineering Shandong University of Science and TechnologyQingdao 266590China National center for Nanoscience and Technology&University of Chinese Academy of Sciences Beijing 100190China Hebei University of Water Resources and Electric Engineering Electrical Automation Department Cangzhou 061001China The MOE Key Laboratory of Weak-Light Nonlinear Photonics and International Sino-Slovenian Join Re-search center on Liquid Crystal Pho-tonics TEDA Institute of Applied Physics and School of PhysicsNankai UniversityTianjin 300457China College of Mathematics and Physics Shandong Advanced Optoelectronic Materials and Technologies Engineering LaboratoryQingdao Uni-versity of Science and TechnologyQingdao 266061China Department of Physics Capital Normal UniversityBeijing 100048China Department of Advanced Materials and computing International Iberian Nanotechnology Laboratory(INL)4715-330 BragaPortugal College of Mathematics and Physics Beijing University of Chemical TechnologyBeijing 100029China

Memristors as non-volatile memory devices have gained numerous attentions owing to their advantages in storage,in-memory computing, synaptic applications, etc. In recent years, two-dimensional(2D) materials with moderate defects have been discovered to exist memristive feature. However, it is very difficult to obtain moderate defect degree in 2D materials, and studied on modulation means and mechanism becomes urgent and essential. In this work, we realized memristive feature with a bipolar switching and a configurable on/off ratio in a two-terminal MoS_(2) device(on/off ratio ~100), for the first time, from absent to present using laser-modulation to few-layer defect-free MoS_(2)(about 10 layers), and its retention time in both high resistance state and low resistance state can reach 2×10^(4) s. The mechanism of the laser-induced memristive feature has been cleared by dynamic Monte Carlo simulations and first-principles calculations. Furthermore, we verified the universality of the laser-modulation by investigating other 2D materials of TMDs. Our work will open a route to modulate and optimize the performance of 2D semiconductor memristive devices.

关键词： 2D-material memristor laser doping laser direct writing memristive mechanism

Severe Global Environmental Issues Caused by Canada’s Record-Breaking Wildfires in 2023

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Advances in Atmospheric Sciences 2024年第4期41卷 565-571页

作者： Zhe WANG Zifa WANG Zhiyin ZOU Xueshun CHEN Huangjian WU Wending WANG Hang SU Fang LI Wenru XU Zhihua LIU Jiaojun ZHU State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China International center for Climate and Environment Sciences Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Institute of applied Ecology Chinese Academy of SciencesShenyang 110016China

Due to the record-breaking wildfires that occurred in Canada in 2023,unprecedented quantities of air pollutants and greenhouse gases were released into the *** wildfires had emitted more than 1.3 Pg CO_(2)and 0.14 Pg CO_(2)equivalent of other greenhouse gases(GHG)including CH4 and N_(2)O as of 31 *** wildfire-related GHG emissions constituted more than doubled Canada’s planned cumulative anthropogenic emissions reductions in 10 years,which represents a significant challenge to climate mitigation *** model simulations showed that the Canadian wildfires impacted not only the local air quality but also that of most areas in the northern hemisphere due to long-range transport,causing severe PM_(2.5)pollution in the northeastern United States and increasing daily mean PM_(2.5)concentration in northwestern China by up to 2μg *** observed maximum daily mean PM_(2.5)concentration in New York City reached 148.3μg m-3,which was their worst air quality in more than 50 years,nearly 10 times that of the air quality guideline(i.e.,15μg m-3)issued by the World Health Organization(WHO).Aside from the direct emissions from forest fires,the peat fires beneath the surface might smolder for several months or even longer and release substantial amounts of CO_(2).The substantial amounts of greenhouse gases from forest and peat fires might contribute to the positive feedback to the climate,potentially accelerating global *** better understand the comprehensive environmental effects of wildfires and their interactions with the climate system,more detailed research based on advanced observations and Earth System Models is essential.

关键词： Canada forest fire greenhouse gases PM_(2.5) transboundary air pollution

An End-To-End Hyperbolic Deep Graph Convolutional Neural Network Framework

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 537-563页

作者： Yuchen Zhou Hongtao Huo Zhiwen Hou Lingbin Bu Yifan Wang Jingyi Mao Xiaojun Lv Fanliang Bu School of Information Network Security People’s Public Security University of ChinaBeijing100038China Institute of computing Technology China Academy of Railway Sciences Corporation LimitedBeijing100081China

Graph Convolutional Neural Networks(GCNs)have been widely used in various fields due to their powerful capabilities in processing graph-structured ***,GCNs encounter significant challenges when applied to scale-free graphs with power-law distributions,resulting in substantial ***,most of the existing GCN models are shallow structures,which restricts their ability to capture dependencies among distant nodes and more refined high-order node features in scale-free graphs with hierarchical *** more broadly and precisely apply GCNs to real-world graphs exhibiting scale-free or hierarchical structures and utilize multi-level aggregation of GCNs for capturing high-level information in local representations,we propose the Hyperbolic Deep Graph Convolutional Neural Network(HDGCNN),an end-to-end deep graph representation learning framework that can map scale-free graphs from Euclidean space to hyperbolic *** HDGCNN,we define the fundamental operations of deep graph convolutional neural networks in hyperbolic ***,we introduce a hyperbolic feature transformation method based on identity mapping and a dense connection scheme based on a novel non-local message passing *** addition,we present a neighborhood aggregation method that combines initial structural featureswith hyperbolic attention *** the above methods,HDGCNN effectively leverages both the structural features and node features of graph data,enabling enhanced exploration of non-local structural features and more refined node features in scale-free or hierarchical *** results demonstrate that HDGCNN achieves remarkable performance improvements over state-ofthe-art GCNs in node classification and link prediction tasks,even when utilizing low-dimensional embedding ***,when compared to shallow hyperbolic graph convolutional neural network models,HDGCNN exhibits notable advantages and performance enhanceme

关键词： Graph neural networks hyperbolic graph convolutional neural networks deep graph convolutional neural networks message passing framework

A Low Complexity ML-Based Methods for Malware Classification

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Computers, Materials and Continua 2024年第3期80卷 4833-4857页

作者： Farfoura, Mahmoud E. Alkhatib, Ahmad Alsekait, Deema Mohammed Alshinwan, Mohammad El-Rahman, Sahar A. Rosiyadi, Didi AbdElminaam, Diaa Salama Cybersecurity Department Al-Zaytoonah University of Jordan Amman 11733 Jordan Department of Computer Science and Information Technology Applied College Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia Faculty of Information Technology Applied Science Private University Amman 11931 Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-Shoubra Benha University Cairo 11629 Egypt Research center for Artificial Intelligence and Cybersecurity Electronics and Informatics Organization National Research and Innovation Agency (BRIN) KST Samaun Samadikun Bandung 40135 Indonesia Jadara Research center Jadara University Irbid 21110 Jordan MEU Research Unit Middle East University Amman 11831 Jordan

The article describes a new method for malware classification, based on a Machine Learning (ML) model architecture specifically designed for malware detection, enabling real-time and accurate malware identification. Using an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique (IFDRT), the authors have significantly reduced the feature space while retaining critical information necessary for malware classification. This technique optimizes the model’s performance and reduces computational requirements. The proposed method is demonstrated by applying it to the BODMAS malware dataset, which contains 57,293 malware samples and 77,142 benign samples, each with a 2381-feature vector. Through the IFDRT method, the dataset is transformed, reducing the number of features while maintaining essential data for accurate classification. The evaluation results show outstanding performance, with an F1 score of 0.984 and a high accuracy of 98.5% using only two reduced features. This demonstrates the method’s ability to classify malware samples accurately while minimizing processing time. The method allows for improving computational efficiency by reducing the feature space, which decreases the memory and time requirements for training and prediction. The new method’s effectiveness is confirmed by the calculations, which indicate significant improvements in malware classification accuracy and efficiency. The research results enhance existing malware detection techniques and can be applied in various cybersecurity applications, including real-time malware detection on resource-constrained devices. Novelty and scientific contribution lie in the development of the IFDRT method, which provides a robust and efficient solution for feature reduction in ML-based malware classification, paving the way for more effective and scalable cybersecurity measures. Copyright © 2024 The Authors. Published by Tech Science Press.

关键词： Dimensionality reduction

Reliable and explainable machine-learning methods for accelerated material discovery

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npj Computational Materials 2019年第1期5卷 186-194页

作者： Bhavya Kailkhura Brian Gallagher Sookyung Kim Anna Hiszpanski T.Yong-Jin Han center for applied scientific computing Computing DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA Materials Science Division Physical and Life Sciences DirectorateLawrence Livermore National LaboratoryLivermoreCAUSA

Despite ML’s impressive performance in commercial applications,several unique challenges exist when applying ML in materials science *** such a context,the contributions of this work are ***,we identify common pitfalls of existing ML techniques when learning from underrepresented/imbalanced material ***,we show that with imbalanced data,standard methods for assessing quality of ML models break down and lead to misleading ***,we find that the model’s own confidence score cannot be trusted and model introspection methods(using simpler models)do not help as they result in loss of predictive performance(reliability-explainability trade-off).Second,to overcome these challenges,we propose a general-purpose explainable and reliable machine-learning ***,we propose a generic pipeline that employs an ensemble of simpler models to reliably predict material *** also propose a transfer learning technique and show that the performance loss due to models’simplicity can be overcome by exploiting correlations among different material properties.A new evaluation metric and a trust score to better quantify the confidence in the predictions are also *** improve the interpretability,we add a rationale generator component to our framework which provides both model-level and decision-level ***,we demonstrate the versatility of our technique on two applications:(1)predicting properties of crystalline compounds and(2)identifying potentially stable solar cell *** also point to some outstanding issues yet to be resolved for a successful application of ML in material science.

关键词： explain overcome simpler

Microwave-accelerated direct regeneration of LiCoO_(2)cathodes for Li-ion batteries

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Science China Chemistry 2024年第7期67卷 2181-2189页

作者： Xueshan Hu Yun-Chao Yin Chao Li Lihui Zhou Lin Yang Yitian Feng Daxian Zuo Chenhui Ning Das Soham Sheng Dai Longbin Qiu Lin Zeng Haw Jiunn Woo Jiayu Wan Future Battery Research center Global Institute of Future TechnologyShanghai Jiao Tong UniversityShanghai 200240China Department of Mechanical and Energy Engineering Southern University of Science and TechnologyShenzhen 518055China Centre for Ionics University of Malaya Department of PhysicsFaculty of ScienceUniversity of MalayaKuala Lumpur 50603Malaysia Key Laboratory for Advanced Materials and Feringa Nobel Prize Scientist Joint Research center School of Chemistry and Molecular EngineeringEast China University of Science and TechnologyShanghai 200237China Department of Natural and applied Sciences Duke Kunshan UniversityKunshan 215316China

Recycling spent lithium-ion batteries is integral to today's low-carbon environmental protection *** concept of direct regeneration,acknowledged for its environmental sustainability,economic viability,and consistent performance of recycled materials,is gaining *** study presents an efficient and nondestructive approach by utilizing an ultrafast microwave technology to directly regenerate spent lithium cobaltate(LCO)cathode *** contrast to conventional furnacebased processes,this method significantly reduces the regeneration *** subjecting the spent LCO mixed with lithium sources to three microwave heating cycles(at approximately 1,350 K),LCO regeneration is achieved,yielding a specific capacity of 140.8 mAh g^(-1)(0.2 C)with a robust cycle *** further environmental and economic benefits,the ultrafast microwave technology holds scientific promise for directly regenerating cathode materials,while establishing competitiveness for industrial applications.

关键词： battery recycling spent lithium cobaltate microwave directly regeneration cathode materials

Piezoelectric constant temperature dependence in strained [111]-oriented zinc-blende MQW-SOAs

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Chinese Optics Letters 2023年第9期21卷 107-112页

作者： Horacio Soto-Ortiz Gerson Torres-Miranda applied Physics Division Center for Scientific Research and Higher Education of Ensenada

Here,we present a study of the effective piezoelectric constant(e_(14_(e)))temperature dependence in strained[111]-oriented zinc-blende quantum wells(QWs)embedded within a semiconductor optical amplifier(SOA).We determined e_(14_(e)) using a method that was insensitive to the segregation phenomenon and to the temperature dependence of the bandgap energy,which required neither fitting parameters nor temperature-dependent expressions for energy and out-of-plane effective masses of electrons and heavy *** e_(14_(e))=−0.0534±0.0040 C·m^(−2) at 23°C was obtained for an SOA with 1.2 nm[111]-oriented strained In0.687Ga0.313As/In0.807Ga0.193As0.304P0.696 *** previously published research,where e_(14_(e)) magnitude increased as temperature rised,we extracted an e_(14_(e)) magnitude that decreased as temperature increased.

关键词： piezoelectric constant pyroelectric effect quantum-confined Stark effect excitons semiconductor optical amplifiers zinc-blende quantum wells

Discovering the ultralow thermal conductive A_(2)B_(2)O_(7)-type high-entropy oxides through the hybrid knowle dge-assiste d data-driven machine learning

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Journal of Materials Science & Technology 2024年第1期168卷 131-142页

作者： Ying Zhang Ke Ren William Yi Wang Xingyu Gao Ruihao Yuan Jun Wang Yiguang Wang Haifeng Song Xiubing Liang Jinshan Li State Key Laboratory of Solidification Processing Northwestern Polytechnical UniversityXi’an 710072China Innovation center NPU ChongqingChongqing 401135China Institute of Advanced Structure Technology Beijing Institute of TechnologyHaidian DistrictBeijing 100081China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijing 100088China Defense Innovation Institute Academy of Military Sciences of the PLA of ChinaBeijing 100071China

Lattice engineering and distortion have been considered one kind of effective strategies for discovering advanced *** instinct chemical flexibility of high-entropy oxides(HEOs)motivates/accelerates to tailor the target properties through phase transformations and lattice ***,a hybrid knowledge-assisted data-driven machine learning(ML)strategy is utilized to discover the A_(2)B_(2)O_(7)-type HEOs with low thermal conductivity(κ)through 17 rare-earth(RE=Sc,Y,La-Lu)solutes optimized A-site.A designing routine integrating the ML and high throughput first principles has been proposed to predict the key physical parameter(KPPs)correlated to the targetedκof advanced *** the smart-designed 6188(5RE_(0.2))_(2)Zr_(2)O_(7)HEOs,the best candidates are addressed and validated by the princi-ples of severe lattice distortion and local phase transformation,which effectively reduceκby the strong multi-phonon scattering and weak interatomic ***,(Sc_(0.2)Y_(0.2)La_(0.2)Ce_(0.2)Pr_(0.2))_(2)Zr_(2)O_(7)with predictedκbelow 1.59 Wm^(−1)K^(−1)is selected to be verified,which matches well with the ex-perimentalκ=1.69 Wm^(−1)K^(−1)at 300 K and could be further decreased to 0.14 Wm^(−1)K^(−1)at 1473 ***,the coupling effects of lattice vibrations and charges on heat transfer are revealed by the cross-validations of various models,indicating that the weak bonds with low electronegativity and few bond-ing charge density and the lattice distortion(r∗)identified by cation radius ratio(r A/r B)should be the KPPs to decreaseκ*** work supports an intelligent designing strategy with limited atomic and electronic KPPs to accelerate the development of advanced multi-component HEOs with proper-ties/performance at multi-scales.

关键词： High-entropy oxides Thermal conductivity Pyrochlore Key physical parameter First-Principles