检索结果-内蒙古大学图书馆

Critical pore radius and transport properties of disordered hard- and overlapping-sphere models

Physical Review E 2021年第1期104卷 014127-014127页

作者： Michael A. Klatt Robert M. Ziff Salvatore Torquato Department of Physics Princeton University Princeton New Jersey 08544 USA Institut für Theoretische Physik FAU Erlangen-Nürnberg Staudtstr. 7 91058 Erlangen Germany Center for the Study of Complex Systems and Department of Chemical Engineering University of Michigan Ann Arbor Michigan 48109 USA Department of Chemistry Princeton Institute for the Science and Technology of Materials and Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

Transport properties of porous media are intimately linked to their pore-space microstructures. We quantify geometrical and topological descriptors of the pore space of certain disordered and ordered distributions of spheres, including pore-size functions and the critical pore radius δc. We focus on models of porous media derived from maximally random jammed sphere packings, overlapping spheres, equilibrium hard spheres, quantizer sphere packings, and crystalline sphere packings. For precise estimates of the percolation thresholds, we use a strict relation of the void percolation around sphere configurations to weighted bond percolation on the corresponding Voronoi networks. We use the Newman-Ziff algorithm to determine the percolation threshold using universal properties of the cluster size distribution. The critical pore radius δc is often used as the key characteristic length scale that determines the fluid permeability k. A recent study [Torquato, Adv. Wat. Resour. 140, 103565 (2020)] suggested for porous media with a well-connected pore space an alternative estimate of k based on the second moment of the pore size 〈δ2〉, which is easier to determine than δc. Here, we compare δc to the second moment of the pore size 〈δ2〉, and indeed confirm that, for all porosities and all models considered, δc2 is to a good approximation proportional to 〈δ2〉. However, unlike 〈δ2〉, the permeability estimate based on δc2 does not predict the correct ranking of k for our models. Thus, we confirm 〈δ2〉 to be a promising candidate for convenient and reliable estimates of the fluid permeability for porous media with a well-connected pore space. Moreover, we compare the fluid permeability of our models with varying degrees of order, as measured by the τ order metric. We find that (effectively) hyperuniform models tend to have lower values of k than their nonhyperuniform counterparts. Our findings could facilitate the design of porous media with desirable transport properties via targete

关键词： Geophysical networks Materials Patterns in complex systems

来源：评论

学校读者我要写书评

暂无评论

Biases in inverse Ising estimates of near-critical behavior

引用

Physical Review E 2023年第1期108卷 014109-014109页

作者： Maximilian B. Kloucek Thomas Machon Shogo Kajimura C. Patrick Royall Naoki Masuda Francesco Turci School of Physics HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Bristol Centre for Functional Nanomaterials HH Wills Physics Laboratory University of Bristol Tyndall Avenue Bristol BS8 1TL United Kingdom Faculty of Information and Human Sciences Kyoto Institute of Technology Kyoto 606-8585 Japan Gulliver UMR CNRS 7083 ESPCI Paris Université PSL 75005 Paris France Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

Inverse Ising inference allows pairwise interactions of complex binary systems to be reconstructed from empirical correlations. Typical estimators used for this inference, such as pseudo-likelihood maximization (PLM), are biased. Using the Sherrington-Kirkpatrick model as a benchmark, we show that these biases are large in critical regimes close to phase boundaries, and they may alter the qualitative interpretation of the inferred model. In particular, we show that the small-sample bias causes models inferred through PLM to appear closer to criticality than one would expect from the data. Data-driven methods to correct this bias are explored and applied to a functional magnetic resonance imaging data set from neuroscience. Our results indicate that additional care should be taken when attributing criticality to real-world data sets.

关键词： Critical phenomena Cortical networks Spin glasses Functional magnetic resonance imaging Ising model

来源：评论

学校读者我要写书评

暂无评论

Correction to: Mitigation strategies against cascading failures within a project activity network

引用

Journal of computational Social science 2021年第1期5卷 1097-1098页

作者： Ellinas, Christos Nicolaides, Christos Masuda, Naoki Nodes and Links Ltd Cambridge UK Department of Business and Public Administration University of Cyprus Nicosia Cyprus Initiative on the Digital Economy Massachusetts Institute of Technology Cambridge USA Department of Mathematics State University of New York at Buffalo Buffalo USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo USA Faculty of Management and Economics Dalian University of Technology Dalian China

来源：评论

学校读者我要写书评

暂无评论

Deep learning for drug repurposing: methods, databases, and applications

arXiv

引用

arXiv 2022年

作者： Pan, Xiaoqin Lin, Xuan Cao, Dongsheng Zeng, Xiangxiang Yu, Philip S. He, Lifang Nussinov, Ruth Cheng, Feixiong School of Computer Science and Engineering Hunan University China School of Computer Science Xiangtan University China Xiangya School of Pharmaceutical Sciences Central South University China Department of Computer Science University of Illinois at Chicago United States Department of Computer Science and Engineering Lehigh University United States Computational Structural Biology Section Basic Science Program Frederick National Laboratory for Cancer Research National Cancer Institute at Frederick FrederickMD21702 United States Department of Human Molecular Genetics and Biochemistry Sackler School of Medicine Tel Aviv University Tel Aviv69978 Israel Genomic Medicine Institute Lerner Research Institute Cleveland Clinic ClevelandOH44195 United States Department of Molecular Medicine Cleveland Clinic Lerner College of Medicine Case Western Reserve University ClevelandOH44195 United States Case Comprehensive Cancer Center Case Western Reserve University School of Medicine ClevelandOH44106 United States

Drug development is time-consuming and expensive. Repurposing existing drugs for new therapies is an attractive solution that accelerates drug development at reduced experimental costs, specifically for Coronavirus Disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, comprehensively obtaining and productively integrating available knowledge and big biomedical data to effectively advance deep learning models is still challenging for drug repurposing in other complex diseases. In this review, we introduce guidelines on how to utilize deep learning methodologies and tools for drug repurposing. We first summarized the commonly used bioinformatics and pharmacogenomics databases for drug repurposing. Next, we discuss recently developed sequence-based and graph-based representation approaches as well as state-of-the-art deep learning-based methods. Finally, we present applications of drug repurposing to fight the COVID-19 pandemic, and outline its future challenges. © 2022, CC BY-NC-SA.

关键词： Coronavirus

来源：评论

学校读者我要写书评

暂无评论

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials

arXiv

引用

arXiv 2023年

作者： Eastman, Peter Galvelis, Raimondas Peláez, Raúl P. Abreu, Charlles R.A. Farr, Stephen E. Gallicchio, Emilio Gorenko, Anton Henry, Michael M. Hu, Frank Huang, Jing Krämer, Andreas Michel, Julien Mitchell, Joshua A. Pande, Vijay S. Rodrigues, João P.G.L.M. Rodriguez-Guerra, Jaime Simmonett, Andrew C. Singh, Sukrit Swails, Jason Turner, Philip Wang, Yuanqing Zhang, Ivy Chodera, John D. De Fabritiis, Gianni Markland, Thomas E. Department of Chemistry Stanford University StanfordCA94305 United States Acellera Labs C Dr Trueta 183 Barcelona08005 Spain C Dr. Aiguader 88 Barcelona08003 Spain Chemical Engineering Department School of Chemistry Federal University of Rio de Janeiro Rio de Janeiro68542 Brazil Redesign Science Inc. 180 Varick St. New YorkNY10014 United States EaStCHEM School of Chemistry University of Edinburgh EH9 3FJ United Kingdom Department of Chemistry and Biochemistry Brooklyn College The City University of New York NY United States Ph.D. Program in Chemistry Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New YorkNY United States Stream HPC Koningin Wilhelminaplein 1 - 40601 Amsterdam1062 HG Netherlands Computational and Systems Biology Program Sloan Kettering Institute Memorial Sloan Kettering Cancer Center New YorkNY10065 United States Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University 18 Shilongshan Road Zhejiang Hangzhou310024 China Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 12 Berlin14195 Germany The Open Force Field Initiative Open Molecular Software Foundation DavisCA95616 United States Andreessen Horowitz 2865 Sand Hill Rd Menlo ParkCA94025 United States Department of Structural Biology Stanford University StanfordCA94305 United States Charité Universitätsmedizin Berlin In silico Toxicology and Structural Bioinformatics Virchowweg 6 Berlin10117 Germany Laboratory of Computational Biology National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20892 United States Entos Inc. 9310 Athena Circle La Jolla CA92037 United States College of Engineering Virginia Polytechnic Institute State University BlacksburgVA24061 United States Simons Center for Computational Physical Chemistry Center for Data Science New York University 24 Waverly Place New YorkNY10004 United States T

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features on simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein (GFP) chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations at only a modest increase in cost. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

来源：评论

学校读者我要写书评

暂无评论

Phase Transformations, Microstructural Refinement and Defect Evolution Mechanisms in Al-Si Alloys under Non-Hydrostatic Diamond Anvil Cell Compression

SSRN

引用

SSRN 2020年

作者： Liu, Tingkun Olszta, Matthew Gwalani, Bharat Park, Changyong Mathaudhu, Suveen Devaraj, Arun Physical and Computational Science Directorate Pacific Northwest National Laboratory Energy and Environment Directorate Pacific Northwest National Laboratory High Pressure Collaborative Access Team X-ray Science Division Argonne National Laboratory Material Science and Engineering Program University of California Riverside Mechanical Engineering Department University of California Riverside

Non-hydrostatic compression of materials using a diamond anvil cell (DAC) can transform equilibrium microstructure of an alloy to novel, potentially metastable states. In this study, in situ synchrotron X-ray diffraction (XRD) during compression up to 24 GPa and subsequent ex situ, high resolution analytical electron microscopy (AEM) after decompression of an Al-Si alloy provided insight into the crystallographic changes during the compression as well as microstructural refinement, and defect structures caused by such a high pressure compression-decompression process. Pressure resolved in-situ synchrotron XRD was used to detail the phase transformation pathway of the eutectic Si phase in Al-Si alloy, from Si-I → Si-XI → Si-V during compression, and a final transformation predominantly to Si-III after decompression. Using scanning and transmission electron microscopy (S/TEM), site specific analysis of the alloy immediately underneath the anvil contact surface demonstrated a highly complex microstructure. A narrow region of thick amorphous Al oxide interspersed with nanocrystalline grains was found at the top surface. Underneath this Al oxide, while the majority the eutectic Si was transformed into highly-deformed, polycrystalline (PC) Si-III, a complex intermediate layer was discovered at the interface between Al and Si, comprised of a small fraction of Al nanocrystals and a majority of nanocrystalline Si-I. This combination of pressure resolved in-situ synchrotron XRD coupled with subsequent high resolution, electron microscopy resolved the phase transformation as well as non-equilibrium microstructures in a metallic alloy induced by a non-hydrostatic high pressure compression followed by decompression. © 2020, The Authors. All rights reserved.

关键词： Silicon alloys

来源：评论

学校读者我要写书评

暂无评论

Metallic vs. Semiconducting properties of quasi-one-dimensional tantalum selenide van der Waals nanoribbons

arXiv

引用

arXiv 2021年

作者： Kargar, Fariborz Krayev, Andrey Wurch, Michelle Ghafouri, Yassamin Debnath, Topojit Wickramaratne, Darshana Salguero, Tina T. Lake, Roger Bartels, Ludwig Balandin, Alexander A. Center Department of Electrical and Computer Engineering University of California RiversideCA92521 United States HORIBA Scientific NovatoCA94949 United States Department of Chemistry and Material Science and Engineering Program University of California RiversideCA92521 United States Department of Chemistry University of Georgia AthensGA30602 United States Laboratory for Terahertz and Terascale Electronics Department of Electrical and Computer Engineering University of California RiversideCA92521 United States Center for Computational Materials Science U.S. Naval Research Laboratory WashingtonDC20375 United States

We conducted a tip-enhanced Raman scattering spectroscopy (TERS) and photoluminescence (PL) study of quasi-1D TaSe3-δ nanoribbons exfoliated onto gold substrates. At a selenium deficiency of δ~0.25 (Se/Ta=2.75,), the nanoribbons exhibit a strong, broad PL peak centered around ~920 nm (1.35 eV), suggesting their semiconducting behavior. Such nanoribbons revealed a strong TERS response under 785-nm laser excitation, allowing for their nanoscale spectroscopic imaging. Nanoribbons with a smaller selenium deficiency (Se/Ta=2.85, δ~0.15) did not show any PL or TERS response. The confocal Raman spectra of these samples agree with the previously-reported spectra of metallic TaSe3. The differences in the optical response of the nanoribbons examined in this study suggest that even small variations in Se content can induce changes in electronic structure, causing samples to exhibit either metallic or semiconducting character. The temperature-dependent electrical measurements of devices fabricated with both types of materials corroborate these observations. The density-functional-theory calculations revealed that incorporation of an oxygen atom in a Se vacancy can result in band gap opening and thus enable the transition from a metal to a semiconductor. However, the predicted bandgap is substantially smaller than that derived from PL data. These results indicate that the properties of van der Waals materials can vary significantly depending on stoichiometry, defect types and concentration, and possibly environmental and substrate effects. In view of this finding, local probing of nanoribbon properties with TERS becomes essential to understanding such low-dimensional systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Van der Waals forces

来源：评论

学校读者我要写书评

暂无评论

Recurrence in the evolution of air transport networks

arXiv

引用

arXiv 2020年

作者： Sugishita, Kashin Masuda, Naoki State University of New York at Buffalo Department of Mathematics Buffalo14260-2900 United States State University of New York at Buffalo Computational and Data-Enabled Science and Engineering Program Buffalo14260-2900 United States Waseda University Faculty of Science and Engineering Tokyo169-8555 Japan

Changes in air transport networks over time may be induced by competition among carriers, changes in regulations on airline industry, and socioeconomic events such as terrorist attacks and epidemic outbreaks. Such network changes may reflect corporate strategies of each carrier. In the present study, we propose a framework for analyzing evolution patterns in temporal networks in discrete time from the viewpoint of recurrence. Recurrence implies that the network structure returns to one relatively close to that in the past. We applied the proposed methods to four major carriers in the US from 1987 to 2019. We found that the carriers were different in terms of the autocorrelation, strength of periodicity, and changes in these quantities across decades. We also found that the network structure of the individual carriers abruptly changes from time to time. Such a network change reflects changes in their operation at their hub airports rather than famous socioeconomic events that look closely related to airline industry. The proposed methods are expected to be useful for revealing, for example, evolution of airline alliances and responses to natural disasters or infectious diseases, as well as characterizing evolution of social, biological, and other networks over time. Copyright © 2020, The Authors. All rights reserved.

关键词： Competition

来源：评论

学校读者我要写书评

暂无评论

When data disappear: public health pays as US policy strays

引用

The Lancet Digital Health 2025年 100874页

作者： McAndrew, Thomas Lover, Andrew A Hoyt, Garrik Majumder, Maimuna S Department of Biostatistics and Health Data Science College of Health Lehigh University BethlehemPA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts Amherst AmherstMA United States Department of Computer Science and Engineering PC Rossin College of Engineering and Applied Sciences Lehigh University BethlehemPA United States Department of Pediatrics Harvard Medical School BostonMA United States Computational Health Informatics Program Boston Children's Hospital BostonMA United States

Presidential actions on Jan 20, 2025, by President Donald Trump, including executive orders, have delayed access to or led to the removal of crucial public health data sources in the USA. The continuous collection and maintenance of health data support public health, safety, and security associated with diseases such as seasonal influenza. To show how public health data surveillance enhances public health practice, we analysed data from seven US Government-maintained sources associated with seasonal influenza. We fit two models that forecast the number of national incident influenza hospitalisations in the USA: (1) a data-rich model incorporating data from all seven Government data sources;and (2) a data-poor model built using a single Government hospitalisation data source, representing the minimal required information to produce a forecast of influenza hospitalisations. The data-rich model generated reliable forecasts useful for public health decision making, whereas the predictions using the data-poor model were highly uncertain, rendering them impractical. Thus, health data can serve as a transparent and standardised foundation to improve domestic and global health. Therefore, a plan should be developed to safeguard public health data as a public good. © 2025 The Author(s)

关键词： Health insurance

来源：评论

学校读者我要写书评

暂无评论

Scientific discovery in the age of artificial intelligence

引用

NATURE 2023年第7978期621卷 E33-E33页

作者： Wang, Hanchen Fu, Tianfan Du, Yuanqi Gao, Wenhao Huang, Kexin Liu, Ziming Chandak, Payal Liu, Shengchao Van Katwyk, Peter Deac, Andreea Anandkumar, Anima Bergen, Karianne Gomes, Carla P. Ho, Shirley Kohli, Pushmeet Lasenby, Joan Leskovec, Jure Liu, Tie-Yan Manrai, Arjun Marks, Debora Ramsundar, Bharath Song, Le Sun, Jimeng Tang, Jian Velickovic, Petar Welling, Max Zhang, Linfeng Coley, Connor W. Bengio, Yoshua Zitnik, Marinka Department of Engineering University of Cambridge Cambridge UK Department of Computing and Mathematical Sciences California Institute of Technology Pasadena CA USA NVIDIA Santa Clara CA USA Department of Computational Science and Engineering Georgia Institute of Technology Atlanta GA USA Department of Computer Science Cornell University Ithaca NY USA Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science Stanford University Stanford CA USA Department of Physics Massachusetts Institute of Technology Cambridge MA USA Harvard-MIT Program in Health Sciences and Technology Cambridge MA USA Mila – Quebec AI Institute Montreal Quebec Canada Université de Montréal Montreal Quebec Canada HEC Montréal Montreal Quebec Canada CIFAR AI Chair Toronto Ontario Canada Department of Earth Environmental and Planetary Sciences Brown University Providence RI USA Data Science Institute Brown University Providence RI USA Center for Computational Astrophysics Flatiron Institute New York NY USA Department of Astrophysical Sciences Princeton University Princeton NJ USA Department of Physics Carnegie Mellon University Pittsburgh PA USA Department of Physics and Center for Data Science New York University New York NY USA Google DeepMind London UK Department of Computer Science and Technology University of Cambridge Cambridge UK Microsoft Research Beijing China Department of Biomedical Informatics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Harvard Data Science Initiative Cambridge MA USA Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University Cambridge MA USA Department of Systems Biology Harvard Medical School Boston MA USA Deep Forest Sciences Palo Alto CA USA BioMap Beijing China Mo

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：