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arXiv 2022年

作者： Wang, Haiqin Zou, Boyi Su, Jian Wang, Dong Xu, Xinpeng Physics Program Guangdong Technion - Israel Institute of Technology 241 Daxue Road Guangdong Shantou515063 China Technion - Israel Institute of Technology Haifa32000 Israel School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China Shenzhen International Center for Industrial and Applied Mathematics Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Variational methods have been widely used in soft matter physics for both static and dynamic problems. These methods are mostly based on two variational principles: the variational principle of minimum free energy (MFEVP) and Onsager's variational principle (OVP). Our interests lie in the applications of these variational methods to active matter physics. In our former work [Soft Matter, 2021, 17, 3634], we have explored the applications of OVP-based variational methods for the modeling of active matter dynamics. In the present work, we explore variational (or energy) methods that are based on MFEVP for static problems in active elastic solids. We show that MFEVP can be used not only to derive equilibrium equations, but also to develop approximate solution methods, such as Ritz method, for active solid statics. Moreover, the power of Ritz-type method can be further enhanced using deep learning methods if we use deep neural networks to construct the trial functions of the variational problems. We then apply these variational methods and the deep Ritz method to study the spontaneous bending and contraction of a thin active circular plate that is induced by internal asymmetric active contraction. The circular plate is found to be bent towards its contracting side. The study of such a simple toy system gives implications for understanding the morphogenesis of solid-like confluent cell monolayers. In addition, we introduce a so-called activogravity length to characterize the importance of gravitational forces relative to internal active contraction in driving the bending of the active plate. When the lateral plate dimension is larger than the activogravity length (about 100 micron), gravitational forces become important. Such gravitaxis behaviors at multicellular scales may play significant roles in the morphogenesis and in the up-down symmetry broken during tissue development. © 2022, CC BY-NC-ND.

关键词： Deep neural networks

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Local Order Metrics for Two-Phase Media Across Length Scales

arXiv

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arXiv 2022年

作者： Torquato, Salvatore Skolnick, Murray Kim, Jaeuk Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States McKetta Department of Chemical Engineering University of Texas at Austin AustinTX78712 United States

The capacity to devise order metrics for microstructures of multiphase heterogeneous media is a highly challenging task, given the richness of the possible geometries and topologies of the phases that can arise. This investigation initiates a program to formulate order metrics to characterize the degree of order/disorder of the microstructures of two-phase media in d-dimensional Euclidean space Rd across length scales. In particular, we propose the use of the local volume-fraction variance σV2 (R) associated with a spherical window of radius R as an order metric. We determine σV2 (R) as a function of R for 22 different models across the first three space dimensions, including both hyperuniform and nonhyperuniform systems with varying degrees of short- and long-range order. We find that the local volume-fraction variance as well as asymptotic coefficients and integral measures derived from it provide reasonably robust and sensitive order metrics to categorize disordered and ordered two-phase media across all length scales. © 2022, CC BY-SA.

关键词： Volume fraction

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TAROGE-M: Antarctic High-Mountain Radio Antenna Array for Detecting Ultra-High Energy ANITA Anomalous Events 38

TAROGE-M: Antarctic High-Mountain Radio Antenna Array for De...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Wang, Shih-Hao Chen, Pisin Chen, Yaocheng Chen, Ying-Chih Choi, Taejin Ham, Young-Bae Hsu, Shih-Ying Huang, Jian-Jung Huang, MingHuey A. Jee, Geonhwa Jung, Jongil Kim, Jieun Kuo, Chung-Yun Kwon, Hyuck-Jin Lee, Changsup Leung, Chung-Hei Liu, Tsung-Che Nam, Jiwoo Shiao, Yu-Shao J. Shin, Bok-Kyun Su, Shih-Chieh Wang, Min-Zu Wang, Shih-Hao Wang, Yu-Hsin Anker, Astrid Barwick, Steven W. Besson, Dave Z. Bouma, Sjoerd Cataldo, Maddalena Gaswint, Geoffrey Glaser, Christian Hallmann, Steffen Hanson, Jordan C. Henrichs, Jakob Kleinfelder, Stuart A. Lahmann, Robert Meyers, Zachary S. Nelles, Anna Novikov, Alexander Paul, Manuel P. Pyras, Lilly Persichilli, Christopher Plaisier, Ilse Rice-Smith, Ryan Seikh, Mohammad F. H. Tatar, Joulien Welling, Christoph Zhao, Leshan Department of Physics National Taiwan University Taipei10617 Taiwan Leung Center for Cosmology and Particle Astrophysics National Taiwan University Taipei10617 Taiwan Kavli Institute for Particle Astrophysics and Cosmology SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Division of Atmospheric Sciences Korea Polar Research Institute Incheon Korea Republic of Department of Polar Science Korea University of Science and Technology Daejeon Korea Republic of Department of Energy Engineering National United University Miaoli Taiwan Department of Astronomy Space Science and Geology Chungnam National University Daejeon34134 Korea Republic of Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Department of Electrophysics National Yang Ming Chiao Tung University Hsinchu300 Taiwan Department of applied physics National Pingtung university Pingtung900 Taiwan Chung Cheng Institute of Technology National Defense University Taoyuan335 Taiwan Undergraduate Degree Program of Systems Engineering and Technology National Yang Ming Chiao Tung University Hsinchu300 Taiwan National Nano Device Laboratories Hsinchu300 Taiwan Ulsan National Institute of Science and Technology Ulsan44919 Korea Republic of Institute of Astronomy and Astrophysics Academia Sinica Astronomy-Mathematics Building No. 1 Sec. 4 Roosevelt Rd. Taipei10617 Taiwan Department of Physics and Astronomy University of California IrvineCA92697 United States Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States DESY Zeuthen15738 Germany ECAP Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Uppsala University Department of Physics and Astronomy UppsalaSE-752 37 Sweden Whittier College Department of Physics WhittierCA90602 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Resea

TAROGE-M is a radio antenna array atop ∼ 2.7 km-high Mt. Melbourne in Antarctica for detecting ultra-high energy (UHE, E > 1017 eV) air showers in near-horizontal directions. Besides the detection of cosmic rays and Earth-skimming tau neutrinos, its primary goal is to reproduce the discovery and verify the origin of so-called ANITA anomalous events, having feature of upward-going UHE air showers but cannot be explained by tau neutrinos. The detection concept takes advantages of a high altitude for a broad view toward the horizon;strong and near-vertical geomagnetic field for enhancing the radio signal;and quiet radio environment in Antarctica. Its relatively simple design and high duty cycle make it easily to be extended and thus to achieve an exposure competitive with ANITA experiments within a few years. The first TAROGE-M station operating at 180-450 MHz frequencies deployed in 2020 detected seven UHE cosmic ray events within 25.3-day livetime. The events have a mean energy of ∼ 1 EeV and an estimated flux consistent with other experiments, and validate the station as an UHE particle detector. In 2022-2023 season, the system was upgraded for a robust long-term operation, with a more detailed calibration with a drone-borne pulser. We also discuss the planned upgrade, including the implementation of interferometric trigger, which is expected to increase the cosmic ray and tau neutrino acceptances by a factor of 10 at 0.1 EeV, corresponding to a lower energy threshold by a factor of 3. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmic rays

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Meaningful human control: actionable properties for AI system development

arXiv

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arXiv 2021年

作者： Siebert, Luciano Cavalcante Lupetti, Maria Luce Aizenberg, Evgeni Beckers, Niek Zgonnikov, Arkady Veluwenkamp, Herman Abbink, David Giaccardi, Elisa Houben, Geert-Jan Jonker, Catholijn M. van den Hoven, Jeroen Forster, Deborah Lagendijk, Reginald L. AiTech Interdisciplinary Research Program on Meaningful Human Control Delft University of Technology Delft Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Faculty of Industrial Design Engineering Delft University of Technology Delft Netherlands Faculty of Mechanical Maritime and Materials Engineering Delft University of Technology Delft Netherlands Faculty of Technology Policy and Management Delft University of Technology Delft Netherlands

How can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans;however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically-minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control. Copyright © 2021, The Authors. All rights reserved.

关键词： Artificial intelligence

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Higher-order rich-club phenomenon in collaborative research grant networks

arXiv

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arXiv 2022年

作者： Nakajima, Kazuki Shudo, Kazuyuki Masuda, Naoki Department of Mathematical and Computing Science Tokyo Institute of Technology Meguro-ku Tokyo152-8552 Japan Department of Mathematics State University of New York at Buffalo Buffalo14260 United States Academic Center for Computing and Media Studies Kyoto University Sakyo-ku Kyoto606-8501 Japan Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo14260 United States Faculty of Science and Engineering Waseda University Tokyo169-8555 Japan

Modern scientific work, including writing papers and submitting research grant proposals, increasingly involves researchers from different institutions. In grant collaborations, it is known that institutions involved in many collaborations tend to densely collaborate with each other, forming rich clubs. Here we investigate higher-order rich-club phenomena in networks of collaborative research grants among institutions and their associations with research impact. Using publicly available data from the National science Foundation in the US, we construct a bipartite network of institutions and collaborative grants, which distinguishes among the collaboration with different numbers of institutions. By extending the concept and algorithms of the rich club for dyadic networks to the case of bipartite networks, we find rich clubs both in the entire bipartite network and the bipartite subnetwork induced by the collaborative grants involving a given number of institutions up to five. We also find that the collaborative grants within rich clubs tend to be more productive in a per-dollar sense than the control. Our results highlight advantages of collaborative grants among the institutions in the rich clubs. Copyright © 2022, The Authors. All rights reserved.

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Assessment and optimization of As(V) adsorption on hydrogel composite integrating chitosan-polyvinyl alcohol and Fe3O4 nanoparticles and evaluation of their regeneration and reusable capabilities in aqueous media

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science of the Total Environment 2023年 855卷 158877页

作者： Weerasundara, Lakshika Ok, Yong Sik Kumarathilaka, Prasanna Marchuk, Alla Bundschuh, Jochen School of Civil Engineering and Surveying Faculty of Health Engineering and Sciences University of Southern Queensland West Street ToowoombaQLD4350 Australia Korea Biochar Research Centre & Division of Environmental Sciences and Ecological Engineering Korea University Seoul Korea Republic of Institute for Life Sciences and the Environment University of Southern Queensland West Street ToowoombaQLD4350 Australia Doctoral Program in Science Technology Environment and Mathematics Department of Earth and Environmental Sciences National Chung Cheng University 168 University Rd. Min-Hsiung Chiayi County62102 Taiwan

A modified chitosan-polyvinyl alcohol (PVA) hydrogel was developed by incorporating Fe3O4 nanoparticles. Four chitosan-Fe3O4 (ChFe) hydrogel types were developed based on chitosan:Fe3O4 ratio as 1:0, 1:1, 1:0.5 and 1:0.25. Batch sorption experiments were conducted with different pH, dosage, kinetics, and isotherms. The exhausted ChFe hydrogels were evaluated for their regeneration and reuse capability with different acids and bases. The best hydrogel for arsenic (V) [As(V)] adsorption was 1:0.5 ratio ChFe hydrogel. The highest As(V) adsorption (89 %) was at pH 4 and the adsorption capacity gradually decreased with increasing solution pH. Within the pH 4–6 range, the hydrogel surface became positively charged due to protonation of –NH2 and –OH groups in the polymer chain and the positive surface attracted H2AsO4− and HAsO42− oxyanions. The experimental kinetic data was well-fitted to the Elovich model (R2 of 0.99) while the Freundlich isotherm model best described the isotherm data (R2 of 0.97). The models predicted chemisorption mechanisms on ChFe hydrogel composites. Electrostatic attractions with –NH3+ and –OH2+, ligand-exchange inner-sphere complexes formation and bidentate corner-sharing (2C) and bidentate edge-sharing (2E) trimetric surface complexes formation have been proposed as the adsorption mechanism of As(V) into ChFe hydrogel. 0.1 M CH3COOH showed the best regeneration pattern with 75, 96, 81, 53 and 43 % of 1st, 2nd, 3rd, 4th and 5th adsorption respectively. Because of this re-usable capability, the As(V) adsorption capacity is not a single value from one adsorption cycle, but a cumulative value of several adsorption cycles and it was 17.39 mg/g for five adsorption cycles. Open for regeneration and reuse, no post-treatment is needed for adsorbent-water separation, allow applications of the ChFe hydrogel composite in a wide range of applications such as water filtration and purification systems. The modification with ChFe further expands the application

关键词： Nanoparticles

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Magnetized and quadratic convection based thermal transport in ternary radiative bio-nanofluid via intelligent neural networks: Two hidden layers mechanism

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Results in Physics 2024年 65卷

作者： Ayub, Assad Darvesh, Adil Shah, Syed Zahir Hussain Junjua, Moin-ud-Din Santos, Elizaldo Domingues dos Ahmad, Shabbir Hendy, Ahmed S. Ragab, Adham E. Department of Mathematics & Statistics Hazara University Manshera Pakistan School of Mathematical Sciences Zhejiang Normal University Zhejiang Jinhua 321004 China Programa de Pós-Graduação em Modelagem Computacional/LMCE Escola de Engenharia Universidade Federal do Rio Grande – FURG RS Rio Grande 96203900 Brazil FURG Escola de Engenharia RS Rio Grande Brazil Department of Basic Sciences and Humanities Muhammad Nawaz Sharif University of Engineering and Technology Multan 60000 Pakistan Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University 19 Mira St. Yekaterinburg 620002 Russian Federation Department of Industrial Engineering College of Engineering King Saud University P.O. Box 800 Riyadh 11421 Saudi Arabia Department of Mathematics Ghazi University Dera Ghazi Khan 32200 Pakistan Graduate Program of Ocean Engineering School of Engineering Universidade Federal do Rio Grande Italia Avenue km 8 96201-900 Rio Grande Brazil Department of Mechanics and Mathematics Western Caspian University Baku 1001 Azerbaijan

Significance: The thermal analysis of nanofluid in a vertical cylinder (artery) in a magnetized environment holds significant implications in physiological and thermal regulation networks. This research is significant in biomedical engineering with applications like medical diagnostics and treatment strategies. Motive: This study investigates the thermal behavior of a magnetized blood-based ternary Carreau nanofluid flowing through a vertically bounded artery with quadratic convection. Velocity and heat transfer analysis is conducted within the artery, utilizing thermal radiation and quadratic convection in a magnetized setting. The base fluid consists of blood, augmented with three nanoparticles: CuO, Al₂O₃, and TiO₂. The investigation centers on examining the properties of blood nanofluid, arterial geometry, and thermal dynamics. Methodology: The physical model generates a set of partial differential equations (PDEs) and similarities mechanism is utilized to fetch its non dimensional form in terms of ordinary differential equations (ODEs). Furthermore, numerical outcomes are obtained with Matlab function bvp4c and obtained data set is trained through robust scheme Levenberg-Marquardt neural network (LMNN) procedure to predict the solution. Findings: Velocity of blood is reduced with increased values of wiessenberg number, magnetic parameter and mixed convection parameter and velocity increases for Second-order convection parameter. Temperature profile decreases with curvature parameter, mixed convection parameter and permeability parameter. © 2024

关键词： Blood fluid Intelligent neural network Magnetized environment Ternary nanoparticles Thermal scrutiny

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Metal ratio and bimetal nanoarchitectonics of ammonia-based fluoride complex induced nickel hydroxide and manganese oxide composites as active materials of an energy storage device

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Journal of Energy Storage 2024年 80卷

作者： Hsu, Chuan-Chih Yu, You Zher Wu, Chung-Hsien Lee, Pin-Yan Chen, Hung-Ming Husain, Sadang Kongvarhodom, Chutima Hsiao, Yu-Cheng Lin, Lu-Yin Division of Cardiovascular Surgery Department of Surgery School of Medicine College of Medicine Taipei Medical University Taiwan Division of Cardiovascular Surgery Department of Surgery Taipei Medical University Hospital Taiwan Department of Chemical Engineering and Biotechnology National Taipei University of Technology Taipei Taiwan Gingen Technology Co. LTD. Rm. 7 10F. No.189 Sec. 2 Keelung Rd. Xinyi Dist. Taipei11054 Taiwan Department of Physics Faculty of Mathematics and Natural Science Lambung Mangkurat University Banjarmasin70124 Indonesia Department of Chemical Engineering King Mongkut's University of Technology Thonburi 126 Pracha-u-thit Toong-kru Bangkok10140 Thailand Department of Chemical Engineering Ming Chi University of Technology New Taipei City24301 Taiwan Cell Physiology and Molecular Image Research Center Wan Fang Hospital Taipei Medical University Taipei11696 Taiwan International PhD Program in Biomedical Engineering College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan Stanford Byers Center for Biodesign Stanford University CA94305-5428 United States Graduate Institute of Biomedical Optomechatronics College of Biomedical Engineering Taipei Medical University Taipei11031 Taiwan

Nickel and manganese compounds are widely applied as efficient battery-type active materials for energy storage devices, due to high theoretical capacities and abundant redox states for generating redox reactions to store charges. Morphology plays significant roles on energy storage ability. Structure directing agent (SDA) is widely reported to have ability for modulating configurations of active materials and to achieve excellent surface properties. In this work, it is the first time to use NH4BF4 and NH4HF2 as structure directing agents for synthesizing nickel manganese compounds derived from ZIF67 as active materials of energy storage devices. Bimetallic compounds are composed of nickel hydroxide and manganese oxide, which presents the favorable two-dimensional flower-like structure with high surface area and abundant redox states. The largest specific capacitance (CF) of 1358.2 F/g corresponding to the capacity of 226.4 mAh/g are achieved for the bimetallic compound synthesized using the Ni to Mn precursor ratio of 1 to 1 (NiMn11). The energy storage device assembled using NiMn11 and reduced graphene oxide on Ni foams presents a maximum energy density of 11.45 Wh/kg at the power density of 0.35 kW/kg, and excellent long-term stability with the CF retention of 85.6% and Coulombic efficiency of 87.5% after 10,000 cycles. © 2023

关键词： Fluorine compounds

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A temperature or FUV tracer? The HNC/HCN ratio in M83 on the GMC scale

arXiv

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arXiv 2024年

作者： Harada, Nanase Saito, Toshiki Nishimura, Yuri Watanabe, Yoshimasa Sakamoto, Kazushi National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Astronomical Science Program Graduate Institute for Advanced Studies SOKENDAI 2-21-1 Osawa Mitaka Tokyo181-1855 Japan Department of Astronomy The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-0033 Japan Materials Science and Engineering College of Engineering Shibaura Institute of Technology 3-7-5 Toyosu Koto-ku Tokyo135-8548 Japan Institute of Astronomy and Astrophysics Academia Sinica 11F of AS/NTU Astronomy-Mathematics Building No.1 Sec. 4 Roosevelt Rd Taipei10617 Taiwan

The HNC/HCN ratio is observationally known as a thermometer in Galactic interstellar molecular clouds. A recent study has alternatively suggested that the HNC/HCN ratio is affected by the ultraviolet (UV) field, not by the temperature. We aim to study this ratio on the scale of giant molecular clouds in the barred spiral galaxy M83 towards the southwestern bar end and the central region from ALMA observations, and if possible, distinguish the above scenarios. We compare the high (40-50 pc) resolution HNC/HCN ratios with the star formation rate from the 3-mm continuum intensity and the molecular mass inferred from the HCN intensities. Our results show that the HNC/HCN ratios do not vary with the star formation rates, star formation efficiencies, or column densities in the bar-end region. In the central region, the HNC/HCN ratios become higher with higher star formation rates, which tend to cause higher temperatures. This result is not consistent with the previously proposed scenario in which the HNC/HCN ratio decreases with increasing temperature. Spectral shapes suggest that this trend may be due to optically thick HCN and optically thin HNC. In addition, we compare the large-scale (∼ 200 pc) correlation between the dust temperature from the FIR ratio and the HNC/HCN ratio for the southwestern bar-end region. The HNC/HCN ratio is lower when the dust temperatures are higher. We suggest from the above results that the HNC/HCN ratio depends on the UV radiation field that affects the interstellar medium on the ∼ 100 pc scale where the column densities are low. © 2024, CC BY.

关键词： Dust

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Deep potentials for materials science

材料展望（英文）

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材料展望（英文） 2022年第2期 89-115页

作者： Tongqi Wen Linfeng Zhang Han Wang Weinan E David J Srolovitz Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China DP Technology BeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China Laboratory of Computational Physics Institute of Applied Physics and Computational MathematicsBeijingPeople's Republic of China HEDPS CAPTCollege of EngineeringPeking UniversityBeijingPeople's Republic of China AI for Science Institute BeijingPeople's Republic of China School of Mathematical Sciences Peking UniversityBeijingPeople's Republic of China Department of Mathematics and Program in Applied and Computational Mathematics Princeton UniversityPrincetonNJUnited States of America Department of Mechanical Engineering The University of Hong KongHong KongHong Kong Special Administrative Region of China International Digital Economy Academy(IDEA) ShenzhenPeople's Republic of China

To fill the gap between accurate(and expensive)ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials,a new class of descriptions of atomic interactions has emerged and been widely applied;*** learning potentials(MLPs).One recently developed type of MLP is the deep potential(DP)*** this review,we provide an introduction to DP methods in computational materials *** theory underlying the DP method is presented along with a step-by-step introduction to their development and *** also review materials applications of DPs in a wide range of materials *** DP Library provides a platform for the development of DPs and a database of extant *** discuss the accuracy and efficiency of DPs compared with ab initio methods and empirical potentials.

关键词： deep potential atomistic simulation machine learning potential neural network

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