检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Siagian, Viktor Siregar, Hermanto Fariyanti, Anna Nainggolan, Kaman Haloho, Lermansius Hutapea, Ronald Suratman Salim, Zamroni Wamaer, Demas da Silva, Helena Ekaswati, Rini Rusdiana, Supardi Marwati, Tri Toha, Mohammad Sondakh, Joula Marpaung, Imelda Research Center for Macro Economic and Finance Research Organization for Governance Economy and Community Welfare National Research and Innovation Agency Jakarta12710 Indonesia IPB University Faculty of Economics and Management Bogor16680 Indonesia College of Management and Computer Science Nusa Mandiri Jakarta12540 Indonesia Research Center for Cooperative Corporation and People's Economy National Research and Innovation Agency Jakarta12710 Indonesia Universitas Islam Malang Indonesia Research Center for Behavioral and Circular Economics National Research and Innovation Agency Jakarta12710 Indonesia Research Center for Animal Research Organization for Agriculture and Food National Research and Innovation Agency Cibinong-Bogor 16128 Indonesia Research Center for Food and Technology Processing Research Organization for Agriculture and Food National Research and Innovation Agency Serpong15314 Indonesia Ph.D. Study Program Natural Resources and Environmental Management Graduate School of IPB University Campus IPB Baranangsiang Bogor16144 Indonesia National Research and Innovation Agency Indonesia

The area of the rice harvest in Banten Province in 2021 was 319,558 ha with a production of 1,63 million tons of unhusked rice or with a productivity of 5.1 tons/ha (CBS,2022). The objectives of this study were: 1) to optimize the target of rice and corn production in Banten Province, and 2) to optimize the target farmers' income in Banten Province. The study method used was a survey, with purposive sampling at the farmer level. Data were analyzed qualitatively (uses descriptive tabulation) and quantitatively (uses goal programming). The results of this study were:1) The target of rice production in the Banten Province of 2.1 million tons of unhusked rice can be achieved with the optimal area of irrigated paddy of 299,755.7 ha and the optimal area of field/dry land of 87,750.9 ha. The target for maize production of 1.75 million tonnes in Banten Province can be achieved with an optimal planting area of 342.211.7 ha of dry land. 2) The optimal area of food crops to achieve the farmer's income target in Banten Province in 2018 is IDR. 8.79 trillion comes from the planting area of lowland rice covering an area of 391,222.1 ha, the corn planting area of 169,059.9 ha, soybean planting area of 23,074.82 ha and cassava planting area of 2,832.77 ha. © 2023, The Authors. All rights reserved.

关键词： Agriculture

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Workshop on imaging science development for cancer prevention and preemption

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Cancer Biomarkers 2007年第1期3卷 1-33页

作者： Kelloff, Gary J. Sullivan, Daniel C. Baker, Houston Clarke, Lawrence P. Nordstrom, Robert Tatum, James L. Dorfman, Gary S. Jacobs, Paula Berg, Christine D. Pomper, Martin G. Birrer, Michael J. Tempero, Margaret Higley, Howard R. Petty, Brenda Gumbs Sigman, Caroline C. Maley, Carlo Sharma, Prateek Wax, Adam Ginsberg, Gregory G. Dannenberg, Andrew J. Hawk, Ernest T. Messing, Edward M. Grossman, H. Barton Harisinghani, Mukesh Bigio, Irving J. Griebel, Donna Henson, Donald E. Fabian, Carol J. Ferrara, Katherine Fantini, Sergio Schnall, Mitchell D. Zujewski, Jo Anne Hayes, Wendy Klein, Eric A. DeMarzo, Angelo Ocak, Iclal Ketterling, Jeffrey A. Tempany, Clare Shtern, Faina Parnes, Howard L. Gomez, Jorge Srivastava, Sudhir Szabo, Eva Lam, Stephen Seibel, Eric J. Massion, Pierre McLennan, Geoffrey Cleary, Kevin Suh, Robert Burt, Randall W. Pfeiffer, Ruth M. Hoffman, John M. Roy, Hemant K. Wang, Thomas Limburg, Paul J. El-Deiry, Wafik S. Papadimitrakopoulou, Vali Hittelman, Walter N. MacAulay, Calum Veltri, Robert W. Solomonzz, Diane Jeronimo, Jose Richards-Kortum, Rebecca Johnson, Karen A. Viner, Jaye L. Stratton, Steven P. Rajadhyaksha, Milind Dhawan, Atam NIH/NCI/DCTD Cancer Imaging Program Bethesda MD United States NIH/NCI/DCTD Cancer Imaging Program NCI-Frederick Frederick MD United States NIH/NCI/DCP Early Detection Research Group Bethesda MD United States The Johns Hopkins University School of Medicine Departments of Radiology Pharmacology and Oncology Division of Neuroradiology Baltimore MD United States NIH/NCI/CCR Cell and Cancer Biology Branch Bethesda MD United States University of California San Francisco Department of Medicine Division of Medical Oncology San Francisco CA United States CCS Associates Inc. Mountain View CA United States Wistar Institute Molecular and Cellular Oncogenesis Program Philadelphia PA United States University of Kansas School of Medicine VA Hospital Kansas City MO United States Duke University Department of Biomedical Engineering Medical Physics Program Durham NC United States University of Pennsylvania School of Medicine Department of Gastroenterology Philadelphia PA United States New York Presbyterian Hospital Weill Medical College of Cornell University New York NY United States NIH/NCI/OD Office of Centers for Training and Resources Bethesda MD United States University of Rochester Medical Center Department of Urology Rochester NY United States University of Texas MD Anderson Cancer Center Department of Urology Houston TX United States Massachusetts General Hospital Abdominal Imaging/Interventional Radiology Boston MA United States Boston University Biomedical Engineering Electrical and Computer Engineering and Physics Boston MA United States NIH/NCI/DCP Gastrointestinal and Other Cancers Research Group Bethesda MD United States The George Washington University Cancer Institute Office of Cancer Prevention and Control Washington DC United States University of Kansas Cancer Center Clinical Oncology Kansas City KS United States University of California Davis Department of Biomedical Engineering Davis CA United States Tufts University De

The concept of intraepithelial neoplasm (IEN) as a near-obligate precursor of cancers has generated opportunities to examine drug or device intervention strategies that may reverse or retard the sometimes lengthy process of carcinogenesis. Chemopreventive agents with high therapeutic indices, well-monitored for efficacy and safety, are greatly needed, as is development of less invasive or minimally disruptive visualization and assessment methods to safely screen nominally healthy but at-risk patients, often for extended periods of time and at repeated intervals. Imaging devices, alone or in combination with anticancer drugs, may also provide novel interventions to treat or prevent precancer.

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

引用

arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Machine Learning for Improved Current Density Reconstruction from 2D Vector Magnetic Images

arXiv

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arXiv 2024年

作者： Reed, Niko R. Bhutto, Danyal Turner, Matthew J. Daly, Declan M. Oliver, Sean M. Tang, Jiashen Olsson, Kevin S. Langellier, Nicholas Ku, Mark J.H. Rosen, Matthew S. Walsworth, Ronald L. Department of Physics University of Maryland College ParkMD United States Quantum Technology Center University of Maryland College ParkMD United States Joint Quantum Institute University of Maryland College ParkMD United States Department of Biomedical Engineering Boston University BostonMA United States A. A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Electrical and Computer Engineering University of Maryland College ParkMD United States The MITRE Corporation McLeanVA22102 United States Intelligence Community Postdoctoral Research Fellowship Program University of Maryland College ParkMD20742 United States Department of Physics Harvard University CambridgeMA United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States Department of Physics and Astronomy University of Delaware NewarkDE19716 United States Harvard Medical School 25 Shattuck St. BostonMA United States

The reconstruction of electrical current densities from magnetic field measurements is an important technique with applications in materials science, circuit design, quality control, plasma physics, and biology. Analytic reconstruction methods exist for planar currents, but break down in the presence of high spatial frequency noise or large standoff distance, restricting the types of systems that can be studied. Here, we demonstrate the use of a deep convolutional neural network for current density reconstruction from two-dimensional (2D) images of vector magnetic fields acquired by a quantum diamond microscope (QDM). Trained network performance significantly exceeds analytic reconstruction for data with high noise or large standoff distances. This machine learning technique can perform quality inversions on lower SNR data, significantly reducing the data collection time and permitting reconstructions of weaker and three-dimensional current sources. © 2024, CC BY.

关键词： Magnetic fields

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SURG-01. DIFFERENTIATION OF GLIOBLASTOMA AND MALIGNANT MENINGIOMA FROM NORMAL BRAIN BY TISSUE STIFFNESS UTILIZING MICROSURGICAL FORCEPS EQUIPPED WITH HAPTICS technology

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Neuro-Oncology 2023年第SUPPLEMENT_5期25卷 v260–v261页

作者： Ezaki, Taketo Kisima, Kohki Shibao, Shunsuke Matsunaga, Takuya Pareira, Eriel Sandika Kitamura, Yohei Takahara, Kento Iwama, Takashi Sampetrean, Oltea Ohnishi, Kouhei Shimono, Tomoyuki Sasaki, Hikaru Keio University School of Medicine Department of Neurosurgery Shinjuku-ku Tokyo Japan Yokohama National University School of Engineering Science Department of Educational Program for Electrical and Computer Engineering Yokohama-shi Japan Keio University School of Medicine Department of Neurosurgery Shinjuku-ku Japan Kanagawa Institute of Industrial Science and Technology Kawasaki-shi Japan Keio University School of Medicine Department of Microbiology and Immunology Shinjuku-ku Japan Keio University Haptics Research Center Kawasaki-shi Japan Tokyo Dental College Ichikawa General Hospital Department of Neurosurgery Ichikawa-shi Chiba Japan

Stiffness of human cancers strongly correlate with their pathology, and it is reported that stiffness can be used as a biomarker. Furthermore, comprehensive analysis has suggested the usefulness of cancer stiffness in diagnosis and malignancy prediction and its association with molecular expression, as well as its potential for predicting and diagnosing postoperative complications. Neurosurgeons perform tumor resection relying on the sensations that they feel with their own fingertips (tactile sensation), but it takes years of surgical experience to accurately distinguish brain tumor from the surrounding normal brain *** is a new technology regarding touching sensation. Especially, our technology, real haptics, can communicate, record, and reproduce the real sensation. It is also possible to measure and analyze the physical property such as stiffness of the touching object from the obtained haptic *** developed a surgical forceps equipped with haptics technology to distinguish brain tumor tissue from surrounding normal brain tissue. three glioblastoma cell lines (SF126, U87, U251) and a malignant meningioma cell line (IOMM-Lee) were each transplanted intracranially into 5 nude mice. Tumor size was periodically monitored and mice were euthanized at appropriate time points. The stiffness of the tumor and normal brain tissue was measured using a master-slave integrated surgical forceps prototype. The results statistically showed that all four types of brain tumor tissues were stiffer than normal brain tissues. (p = 0.001) In addition, malignant meningioma was statistically stiffer than glioblastomas. (p = 0.025) This result is consistent with actual surgical *** have shown that our surgical forceps has the ability to distinguish pathology of brain tumors as well as to distinguish brain tumor from normal brain tissue. The forceps is also helpful for the neurosurgeons to sense minute change in tissue stiffness.

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RETRACTED ARTICLE: Scenario analysis for low carbon development in Nigeria

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Cogent Engineering 2016年第1期3卷 Article: 1218818页

作者： Emodi, Nnaemeka Vincent Birku, Reta Entele Marc Nwosu Ogbuagu Chinenye Comfort Emodi Adaeze Saratu Augusta Emodi Okoro, Felix Chimezirim College of Business Law and Governance James Cook University P.O. Box 6811 Cairns 4870 Queensland Australia Department of Technology Management Economics and Policy Program College of Engineering Seoul National University Seoul 151-744 South Korea Department of Chemistry College of Physical and Applied Sciences Michael Okpara University of Agriculture Umudike Nigeria Department of Computer Science College of Physical and Applied Science Michael Okpara University of Agriculture Umudike Nigeria Faculty of Law Abia State University Uturu Nigeria Department of Vocational Teacher Education University of Nigeria Nsukka Road Nsukka 410001 Nigeria

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OPTIMADE, an API for exchanging materials data

arXiv

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arXiv 2021年

作者： Andersen, Casper W. Armiento, Rickard Blokhin, Evgeny Conduit, Gareth J. Dwaraknath, Shyam Evans, Matthew L. Fekete, Ádám Gopakumar, Abhijith Gražulis, Saulius Merkys, Andrius Mohamed, Fawzi Oses, Corey Pizzi, Giovanni Rignanese, Gian-Marco Scheidgen, Markus Talirz, Leopold Toher, Cormac Winston, Donald Aversa, Rossella Choudhary, Kamal Colinet, Pauline Curtarolo, Stefano Di Stefano, Davide Draxl, Claudia Er, Suleyman Esters, Marco Fornari, Marco Giantomassi, Matteo Govoni, Marco Hautier, Geoffroy Hegde, Vinay Horton, Matthew K. Huck, Patrick Huhs, Georg Hummelshøj, Jens Kariryaa, Ankit Kozinsky, Boris Kumbhar, Snehal Liu, Mohan Marzari, Nicola Morris, Andrew J. Mostofi, Arash A. Persson, Kristin A. Petretto, Guido Purcell, Thomas Ricci, Francesco Rose, Frisco Scheffler, Matthias Speckhard, Daniel Uhrin, Martin Vaitkus, Antanas Villars, Pierre Waroquiers, David Wolverton, Chris Wu, Michael Yang, Xiaoyu École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Materials Design and Informatics Unit Department of Physics Chemistry and Biology Linköping University Sweden Tilde Materials Informatics Straßmannstraße 25 Berlin10249 Germany Materials Platform for Data Science Sepapaja 6 Tallinn15551 Estonia Theory of Condensed Matter Group Cavendish Laboratory 19 J.J. Thomson Avenue CambridgeCB3 0HE United Kingdom Lawrence Berkeley Labs BerkeleyCA United States Chemin des Étoiles 8 Louvain-la-Neuve1348 Belgium Department of Physics King's College London London United Kingdom Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Institute of Biotechnology Life Science Center Vilnius University Saulétekio av. 7 VilniusLT-10257 Lithuania Institute of Computer Science Faculty of Mathematics and Informatics Vilnius University Naugarduko g. 24 VilniusLT-03225 Lithuania Fritz-Haber-Institut der Max-Planck-Gesellschaft Faradayweg 4-6 Berlin14195 Germany Center for Autonomous Materials Design Duke University DurhamNC27708 United States Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Humboldt-Universität zu Berlin Institut für Physik and IRIS Adlershof Berlin12489 Germany École Polytechnique Fédérale de Lausanne Sion1951 Switzerland Trieste34136 Italy Hermann-von-Helmholtz-Platz 1 Eggenstein-Leopoldshafen76344 Germany Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Ansys 300 Rustat House 62 Clifton Rd CambridgeCB1 7EG United Kingdom De Zaale 20 Eindhoven5612 AJ Netherlands Department of Physics and Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Argonne National Laboratory LemontIL60439 United States Thayer School of Engineering Dartmouth College HanoverNH03755 United States Los AltosCA940

The Open Databases Integration for Materials Design (OPTIMADE) consortium has designed a universal application programming interface (API) to make materials databases accessible and interoperable. We outline the first stable release of the specification, v1.0, which is already supported by many leading databases and several software packages. We illustrate the advantages of the OPTIMADE API through worked examples on each of the public materials databases that support the full API specification. © 2021, CC BY.

关键词： Specifications

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Author Correction: Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways

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Scientific reports 2018年第1期8卷 8995页

作者： Renchu Guan Xu Wang Mary Qu Yang Yu Zhang Fengfeng Zhou Chen Yang Yanchun Liang Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. MidSouth Bioinformatics Center and Joint Bioinformatics Ph.D. Program of University of Arkansas at Little Rock and Univ. of Arkansas Medical Sciences Little Rock AR 72204 USA. Institute of Information Engineering Chinese Academy of Sciences School of Cyber Security University of Chinese Academy of Sciences Beijing 100093 China. College of Earth Sciences Jilin University Changchun 130061 China. yangc616@***. Key Laboratory for Symbol Computation and Knowledge Engineering of National Education Ministry College of Computer Science and Technology Jilin University Changchun 130012 China. ycliang@***. Zhuhai Laboratory of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Zhuhai 519041 China. ycliang@***.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has not been fixed in the paper.

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Two-Step Electronic Response to Magnetic Ordering in a van der Waals Ferromagnet

arXiv

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arXiv 2023年

作者： Wu, Han Zhu, Jian-Xin Chen, Lebing Butcher, Matthew W. Yue, Ziqin Yuan, Dongsheng He, Yu Oh, Ji Seop Huang, Jianwei Wu, Shan Gong, Cheng Guo, Yucheng Mo, Sung-Kwan Denlinger, Jonathan D. Lu, Donghui Hashimoto, Makoto Stone, Matthew B. Kolesnikov, Alexander I. Chi, Songxue Kono, Junichiro Nevidomskyy, Andriy H. Birgeneau, Robert J. Dai, Pengcheng Yi, Ming Department of Physics and Astronomy Rice Center for Quantum Materials Rice University HoustonTX77005 United States Theoretical Division Los Alamos National Laboratory Los AlamosNM87545 United States Center for Integrated Nanotechnologies Los Alamos National Laboratory Los AlamosNM87545 United States Department of Physics University of California at Berkeley BerkeleyCA94720 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States 1-1 Namiki Ibaraki Tsukuba305-0044 Japan Department of Applied Physics Yale University New HavenCT06511 United States Department of Electrical and Computer Engineering Quantum Technology Center University of Maryland College ParkMD20742 United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Stanford Synchrotron Radiation Lightsource SLAC National Accelerator Laboratory Menlo ParkCA94025 United States Neutron Scattering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Material Science and NanoEngineering Rice University HoustonTX77005 United States Department of Materials Science and Engineering University of California Berkeley United States

The two-dimensional (2D) material Cr2Ge2Te6 is a member of the class of insulating van der Waals magnets. Here, using high resolution angle-resolved photoemission spectroscopy in a detailed temperature dependence study, we identify a clear response of the electronic structure to a dimensional crossover in the form of two distinct temperature scales marking onsets of modifications in the electronic structure. Specifically, we observe Te p-orbital-dominated bands to undergo changes at the Curie transition temperature TC while the Cr d-orbital-dominated bands begin evolving at a higher temperature scale. Combined with neutron scattering, density functional theory calculations, and Monte Carlo simulations, we find that the electronic system can be consistently understood to respond sequentially to the distinct temperatures at which in-plane and out-of-plane spin correlations exceed a characteristic length scale. Our findings reveal the sensitivity of the orbital-selective electronic structure for probing the dynamical evolution of local moment correlations in vdW insulating magnets. © 2023, CC BY.

关键词： Electronic structure

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computer-SYSTEM ARCHITECTURE CONCEPTS FOR FUTURE COMBAT SYSTEMS

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NAVAL ENGINEERS JOURNAL 1990年第3期102卷 43-62页

作者： ZITZMAN, LH FALATKO, SM PAPACH, JL Dr. Lewis H. Zitzman:is the group supervisor of the Advanced Systems Design Group Fleet Systems Department The Johns Hopkins University Applied Physics Laboratory (JHU/APL). He has been employed at JHU/APL since 1972 performing applied research in computer science and in investigating and applying advanced computer technologies to Navy shipboard systems. He is currently chairman of Aegis Computer Architecture Data Bus and Fiber Optics Working Group from which many concepts for this paper were generated. Dr. Zitzman received his B.S. degree in physics from Brigham Young University in 1963 and his M.S. and Ph.D. degrees in physics from the University of Illinois in 1967 and 1972 respectively. Stephen M. Falatko:was a senior engineering analyst in the Combat Systems Engineering Department Comptek Research Incorporated for the majority of this effort. He is currently employed at ManTech Services Corporation. During his eight-year career first at The Johns Hopkins University Applied Physics Laboratory and currently with ManTech Mr. Falatko's work has centered around the development of requirements and specifications for future Navy systems and the application of advanced technology to Navy command and control systems. He is a member of both the Computer Architecture Fiber Optics and Data Bus Working Group and the Aegis Fiber Optics Working Group. Mr. Falatko received his B.S. degree in aerospace engineering with high distinction from the University of Virginia in 1982 and his M.S. degree in applied physics from The Johns Hopkins University in 1985. Mr. Falatko is a member of Tau Beta Pi Sigma Gamma Tau the American Society of Naval Engineers and the U.S. Naval Institute. Janet L. Papach:is a section leader and senior engineering analyst in the Combat Systems Engineering Department Comptek Research Incorporated. She has ten years' experience as an analyst supporting NavSea Spa War and the U.S. Department of State. She currently participates in working group efforts under Aegis Combat System Doctrin

This paper sets forth computer systems architecture concepts for the combat system of the 2010–2030 timeframe that satisfy the needs of the next generation of surface combatants. It builds upon the current Aegis computer systems architecture, expanding that architecture while preserving, and adhering to, the Aegis fundamental principle of thorough systems engineering, dedicated to maintaining a well integrated, highly reliable, and easily operable combat system. The implementation of these proposed computer systems concepts in a coherent architecture would support the future battle force capable combat system and allow the expansion necessary to accommodate evolutionary changes in both the threat environment and the technology then available to effectively counter that threat. Changes to the current Aegis computer architecture must be carefully and effectively managed such that the fleet will retain its combat readiness capability at all times. This paper describes a possible transition approach for evolving the current Aegis computer architecture to a general architecture for the future. The proposed computer systems architecture concepts encompass the use of combinations of physically distributed, microprocessor-based computers, collocated with the equipment they support or embedded within the equipment itself. They draw heavily on widely used and available industry standards, including instruction set architectures (ISAs), backplane busses, microprocessors, computer programming languages and development environments, and local area networks (LANs). In this proposal, LANs, based on fiber optics, will provide the interconnection to support system expandability, redundancy, and higher data throughput rates. A system of cross connected LANs will support a high level of combat system integration, spanning the major warfare areas, and will facilitate the coordination and development of a coherent multi-warfare tactical picture supporting the future combatant command st

关键词： computer Architecture

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