检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Reji, Varghese Kanodia, Shubham Ninan, Joe P. Cañas, Caleb I. Libby-Roberts, Jessica Lin, Andrea S.J. Gupta, Arvind F. Swaby, Tera N. Larsen, Alexander Kobulnicky, Henry A. Choi, Philip I. Evans, Nez Santomenna, Sage Winnick, Isabelle Yu, Larry Alvarado-Montes, Jaime A. Bender, Chad F. Bernabò, Lia Marta Blake, Cullen H. Cochran, William D. Diddams, Scott A. Halverson, Samuel Han, Te Hearty, Fred Logsdon, Sarah E. Mahadevan, Suvrath McElwain, Michael W. Monson, Andrew Robertson, Paul Ojha, Devendra K. Roy, Arpita Schwab, Christian Stefansson, Gudmundur Wright, Jason Department of Astronomy and Astrophysics Tata Institute of Fundamental Research Homi Bhabha Road Colaba Mumbai400005 India Earth and Planets Laboratory Carnegie Science 5241 Broad Branch Road NW WashingtonDC20015 United States NASA Postdoctoral Fellow NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Department of Astronomy & Astrophysics 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States Center for Exoplanets and Habitable Worlds 525 Davey Laboratory The Pennsylvania State University University ParkPA16802 United States NSF NOIRLab 950 N. Cherry Ave. TucsonAZ85719 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States Pomona College 333 N. College Way ClaremontCA91711 United States School of Mathematical and Physical Sciences Macquarie University Balaclava Road North RydeNSW2109 Australia The Macquarie University Astrophysics and Space Technologies Research Centre Macquarie University Balaclava Road North RydeNSW2109 Australia Steward Observatory University of Arizona 933 N. Cherry Ave TucsonAZ85721 United States Rutherfordstrasse 2 Berlin12489 Germany McDonald Observatory Center for Planetary Systems Habitability The University of Texas at Austin AustinTX78730 United States Department of Physics and Astronomy University of Pennsylvania 209 S 33rd Street PhiladelphiaPA19104 United States Electrical Computer & Energy Engineering University of Colorado 425 UCB BoulderCO80309 United States Department of Physics University of Colorado 2000 Colorado Avenue BoulderCO80309 United States Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive PasadenaCA91109 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States Astrophysics & Space Institute Schmidt Sciences New YorkNY10011 United States Anton Pannekoek Institute

We present the discovery of a low-density planet orbiting the high-metallicity early M-dwarf TOI-5688 A b. This planet was characterized as part of the search for transiting giant planets (R 8 R⊕) through the Searching for GEMS (Giant Exoplanets around M-dwarf Stars) survey. The planet was discovered with the Transiting Exoplanet Survey Satellite (TESS), and characterized with ground-based transits from Red Buttes Observatory (RBO), the Table Mountain Observatory of Pomona College, and radial velocity (RV) measurements with the Habitable-Zone Planet Finder (HPF) on the 10 m Hobby Eberly Telescope (HET) and NEID on the WIYN 3.5 m telescope. From the joint fit of transit and RV data, we measure a planetary mass and radius of 124 ± 24 M⊕ (0.39 ± 0.07 MJ) and 10.4 ± 0.7 R⊕ (0.92 ± 0.06 RJ) respectively. The spectroscopic and photometric analysis of the host star TOI-5688 A shows that it is a metal-rich ([Fe/H] = 0.47 ± 0.16 dex) M2V star, favoring the core-accretion formation pathway as the likely formation scenario for this planet. Additionally, Gaia astrometry suggests the presence of a wide-separation binary companion, TOI-5688 B, which has a projected separation of ∼ 5" (1110 AU) and is an M4V, making TOI-5688 A b part of the growing number of GEMS in wide-separation binary systems. © 2024, CC BY.

关键词： Exoplanets

来源：评论

学校读者我要写书评

暂无评论

Quantum Simulation for High Energy Physics

arXiv

引用

arXiv 2022年

作者： Bauer, Christian W. Davoudi, Zohreh Balantekin, A. Baha Bhattacharya, Tanmoy Carena, Marcela de Jong, Wibe A. Draper, Patrick El-Khadra, Aida Gemelke, Nate Hanada, Masanori Kharzeev, Dmitri Lamm, Henry Li, Ying-Ying Liu, Junyu Lukin, Mikhail Meurice, Yannick Monroe, Christopher Nachman, Benjamin Pagano, Guido Preskill, John Rinaldi, Enrico Roggero, Alessandro Santiago, David I. Savage, Martin J. Siddiqi, Irfan Siopsis, George Van Zanten, David Wiebe, Nathan Yamauchi, Yukari Yeter-Aydeniz, Kübra Zorzetti, Silvia Physics Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Physics Maryland Center for Fundamental Physics University of Maryland College ParkMD20742 United States Department of Physics University of Wisconsin MadisonWI53706 United States T-2 Los Alamos National Laboratory Los AlamosNM87545 United States Fermi National Accelerator Laboratory BataviaIL60510 United States Enrico Fermi Institute University of Chicago ChicagoIL60637 United States Kavli Institute for Cosmological Physics University of Chicago ChicagoIL60637 United States Department of Physics University of Chicago ChicagoIL60637 United States Department of Physics Illinois Center for the Advanced Study of the Universe and Illinois Quantum Information Science and Technology Center University of Illinois UrbanaIL61801 United States QuEra Computing Inc BostonMA02135 United States Department of Mathematics University of Surrey Guildford Surrey GU2 7XH United Kingdom Center for Nuclear Theory Department of Physics and Astronomy Stony Brook University NY11794-3800 United States Department of Physics Brookhaven National Laboratory UptonNY11973 United States Pritzker School of Molecular Engineering Chicago Quantum Exchange Kadanoff Center for Theoretical Physics University of Chicago IL60637 United States QBraid Co. Harper Court 5235 ChicagoIL60615 United States Department of Physics Harvard University CambridgeMA02138 United States Department of Physics and Astronomy University of Iowa Iowa CityIA52242 United States Duke Quantum Center Duke University DurhamNC27701 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Physics and Astronomy Department Rice University HoustonTX77005 United States Institute for Quantum Information and Matter Californi

It is for the first time that quantum simulation for High Energy Physics (HEP) is studied in the U.S. decadal particle-physics community planning, and in fact until recently, this was not considered a mainstream topic in the community. This fact speaks of a remarkable rate of growth of this subfield over the past few years, stimulated by the impressive advancements in Quantum Information Sciences (QIS) and associated technologies over the past decade, and the significant investment in this area by the government and private sectors in the U.S. and other countries. High-energy physicists have quickly identified problems of importance to our understanding of nature at the most fundamental level, from tiniest distances to cosmological extents, that are intractable with classical computers but may benefit from quantum advantage. They have initiated, and continue to carry out, a vigorous program in theory, algorithm, and hardware co-design for simulations of relevance to the HEP mission. This community whitepaper is an attempt to bring this exciting and yet challenging area of research to the spotlight, and to elaborate on what the promises, requirements, challenges, and potential solutions are over the next decade and beyond. © 2022, CC BY.

关键词： High energy physics

来源：评论

学校读者我要写书评

暂无评论

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

arXiv

引用

arXiv 2022年

作者： Pati, Sarthak Baid, Ujjwal Edwards, Brandon Sheller, Micah Wang, Shih-Han Reina, G. Anthony Foley, Patrick Gruzdev, Alexey Karkada, Deepthi Davatzikos, Christos Sako, Chiharu Ghodasara, Satyam Bilello, Michel Mohan, Suyash Vollmuth, Philipp Brugnara, Gianluca Preetha, Chandrakanth J. Sahm, Felix Maier-Hein, Klaus Zenk, Maximilian Bendszus, Martin Wick, Wolfgang Calabrese, Evan Rudie, Jeffrey Villanueva-Meyer, Javier Cha, Soonmee Ingalhalikar, Madhura Jadhav, Manali Pandey, Umang Saini, Jitender Garrett, John Larson, Matthew Jeraj, Robert Currie, Stuart Frood, Russell Fatania, Kavi Huang, Raymond Y. Chang, Ken Balana, Carmen Capellades, Jaume Puig, Josep Trenkler, Johannes Pichler, Josef Necker, Georg Haunschmidt, Andreas Meckel, Stephan Shukla, Gaurav Liem, Spencer Alexander, Gregory S. Lombardo, Joseph Palmer, Joshua D. Flanders, Adam E. Dicker, Adam P. Sair, Haris I. Jones, Craig K. Venkataraman, Archana Jiang, Meirui So, Tiffany Y. Chen, Cheng Heng, Pheng Ann Dou, Qi Kozubek, Michal Lux, Filip Michálek, Jan Matula, Petr Keřkovský, Miloš Kopřivová, Tereza Dostál, Marek Vybíhal, Václav Vogelbaum, Michael A. Mitchell, J. Ross Farinhas, Joaquim Maldjian, Joseph A. Yogananda, Chandan Ganesh Bangalore Pinho, Marco C. Reddy, Divya Holcomb, James Wagner, Benjamin C. Ellingson, Benjamin M. Cloughesy, Timothy F. Raymond, Catalina Oughourlian, Talia Hagiwara, Akifumi Wang, Chencai To, Minh-Son Bhardwaj, Sargam Chong, Chee Agzarian, Marc Falcão, Alexandre Xavier Martins, Samuel B. Teixeira, Bernardo C.A. Sprenger, Flávia Menotti, David Lucio, Diego R. LaMontagne, Pamela Marcus, Daniel Wiestler, Benedikt Kofler, Florian Ezhov, Ivan Metz, Marie Jain, Rajan Lee, Matthew Lui, Yvonne W. McKinley, Richard Slotboom, Johannes Radojewski, Piotr Meier, Raphael Wiest, Roland Derrick, Murcia Fu, Eric Haas, Rourke Thompson, John Ormond, David Ryan Badve, Chaitra Sloan, Andrew E. Vadmal, Vachan Waite, Kristin Colen, Rivka R. Pei, Linmin Ak, Murat Srinivasan, Ashok Bapuraj, J. Rajiv Rao, Arvind Wang, Nicholas Yoshiaki, Ota Moritani, Tos University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Informatics Technical University of Munich Bavaria Munich Germany Intel Corporation Santa ClaraCA United States Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Heidelberg Germany Department of Neuropathology Heidelberg University Hospital Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Neurology Clinic Heidelberg University Hospital Heidelberg Germany Department of Radiology & Biomedical Imaging University of California San Francisco San FranciscoCA United States Symbiosis Center for Medical Image Analysis Symbiosis International University Maharashtra Pune India Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Karnataka Bangalore India Department of Radiology School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin MadisonWI United States Department of Neuroradiology Clinical Neuroscience Center University Hospital Zurich University of Zurich Zurich Switzerland Leeds Teaching Hospitals Trust Department of Radiology Leeds United Kingdom Department of Radiology Brigham and Women's Hospital Harvard Medical School BostonMA United States Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Catalan Institute of Oncology Badalona Spain Consorci MAR Parc de Salut de Barcelona Catalonia Spain Josep Trueta University Hospital Girona Spain

Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally sharing ample, and importantly diverse, data from multiple sites. However, such centralization is challenging to scale (or even not feasible) due to various limitations. Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here we present findings from the largest FL study to-date, involving data from 71 healthcare institutions across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, utilizing the largest dataset of such patients ever used in the literature (25,256 MRI scans from 6,314 patients). We demonstrate a 33% improvement over a publicly trained model to delineate the surgically targetable tumor, and 23% improvement over the tumor's entire extent. We anticipate our study to: 1) enable more studies in healthcare informed by large and diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further quantitative analyses for glioblastoma via performance optimization of our consensus model for eventual public release, and 3) demonstrate the effectiveness of FL at such scale and task complexity as a paradigm shift for multi-site collaborations, alleviating the need for data sharing. © 2022, CC BY.

关键词： Tumors

来源：评论

学校读者我要写书评

暂无评论

Roadmap on spin-wave computing

arXiv

引用

arXiv 2021年

作者： Chumak, Andrii V. Kabos, P. Wu, M. Abert, C. Adelmann, C. Adeyeye, A. Åkerman, J. Aliev, F.G. Anane, A. Awad, A. Back, C.H. Barman, A. Bauer, G.E.W. Becherer, M. Beginin, E.N. Bittencourt, V.A.S.V. Blanter, Y.M. Bortolotti, P. Boventer, I. Bozhko, D.A. Bunyaev, S.A. Carmiggelt, J.J. Cheenikundil, R.R. Ciubotaru, F. Cotofana, S. Csaba, G. Dobrovolskiy, O.V. Dubs, C. Elyasi, M. Fripp, K.G. Fulara, H. Golovchanskiy, I.A. Gonzalez-Ballestero, C. Graczyk, P. Grundler, D. Gruszecki, P. Gubbiotti, G. Guslienko, K. Haldar, A. Hamdioui, S. Hertel, R. Hillebrands, B. Hioki, T. Houshang, A. Hu, C.-M. Huebl, H. Huth, M. Iacocca, E. Jungfleisch, M.B. Kakazei, G.N. Khitun, A. Khymyn, R. Kikkawa, T. Kläui, M. Klein, O. Klos, J.W. Knauer, S. Koraltan, S. Kostylev, M. Krawczyk, M. Krivorotov, I.N. Kruglyak, V.V. Lachance-Quirion, D. Ladak, S. Lebrun, R. Li, Y. Lindner, M. Macêdo, R. Mayr, S. Melkov, G.A. Mieszczak, S. Nakamura, Y. Nembach, H.T. Nikitin, A.A. Nikitov, S.A. Novosad, V. Otalora, J.A. Otani, Y. Papp, A. Pigeau, B. Pirro, P. Porod, W. Porrati, F. Qin, H. Rana, B. Reimann, T. Riente, F. Romero-Isart, O. Ross, A. Sadovnikov, A.V. Safin, A.R. Saitoh, E. Schmidt, G. Schultheiss, H. Schultheiss, K. Serga, A.A. Sharma, S. Shaw, J.M. Suess, D. Surzhenko, O. Szulc, K. Taniguchi, T. Urbánek, M. Usami, K. Ustinov, A.B. van der Sar, T. van Dijken, S. Vasyuchka, V.I. Verba, R. Viola Kusminskiy, S. Wang, Q. Weides, M. Weiler, M. Wintz, S. Wolski, S.P. Zhang, X. Faculty of Physics University of Vienna Boltzmanngasse 5 ViennaA-1090 Austria National Institute of Standards and Technology BoulderCO80305 United States Department of Physics Colorado State University Fort CollinsCO80523 United States Research Platform MMM Mathematics-Magnetism-Materials University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria Imec Leuven3001 Belgium Department of Physics Durham University South Road DurhamDH1 3LE United Kingdom Department of Physics University of Gothenburg Gothenburg412 96 Sweden Universidad Autónoma de Madrid Madrid28049 Spain Unité Mixte de Physique CNRS Thales Université Paris Saclay Palaiseau91767 France Department of Physics Technical University of Munich Garching85748 Germany Department of Condensed Matter Physics and Material Sciences S N Bose National Centre for Basic Sciences Salt Lake Kolkata700106 India Advanced Institute for Materials Research Tohoku University Sendai980-8577 Japan Zernike Institute for Advanced Materials University of Groningen Netherlands Department of Electrical and Computer Engineering Technical University of Munich Munich80333 Germany Laboratory of Magnetic Metamaterials Saratov State University Saratov410012 Russia Max Planck Institute for the Science of Light Erlangen91058 Germany Department of Quantum Nanoscience Kavli Institute of Nanoscience Delft University of Technology Delft2628 CJ Netherlands Department of Physics and Energy Science University of Colorado Colorado Springs Colorado SpringsCO80918 United States Departamento de Física e Astronomia Universidade do Porto Porto4169-007 Portugal Institut de Physique et Chimie des Matériaux de Strasbourg CNRS Université de Strasbourg StrasbourgF-67000 France Department of Quantum and Computer Engineering Delft University of Technology Delft2628 CD Netherlands Faculty for Information Technology and Bionics Pazmany Peter Catholic University Prater u. 50/A BudapestH-1083 Hungary INNOVENT e.V.

Magnonics is a field of science that addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operations in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of the current challenges and the outlook of the further development of the research directions. © 2021, CC BY.

关键词： Spin waves

来源：评论

学校读者我要写书评

暂无评论

International Society for Therapeutic Ultrasound Conference 2016

引用

Journal of Therapeutic Ultrasound 2017年第1期5卷 1-114页

作者： Brian Fowlkes Pejman Ghanouni Narendra Sanghvi Constantin Coussios Paul C. Lyon Michael Gray Christophoros Mannaris Marie de Saint Victor Eleanor Stride Robin Cleveland Robert Carlisle Feng Wu Mark Middleton Fergus Gleeson Jean-Franҫois Aubry Kim Butts Pauly Chrit Moonen Jacob Vortman Shirley Sharabi Dianne Daniels David Last David Guez Yoav Levy Alexander Volovick Javier Grinfeld Itay Rachmilevich Talia Amar Zion Zibly Yael Mardor Sagi Harnof Michael Plaksin Yoni Weissler Shy Shoham Eitan Kimmel Omer Naor Nairouz Farah Dong-Guk Paeng Zhiyuan Xu John Snell Anders H. Quigg Matthew Eames Changzhu Jin Ashli C. Everstine Jason P. Sheehan Beatriz S. Lopes Neal Kassell Thomas Looi Vera Khokhlova Charles Mougenot Kullervo Hynynen James Drake Michael Slayton Richard C. Amodei Keegan Compton Ashley McNelly Daniel Latt John Kearney David Melodelima Aurelien Dupre Yao Chen David Perol Jeremy Vincenot Jean-Yves Chapelon Michel Rivoire Wei Guo Guoxin Ren Guofeng Shen Michael Neidrauer Leonid Zubkov Michael S. Weingarten David J. Margolis Peter A. Lewin Nathan McDannold Jonathan Sutton Natalia Vykhodtseva Margaret Livingstone Thiele Kobus Yong-Zhi Zhang Michael Schwartz Yuexi Huang Nir Lipsman Jennifer Jain Martin Chapman Tejas Sankar Andres Lozano Robert Yeung Christakis Damianou Nikolaos Papadopoulos Omer Brokman Eyal Zadicario Ori Brenner David Castel Shih-Ying Wu Julien Grondin Wenlan Zheng Marc Heidmann Maria Eleni Karakatsani Carlos J. Sierra Sánchez Vincent Ferrera Elisa E. Konofagou Marinos Yiannakou HongSeok Cho Hwayoun Lee Mun Han Jong-Ryul Choi Taekwan Lee Sanghyun Ahn Yongmin Chang Juyoung Park Nicholas Ellens Ari Partanen Keyvan Farahani Raag Airan Alexandre Carpentier Michael Canney Alexandre Vignot Cyril Lafon Jean-yves Delattre Ahmed Idbaih Henrik Odéen Bradley Bolster Eun Kee Jeong Dennis L. Parker Pooja Gaur Xue Feng Samuel Fielden Craig Meyer Beat Werner William Grissom Michael Marx Hans Weber Valentina Taviani Brian Hargreaves Jun Tanaka Kentaro Kikuchi Ayumu Ishijima Takashi Azuma Kosuke Minamihata Satoshi Basic Radiological Sciences Division Department of Radiology University of Michigan Ann Arbor USA Stanford School of Medicine Stanford USA SonaCare Medical Indianapolis USA Institute of Biomedical Engineering University of Oxford Oxford UK HIFU Unit Churchill Hospital Oxford UK Department of Oncology University of Oxford Oxford UK Department of Radiology Churchill Hospital Oxford UK Institut Langevin Paris France Center for Imaging Sciences Imaging Division University Medical Center Utrecht The Netherlands InSighTec Haifa Israel Sheba Medical Center Ramat-Gan Israel Tel-Aviv University Tel-Aviv Israel Faculty of Biomedical Engineering & Russell Berrie Nanotechnology Institute Technion – Israel Institute of Technology Haifa Israel ELSC Center for Brain Sciences Hebrew University Jerusalem Israel Biomedical Engineering Technion- Israel Institute of Technology Haifa Israel Faculty of Life Sciences Bar Ilan University Ramat Gan Israel Focused Ultrasound Foundation Charlottesville USA Ocean System Engineering Jeju National University Jeju Republic of Korea Neurosurgery University of Virginia Charlottesville USA Biology University of Virginia Charlottesville USA Pathology University of Virginia Charlottesville USA Hospital for Sick Children Toronto Canada University of Toronto Toronto Canada University of Washington Seattle USA Philips Healthcare Markham Canada Sunnybrook Research Institute Toronto Canada Guided Therapy Systems LLC Mesa USA School of Medicine University of Arizona Tucson USA The CORE Institute Phoenix USA LabTAU - U1032 INSERM Lyon France Centre Leon Berard Lyon France Department of Oral Maxillofacial and Head Neck Oncology Shanghai 9th People’s Hospital Shanghai China The School of Biomedical Engineering Science and Health Systems Drexel University Philadelphia USA Department of Surgery College of Medicine Drexel University Philadelphia USA Biostatistics and Epidemiology University of Pennsylvania Philadelphi

来源：评论

学校读者我要写书评

暂无评论

TOI-3984 A b and TOI-5293 A b: two temperate gas giants transiting mid-M dwarfs in wide binary systems

arXiv

引用

arXiv 2023年

作者： Cañas, Caleb I. Kanodia, Shubham Libby-Roberts, Jessica Lin, Andrea S.J. Schutte, Maria Powers, Luke Jones, Sinclaire Monson, Andrew Wang, Songhu Stefánsson, Guðmundur Cochran, William D. Robertson, Paul Mahadevan, Suvrath Kowalski, Adam F. Wisniewski, John Parker, Brock A. Larsen, Alexander Chapman, Franklin A.L. Kobulnicky, Henry A. Gupta, Arvind F. Everett, Mark E. Penprase, Bryan Edward Zeimann, Gregory Beard, Corey Bender, Chad F. Colón, Knicole D. Diddams, Scott A. Fredrick, Connor Halverson, Samuel Ninan, Joe P. Ramsey, Lawrence W. Roy, Arpita Schwab, Christian NASA Goddard Space Flight Center 8800 Greenbelt Road GreenbeltMD20771 United States Department of Astronomy & Astrophysics The Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States Center for Exoplanets and Habitable Worlds The Pennsylvania State University 525 Davey Laboratory University ParkPA16802 United States Earth and Planets Laboratory Carnegie Institution for Science 5241 Broad Branch Road NW WashingtonDC20015 United States Homer L. Dodge Department of Physics and Astronomy University of Oklahoma 440 W. Brooks Street NormanOK73019 United States Department of Astronomy The Ohio State University 4055 McPherson Laboratory ColumbusOH43210 United States Department of Astrophysical Sciences Princeton University 4 Ivy Lane PrincetonNJ08540 United States Steward Observatory The University of Arizona 933 N. Cherry Avenue TucsonAZ85721 United States Department of Astronomy Indiana University 727 East 3rd Street BloomingtonIN47405-7105 United States McDonald Observatory Center for Planetary Systems Habitability The University of Texas at Austin AustinTX78730 United States Department of Physics & Astronomy The University of California Irvine IrvineCA92697 United States ETH Zurich Institute for Particle Physics & Astrophysics Zurich Switzerland National Solar Observatory University of Colorado Boulder 3665 Discovery Drive BoulderCO80303 United States Department of Astrophysical and Planetary Sciences University of Colorado 2000 Colorado Ave BoulderCO80305 United States Laboratory for Atmospheric and Space Physics University of Colorado Boulder 3665 Discovery Drive BoulderCO80303 United States Department of Physics and Astronomy George Mason University FairfaxVA22030 United States Department of Physics & Astronomy University of Wyoming LaramieWY82070 United States NSF’s National Optical-Infrared Astronomy Research Laboratory 950 N. Cherry Ave. TucsonAZ85719 United States Soka Universit

We confirm the planetary nature of two gas giants discovered by TESS to transit M dwarfs with stellar companions at wide separations. TOI-3984 A (J = 11.93) is an M4 dwarf hosting a short-period (4.353326 ±0.000005 days) gas giant (Mp = 0.14 ±0.03 MJ and Rp = 0.71 ± 0.02 RJ) with a wide separation white dwarf companion. TOI-5293 A (J = 12.47) is an M3 dwarf hosting a short-period (2.930289 ± 0.000004 days) gas giant (Mp = 0.54 ± 0.07 MJ and Rp = 1.06 ± 0.04 RJ) with a wide separation M dwarf companion. We characterize both systems using a combination of ground-based and space-based photometry, speckle imaging, and high-precision radial velocities from the Habitable-zone Planet Finder and NEID spectrographs. TOI-3984 A b (Teq = 563 ± 15 K and TSM = 138+29−27) and TOI-5293 A b (Teq = 675+42−30 K and TSM = 92 ±14) are two of the coolest gas giants among the population of hot Jupiter-sized gas planets orbiting M dwarfs and are favorable targets for atmospheric characterization of temperate gas giants and three-dimensional obliquity measurements to probe system architecture and migration scenarios. © 2023, CC BY.

关键词： Gases

来源：评论

学校读者我要写书评

暂无评论

Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

引用

BMC bioinformatics 2016年第3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca USA Graduate Field of Computational Biology Cornell University Ithaca USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Antwerpen Belgium Department of Computer Science University of Arizona Tucson USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Minds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai India Philips Research North America Briarcliff Manor USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Tokyo Japan Systems Biology Centre University of Warwick Senate House Coventry UK Systems Biology Doctoral Training Centre University of Warwick Senate House Coventry UK Center for B

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

来源：评论

学校读者我要写书评

暂无评论

A strategic review on sustainable approaches in municipal solid waste management andenergy recovery: Role of artificial intelligence,economic stability andlife cycle assessment

引用

Bioresource technology 2023年 379卷 129044页

作者： Rajendiran Naveenkumar Jayaraj Iyyappan Ravichandran Pravin Seifedine Kadry Jeehoon Han Raveendran Sindhu Mukesh Kumar Awasthi Samuel Lalthazuala Rokhum Gurunathan Baskar Biological Systems Engineering University of Wisconsin-Madison Madison WI 53706 United States Forest Products Laboratory USDA Forest Service Madison WI 53726 United States. Department of Biotechnology Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Thandalam Chennai 602107 India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119. India. Department of Applied Data Science Noroff University College Kristiansand Norway Artificial Intelligence Research Center (AIRC) Ajman University Ajman 346 United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon. Department of Chemical Engineering Pohang University of Science and Technology Pohang 37673 Republic of Korea. Department of Food Technology TKM Institute of Technology Kollam Kerala India. College of Natural Resources and Environment Northwest A&F University Yangling Shaanxi Province 712100 China. Department of Chemistry National Institute of Technology Silchar 788010 Assam India. Department of Biotechnology St. Joseph's College of Engineering Chennai 600119. India Department of Applied Data Science Noroff University College Kristiansand Norway. Electronic address: basg2002@***.

The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to explore various technological advancements for the utilization of waste to energy. Municipal Solid Waste (MSW) has been reported as constructive feedstock to produce biofuels, biofuel carriers and biochemicals using energy-efficient technologies in risk freeways. The present review contemplates risk assessment and challenges in sorting and transportation of MSW and different aspects of conversion of MSW into energy are critically analysed. The circular bioeconomy of energy production strategies and management of waste are also analysed. The current scenario on MSW and its impacts on the environment are elucidated in conjunction with various policies and amendments equipped for the competent management of MSW in order to fabricate a sustained environment.

关键词： Circular bioeconomy Municipal Solid Waste Risk assessment Sustainable Waste Management

来源：评论

学校读者我要写书评

暂无评论

Predicting Spring Phenology in Deciduous Broadleaf Forests: An Open Community Forecast Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chenp, Min Goldstein, Benjamin Guo, Jessica S. Haoq, Dalei Jones, Chris Kelly-Fair, Mira Liup, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Ross, Arun A. Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Institute State University 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Polytechnic Institute State University 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building University of California Berkeley BerkeleyCA94720 United States Department of For

Accurate phenology models are important to predict how global climate change will continue to alter the timing of plant phenological events, such as spring greenup in deciduous broadleaf forests. While there is merit in long-term predictions, investigating how models can predict near-term (1– 35 days) canopy greenness throughout the spring allows us to validate performance and understanding now. The Ecological Forecasting Initiative’s NEON Forecasting Challenge, is an open challenge to the community to predict daily greenness values, measured through digital images collected by the PhenoCam Network at National Ecological Observatory Network (NEON) sites. For the first round of the challenge, which is presented here, teams were tasked to forecast phenology at eight deciduous broadleaf sites. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts, which future rounds will continue to investigate. © 2023, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Predicting Spring Phenology in Deciduous Broadleaf Forests: Neon Phenology Forecasting Community Challenge

SSRN

引用

SSRN 2023年

作者： Wheeler, Kathryn I. Dietze, Michael C. LeBauer, David Peters, Jody Richardson, Andrew D. Ross, Arun A. Thomas, R. Quinn Zhu, Kai Bhat, Uttam Munch, Stephan Buzbee, Raphaela Floreani Chen, Min Goldstein, Benjamin Guo, Jessica Hao, Dalei Jones, Chris Kelly-Fair, Mira Liu, Haoran Malmborg, Charlotte Neupane, Naresh Pal, Debasmita Shirey, Vaughn Song, Yiluan Steen, McKalee Vance, Eric A. Woelmer, Whitney M. Wynne, Jacob Zachmann, Luke Department of Earth and Environment Boston University 685 Commonwealth Avenue BostonMA02215 United States Cooperative Programs for the Advancement of Earth System Science University Corporation for Atmospheric Research Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States National Oceanic and Atmospheric Administration Foothills Laboratory Bldg 4 3300 Mitchell Lane BoulderCO80301 United States Department of Civil and Environmental Engineering Massachusetts Institute of Technology 15 Vassar St CambridgeMA02142 United States Arizona Experiment Station University of Arizona 1230 N Cherry Ave TucsonAZ85721 United States Department of Biological Sciences University of Notre Dame 100 Galvin Notre DameIN46556 United States School of Informatics Computing and Cyber Systems Northern Arizona University 1295 Knoles Dr. FlagstaffAZ86011 United States Center for Ecosystem Science and Society Northern Arizona University PO Box 5620 FlagstaffAZ86011 United States Department of Computer Science and Engineering Michigan State University 428 South Shaw Lane East LansingMI48824 United States Department of Forest Resources and Environmental Conservation Virginia Polytechnic Instit 310 West Campus Drive BlacksburgVA24061 United States Department of Biological Sciences Virginia Tech 926 West Campus Drive BlacksburgVA24061 United States Institute for Global Change Biology University of Michigan 440 Church Street Ann ArborMI48109 United States School for Environment and Sustainability University of Michigan 440 Church Street Ann ArborMI48109 United States Institute of Marine Sciences University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Applied Mathematics University of California Santa Cruz 1156 High Street Santa CruzCA95060 United States Department of Environmental Science Policy and Management University of California Berkeley 4004 Valley Life Sciences Building Universi

Accurate models are important to predict how global climate change will continue to alter plant phenology and near-term ecological forecasts can be used to iteratively improve models and evaluate predictions that are made a priori. The Ecological Forecasting Initiative’s National Ecological Observatory Network (NEON) Forecasting Challenge, is an open challenge to the community to forecast daily greenness values, measured through digital images collected by the PhenoCam Network at NEON sites before the data are collected. For the first round of the challenge, which is presented here, we forecasted canopy greenness throughout the spring at eight deciduous broadleaf sites to investigate what factors impact current predictive skill of phenology forecasts. A total of 192,536 predictions were submitted, representing eighteen models, including a persistence and a day of year mean null models. We found that overall forecast skill was highest when forecasting earlier in the greenup curve compared to the end, for shorter lead times, for sites that greened up earlier, and when submitting forecasts during times other than near budburst. The models based on day of year historical mean had the highest predictive skill across the challenge period. In this first round of the challenge, by synthesizing across forecasts, we started to elucidate what factors affect the predictive skill of near-term phenology forecasts. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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：