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Coronal hole picoflare jets are progenitors of both fast and Alfvénic slow solar wind

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arXiv 2024年

作者： Chitta, L.P. Huang, Z. D’Amicis, R. Calchetti, D. Zhukov, A.N. Kraaikamp, E. Verbeeck, C. Cuadrado, R. Aznar Hirzberger, J. Berghmans, D. Horbury, T.S. Solanki, S.K. Owen, C.J. Harra, L. Peter, H. Schühle, U. Teriaca, L. Louarn, P. Livi, S. Giunta, A.S. Hassler, D.M. Wang, Y.-M. Max-Planck-Institut für Sonnensystemforschung Göttingen37077 Germany Via Fosso del Cavaliere 100 Rome00133 Italy Solar-Terrestrial Centre of Excellence Solar Influences Data analysis Centre Royal Observatory of Belgium Brussels1180 Belgium Skobeltsyn Institute of Nuclear Physics Moscow State University Moscow119991 Russia Department of Physics Imperial College London LondonSW7 2AZ United Kingdom Mullard Space Science Laboratory Holmbury St MaryRH5 6NT United Kingdom Physikalisch-Meteorologisches Observatorium Davos World Radiation Center Davos Dorf7260 Switzerland Die Eidgenössische Technische Hochschule Zürich Zürich8093 Switzerland Georges-Köhler-Allee 401a Freiburg79110 Germany Institut de Recherche en Astrophysique et Planétologie CNRS Université de Toulouse CNES Toulouse France Southwest Research Institute San AntonioTX78238 United States RAL Space UKRI STFC Rutherford Appleton Laboratory DidcotOX11 0QX United Kingdom Southwest Research Institute BoulderCO80302 United States Space Science Division Naval Research Laboratory WashingtonDC20375 United States

Solar wind, classified by its bulk speed and the Alfvénic nature of its fluctuations, generates the heliosphere. The elusive physical processes responsible for the generation of the different types of this wind are a topic of active debate. Recent observations reveal intermittent jets, with kinetic energy in the picoflare range, emerging from dark areas of a polar coronal hole threaded by open magnetic field lines. These could substantially contribute to solar wind. However, their ubiquity and direct links to solar wind have not been established. Here, we report a unique set of remote-sensing and in situ observations from the Solar Orbiter spacecraft that establish a unified picture of fast and Alfvénic slow wind, connected to the similar widespread picoflare jet activity in two coronal holes. Radial expansion of coronal holes ultimately regulates the speed of the emerging wind. Copyright © 2024, The Authors. All rights reserved.

关键词： Solar wind

Modern applications of machine learning in quantum sciences

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arXiv 2022年

作者： Dawid, Anna Arnold, Julian Requena, Borja Gresch, Alexander Plodzien, Marcin Donatella, Kaelan Nicoli, Kim A. Stornati, Paolo Koch, Rouven Büttner, Miriam Okula, Robert Muñoz–Gil, Gorka Vargas–Hernández, Rodrigo A. Cervera-Lierta, Alba Carrasquilla, Juan Dunjko, Vedran Gabrié, Marylou Huembeli, Patrick van Nieuwenburg, Evert Vicentini, Filippo Wang, Lei Wetzel, Sebastian J. Carleo, Giuseppe Greplová, Eliška Krems, Roman Marquardt, Florian Tomza, Michal Lewenstein, Maciej Dauphin, Alexandre Faculty of Physics University of Warsaw Poland ICFO - Institut de Ciències Fotòniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Center for Computational Quantum Physics Flatiron Institute New York United States Department of Physics University of Basel Switzerland Institute for Theoretical Physics Heinrich Heine University Düsseldorf Germany Institute for Quantum Inspired and Quantum Optimization Hamburg University of Technology Germany Université de Paris CNRS Laboratoire Matériaux et Phénomènes Quantiques France Machine Learning Group Technische Universität Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Applied Physics Aalto University Espoo Finland Institute of Physics Albert-Ludwig University of Freiburg Germany International Centre for Theory of Quantum Technologies University of Gdańsk Poland Department of Algorithms and System Modeling Faculty of Electronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Poland Institute for Theoretical Physics University of Innsbruck Austria Department of Chemistry University of Toronto Canada Vector Institute for Artificial Intelligence MaRS Centre Toronto Canada Department of Chemistry and Chemical Biology McMaster University Hamilton Canada Barcelona Supercomputing Center Spain LIACS Leiden University Netherlands CMAP École Polytechnique France Switzerland Menten AI Inc. Palo AltoCA United States Niels Bohr Institute Copenhagen Denmark CPHT CNRS École Polytechnique Institut Polytechnique de Paris PalaiseauF-91128 France Beijing National Lab for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Songshan Lake Materials Laboratory Dongguan China Perimeter Institute for Theoretical Physics Waterloo Canada Kavli Institute of Nanoscience Delft University of Technology DelftNL-2600 GA Netherlands Department of

In this book, we provide a comprehensive introduction to the most recentadvances in the application of machine learning methods in quantum sciences. Wecover the use of deep learning and kernel methods in supervised, unsupervised,and reinforcement learning algorithms for phase classification, representationof many-body quantum states, quantum feedback control, and quantum circuitsoptimization. Moreover, we introduce and discuss more specialized topics suchas differentiable programming, generative models, statistical approach tomachine learning, and quantum machine learning. © 2022, CC BY.

关键词： Deep learning

A magnetic avalanche as the central engine powering a solar flare

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arXiv 2025年

作者： Chitta, L.P. Pontin, D.I. Priest, E.R. Berghmans, D. Kraaikamp, E. Rodriguez, L. Verbeeck, C. Zhukov, A.N. Krucker, S. Cuadrado, R. Aznar Calchetti, D. Hirzberger, J. Peter, H. Schühle, U. Solanki, S.K. Teriaca, L. Giunta, A.S. Auchère, F. Harra, L. Müller, D. Max-Planck-Institut für Sonnensystemforschung Göttingen37077 Germany School of Information and Physical Sciences University of Newcastle CallaghanNSW2308 Australia School of Mathematics and Statistics University of St Andrews St AndrewsKY16 9SS United Kingdom Solar-Terrestrial Centre of Excellence Solar Influences Data analysis Centre Royal Observatory of Belgium Brussels1180 Belgium Skobeltsyn Institute of Nuclear Physics Moscow State University Moscow119991 Russia Space Sciences Laboratory University of California BerkeleyCA94720 United States University of Applied Sciences and Arts Northwestern Switzerland WindischCH-5210 Switzerland Georges-Köhler-Allee 401a Freiburg79110 Germany RAL Space UKRI STFC Rutherford Appleton Laboratory DidcotOX11 0QX United Kingdom University of Catania Astrophysics Section Dept. of Physics and Astronomy Via S. Sofia 78 Catania95123 Italy Université Paris-Saclay CNRS Institut d’astrophysique spatiale Orsay91405 France Physikalisch-Meteorologisches Observatorium Davos World Radiation Center Davos Dorf7260 Switzerland Die Eidgenössische Technische Hochschule Zürich Zürich8093 Switzerland PO Box 299 Noordwijk2200 AG Netherlands

Solar flares are the most powerful, magnetically-driven, explosions in the heliosphere. The nature of magnetic energy release in the solar corona that heats the plasma and accelerates particles in a flare, however, remains poorly understood. Here, we report high-resolution coronal observations of a flare (SOL2024-09-30T23:47) by the Solar Orbiter mission that reveal initially weaker but rapid reconnection events, on timescales of at most a few seconds, leading to a more prominent activity of similar nature that explosively cause a flare. Signatures of this process are further imprinted on the widespread raining plasma blobs with short lifetimes, giving rise to the characteristic ribbon-like emission pattern associated with the flare. Our novel observations unveil the central engine of a flare and emphasize the crucial role of an avalanche-like magnetic energy release mechanism at work. Copyright © 2025, The Authors. All rights reserved.

关键词： Electric corona

Heterogeneous bacterial swarms with mixed lengths

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arXiv 2020年

作者： Peled, Shlomit Ryan, Shawn D. Heidenreich, Sebastian Bär, Markus Ariel, Gil Be’er, Avraham Zuckerberg Institute for Water Research Jacob Blaustein Institutes for Desert Research Ben-Gurion University of the Negev Sede Boqer Campus Midreshet Ben-Gurion84990 Israel Department of Mathematics and Statistics Cleveland State University ClevelandOH United States Center for Applied Data Analysis and Modeling Cleveland State University ClevelandOH United States Department of Mathematical Modelling and Data Analysis Physikalisch-Technische Bundesanstalt Braunschweig und Berlin Abbestrasse 2-12 BerlinD-10587 Germany Department of Mathematics Bar-Ilan University Ramat Gan52000 Israel Department of Physics Ben-Gurion University of the Negev Beer-Sheva84105 Israel

Heterogeneous systems of active matter exhibit a range of complex emergent dynamical patterns. In particular, it is difficult to predict the properties of the mixed system based on its constituents. These considerations are particularly significant for understanding realistic bacterial swarms, which typically develop heterogeneities even when grown from a single cell. Here, mixed swarms of cells with different aspect ratios are studied both experimentally and in simulations. In contrast with previous theory, there is no macroscopic phase segregation. However, locally, long cells act as nucleation cites, around which aggregates of short, rapidly moving cells can form, resulting in enhanced swarming speeds. On the other hand, high fractions of long cells form a bottle-neck for efficient swarming. Our results suggest a new physical advantage for the spontaneous heterogeneity of bacterial swarm populations. Copyright © 2020, The Authors. All rights reserved.

关键词： Cells

Comparaison de stratégies d’initiation de l’épuration extra-rénale en réanimation : apport des modèles multi-états

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Revue d'Épidémiologie et de Santé Publique 2021年 69卷 S16-S16页

作者： D. Hajage M. Von Cube D. Dreyfuss S. Gaudry L. Jérome Sorbonne université Inserm institut Pierre-Louis d’épidémiologie et de santé publique AP–HP Sorbonne université hôpital Pitié-Salpêtrière département de santé publique centre de pharmacoépidémiologie (Cephepi) CIC-1421 Paris France Institute of Medical Biometry and Statistics Faculty of Medicine and Medical Center University of Freiburg Freiburg Center for Data Analysis and Modeling University of Freiburg Freiburg Center for Data Analysis and Modeling Freiburg Allemagne AP–HP hôpital Louis-Mourier service de médecine intensive et réanimation Colombes France AP–HP hôpital Avicenne département de réanimation médico-chirurgicale Bobigny France Université de Paris Inserm U1153 CRESS Epidemiology and Clinical Statistics for Tumor Respiratory and Resuscitation Assessments (ECSTRRA) Team AP–HP hôpital Saint-Louis service de biostatistique et information médicale Paris France

Introduction Chez les patients hospitalisés en réanimation et présentant une insuffisance rénale aiguë, l’épuration extra-rénale (EER) peut être débutée soit pré...

Introduction Chez les patients hospitalisés en réanimation et présentant une insuffisance rénale aiguë, l’épuration extra-rénale (EER) peut être débutée soit précocement, soit selon une stratégie d’attente, c’est-à-dire seulement en cas d’apparition de complications métaboliques ou respiratoires mettant en jeu le pronostic vital. Plusieurs essais randomisés et une méta-analyse récente n’ont pas montré de différence de mortalité entre les deux stratégies. En l’absence de différence de mortalité, la meilleure stratégie est celle qui permet de réduire le nombre de patients chez qui une EER est débutée ainsi que le temps passé sous EER. Mais quantifier ces éléments en présence de données censurées, quand l’EER est débutée à des délais variables selon les individus (voir non débutée), et en tenant compte des risques compétitifs de sortie vivant de réanimation et de décès, n’est pas immédiat. Méthodes Nous proposons de réanalyser les données de l’essai contrôlé randomisé AKIKI, comparant une stratégie d’initiation précoce et à une stratégie d’attente, à l’aide d’un modèle multi-état. Cinq états sont définis au cours du suivi : « en réanimation et EER non débutée », « en réanimation sous EER », « en réanimation et sevré de l’EER », « sorti vivant de réanimation », « décédé ». Sous l’hypothèse markovienne, les probabilités d’occupation de chaque état au cours du temps, ainsi que les durées moyennes tronquées d’occupation sont estimées et comparées entre les deux bras. Résultats La représentation des probabilités d’occupation de chaque état au cours du temps met en évidence les différents événements d’intérêt sur un seul graphique ( Fig. 1 ). Pendant les 28 premiers jours, cette réanalyse suggère que les patients randomisés dans la stratégie d’attente sont épurés significativement moins longtemps (différence moyenne = −2,0 jours [IC95 % : −3,7 ; −0,1]) par rapport à la stratégie précoce, pour un pronostic similaire (différence moyenne de temps passé en vie hors de la réa

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INTRODUCTION

Bulletin of the American Meteorological Society 2024年第8期105卷 S1-S11页

作者： Boyer, T. Blunden, J. Dunn, R.J.H. NOAA/NESDIS National Centers for Environmental Information Silver Spring Maryland NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Met Office Hadley Centre Exeter United Kingdom European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland Scripps Institution of Oceanography University of California San Diego La Jolla California NOAA/NWS National Centers for Environmental Prediction Climate Prediction Center College Park Maryland Brandenburg University of Technology (BTU) Cottbus-Senftenberg Germany Servicio Meteorológico Nacional Buenos Aires Argentina NOAA/OAR Physical Sciences Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Bonn Germany Center for Geophysical Research and School of Physics University of Costa Rica San José Costa Rica Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Centro Nacional de Monitoramento e Alertas de Desastres Naturais CEMADEN São Paulo Brazil Université Grenoble Alpes Institut des Géosciences de l’Environnement IRD CNRS Grenoble INP Grenoble France Hampton University Hampton Virginia Seychelles Meteorological Authority Mahe Seychelles Section for Glaciers Ice and Snow Norwegian Water Resources and Energy Directorate Oslo Norway National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany NOAA Global Monitoring Laboratory Boulder Colorado Servicio Nacional de Meteorología e Hidrología del Perú Lima Perú Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Department o

In 2023, La Niña conditions that generally prevailed in the eastern Pacific Ocean from mid-2020 into early 2023 gave way to a strong El Niño by October. Atmospheric concentrations of Earth’s major greenhouse gases—carbon dioxide, methane, and nitrous oxide—all increased to record-high levels. The annual global average carbon dioxide concentration in the atmosphere rose to 419.3±0.1 ppm, which is 50% greater than the pre-industrial level. The growth from 2022 to 2023 was 2.8 ppm, the fourth highest in the record since the 1960s. The combined short-term effects of El Niño and the long-term effects of increasing levels of heat-trapping gases in the atmosphere contributed to new records for many essential climate variables reported here. The annual global temperature across land and oceans was the highest in records dating as far back as 1850, with the last seven months (June–December) having each been record warm. Over land, the globally averaged temperature was also record high. Dozens of countries reported record or near-record warmth for the year, including China and continental Europe as a whole (warmest on record), India and Russia (second warmest), and Canada (third warmest). Intense and widespread heatwaves were reported around the world. In Vietnam, an all-time national maximum temperature record of 44.2°C was observed at Tuong Duong on 7 May, surpassing the previous record of 43.4°C at Huong Khe on 20 April 2019. In Brazil, the air temperature reached 44.8°C in Araçuaí in Minas Gerais on 20 November, potentially a new national record and 12.8°C above normal. The effect of rising temperatures was apparent in the cryosphere, where snow cover extent by June 2023 was the smallest in the 56-year record for North America and seventh smallest for the Northern Hemisphere overall. Heatwaves contributed to the greatest average mass balance loss for Alpine glaciers around the world since the start of the record in 1970. Due to rapid volume loss beginning in 2021, St. A

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Optogenetic manipulation of cardiac electrical dynamics using sub-threshold illumination: Dissecting the role of cardiac alternans in terminating rapid rhythms

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Vascular Pharmacology 2022年 146卷 107032-107032页

作者： Biasci, Valentina Santini, Lorenzo Hussaini, Sayedeh Ferrantini, Cecilia Coppini, Raffaele Loew, Leslie Luther, Stefan Campione, Marina Poggesi, Corrado Pavone, Francesco Saverio Cerbai, Elisabetta Bub, Gil Sacconi, Leonardo European Laboratory for Non-Linear Spectroscopy - LENS University of Florence 50019 Sesto Fiorentino Italy Department of Experimental and Clinical Medicine University of Florence 50134 Florence Italy Department of Neurology Psychology Drug Sciences and Child Health University of Florence 50134 Florence Italy Max Planck Institute for Dynamics and Self- Organization 37077 Gottingen Germany Center for Cell Analysis and Modeling University of Connecticut Farmington CT United States Institute of Neuroscience (IN-CNR) and Department of Biomedical Science University of Padova 35122 Padua Italy Department of Physics and Astronomy University of Florence 50019 Sesto Fiorentino Italy National Institute of Optics (INO-CNR) 50019 Sesto Fiorentino Italy Department of Physiology McGill University Montreal Quebec H3A 0G4 Canada Institute for Experimental Cardiovascular Medicine University Heart Center and Medical Faculty University of Freiburg 79085 Freiburg Germany

Community-developed checklists for publishing images and image analyses

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arXiv 2023年

作者： Schmied, Christopher Nelson, Michael S. Avilov, Sergiy Bakker, Gert-Jan Bertocchi, Cristina Bischof, Johanna Boehm, Ulrike Brocher, Jan Carvalho, Mariana Chiritescu, Catalin Christopher, Jana Cimini, Beth A. Conde-Sousa, Eduardo Ebner, Michael Ecker, Rupert Eliceiri, Kevin Fernandez-Rodriguez, Julia Gaudreault, Nathalie Gelman, Laurent Grunwald, David Gu, Tingting Halidi, Nadia Hammer, Mathias Hartley, Matthew Held, Marie Jug, Florian Kapoor, Varun Koksoy, Ayse Aslihan Lacoste, Judith Le Dévédec, Sylvia Le Guyader, Sylvie Liu, Penghuan Martins, Gabriel G. Mathur, Aastha Miura, Kota Llopis, Paula Montero Nitschke, Roland North, Alison Parslow, Adam C. Payne-Dwyer, Alex Plantard, Laure Ali, Rizwan Schroth-Diez, Britta Schütz, Lucas Scott, Ryan T. Seitz, Arne Selchow, Olaf Sharma, Ved P. Spitaler, Martin Srinivasan, Sathya Strambio-De-Castillia, Caterina Taatjes, Douglas Tischer, Christian Jambor, HelenaKlara Fondazione Human Technopole Viale Rita Levi-Montalcini 1 Milano20157 Italy Department of Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Max Planck Institute of Immunobiology and Epigenetics Freiburg79108 Germany Medical BioSciences department Radboud University Medical Centre Nijmegen Netherlands Laboratory for Molecular mechanics of cell adhesions Pontificia Universidad Católica de Chile Santiago Chile Osaka University Graduate School of Engineering Science Japan Euro-BioImaging ERIC Bio-Hub Meyerhofstr. 1 Heidelberg69117 Germany Carl Zeiss AG Carl-Zeiss-Straße 22 Oberkochen73447 Germany BioVoxxel Scientific Image Processing and Analysis Eugen-Roth-Strasse 8 Ludwigshafen67071 Germany Nanophotonics and BioImaging Facility at INL International Iberian Nanotechnology Laboratory 4715-330 Portugal Phi Optics Inc. 1800 S. Oak St Ste 106 ChampaignIL61820 United States Biochemistry Center Heidelberg Heidelberg University Germany Imaging Platform Broad Institute CambridgeMA02142 United States i3S Instituto de Investigação e Inovação Em Saúde and INEB Instituto de Engenharia Biomédica Universidade do Porto Porto Portugal Translational Research Institute Queensland University of Technology 37 Kent Street WoolloongabbaQLD4102 Australia School of Biomedical Sciences Faculty of Health Queensland University of Technology BrisbaneQLD4059 Australia TissueGnostics GmbH Vienna1020 Austria Department of Medical Physics and Biomedical Engineering University of Wisconsin-Madison MadisonWI53706 United States Centre for Cellular Imaging Core Facility Sahlgrenska Academy University of Gothenburg Sweden Allen Institute for Cell Science SeattleWA United States Friedrich Miescher Institute for Biomedical Research Basel Switzerland RNA Therapeutics Institute University of Massachusetts Chan Medical School WorcesterMA01605 United States University of Oklahoma NormanOK United States Advanced Light Microscopy Unit Ce

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications. © 2023, CC BY-SA.

关键词： Image analysis

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GLOBAL CLIMATE

Bulletin of the American Meteorological Society 2024年第8期105卷 S12-S155页

作者： Dunn, R.J.H. Blannin, J. Gobron, N. Miller, J.B. Willett, K.M. Met Office Hadley Centre Exeter United Kingdom European Commission Joint Research Centre Ispra Italy NOAA Global Monitoring Laboratory Boulder Colorado European Centre for Medium-Range Weather Forecasts Reading United Kingdom University of Maryland College Park Maryland European Centre for Medium-Range Weather Forecasts Bonn Germany Department of Meteorology and National Centre for Earth Observation University of Reading Reading United Kingdom Hampton University Hampton Virginia National Research Institute for Agriculture Food and Environment (INRAE) CARRTEL Université Savoie Mont Blanc Thonon les Bains France Graduate School of Agriculture Osaka Metropolitan University Sakai Japan NOAA/NESDIS National Centers for Environmental Information Asheville North Carolina Science Faculty Universidad Nacional de Colombia Bogotá Colombia University of Bremen Bremen Germany Centro de Investigaciones sobre Desertificación – Spanish National Research Council (CSIC-UV-GVA) Valencia Spain Hydro-Climate Extremes Lab (H-CEL) Ghent University Ghent Belgium Laboratoire des Sciences du Climat et de l’Environnement (LSCE) LSCE/IPSL CEA-CNRS-UVSQ Gif-sur-Yvette France Instituto de Geografía Pontificia Universidad Católica de Valparaíso Valparaíso Chile Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia University of Reading Reading United Kingdom Institute Pierre Simon Laplace (IPSL) Guyancourt France School of Engineering Newcastle University Newcastle-upon-Tyne United Kingdom Université Paris Cité Institut de physique du globe de Paris CNRS IGN F-75005 Paris France Univ Gustave Eiffel ENSG IGN F-77455 Marne-la-Vallée France Laboratoire EDYTEM CNRS/Université Savoie Mont-Blanc Le Bourget-du-Lac France Global Modeling and Assimilation Office NASA Goddard Space Flight Center Greenbelt Maryland Universität Hamburg Hamburg Germany Climate Dynamics Lab University of St Andrews St Andrews United Kingdom Ea