Repositorium für Publikationen und Forschungsdaten

Suchen Sie in der Research Collection der ETH Zürich nach wissenschaftlichen Publikationen und Forschungsdaten oder laden Sie selbst eigenen Forschungsoutput hoch. Weiterlesen

Aktuell 

Vorankündigung: Open Science Call der ETH Domain

Die ETH Zürich stellt zusammen mit der EPF Lausanne, sowie mit den Forschungsanstalten EMPA, PSI, EAWAG und WSL bis zu Fr. 10 Mio. für die Lancierung eines Förderprogramms für Open Research Data (ORD) bereit. Für November 2021 ist im Rahmen dieses Programmes eine erste Ausschreibung zur Unterstützung von ORD Praktiken der Forschenden bis 2024 vorgesehen. Weiterlesen

Forschungsdatenmanagement und verwandte Themen: Know-how für Ihr Forschungsprojekt

Lernen Sie in der Workshop-Reihe der ETH-Bibliothek in Kooperation mit den Scientific IT Services der ETH Zürich die vielseitigen Teilbereiche des Forschungsdatenmanagements kennen. Weiterlesen

Neu: Autorennamen in der Research Collection und Personensuche

Mitarbeitende der ETH Zürich hinterlegen neu ihren bevorzugten Autorennamen inklusive Varianten in ihrem ETH-Profil. So werden Verfasser bei der Publikationsrecherche vollumfänglich berücksichtigt. Weiterlesen

Neueste Publikationen 

  1. Iron homeostasis during anemia of inflammation: a prospective study of patients with tuberculosis 

    Cercamondi C.I.; Stoffel N.U.; Moretti D.; et al. (2021)
    Blood : journal of the American Society of Hematology
    Anemia of inflammation is a hallmark of tuberculosis. Factors controlling iron metabolism during anemia of inflammation and its resolution are uncertain. Whether iron supplements should be given during antituberculosis treatment to support hemoglobin (Hb) recovery is unclear. Before and during treatment of tuberculosis, we assessed iron kinetics, as well as changes in inflammation and iron metabolism indices. In a 26-week prospective ...
    Journal Article
  2. Aerosol-cloud interactions: The representation of heterogeneous ice activation in cloud models 

    Kärcher B.; Marcolli C. (2021)
    Atmospheric Chemistry and Physics
    The homogeneous nucleation of ice in supercooled liquid-water clouds is characterized by time-dependent freezing rates. By contrast, water phase transitions induced heterogeneously by ice-nucleating particles (INPs) are described by time-independent ice-active fractions depending on ice supersaturation (s). Laboratory studies report ice-active particle number fractions (AFs) that are cumulative in s. Cloud models budget INP and ice crystal ...
    Journal Article
  3. Berezinskii-Kosterlitz-Thouless Phase Transitions with Long-Range Couplings 

    Giachetti G.; Defenu N.; Ruffo S.; et al. (2021)
    Physical Review Letters
    The Berezinskii-Kosterlitz-Thouless (BKT) transition is the paradigmatic example of a topological phase transition without symmetry breaking, where a quasiordered phase, characterized by a power-law scaling of the correlation functions at low temperature, is disrupted by the proliferation of topological excitations above the critical temperature TBKT. In this Letter, we consider the effect of long-range decaying couplings ∼r-2-σ on the ...
    Journal Article
  4. Three-port series-resonant dc/dc converter for automotive charging applications 

    Schäfer J.; Kolar J.W. (2021)
    Electronics
    In the energy distribution grid of electric vehicles (EVs), multiple different voltage potentials need to be interconnected, to allow arbitrary power flow between the various energy sources and the different electrical loads. However, between the different potentials, galvanic isolation is absolutely necessary, either due to safety reasons and/or due to different grounding schemes. This paper presents an isolated three-port DC/DC converter ...
    Journal Article
  5. Optimized observable readout from single-shot images of ultracold atoms via machine learning 

    Lode A.U.J.; Lin R.; Büttner M.; et al. (2021)
    Physical Review A
    Single-shot images are the standard readout of experiments with ultracold atoms, the imperfect reflection of their many-body physics. The efficient extraction of observables from single-shot images is thus crucial. Here we demonstrate how artificial neural networks can optimize this extraction. In contrast to standard averaging approaches, machine learning allows both one- and two-particle densities to be accurately obtained from a ...
    Journal Article

Mehr anzeigen