1,721,093 research outputs found
Specific loss power of magnetic nanoparticles: A machine learning approach
Full text linkA machine learning approach has been applied to the prediction of magnetic hysteresis properties (coercive field, magnetic remanence, and hysteresis loop area) of magnetic nanoparticles for hyperthermia applications. Trained on a dataset compiled from numerical simulations, a neural network and a random forest were used to predict power losses of nanoparticles as a function of their intrinsic properties (saturation, anisotropy, and size) and mutual magnetic interactions, as well as of application conditions (temperature, frequency, and applied field magnitude), for values of the parameters not represented in the database. The predictive ability of the studied machine learning approaches can provide a valuable tool toward the application of magnetic hyperthermia as a precision medicine therapy tailored to the patient's needs. (C) 2022 Author(s)
Effect of thermal treatment on high-frequency magneto-impedance in ferromagnetic/Cu/ferromagnetic trilayers
No full textSpin reorientation transition in amorphous FeBSi thin films submitted to thermal treatments
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Morphology and magnetic structure of sputtered Co80Cr20 thin film antidot patterns obtained by Electron Beam Lithography
No full textEnhancement and Correlation of MFM Images: Effect of the Tip on the Magnetic Configuration of Patterned Co Thin Films
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Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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