170,111 research outputs found

    l'Architettura delle Fortificazioni innovazioni e riuso nelle Città del Mediterraneo, contributi al Seminario Internazionale di studi e alla Mostra documentaria

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    Il Volume raccoglie i "Contributi" multidisciplinari del Seminario internazionale e della Mostra documentaria che ha visto coinvolti con comunicazioni e poster docenti universitari, ricercatori, esperti militari italiani e stranieri sul tema delle opere ossidionali dal Quattrocento all’Ottocento

    The discovery of X-ray diffraction

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    Starting from the discovery of Roentgen’s X-ray (Ro ̈ntgen 1895), I here reconstruct the various attempts carried out to find X-ray diffraction using very thin wedge-shaped slits. These experiments allowed A. Sommerfeld to achieve, at the beginning of 1912, a qualitative estimate of the wavelength of X-rays around 4 9 10-9 cm. This value was at the base, together with the adoption of the space-lattice model for the crystal, of Laue idea of using a crystal as a diffraction grating for X-ray. Then we will discuss the feasibility of the Laue project, as well as the realization of the experiment, and the first results of W. Friedrich and P. Knipping, with the theoretical interpretation given to them by Laue. Laue’s discovery of X-ray diffraction was immediately taken up by William Henry Bragg and re-interpreted by his sonWilliam Lawrence in terms of X-ray reflection by the planes of the crystal, thus becoming, in a few months, a formidable tool for the study of crystal structures, as well as for the precise determination of the wavelengths of X-rays (to Bragg father we owe the construction of the first X-ray ionization spectrometer). Particular attention will be given to the works of C. G. Barkla on X-ray absorption, which resulted in the discovery in 1906 of the Roentgen characteristic radiation of the elements. These works strengthened the hypothesis of the wave nature of X-rays, at the expense of the ‘‘corpuscular hypothesis’’ (supported notably by J. Stark and by W.H. Bragg), and represented a solid reference point for the project of Laue

    Multi-class machine learning detection of Edema, Vocal Paralysis and Vocal Nodules through voice

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    This paper aims to differentiate causes of dysphonia, namely Reinke's Edema, Vocal Cord Paralysis, and Vocal Nodules, also including healthy subjects. A proprietary dataset of 245 subjects underwent acoustic feature extraction and selection, and four classifiers were trained for multi-class classification. Loudness/Energy-related features were among the most effective, which is in line with the fact that the three diseases all cause different impairments in terms of voice volume. Cepstrum is also confirmed as an effective domain. The four classifiers obtained comparable performances, with Random Forest having the highest accuracy at 78.4% and Naïve Bayes offering the best compromise in terms of recall. Healthy subjects always lead to a higher recall, which is in line with the fact that identifying dysphonia is an easier task than differentiating among its causes
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