We present a simultaneous dual-wavelength phase-imaging digital holographic technique demonstrated on porous coal samples. The use of two wavelengths enables us to increase the axial range at which the unambiguous phase imaging can be performed, but also increases the noise. We employ a noise reduction "fine map" algorithm, which uses the two-wavelength phase map as a guide to correct a single-wavelength phase image. Then, the resulting noise of a fine map is reduced to the level of single-wavelength noise. A comparison to software unwrapping is also presented. A simple way of correcting a curvature mismatch between the reference and the object beams is offered.
The present objectives of universalization of the academic and research formation for earning scientific degrees in the health care sector are added to the universalization processes of the Cuban medical education. At the same time, they respond to the need of this sector to give solution to the health problems with the help of scientific methods. The current scenarios of the primary and secondary care are real teaching and research settings that allow attaining this goal of the medical university. Universalization of PhD. formation in the health care area is encouraged by the political willingness of the Cuban state, the Commander-in-Chief´s thinking, who has stated that we are called upon to reach high performance in health services, which has been shown by health professionals in the country and in all the internationalist missions. This paper presented the Ministry of Public Health strategy with a view to expanding the formation of PhDs. and showed the results of one of the areas, that is, PhD. in health sciences as an example of the work that is being carried out throughout the country.
Q fever is an uncommon zoonotic rickettsial disease with no exanthem or specific cutaneous lesions. Only nonspecific cutaneous involvement has been reported to date. A 69-year-old Spanish woman with chronic myelogenous leukaemia developed fever and two subcutaneous nodules. The patient complained of extreme pain. Biopsy revealed a granulomatous lobular panniculitis with a characteristic 'fibrin ring' or 'doughnut' appearance: fibrin and inflammatory cells arranged around a central clear space. Changes of membranous lipodystrophy were also found. Q fever serological studies were positive. Our patient had panniculitis with singular histopathological features. These histopathological changes have been described in liver and bone marrow of patients with Q fever. To the best of our knowledge, this cutaneous involvement due to Q fever has not previously been described in the literature.
Quantifying the stress field induced into a piece when it is loaded is important for engineering areas since it allows the possibility to characterize mechanical behaviors and fails caused by stress. For this task, digital photoelasticity has been highlighted by its visual capability of representing the stress information through images with isochromatic fringe patterns. Unfortunately, demodulating such fringes remains a complicated process that, in some cases, depends on several acquisitions, e.g., pixel-by-pixel comparisons, dynamic conditions of load applications, inconsistence corrections, dependence of users, fringe unwrapping processes, etc. Under these drawbacks and taking advantage of the power results reported on deep learning, such as the fringe unwrapping process, this paper develops a deep convolutional neural network for recovering the stress field wrapped into color fringe patterns acquired through digital photoelasticity studies. Our model relies on an untrained convolutional neural network to accurately demodulate the stress maps by inputting only one single photoelasticity image. We demonstrate that the proposed method faithfully recovers the stress field of complex fringe distributions on simulated images with an averaged performance of 92.41% according to the SSIM metric. With this, experimental cases of a disk and ring under compression were evaluated, achieving an averaged performance of 85% in the SSIM metric. These results, on the one hand, are in concordance with new tendencies in the optic community to deal with complicated problems through machine-learning strategies; on the other hand, it creates a new perspective in digital photoelasticity toward demodulating the stress field for a wider quantity of fringe distributions by requiring one single acquisition.
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