Study and characterization of the ancient bricks of monastery of “San Filippo di Fragalà” in Frazzanò (Sicily). Cardiano P., Ioppolo S., De Stefano C., Pettignano A., Sergi S., Piraino, P. (ed.) Cmentarz Salwatora: pierwsza nekropolia wrocławskich protestantów, Wratislavia Antiqua: studia z dziejów Wrocławia/Zespół do Badań Średniowiecznego i Nowożytnego Wrocławia, 21, 197–213, Uniwersytet Wrocławski, Instytut Archeologii, Wrocław. ‘Porównawcze badania pomiarowe cegieł z kościoła Salwatora we Wrocławiu’ Wachowski, K. Brick masonry identification in a complex historic building, the Main College of the University of Alcalá, Madrid (Spain). Barluenga G., Estirado F., Undurraga R., Conde J.F., Agua F., Villegas M. Thus, our work proves that a detailed mineralogical and petrological study may help to identify different construction phases in historic monuments. The observed variability corresponds well to the different constructing phases, identified previously on the basis of archaeological work. Only small differences have been recognized in: 1) clay to aplastic material ratio, 2) amount of accessory minerals, 3) grain-size distribution of aplastic materials, but no significant changes in the brick technology were observed. The results indicate, that low-calcareous raw materials were used, presumably Miocene-Pliocene ‘flamy clays’, exploited a few kilometres away from the castle and tempered with locally obtained sand from the Odra river. Most of the bricks were fired under oxidising conditions, occasionally over a sequence of oxidising and reducing steps, resulting in a sandwich structure. The combined results of these methods suggest that the firing temperature ranges from 950☌, through the most common temperatures of 850–900☌, to the infrequent temperatures below 750☌. This level of accuracy for framboids suggests that less-distinct rock components may require larger training sets to provide satisfactory accuracy.The historic bricks from the ducal castle on Ostrów Tumski (Wrocław), one of the first brickwork structures in the Lower Silesia, which dates back to the XII and XIII century, were studied and characterised by a combination of classical petrographic studies (polarising microscopy), scanning microscopy, thermal analysis and X-ray diffraction.
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We assess the accuracy of the CNN algorithm by comparing the equivalent framboid diameter calculated by the CNN methods to those measured through manual tracing and find greater than 95% accuracy. Significantly, we find that the CNN algorithm can detect up to ~ 95 % of the total number of framboids traced manually. The results of the CNN algorithm were compared to manually traced results using JMicroVision. Among 128 images, 33 were tested and 95 were used for training. A total of ~ 1800 framboids from 128 scanning electron microscope (SEM) images were characterized to delineate the changes in water column oxygenation during the deposition of the Marcellus Shale. Specifically, we use several architectures, including Inception, Resnet, and their modified CNN architectures. We leverage deep learning techniques such as convolutional neural networks (CNNs) to replace manual tracing and to reduce significantly the time for object detection and image segmentation. In this work, we implement deep learning techniques to characterize framboids from 14 samples across depth in the Marcellus Shale (Devonian). In back-scattered electron images and in EDS elemental mapping pyrite framboids stand out in sharp contrast to surrounding silicate and carbonate minerals, making them an interesting test case for machine learning because of the relative ease of their discrimination from other components. Determination of framboid size distributions by manual tracing, is slow, laborious, and prone to human error. Framboid size distributions are established during early diagenesis, are preserved through advanced stages of diagenesis, and are used to infer depositional redox conditions. Pyrite framboids (FeS 2) occur in sediments of all geological ages and are characterized by an external spheroidal form and an internal discrete, equant microcrystalline architecture.
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