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Representative and very uneven texturally bricks having yellow/beige or pale or dark red colors from the Renaissance walls (16th century) of Padua, Northeast Italy, were studied by means of colorimetric, petrographic (MOP), chemical (XRF), mineralogical (PXRD) and microstructural analysis (FESEM-EDS). Starting from the color measurements of the ceramic bodies, the manufacturing technologies and their influence on the physical behavior and durability of the bricks were established. The porous system was characterized by means of hygric tests and mercury intrusion porosimetry; the compactness and structural anisotropy were defined through ultrasound velocity; the uniaxial compressive strength was determined; and durability to salt crystallization and frost action of the bricks was assessed. Mg- and Ca-rich illitic clays fired at temperatures ≥900 °C were used to manufacture the beige hue bodies, while the pale red bricks were made out with Ca- and Fe-rich illitic clays fired at 850–900 °C. A lower carbonate content on the base clays and a lower firing temperature were the main causes responsible for the changing colors from beige to red hue. The increase of the red color was associated to higher silicate inclusions content and lower development of reaction rims around grains. The low sintering degree achieved yielded highly porous bodies with diverse porous systems, leading to differential physical performance and durability of the bricks that may turn out beneficial for the conservation of the historic walls. [ABSTRACT FROM AUTHOR]

Physical function is a patient-oriented indicator and can be considered a proxy for the assignment of healthcare personnel. The study aims to create an algorithm that classifies patients into homogeneous groups according to physical function. A two-step machine-learning algorithm was applied to administrative data recorded between 2015 and 2018 at the University Hospital of Padova. A clustering-large-applications (CLARA) algorithm was used to partition patients into homogeneous groups. Then, machine learning technique (MLT) classifiers were used to categorize the doubtful records. Based on the results of the CLARA algorithm, records were divided into three groups according to the Barthel index: <45, >65, ≥45 and ≤65. The support vector machine was the MLT showing the best performance among doubtful records, reaching an accuracy of 66%. The two-step algorithm, since it splits patients into low and high resource consumption, could be a useful tool for organizing healthcare personnel allocation according to the patients' assistance needs. [ABSTRACT FROM AUTHOR]