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The goal of the present study was to develop and evaluate machine learning algorithms for the prediction of blood transfusion donation. The machine learning algorithms studied included multilayer perceptrons (MLPs) and support vector machines (SVMs). The methods were evaluated retrospectively in a group of 600 patients and validated prospectively in a group of 148 patients. We reach a sensitivity of 65.8% and a specificity of 78.2% in the prospective group. This discrimination is very interesting because it could allow to propose to the patients, classified as non-donators, to give their blood in the future. Furthermore, the blood transfusion donation VCI corpus used, has been processed in a different manner than the initial marketing one. Therefore, this recent corpus could give a new training set for testing and improving machine learning methods in the future.

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Lebanon is one of the countries which are at high risk of experiencing rock falls. In order to ensure public safety, engineers must take into consideration this risk. In the past years, numerous researches were conducted on the behavior of horizontal structural elements, slabs, of different types under dynamic impact load. Reinforced concrete flat slabs are commonly used slabs in residential buildings. To build a profound understanding of the structural behavior of the slabs under such loadings, it is important to investigate the effect of energy dissipation on the equivalent impact force, mid-span deflection and damage pattern. In this study a sample reinforced concrete slab of 500 x 1000 x 100 mm dimensions is considered. The aim of this paper is to find how these factors vary with the increase in energy as the drop load resembling the real rock fall is left to drop freely from different heights 0.6 m and 1 m.