Physically based modeling approach has been widely developed in recent years for the simulation of dam failure process due to the lack of field data. This paper provides and describes a physically-based model depending on dimensional analysis and hydraulic simulation methods for estimating the maximum water level and the wave propagation time from breaching of field test dams. The field physical model has been constructed in Dabbah city to represent the collapse of the Roseires dam in Sudan. Five cases of a dam failure were studied to simulate water flood conditions by changing initial water height in the reservoir (0.8, 1.0, 1.2, 1.4 and 1.5 m respectively).The physical model working under five cases, case 5 had the greatest influence of the water wave movement in the canal downstream. The results showed the collapse of the dam and the sides of the canal due to the high water levels recorded 277.001, 277.084 and 277.161 m Above Mean Sea Level (AMSL) at section 13 in the canal. The wave vanishing time was 70 minutes from the beginning of the failure at a distance of 590 m downstream. The dam collapse leads to cover the sides of the canal completely, especially at the distance of 371 m.
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The intellectual and religious characteristics were an influential presence in the same Andalusian poet, especially among the poets of Beni El-Ahmar because they are part of the heritage of poets, and that is to push them towards the glory of this heritage and to take care of it and benefit from its inclusion, inspiration and similarity.
That this inflection on the poetic heritage is justified by the poets of the sons of the Red were inclined to preserve the inherited values, especially as it was related to their poetry, especially that the Andalusian poet did not find embarrassment in the inspiration of heritage and emerged when he mentioned the homes and the ruins and the camel and the journey, although the community Andalusian
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In the current worldwide health crisis produced by coronavirus disease (COVID-19), researchers and medical specialists began looking for new ways to tackle the epidemic. According to recent studies, Machine Learning (ML) has been effectively deployed in the health sector. Medical imaging sources (radiography and computed tomography) have aided in the development of artificial intelligence(AI) strategies to tackle the coronavirus outbreak. As a result, a classical machine learning approach for coronavirus detection from Computerized Tomography (CT) images was developed. In this study, the convolutional neural network (CNN) model for feature extraction and support vector machine (SVM) for the classification of axial
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Aerial manipulation of objects has a number of advantages as it is not limited by the morphology of the terrain. One of the main problems of the aerial payload process is the lack of real-time prediction of the interaction between the gripper of the aerial robot and the payload. This paper introduces a digital twin (DT) approach based on impedance control of the aerial payload transmission process. The impedance control technique is implemented to develop the target impedance based on emerging the mass of the payload and the model of the gripper fingers. Tracking the position of the interactional point between the fingers of gripper and payload, inside the impedance control, is achieved using model predictive control (MPD) approach.
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