Shear wall are used in tall buildings as supporting element to resist earthquake loading. The RC shear wall is the most reliable method of construction of shear wall which makes the structure resistant against lateral forces. Shear wall systems are one of the most commonly used lateral load resisting systems in high rise buildings. A study on an high rise building with shear wall and without shear wall at different locations was studied to understand the lateral loads and shear effects.in this paper six models are analysed with and without shear wall at different location, one at the corners, one at the periphery and center core of the building through ETABS software. Estimation of structural response such as; base shear, storey displacement, storey acceleration and storey drift is carried out. In equivalent static load method and response spectrum method is used.
Situs Inversus Totalis is a rare congenital anomaly characterized by transposition of thoracic and abdominal viscera. Its anaesthetic plans & implications have not been thoroughly discussed. It poses a magnitude of problems to the anaesthesiologist in terms of airway management, endotracheal intubation, lung separation during thoracic surgeries, providing ventilation, placement of ECG leads and electrodes during defibrillation. So we report the problems encountered in handling the case of 4 days old baby diagnosed as Situs Inversus Totalis with cardiac abnormalities scheduled for duodenal atresia corrective surgery. The surgery was abandoned due to single ventricle which is not compatible with life.
These days, a basic appraisal effort in clinical affiliations biometrics is finding careful biomarkers that permit settling on clinical choice assistance instruments. Parkinson ’s disease (PD) is a chronic and progressive illness that affects hundreds of thousands of people worldwide. Although it is quite easy to identify someone affected by PD when the illness shows itself (e.g. tremors, slowness of movement and freezing-of-gait).Most works have focused on studying the working mechanism and detection of the disease in its very early stages. In such cases, drugs can be administered in order to increase the quality of life of the patients. Since the beginning, it is well-known that PD patients feature the micrography, which is related to muscle rigidity and tremors. As such, most of the approaches to detect Parkinson’s Disease make use of handwritten assessment tools, where the individual is asked to perform some predefined tasks, such as drawing spirals and meanders on a template paper. Later, an expert analyses the drawings in order to classify the progressive of the disease. In this work, we are interested into aiding physicians in such task by means of machine learning techniques, which can learn proper information from digitized versions of the exams, and them recommending a probability of a given individual being affected by PD depending on its handwritten skills. Particularly, we are interested in deep learning techniques (i.e. Convolutional Neural Networks) due to their ability into learning features without human interaction.
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