Simón Bolívar University

About: Simón Bolívar University is a education organization based out in Caracas, Venezuela. It is known for research contribution in the topics: Population & Crystallization. The organization has 5912 authors who have published 8294 publications receiving 126152 citations.

Towards the development of knee prostheses: review of current researches

Abstract: Purpose – The aim is to set a state‐of‐the‐art in scientific research towards the development of knee prostheses for transfemoral amputees, by reviewing the literature in the field and by identifying different scientific research lines that have brought out through the years. Also, to provide the information about possible outcomes in the near future, and their links to cybernetics, given the present trends in the field.Design/methodology/approach – Literature related to scientific research carried out up‐to‐date in the field of knee prostheses, is reviewed in scientific articles, books and electronic sources. Then, different research lines are identified from the obtained information, and finally classified as presented in this work.Findings – Three scientific research lines regarding the development of knee prostheses were found, each one dealing with: the design of knee prostheses; the performance assessment of these prostheses; and the creation of control strategies for these prostheses which use elec...

Braneworld Stars: Anisotropy Minimally Projected Onto the Brane

Abstract: In the context of the Randall-Sundrum braneworld, an exhaustive and detailed description of the approach based in the minimal anisotropic consequence onto the brane, which has been successfully used to generate exact interior solutions to Einstein’s field equations for static and non-uniform braneworld stars with local and non-local bulk terms, is carefully presented. It is shown that this approach allows the generation of a braneworld version for any known general relativistic solution.

Extending the geometric deformation: New black hole solutions

Abstract: We use the extension of the Minimal Geometric Deformation approach, recently developed to investigate the exterior of a self-gravitating system in the Braneworld, to identified a master solution for the deformation undergone by the radial metric component when time deformations are produced by bulk gravitons. A specific form for the temporal deformation is used to generate a new exterior solution with a tidal charge $Q$. The main feature of this solution is the presence of higher-order terms in the tidal charge, thus generalizing the well known tidally charged solution. The horizon of the black hole lies inside the Schwarzschild radius, $h

Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection.

Abstract: Objective: Atrial fibrillation is a common type of heart rhythm abnormality caused by a problem with the heart's electrical system. Early detection of this disease has important implications for stroke prevention and management. Our objective is to construct an intelligent tool that assists cardiologists in identifying automatically cardiac arrhythmias and noise in electrocardiogram (ECG) recordings. Approach: Our base deep classifier combined a convolutional neural network (CNNs) and a sequence of long short-term memory units, with pooling, dropout and normalization techniques to improve their accuracy. The network predicted a classification at every 18th input sample and the final prediction was selected for classification. Ten standalone models that used our base classifier architecture were first cross-validated separately on 90% of the PhysioNet/CinC Challenge 2017 dataset and then tested on 10%. An ensemble classifier selected the label of the best average probability from the ten sub-models to improve prediction quality. Main results: Our original result submitted to the challenge gave a mean F1-measure of 80%. The new proposed method improved the test score to 82%, which was tied for the third-highest score in the follow-up phase of the challenge. Significance: Without employing a time-consuming feature engineering step, the ensemble classifier trained with this architecture provided a robust solution to the problem of detecting cardiac arrhythmia from noisy ECG signals. In addition, interpretation of the classifier by inspection of its network parameters and predictions revealed what aspects of the ECG signal the classifier considered most discriminating.