Evidence that elevated blood glucose is a significant risk factor for severe COVID-19
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel coronavirus responsible for the outbreak of the COVID-19 disease. The virus emerged in China in late 2019, with viral transmission reaching pandemic proportions in early 2020. The average mortality rate of COVID-19 is under 2%, as this virus also causes asymptomatic infections in a large proportion of the population. However, in a small percentage of infected individuals, the virus causes symptomatic infections, leading to severe disease, hospitalizations, and deaths. There are also reports of persistent symptoms and long-term sequelae post recovery from COVID-19, which indicates a potentially more profound health crisis.
Professor/Assistant Professor/Associate Professor in Biomedical Engineering job with KHALIFA UNIVERSITY
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Cleaning frequency key to limiting SARS-CoV-2 spread at theme parks
As vaccine rollout begins to reach clinically effective levels in many countries, the question of reopening public entertainment facilities has become a burning one. However, this step must include considered policies on how to safeguard public health as well as open up public spaces.
A new preprint research paper provides some insights into how this may be done using computational modeling. The study, which appeared on the
medRxiv server, deals with the mitigation strategies that must be implemented in order to contain the virus.
Existing protocols include restricting the number of visitors, more frequent and intensive cleaning, the mandatory wearing of masks, the use of personal protective equipment (PPE) by park staff, entry temperature checks, and hand sanitization on a routine basis.
The task of multi-label image recognition is to predict a set of object labels that present in an image. As objects normally co-occur in an image, it is desirable to model label dependencies to improve recognition performance. To capture and explore such important information, we propose Graph Convolutional Networks based models for multi-label recognition, where directed graphs are constructed over classes and information is propagated between classes to learn inter-dependent class-level representations. Following this idea, we design two particular models that approach multi-label classification from different views. In our first model, the prior knowledge about the class dependencies is integrated into classifier learning. Specifically, we propose Classifier-Learning-GCN to map class-level semantic representations (\eg, word embedding) into classifiers that maintain the inter-class topology. In our second model, we decompose the visual representation of an image into a set of label-
Deep learning has gained much traction in application to state-of-charge (SOC) estimation for Li-ion batteries in electric vehicle applications. However, with the vast selection of architectures and hyperparameter combinations, it remains challenging to design an accurate and robust SOC estimation model with a sufficiently low computation cost. Therefore, this study provides a comparative evaluation among commonly used deep learning models from the recurrent, convolutional and feedforward architecture benchmarked on an openly available Li-ion battery dataset. To evaluate model robustness and generalization capability, we train and test models on different drive cycles at various temperatures and compute the RMSE and MAE error metric. To evaluate model computation costs, we run models in real-time and record the model size, floating point operations per second (FLOPS), and run-time duration per datapoint. This study proposes a two-hidden layer stacked gated recurrent unit (GRU) model tr
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