University of New Mexico

About: University of New Mexico is a education organization based out in Albuquerque, New Mexico, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 28870 authors who have published 64767 publications receiving 2578371 citations. The organization is also known as: UNM & Universitatis Novus Mexico.

Ionic polymer–metal composites: III. Modeling and simulation as biomimetic sensors, actuators, transducers, and artificial muscles

Abstract: This paper, the third in a series of four review papers to appear in this journal, presents a number of descriptions of various modeling and simulation techniques and, briefly, the associated experimental results in connection with ionic polymer–metal composites and, in general, ionic polymer–conductor composites, as soft biomimetic distributed sensors, actuators, transducers, and artificial muscles.

Within the lack of chest COVID-19 X-ray dataset: A novel detection model based on GAN and deep transfer learning

Abstract: The coronavirus (COVID-19) pandemic is putting healthcare systems across the world under unprecedented and increasing pressure according to the World Health Organization (WHO). With the advances in computer algorithms and especially Artificial Intelligence, the detection of this type of virus in the early stages will help in fast recovery and help in releasing the pressure off healthcare systems. In this paper, a GAN with deep transfer learning for coronavirus detection in chest X-ray images is presented. The lack of datasets for COVID-19 especially in chest X-rays images is the main motivation of this scientific study. The main idea is to collect all the possible images for COVID-19 that exists until the writing of this research and use the GAN network to generate more images to help in the detection of this virus from the available X-rays images with the highest accuracy possible. The dataset used in this research was collected from different sources and it is available for researchers to download and use it. The number of images in the collected dataset is 307 images for four different types of classes. The classes are the COVID-19, normal, pneumonia bacterial, and pneumonia virus. Three deep transfer models are selected in this research for investigation. The models are the Alexnet, Googlenet, and Restnet18. Those models are selected for investigation through this research as it contains a small number of layers on their architectures, this will result in reducing the complexity, the consumed memory and the execution time for the proposed model. Three case scenarios are tested through the paper, the first scenario includes four classes from the dataset, while the second scenario includes 3 classes and the third scenario includes two classes. All the scenarios include the COVID-19 class as it is the main target of this research to be detected. In the first scenario, the Googlenet is selected to be the main deep transfer model as it achieves 80.6% in testing accuracy. In the second scenario, the Alexnet is selected to be the main deep transfer model as it achieves 85.2% in testing accuracy, while in the third scenario which includes two classes (COVID-19, and normal), Googlenet is selected to be the main deep transfer model as it achieves 100% in testing accuracy and 99.9% in the validation accuracy. All the performance measurement strengthens the obtained results through the research.

Are Overweight Children Unhappy?: Body Mass Index, Depressive Symptoms, and Overweight Concerns in Elementary School Children

Abstract: Background: It is commonly believed that overweight children are unhappy with their weight. However, population-based data addressing this association are lacking. Objectives: To evaluate the association between obesity and depressive symptoms in a diverse, school-based sample of preadolescent children, and to examine whether overweight concerns play a role in this association. Design, Setting, and Participants: Third-grade students (N=868, mean age, 8.4 years) attending 13 public elementary schools in Northern California were measured for weight and height, and were asked to complete self-report assessments of depressive symptoms and overweight concerns. Results: A modest association between depressive symptoms and body mass index (BMI; calculated as weight in kilograms divided by the square of height in meters) was found for girls (r=0.14, P,.01), but not for boys (r=0.01, P,.78). Among girls, depressive symptoms were strongly associated with overweight concerns (r=0.32, P,.001). After controlling for level of overweight concerns, BMI was no longer significantly associated with depressive symptoms among girls. In contrast, after controlling for BMI, overweight concerns remained significantly associated with depressive symptoms. Conclusions: This study provides cross-sectional evidence for a relationship between depressive symptoms and BMI in preadolescent girls, but not in preadolescent boys. This relationship seems to be explained by an excess of overweight concerns. Assessing overweight concerns may be a useful method to identify those overweight girls who are at highest risk for associated depressive symptoms. Arch Pediatr Adolesc Med. 2000;154:931-935

Survey of robust control for rigid robots

Abstract: Current approaches to the robust control of the motion of rigid robots are surveyed, and the available literature is summarized. The five major design approaches discussed are the linear-multivariable approach, the passivity approach, the variable-structure approach, the saturation approach, and the robust-adaptive approach. Some guidelines for choosing a method are offered. >