University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Acceleration of tissue plasminogen activator-mediated thrombolysis by magnetically powered nanomotors.

Abstract: Dose control and effectiveness promotion of tissue plasminogen activator (t-PA) for thrombolysis are vitally important to alleviate serious side effects such as hemorrhage in stroke treatments. In order to increase the effectiveness and reduce the risk of stroke treatment, we use rotating magnetic nanomotors to enhance the mass transport of t-PA molecules at the blood clot interface for local ischemic stroke therapy. The in vitro experiments demonstrate that, when combined with magnetically activated nanomotors, the thrombolysis speed of low-concentration t-PA (50 μg mL–1) can be enhanced up to 2-fold, to the maximum lysis speed at high t-PA concentration. Based on the convection enhanced transport theory due to rotating magnetic nanomotors, a theoretical model is proposed and predicts the experimental results reasonably well. The validity and efficiency of this enhanced treatment has been demonstrated in a rat embolic model.

Short-term wind power prediction based on wavelet transform-support vector machine and statistic characteristics analysis

Abstract: The prediction algorithm is an important key factor in wind power prediction. However, there are pros and cons on different forecasting algorithms. Based on the principles of wavelet transform (WT), support vector machine (SVM) as well as characteristics of wind turbine generation systems, two prediction methods are presented and compared in this paper. In method 1, the time series of model input are decomposed into different frequency composes and models are set up separately based on SVM. The results are combined together to obtain the final wind power output. In method 2, the wavelet kernel function is applied in place of RBF kernel function in SVM training. To supply more valuable suggestions, the means of evaluating prediction algorithm precision is proposed. The operation data from two wind farms both in North China and U.S.A are used to test the usability of the method. The mean relative error of WT-SVM model (method 1) is less than that of traditional SVM model.

Search for high-mass diphoton resonances in pp collisions at √s = 8 TeV with the ATLAS detector

Abstract: Search for high-mass diphoton resonances in pp collisions at pffisffi root s=8 TeV with the ATLAS detector