Crack Propagation Simulation Tool

In the massively engineered world that exists today, understanding material behavior is of paramount importance in caring for human safety in design. Molecular dynamic simulations on crack propagation through materials allow visualization of material behavior under stress. The tool, developed by the nanoHUB group as a part of the Network for Computational Nanotechnology at Purdue University, makes performing such simulations accessible to undergraduate students, highly qualified researchers, and all those in between. First, the input deck for the simulation parameters was simplified from the complex, language-specific code into a simple, user-friendly Graphic User Interface (GUI). Several interesting example cases were run through using the GUI and developed to help the user understand the functionality of the tool. The output of the GUI was developed to allow the user to have both numerical and visual depictions of what occurred. The resulting tool allows for a step-by-step walkthrough of generating the case in situations where the user may be unfamiliar with the required code. The user can manipulate the parameters to fit their individual needs in regards to size and strain rate for example. The tool can be used as instructional material in classes such as materials science and validation material for the varied clientele that exists. This nanoHUB tool will contribute to educating future engineers and scientists in materials behavior. Furthermore, it provides engineers and scientists a simple process to model and validate their ongoing projects and research

Bivalirudin during thrombolysis with catheter‐directed tPA in a heparin‐refractory patient: A case report

Venous thromboembolism has increasing significance in hospitalized pediatric patients. Patients who have life‐threatening or limb‐threatening thrombotic events require thrombolysis in addition to anticoagulation. In patients who show signs of heparin resistance or heparin‐induced thrombocytopenia, it is imperative to identify alternative therapeutic options. We present a child in whom bivalirudin was used for systemic anticoagulation during catheter‐directed thrombolysis along with tissue plasminogen activator (Alteplase®) for the treatment of a near‐occlusive organ‐threatening thrombus. We also review the currently available literature on the use of combination therapy of an intravenous direct thrombin inhibitor with alteplase.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152619/1/pbc28094_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/152619/2/pbc28094.pd

XNet: A convolutional neural network (CNN) implementation for medical X-Ray image segmentation suitable for small datasets

X-Ray image enhancement, along with many other medical image processing applications, requires the segmentation of images into bone, soft tissue, and open beam regions. We apply a machine learning approach to this problem, presenting an end-to-end solution which results in robust and efficient inference. Since medical institutions frequently do not have the resources to process and label the large quantity of X-Ray images usually needed for neural network training, we design an end-to-end solution for small datasets, while achieving state-of-the-art results. Our implementation produces an overall accuracy of 92%, F1 score of 0.92, and an AUC of 0.98, surpassing classical image processing techniques, such as clustering and entropy based methods, while improving upon the output of existing neural networks used for segmentation in non-medical contexts. The code used for this project is available online.Comment: 11 pages, 5 figures, 2 table