Left main coronary artery aneurysm

Left main coronary artery aneurysm is rare finding at coronary angiography. We report a case of a large left main coronary artery aneurysm in a 59-year-old male who had cardiac catheterization for effort angina and inducible myocardial ischemia.     

Simulation of Depth of Wear of Eco-Friendly Concrete Using Machine Learning Based Computational Approaches

To avoid time-consuming, costly, and laborious experimental tests that require skilled personnel, an effort has been made to formulate the depth of wear of fly-ash concrete using a comparative study of machine learning techniques, namely random forest regression (RFR) and gene expression programming (GEP). A widespread database comprising 216 experimental records was constructed from available research. The database includes depth of wear as a response parameter and nine different explanatory variables, i.e., cement content, fly ash, water content, fine and coarse aggregate, plasticizer, air-entraining agent, age of concrete, and time of testing. The performance of the models was judged via statistical metrics. The GEP model gives better performance with R² and ρ equals 0.9667 and 0.0501 respectively and meet with the external validation criterion suggested in the previous literature. The k-fold cross-validation also verifies the accurateness of the model by evaluating R², RSE, MAE, and RMSE. The sensitivity analysis of GEP equation indicated that the time of testing is the influential parameter. The results of this research can help the designers, practitioners, and researchers to quickly estimate the depth of wear of fly-ash concrete thus shortening its ecological susceptibilities that push to sustainable and faster construction from the viewpoint of environmentally friendly waste management.     

High performance and remarkable cyclic stability of a nanostructured RGO–CNT-WO3 supercapacitor electrode

One of the most pressing concerns in today''s power networks is ensuring that consumers (both home and industrial) have access to efficient and long-lasting economic energy. Due to improved power accessibility and high specific capacitance without deterioration over long working times, supercapacitor-based energy storage systems can be a viable solution to this problem. So, here, tungsten trioxide (WO₃) nanocomposites containing reduced graphene oxide and carbon nanotubes i.e. (RGO-WO₃), (CNT-WO₃), and (RGO–CNT-WO₃), as well as pure WO₃ nanostructures as electrode materials, were synthesized using a simple hydrothermal process. The monoclinic phase of WO₃ with high diffraction peaks is visible in X-ray diffraction analysis, indicating good crystallinity of all electrode materials. Nanoflowers of WO₃ were well-decorated on the RGO/CNTs conductive network in SEM micrographs. In a three-electrode system, the specific capacitance of the RGO–CNT-WO₃ electrode is 691.38 F g⁻¹ at 5 mV s⁻¹ and 633.3 F g⁻¹ at 2 A g⁻¹, which is significantly higher than that of pure WO₃ and other binary electrodes. Furthermore, at 2 A g⁻¹, it achieves a coulombic efficiency of 98.4%. After 5000 cycles, RGO–CNT-WO₃ retains 89.09% of its capacitance at 1000 mV s⁻¹, indicating a promising rate capability and good cycling stability performance.

One of the most pressing concerns in today''s power networks is ensuring that consumers (both home and industrial) have access to efficient and long-lasting economic energy.     

Anatomically Anchored Template-Based Level Set Segmentation: Application to Quadriceps Muscles in MR Images from the Osteoarthritis Initiative

In this paper, we present a semi-automated segmentation method for magnetic resonance images of the quadriceps muscles. Our method uses an anatomically anchored, template-based initialization of the level set-based segmentation approach. The method only requires the input of a single point from the user inside the rectus femoris. The templates are quantitatively selected from a set of images based on modes in the patient population, namely, sex and body type. For a given image to be segmented, a template is selected based on the smallest Kullback–Leibler divergence between the histograms of that image and the set of templates. The chosen template is then employed as an initialization for a level set segmentation, which captures individual anatomical variations in the image to be segmented. Images from 103 subjects were analyzed using the developed method. The algorithm was trained on a randomly selected subset of 50 subjects (25 men and 25 women) and tested on the remaining 53 subjects. The performance of the algorithm on the test set was compared against the ground truth using the Zijdenbos similarity index (ZSI). The average ZSI means and standard deviations against two different manual readers were as follows: rectus femoris, 0.78 ± 0.12; vastus intermedius, 0.79 ± 0.10; vastus lateralis, 0.82 ± 0.08; and vastus medialis, 0.69 ± 0.16.