Fundamental study for solvent growth of silicon carbide utilizing Fe-Si melt

Published under licence in Journal of Physics: Conference Series by IOP Publishing Ltd

Response of angina and ischemia to long-term treatment in patients with chronic stable angina: A double-blind randomised individualized dosing trial of nifedipine, propranolol and their combination

AbstractSeventy-four patients with chronic stable mild angina, mild coronary artery disease (83% had one- or two-vessel disease) and normal left ventricular function were studied to measure the response of treadmill exercise performance and painful and silent ischemia in the ambulatory setting to randomly assigned treatment with nifedipine or propranolol and their combination; titration to maximal tolerated dosages was performed in doubleblind manner.At 3 months both nifedipine and propranolol reduced the weekly angina rate (p < 0.05); during treadmill exercise testing, increases (p < 0.05) were noted in time to angina and total exercise time and decreases in maximal ST depression at the end of exercise. There were no differences between the responses to nifedipine and propranolol and no significant additional changes were seen after another 3 months of therapy. The combination of nifedipine and propranolol reduced the number of patients with angina on exercise treadmill testing from 64% to 38% (p < 0.05).During ambulatory electrocardiographic monitoring before treatment, there were 1.4 ± 2.4 (mean ± SD) episodes/24 h of painful ischemia and a very low silent ischemia frequency: mean 1.1 ± 2.7 episodes/24 h, mean duration 16 ± 25 min/24 h. Treatment with propranolol and nifedipine resulted in reduction of episodes and duration of painful and painless ischemia; approximately 77% of patients were free of all ischemic episodes.It is concluded that patients with chronic stable mild angina have a low incidence of silent ischemia. Nifedipine or propranolol alone, titrated to individualized maximally tolerated dosages, are equally effective in long-term control of painful and painless ischemia, anginal episodes and exercise-induced ischemia. Combination therapy further reduced only exercise-induced angina and maximal exercise-induced ST depression

Isotopic Niche Differentiation Among Mammals from a Rainforest in Peninsular Malaysia

We performed stable isotope analysis on eight mammalian species: pig-tailed macaque (Macaca nemestrina), long-tailed macaque (M. fascicularis), dusky leaf monkey (Trachypithecus obscurus), brush-tailed porcupine (Atherurus macrourus), wild boar (Sus scrofa), lesser mouse-deer (Tragulus javanicus), greater mouse-deer (T. napu), and barking deer (Muntiacus muntjac), to test the hypothesis that the differences in diet and habitat types among species, guilds and foraging strategies are reflected in the δ15N and δ13C signatures of their tissues. Whereas the isotopic ratios differed among taxa, the four major isotopic groups observed were: mouse-deer species, primate species, brush-tailed porcupine, and wild boar. The brush-tailed porcupine showed the most divergent isotopic signatures, depleted in both δ15N and δ13C, and the wild boar had isotopic signatures enriched in both δ15N and δ13C. Although results are only indicative, the three habitat types occupied by the species were reflected by differences in isotopic signatures, with the ground-dwelling species having the most divergent isotopic values from arboreal and semi-arboreal species. Likewise, among the four different types of dietary lifestyle groups tested, each group showed either significantly different δ15N or δ13C from other groups. Omnivores had the highest isotopic values, and bark-eater/frugivores had the lowest. By increasing the sample sizes both within the species and the number of species in future analyses, this isotopic technique provides opportunity to elucidate the diets of their putative predators in the rainforests of Peninsular Malaysia

Attribute-aware Semantic Segmentation of Road Scenes for Understanding Pedestrian Orientations

Semantic segmentation is an interesting task for many deep learning researchers for scene understanding. However, recognizing details about objects' attributes can be more informative and also helpful for a better scene understanding in intelligent vehicle use cases. This paper introduces a method for simultaneous semantic segmentation and pedestrian attributes recognition. A modified dataset built on top of the Cityscapes dataset is created by adding attribute classes corresponding to pedestrian orientation attributes. The proposed method extends the SegNet model and is trained by using both the original and the attribute-enriched datasets. Based on an experiment, the proposed attribute-aware semantic segmentation approach shows the ability to slightly improve the performance on the Cityscapes dataset, which is capable of expanding its classes in this case through additional data training