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The lithium-ion secondary battery is manufactured using an electrode active material which absorbs and releases lithium ions, a binder which binds the electrode active material together, and a conductive aid (collectively referred to as “a positive electrode”) and a conductive aid (collectively referred to as “a negative electrode”).
In order to improve battery performance, such as battery capacity, output voltage, and the like, there has been conventionally proposed a positive electrode including a positive electrode mixture that mainly contains a positive electrode active material such as lithium cobaltate, lithium nickelate, or the like, and a binder. Meanwhile, there has been conventionally known a method in which a negative electrode mixture including a negative electrode active material is impregnated in a binder layer on a negative electrode base material to form a positive electrode mixture, and a solvent and the like are then added, for example, to form a slurry.
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Feb 2, 2018. a new, fully portable app (Mac OSX/ Windows/ Linux) that lets users easily .[Correlation of the wall thickness with the degree of stenosis in obstructive atherosclerotic lesions of the carotid artery].
Thickening of the intima of the carotid artery has a correlation with the degree of stenosis. This is a well-known fact, but it is important to determine this correlation in the different stenosis degrees. It is very important, because when the intima thins, the chance of future complications increases. By studying 57 patients with carotid artery stenosis, we were able to observe intima wall thickenings that were statistically associated with different degree of stenosis. The intima of the patients with stenosis of 70 to 99% was statistically thicker than that in the patients with stenosis #ifdef USE_CUDNN
#include

#include “caffe/layers/cudnn_conv_layer.hpp”

namespace caffe {

template
void CuDNNConvolutionLayer::Forward_gpu(
const vector*>& bottom, const vector*>& top) {
const Dtype* bottom_data = bottom[0]->gpu_data();
Dtype* top_data = top[0]->mutable_gpu_data();
CUDNN_CHECK(cudnnConvolutionForward(handle_,
bottom[0]->desc(), bottom_data,
filter_desc_, num_output_,
bottom[1]->desc(), top_data,
pad_desc_, stride_,
weight_desc_,
bias_desc_,

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