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The final shape of the gastric sleeve as depicted by radiological studies seems to have an impact on reflux symptoms after laparoscopic sleeve gastrectomy.

Post-operative radiological evaluation by UGI and CT is important for diagnosis and management of complications following LSG. Familiarity with the anatomy of the gastric remnant at UGI is essential for correct image interpretation.

Abstract-A novel frequency-domain technique for image blocking artifact detection and reduction is presented in this paper. The algorithm first detects the regions of the image which present visible blocking artifacts. This detection is performed in the frequency domain and uses the estimated relative quantization error calculated when the discrete cosine transform (DCT) coefficients are modeled by a Laplacian probability function. Then, for each block affected by blocking artifacts, its dc and ac coefficients are recalculated for artifact reduction. To achieve this, a closed-form representation of the optimal correction of the DCT coefficients is produced by minimizing a novel enhanced form of the mean squared difference of slope for every frequency separately. This correction of each DCT coefficient depends on the eight neighboring coefficients in the subband-like representation of the DCT transform and is constrained by the quantization upper and lower bound. Experimental results illustrating the performance of the proposed method are presented and evaluated.

Compression tests between 1250 and 1550°C and 10 À5 and 5 • 10 À3 s À1 and transmission electron microscopy have been employed to investigate the high temperature mechanical properties and the deformation mechanisms of the C15 Cr 2 Nb Laves phase. The stresspeaks in the compression curves during yielding were explained using a mechanism similar to strain aging combined with a low initial density of mobile dislocations. The primary deformation mechanism is slip by extended dislocations with Burgers vector 1/2AE1 1 0ae, whereas twinning is more frequent at 10 À4 s À1. Schmid factor analysis indicated that twinning is more probable in grains oriented so as to have two co-planar twinning systems with high and comparable resolved shear stresses. Twinning produced very anisotropic microstructures. This may be due to synchroshear: a self-pinning mechanism which requires cooperative motion of zonal dislocations.

In this paper we address the problem of recognising the Broad-leaved dock (Rumex obtusifolius L.) in grasslands from high-resolution 2D images. We discuss and present the determining factors for developing and implementing weed visual recognition algorithms using deep learning. This analysis, leads to the formulation of the proposed algorithm. Our implementation exploits Transfer Learning techniques for deep learning-based feature extraction, in combination with a classifier for weed recognition. A prototype robotic platform has been used to make available an image dataset from a dairy farm containing broad-leaved docks. The evaluation of the proposed algorithm on this dataset shows that it outperforms competing weed/plant recognition methods in recognition accuracy, while producing low false-positive rates under real-world operation conditions.

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In this paper, a software-based