Among the various metal oxides, SnO2 has been most widely exploited as a semiconductor gas sensor for its excellent functionalities. Models illustrating the sensing mechanism of SnO2 have been proposed and tested to explain experimentally derived "power laws". The models, however, are often based on somewhat simplistic assumptions; for instance, the net charge transfer from an adsorbate to a sensor surface site is assumed to occur only by integer values independent of the crystallographic planes. In this work, we use layer-shaped SnO2 crystallites with one nanodimension (1ND-crystallites) as NO2 gas sensing elements under flat band conditions, and derive appropriate "power laws" by combining the dynamics of gas molecules on the sensor surface with a depletion theory of semiconductor. Our experimentally measured sensor response as a function of NO2 concentration when compared with the theoretically derived power law indicates that sensing occurs primarily through the chemisorption of single NO2 molecules at oxygen vacancy sites on the sensor surface.

There has been a significant increase from 2010 to 2016 in the number of people suffering from spine problems. The automatic image segmentation of the spine obtained from a computed tomography (CT) image is important for diagnosing spine conditions and for performing surgery with computer-assisted surgery systems. The spine has a complex anatomy that consists of 33 vertebrae, 23 intervertebral disks, the spinal cord, and connecting ribs. As a result, the spinal surgeon is faced with the challenge of needing a robust algorithm to segment and create a model of the spine. In this study, we developed an automatic segmentation method to segment the spine, and we compared our segmentation results with reference segmentations obtained by experts. We developed a fully automatic approach for spine segmentation from CT based on a hybrid method. This method combines the convolutional neural network (CNN) and fully convolutional network (FCN), and utilizes class redundancy as a soft constraint to greatly improve the segmentation results. The proposed method was found to significantly enhance the accuracy of the segmentation results and the system processing time. Our comparison was based on 12 measurements: the Dice coefficient (94%), Jaccard index (93%), volumetric similarity (96%), sensitivity (97%), specificity (99%), precision (over segmentation; 8.3 and under segmentation 2.6), accuracy (99%), Matthews correlation coefficient (0.93), mean surface distance (0.16 mm), Hausdorff distance (7.4 mm), and global consistency error (0.02). We experimented with CT images from 32 patients, and the experimental results demonstrated the efficiency of the proposed method.

Surgeons should be aware of the re-increase in anterior capsular volume or restretching trait of the anterior capsule over time, even after successful arthroscopic Bankart repair and capsular shift. In this study, women, elite athletes, and those with frequent dislocations were at high risk of capsular restretching. An increase in capsular volume was related to redislocation and positive apprehension sign as well as with Rowe score.