Steady Characteristics of the Water-Lubricated Conical Bearings

The water-lubricated conical bearing has attracted attentions of researchers for its simple structure, easily adjusted gap （fdm thickness ）, lower friction loss, and less pollution in application. A mathematic model with consideration of the effects of turbulence, two-phase flow, and temperature on the pressure field at bearing surface is proposed here. Using this model, the Reynolds＇ equation and energy equation are solved in which the thermo- physical properties of the water as lubricant are taken into account. The dependency of characteristics of bearing, such as load-earrying capacity, flow rate （pumping losses ）, and frictional losses, on angular velocity, conical angle, and radial eccentricities, is presented. The research results are beneficial to the improvement of the efficiency of conical bearing and the environmental protection.

From bench to bedside: Fecal calprotectin in inflammatory bowel diseases clinical setting

Fecal calprotectin(FC) has emerged as one of the most useful tools for clinical management of inflammatory bowel diseases(IBD). Many different methods of assessment have been developed and different cutoffs have been suggested for different clinical settings. We carried out a comprehensive literature review of the most relevant FC-related topics: the role of FC in discriminating between IBD and irritable bowel syndrome(IBS) and its use in managing IBD patients In patients with intestinal symptoms, due to the high negative predictive value a normal FC level reliably rules out active IBD. In IBD patients a correlation with both mucosal healing and histology was found, and there is increasing evidence that FC assessment can be helpful in monitoring disease activity and response to therapy as well as in predicting relapse, post-operative recurrence or pouchitis. Recently, its use in the context of a treat-to-target approach led to a better outcome than clinically-based therapy adjustment in patients with early Crohn's disease. In conclusion, FC measurement represents a cheap, safe and reliable test, easy to perform and with a good reproducibility. The main concerns are still related to the choice of the optimal cut-off, both for differentiating IBD from IBS, and for the management of IBD patients.

Pedestrian and Vehicle Detection Based on Pruning YOLOv4 with Cloud-Edge Collaboration

Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.

Fundus Lesion Detection Based on Visual Attention Model

Reliable detection of fundus lesion is important for automated screening of diabetic retinopathy. This paper presents a novel method to detect the fundus lesion in retinal fundus image based on a visual attention model. The proposed method intends to model the visual attention mechanism of ophthalmologists during observing fundus images. That is, the abnormal structures, such as the dark and bright lesions in the image, usually attract the most attention of experts, however, the normal structures, such as optic disc and vessels, have been usually selectively ignored. To measure the visual attention for abnormal and normal areas, the incremental coding length is computed in local and global manner respectively. The final saliency map of fundus lesion is a fusion of attention maps computed for the abnormal and normal areas. Experimental results conducted on the publicly DiaRetDB1 dataset show that the proposed method achieved a sensitivity of 0.71 at a specificity of 0.82 and an AUC of 0.76 for fundus lesion detection, and achieved an accuracy of 100% for normal area (optic disc) detection. The proposed method can assist the ophthalmologists in the inspection of fundus lesion.

Automated Artery-Vein Classification in Fundus Color Images

The estimation of Arterio-Venous ratio (AVR) is an important phase in diagnosing various vascular diseases e.g. Diabetic Retinopathy. For calculating this value, it is essential to differentiate the vessels into arteries and veins. This paper presents a novel structural and automated method for artery/vein vessels classification in retinal images. Our method is tested on DRIVE database and the classification accuracy is 88.7 % for pixels and 89.07 % for vessel lines, respectively, which demonstrate the effectives of our approach. Our method will help to achieve the fundus disease surveillance on mobile and remote medical treatment. It has a remarkable social significance.

Modeling the defect distribution and degradation of CdTe ultrathin films

The defect distribution across an ultrathin film CdTe layer of a CdS/CdTe solar cell is modelled by solving the balance equation in steady state. The degradation of the device parameters due to the induced defects during ion implantation is considered where the degradation rate is accelerated if the defect distribution is considerable.The defect concentration is maximum at the surface of the CdTe layer where implantation is applied and it is minimum at the junction with the CdS layer. It shows that ultrathin devices degrade faster if the defect concentration is high at the junction rather than the back region（CdTe/Metal）. Since the front and back contacts of the device are close in ultrathin films and the electric field is strong to drive the defects into the junction, the p-doping process might be precisely controlled during ion implantation. The modeling results presented here are in agreement with the few available experimental reports in literature about the degradation and defect configuration of the ultrathin CdTe films.