Atomically precise gold nanoclusters for healthcare applications

The potential application of gold nanoparticles(GNPs)in biomedicine has been extensively reported.However,there is still too much puzzle about their real face and potential health risks in comparison with the commercial drug molecules.The emergence of atomically precise gold nanoclusters(APGNCs)provides the opportunity to address the puzzle due to their ultrasmall size,defined molecular formula,editable surface engineering,available structures and unique physicochemical properties including excellent biocompatibility,strong luminescence,enzyme-like activity and efficient renal clearance,et al.Recently,these advantages of APGNCs also endow them promising performances in healthcare such as bioimaging,drug delivery,antibacterial and cancer therapy.Especially,their clear composition and structures like the commercial drug molecules facilitate the study of their functions and the structure-activity relationship in healthcare,which is essential for the guided design of APGNC nanomedicine.Therefore,this review will focus the advantages and recent progress of APGNCs in health care and envision their prospects for the future.

聚四氟乙烯微粉对改性氟橡胶摩擦磨损性能的影响

通过加入聚四氟乙烯(PTFE)微粉对氟橡胶进行改性,采用力学试验和环-块摩擦磨损试验研究了不同含量的聚四氟乙烯对氟橡胶的力学性能和摩擦磨损性能的影响,使用扫描电子显微镜分析了磨损机制,并结合力学性能讨论了摩擦磨损性能变化的原因。结果表明,随着PTFE含量增加,硫化胶的拉伸强度降低但撕裂强度升高;PTFE质量含量在8%以下的改性氟橡胶摩擦磨损性能得以改善,体积磨损率和摩擦系数均小于未改性氟橡胶;5%的PTFE改性氟橡胶耐磨性最好,10%的PTFE改性氟橡胶摩擦系数最小。

Preparation and properties of dental zirconia ceramics

Y2O3-stabilized tetragonal zirconia polycrystalline （Y-TZP） ceramics with high-performance were prepared for dental ap- plication by use of the micro-emulsion and two-step sintering method. The crystal phase, morphology, and microstructure of the reaction products were characterized by X-ray diffraction （XRD）, scanning electron microscopy （SEM）, and transmission electron mi- croscopy （TEM）. XRD results show that the ceramics mainly consist of tetragonal zirconia. Physical and mechanical properties test results show that the bending strength, fracture toughness, and the density of full sintered Y-TZP ceramics are 1150 MPa, 5.53 MPa.m1/2, and 6.08 g/cm3, respectively, which suggest that the material is relatively suitable for dental restoration. The dental base crown machined with this material by CAD/CAM system exhibits a verisimilitude configuration and the material＇s expansion coefficient well matches that of the glaze. These results further indicate that the product can be used as a promising new ceramic material to fabricate dental base crowns and bridges.

Automated classification and detection of multiple pavement distress images based on deep learning

To achieve automatic,fast,efficient and high-precision pavement distress classification and detection,road surface distress image classification and detection models based on deep learning are trained.First,a pavement distress image dataset is built,including 9017pictures with distress,and 9620 pictures without distress.These pictures were captured from 4 asphalt highways of 3 provinces in China.In each pavement distress image,there exists one or more types of distress,including alligator crack,longitudinal crack,block crack,transverse crack,pothole and patch.The distresses are labeled by a rectangle bounding box on the pictures.Then ResNet networks and VGG networks are used respectively as binary classification models for distressed and non-distressed imagines classification,and as multi-label classification models for six types of distress classification.Training techniques,such as data augmentation,batch normalization,dropout,momentum,weight decay,transfer learning,and discriminative learning rate are used in training the model.Among the 4 CNNs considered in this study,namely ResNet 34 and 50,and VGG 16 and 19,for the binary classification,ResNet 50 has the highest Accuracy of 96.243%,Precision of 95.183%,and ResNet 34 has the highest Recall of 97.824%,and F2 score of 97.052%.For multi-label classification,ResNet 50 has the best performance,with the highest Accuracy of 90.257%,higher than 90%required by the Chinese standard(JTG H20-2018)for road distresses detection,F2 score-82.231%,and Precision-76.509%,and ResNet34 has the highest Recall of 87.32%.To locate and quantify the distress areas in the images,the single shot multibox detector(SSD)model is developed,in which the ResNet 50 is used as the base network to extract features.When the intersection over union(IoU)is set to 0,0.25,0.50,0.75,the mean average precision(mAP)of the model are found to be 74.881%,50.511%,28.432%,3.969%,respectively.

Recent Progress in Fiber-Optic Hydrophones

Fiber-optic hydrophone (FOH) is a significant type of acoustic sensor, which can be used in both military and civilian fields such as underwater target detection, oil and natural gas prospecting, and earthquake inspection. The recent progress of FOH is introduced from five aspects, including large-scale FOH array, very-low-frequency detection, fiber-optic vector hydrophone (FOVH), towed linear array, and deep-sea and long-haul transmission. The above five aspects indicate the future development trends in the FOH research field, and they also provide a guideline for the practical applications of FOH as well as its array.

Attention based simplified deep residual network for citywide crowd flows prediction

Crowd flows prediction is an important problem of urban computing whose goal is to predict the number of incoming and outgoing people of regions in the future.In practice,emergency applications often require less training time.However,there is a little work on how to obtain good prediction performance with less training time.In this paper,we propose a simplified deep residual network for our problem.By using the simplified deep residual network,we can obtain not only less training time but also competitive prediction performance compared with the existing similar method.Moreover,we adopt the spatio-temporal attention mechanism to further improve the simplified deep residual network with reasonable additional time cost.Based on the real datasets,we construct a series of experiments compared with the existing methods.The experimental results confirm the efficiency of our proposed methods.