Research of thoracolumbar spine lateral vascular anatomy and imaging

This study introduces an anatomical basis for surgeries such as thoracoscopeassisted thoracolumbar spinal anterior interbody fusion in terms of image observing and corpse specimen anatomising. The observation of the 3-dimensional computed tomography (CT) image indicates that segmental arteries are visible and run in the central supersulcus of the corresponding vertebral body’s side, while the branches are invisible. The distances between adjacent segmental arteries in T10/11, T11/12, T12/L1, L1/2, and L2/3 are 23.35 ± 1.48, 25.61 ± 2.08, 29.12 ± 2.30, 32.53 ± 2.18, and 33.73 ± 2.29 (mm), respectively. And the observation by the thoracolumbar spine side of the adult corpse specimens shows that segmental arteries and veins constantly exist and run in the central supersulcus of the corresponding vertebral body’s side; each segmental artery has some small branches; the zone between the upper and lower segmental arteries form a relatively non-vascular nerve safe zone, where the intervertebral space (disc) locates. The distances between adjacent segmental arteries in T10/11, T11/12, T12/L1,L1/2,L2/3 are 23.34 ± 0.78, 25.54 ± 0.85, 29.11 ± 1.01, 32.82 ± ± 1.28, and 33.71 ± 1.42 (mm), respectively. The safe zone, with the intervertebral disc as the reference mark, can provide enough operation space for surgeries like thoracoscope-assisted anterior interbody fusion and reducing damage to blood vessels as well as surgical complications. Additionally, the arrangement and distribution of segmental arteries can be clearly displayed on the 3-dimensional CT image and the result is basically consistent with that of corpse specimens. Therefore, the 3-dimensional CT image can be regarded as the reference for video-assisted thoracoscopic surgery plans. (Folia Morphol 2010; 69, 3: 128-133

A surface defect detection method of steel plate based on YOLOV3

At present, the steel plate surface defect detection technology based on machine vision and convolutional neural network (CNN) has achieved good results. However, these models are mostly two-stage methods, extracting features first and then classifying them, which is slow and inaccurate. Therefore, this paper proposes a single-stage surface defect detection method of steel plate based on yolov3, which can classify defects, determine the location of defects, and greatly improve the detection speed. It is of great significance to realize the automation of cold rolling production line. The experiment shows that the detection speed of this model reaches 62 fps and the accuracy reaches 73 %, which has a good prospect in industry

Effect of Volume of Heat Sink on Process and Physical Properties of Parts Built by Welding Based SFF

A new numerical simulation of the effect of the volume of the heat sink on the welding–based deposition process is performed. For this purpose, the ANSYS parametric design language (APDL) is applied. Due to the complex internal and/or external shapes of the designed threedimensional (3D) part, different heat transfer conditions are met during the building process. The influences of the different heat transfer conditions on the physical part properties are also investigated. The influence of the volume of the heat sink on the process and on the physical properties is significant and can not be neglected. Extensive experiments are designed and executed in order to verify the conclusions derived from the finite elements model results and to investigate the material properties of the built part.Mechanical Engineerin

A surface defect detection method of steel plate based on YOLOV3

At present, the steel plate surface defect detection technology based on machine vision and convolutional neural network (CNN) has achieved good results. However, these models are mostly two-stage methods, extracting features first and then classifying them, which is slow and inaccurate. Therefore, this paper proposes a single-stage surface defect detection method of steel plate based on yolov3, which can classify defects, determine the location of defects, and greatly improve the detection speed. It is of great significance to realize the automation of cold rolling production line. The experiment shows that the detection speed of this model reaches 62 fps and the accuracy reaches 73 %, which has a good prospect in industry

Incremental association rule mining based on matrix compression for edge computing

A growing amount of data is being generated, communicated and processed at the edge nodes of cloud systems; this has the potential to improve response times and thus reduce communication bandwidth. We found that traditional static association rule mining cannot solve certain real-world problems with dynamically changing data. Incremental association rule mining algorithms have been studied. This paper combines the fast update pruning (FUP) algorithm with a compressed Boolean matrix and proposes a new incremental association rule mining algorithm, named the FUP algorithm based on a compression matrix (FBCM). This algorithm requires only a single scan of both the database and incremental databases, establishes two compressible Boolean matrices, and applies association rule mining to those matrices. The FBCM algorithm effectively improves the computational efficiency of incremental association rule mining and hence is suitable for knowledge discovery in the edge nodes of cloud systems

A REEVALUATION OF THE GROWTH DECLINE IN PINE IN GEORGIA, AND IN GEORGIA-ALABAMA COMBINED

Using an improved testing procedure based on bootstrap and weighted jack-knife confidence intervals with the same model as used in Bechtold et al. (1991) and Ruark et al. (1991), analysis in this paper generally confirm the results of a significant decrease in growth rate in pine in Georgia and Alabama for 1972 - 1982 (5th cycle) relative to 1961 - 1972 (4th cycle) discussed in these papers

Design Principles for High-Capacity Mn-Based Cation-Disordered Rocksalt Cathodes

Mn-based Li-excess cation-disordered rocksalt (DRX) oxyfluorides are promising candidates for next-generation rechargeable battery cathodes owing to their large energy densities, the earth abundance, and low cost of Mn. In this work, we synthesized and electrochemically tested four representative compositions in the Li-Mn-O-F DRX chemical space with various Li and F content. While all compositions achieve higher than 200 mAh g−1 initial capacity and good cyclability, we show that the Li-site distribution plays a more important role than the metal-redox capacity in determining the initial capacity, whereas the metal-redox capacity is more closely related to the cyclability of the materials. We apply these insights and generate a capacity map of the Li-Mn-O-F chemical space, LixMn2-xO2-yFy (1.167 ≤ x ≤ 1.333, 0 ≤ y ≤ 0.667), which predicts both accessible Li capacity and Mn-redox capacity. This map allows the design of compounds that balance high capacity with good cyclability