宇宙线缪子散射成像模拟与算法研究

本文验证了基于Micromegas探测器的宇宙线缪子散射成像系统进行快速核材料检测的可行性，并对实验室宇宙线缪子成像系统原型进行参数估算。基于Geant4程序开发了用于模拟宇宙线缪子物理过程、传输径迹及Micromegas探测器响应的模拟程序。在模拟数据的基础上，实现并改进了两种主要的宇宙线缪子散射成像算法。根据模拟和成像结果，1 m×1 m成像系统可在10 min内检测到被重元素屏蔽的核材料。10 cm×10 cm成像系统的缪子事例触发率为0.16 s^-1，要获得较为清晰的成像结果，要求探测器位置分辨率达到300 μm，探测器增益为1 000时实际测量事例至少需要20 h。     

R2D2: A scalable deep learning toolkit for medical imaging segmentation

Deep learning has gained a significant popularity in recent years thanks to its tremendous success across a wide range of relevant fields of applications, including medical image analysis domain in particular. Although convolutional neural networks (CNNs) based medical applications have been providing powerful solutions and revolutionizing medicine, efficiently training of CNNs models is a tedious and challenging task. It is a computationally intensive process taking long time and rare system resources, which represents a significant hindrance to scientific research progress. In order to address this challenge, we propose in this article, R2D2, a scalable intuitive deep learning toolkit for medical imaging semantic segmentation. To the best of our knowledge, the present work is the first that aims to tackle this issue by offering a novel distributed versions of two well-known and widely used CNN segmentation architectures [ie, fully convolutional network (FCN) and U-Net]. We introduce the design and the core building blocks of R2D2. We further present and analyze its experimental evaluation results on two different concrete medical imaging segmentation use cases. R2D2 achieves up to 17.5× and 10.4× speedup than single-node based training of U-Net and FCN, respectively, with a negligible, though still unexpected segmentation accuracy loss. R2D2 offers not only an empirical evidence and investigates in-depth the latest published works but also it facilitates and significantly reduces the effort required by researchers to quickly prototype and easily discover cutting-edge CNN configurations and architectures.     

Experimental investigation and numerical simulation analysis of sintered micro-fluidic devices

ABSTRACT

This study investigates the use of numerical simulations to describe the solid-state diffusion of a sintering stage during a metal injection moulding process for micro-fluidic components with 316L stainless steel powders. Finite element (FE) analysis based on a thermo-elasto-viscoplastic model was conducted to describe the densification process of a stainless steel porous component during solid-state sintering. The numerical analyses, which were performed on a 3D micro-structured component with various powder volume loadings to take into account the thermal debinding effect to propose a full debinding sintering simulation, demonstrated that the FE simulation results are in agreement with the experimental ones.     

基于局部特征的青年女性腿部形态分类

为提高女性裤装的合体性与舒适性,采用三维测量法、图像测量法及手工测量法相结合的方式,对236名18~25岁的青年女性腿部形态进行测量,获取20个相关特征变量;通过主成分因子分析得到影响腿部形态的主要特征因子,通过聚类分析得到青年女性腿部形态的分类结果;最后结合膝曲角的正负归纳出各类腿部形态的辨别规则并进行辨别验证。研究得出:影响腿部形态的主要特征因子有水平围度因子、长度因子、小腿围度差因子、腿部曲度因子、腿部轮廓因子和大腿围度差因子;人体腿部形态可分成圆体型、稍扁体型和扁体型3类;以大腿宽厚比、大腿根围膝围比、大小腿长比及膝曲角4个变量建立腿部形态分类规则。     

Effects of polysaccharides and polyphenolics fractions of Zijuan tea (Camellia sinensis var. kitamura) on α‐glucosidase activity and blood glucose level and glucose tolerance of hyperglycaemic mice

The effects of polysaccharides, theaflavins, thearubigins and theabrownin fractions of Zijuan tea on α‐glucosidase and blood glucose level and intolerance of hyperglycaemic mice were evaluated. The polysaccharides or theaflavins fraction exhibited greater inhibition rate of α‐glucosidase than acarbose positive control, thearubigins fraction or theabrownin fraction. The four fractions were delivered to the treatment mice through oval gavage each day for 15 days. The mice in polysaccharides and theaflavins high‐ and low‐dose and theabrownin high‐dose treatments significantly lowered their blood glucose levels while all the treatment mice gained body weight. The mice in polysaccharides, theaflavins and theabrownin high‐ and low‐dose treatments had greater glucose tolerance as well. Thus, the theaflavins and polysaccharides fractions of Zijuan tea effectively moderated the complications of hyperglycaemic mice. The lower effectiveness of thearubigins and theabrownin fractions may be caused by the highly polymerised polyphenolics which decreased their accessibility to α‐glucosidase and digestibility in mice.     

Physical and thermal properties of l‐lactide/ϵ‐caprolactone copolymers: the role of microstructural design

Understanding the underlying role of microstructural design in polymers allows for the manipulation and control of properties for a wide range of specific applications. As such, this work focuses on the study of microstructure–property relationships in l‐ lactide/?‐caprolactone (LL/CL) copolymers. One‐step and two‐step bulk ring‐opening polymerization (ROP) procedures were employed to synthesize LL/CL copolymers of various compositions and chain microstructures. In the one‐step procedure, LL and CL were simultaneously copolymerized to yield P(LL‐stat‐CL) statistical copolymers. In the two‐step procedure, poly(l‐ lactide) (PLL) and poly(?‐caprolactone) (PCL) prepolymers were synthesized in the first step before CL and LL respectively were added in the second step to yield P[LL‐b‐(CL‐stat‐LL)‐b‐LL] and P[CL‐b‐(LL‐stat‐CL)‐b‐CL] block copolymers as the final products. The findings reveal that, in addition to the copolymerization procedure employed, the length and type of the prepolymer play important roles in determining the chain microstructure and thereby the overall properties of the final copolymer. Moreover, control over the degree of crystallinity and the type of crystalline domains, which is controlled during the polymer chemistry process, heavily influences the physical and mechanical properties of the final polymer. In summary, this work describes an interesting approach to the microstructural design of biodegradable copolymers of LL and CL for potential use in biomedical applications. © 2019 Society of Chemical Industry