Ultrahigh molecular weight polyethylene with improved crosslink density,oxidation stability,and microbial inhibition by chemical crosslinking and tea polyphenols for total joint replacements

Highly crosslinked ultrahigh molecular weight polyethylene (UHMWPE) stabilized by vitamin E (VE) is widely applied in artificial joints as the bearings. Despite the approval, there is a discord that VE lowers the crosslinking efficiency, limiting its use at high concentration. In this work, we aim to obtain highly crosslinked and oxidation resistant UHMWPE through the conjunction of tea polyphenol and chemical crosslinking. We hypothesized that highly incorporated tea polyphenol with multiple reactive sites can ameliorate crosslinking efficiency of chemical crosslinked UHMWPE in comparison to VE. Epigallocatechin gallate (EGCG) as representative tea polyphenol was incorporated into UHMWPE at high concentration (2–8 wt%), followed by chemical crosslinking with 2 wt% organic peroxide. Unlike VE/UHMWPE blends as the control, chemical crosslinking achieved an increasing trend in crosslink density of EGCG/UHMWPE blends with increasing antioxidant concentration. High concentration of EGCG also enhanced the oxidation stability of UHMWPE. Intriguingly, EGCG endowed UHMWPE with an excellent antimicrobial property, which was inefficient in VE/UHMWPE. Cell viability was hardly affected by the high loaded antioxidant and peroxide. The chemically crosslinked UHMWPE blended with EGCG is proved to be a reasonable, cost effective and realistic alternative for use in artificial joints.     

基于UML活动图的用例识别

在面向对象的软件开发过程中,统一建模语言(unified modeling language, UML)的用例图用于捕获用户的需求.传统描述用例的方法一般是开发者根据自己的经验,从需求中人工获取用例.然而,如何自动生成准确的用例仍然是一个待解决的问题.本文提出了一种通过用UML活动图半自动生成用例的方法.首先通过引入形式化模型——统一结构来描述用例图与活动图,其次给出分解活动图的算法,然后根据分解活动图得到的依赖链生成对应的用例的事件流,从而得到用例模型,最后通过所开发的原型CASE工具进行案例的演示,验证了本文所提出的方法的可行性。     

基于XML的个性化产品信息建模技术研究

为便于在Internet中构建形式化、易操作、易理解的个性化产品信息模型,通过对产品特征信息结构及零件信息模型的研究,讨论了网络环境下建模语言的特征,提出基于STEP的面向对象描述语言EXPRESS向XML映射的方式来实现个性化产品的信息建模技术,并通过控件接口类的设计来实现信息模型的基本操作.     

A Green Synthesis of Fused Polycyclic 5H-Chromeno[3,2-c]quinoline-6,8(7H,9H)-dione Derivatives Catalyzed by TsOH in Ionic Liquids

A three-component reaction of benzaldehyde, 5,5-dimethyl-3-(arylamino)cyclohex-2-enone and 4-hydroxyquinolin-2(1H)-one gave a series of 3-((4,4-dimethyl-6-oxo-2-(arylamino) cyclohex-1-en-1-yl)(aryl)methyl)-4-hydroxyquinolin-2(1H)-one derivatives in ionic liquids at 80°C under catalyst-free conditions. In the presence of TsOH at 140°C, the same reaction provided an efficient method for the synthesis of 7-aryl-10,10-dimethyl-10,11-dihydro-5H-chromeno[3,2-c] quinoline-6,8(7H,9H)-dione derivatives in high yields while aromatic amine losing unexpectedly.     

Replies to the comments on “Faber series method for plane problems of an arbitrarily shaped inclusion [1]”

This work presents a brief introduction to the Faber polynomials and Faber series in order to reply to the comments made by Prof. Y. Z. Chen on “Faber series method for plane problems of an arbitrarily shaped inclusion (Luo and Gao in Acta Mech 208:133–145, 2009),” and finally, it is shown that his comments and conclusion are hasty and incorrect.     

Grinding Chatter Detection and Identication Based on BEMD and LSSVM

Grinding chatter is a self?induced vibration which is unfavorable to precision machining processes. This paper proposes a forecasting method for grinding state identification based on bivarition empirical mode decomposition(BEMD) and least squares support vector machine(LSSVM), which allows the monitoring of grinding chatter over time. BEMD is a promising technique in signal processing research which involves the decomposition of two?dimen?sional signals into a series of bivarition intrinsic mode functions(BIMFs). BEMD and the extraction criterion of its true BIMFs are investigated by processing a complex?value simulation chatter signal. Then the feature vectors which are employed as an amplification for the chatter premonition are discussed. Furthermore, the methodology is tested and validated by experimental data collected from a CNC guideway grinder KD4020 X16 in Hangzhou Hangji Machine Tool Co., Ltd. The results illustrate that the BEMD is a superior method in terms of processing non?stationary and nonlinear signals. Meanwhile, the peak to peak, real?time standard deviation and instantaneous energy are proven to be e ec?tive feature vectors which reflect the di erent grinding states. Finally, a LSSVM model is established for grinding status classification based on feature vectors, giving a prediction accuracy rate of 96%.     

Geometry-Aware ICP for Scene Reconstruction from RGB-D Camera

The Iterative Closest Point (ICP) scheme has been widely used for the registration of surfaces and point clouds. However, when working on depth image sequences where there are large geometric planes with small (or even without) details, existing ICP algorithms are prone to tangential drifting and erroneous rotational estimations due to input device errors. In this paper, we propose a novel ICP algorithm that aims to overcome such drawbacks, and provides significantly stabler registration estimation for simultaneous localization and mapping (SLAM) tasks on RGB-D camera inputs. In our approach, the tangential drifting and the rotational estimation error are reduced by: 1) updating the conventional Euclidean distance term with the local geometry information, and 2) introducing a new camera stabilization term that prevents improper camera movement in the calculation. Our approach is simple, fast, effective, and is readily integratable with previous ICP algorithms. We test our new method with the TUM RGB-D SLAM dataset on state-of-the-art real-time 3D dense reconstruction platforms, i.e., ElasticFusion and Kintinuous. Experiments show that our new strategy outperforms all previous ones on various RGB-D data sequences under different combinations of registration systems and solutions.

     

面向比特币交易网络的拓扑结构可视探索方法

分析比特币交易网络有助于人们理解交易者在比特币交易中的交易模式.比特币交易网络的匿名性和其巨大的规模使得用户很难在分析前对整个交易网络产生大致的认知.提出了一种基于拓扑结构推荐的比特币交易网络可视分析方法.核心思想是为每个节点生成一个向量化表达,在用户交互的基础上,所提算法即可检测一系列相似的结构.案例分析证明了系统能够支持用户对比特币交易中的交易模式进行探索和分析.     

视频监控场景下基于单视角步态的人体身份及属性识别系统

基于步态的特征识别是一种新兴的生物特征识别技术, 旨在通过人们走路的姿态进行身份和相关属性的分析. 与其他的生物识别技术相比, 基于步态的识别方法具有难隐藏性、非接触性和可远距离使用的优点. 本文设计出一个视频监控场景下基于单视角步态的人体身份及属性识别系统, 该系统通过图像处理方法从复杂的监控视频中实时检测出人体的步态, 经过利用深度学习训练过的算法进行分析后, 获取人体的身份、性别和年龄信息. 实验表明, 系统的身份识别准确率达98.1%, 性别预测准确率达97.1%, 年龄预测平均绝对误差为6.21岁, 实验结果均优于传统基准算法, 且系统开发成本低, 支持实时检测, 能充分满足中小规模步态研究与分析的需要.     

PMMA光子晶体及PMMA/TiO2光子晶体的制备及表征

用无皂乳液聚合法,制备单分散的聚甲基丙烯酸甲酯（PMMA）微球,并使其在重力和表面张力作用下自组装成PMMA光子晶体。用SEM研究其表面形貌,并对其进行光学性质的研究。此外还制备了PMMA／TiO2光子晶体,用AFM表征了其形貌,XRD表征了混合体系的TiO2的晶型。经光学性质研究表明,TiO2纳米粒子提高了光子晶体的折射率比,光子带隙红移。