Influence of protein and lipid concentration of the test lubricant on the wear of ultra high molecular weight polyethylene

To gain a better understanding of the ultra-high molecular weight polyethylene (UHMWPE) wear mechanism in the physiological environment, the effects of protein and lipid constituents of synovial fluid on the specific wear rate of UHMWPE were examined experimentally. The multidirectional sliding pin-on-plate wear tester was employed to simulate the simplified sliding condition of hip joint prostheses. Bovine serum γ-globulin and synthetic l--DPPC were used as model protein and lipid constituents of synovia, respectively. Results of the wear test indicated that the UHMWPE wear rate primarily depended on the protein concentration of the test lubricant. Lipids acted as a boundary lubricant and reduced polyethylene wear in the low protein lubricants. However, the polyethylene wear rate increased with increasing lipid concentrations if the protein concentration was within the physiological level. Increased interactions between protein and lipid molecules and lipid diffusion to polyethylene surface might be responsible for the increased wear.     

基于GWO-SVM的下肢假肢穿戴者骑行相位识别

针对下肢假肢穿戴者骑行相位识别的问题,提出基于灰狼算法优化的支持向量机(GWO-SVM)分类模型. 建立下肢多源信息系统,采集膝关节、踝关节的加速度信号以及膝关节角度信号. 应用奇异值分解,对采集到的信号进行降噪处理. 在对信号进行降噪处理之后,为了避免单一信号不确定的影响,从数据冗余角度,选取各信号的特征点,开展归一化处理,组成多维特征向量,作为SVM分类模型的输入. 为了能够进一步提高分类精度,加强全局优化能力,利用GWO算法对核参数进行优化. 通过与PSO-SVM分类模型、GA-SVM分类模型对比表明,基于GWO优化的SVM分类模型对骑行相位的识别率为94%,高于其他方法优化的SVM分类模型.     

电脑辅助钛合金人工髋关节锻件的开发

人工股骨柄是人工髋关节组件之重要分件 ,又是影响手术后人工髋关节使用寿命的重要因素 .由于人工股骨柄截面形状及细长比差异很大 ,为考虑钛合金锻造特性及省料、预成形之设计相当重要 ;本文针对钛合金人工髋关节股骨柄锻造工艺之设计要项 ,应用电脑辅助设计及锻造工艺模拟解析技术于锻坯和模具设计 ,期能缩短开发时程并降低开发成本     

Chemical properties of femoral gland secretions in the desert iguana,Dipsosaurus dorsalis

This study investigates the chemistry of femoral gland secretions in the desert iguana,Dipsosaurus dorsalis (Lacertilia: Iguanidae), and discusses their possible functional significance. Electrophoretic and proton NMR studies indicated that the secretions are composed of approximately 80 % protein and 20% lipid material. Individual differences in polyacrylamide gel banding patterns of femoral gland proteins were found. Reflectance spectroscopy revealed that the secretions strongly absorb longwave ultraviolet light, a feature that may be important in the localization of secretion deposits in the environment.     

口腔修复用Y-TZP陶瓷材料制备工艺研究

采用国产3Y-TZP亚微米粉体，通过双向模压结合等静压成形有效克服了条形试样的烧结弯曲，探讨了24h排胶工艺对预烧试样力学性能的影响，通过对预烧试样热膨胀曲线的测定、不同烧结温度试样力学性能和密度的比较及XRD定量分析确定了适宜的烧结工艺。结果表明，牙科支架用陶瓷，将预烧试样升至1550℃保温0．5h，排胶工艺对预烧试样的抗弯强度和断裂韧性并无显著影响，对于即可获得足够的抗弯强度，提高其断裂韧性。     

Preparation for femur prosthesis of ceramic-metal combination artificial hip joint

1　ＩＮＴＲＯＤＵＣＴＩＯＮＴｈｅａｒｔｉｆｉｃｉａｌｈｉｐｊｏｉｎｔｆｅｍｕｒｓｕｓｅｄｉｎｃｌｉｎｉｃａｌａｔｐｒｅｓｅｎｔａｒｅｍａｄｅｏｆｍｅｔａｌａｎｄａｌｌｏｙ ,ｗｈｉｃｈｍａｉｎｌｙｉｎ ｃｌｕｄｅｓｔａｉｎｌｅｓｓｓｔｅｅｌ,Ｔｉａｌｌｏｙａ     

一种高性能人造视网膜微刺激器的设计

微刺激器是人造视网膜系统的核心组成部分,其设计面临诸多挑战.设计了一个驱动4×4微电极阵列的人造视网膜微刺激器,通过采用内部计数和内部RAM策略,将传输数据量减小到1/16.通过采用宽摆幅共源共栅结构使微刺激器的输出阻抗和输出电压摆幅分别达到2～4兆欧姆和90%以上,提高了其生物环境适应性.通过引入电荷平衡机制,减小了失配引起的电荷积累,提高了人造视网膜长期工作的安全性.仿真结果验证了该微刺激器方案的正确性.     

基于OpenGL的三维牙颌模型可视化研究

为了实现牙颌模型的三维数据可视化,通过计算机逼真的显示牙列形态,并进行交互测量及变形等操作。在口腔修复体计算机辅助设计中,以OpenGL为渲染工具,结合自主设计的牙颌模型三维激光扫描仪获取的点云数据,采用基于B样条曲面的三维重建技术,以Visual C＋＋为开发工具实现数字化牙颌模型的三维可视化。以Visual C＋＋和OpenGL为工具,减少了程序设计难度,提高了执行效率,图形显示更加逼真流畅。本系统的设计满足了口腔医生的需求,给患者带来了便利,并促进了口腔修复体的开发制作。     

Comparison of three-dimensional surface scanning techniques for capturing the external ear

Congenital facial anomalies, such as microtia (malformation of the external ear), lead to significant psychosocial effects starting from early childhood. Three-dimensional (3D) scanning and advanced manufacturing are being investigated as a cheaper and more personalised method of fabricating reconstructive treatments for patients compared to traditional approaches. To date, most case studies have used expensive 3D scanners, yet, there is potential for low-cost devices to provide comparable results. This study aimed to investigate these different approaches. Both ears of 16 adult participants were scanned with three devices: Artec Spider (Artec Group), Intel^® RealSense? (Intel), and the Apple iPhone^® 7 (Apple Inc.) combined with photogrammetry using 90, 60 and 30 photographs. The scanning time, processing time, accuracy, completeness, resolution and repeatability of each technique were assessed using the Artec Spider as a reference scanner. Our results show that the iPhone had the longest processing time however, this decreased nine-fold when reducing the number of photos from 90 to 30. There was no significant difference in the accuracy, completeness or repeatability of the iPhone scans with 90 photographs (1.4?±?0.6?mm, 79.9%, 1.0?±?0.1?mm), 60 photographs (1.2?±?0.2, 79.3%, 0.9?±?0.2?mm) or 30 photographs (1.2?±?0.3?mm, 74.3%, 1.0?±?0.2?mm). The Intel RealSesne performed significantly worse in each parameter (1.8?±?03?mm; 46.6%, 1.4?±?0.5). Additionally, the RealSense had significantly lower resolution with not enough detail captured for the application. These results demonstrate that the ear can be accurately 3D scanned using iPhone photographs. We would recommend capturing between 30 and 60 photographs of the ear to create a scan that is accurate but without the downfall of long processing time. Using these methods we aim to provide a more comfortable setting for the patient and a lower-cost and more personalised ear prosthesis.     

Differentiating types of muscle movements using a wavelet based fuzzy clustering neural network

Abstract: The electromyographic signals observed at the surface of the skin are the sum of many small action potentials generated in the muscle fibres. After the signals are processed, they can be used as a control source of multifunction prostheses. The myoelectric signals are represented by wavelet transform model parameters. For this purpose, four different arm movements (elbow extension, elbow flexion, wrist supination and wrist pronation) are considered in studying muscle contraction. Wavelet parameters of myoelectric signals received from the muscles for these different movements were used as features to classify the electromyographic signals in a fuzzy clustering neural network classifier model. After 1000 iterations, the average recognition percentage of the test was found to be 97.67% with clustering into 10 features. The fuzzy clustering neural network programming language was developed using Pascal under Delphi.