Fabrication,characterization and evaluation of the effect of PLGA and PLGA–PEG biomaterials on the proliferation and neurogenesis potential of human neural SH-SY5Y cells

In recent years with regard to the development of nanotechnology and neural stem cell discovery, the combinatorial therapeutic strategies of neural progenitor cells and appropriate biomaterials have raised the hope for brain regeneration following neurological disorders. This study aimed to explore the proliferation and neurogenic effect of PLGA and PLGA–PEG nanofibers on human SH-SY5Y cells in in vitro condition. Nanofibers of PLGA and PLGA–PEG biomaterials were synthesized and fabricated using electrospinning method. Physicochemical features were examined using HNMR, FT-IR, and water contact angle assays. Ultrastructural morphology, the orientation of nanofibers, cell distribution and attachment were visualized by SEM imaging. Cell survival and proliferation rate were measured. Differentiation capacity was monitored by immunofluorescence staining of Map-2. HNMR, FT-IR assays confirmed the integration of PEG to PLGA backbone. Water contact angel assay showed increasing surface hydrophilicity in PLGA–PEG biomaterial compared to the PLGA substrate. SEM analysis revealed the reduction of PLGA–PEG nanofibers' diameter compared to the PLGA group. Cell attachment was observed in both groups while PLGA–PEG had a superior effect in the promotion of survival rate compared to other groups (p < .05). Compared to the PLGA group, PLGA–PEG increased the number of Ki67⁺ cells (p < .01). PLGA–PEG biomaterial induced neural maturation by increasing protein Map-2 compared to the PLGA scaffold in a three-dimensional culture system. According to our data, structural modification of PLGA with PEG could enhance orientated differentiation and the dynamic growth of neural cells.     

面向快速拆装的模块化机器人柔顺控制方法

在进行快速拆装任务时,模块化机器人既要有较高的伺服刚度与结构强度,又要对接触物体表现出一定的顺从特性,为此,提出一种面向快速拆装的模块化机器人柔顺控制方法。分析机器人拆装时的正逆向运动学,获取末端执行器与基座间的总坐标转换形式,以及对应关节的旋转角度,调整模块力的反馈作用,令阻抗与导纳趋于动态均衡。基于机器人与外界环境间作用力,建立模块阻抗关系,推算柔顺控制规律,利用设计的自适应柔顺控制模型,取得期望控制力,令模块到达期望位置。实验测试中,使机器人模块随实验人员手部移动,跟随运动轨迹表明,其能够较好地适应手部给予的未知力,各方向上的跟随速度与作用力拟合情况以及零件实际装配结果较为优越,具备良好的控制稳定性,且满足快速拆装时的柔顺控制基本需求。     

Machine learning for post-traumatic stress disorder identification utilizing resting-state functional magnetic resonance imaging

Early detection of post-traumatic stress disorder (PTSD) is essential for proper treatment of the patients to recover from this disorder. The aligned purpose of this study was to investigate the performance deviations in regions of interest (ROI) of PTSD than the healthy brain regions, to assess interregional functional connectivity and applications of machine learning techniques to identify PTSD and healthy control using resting-state functional magnetic resonance imaging (rs-fMRI). The rs-fMRI data of 10 ROI was extracted from 14 approved PTSD subjects and 14 healthy controls. The rs-fMRI data of the selected ROI were used in ANOVA to measure performance level and Pearson's correlation to investigate the interregional functional connectivity in PTSD brains. In machine learning approaches, the logistic regression, K-nearest neighbor (KNN), support vector machine (SVM) with linear, radial basis function, and polynomial kernels were used to classify the PTSD and control subjects. The performance level in brain regions of PTSD deviated as compared to the regions in the healthy brain. In addition, significant positive or negative functional connectivity was observed among ROI in PTSD brains. The rs-fMRI data have been distributed in training, validation, and testing group for maturity, implementation of machine learning techniques. The KNN and SVM with radial basis function kernel were outperformed for classification among other methods with high accuracies (96.6%, 94.8%, 98.5%) and (93.7%, 95.2%, 99.2%) to train, validate, and test datasets, respectively. The study's findings may provide a guideline to observe performance and functional connectivity of the brain regions in PTSD and to discriminate PTSD subject using only the suggested algorithms.     

Review of human–robot coordination control for rehabilitation based on motor function evaluation

As a wearable and intelligent system, a lower limb exoskeleton rehabilitation robot can provide auxiliary rehabilitation training for patients with lower limb walking impairment/loss and address the existing problem of insufficient medical resources. One of the main elements of such a human–robot coupling system is a control system to ensure human–robot coordination. This review aims to summarise the development of human–robot coordination control and the associated research achievements and provide insight into the research challenges in promoting innovative design in such control systems. The patients’ functional disorders and clinical rehabilitation needs regarding lower limbs are analysed in detail, forming the basis for the human–robot coordination of lower limb rehabilitation robots. Then, human–robot coordination is discussed in terms of three aspects: modelling, perception and control. Based on the reviewed research, the demand for robotic rehabilitation, modelling for human–robot coupling systems with new structures and assessment methods with different etiologies based on multi-mode sensors are discussed in detail, suggesting development directions of human–robot coordination and providing a reference for relevant research.     

基于改进遗传算法的清洁移动机械臂运动学逆解

以研制的塔式太阳能热发电站定日镜表面清洁作业的车载清洁移动机械臂为研究对象,建立运动学模型并分析其逆解集。采用遗传算法求解车载清洁移动机械臂逆解,在解决多解问题时,利用“多移动小关节,少移动大关节”的原则进行约束,并求出最优解,对机械臂的轨迹规划提供基础。实验证明该文采用的算法能得到可行的最优解,并使得关节1的移动最小,提高清洁机械臂的控制度,满足实际清洁要求。     

白龙江中上游径流变化及其对气候和人类活动的响应

河川径流是水资源重要组成部分,径流变化归因分析对于流域水资源安全及管理具有重要意义。基于白龙江中上游1960~2015年水文气象资料,采用多种统计学方法、基于Budyko假设的弹性分析法研究径流变化特征及归因分析。结果表明,径流呈显著下降趋势,线性倾向变化率为-2.69×10⁸m³/10a,其临界突变发生在1986年,呈先上升后下降的趋势;径流量分布集中,6~10月占比63.59%,夏秋两季占比70.62%;径流减少受气候变化和人类活动影响,双累积曲线法影响率分别为33.02%、66.98%,累积量斜率变化率法影响率分别为44.17%、55.83%,弹性分析法影响率分别为35%~39%、61%~65%,可见人类活动为径流减少的主要影响因素,同时,弹性分析法计算结果介于其他两种方法计算结果之间,最为可靠。     

基于蒙特卡罗法的星载太阳辐照度光谱仪对日指向误差分析

为保证光谱仪能够获取到有效太阳光谱数据,要求搭载光谱仪的二维转台能够长时间高精度跟踪太阳,而跟踪太阳的前提是转台实现精准指向,使太阳进入导行镜的有效视场。根据从卫星接收的太阳矢量和姿态数据对安装误差进行修正,计算修正后转台指向的参考值,并确保指向误差小于1°。在转台各个基准立方镜上建立坐标系,采用坐标变换法建立了对日指向的数学模型,通过运动学反解给出转台调整角度与轨道坐标系下太阳矢量的解析关系。然后,结合光谱仪的在轨运动形式,对其工作过程中的主要误差来源进行分析,并在MatLab平台上搭建基于蒙特卡罗法的指向误差模型。仿真结果表明指向误差优于0.35°。最后,在地面开展太阳指向模拟实验,测得春分轨道下指向误差低于0.16°,满足导行镜捕获视场需求。实验结果验证了数学模型的正确性及有效性,同时为光谱仪在轨工作提供了设计参考。     

洪涝灾害人口损失研究进展

洪涝灾害经常造成大量的人员死亡,不仅给人们心理上造成巨大创伤,还给生产生活带来严重的破坏,成为对社会影响最为严重的自然灾害之一。随着民生水利的提出,洪涝灾害中人口死亡研究受到各级水行政主管部门的关注。现详细介绍了洪涝灾害中影响人员损失的主要影响因子(即水深、流速、水位上涨速率、水温、疏散撤离和救援等)方面的国内外研究进展,以及溃坝洪水、沿海风暴潮、海啸和江河洪水等4种主要洪涝灾害类型中人口损失的估算方法。最后结合我国灾害人口损失研究的现状,对人口损失研究的发展趋势进行了展望。     

机器人运动学逆解及奇异和多解的处理

针对后3个关节轴线相交于一点的6R工业机器人,提出一种有别于传统方法的位姿分离逆解算法,对逆解涉及的奇异和多解处理也做了详细分析,并仿真验证了该算法的正确性。该算法完全避免了矩阵求逆的运算,因此比一般的解析算法更加简单高效,便于实时控制。     

Kinematics of the trunk in sitting posture: An analysis based on the instantaneous axis of rotation

This paper presents a new approach for analysing trunk kinematics in sitting posture based on the characterisation of thorax and pelvis motion by means of ranges of motion and instantaneous axes of rotation (IAR). These variables are estimated from videophotogrammetric data. An experiment was carried out in order to analyse three motions associated with the flexion–extension movement: the absolute motions of the pelvis and thorax and the relative motion between the thorax and pelvis. The results obtained suggest a sequential activation of lumbar vertebrae in the flexion–extension motion. On the other hand, the location of the pelvis IAR shows that the movement of the pelvis on the seat is not just a rolling motion but a rolling with some level of sliding. Finally, the location of the IAR in the thorax-pelvis relative motion shows a mismatch between the trunk IAR and the backrest axis of rotation in several office chairs. The proposed technique provides a new approach for the kinematic analysis of sitting posture. The results can be applied to the improvement of biomechanical models of seated posture as well as to define some design criteria of work seats based on the fit between the trunk and backrest movements.