Porous and self-strengthened poly(ε-caprolactone)/calcium sulfate hemihydrate composite fibers for bone regeneration

Porous polycaprolactone (PCL)/calcium sulfate hemihydrate (CSH) composite fibers with different compositions were fabricated via electrospinning by using chloroform/dimethyl sulfoxide (volume ratio = 8:2) as the co-solvent. It was found that the incorporation of fine CSH powders greatly improved the morphology of the PCL fibers and generated continuous, bead-free PCL/CSH composite fibers with the pore diameter varying from 0.2 to 1.1 μm when 20% CSH was added. As compared with the PCL fibers, the PCL/CSH composite fibers also exhibited enhanced thermal stability and hydrophilicity. The tensile strength and elastic modulus of the PCL/CSH fibrous membranes first increased and then decreased with the increase of the CSH content, while their ductility exhibited a sustained decrease. Moreover, after immersion in water, the PCL-20%CSH composite fibers presented a much higher tensile strength than the pristine ones probably due to the self-setting of the CSH powders through the porous structure. In addition, the PCL-20%CSH composite fibers displayed a strong ability to form bone-like apatite after immersion in simulated body fluid for 3 days, which suggests their potential applications in bone tissue regeneration as a novel type of substitute.     

基于改进人工免疫方法的混合模糊聚类算法

为了解决传统模糊聚类算法对初始值敏感、目标函数易陷入局部极小值等问题,将模糊核聚类方法与人工免疫算法相结合,提出了一种基于改进人工免疫方法的混合模糊聚类算法.算法通过借鉴生物免疫系统中的克隆选择原理和记忆机制,自动确定聚类类目及中心位置,同时还集成了模糊c均值搜索算子用于加快收敛速度.仿真实验结果表明了本算法在收敛性、收敛速度和分类性能的有效性.     

Students’ perceptions of collaboration, self-regulated learning, and information seeking in the context of Internet-based learning and traditional learning

This study aims to investigate students’ perceptions of three aspects of learning - collaboration, self-regulated learning (SRL), and information seeking (IS) in both Internet-based and traditional face-to-face learning contexts. A multi-dimensional questionnaire was designed to evaluate each aspect in terms of perceived capability, experience, and interest. The analyses explore (1) potential differences of students’ perceptions between Internet-based and face-to-face learning environments and (2) potential differences in the three aspects in relation to learners’ attributes and the use of the Internet and enrollment in online courses. This study surveyed students in a higher education institute who had had experiences with Internet-based and face-to-face learning. The results showed that students perceived higher levels of collaboration (capability only), SRL (capability and experience) and IS (capability, interest, and experience) in Internet-based learning than in traditional learning environments. In terms of students’ education level, graduate students perceived higher levels of capabilities and interests in some of the aspects, than undergraduate students. In addition, for Internet-based learning, significant differences in collaboration and SRL were found derived from time spent on the Internet related to learning; and students’ perceptions of collaboration, SRL, and IS were all positively correlated to students’ online course-taking experience. Implications for online learning practices and instructor’s facilitation are discussed.     

The feedrate scheduling of NURBS interpolator for CNC machine tools

This paper proposes an off-line feedrate scheduling method of CNC machines constrained by chord tolerance, acceleration and jerk limitations. The off-line process for curve scanning and feedrate scheduling is realized as a pre-processor, which releases the computational burden in real-time task. The proposed method first scans a non-uniform rational B-spline (NURBS) curve and finds out the crucial points with large curvature (named as critical point) or G⁰ continuity (named as breakpoint). Then, the NURBS curve is divided into several NURBS sub-curves using curve splitting method which guarantees the convergence of predictor–corrector interpolation (PCI) algorithm. The suitable feedrate at critical point is adjusted according to the limits of chord error, centripetal acceleration and jerk, and at breakpoint is adjusted based on the formulation of velocity variation. The feedrate profile corresponding to each NURBS block is constructed according to the block length and the given limits of acceleration and jerk. In addition, feedrate compensation method for short NURBS blocks is performed to make the jerk-limited feedrate profile more continuous and precise. Because the feedrate profile is established in off-line, the calculation of NURBS interpolation is extremely efficient for CNC high-speed machining. Finally, simulations and experiments with two free-form NURBS curves are conducted to verify the feasibility and applicability of the proposed method.     

TAFRO Syndrome with Renal Thrombotic Microangiopathy: Insights into the Molecular Mechanism and Treatment Opportunities

TAFRO syndrome is an extremely rare form of idiopathic MCD, characterized by thrombocytopenia, anasarca, fever, reticulin fibrosis on bone marrow biopsy, and organomegaly. Like idiopathic MCD, renal involvement is also a common presentation in patients with TAFRO syndrome. Furthermore, membranoproliferative glomerulonephritis (MPGN)-like injury and thrombotic microangiopathy (TMA) are the most reported histopathologic findings of renal biopsy. Several molecular mechanisms have been previously postulated in order to explain the TAFRO syndrome symptoms, including abnormal production of interleukin-6 (IL-6), vascular endothelial growth factor (VEGF), etc. The role of these cytokines in renal injury, however, is not well understood. The aim of this review article is to summarize the latest knowledge of molecular mechanisms behind the TAFRO syndrome and their potential role in renal damage.     

Robot Control Optimization Using Reinforcement Learning

Conventional robot control schemes are basically model-based methods. However, exact modeling of robot dynamics poses considerable problems and faces various uncertainties in task execution. This paper proposes a reinforcement learning control approach for overcoming such drawbacks. An artificial neural network (ANN) serves as the learning structure, and an applied stochastic real-valued (SRV) unit as the learning method. Initially, force tracking control of a two-link robot arm is simulated to verify the control design. The simulation results confirm that even without information related to the robot dynamic model and environment states, operation rules for simultaneous controlling force and velocity are achievable by repetitive exploration. Hitherto, however, an acceptable performance has demanded many learning iterations and the learning speed proved too slow for practical applications. The approach herein, therefore, improves the tracking performance by combining a conventional controller with a reinforcement learning strategy. Experimental results demonstrate improved trajectory tracking performance of a two-link direct-drive robot manipulator using the proposed method.     

最多叶子生成树问题的核化算法

对算法领域的最多叶子生成树问题进行了深入研究,提出了对简单连通图2度节点的化简规则,并证明了不含2度节点的图的生成树的叶子节点数的下限为(N+6)/4,给出了构造这样一棵生成树的构造性方法.基于上述化简规则和所证明的结论,给出了最多叶子生成树问题的核化算法,该核化算法可以在O(n2)时间内得到一个4k-6大小的线性核.对于这样一个较小的核,将大大提高相关的参数算法和近似算法的性能.     

闪存数据库:现状、技术与展望

随着闪存存储技术的发展,闪存已经广泛应用于各种移动设备、PC机和服务器中.作为一种完全不同于磁盘的新型存储介质,闪存具有非易失、高速读写、抗震、低功耗、高存储密度等物理特性,这使得基于闪存的数据管理问题成为新的挑战.数据库系统是数据管理的重要技术,将现有的数据库系统直接移植到闪存上并不能充分发挥其硬件特性,设计实现基于闪存的数据库系统是当前的一个研究热点.文中介绍了闪存的特性和闪存转换层;总结了缓冲区、索引、查询和事务等数据库关键技术;讨论了基于闪存的混合存储数据管理.最后,基于该领域亟待解决的诸多问题,指出了未来的研究方向.     

On Non-Uniform Rational B-Splines Surface Neural Networks

This article presents a simulation study for validation of an adaptation methodology for learning weights of a Hopfield neural network configured as a static optimizer. The quadratic Liapunov function associated with the Hopfield network dynamics is leveraged to map the set of constraints associated with a static optimization problem. This approach leads to a set of constraint-specific penalty or weighting coefficients whose values need to be defined. The methodology leverages a learning-based approach to define values of constraint weighting coefficients through adaptation. These values are in turn used to compute values of network weights, effectively eliminating the guesswork in defining weight values for a given static optimization problem, which has been a long-standing challenge in artificial neural networks. The simulation study is performed using the Traveling Salesman problem from the domain of combinatorial optimization. Simulation results indicate that the adaptation procedure is able to guide the Hopfield network towards solutions of the problem starting with random values for weights and constraint weighting coefficients. At the conclusion of the adaptation phase, the Hopfield network acquires weight values which readily position the network to search for local minimum solutions. The demonstrated successful application of the adaptation procedure eliminates the need to guess or predetermine the values for weights of the Hopfield network.