珍珠层粉/壳聚糖多孔支架的制备及分析测试

用负压真空冷冻干燥机在-20℃条件下冷冻干燥48h制备珍珠层粉/壳聚糖复合多孔支架。用扫描电子显微镜(SEM)观察支架表面形貌,通过X射线衍射分析(XRD)、傅里叶红外光谱(FTIR)等手段对材料进行表征测试,检测其降解性能,并将兔骨髓间充质干细胞与支架共培养检测材料细胞毒性。结果表明复合支架具有稳固的三维多孔结构,孔隙率最大至91.64%,孔径在100~300μm范围,降解性能适宜,且冷冻干燥制备方法对珍珠层粉的结构无明显破坏,并具有良好的生物相容性,有利于细胞的生长。     

Graphene oxide-decorated Fe<Subscript>2</Subscript>(MoO<Subscript>4</Subscript>)<Subscript>3</Subscript> microflowers as a promising anode for lithium and sodium storage

Mixed transition metal oxides (MTMOs) have received intensive attention as promising anode materials for lithium-ion batteries (LIBs) and sodium-ion batteries (SIBs). In this work, we demonstrate a facile one-step water-bath method for the preparation of graphene oxide (GO) decorated Fe₂(MoO₄)₃ (FMO) microflower composite (FMO/GO), in which the FMO is constructed by numerous nanosheets. The resulting FMO/GO exhibits excellent electrochemical performances in both LIBs and SIBs. As the anode material for LIBs, the FMO/GO delivers a high capacity of 1,220 mAh·g^–1 at 200 mA·g^–1 after 50 cycles and a capacity of 685 mAh·g^–1 at a high current density of 10 A·g^–1. As the anode material for SIBs, the FMO/GO shows an initial discharge capacity of 571 mAh·g^–1 at 100 mA·g^–1, maintaining a discharge capacity of 307 mAh·g^–1 after 100 cycles. The promising performance is attributed to the good electrical transport from the intimate contact between FMO and graphene oxide. This work indicates that the FMO/GO composite is a promising anode for high-performance lithium and sodium storage.

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嵌入式VxWorks系统下的CPCI设备驱动程序开发

具体分析了VxWorks下PCI总线的配置空间结构和地址映射,并采用CPCI9054芯片,结合一个信号产生、数据采集处理系统的工程实例,详细阐述了VxWorks下CPCI设备驱动程序的开发实现方法和具体步骤,并给出了部分关键代码.应用结果表明,该方法可移植性较高,可操作性强,对于了解和开发CPCI驱动程序具有一定的参考...     

无线传感器网络中一种自适应的退避算法

802.15.4协议中CSMA/CA信道接入方式为了避免再次碰撞使用BEB退避算法,在重负载的情况下网络性能较差。提出一种自适应的退避算法,它用报文的冲突概率来预测节点附近信道的争用状况,在不同的网络状况下,竞争窗口的取值采用相应的退避策略,动态地控制节点的退避范围,以达到改善网络性能的目的。分析与仿真结果表明,新的算法与传统的退避算法BEB算法相比,在网络负载重的情况下,有效地降低了数据包的冲突概率,减少了时延,提高了网络的吞吐量,同时对信道的公平性也有所改善。     

The influence of goal clarity,curiosity, and enjoyment on intention to code

The purpose of this study is to examine the influence of goal clarity, curiosity, and enjoyment – dimensions of flow theory – on the intention to write programming code. This research refines and extends previous information systems (IS) research in two significant ways: first, this research is focused specifically on systems development behaviour; second; this is the first research that isolates specific flow theory constructs associated with systems development behaviour. We used SmartPLS to test our model, as partial least squares is the appropriate statistical methodology for theory building and model testing. Findings are based on survey data from computer IS classes at two different universities. Goal clarity and curiosity independently and significantly contributed to enjoyment when programming, which significantly and positively influenced a future intention to code. Recommendations for practitioners and faculty include testing for curiosity characteristics, providing clear goals, and providing stimuli to pique curiosity.     

Multiple kernel ensemble learning for software defect prediction

Software defect prediction aims to predict the defect proneness of new software modules with the historical defect data so as to improve the quality of a software system. Software historical defect data has a complicated structure and a marked characteristic of class-imbalance; how to fully analyze and utilize the existing historical defect data and build more precise and effective classifiers has attracted considerable researchers’ interest from both academia and industry. Multiple kernel learning and ensemble learning are effective techniques in the field of machine learning. Multiple kernel learning can map the historical defect data to a higher-dimensional feature space and make them express better, and ensemble learning can use a series of weak classifiers to reduce the bias generated by the majority class and obtain better predictive performance. In this paper, we propose to use the multiple kernel learning to predict software defect. By using the characteristics of the metrics mined from the open source software, we get a multiple kernel classifier through ensemble learning method, which has the advantages of both multiple kernel learning and ensemble learning. We thus propose a multiple kernel ensemble learning (MKEL) approach for software defect classification and prediction. Considering the cost of risk in software defect prediction, we design a new sample weight vector updating strategy to reduce the cost of risk caused by misclassifying defective modules as non-defective ones. We employ the widely used NASA MDP datasets as test data to evaluate the performance of all compared methods; experimental results show that MKEL outperforms several representative state-of-the-art defect prediction methods.     

Investigation of fused silica glass etching using C4F8/Ar inductively coupled plasmas for through glass via (TGV) applications

Through glass via (TGV) technology is considered to be a cost effective enabler for the integration of micro electromechanical systems and radio frequency devices. Inductively coupled plasma and Bosch etching process comprise one of the most pervasive methods for through silicon via (TSV) formation. Unfortunately an equivalent process for glass etching remains elusive. In this paper, the influence of plasma etching for fused silica glass were investigated to find the best tradeoff between etch rate and profile of TGVs. The process parameters including bias power, gas flow rate, ratio of etching gases and reaction chamber pressure using Ar/C₄F₈ inductively coupled plasmas were studied. The etching results show that all these three parameters have a significant impact on the etch rate. Furthermore, the adjustment including total flow rate and ratio of Ar/C₄F₈ and chamber pressure can be used to control the via profile. Constant fused silica glass etch rate greater than 1 μm/min was obtained when chiller temperature was 40 °C with etching time of 60 min. The profile angle of TGVs with nearly 90° was also achieved.     

Hybrid fire detection using hidden Markov model and luminance map

Recently, fire detection is a hot research topic. Although many detection methods have been proposed, there exist high false alarms because of the interference of fire-colored moving object in the complex environments. In this paper, a hybrid method is proposed. First, we get the set of candidate fire regions. Then these candidate fire regions are analyzed to exclude the fire-colored moving object. Our contributions are using the hidden Markov model (HMM) based on spatio-temporal feature and the variance of luminance map motivated by visual attention, and combining both for fire detection. The wrong detection can be reduced greatly. Experiment results show our proposed method has a good performance and it is robust to be used in complex environment compared with previous algorithms.     

智能家居照明控制无线开关系统

设计了一种用于智能家居照明控制的可编程四路无线开关系统。该系统利用芯片组PT2262／2272实现编码一发射和接收一解码,采用AT89S52单片机对接收信号进行控制。此方案电路设计灵活,能充分利用系统软硬件资源,系统扩展性好,提供了一种廉价的智能家居照明控制解决方案。