构件测试元数据及其提取与展现技术

构件具有的封装性给构件测试带来了困难,而目前构件包含的元数据尚不完整。为了充分利用构件元数据进行构件自动化测试,从构件使用者和测试者的角度设计了内涵丰富的构件元数据,并且针对COM构件,通过访问类型库来自动获取构件结构信息元数据,按层次提取并用XML形式化描述类型信息,得到了COM构件辅助测试规格说明。实例表明,该方法能直接操纵COM构件,自动获取元数据,便于测试脚本自动生成。     

Comparison of gas sensing properties based on hollow and porous α-Fe<Subscript>2</Subscript>O<Subscript>3</Subscript> nanotubes

Hollow and porous α-Fe₂O₃ nanotubes were successfully synthesized by single nozzle electrospinning method followed by annealing treatment. The crystal structures and morphologies of the as-prepared materials were characterized by X-ray diffraction and scanning electron microscopy, respectively. The as-prepared materials were applied to construct gas sensor devices which gas sensing properties were further investigated. The obtained results revealed that porous α-Fe₂O₃ nanotube gas sensors exhibit a markedly enhanced gas sensing performance compared with hollow α-Fe₂O₃ nanotube gas sensors, which was about three times higher to 100 ppm acetone at 240 °C. Interestingly, hollow and porous α-Fe₂O₃ nanotube gas sensors both showed fast response–recovery time and good selectivity, but the porous ones possessed the shorter recovery time. The improved properties could be attributed to the unique morphology of porous nanotubes. Thus, further improvement of performance in metal-oxide-semiconductors materials could be realized by preparation the unique porous structures of nanotubes. Moreover, it is expected that porous metal-oxide-semiconductors nanotubes could be further design as promising candidates for gas sensing materials.     

La<Subscript>1−x</Subscript>Ag<Subscript>x</Subscript>FeO<Subscript>3</Subscript>/halloysites nanocomposite with enhanced visible light photocatalytic performance

La_1?xAg_xFeO₃/halloysites nanotubes (HNTs) nanocomposite was synthesized by sol–gel method. It was characterized by X-ray diffraction, transmission electron microscope, Fourier transform infrared spectroscopy and UV–visible diffused reflectance spectroscopy measurements. The photo-activity of the La_1?xAg_xFeO₃/HNTs nanocomposite was evaluated via degradation of methylene blue (MB) under visible-light irradiation. The results showed that the HNTs with unique pore structure favored the adsorption of organic molecules. Adequate Ag⁺ doping improved the absorption ability for visible light. The La_0.95Ag_0.05FeO₃/HNTs demonstrated the best photocatalytic performance, which achieved as high as 99 % for MB degradation exposed 2 h irradiation. However,further increasing of Ag⁺ doping gradually reduced the photocatalytic activity. The nanocomposite catalyst showed outstanding recyclability after eight cycles which still remained up to 90 %.     

强磁场对硅锰铸钢于A_1点附近等温珠光体相变的影响

通过开展12 T强磁下硅锰铸钢(50Si_2Mn_3)于A_1点温度附近不同等温时间的热处理试验,并对比强磁场与无磁场条件下等温珠光体相变试验结果,研究了磁场对珠光体转变量及组织形貌的影响。研究表明,对于50Si_2Mn_3在A1点以下较高温度范围内,强磁场可缩短珠光体相变的孕育期,促进硅锰铸钢珠光体转变,使其转变量明显增加;强磁场能够提高珠光体转变临界点,使50Si_2Mn_3在A1点以上等温过程中能够发生珠光体相变。     

Aggregation-induced emission luminogen-assisted stimulated emission depletion nanoscopy for super-resolution mitochondrial visualization in live cells

Nano Research - Aggregation-induced emission luminogens (AIEgens) are fluorescent agents that are ideal for bioimaging and have been widely used for organelle targeting, cellular mapping, and...     

A new approach to the prediction of passenger flow in a transit system

A non-linear model is proposed for predicting the rate of passenger flow in a transit system, and its chaotic characteristic is observed. Using wavelets analysis, the passenger flow data for a whole day are decomposed in a multi-scale way to obtain decomposition sequences. Subsequently, a neural network approach is used to predict the sequences. Finally the passenger flow value can be predicted when the predicted sequences are reconstructed. Results show that the present approach is a feasible method for passenger flow prediction.     

Comments on “A new robust control for a class of uncertaindiscrete-time systems”

A completed proof for a theorem on the robust controller design proposed in Wang and Ghosh (ibid. vol.43 (1997)) is presented in this paper     

Tracking mobile users in wireless networks via semi-supervised colocalization

Recent years have witnessed the growing popularity of sensor and sensor-network technologies, supporting important practical applications. One of the fundamental issues is how to accurately locate a user with few labeled data in a wireless sensor network, where a major difficulty arises from the need to label large quantities of user location data, which in turn requires knowledge about the locations of signal transmitters or access points. To solve this problem, we have developed a novel machine learning-based approach that combines collaborative filtering with graph-based semi-supervised learning to learn both mobile users' locations and the locations of access points. Our framework exploits both labeled and unlabeled data from mobile devices and access points. In our two-phase solution, we first build a manifold-based model from a batch of labeled and unlabeled data in an offline training phase and then use a weighted k-nearest-neighbor method to localize a mobile client in an online localization phase. We extend the two-phase colocalization to an online and incremental model that can deal with labeled and unlabeled data that come sequentially and adapt to environmental changes. Finally, we embed an action model to the framework such that additional kinds of sensor signals can be utilized to further boost the performance of mobile tracking. Compared to other state-of-the-art systems, our framework has been shown to be more accurate while requiring less calibration effort in our experiments performed on three different testbeds.