一类均匀康托集的Hausdorff中心测度

本文研究了均匀2n部分康托集的Hausdorff中心测度.利用极大中心密度与Hausdorff 中心测度之间的关系,确定了均匀2n部分康托集Hausdorff中心测度的精确值.     

Characteristics of stable L-band SFS using dual-forward synchronous pumping technique

We investigate the characteristics of the dual-forward synchronously pumped L-band erbium-doped su- perfiuorescent fiber source （SFS）. The effects of pump ratio and fiber length arrangements on the output characteristics of the L-band SFS in terms of mean wavelength, spectral linewidth, and output power are analyzed. It is shown that the optimized pump ratio and fiber length arrangements provide broadening spectral linewidth and enhanced pumping efficiency, while the synchronous pump ensures stable mean wavelength operation. A new single-forward pumping scheme with a section of unpumped fiber is pro- posed to achieve a mean wavelength stable L-band SFS with a broadening linewidth of 50.4 nm and a pumping efficiency of 33.5%.     

Some Two-Point Resistances of the Sierpinski Gasket Network

In this paper, we use the principle of substitution to replace sub-gaskets of the Sierpinski gasket network by an equivalent Y-network which enables the use of only the Delta–Wye transformation and the series and parallel principles to derive some two-point resistances of the Sierpinski gasket network with dimension two.     

Hg2+在大鼠组织器官中的分布及其存在形态研究

汞是环境中重要的污染物之一,对人类的健康构成了巨大的威胁。文章通过建立电感耦合等离子体质谱及凝胶排阻色谱分离法与紫外和电感耦合等离子体质谱在线检测的联用技术,研究大鼠灌喂HgCl2后Hg在大鼠中的分布累积规律和及其与蛋白的结合,实验结果表明肝脏和肾脏中在Hg刺激下诱导产生大量硫蛋白,大量产生的金属硫蛋白可能因此而优先与通过胃肠吸收摄入的Hg结合,减少了Hg对其它蛋白的正常作用的干扰,从而大大降低了Hg^2＋的毒性。     

Rapid detection of malachite green in fish with a fluorescence probe of molecularly imprinted polymer

A fluorescent probe based on CdTe/CdS quantum dots coated with molecularly imprinted polymer shell was designed. The fluorescence emission of the probe was at 622?nm. The probe presented selective adsorption for malachite green and the adsorption caused fluorescence quenching due to fluorescence resonance energy transfer. A linear relationship ranging from 0.05 to 10?μmol·L^?1 between relative fluorescence quenching intensities and malachite green concentrations was obtained with a detection limit of 0.029?μmol·L^?1. The fluorescent probe was successfully applied to the determination of malachite green in fish with recoveries ranging from 93.3 to 107.7%.     

Emerging approach in semiconductor photocatalysis: Towards 3D architectures for efficient solar fuels generation in semi-artificial photosynthetic systems

Interest in the application of semiconductors toward the photocatalytic generation of solar fuels, including hydrogen from water-splitting and hydrocarbons from the reduction of carbon dioxide, remains strong due to concerns over the continued emission of greenhouse gases as well as other environmental impacts from the use of fossil fuels. While the efficiency and durability of such systems will depend heavily on the types of the semiconductors, co-catalysts, and mediators employed, the dimensionality of the semiconductors employed can also have a significant impact. Recognizing the broad nature of this field and the many recent advances in it, this review focuses on the emerging approaches from 0-dimensional (0D) to 3-dimensional (3D) semiconductor photocatalysts towards efficient solar fuels generation. We place particular emphasis on systems that are “semi-artificial”, that is, hybrid systems that integrate naturally occurring enzymes or whole cells with semiconductor components that harvest light energy. The semiconductors in these systems must have suitable interfacial properties for immobilization of enzymes to be effective photocatalysts. These requirements are particularly sensitive to surface structures and morphology, making the semiconductor dimensionality a critical factor. In addition to providing an overview of advances towards designing 3D architecture in semi-artificial photosynthetic field, we also present recent advances in fabrication strategies for 3D inorganic photocatalysts.     

A comparative study of the effect of the position of outliers on classical and nontraditional approaches to the two-group classification problem

Popularity of nontraditional approaches to the statistical classification problem has resulted from the potential of these techniques to outperform the standard parametric procedures under conditions when nonnormality is present. Thus proponents of these nontraditional models have recommended these models when outliers are in the data. However, research showing that these nontraditional models' performances can vary widely depending on where the outlier data are located has not been fully illustrated. The research in this paper demonstrates how the mathematical programming approaches and the nearest neighbor discriminant models can be affected by the position of contaminated normal data and that each of the models studied in this paper may not be robust to all types of outliers in the data. The results of this paper are also important because the study compares two recently proposed mathematical programming models as well as two versions of the nearest neighbor model with the standard classical parametric models. This combination of classification models does not appear to have been studied together under conditions of contaminated normal data in which numerous positions of the outliers are considered.     

Geographical networks: geographical effects on network properties

Complex networks describe a wide range of systems in nature and society. Since most real systems exist in certain physical space and the distance between the nodes has influence on the connections, it is helpful to study geographical complex networks and to investigate how the geographical constrains on the connections affect the network properties. In this paper, we briefly review our recent progress on geographical complex networks with respect of statistics, modelling, robustness, and synchronizability. It has been shown that the geographical constrains tend to make the network less robust and less synchronizable. Synchronization on random networks and clustered networks is also studied.      

SCALE-TYPE STABILITY FOR NEURAL NETWORKS WITH UNBOUNDED TIME-VARYING DELAYS

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.