20,492 research outputs found
sj-pdf-1-asp-10.1177_00037028231192124 - Supplemental material for A Sensitive Fluorescence Sensor for Tetracycline Determination Based on Adenine Thymine-Rich Single-Stranded DNA-Templated Copper Nanoclusters
No full textSupplemental material, sj-pdf-1-asp-10.1177_00037028231192124 for A Sensitive Fluorescence Sensor for Tetracycline Determination Based on Adenine Thymine-Rich Single-Stranded DNA-Templated Copper Nanoclusters by Ning-Ning Wu, Lin-Ge Chen and Hai-Bo Wang in Applied Spectroscopy</p
Graphene nanocomposites
No full textGraphene, one of the allotropes (diamond, carbon nanotube, and fullerene) of carbon, is a monolayer of honeycomb lattice of carbon atoms discovered in 2004. The Nobel Prize in Physics 2010 was awarded to Andre Geim and Konstantin Novoselov for their ground breaking experiments on the twodimensional graphene [1]. Since its discovery, the research communities have shown a lot of interest in this novel material owing to its unique properties. As shown in Figure 1, the number of publications on graphene has dramatically increased in recent years. It has been confirmed that graphene possesses very peculiar electrical properties such as anomalous quantum hall effect, and high electron mobility at room temperature (250000 cm2/Vs). Graphene is also one of the stiffest (modulus ~1 TPa) and strongest (strength ~100 GPa) materials. In addition, it has exceptional thermal conductivity (5000 Wm-1K-1). Based on these exceptional properties, graphene has found its applications in various fields such as field effect devices, sensors, electrodes, solar cells, energy storage devices and nanocomposites. Only adding 1 volume per cent graphene into polymer (e.g. polystyrene), the nanocomposite has a conductivity of ~0.1 Sm-1 [2], sufficient for many electrical applications. Significant improvement in strength, fracture toughness and fatigue strength has also been achieved in these nanocomposites [3-5]. Therefore, graphene-polymer nanocomposites have demonstrated a great potential to serve as next generation functional or structural materials
FIGURE 1. Primulina qingyuanensis. A. Habit. B. Bracts. C. Pistil. D. Stigma. E. Flower side view. F. Stamens. G. Capsule. H in Primulina qingyuanensis (Gesneriaceae), a new species from limestone areas in Guangdong, China
No full textFIGURE 1. Primulina qingyuanensis. A. Habit. B. Bracts. C. Pistil. D. Stigma. E. Flower side view. F. Stamens. G. Capsule. H. Corolla opened showing stamens and staminodes. Illustration by Yun-Xiao Liu, based on the holotype.Published as part of Ning, Zu-Lin, Wang, Jing, Smith, James F. & Kang, Ming, 2013, Primulina qingyuanensis (Gesneriaceae), a new species from limestone areas in Guangdong, China, pp. 48-52 in Phytotaxa 137 (1) on page 49, DOI: 10.11646/phytotaxa.137.1.5, http://zenodo.org/record/508630
Grey swan tropical cyclones
No full textWe define ‘grey swan’ tropical cyclones as high-impact storms that would not be predicted based on history but may be foreseeable using physical knowledge together with historical data. Here we apply a climatological–hydrodynamic method to estimate grey swan tropical cyclone storm surge threat for three highly vulnerable coastal regions. We identify a potentially large risk in the Persian Gulf, where tropical cyclones have never been recorded, and larger-than-expected threats in Cairns, Australia, and Tampa, Florida. Grey swan tropical cyclones striking Tampa, Cairns and Dubai can generate storm surges of about 6 m, 5.7 m and 4 m, respectively, with estimated annual exceedance probabilities of about 1/10,000. With climate change, these probabilities can increase significantly over the twenty-first century (to 1/3,100–1/1,100 in the middle and 1/2,500–1/700 towards the end of the century for Tampa). Worse grey swan tropical cyclones, inducing surges exceeding 11 m in Tampa and 7 m in Dubai, are also revealed with non-negligible probabilities, especially towards the end of the century
Epidemiology of respiratory syncytial virus infection among paediatric inpatients in northern Taiwan
No full text
Hierarchical Bayes based Adaptive Sparsity in Gaussian Mixture Model
No full textGaussian Mixture Model (GMM) has been widely used in statistics for its great flexibility. However, parameter estimation for GMM with high dimensionality is a challenge because of the large number of parameters and the lack of observation data. In this paper, we propose an effective method named hierarchical Bayes based Adaptive Sparsity in Gaussian Mixture Model (ASGMM) to estimate the parameters in a GMM by incorporating a two-layer hierarchical Bayes based adaptive sparsity prior. The prior we impose on the precision matrices can encourage sparsity and hence reduce the dimensionality of the parameters to be estimated. In contrast to the l(1)-norm penalty or Laplace prior, our approach does not involve any hyperparameters that must be tuned, and the sparsity adapts to the observation data. The proposed method is achieved by three steps: first, we formulate an adaptive hierarchical Bayes model of the precision matrices in the GMM with a Jeffrey's noninformative hyperprior, which expresses scale-invariance and, more importantly, is hyperparameter-free and unbiased. Second, we perform a Cholesky decomposition on the precision matrices to impose the positive definite property. Finally, we exploit the expectation maximization (EM) algorithm to obtain the final estimated parameters in the GMM. Experimental results on synthetic and real-world datasets demonstrate that ASGMM cannot only adapt the sparsity of high-dimensional data with small estimated error, but also achieve better clustering performance comparing with several classical methods. (C) 2014 Elsevier B.V. All rights reserved
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