Optical temperature sensing based on the Goos-Hänchen effect

The possibility of constructing an optical sensor for temperature monitoring based on the Goos-H?nchen (GH) effect is explored using a theoretical model. This model considers the lateral shift of the incident beam upon reflection from a metal-dielectric interface, with the shift becoming a function of temperature due mainly to the temperature dependence of the optical properties of the metal. It is found that such a sensor can be most effective by using long wavelength p-polarized incident light at almost grazing incidence onto the metal, where significant variation of negative GH shifts can be observed as a function of the temperature.     

Green synthesis of silver nanoparticles using biopolymers,carboxymethylated-curdlan and fucoidan

There is a growing need in developing a reliable and eco-friendly methodology for the synthesis of metallic nanoparticles, which may be applied for many nanotechnological applications. Natural compounds such as biopolymers are one of the resources which could be used for this purpose. The present study involves the development of a simple, ecological and user-friendly method in synthesizing silver nanoparticles by using carboxymethylated-curdlan or fucoidan as reducing and stabilizing agents. Reduction of silver ions by these biopolymers occurred when heating at 100 °C, led to the formation of silver nanoparticles in the range of 40–80 nm in dimensions. The silver nanoparticles were formed readily within 10–15 min. Morphological observation and characterization of the silver nanoparticles were performed by using dynamic light scattering (DLS), high-resolution transmission electron microscopy (HRTEM), and UV–vis absorption spectrophotometer. The size of silver nanoparticles can be controlled by using different concentrations of carboxymethylated-curdlan, fucoidan or silver nitrate. This way of silver nanoparticles preparation is easy, fast, user-friendly and suitable for large-scale production.     

An efficient data mining method for learning Bayesian networks using an evolutionary algorithm-based hybrid approach

Given the explosive growth of data collected from current business environment, data mining can potentially discover new knowledge to improve managerial decision making. This paper proposes a novel data mining approach that employs an evolutionary algorithm to discover knowledge represented in Bayesian networks. The approach is applied successfully to handle the business problem of finding response models from direct marketing data. Learning Bayesian networks from data is a difficult problem. There are two different approaches to the network learning problem. The first one uses dependency analysis, while the second one searches good network structures according to a metric. Unfortunately, both approaches have their own drawbacks. Thus, we propose a novel hybrid algorithm of the two approaches, which consists of two phases, namely, the conditional independence (CI) test and the search phases. In the CI test phase, dependency analysis is conducted to reduce the size of the search space. In the search phase, good Bayesian network models are generated by using an evolutionary algorithm. A new operator is introduced to further enhance the search effectiveness and efficiency. In a number of experiments and comparisons, the hybrid algorithm outperforms MDLEP, our previous algorithm which uses evolutionary programming (EP) for network learning, and other network learning algorithms. We then apply the approach to two data sets of direct marketing and compare the performance of the evolved Bayesian networks obtained by the new algorithm with those by MDLEP, the logistic regression models, the na/spl inodot//spl uml/ve Bayesian classifiers, and the tree-augmented na/spl inodot//spl uml/ve Bayesian network classifiers (TAN). In the comparison, the new algorithm outperforms the others.     

Sparse and Truncated Nuclear Norm Based Tensor Completion

One of the main difficulties in tensor completion is the calculation of the tensor rank. Recently a tensor nuclear norm, which is equal to the weighted sum of matrix nuclear norms of all unfoldings of the tensor, was proposed to address this issue. However, in this approach, all the singular values are minimized simultaneously. Hence the tensor rank may not be well approximated. In addition, many existing algorithms ignore the structural information of the tensor. This paper presents a tensor completion algorithm based on the proposed tensor truncated nuclear norm, which is superior to the traditional tensor nuclear norm. Furthermore, to maintain the structural information, a sparse regularization term, defined in the transform domain, is added into the objective function. Experimental results showed that our proposed algorithm outperforms several state-of-the-art tensor completion schemes.     

Green cell association for multimedia transmission in cognitive heterogeneous networks

With the introduction of low‐powered pico/femto‐base stations and relay nodes into the macro‐cell, recent heterogeneous networks provide an attractive approach for future wireless communication. Although it may achieve better coverage and higher capacity, several problems remain unsolved before practical deployment. For example, how to select the proper cell from neighbor low‐powered cells and then occupy the radio resource without interference on macro‐users is both important and challenging, especially for rigorous multimedia applications. The traditional cell access algorithms and quality‐control parameters such as delay or throughput no longer suit well in this complex environment. An effective approach should be pursued. In this paper, we investigate this interesting cell association problem and propose a complete green resolution on the basis of thorough discussions about the multimedia transmission under these concerns. Cognitive radio is introduced to share spectrum between macro‐cell and low‐powered cells while securing the transmission of authorized macro‐users. We also bring forth the concept of ‘interference balance’ to better manage the overall interference and energy consumption in the network. Restless bandit model is formulated on the basis of channel state, data rate, interference control, and the carefully chosen intra‐refreshing rate for multimedia traffic. Then the cell association scheme is designed to be efficient and practical because of the simple index property of our model output. Simulation results have proven the performance of our proposed resolution compared with existing algorithms on interference constraint, multimedia distortion, and overall network energy consumption balance. Copyright © 2013 John Wiley & Sons, Ltd.     

MPPA光动力作用诱导人鼻咽癌细胞凋亡的实验研究

为观察MPPa光动力作用对鼻咽癌细胞凋亡的影响,应用AnnexinV—PI双染结合流式细胞仪分析MPPa光动力作用后人鼻咽癌细胞株CNE2细胞发生凋亡和继发性坏死的比率。结果显示MPPa光动力作用实验组人鼻咽癌细胞株CNE2细胞发生凋亡和继发性坏死的比率分别增加到16.43 %和4.64 % ,且均显著高于单纯光照射组、单纯MMPa光敏剂处理组和假照射组(P <0 .0 1) ,而三对照组间无明显差异(P >0 .0 5 )。表明MPPa光动力作用能有效诱导低分化人鼻咽癌细胞株CNE2细胞凋亡的发生。这也可能是MPPa光动力作用杀伤鼻咽癌的重要机制之一。     

Aligning RFID applications with supply chain strategies

Many large retailers decided to adopt RFID as their new supply chain technology, but RFID adoption by all industries did not spread as rapidly as initially expected. We believe that its benefits constituted the major barrier to its adoption, and argued that a major contributor to the delay has been its inappropriate implementation. Our study reports the result of six in-depth case studies, which, together with case content analysis of 88 reported RFID applications, provided us with a clear view of the RFID implementation landscape, suggesting that organizations often mindlessly adopted RFID applications that were misaligned with their supply chain strategies, leading to unsatisfactory benefits. Insights into how organizations should adopt RFID were developed from our results and were discussed.     

Centering of organic-inorganic hybrid liposomal cerasomes in electrospun gelatin nanofibers

A study investigating the embedding of stabilized organic-inorganic liposomal cerasomes in gelatin nanofibers through the electrospinning of cerasome-dispersed gelatin aqueous solution is presented. Fluorescent and transmission electron microscopy confirm the embedding and centering of cerasomes in the electrospun nanofibers. A simple mechanism is proposed for the centering of cerasomes in gelatin nanofibers. The ability to incorporate cerasomes capable of encapsulating a variety of bioactive molecules provides a promising method to functionalize polymer nanofibers.     

Transmission Electron Microscopy of Fe79.5B6.5C14 Network Alloys: Part II

When undercooled deeply into its undercooling regime, a molten Fe_79.5B_6.5C₁₄ ingot undergoes spinodal decomposition, splitting into two interpenetrating subnetworks which are denoted by α-(liquid subnetwork) and β-(liquid subnetwork). Transmission electron microscopy results suggest that there are three constraints on crystal growth when the undercooled, decomposed molten ingot solidifies, which are (i) the solidification is due to the advancement of a bulk solid/liquid interface. It consists of a number of α-solid/liquid interfaces moving in the α-(liquid subnetwork) and a number of β-solid/liquid interfaces moving in the β-(liquid subnetwork); (ii) to overcome two long-range composition gradients of C and B atoms; and (iii) the two long-range composition gradients stabilize the bulk solid/liquid interface. The morphology of a solidified or network Fe_79.5B_6.5C₁₄ ingot is explained in terms of the obtained microscopic results.