Enhancement of thermal,mechanical, ignition and damping response of magnesium using nano-ceria particles

Magnesium (Mg)-based nanocomposites owing to their low density and biocompatibility are being targeted for transportation and biomedical sectors. In order to support a sustainable environment, the prime aim of this study was to develop non-toxic magnesium-based nanocomposites for a wide spectrum of applications. To support this objective, cerium oxide nanoparticles (0.5?vol%, 1?vol%, and 1.5?vol%) reinforced Mg composites are developed in this study using blend-press-sinter powder metallurgy technique. The microstructural studies exhibited limited amounts of porosity in Mg and Mg-CeO₂ samples (< 1%). Increasing presence of CeO₂ nanoparticles (up to 1.5?vol%) led to a progressive increase in microhardness, dimensional stability, damping capacity and ignition resistance of magnesium. The compressive strengths increased with the increasing addition of the nanoparticles with a significant enhancement in the fracture strain (up to ~48%). Superior energy absorption was observed for all the composite samples prior to compressive fracture. Further, enhancement in thermal, mechanical and damping characteristics of pure Mg is correlated with microstructural changes due to the presence of the CeO₂ nanoparticles.     

Top-Seeded Melt-Growth of YBa2Cus3Ox Crystals for Neutron Diffraction Studies

We have grown cubic centimeter-size crystals of YBa₂Cu₃O _x suitable for neutron studies, by a top-seeded melt-growth technique. Growth conditions were optimized with an eye toward maximizing phase purity. It was found that the addition of 2% Y₂BaCuO₅ and 0.5% Pt (by mass) were required to prevent melt loss and to obtain the highest crystallinity. A neutron diffraction study on a mosaic of six such crystals found that the final Y₂BaCuO₅ concentration was 5%, while other impurity phases comprised less than 1% by volume. The oxygen content was set to x = 6.5, the crystals were detwinned, and then carefully annealed to give the well-ordered ortho-II phase. The neutron study determined that 70% of the mosaic's volume was in the majority orthorhomic domain. The neutron (006) and (110) rocking curve widths were ～1° per crystal and ～2.2° for the mosaic, and the oxygen chain correlation lengths were >100 Å in the a- and b-directions and ～50 Å in the c-direction.     

Video compression using spatiotemporal regularity flow.

We propose a new framework in wavelet video coding to improve the compression rate by exploiting the spatiotemporal regularity of the data. A sequence of images creates a spatiotemporal volume. This volume is said to be regular along the directions in which the pixels vary the least, hence the entropy is the lowest. The wavelet decomposition of regularized data results in a fewer number of significant coefficients, thus yielding a higher compression rate. The directions of regularity of an image sequence depend on both its motion content and spatial structure. We propose the representation of these directions by a 3-D vector field, which we refer to as the spatiotemporal regularity flow (SPREF). SPREF uses splines to approximate the directions of regularity. The compactness of the spline representation results in a low storage overhead for SPREF, which is a desired property in compression applications. Once SPREF directions are known, they can be converted into actual paths along which the data is regular. Directional decomposition of the data along these paths can be further improved by using a special class of wavelet basis called the 3-D orthonormal bandelet basis. SPREF -based video compression not only removes the temporal redundancy, but it also compensates for the spatial redundancy. Our experiments on several standard video sequences demonstrate that the proposed method results in higher compression rates as compared to the standard wavelet based compression.     

病险水工建筑物可靠性评估方法初探

 水工建筑物可靠性评估是水利工程管理中急需解决的问题，将水工建筑物分解为由分部工程组成的层次结构，引入子系统概率重要性系数，用层次分析法导出水工建筑物系统可靠度指标的实用计算方法。     

Nitrogen management in reservoir catchments through constructed wetland systems.

In this study, nitrogen removal was investigated in pilot-scale subsurface flow (SSF) and in free water surface flow (FWS) constructed wetlands installed in the campus of TUBITAK-Marmara Research Center, Gebze, near Istanbul, Turkey. The main purposes of this study are to apply constructed wetlands for the protection of water reservoirs and to reuse wastewater. Experiments were carried out at continuous flow reactors. The effects of the type of plants on the removal were investigated by using emergent (Canna, Cyperus, Typhia spp., Phragmites spp., Juncus, Poaceae, Paspalum and Iris.), submerged (Elodea, Egeria) and floating (Pistia, Salvina and Lemna) marsh plants at different conditions. During the study period HLRs were 30, 50, 70, 80 and 120 L m(2)d(-1) respectively. The average annual NH4-N, NO(3)-N, organic N and TN treatment efficiencies in SSF and FWS wetlands are 81% and 68%, 37% and 49%, 75% and 68%, 47% and 53%, respectively. Nitrification, denitrification and ammonification rate constant (k20) values in SSF and FNS systems have been found as 0.898 d(-1) and 0.541 d(-1), 0.488 d(-1) and 0.502 d(-1), 0.986 d(-1) and 0.908 respectively. Two types of the models (first-order plug flow and multiple regression) were tried to estimate the system performances.     

家用电器纳米抗菌材料

家用电器已经成为越来越多家庭中不可或缺的生活品,同时由于电器使用过程中细菌滋生导致的健康问题越来越受到人们的关注,因此,家用电器的抗菌效果成为了大众评判健康生活的指标之一.而纳米材料由于其成本低廉并拥有较好的抗菌效果,可以作为解决这一问题的途径之一.对家电行业已经初步应用的三种成本低、无需附加苛刻条件、抗菌效果好的纳米材料进行分析并研究,指出开发更加优异的纳米抗菌材料是当前抗菌行业的热点.     

Activities of MFI-Supported Rhenium Catalysts for the Aromatization of Methane: Effect of Cationic Form of the Inorganic Carrier

MFI type inorganic carrier was used in two different cationic forms, hydrogen and calcium respectively. MFI-supported molybdenum and rhenium catalysts were prepared. The activities of the catalysts were compared for the aromatization reaction of methane. Higher activity values were attained with the catalysts supported on HZSM-5. Aromatics were also observed with the catalysts supported on CaZSM-5, despite their deficiency in acid sites. Highly dispersed rhenium is expected to be formed with the use of the inorganic carrier in calcium form. On the other hand, lower reaction rates were observed with rhenium supported on CaZSM-5, in spite of the improved dispersion of the active rhenium species on this catalyst. This was interpreted in terms of the critical role of the acid sites in the conversion of methane to aromatics, compared to the improved dispersion of the active metal.     

Chlorophyll adsorption on acid-activated clay

An acid-activated clay (Çanakkale montmorillonite from Turkey) was used to adsorb chlorophyll from hexane solutions. The phenomenon seems to be mainly driven by the interaction of chlorophyll with acid sites. The adsorption of chlorophyll on Brönsted acid sites was indicated by a characteristic infrared band for the -OH group at 3671 cm^?1. The variations in the structure of clay mineral and chlorophyll during adsorption have been examined by differential thermal analysis, thermogravimetry and infrared spectroscopy of the activated clay before and after adsorption of chlorophyll. Oxidation of adsorbed chlorophyll was completed at quite a high temperature.     

基于卡口上下文和深度置信网络的车辆轨迹预测模型研究

针对车辆轨迹预测中节点序列的时序特性和实际路网中的空间关联性,该文提出一种基于深度置信网络和SoftMax (DBN-SoftMax)轨迹预测方法.首先,考虑到轨迹在节点集合中的强稀疏性和一般特征学习方法对新特征的泛化能力不足,该文利用深度置信网络(DBN)较强的无监督特征学习能力,达到提取轨迹局部空间特性的目的;然后,针对轨迹的时序特性,该文采用逻辑回归的预测思路,用当前轨迹集在路网特征空间中的线性组合来预测轨迹;最后,结合自然语言处理领域中的词嵌入的思想,基于实际轨迹中节点存在的上下文关系,运用节点的向量集表征了节点间的交通时空关系.实验结果表明该模型不仅能够有效地提取轨迹特征,并且在拓扑结构复杂的路网中也能得到较好的预测结果.     

Comparative study on classifying human activities with miniature inertial and magnetic sensors

This paper provides a comparative study on the different techniques of classifying human activities that are performed using body-worn miniature inertial and magnetic sensors. The classification techniques implemented and compared in this study are: Bayesian decision making (BDM), a rule-based algorithm (RBA) or decision tree, the least-squares method (LSM), the k-nearest neighbor algorithm (k-NN), dynamic time warping (DTW), support vector machines (SVM), and artificial neural networks (ANN). Human activities are classified using five sensor units worn on the chest, the arms, and the legs. Each sensor unit comprises a tri-axial gyroscope, a tri-axial accelerometer, and a tri-axial magnetometer. A feature set extracted from the raw sensor data using principal component analysis (PCA) is used in the classification process. A performance comparison of the classification techniques is provided in terms of their correct differentiation rates, confusion matrices, and computational cost, as well as their pre-processing, training, and storage requirements. Three different cross-validation techniques are employed to validate the classifiers. The results indicate that in general, BDM results in the highest correct classification rate with relatively small computational cost.