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With the emerging technology in the 21st century, which requires higher electrochemical performances, metal oxide composite electrodes in particular o®er complementary properties of individual materials via the incorporation of both physical and chemical charge storage mechanism together in a single electrode. Numerous works reviewed herein have identi¯ed a wide variety of attractive metal oxide-based composite electrode material for symmetric and asymmetric electrochemical capacitors. The focus of the review is the detailed literature data and discussion regarding the electrochemical performance of various metal oxide composite electrodes fabricated from di®erent con¯gurations including binary and ternary composites. Additionally, projection of future development in hybrid capacitor coupling lithium metal oxides and carbonaceous materials are found to obtain signi¯cantly higher energy storage than currently available commercial electrochemical capacitors. This review describes the novel concept of lithium metal oxide electrode materials which are of value to researchers in developing high-energy and 1430002-1 NANO: Brief Reports and Reviews Vol. 9, No. 6 (2014) enhanced-cyclability electrochemical capacitors comparable to Li-ion batteries. In order to fully exploit the potential of metal oxide composite electrode materials, developing low cost, environment-friendly nanocomposite electrodes is certainly a research direction that should be extensively investigated in the future.

This paper presents the implementation of a new text document classification framework that uses the Support Vector Machine (SVM) approach in the training phase and the Euclidean distance function in the classification phase, coined as Euclidean-SVM. The SVM constructs a classifier by generating a decision surface, namely the optimal separating hyper-plane, to partition different categories of data points in the vector space. The concept of the optimal separating hyper-plane can be generalized for the nonlinearly separable cases by introducing kernel functions to map the data points from the input space into a high dimensional feature space so that they could be separated by a linear hyper-plane. This characteristic causes the implementation of different kernel functions to have a high impact on the classification accuracy of the SVM. Other than the kernel functions, the value of soft margin parameter, C is another critical component in determining the performance of the SVM classifier. Hence, one of the critical problems of the conventional SVM classification framework is the necessity of determining the appropriate kernel function and the appropriate value of parameter C for different datasets of varying characteristics, in order to guarantee high accuracy of the classifier. In this paper, we introduce a distance measurement technique, using the Euclidean distance function to replace the optimal separating hyper-plane as the classification decision making function in the SVM. In our approach, the support vectors for each category are identified from the training data points during training phase using the SVM. In the classification phase, when a new data point is mapped into the original vector space, the average distances between the new data point and the support vectors from different categories are measured using the Euclidean distance function. The classification decision is made based on the category of support vectors which has the lowest average distance with the new data point, and this makes the classification decision irrespective of the efficacy of hyper-plane formed by applying the particular kernel function and soft margin parameter. We tested our proposed framework using several text datasets. The experimental results show that this approach makes the accuracy of the Euclidean-SVM text classifier to have a low impact on the implementation of kernel functions and soft margin parameter C.

In the present study, Fe3O4/ZnO core/shell nanocrystals (NCs) are synthesized via seed-meditated growth approach in nonhydrolytic condition. The controlling process of thermal pyrolysis of zinc acetate (ZnAc) renders a condition to overgrow ZnO layer on the surface of Fe3O4 NCs (seeds). The transmission electron microscope (TEM) micrograph shows that Fe3O4/ZnO NCs are spherical in shape, highly monodispersed, and exhibiting responding shell thickness by varying the mole ratio of seed to shell precursor. The X-ray powder diffraction patterns (XRD) for Fe3O4/ZnO NCs reveal the coexistence of both Fe3O4 and ZnO crystal structures, which the patterns can be well indexed with the standard powder diffraction patterns of both materials. The NCs exhibit superparamagnetism corresponding to an external magnet field provided by vibrating sample magnetometer (VSM) and show red-shift phenomenon under UV excitation at room temperature. The NCs are magnetically separable upon application of 0.6 T magnet, which shows that high potentiality of the NCs to be used as regenerable catalyst material.