Double-plane pattern enhancement of rectangular slotted waveguide antennas using slotted concave surfaces

Conventional slotted waveguide antennas are well known and prominently used for many military and even marketable applications. This is due to their different advantageous electrical and mechanical characteristics. It is also well known that this type of antennas is a typical array of slots with which the width of the main beam can be controlled in only one plane by using a different number of elements in such antennas. Numerous research, discuss the capability of using an array of such antennas in order to narrow down the main beam in both planes and even achieve a scanning phased array. In this paper, a new and broader approach is presented concerning beam width control of the typical configuration of such antennas. This work presents the ability to control the main lobe in both planes using only one slotted waveguide to a certain extent. Different simulation results are considered at the 2.4 GHz frequency demonstrating improved effectiveness in terms of directivity and high gain as well as minimizing the side-lobe level and mechanically controlling the main lobe of the antenna.     

Selection of optimal denoising-based regularization hyper-parameters for performance improvement in a sensor validation model

Multilayered auto-associative neural architectures have widely been used in empirical sensor modeling. Typically, such empirical sensor models are used in sensor calibration and fault monitoring systems. However, simultaneous optimization of related performance metrics, i.e., auto-sensitivity, cross-sensitivity, and fault-detectability, is not a trivial task. Learning procedures for parametric and other relevant non-parametric empirical models are sensitive to optimization and regularization methods. Therefore, there is a need for active learning strategies that can better exploit the underlying statistical structure among input sensors and are simple to regularize and fine-tune. To this end, we investigated the greedy layer-wise learning strategy and denoising-based regularization procedure for sensor model optimization. We further explored the effects of denoising-based regularization hyper-parameters such as noise-type and noise-level on sensor model performance and suggested optimal settings through rigorous experimentation. A visualization procedure was introduced to obtain insight into the internal semantics of the learned model. These visualizations allowed us to suggest an implicit noise-generating process for efficient regularization in higher-order layers. We found that the greedy-learning procedure improved the overall robustness of the sensor model. To keep experimentation unbiased and immune to noise-related artifacts in real sensors, the sensor data were sampled from simulators of a nuclear steam supply system of a pressurized water reactor and a Tennessee Eastman chemical process. Finally, we compared the performance of an optimally regularized sensor model with auto-associative neural network, auto-associative kernel regression, and fuzzy similarity-based sensor models.     

The production of a cellular graphene array by scanning probe lithography and its ability to store electrical charge

A graphene cellular array on an insulating SiO₂ layer was fabricated by scanning probe lithography. The graphene layer was oxidized by an electric field which was applied between the cantilever tip and Si substrate without any electrode directly connected to the graphene layer. When the bias voltage was applied on a cell of patterned graphene through the cantilever tip, charge was accumulated on the cell and preserved for a long time without decay. The accumulated charge and the surface potential were measured by an electrostatic force microscope. The charge retention was measured as a function of time, and the decay time constant was estimated to be ～70 min.     

Review of titania nanotubes: Fabrication and cellular response

Titania (TiO₂) nanotube is gaining prominence as an implantation material due to its unique properties such as high specific surface area and the ability to exhibit positive cellular response. In this paper, we briefly review the current state of fabrication methods to synthesize nanotubular TiO₂ surface topography, and discuss its effect on cellular response of different cells in terms of cell adhesion, proliferation and differentiation. In vitro and in vivo studies by using TiO₂ nanotubes are also presented establishing the potential of nanotubes in biomedical applications. Finally, an outlook of future growth of research in TiO₂ nanostructures beyond the nanotubes is provided     

3D numerical investigation of ZnO/Zn hydrolysis for hydrogen production

The current research is focused on the hydrogen production through a two‐step ZnO/Zn thermochemical water splitting cycle. In the present paper, numerical modeling of the second step is conducted using Computational Fluid Dynamics (CFD)2, where steam reacts with zinc to produce hydrogen. The parametric study shows that the hydrogen yield is relatively insensitive to the steam/zinc molar ratio and inversely proportional to the argon/steam molar ratio. For large argon to steam molar ratios, hydrogen yield is relatively insensitive to the inlet temperature of zinc and steam, and increases marginally with an increase in the argon inlet temperature. Five different reactor configurations were evaluated comprehensively. Among all configurations, a cylindrical reactor with a tangential inlet for argon and zinc, and a radial inlet for steam (both in the bottom plane of the reactor) and a tangential outlet in the top plane of the reactor produced the highest hydrogen yield of 88%. Copyright © 2013 John Wiley & Sons, Ltd.     

A Puzzle of Quantum Phase Transition on Gd-IMC by Tuning of Conduction Electron Concentration

Based on the critical unstable of both crystal and magnetic structure of Gd-intermetallic compound near the competition of two strongly independent subsystem （＂local 4f7＂ and ＂conduction electron concentration＂）, a new QPT （quantum point transition） is predicted by calculation of： （1） The band structure and density of state by density functional theory where a strong narrowing fluidity of fermions around EF with shifted to negative value ＂-0.8 eV ＂is observable in the Gd-intermetalliccompound system while in the Y-case, it is not dominated. An antiferromagnetic state on the fluidity of conduction band can be investigated; （2） The internal magnetic field due to short range exchange interaction Jij between the nearest neighbor of local magnetic moment of stable s-state of Gd （L = 0） through the mean field approximation and of Eigenvalue-Eigenfunction ~.（k） are calculated. While a strong negative value of Jy is predicted, the eigenvalue L（k） of the system shows a strong antiferromagnetic phase in the reciprocal lattice direction 〈010〉, 〈001〉 in the correlation length 3.38 ~A. Although the antiferromagnetic state at Rc 〈_ 3.38 °A is a puzzle but it is completely dominated at Rc = 9 °A after passing through the competition between ）.λmin（O） and λmin（π） in the ranger of 3.2 °A 〈 Rc 〈 9 °A. Since both of the antiferromagnetic subsystems are sensitive to the predicted KF, the effect of decreasing of conduction electron is proposed to investigate, the change of the antiferromagnetic ordering state to the competition of ferromagnetic state （in direction 〈010〉） and antiferromagnetic state （in direction 〈001 〉 and 〈 100〉） resulted to paramagnetic state in the long range Rc = 9 °A.