Effects of anodizing voltage on the anodized and hydrothermally treated titanium surface

Anodic oxidation is the process of creating a titanium oxide layer with various defects more dense and stable. In this study, a dense, stable and porous oxide layer was formed using anodic spark oxidation on pure titanium surface and hydroxyapatite crystals were formed on its surface via a hydrothermal treatment. A mixture of 0.02M−GP (Glycerolphosphate disodium salt) and 0.2M-CA (Calcium acetate) was used as an electrolyte. By increasing the anodizing voltage to 220, 260, 300, and 360 V, the effects of the anodizing voltage were examined by evaluating the film properties after anodization and a hydrothermal treatment. Breakdown occurred around 230 V. As the voltage increased after breakdown, the pore size increased. After the hydrothermal treatment, the amount of HA crystal precipitation was also increased as the voltage increased. The mean surface roughness (Ra) of the anodizing surface was also increased as the voltage increased. The Ra value was larger in the hydrothermally treated group compared with the group treated with anodization as a result of the HA crystals present on the surface after the hydrothermal treatment. Corrosion resistance of the surface modified by anodization was significantly increased in a saline solution compared to that for the non-treated group; this increased further after the hydrothermal treatment. These increases were most likely due to a thick stable oxide layer formed through anodization. Thus, it is believed that titanium with its surface modified through anodic spark oxidation would be a suitable biomaterial due to its corrosion resistance and biocompatibility.     

Micro-end-milling of single-crystal silicon

Ductile-regime machining of silicon using micro-end-mill is almost impossible because of the brittle properties of silicon, crystal orientation effects, edge radius of the cutter and the hardness of tool materials. Micro-end-milling can potentially be used to create desired three dimensional (3D) free form surface features using the ductile machining technology for single-crystal silicon. There is still a lack of fundamental understanding of micro-end-milling of single-crystal silicon using diamond-coated tool, specifically basic understanding of material removal mechanism, cutting forces and machined surface integrity in micro-scale machining of silicon. In this paper, further research to understand the chip formation mechanism was conducted. An analysis was performed to discover how the chips are removed during the milling process. Brittle and ductile cutting regimes corresponding to machined surfaces and chips are discussed. Experiments have shown that single-crystal silicon can be ductile machined using micro-end-milling process. Forces generated when micro-end-milling single-crystal silicon are used to determine the performance of the milling process. Experimental results show that the dependence of the cutting force on the uncut chip thickness can be well described by a polynomial function order n. As cutting regime becomes more brittle, the cutting force has more complex function.     

Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data

Uncertainty-based multidisciplinary design optimization (UMDO) has been widely acknowledged as an advanced methodology to address competing objectives and reliable constraints of complex systems by coupling relationship of disciplines involved in the system. UMDO process consists of three parts. Two parts are to define the system with uncertainty and to formulate the design optimization problem. The third part is to quantitatively analyze the uncertainty of the system output considering the uncertainty propagation in the multidiscipline analysis. One of the major issues in the UMDO research is that the uncertainty propagation makes uncertainty analysis difficult in the complex system. The conventional methods are based on the parametric approach could possibly cause the error when the parametric approach has ill-estimated distribution because data is often insufficient or limited. Therefore, it is required to develop a nonparametric approach to directly use data. In this work, the nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data is proposed. To handle limited data, three processes are also adopted. To verify the performance of the proposed method, mathematical and engineering examples are illustrated.     

An online fault tolerant actor-critic neuro-control for a class of nonlinear systems using neural network HJB approach

In this paper, we propose an actor-critic neuro-control for a class of continuous-time nonlinear systems under nonlinear abrupt faults, which is combined with an adaptive fault diagnosis observer (AFDO). Together with its estimation laws, an AFDO scheme, which estimates the faults in real time, is designed based on Lyapunov analysis. Then, based on the designed AFDO, a fault tolerant actor- critic control scheme is proposed where the critic neural network (NN) is used to approximate the value function and the actor NN updates the fault tolerant policy based on the approximated value function in the critic NN. The weight update laws for critic NN and actor NN are designed using the gradient descent method. By Lyapunov analysis, we prove the uniform ultimately boundedness (UUB) of all the states, their estimation errors, and NN weights of the fault tolerant system under the unpredictable faults. Finally, we verify the effectiveness of the proposed method through numerical simulations.     

Ultrastructural investigation of intact orbital implant surfaces using atomic force microscopy

This study examined the surface nanostructures of three orbital implants: nonporous poly(methyl methacrylate) (PMMA), porous aluminum oxide and porous polyethylene. The morphological characteristics of the orbital implants surfaces were observed by atomic force microscopy (AFM). The AFM topography, phase shift and deflection images of the intact implant samples were obtained. The surface of the nonporous PMMA implant showed severe scratches and debris. The surface of the aluminum oxide implant showed a porous structure with varying densities and sizes. The PMMA implant showed nodule nanostructures, 215.56 ± 52.34 nm in size, and the aluminum oxide implant showed crystal structures, 730.22 ± 341.02 nm in size. The nonporous PMMA implant showed the lowest roughness compared with other implant biomaterials, followed by the porous aluminum oxide implant. The porous polyethylene implant showed the highest roughness and severe surface irregularities. Overall, the surface roughness of orbital implants might be associated with the rate of complications and cell adhesion. SCANNING 33: 211–221, 2011. © 2011 Wiley Periodicals, Inc.     

Perceived Visual Complexity and Visual Search Performance of Automotive Instrument Cluster: A Quantitative Measurement Study

Advancements in technology have spurred the development of new in-vehicle applications. Drivers are faced with different driving contexts due to an increase in the number of devices that provide a wealth of diverse information. However, such a scenario can cause drivers to become distracted. Therefore, research on how the presentation of visual information can affect drivers’ performance is important. In this study, an analysis of quantifiable measurements that affect drivers’ perception of visual complexity and visual search performance was conducted. A questionnaire was administered to assess subjective perception of visual complexity, and a user experiment using eye tracking was designed to explore participants’ visual search performance. The results of subjective visual complexity perception and visual search performance suggested that some objective measurement variables were significantly related only to perceived visual complexity, whereas others affected both subjective and behavioral measurements. Thus it is possible to predict which quantifiable measurement variables affect subjective perception of visual complexity and which affect visual search performance. Therefore, this study allows understanding and explaining of perception of visual complexity by quantifiable measurements and the different ways by which these measurements affect visual search performance.     

Extraction of enhanced evoked potentials using wavelet filtering

In order to remove physiological artefacts and gain the improved evoked potentials, we propose a filtering method using the multi-resolution wavelet transform. The wavelet transform is repeatedly performed until all resolution levels are obtained. It decomposes the measured evoked potentials into scale coefficients corresponding to low frequency components and wavelet coefficients corresponding to high frequency components. In the wavelet domain, artefacts are dispersed mainly at the wavelet coefficients rather than the scaling coefficients. Thus, when the inverse wavelet transform is performed, this method shrinks the wavelet coefficients to reduce artefacts with shrinkage functions. By repeatedly performing the inverse wavelet transform, an evoked potential having the reduced artefacts and background noise is obtained. In this study, quantitative evaluation with simulation data and actual clinical data were conducted. As a result, characteristic peaks of evoked potential could be gained removing background EEG and artefacts using suggested shrinkage function. It was improved more than 0.2–1.6Db compared to the conventional averaging method. Also, the system for measuring and analyzing evoked potentials using DSP is implemented.     

A comparison of generalized linear discriminant analysis algorithms

Linear discriminant analysis (LDA) is a dimension reduction method which finds an optimal linear transformation that maximizes the class separability. However, in undersampled problems where the number of data samples is smaller than the dimension of data space, it is difficult to apply LDA due to the singularity of scatter matrices caused by high dimensionality. In order to make LDA applicable, several generalizations of LDA have been proposed recently. In this paper, we present theoretical and algorithmic relationships among several generalized LDA algorithms and compare their computational complexities and performances in text classification and face recognition. Towards a practical dimension reduction method for high dimensional data, an efficient algorithm is proposed, which reduces the computational complexity greatly while achieving competitive prediction accuracies. We also present nonlinear extensions of these LDA algorithms based on kernel methods. It is shown that a generalized eigenvalue problem can be formulated in the kernel-based feature space, and generalized LDA algorithms are applied to solve the generalized eigenvalue problem, resulting in nonlinear discriminant analysis. Performances of these linear and nonlinear discriminant analysis algorithms are compared extensively.     

A study of roll‐printing technology for TFT‐LCD fabrication

Abstract— Recently, potential breakthrough technologies for low‐cost processing of TFT‐LCDs and new process developments for flexible‐display fabrication have been widely studied. A roll‐printing process using etch‐resist material as a replacement for photolithographic patterning was investigated. The characterization of the properties of patterns formed in roll printing, a method to fabricate cliché plates for fine patterns, and the design of a new formulation for resist printing ink is reported. The pattern position accuracy, which is one of the most important issues for the successful application of printing processes in display manufacturing was studied and how it can be improved by optimizing the blanket roll structure is explained. New design rules for the layout of the thin‐film‐transistor array was derived to improve the compatibility of roll printing. As a result, a prototype 15‐in.‐XGA TFT‐LCD panel was fabricated by using printing processes to replace all the photolithographic patterning steps conventionally used.     

Pharmacophore and docking-based combined in-silico study of KDR inhibitors

The growth and metastasis of solid tumors is dependent on angiogenesis. The vascular endothelial growth factor (VEGF) and its cell surface receptor in human KDR (kinase domain containing receptor or VEGFR-2) have particular interest because of their importance in angiogenesis. The development of novel inhibitors of VEGFR-2 would be helpful to check the growth of tumors. Quantitative structure activity relationship (QSAR) analyses used to understand the structural factors affecting inhibitory potency of thiazole-substituted pyrazolone derivatives. Several pharmacophore-based models indicated the importance of steric, hydrophobic and hydrogen bond acceptor groups to inhibitory activity. The comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analyses (CoMSIA) based 3D-QSAR models were derived using pharmacophore-based alignment. Both CoMFA (q² = 0.70, r² = 0.97 and ) and CoMSIA (q² = 0.54, r² = 0.82 and ) gave reasonable results. The molecular docking (receptor-guided technique) with a recently reported receptor structure (PDB = 1YWN) were performed. The docked alignment was subsequently used for 3D-QSAR (CoMFA; q² = 0.56, r² = 0.97, , CoMSIA; q² = 0.58 r² = 0.91, ). The overall both studies were indicated, steric, electrostatic and hydrogen bond acceptor effects contribute to the inhibitory activity. CoMFA and CoMSIA models suggested that a positive bulk with hydrophobic effect is desirable around position 4 and 5 and hydrogen bond acceptor groups around pyrazolones ring will be helpful.