Concentration-dependent growth and morphology of doped polyaniline nanowires

Polyaniline nanowires with entangled network structures were synthesised through an electropolymerisation route using a step galvanostatic method on titanium substrate. The morphology of the synthesised polyaniline was investigated by scanning electron microscopy and transmission electron microscopy. The dependence of morphology on the concentration and duration of synthesis has also been studied. Structural characterisation has been done by UV–VIS spectroscopy and Fourier transform infrared spectroscopy. The concentration of the monomer and the duration of synthesis affect the morphology as well as the amount of nanowires deposited.     

Effect of nitrogen ion implantation on the localized corrosion behavior of titanium modified type 316L stainless steel in simulated body fluid

Nitrogen ion implantation on titanium-modified type 316L stainless steel (SS) at the energy of 70 keV was carried out at different doses ranging from 1×10¹⁵ to 2.5×10¹⁷ ions/cm². These samples were subjected to open circuit potential (OCP)—time measurement, cyclic polarization, and accelerated leaching studies—in order to discover the optimum dose that can provide good localized corrosion resistance in a simulated body fluid condition. The results showed that the localized corrosion resistance improved with an increase in doses up to 1×10¹⁷ ions/cm², beyond which it started to deteriorate. The results of the accelerated leaching studies showed that the leaching of the major alloying elements was arrested upon nitrogen ion implantation. Gracing incidence x-ray diffraction studies showed the formation of chromium nitrides at a dose of 2.5×10¹⁷ ions/cm². X-ray photoelectron spectroscopy studies revealed the presence of these chromium nitrides in the passive film, which was attributed to the decreased corrosion resistance at a higher dose. Secondary ion mass spectroscopy studies on the passive film showed the variation in the depth profile upon nitrogen ion implantation. Thus, nitrogen ion implantation can be effectively used as a method to improve the corrosion resistance of the orthopedic implant devices made of titanium-modified type 316L SS. The nature of the passive film and its influence on corrosion resistance are discussed in this article.     

Wing-body interference using a hybrid panel method

Summary The problem of determining the pressures acting on bodies in the region of wing-body interference in subsonic, inviscid flows is dealt with in detail in the current paper in the framework of a hybrid vortex lattice/first order panel method. Here the lift carryover and the wing-body interference effects are accounted for by the use of interpenetrating singularity panels. The non-lifting components are modeled by constant source panels and lifting elements by horse shoe vortices. Several alternative models have been programmed to study their effects on the pressure distribution in the wing-body interference region. The final procedure has been validated by comparing the pressures from experiments for several wing-body combinations.Notation AR Wing aspect ratio - b Wing span - B Body - C _p ,C _p Pressure coefficient,P-P /1/2 U ² - ,n Unit normal to panel; also direction of normal - r(p, q) Distance between two points in three dimensional space; especially between a point where a source is located and a point where the velocities are computed - U ,V _FS Free stream velocity - V _B Velocity due to body sources - V _D Velocity due to distributed vortices - V _T Velocity due to wing thickness sources - V _P Total velocity at pointP - V _c+ Velocity due to camber and incidence - W Wing - X _B Distance along length of body - Angle of attack - , Vortex strength - Air density - Azimuthal distribution of panels on fuselage - Non-dimensional spanwise station - Source density - Free stream conditions     

Development of Silica Glass Coatings on 316L SS and Evaluation of its Corrosion Resistance Behavior in Ringer’s Solution

Sol-gel derived silica glasses have many promising features, including low-temperature preparation as well as chemical and physical stability. Two silica glasses with Si₁₀₀ and Si₈₀ composition were prepared to understand the factors contributing to the rate of bioactivity. The effects of pH, solution aging temperature, and molar ratio of H₂O/tetraethyl orthosilicate (TEOS) were studied, and the obtained powder sample was characterized by Fourier transform infrared spectroscopy, X-ray diffraction studies, and scanning electron microscopy. The synthesized silica glasses were deposited on 316L SS by the spin coating method at the optimized speed of 2000?revolutions per minute. The corrosion resistance behavior of the coatings was determined by (1) open-circuit potential vs time of exposure, (2) electrochemical impedance spectroscopy, and (3) cyclic polarization in Ringer??s solution. A higher breakdown potential (E _b) and repassivation potential (E _p) value with lower current density was obtained from cyclic polarization. Similar results were observed from impedance analysis with higher charge transfer resistance (R _ct) and lower double layer capacitance (C _dl) indicating the corrosion resistance behavior of the coatings compared with the uncoated 316L stainless steel. From the results, it was observed that both Si₁₀₀ and Si₈₀ glass coatings had a positive effect on the corrosion resistance behavior. An adhesive strength of 46?MPa and 45?MPa was obtained for the Si₁₀₀ and Si₈₀ coatings, respectively. An accelerated leach out study was carried out by impressing the potential at their breakdown potential to determine the effect of glass coating for long-term contact between the implant and a normal biological medium.     

A survey of the state of the art in learning the kernels

In recent years, the machine learning community has witnessed a tremendous growth in the development of kernel-based learning algorithms. However, the performance of this class of algorithms greatly depends on the choice of the kernel function. Kernel function implicitly represents the inner product between a pair of points of a dataset in a higher dimensional space. This inner product amounts to the similarity between points and provides a solid foundation for nonlinear analysis in kernel-based learning algorithms. The most important challenge in kernel-based learning is the selection of an appropriate kernel for a given dataset. To remedy this problem, algorithms to learn the kernel have recently been proposed. These methods formulate a learning algorithm that finds an optimal kernel for a given dataset. In this paper, we present an overview of these algorithms and provide a comparison of various approaches to find an optimal kernel. Furthermore, a list of pivotal issues that lead to efficient design of such algorithms will be presented.     

Forecasting of science & technology expenditure of India by simulation method

In this paper, an attempt has been made to forecast science and technology expenditure of India by simulation method as well as by regression method. The base data used are the average yearly growth rates of science and technology expenditure both at current and constant prices. For the regression analyses, the yearly growth rates of the gross national product at factor cost both at current and constant prices are used as independent variable. The forecast values of S&T expenditure have been given up to seven years from 1982–83, for both simulation method and regression method.The content of the paper is the work of author's personal research and it does not reflect the views of the Government of India.     

Formalizing real-time scheduling using priority-based supervisory control of discrete-event systems

In this note, we formalize real-time task scheduling by applying an extension of supervisory control theory (SCT) of discrete-event systems to real-time models. The set of all possible timed traces of the system is specified by a discrete timed automaton where each transition is associated with an event occurrence or the passage of one unit of time. We introduce priorities to SCT, and apply them to the setting of discrete timed automata in order to develop a formal and unified framework for task scheduling on a single CPU.     

Novel direct and self-regulating approaches to determine optimum growing multi-experts network structure

This work presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportion error among the local experts. After reaching the specified size of the network, redundant experts removal algorithm is invoked to prune the size of the network based on the ranking of the experts. However, GMN is not ergonomic due to too many network control parameters. Therefore, a self-regulating GMN (SGMN) algorithm is proposed. SGMN adopts self-adaptive learning rates for gradient-descent learning rules. In addition, SGMN adopts a more rigorous clustering method called fully self-organized simplified adaptive resonance theory in a modified form. Experimental results show SGMN obtains comparative or even better performance than GMN in four benchmark examples, with reduced sensitivity to learning parameters setting. Moreover, both GMN and SGMN outperform the other neural networks and statistical models. The efficacy of SGMN is further justified in three industrial applications and a control problem. It provides consistent results besides holding out a profound potential and promise for building a novel type of nonlinear model consisting of several local linear models.     

The variants of the harmony search algorithm: an overview

The harmony search (HS) algorithm is a relatively new population-based metaheuristic optimization algorithm. It imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. Since the emergence of this algorithm in 2001, it attracted many researchers from various fields especially those working on solving optimization problems. Consequently, this algorithm guided researchers to improve on its performance to be in line with the requirements of the applications being developed. These improvements primarily cover two aspects: (1) improvements in terms of parameters setting, and (2) improvements in terms of hybridizing HS components with other metaheuristic algorithms. This paper presents an overview of these aspects, with a goal of providing useful references to fundamental concepts accessible to the broad community of optimization practitioners.     

An unsupervised approach to learn the kernel functions: from global influence to local similarity

Recently there has been a steep growth in the development of kernel-based learning algorithms. The intrinsic problem in such algorithms is the selection of the optimal kernel for the learning task of interest. In this paper, we propose an unsupervised approach to learn a linear combination of kernel functions, such that the resulting kernel best serves the objectives of the learning task. This is achieved through measuring the influence of each point on the structure of the dataset. This measure is calculated by constructing a weighted graph on which a random walk is performed. The measure of influence in the feature space is probabilistically related to the input space that yields an optimization problem to be solved. The optimization problem is formulated in two different convex settings, namely linear and semidefinite programming, dependent on the type of kernel combination considered. The contributions of this paper are twofold: first, a novel unsupervised approach to learn the kernel function, and second, a method to infer the local similarity represented by the kernel function by measuring the global influence of each point toward the structure of the dataset. The proposed approach focuses on the kernel selection which is independent of the kernel-based learning algorithm. The empirical evaluation of the proposed approach with various datasets shows the effectiveness of the algorithm in practice.