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.     

Electrochemical,SEM and XPS investigations on phosphoric acid treated surgical grade type 316L SS for biomedical applications

A simple surface pre-treatment method was attempted to establish a stable passive layer on the surface of surgical grade stainless steel (SS) of type 316L for biomedical applications. Surgical grade type 316L SS specimens were subjected to H₃PO₄ treatment for 1 h by completely immersing them in the acid solutions to develop a passive barrier film. The effect of various concentrations of phosphoric acid on the localized corrosion resistance behavior of type 316L SS was investigated through electrochemical techniques using cyclic polarization studies and electrochemical impedance spectroscopy (EIS). X-ray photoelectron spectroscopy (XPS) was used to evaluate the nature and composition of the passive films. The surface morphology and relative elemental composition of the untreated and acid treated surfaces subjected to anodic polarization was studied by scanning electron microscopy (SEM) and energy dispersive X-ray analysis (EDAX) techniques, respectively. Compared with untreated (pristine) 316L SS, the 40% acid treated surface formed a stable passive layer that had superior corrosion resistance.     

Minimally invasive injectable short nanofibers of poly(glycerol sebacate) for cardiac tissue engineering

Myocardial tissue lacks the ability to appreciably regenerate itself following myocardial infarction (MI) which ultimately results in heart failure. Current therapies can only retard the progression of disease and hence tissue engineering strategies are required to facilitate the engineering of a suitable biomaterial to repair MI. The aim of this study was to investigate the in?vitro properties of an injectable biomaterial for the regeneration of infarcted myocardium. Fabrication of core/shell fibers was by co-axial electrospinning, with poly(glycerol sebacate) (PGS) as core material and poly-l-lactic acid (PLLA) as shell material. The PLLA was removed by treatment of the PGS/PLLA core/shell fibers with DCM:hexane (2:1) to obtain PGS short fibers. These PGS short fibers offer the advantage of providing a minimally invasive injectable technique for the regeneration of infarcted myocardium. The scaffolds were characterized by SEM, FTIR and contact angle and cell-scaffold interactions using cardiomyocytes. The results showed that the cardiac marker proteins actinin, troponin, myosin heavy chain and connexin 43 were expressed more on short PGS fibers compared to PLLA nanofibers. We hypothesized that the injection of cells along with short PGS fibers would increase cell transplant retention and survival within the infarct, compared to the standard cell injection system.     

Multi-capping agents in size confinement of ZnO nanostructured particles

In the present paper, we report a new approach to synthesize crystalline zinc oxide (ZnO) nanoparticles in the presence of multi-capping agents namely poly-vinylpyrrolidone (PVP) and citric acid (CA), with zinc acetate dihydrate and sodium hydroxide (NaOH as pellets) as a source material and their characteristic studies. The ZnO nanoparticles grown under this simple chemical process involve a heterogeneous chemical reaction in the presence of water as a solvent medium and reaction temperature of 100 °C for 48 h in a closed environment. The structural, optical and chemical features of ZnO nanoparticles were systematically studied by X-ray powder diffraction (XRD), Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FT-IR) and Ultra Violet–visible (UV–vis) absorption spectroscopy. The Williamson–Hall (W–H) plot was also performed to distinguish the effect of crystalline size-induced broadening and strain-induced broadening at Full Width Half Maximum (FWHM) of the XRD profile. The growth mechanism of ZnO nanoparticles and the effective capping mechanism shown by each capping agent are briefly discussed.     

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.     

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     

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.     

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.     

Investigation of fatigue failure of a stainless steel orthopedic implant device

An orthopedic implant (rush nail) fractured in a patient at a location that corresponded to the site of a prior fracture of the bone (right femur). The crack propagation in the implant proceeded from both sides of the nail, and the final fracture occurred by ductile shear in the midsection of the nail. Dimple structures and tear ridges between fatigue striation patches were observed on the fractured surface. Moreover, the device fractured within a short period of use. Contrary to post-procedure instructions, the patient placed the body’s full weight on the implanted leg at least once, perhaps twice, causing overload-induced fatigue failure of the implant.     

In vitro corrosion evaluation of nitrogen ion implanted titanium in simulated body fluid

Surface modification of commercially pure (CP) titanium was attempted by nitrogen ion implantation to investigate corrosion resistance in simulated body fluid. Nitrogen ion was implanted at 70 keV energy for different doses ranging from 5 × 10¹⁵ to 2.5 × 10¹⁷ ions/cm². In Vitro Open Circuit Potential (OCP-time measurements and cyclic polarization studies were carried out to evaluate the corrosion resistance of the implanted specimens with reference to the unimplanted one. Specimens implanted at 4 × 10¹⁶ and 7 × 10¹⁶ ions/cm² showed optimum corrosion resistance, and implantation beyond this dose deteriorated the corrosion resistance. Gracing Incidence X-ray diffraction (GIXD) was employed on implanted specimens to understand the phases formed with increasing doses. The results of the present investigation indicated that nitrogen ion implantation can be used as a viable method for improving corrosion resistance of titanium. Nature of the surface and reason for the variation and improvement in corrosion resistance are discussed in detail.