Attenuated total reflection fourier transform infrared spectroscopy towards disclosing mechanism of bacterial adhesion on thermally stabilized titanium nano-interfaces

Titanium is widely used as medical implant material and as condenser material in the nuclear industry where its integrity is questioned due to its susceptibility to bacterial adhesion. A systematic investigation on the influence of thermally (50–800 °C) stabilized titanium (TS-Ti) nano oxide towards bacterial adhesion was carried out. The results showed that below 350 °C significant bacterio-phobicity was observed, while above 500 °C significant affinity towards bacterial cells was recorded. Conventional characterization tools such as HR-TEM and XRD did not provide much insight on the changes occurring on the oxide film with heat treatment, however, attenuated total reflection fourier transform infrared spectroscopy (ATR-FTIR) of the surface showed significant changes in the spectral pattern as a function of increasing heat treatment. It was observed that elevated OH, N–H and C=O groups and rutile titania on the TS-Ti oxide films led to higher affinity for bacterial adhesion. On the other hand low temperature TS-Ti nanooxide films (<350 °C) showed high C–H groups and decreased OH groups on their surface, which possibly contributed towards their bacterio-phobicity. The TS-Ti nanooxide film grown at 50 °C was observed to be the most efficient anti-bacterial adhesion interface, while the 800 °C interface was the one showing highest affinity towards bacterial adhesion. This study confirms the successful application of ATR-FTIR technique for nano-oxide film characterization and towards understanding the variations in bacterial interaction of such nano interfaces.     

Characterization and evaluating the mechanical performance of halloysite nanotubes reinforced acrylonitrile butadiene styrene/polycarbonate composites exposed to aggressive environmental conditions

Developing light weight polymer based composites dispersed with novel reinforcements which can function well in the presence of aggressive environments is an active research field in the materials engineering. Hence, in the current work, halloysite nanotubes (1 %, 2 %, 4 %, 6 %, 8 % and 10 % by weight) were reinforced into acrylonitrile butadiene styrene/polycarbonate blend and the role of reinforcing phases on the mechanical performance under aggressive environmental conditions has been evaluated. Hardness was measured as gradually increased in the composites with the increased content of the reinforcements. Impact strength of the composites was observed as increased in the composites up to 4 % reinforcement and further decreased. Increased strength was measured for the composite up to 2 % reinforcement. Ductility of the composites was decreased as reflected form the decreased % of elongation with the higher fraction of reinforcements due to induced brittleness. The composites were exposed to diluted sulfuric acid for 3 h and 6 h at 60 °C and then subjected to tensile loading. With the increased time of exposure, composites with 1 % and 2 % reinforcement exhibited relatively better performance.     

Chromatic aberrations of radial gradient-index lenses. I. Theory

Chromatic effects of radial gradient-index materials have been analyzed, and several important conclusions have been derived in terms of material dispersion data. The use of Buchdahl dispersion data, both for base glass materials and ion-exchange pairs, provides some simple relationships for chromatic aberration and helps in selecting suitable materials for producing achromatic radial gradient-index lenses.     

Delay‐dependent LMI condition for global asymptotic stability of discrete‐time uncertain state‐delayed systems using quantization/overflow nonlinearities

A delay‐dependent criterion for the global asymptotic stability of a class of uncertain discrete‐time state‐delayed systems using various combinations of quantization and overflow nonlinearities is presented. The proposed criterion is in the form of a linear matrix inequality and, hence, computationally tractable. A numerical example highlighting the usefulness of the proposed criterion is given. Copyright © 2010 John Wiley & Sons, Ltd.     

Visual quality assessment algorithms: what does the future hold?

Creating algorithms capable of predicting the perceived quality of a visual stimulus defines the field of objective visual quality assessment (QA). The field of objective QA has received tremendous attention in the recent past, with many successful algorithms being proposed for this purpose. Our concern here is not with the past however; in this paper we discuss our vision for the future of visual quality assessment research. We first introduce the area of quality assessment and state its relevance. We describe current standards for gauging algorithmic performance and define terms that we will use through this paper. We then journey through 2D image and video quality assessment. We summarize recent approaches to these problems and discuss in detail our vision for future research on the problems of full-reference and no-reference 2D image and video quality assessment. From there, we move on to the currently popular area of 3D QA. We discuss recent databases, algorithms and 3D quality of experience. This yet-nascent technology provides for tremendous scope in terms of research activities and we summarize each of them. We then move on to more esoteric topics such as algorithmic assessment of aesthetics in natural images and in art. We discuss current research and hypothesize about possible paths to tread. Towards the end of this article, we discuss some other areas of interest including high-definition (HD) quality assessment, immersive environments and so on before summarizing interesting avenues for future work in multimedia (i.e., audio-visual) quality assessment.     

Motion in ambiguity: Coordinated active global localization for multiple robots

The task of the robot in localization is to find out where it is, through sensing and motion. In environments which possess relatively few features that enable a robot to unambiguously determine its location, global localization algorithms can result in ‘multiple hypotheses’ locations of a robot. This is inevitable with global localization algorithms, as the local environment seen by a robot repeats at other parts of the map. Thus, for effective localization, the robot has to be actively guided to those locations where there is a maximum chance of eliminating most of the ambiguous states — which is often referred to as ‘active localization’. When extended to multi-robotic scenarios where all robots possess more than one hypothesis of their position, there is an opportunity to do better by using robots, apart from obstacles, as ‘hypotheses resolving agents’. The paper presents a unified framework which accounts for the map structure as well as measurement amongst robots, while guiding a set of robots to locations where they can singularize to a unique state. The strategy shepherds the robots to places where the probability of obtaining a unique hypothesis for a set of multiple robots is a maximum. Another aspect of framework demonstrates the idea of dispatching localized robots to locations where they can assist a maximum of the remaining unlocalized robots to overcome their ambiguity, named as ‘coordinated localization’. The appropriateness of our approach is demonstrated empirically in both simulation & real-time (on Amigo-bots) and its efficacy verified. Extensive comparative analysis portrays the advantage of the current method over others that do not perform active localization in a multi-robotic sense. It also portrays the performance gain by considering map structure and robot placement to actively localize over methods that consider only one of them or neither. Theoretical backing stems from the proven completeness of the method for a large category of diverse environments.     

Reduced one-against-all method for multiclass SVM classification

We present an improved version of one-against-all method for multiclass SVM classification based on subset sample selection, named reduced one-against-all, to achieve high performance in large multiclass problems. Reduced one-against-all drastically decreases the computing effort involved in training one-against-all classifiers, without any compromise in classification accuracy. Computational comparisons on publicly available datasets indicate that the proposed method has comparable accuracy to that of conventional one-against-all method, but with an order of magnitude faster. On the largest dataset considered, reduced one-against-all method achieved 50% reduction in computing time over one-against-all method for almost the same classification accuracy. We further investigated reduced one-against-all with linear kernel for multi-label text categorization applications. Computational results demonstrate the effectiveness of the proposed method on both the text corpuses considered.     

Express Virtual Channels with Capacitively Driven Global Links

Networks on chip must deliver high bandwidth at low latencies while keeping within a tight power envelope. Using express virtual channels for flow control improves energy-delay throughput by letting packets bypass intermediate routers, but EVCs have key limitations. Nochi (NoC with hybrid interconnect) overcomes these limitations by transporting data payloads and control information on separate planes, optimized for bandwidth and latency respectively.