Comparative investigation of the biocompatibility of various silicon nitride ceramic qualities in vitro

There is a controversy about the biocompatibility of silicon nitride ceramics contained in the literature, which appears to be related to a factor of the individual chemical composition of different qualities of silicon nitride ceramics and of the different surface properties. This study attempts to investigate the cytotoxicity of different qualities of industrial silicon nitride ceramics applying an L929-cell culture model in a direct contact assay combined with a cell viability assessment. Five different qualities of industrial standard silicon nitride ceramics were chosen for in vitro testing. The chemical composition was determined by EDS analysis. Different biomedically approved aluminium oxide qualities, a titanium alloy, glass and polyvinylchloride (PVC) served as control materials. L929 mice fibroblasts were incubated directly on the materials for 24 h, stained with bisbenzimide and propidium iodine for double fluorochromasia viability testing, and evaluated by inversion-fluorescence microscopy to control cell morphology, viability and cell counts compared to empty well values. Scanning electron microscopy was applied to additionally investigate cell morphology. There was no observation of cytotoxic effects on the silicon nitride ceramic samples; moreover cell morphology remained the same as on aluminium oxide and titanium. Viability testing revealed the presence of avital cells exclusively on PVC, which served as a negative control. Cell counts on all polished surfaces showed significantly higher numbers, whereas some rough surface samples showed significantly lower numbers. We conclude that silicon nitride ceramics show no cytotoxic effects and should be considered for biomedical application owing to its favourable physiochemical properties, especially its superior resistance to mechanical stress, which would be useful for compression loaded conditions. Polished surfaces would appear to promote advanced biocompatibility.     

Two software tools for plane-wave scattering on nonspherical particles in the German Aerospace Center's Virtual Laboratory

We present the methodological background, the range of applicability, and the on-line usage of two software packages, MIESCHKA and CYL, which we have developed for light-scattering analysis on nonspherical particles. MIESCHKA solves Maxwell's equations in a rigorous way but is restricted to axisymmetric geometries, whereas CYL is an approximation for finite columns with nonspherical cross sections. We have established an easy on-line access to both of these programs through the Virtual Laboratory. Its generic software infrastructure was designed to simplify the web-based usage and to support the intercomparability of scientific software.     

Assessing forest fire as a potential PCDD/F source in Queensland, Australia

Forest fires are suggested as a potential and significant source of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs), even though no studies to date provide sufficient evidence to confirm forest fires as a source of PCDD/Fs. Recent investigations in Queensland, Australia have identified a widespread contamination of PCDDs (in particular OCDD) in soils and sediments in the coastal region from an unknown source of PCDD/Fs. Queensland is predominately rural; it has few known anthropogenic sources of PCDD/Fs, whereas forest fires are a frequent occurrence. This study was conducted to assess forest fires as a potential source of the unknown PCDD/F contamination in Queensland. A combustion experiment was designed to assess the overall mass of PCDD/Fs before and after a simulated forest fire. The results from this study did not identify an increase in sigmaPCDD/Fs or OCDD after the combustion process. However, specific non-2,3,7,8 substituted lower chlorinated PCDD/Fs were elevated after the combustion process, suggesting formation from a precursor. The results from this study indicate that forest fires are unlikely to be the source of the unknown PCDD contamination in Queensland, rather they are a key mechanism for the redistribution of PCDD/Fs from existing sources and precursors.     

Graph separators: a parameterized view

Graph separation is a well-known tool to make (hard) graph problems accessible to a divide-and-conquer approach. We show how to use graph separator theorems in combination with (linear) problem kernels in order to develop fixed parameter algorithms for many well-known NP-hard (planar) graph problems. We coin the key notion of glueable select&verify graph problems and derive from that a prospective way to easily check whether a planar graph problem will allow for a fixed parameter algorithm of running time for constant c. One of the main contributions of the paper is to exactly compute the base c of the exponential term and its dependence on the various parameters specified by the employed separator theorem and the underlying graph problem. We discuss several strategies to improve on the involved constant c.     

Rotation Averaging

This paper is conceived as a tutorial on rotation averaging, summarizing the research that has been carried out in this area; it discusses methods for single-view and multiple-view rotation averaging, as well as providing proofs of convergence and convexity in many cases. However, at the same time it contains many new results, which were developed to fill gaps in knowledge, answering fundamental questions such as radius of convergence of the algorithms, and existence of local minima. These matters, or even proofs of correctness have in many cases not been considered in the Computer Vision literature. We consider three main problems: single rotation averaging, in which a single rotation is computed starting from several measurements; multiple-rotation averaging, in which absolute orientations are computed from several relative orientation measurements; and conjugate rotation averaging, which relates a pair of coordinate frames. This last is related to the hand-eye coordination problem and to multiple-camera calibration.     

Consumer credit risk: Individual probability estimates using machine learning

Consumer credit scoring is often considered a classification task where clients receive either a good or a bad credit status. Default probabilities provide more detailed information about the creditworthiness of consumers, and they are usually estimated by logistic regression. Here, we present a general framework for estimating individual consumer credit risks by use of machine learning methods. Since a probability is an expected value, all nonparametric regression approaches which are consistent for the mean are consistent for the probability estimation problem. Among others, random forests (RF), k-nearest neighbors (kNN), and bagged k-nearest neighbors (bNN) belong to this class of consistent nonparametric regression approaches. We apply the machine learning methods and an optimized logistic regression to a large dataset of complete payment histories of short-termed installment credits. We demonstrate probability estimation in Random Jungle, an RF package written in C++ with a generalized framework for fast tree growing, probability estimation, and classification. We also describe an algorithm for tuning the terminal node size for probability estimation. We demonstrate that regression RF outperforms the optimized logistic regression model, kNN, and bNN on the test data of the short-term installment credits.     

Application of a new algorithm using Doppler information to retrieve complex wind fields over the Black Sea from ENVISAT SAR images

Several algorithms have been proposed to retrieve near-surface wind fields from C-band synthetic aperture radar (SAR) images acquired over the ocean. They mainly differ in the way they retrieve the wind direction. Conventionally, the wind direction is taken from atmospheric models or is extracted from the linear features sometimes visible in SAR images. Recently, a new wind retrieval algorithm has been proposed, which also includes the Doppler shift induced by motions of the sea surface. In this article, we apply three wind retrieval algorithms, including the one using Doppler information, to three complex wind events encountered over the Black Sea and compare the SAR-derived wind fields with model wind fields calculated using the high-resolution weather research and forecasting (WRF) model. It is shown that the new algorithm is very efficient in resolving the 180° ambiguity in the wind direction, which is often a problem in the streak-based wind retrieval algorithms. However, the Doppler-based algorithm only yields good results for wind directions that have a significant component in the look direction of the SAR antenna. Furthermore, it is dependent on good separation of the contributions to the Doppler shift induced by surface currents and wind-related effects (wind drift and wind-sea components of the ocean wave spectrum). We conclude that an optimum wind retrieval algorithm should consist of a combination of the algorithms based on linear features and Doppler information.     

Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery

This Letter evaluates several narrow-band indices from EO-1 Hyperion imagery in discriminating sugarcane areas affected by ‘orange rust’ (Puccinia kuehnii) disease. Forty spectral vegetation indices (SVIs), focusing on bands related to leaf pigments, leaf internal structure, and leaf water content, were generated from an image acquired over Mackay, Queensland, Australia. Discriminant function analysis was used to select an optimum set of indices based on their correlations with the discriminant function. The predictive ability of each index was also assessed based on the accuracy of classification. Results demonstrated that Hyperion imagery can be used to detect orange rust disease in sugarcane crops. While some indices that only used visible near-infrared (VNIR) bands (e.g. SIPI and R800/R680) offer separability, the combination of VNIR bands with the moisture-sensitive band (1660?nm) yielded increased separability of rust-affected areas. The newly formulated ‘Disease–Water Stress Indices' (DWSI-1=R800/R1660; DSWI-2=R1660/R550; DWSI-5=(R800+R550)/(R1660+R680)) produced the largest correlations, indicating their superior ability to discriminate sugarcane areas affected by orange rust disease.