In Situ EXAFS Study on the Chemical State of Arsenic Deposited on a NiMoP/Al2O3 Hydrotreating Catalyst

The chemical state of arsenic deposited on a NiMoP/Al₂O₃ hydrotreating catalyst exposed to ppb levels of arsenic over several years in a refinery reactor has been studied by in situ EXAFS. In the as-received As-NiMoP catalyst, arsenic is exclusively coordinated to oxygen atoms. Upon sulfiding the sample in 2%H₂S/2%H₂/96%He, the As atoms become surrounded by approximately two sulfur atoms. No evidence was found for Ni–As bond formation. A possible model for the As local environment is suggested on the basis of combined EXAFS results, STM data and FEFF8.0 simulations (program for ab initio calculations on multiple scattering XAFS and XANES). The FEFF8.0 simulations of the proposed model are in accord with the experimental data measured at the As K edge. In this model, an As atom is located at the edge of a hexagonally truncated Ni-MoS₂ slab and is blocking the active NiMoS site.     

Polyunsaturated monoglycerides and a pregnadiene in defensive glands of the water beetle Agabus affinis

In addition to the C₂₁ steroid 15α-hydroxypregna-4,6-dien-3,20-dione, four 1- or 2-acylated polyunsaturated monoglycerides, 1- or 2-(cis-5,8,11,14-eicosatetraenoyl)-glycerol and 1- or 2-(cis-5,8,11,14,17-eicosapentaenoyl)glycerol were identified as constituents of the prothoracic defensive gland secretion of the dytiscid beetle Agabus affinis by gas chromatography-mass spectrometry of trimethylsilylated gland extracts. In a feeding assay with minnows, synthetic samples of the two 2-acylated monoglycerides showed only a weak activity as a feeding deterrent. For that reason, other possible functions of the monoglycerides are discussed, such as roles as emulsifiers of cannabimimetics.     

Light metal compound casting

‘Compound casting’simplifies joining processes by directly casting a metallic melt onto a solid metal substrate. A continuously metallurgic transition is very important for industrial applications, such as joint structures of spaceframe constructions in transport industry. In this project, ‘compound casting’ of light metals is investigated, aiming at weight-saving. The substrate used is a wrought aluminium alloy of type AA5xxx, containing magnesium as main alloying element. The melts are aluminium alloys, containing various alloying elements (Cu, Si, Zn), and magnesium. By replacing the natural oxygen layer with a zinc layer, the inherent wetting difficulties were avoided, and compounds with flawless interfaces were successfully produced (no contraction defects, cracks or oxides). Electron microscopy and EDX investigations as well as optical micrographs of the interfacial areas revealed their continu- ously metallic constitution. Diffusion of alloying elements leads to heat-treatable microstructures in the vicinity of the joining interfaces in Al-Al couples. This permits significant variability of mechanical properties. Without significantly cutting down on wettability, the formation of low-melting intermetallic phases (Al3Mg2 and Al12Mg17 IMPs) at the interface of Al-Mg couples was avoided by applying a protective coating to the substrate.     

Object detection by global contour shape

We present a method for object class detection in images based on global shape. A distance measure for elastic shape matching is derived, which is invariant to scale and rotation, and robust against non-parametric deformations. Starting from an over-segmentation of the image, the space of potential object boundaries is explored to find boundaries, which have high similarity with the shape template of the object class to be detected. An extensive experimental evaluation is presented. The approach achieves a remarkable detection rate of 83-91% at 0.2 false positives per image on three challenging data sets.     

Comparison of satellite-derived and in-situ observations of ice and snow surface temperatures over Greenland

The most practical way to get spatially broad and continuous measurements of the surface temperature in the data-sparse cryosphere is by satellite remote sensing. The uncertainties in satellite-derived LSTs must be understood to develop internally-consistent decade-scale land surface temperature (LST) records needed for climate studies. In this work we assess satellite-derived “clear-sky” LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and LSTs derived from the Enhanced Thematic Mapper Plus (ETM+) over snow and ice on Greenland. When possible, we compare satellite-derived LSTs with in-situ air temperature observations from Greenland Climate Network (GC-Net) automatic weather stations (AWS). We find that MODIS, ASTER and ETM+ provide reliable and consistent LSTs under clear-sky conditions and relatively-flat terrain over snow and ice targets over a range of temperatures from ? 40 to 0 °C. The satellite-derived LSTs agree within a relative RMS uncertainty of ~ 0.5 °C. The good agreement among the LSTs derived from the various satellite instruments is especially notable since different spectral channels and different retrieval algorithms are used to calculate LST from the raw satellite data. The AWS record in-situ data at a “point” while the satellite instruments record data over an area varying in size from: 57 × 57 m (ETM+), 90 × 90 m (ASTER), or to 1 × 1 km (MODIS). Surface topography and other factors contribute to variability of LST within a pixel, thus the AWS measurements may not be representative of the LST of the pixel. Without more information on the local spatial patterns of LST, the AWS LST cannot be considered valid ground truth for the satellite measurements, with RMS uncertainty ~ 2 °C. Despite the relatively large AWS-derived uncertainty, we find LST data are characterized by high accuracy but have uncertain absolute precision.     

Toward the Proteome of the Human Peripheral Blood Eosinophil

Eosinophils (EOSs) are granular leukocytes that have significant roles in many inflammatory and immunoregulatory responses, especially asthma and allergic diseases. We have undertaken a fairly comprehensive proteomic analysis of purified peripheral blood EOSs from normal human donors primarily employing 2‐DE with protein spot identification by MALDI‐MS. Protein subfractionation methods employed included IEF (Zoom^® Fractionator) and subcellular fractionation using differential protein solubilization. We have identified 3141 proteins, which had Mascot expectation scores of 10^?3 or less. Of these 426 were unique and non‐redundant of which 231 were novel proteins not previously reported to occur in EOSs. Ingenuity Pathway Analysis showed that some 70% of the non‐redundant proteins could be subdivided into categories that are clearly related to currently known EOS biological activities. Cytoskeletal and associated proteins predominated among the proteins identified. Extensive protein posttranslational modifications were evident, many of which have not been previously reported that reflected the dynamic character of the EOS. This data set of eosinophilic proteins will prove valuable in comparative studies of disease versus normal states and for studies of gender differences and polymorphic variation among individuals.     

Patch layout from feature graphs

The structuring of surface meshes is a labor intensive task in reverse engineering. For example, in CAD, scanned triangle meshes must be divided into characteristic/uniform patches to enable conversion into high-level spline surfaces. Typical industrial techniques, like rolling ball blends, are very labor intensive.We provide a novel, robust and quick algorithm for the automatic generation of a patch layout based on a topology consistent feature graph. The graph separates the surface along feature lines into functional and geometric building blocks. Our algorithm then thickens the edges of the feature graph and forms new regions with low varying curvature. Further, these new regions-so-called fillets and node patches-will have highly smooth boundary curves, making the algorithm an ideal preprocessor for a subsequent spline fitting algorithm.     

Method of classifier selection using the genetic approach

Abstract: The paper presents a novel machine learning algorithm used for training a compound classifier system that consists of a set of area classifiers. Area classifiers recognize objects derived from the respective competence area. Splitting feature space into areas and selecting area classifiers are two key processes of the algorithm; both take place simultaneously in the course of an optimization process aimed at maximizing the system performance. An evolutionary algorithm is used to find the optimal solution. A number of experiments have been carried out to evaluate system performance. The results prove that the proposed method outperforms each elementary classifier as well as simple voting.