Projection-based metal-artifact reduction for industrial 3D X-ray computed tomography

Multi-material components, which contain metal parts surrounded by plastic materials, are highly interesting for inspection using industrial 3D X-ray computed tomography (3DXCT). Examples of this application scenario are connectors or housings with metal inlays in the electronic or automotive industry. A major problem of this type of components is the presence of metal, which causes streaking artifacts and distorts the surrounding media in the reconstructed volume. Streaking artifacts and dark-band artifacts around metal components significantly influence the material characterization (especially for the plastic components). In specific cases these artifacts even prevent a further analysis. Due to the nature and the different characteristics of artifacts, the development of an efficient artifact-reduction technique in reconstruction-space is rather complicated. In this paper we present a projection-space pipeline for metal-artifacts reduction. The proposed technique first segments the metal in the spatial domain of the reconstructed volume in order to separate it from the other materials. Then metal parts are forward-projected on the set of projections in a way that metal-projection regions are treated as voids. Subsequently the voids, which are left by the removed metal, are interpolated in the 2D projections. Finally, the metal is inserted back into the reconstructed 3D volume during the fusion stage. We present a visual analysis tool, allowing for interactive parameter estimation of the metal segmentation. The results of the proposed artifact-reduction technique are demonstrated on a test part as well as on real world components. For these specimens we achieve a significant reduction of metal artifacts, allowing an enhanced material characterization.     

Quinone Methide-Based Organophosphate Hydrolases Inhibitors: Trans Proximity Labelers versus Cis Labeling Activity-Based Probes

Quinone methide (QM) chemistry is widely applied including in enzyme inhibitors. Typically, enzyme-mediated bond breaking releases a phenol product that rearranges into an electrophilic QM that in turn covalently modifies protein side chains. However, the factors that govern the reactivity of QM-based inhibitors and their mode of inhibition have not been systematically explored. Foremost, enzyme inactivation might occur in cis, whereby a QM molecule inactivates the very same enzyme molecule that released it, or by trans if the released QMs diffuse away and inactivate other enzyme molecules. We examined QM-based inhibitors for enzymes exhibiting phosphoester hydrolase activity. We tested different phenolic substituents and benzylic leaving groups, thereby modulating the rates of enzymatic hydrolysis, phenolate-to-QM rearrangement, and the electrophilicity of the resulting QM. By developing assays that distinguish between cis and trans inhibition, we have identified certain combinations of leaving groups and phenyl substituents that lead to inhibition in the cis mode, while other combinations gave trans inhibition. Our results suggest that cis-acting QM-based substrates could be used as activity-based probes to identify various phospho- and phosphono-ester hydrolases, and potentially other hydrolases.     

Toward Design of Novel Materials for Organic Electronics

Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large‐area devices, and thin‐film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly infinite chemical space that needs to be explored to identify the most suitable material candidates. Established experimental approaches struggle with the size and complexity of this chemical space. Herein, the development of simulation methods is addressed, with a particular emphasis on predictive multiscale protocols, to complement experimental research in the identification of novel materials and illustrate the potential of these methods with a few prominent recent applications. Finally, the potential of machine learning and methods based on artificial intelligence is discussed to further accelerate the search for new materials.     

Photoquantum Hall Effect and Light‐Induced Charge Transfer at the Interface of Graphene/InSe Heterostructures

The transfer of electronic charge across the interface of two van der Waals crystals can underpin the operation of a new class of functional devices. Among van der Waals semiconductors, an exciting and rapidly growing development involves the “post‐transition” metal chalcogenide InSe. Here, field effect phototransistors are reported where single layer graphene is capped with n‐type InSe. These device structures combine the photosensitivity of InSe with the unique electrical properties of graphene. It is shown that the light‐induced transfer of charge between InSe and graphene offers an effective method to increase or decrease the carrier density in graphene, causing a change in its resistance that is gate‐controllable and only weakly dependent on temperature. The charge transfer at the InSe/graphene interface is probed by Hall effect and photoconductivity measurmentes and it is demonstrated that light can induce a sign reversal of the quantum Hall voltage and photovoltaic effects in the graphene layer. These findings demonstrate the potential of light‐induced charge transfer in gate‐tunable InSe/graphene phototransistors for optoelectronics and quantum metrology.     

Effect of grinding on the fractional composition of polymineral laminated bituminous shales

The effects of size reduction on oil shales from the Yaregskoye oil field were conducted. These shales are characterized by lamination and by high degree of schistosity and expressed zones of pyritization and bituminization. The influence of the level of crushing on the distribution and composition functions was studied. Size separation as a method for the preconcentration of this material at the ore preparation stage was substantiated. The results obtained using predictive formulas showed the possibility of obtaining a product with an increased TiO₂ content at the ore preparation stage and a reduced amount of material requiring processing.     

InSpectr: Multi‐Modal Exploration,Visualization, and Analysis of Spectral Data

This paper addresses the increasing demand in industry for methods to analyze and visualize multimodal data involving a spectral modality. Two data modalities are used: high‐resolution X‐ray computed tomography (XCT) for structural characterization and low‐resolution X‐ray fluorescence (XRF) spectral data for elemental decomposition. We present InSpectr, an integrated tool for the interactive exploration and visual analysis of multimodal, multiscalar data. The tool has been designed around a set of tasks identified by domain experts in the fields of XCT and XRF. It supports registered single scalar and spectral datasets optionally coupled with element maps and reference spectra. InSpectr is instantiating various linked views for the integration of spatial and non‐spatial information to provide insight into an industrial component's structural and material composition: views with volume renderings of composite and individual 3D element maps visualize global material composition; transfer functions defined directly on the spectral data and overlaid pie‐chart glyphs show elemental composition in 2D slice‐views; a representative aggregated spectrum and spectra density histograms are introduced to provide a global overview in the spectral view. Spectral magic lenses, spectrum probing and elemental composition probing of points using a pie‐chart view and a periodic table view aid the local material composition analysis. Two datasets are investigated to outline the usefulness of the presented techniques: a 3D virtually created phantom with a brass metal alloy and a real‐world 2D water phantom with insertions of gold, barium, and gadolinium. Additionally a detailed user evaluation of the results is provided.     

Condensation of Organoyttriumoxanalumoxanes with Chromium Acetylacetonate

Journal of Inorganic and Organometallic Polymers and Materials - The process of organoyttriumoxanealumoxanes co-condensation with chromium acetylacetonate has been studied, as a result...     

On the expressive power of behavioral profiles

Behavioral profiles have been proposed as a behavioral abstraction of dynamic systems, specifically in the context of business process modeling. A behavioral profile can be seen as a complete graph over a set of task labels, where each edge is annotated with one relation from a given set of binary behavioral relations. Since their introduction, behavioral profiles were argued to provide a convenient way for comparing pairs of process models with respect to their behavior or computing behavioral similarity between process models. Still, as of today, there is little understanding of the expressive power of behavioral profiles. Via counter-examples, several authors have shown that behavioral profiles over various sets of behavioral relations cannot distinguish certain systems up to trace equivalence, even for restricted classes of systems represented as safe workflow nets. This paper studies the expressive power of behavioral profiles from two angles. Firstly, the paper investigates the expressive power of behavioral profiles and systems captured as acyclic workflow nets. It is shown that for unlabeled acyclic workflow net systems, behavioral profiles over a simple set of behavioral relations are expressive up to configuration equivalence. When systems are labeled, this result does not hold for any of several previously proposed sets of behavioral relations. Secondly, the paper compares the expressive power of behavioral profiles and regular languages. It is shown that for any set of behavioral relations, behavioral profiles are strictly less expressive than regular languages, entailing that behavioral profiles cannot be used to decide trace equivalence of finite automata and thus Petri nets.