Finite-size effects in amorphous Fe90Zr10/Al75Zr25 multilayers

The thickness dependence of the magnetic properties of amorphous Fe90Zr10 layers has been explored using Fe90Zr10/Al75Zr25 multilayers. The Al75Zr25 layer thickness is kept at 40 \AA, while the thickness of the Fe90Zr10 layers is varied between 5 and 20 \AA. The thickness of the Al75Zr25 layers is sufficiently large to suppress any significant interlayer coupling. Both the Curie temperature and the spontaneous magnetization decrease non-linearly with decreasing thickness of the Fe90Zr10 layers. No ferromagnetic order is observed in the multilayer with 5 {\AA} Fe90Zr10 layers. The variation of the Curie temperature $T_c$ with the Fe90Zr10 layer thickness $t$ is fitted with a finite-size scaling formula [1-\Tc(t)/\Tc(\infty)]=[(t-t')/t_0]^{-\lambda}, yielding $\lambda=1.2$, and a critical thickness $t'=6.5$ \AA, below which the Curie temperature is zero.Comment: 8 pages, 8 figure

Visual Analytics for Understanding Spatial Situations from Episodic Movement Data

Continuing advances in modern data acquisition techniques result in rapidly growing amounts of geo-referenced data about moving objects and in emergence of new data types. We define episodic movement data as a new complex data type to be considered in the research fields relevant to data analysis. In episodic movement data, position measurements may be separated by large time gaps, in which the positions of the moving objects are unknown and cannot be reliably reconstructed. Many of the existing methods for movement analysis are designed for data with fine temporal resolution and cannot be applied to discontinuous trajectories. We present an approach utilizing Visual Analytics methods to explore and understand the temporal variation of spatial situations derived from episodic movement data by means of spatio-temporal aggregation. The situations are defined in terms of the presence of moving objects in different places and in terms of flows (collective movements) among the places. The approach, which combines interactive visual displays with clustering of the spatial situations, is presented by example of a real dataset collected by Bluetooth sensors

Single Track Performance of the Inner Detector New Track Reconstruction (NEWT)

In a previous series of documents we have presented the new ATLAS track reconstruction chain (NEWT) and several of the involved components. It has become the default reconstruction application for the Inner Detector. However, a large scale validation of the reconstruction performance in both efficiency and track resolutions has not been given yet. This documents presents the results of a systematic single track validation of the new track reconstruction and puts it in comparison with results obtained with different reconstruction applications

Concepts, Design and Implementation of the ATLAS New Tracking (NEWT)

The track reconstruction of modern high energy physics experiments is a very complex task that puts stringent requirements onto the software realisation. The ATLAS track reconstruction software has been in the past dominated by a collection of individual packages, each of which incorporating a different intrinsic event data model, different data flow sequences and calibration data. Invoked by the Final Report of the Reconstruction Task Force, the ATLAS track reconstruction has undergone a major design revolution to ensure maintainability during the long lifetime of the ATLAS experiment and the flexibility needed for the startup phase. The entire software chain has been re-organised in modular components and a common Event Data Model has been deployed during the last three years. A complete new track reconstruction that concentrates on common tools aimed to be used by both ATLAS tracking devices, the Inner Detector and the Muon System, has been established. It has been already used during many large scale tests with data from Monte Carlo simulation and from detector commissioning projects such as the combined test beam 2004 and cosmic ray events. This document concentrates on the technical and conceptual details of the newly developed track reconstruction, also known as New Tracking

Thermal Conversion of Guanylurea Dicyanamide into Graphitic Carbon Nitride via Prototype CNx Precursors

Guanylurea dicyanamide, [(H2N)C(-O)NHC(NH2)2][N(CN)2], has been synthesized by ion exchange reaction in aqueous solution and structurally characterized by single-crystal X-ray diffraction (C2/c, a = 2249.0(5) pm, b = 483.9(1) pm, c = 1382.4(3) pm, β = 99.49(3)°, V = 1483.8(5) × 106 pm3, T = 130 K). The thermal behavior of the molecular salt has been studied by thermal analysis, temperature-programmed X-ray powder diffraction, FTIR spectroscopy, and mass spectrometry between room temperature and 823 K. The results were interpreted on a molecular level in terms of a sequence of thermally induced addition, cyclization, and elimination reactions. As a consequence, melamine (2,4,6-triamino-1,3,5-triazine) is formed with concomitant loss of HNCO. Further condensation of melamine yields the prototypic CNx precursor melem (2,6,10-triamino-s-heptazine, C6N7(NH2)3), which alongside varying amounts of directly formed CNxHy material transforms into layered CNxHy phases without significant integration of oxygen into the core framework owing to the evaporation of HNCO. Thus, further evidence can be added to melamine and its condensation product melem acting as “key intermediates” in the synthetic pathway toward graphitic CNxHy materials, whose exact constitution is still a point at issue. Due to the characteristic formation process and hydrogen content a close relationship with the polymer melon is evident. In particular, the thermal transformation of guanylurea dicyanamide clearly demonstrates that the formation of volatile compounds such as HNCO during thermal decomposition may render a large variety of previously not considered molecular compounds suitable CNx precursors despite the presence of oxygen in the starting material

Implementation and comparison of algebraic and machine learning based tensor interpolation methods applied to fiber orientation tensor fields obtained from CT images

Fiber orientation tensors (FOT) are used as a compact form of representing the mechanically important quantity of fiber orientation in fiber reinforced composites. While they can be obtained via image processing methods from micro computed tomography scans (CT), the specimen size needs to be sufficiently small for adequate resolution – especially in the case of carbon fibers. In order to avoid massive workload by scans and image evaluation when determining full-field FOT distributions for a plaque or a part, e.g., for comparison with process simulations, the possibilities of a direct interpolation of a few measured FOT at specific support points were opened in this paper. Hence, three different tensor interpolation methods were implemented and compared qualitatively with the help of visualization through tensor glyphs and quantitatively by calculating originally measured tensors at support points and evaluating the deviations. The methods compared in this work include two algebraic approaches, firstly, a Euclidean component averaging and secondly, a decomposition approach based on separate invariant and quaternion weighting, as well as an artificial intelligence (AI)-based method using an artificial neural network (ANN). While the decomposition method showed the best results visually, quantitatively the component averaging method and the neural network behaved better (that is for the type of quantitative error assessment used in this paper) with mean absolute errors of 0.105 and 0.114 when calculating previously measured tensors and comparing the components. With each method providing different advantages, the use for further application as well as necessary improvement is discussed. The authors would like to highlight the novelty of the methods being used with small and CT-based tensor datasets