75 research outputs found

    Transverse Compression Behavior of Wood in Saturated Steam at 150-170°C

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    The transverse compression behavior of wood in high temperature (150, 160, and 170°C) and saturated steam conditions was studied. The effect of the temperature on the stress-strain response, nonlinear strain function, and relative density change was examined by a modified Hooke's law based on the load-compression behavior of flexible foams. The influence of environmental conditions during compression on the set recovery of the compression deformation was determined. It was found that temperature and moisture content affected the compression behavior of wood in saturated steam conditions. A small difference in moisture content of specimens compressed at 160 and 170°C caused approximately the same stress-strain and relative density curves with minimum temperature affect on the compression behavior. The compressive modulus of the wood and cell wall modulus were found to decrease with increasing temperature from 150 to 160°C with no change when increased to 170°C. The densification region was entered at notably lower stress levels at 160 and 170°C when compared with 150°C. The results established that temperature and moisture content did not affect the nonlinear strain function at strain levels lower than 0.63. Furthermore, it was found that the set recovery of compressive deformation decreased with increasing temperature of compression from 150 to 160°C. In addition, the results showed that compression at 160 and 170°C significantly lowered the equilibrium moisture content

    Hamiltonicity of 3-arc graphs

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    An arc of a graph is an oriented edge and a 3-arc is a 4-tuple (v,u,x,y)(v,u,x,y) of vertices such that both (v,u,x)(v,u,x) and (u,x,y)(u,x,y) are paths of length two. The 3-arc graph of a graph GG is defined to have vertices the arcs of GG such that two arcs uv,xyuv, xy are adjacent if and only if (v,u,x,y)(v,u,x,y) is a 3-arc of GG. In this paper we prove that any connected 3-arc graph is Hamiltonian, and all iterative 3-arc graphs of any connected graph of minimum degree at least three are Hamiltonian. As a consequence we obtain that if a vertex-transitive graph is isomorphic to the 3-arc graph of a connected arc-transitive graph of degree at least three, then it is Hamiltonian. This confirms the well known conjecture, that all vertex-transitive graphs with finitely many exceptions are Hamiltonian, for a large family of vertex-transitive graphs. We also prove that if a graph with at least four vertices is Hamilton-connected, then so are its iterative 3-arc graphs.Comment: in press Graphs and Combinatorics, 201

    Horizon Europe-New European Bauhaus Nexus Report: Conclusions of the High-Level Workshop on ‘Research and Innovation for the New European Bauhaus’, jointly organised by DG Research and Innovation and the Joint Research Centre

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    The ‘Horizon Europe-New European Bauhaus Nexus Report’ (2022) is an independent expert report. The report offers a set of guiding principles that can shape the goals and ambitions of the New European Bauhaus initiative. The recommendations aim to support the New European Bauhaus core values – sustainability, inclusion, and aesthetics. The recommendations are based on the current and future organisational structures and timeframes of the Horizon Europe research and innovation framework programme. They consider opportunities for the funding period 20212022, mid-term goals for the period 2023-2024, and long-term actions beyond 2024 that reflect the transdisciplinary and trans-sectoral vision of the New European Bauhaus. The report also provides a roadmap with strategic priorities and associated benchmarks that describe a possible path forward for European society and its response to the climate crisis, together with the EU’s global partners

    Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021

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    Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and interrater variability. Automated rating may benefit biomedical research, as well as clinical assessment, but diagnostic reliability of existing algorithms is unknown. Here, we present the results of the VAscular Lesions DetectiOn and Segmentation (Where is VALDO?) challenge that was run as a satellite event at the international conference on Medical Image Computing and Computer Aided Intervention (MICCAI) 2021. This challenge aimed to promote the development of methods for automated detection and segmentation of small and sparse imaging markers of cerebral small vessel disease, namely enlarged perivascular spaces (EPVS) (Task 1), cerebral microbleeds (Task 2) and lacunes of presumed vascular origin (Task 3) while leveraging weak and noisy labels. Overall, 12 teams participated in the challenge proposing solutions for one or more tasks (4 for Task 1-EPVS, 9 for Task 2-Microbleeds and 6 for Task 3-Lacunes). Multi-cohort data was used in both training and evaluation. Results showed a large variability in performance both across teams and across tasks, with promising results notably for Task 1-EPVS and Task 2-Microbleeds and not practically useful results yet for Task 3-Lacunes. It also highlighted the performance inconsistency across cases that may deter use at an individual level, while still proving useful at a population level
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