@article{OrcesiO'ConnorDiamantidisetal., author = {Orcesi, Andr{\´e} and O'Connor, Alan and Diamantidis, Dimitris and S{\´y}kora, Miroslav and Wu, Teng and Akiyama, Mitsuyoshi and Alhamid, Abdul Kadir and Schmidt, Franziska and Pregnolato, Maria and Li, Yue and Salarieh, Babak and Salman, Abdullahi M. and Bastidas-Arteaga, Emilio and Markogiannaki, Olga and Schoefs, Franck}, title = {Investigating the Effects of Climate Change on Structural Actions}, series = {Structural Engineering International}, volume = {32}, journal = {Structural Engineering International}, number = {4}, publisher = {Taylor \& Francis}, doi = {10.1080/10168664.2022.2098894}, pages = {1 -- 14}, abstract = {The changing climate with resulting more extreme weather events will likely impact infrastructure assets and services. This phenomenon can present direct threats to the assets as well as significant indirect effects for those relying on the services those assets deliver. Such threats are path-dependent and place-specific, as they strongly depend on current and future climate variability, location, asset design life, function and condition. One key question is how climate change is likely to increase both the probability and magnitude of extreme weather events under different scenarios of climate change. To address this issue, this paper investigates selected effects of climate change and their consequences on structural performance, in the context of evolving loading scenarios in three different continental regions: Europe, North America, and Asia. The aim is to investigate some main place-specific changes of the exposure in terms of intensity/frequency of extreme events as well as the associated challenges, considering some recent activities of members of the IABSE TG6.1. Climate change can significantly affect built infrastructure and the society by increasing the occurrence and magnitude of extreme events and increasing potential losses. Therefore, specific relationships relating hazard levels and structural vulnerability to climate change effects should be determined.}, language = {en} } @article{LaunhardtWoerzLodereretal., author = {Launhardt, M. and W{\"o}rz, A. and Loderer, A. and Laumer, Tobias and Drummer, Dietmar and Hausotte, Tino and Schmidt, Michael}, title = {Detecting surface roughness on SLS parts with various measuring techniques}, series = {Polymer Testing}, volume = {53}, journal = {Polymer Testing}, publisher = {Elsevier}, address = {Amsterdam}, isbn = {0142-9418}, issn = {1873-2348}, doi = {10.1016/j.polymertesting.2016.05.022}, pages = {217 -- 226}, abstract = {Selective Laser Sintering (SLS) is an additive manufacturing technique whereby a laser melts polymer powder layer by layer to generate three-dimensional parts. It enables the fabrication of parts with high degrees of complexity, nearly no geometrical restrictions, and without the necessity of a tool or a mold. Due to the orientation in the building space, the processing parameters, and the powder properties, the resulting parts possess an increased surface roughness. In comparison to other manufacturing techniques, e.g. injection molding, the surface roughness of SLS parts results from partially melted powder particles on the surface layer. The actual surface roughness must thus be characterized with respect to the part's eventual application. At the moment, there is no knowledge regarding which measuring technique is most suitable for detecting and quantifying SLS parts' surface roughness. The scope of this paper is to compare tactile profile measurement methods, as established in industry, to optical measurement techniques such as Focus Variation, Fringe Projection Technique (FPT), and Confocal Laser Scanning Microscope (CLSM). The advantages and disadvantages of each method are presented and, additionally, the effect of tactile measurement on a part's surface is investigated.}, language = {en} } @article{KleinNabbefeldHattabetal., author = {Klein, C. and Nabbefeld, T. and Hattab, H. and Meyer, D. and Jnawali, G. and Kammler, Martin and Meyer zu Heringdorf, Frank-Joachim and Golla-Franz, A. and M{\"u}ller, B. H. and Schmidt, Thomas and Henzler, M. and Horn-von Hoegen, Michael}, title = {Lost in reciprocal space? Determination of the scattering condition in spot profile analysis low-energy electron diffraction}, series = {Review of scientific instruments}, volume = {82}, journal = {Review of scientific instruments}, number = {3}, publisher = {American Institute of Physics}, doi = {10.1063/1.3554305}, abstract = {The precise knowledge of the diffraction condition, i.e., the angle of incidence and electron energy, is crucial for the study of surface morphology through spot profile analysis low-energy electron diffraction (LEED). We demonstrate four different procedures to determine the diffraction condition: employing the distortion of the LEED pattern under large angles of incidence, the layer-by-layer growth oscillations during homoepitaxial growth, a G(S) analysis of a rough surface, and the intersection of facet rods with 3D Bragg conditions.}, language = {en} } @article{ScheppachMendelMuzalyovaetal., author = {Scheppach, Markus W. and Mendel, Robert and Muzalyova, Anna and Rauber, David and Probst, Andreas and Nagl, Sandra and R{\"o}mmele, Christoph and Yip, Hon Chi and Lau, Louis Ho Shing and G{\"o}lder, Stefan Karl and Schmidt, Arthur and Kouladouros, Konstantinos and Abdelhafez, Mohamed and Walter, Benjamin M. and Meinikheim, Michael and Chiu, Philip Wai Yan and Palm, Christoph and Messmann, Helmut and Ebigbo, Alanna}, title = {Artificial intelligence improves submucosal vessel detection during third space endoscopy}, series = {Endoscopy}, journal = {Endoscopy}, publisher = {Thieme}, address = {Stuttgart}, doi = {10.1055/a-2534-1164}, abstract = {Background and study aims: While artificial intelligence (AI) shows high potential in decision support for diagnostic gastrointestinal endoscopy, its role in therapeutic endoscopy remains unclear. Third space endoscopic procedures pose the risk of intraprocedural bleeding. Therefore, we aimed to develop an AI algorithm for intraprocedural blood vessel detection. Patients and Methods: Using a test dataset with 101 standardized video clips containing 200 predefined submucosal blood vessels, 19 endoscopists were evaluated for the vessel detection rate (VDR) and time (VDT) with and without support of an AI algorithm. Test subjects were grouped according to experience in ESD. Results: With AI support, endoscopists VDR increased from 56.4\% [CI 54.1-58.6] to 72.4\% [CI 70.3-74.4]. Endoscopists' VDT dropped from 6.7sec [CI 6.2-7.1] to 5.2sec [CI 4.8-5.7]. False positive (FP) readings appeared in 4.5\% of frames and were marked significantly shorter than true positives (6.0sec [CI 5.28-6.70] vs. 0.7sec [CI 0.55-0.87]). Conclusions: AI improved the vessel detection rate and time of endoscopists during third space endoscopy. While these data need to be corroborated by clinical trials, AI may prove to be an invaluable tool for the improvement of endoscopic interventions.}, language = {en} }