TY - GEN A1 - Kirchner, Änne A1 - Diermayr, Gudrun A1 - Becker, Monika A1 - Rösner, Katrin A1 - Kopkow, Christian A1 - Saal, Susanne T1 - Anpassung internationaler Leitlinien in der Physiotherapie – ein Methodenpapier der Deutschen Gesellschaft für Physiotherapiewissenschaft e. V. (DGPTW) T2 - Physioscience Y1 - 2020 SN - 1860-3092 SN - 1860-3351 VL - 16 IS - 3 SP - 132 EP - 137 ER - TY - GEN A1 - Lange, Toni A1 - Kopkow, Christian A1 - Lützner, Jörg A1 - Günther, Klaus-Peter A1 - Gravius, Sascha A1 - Scharf, Hanns-Peter A1 - Stöve, Johannes A1 - Wagner, Richard A1 - Schmitt, Jochen T1 - Comparison of different rating scales for the use in Delphi studies: different scales lead to different consensus and show different test-retest reliability T2 - BMC Medical Research Methodology Y1 - 2020 U6 - https://doi.org/10.1186/s12874-020-0912-8 SN - 1471-2288 VL - 20 IS - 1 ER - TY - GEN A1 - Kraljevski, Ivan A1 - Duckhorn, Frank A1 - Ju, Yong Chul A1 - Tschöpe, Constanze A1 - Richter, Christian A1 - Wolff, Matthias T1 - Acoustic Resonance Recognition of Coins T2 - 2020 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 25-28 May 2020, Dubrovnik, Croatia N2 - In this study, we compare different machine learning approaches applied to acoustic resonance recognition of coins. Euro-cents and Euro-coins were classified by the sound emerging when throwing the coins onto a hard surface.The used dataset is a representative example of a small data which was collected in carefully prepared experiments.Due to the small number of coin specimens and the count of the collected observations, it was interesting to see whether deep learning methods can achieve similarly or maybe even better classification performances compared with more traditional methods.The results of the multi-class prediction of coin denominations are presented and compared in terms of balanced accuracy and Matthews Correlation Coefficient metrics. The feature analysis methods combined with the employed classifiers achieved acceptable results, despite the relatively small dataset. Y1 - 2020 SN - 978-1-7281-4460-3 SN - 978-1-7281-4461-0 U6 - https://doi.org/10.1109/I2MTC43012.2020.9129256 PB - IEEE Xplore ER - TY - GEN A1 - Kanzler, Michael A1 - Böhm, Christian A1 - Freese, Dirk T1 - The development of soil organic carbon under young black locust (Robinia pseudoacacia L.) trees at a post-mining landscape in eastern Germany T2 - New Forests N2 - The aim of this study was to evaluate the potential of short rotation alley cropping systems (SRACS) to improve the soil fertility of marginal post-mining sites in Brandenburg, Germany. Therefore, we annually investigated the crop alleys (AC) and black locust hedgerows (ABL) of a SRACS field trail under initial soil conditions to identify the short-term effects of tree planting on the storage of soil organic carbon (SOC) and its degree of stabilization by density fractionation. We detected a significant increase in SOC and hot-water-extractable organic C (HWEOC) at ABL, which was mainly restricted to the uppermost soil layer (0–10 cm). After 6 years, the SOC and HWEOC accumulation rates at ABL were 0.6 Mg and 46 kg ha−1 year−1, which were higher than those in the AC. In addition, comparatively high stocks of approximately 4.6 Mg OC and 182 kg HWEOC ha−1 were stored in the ABL litter layer. Density fractionation of the 0–3 cm soil layer at ABL revealed that the majority of the total SOC (47%) was stored in the free particulate organic matter fraction, which was more than twice that of the AC. At the same time, a higher and steadily increasing amount of SOC was stored in the occluded particulate organic matter fraction at ABL, which indicated a high efficiency for SOC stabilization. Overall, our findings support the suitability of black locust trees for increasing the soil fertility of the reclaimed mining substrate and, consequently, the high potential for SRACS to serve as an effective recultivation measure at marginal sites. KW - Marginal sites KW - SOC sequestration KW - Agroforestry KW - Density fractionation Y1 - 2020 U6 - https://doi.org/10.1007/s11056-020-09779-1 SN - 1573-5095 SN - 0169-4286 VL - 52 (2021) IS - 1 SP - 68 ER -