High- and low pressure systems of the large-scale atmospheric circulation in the mid-latitudes drive European weather and climate. Potential future changes in the occurrence of circulation types are highly relevant for society. Classifying the highly dynamic atmospheric circulation into discrete classes of circulation types helps to categorize the linkages between atmospheric forcing and surface conditions (e.g. extreme events). Previous studies have revealed a high internal variability of projected changes of circulation types. Dealing with this high internal variability requires the employment of a single-model initial-condition large ensemble (SMILE) and an automated classification method, which can be applied to large climate data sets. One of the most established classifications in Europe are the 29 subjective circulation types called Grosswetterlagen by Hess & Brezowsky (HB circulation types). We developed, in the first analysis of its kind, an automated version of this subjective classification using deep learning. Our classifier reaches an overall accuracy of 41.1% on the test sets of nested cross-validation. It outperforms the state-of-the-art automatization of the HB circulation types in 20 of the 29 classes. We apply the deep learning classifier to the SMHI-LENS, a SMILE of the Coupled Model Intercomparison Project phase 6, composed of 50 members of the EC-Earth3 model under the SSP37.0 scenario. For the analysis of future frequency changes of the 29 circulation types, we use the signal-to-noise ratio to discriminate the climate change signal from the noise of internal variability. Using a 5%-significance level, we find significant frequency changes in 69% of the circulation types when comparing the future (2071–2100) to a reference period (1991–2020).
Background Many radiographic lower limb alignment measurements are dependent on patients’ position, which makes a standardised image acquisition of long-leg radiographs (LLRs) essential for valid measurements. The purpose of this study was to investigate the influence of rotation and flexion of the lower limb on common radiological alignment parameters using three-dimensional (3D) simulation. Methods Joint angles and alignment parameters of 3D lower limb bone models (n = 60), generated from computed tomography (CT) scans, were assessed and projected into the coronal plane to mimic radiographic imaging. Bone models were subsequently rotated around the longitudinal mechanical axis up to 15° inward/outward and additionally flexed along the femoral intercondylar axis up to 30°. This resulted in 28 combinations of rotation and flexion for each leg. The results were statistically analysed on a descriptive level and using a linear mixed effects model. Results A total of 1680 simulations were performed. Mechanical axis deviation (MAD) revealed a medial deviation with increasing internal rotation and a lateral deviation with increasing external rotation. This effect increased significantly (p < 0.05) with combined flexion up to 30° flexion (− 25.4 mm to 25.2 mm). With the knee extended, the mean deviation of hip–knee–ankle angle (HKA) was small over all rotational steps but increased toward more varus/valgus when combined with flexion (8.4° to − 8.5°). Rotation alone changed the medial proximal tibial angle (MPTA) and the mechanical lateral distal femoral angle (mLDFA) in opposite directions, and the effects increased significantly (p < 0.05) when flexion was present. Conclusions Axial rotation and flexion of the 3D lower limb has a huge impact on the projected two-dimensional alignment measurements in the coronal plane. The observed effects were small for isolated rotation or flexion, but became pronounced and clinically relevant when there was a combination of both. This must be considered when evaluating X-ray images. Extension deficits of the knee make LLR prone to error and this calls into question direct postoperative alignment controls. Level of evidence III (retrospective cohort study).
This study investigates how age, period, and birth cohorts are related to altering travel distances. We analyze a repeated cross-sectional survey of German pleasure travels for the period 1971–2018 using a holistic age–period–cohort (APC) analysis framework. Changes in travel distances are attributed to the life cycle (age effect), macro-level developments (period effect), and generational membership (cohort effect). We introduce ridgeline matrices and partial APC plots as innovative visualization techniques facilitating the intuitive interpretation of complex temporal structures. Generalized additive models are used to circumvent the identification problem by fitting a bivariate tensor product spline between age and period. The results indicate that participation in short-haul trips is mainly associated with age, while participation in long-distance travel predominantly changed over the period. Generational membership shows less association with destination choice concerning travel distance. The presented APC approach is promising to address further questions of interest in tourism research.
For Europe to achieve “climate neutrality” by 2050, emissions from all economic sectors must be reduced to the absolute minimum. In addition to changes in raw material extraction and building material production, the construction industry must embrace emission-free construction sites. The present paper suggests a method to calculate carbon emissions on construction sites by defining all fuel-consuming processes while relying on established European standards. A set of system boundaries is defined to single out emissions that occur in the construction industry sphere. These definitions are essential to calculate savings through the entire construction process. This method is subsequently used to assess the carbon balance of four exemplary construction sites in Austria, which cover the total span of the construction life cycle. Results show that the largest share of emissions is attributed to transport during the construction of new buildings, followed by emissions from demolition and building processes.
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