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Futurehotel - Employee Profiles : Berufsprofile im Gastgewerbe. Heute und in Zukunft ; ein Bericht aus dem Forschungsprojekt Futurehotel
No full textAus dem Vorwort: Die Studie "Futurehotel - Employee Profiles : Berufsprofile im Gastgewerbe. Heute und in Zukunft" ermöglicht dem Leser, sich dem Thema mit Hilfe eines wissenschaftlichen Ansatzes zu nähern. Anhand der vier Säulen aus Desktop-Recherche, Expertenworkshops, qualitativen Leitfadeninterviews sowie der Erfassung von knapp 4.000 empirischen Daten erörtert der Bericht überzeugend, wie die verschiedenen Tätigkeitsfelder des Gastgewerbes von Direktion bis hin zu Haustechnik in Zukunft aussehen könnten. Auch wenn die mittel- und langfristigen Auswirkungen der Corona-Krise noch nicht abzusehen sind, zeigt sich bereits jetzt, wie wichtig die Auseinandersetzung mit Digitalisierung und Flexibilisierung für die Zukunft des Gastgewerbes ist. Hotelcareer by StepStone trat vor über 20 Jahren mit dem Bestreben an, trotz viel Gegenwind die Rekrutierung in der Hotellerie und Gastronomie mit Hilfe einer branchenspezifischen Internet-Jobbörse digital zu revolutionieren
Volunteering 4.0? : What opportunities and challenges does the digital transformation hold for volunteer work?
Get PDFInhalt
1 Zwischen Angst und Aufbruch
2 Digitalisierung - ein Querschnittsthema der Zivilgesellschaft
3 Das Ehrenamt in Deutschland
4 Status Quo der Digitalisierung im ehrenamtlichen Engagement und Engagement-Strukturen
5 Zwischenfazit
6 Chancen einer digitalen Transformation für Organisationen mit Ehrenamtsstrukturen, Ehrenamtliche und die Zivilgesellschaft
6.1. Chancen für Organisationen
6.2. Chancen für die Ehrenamtlichen
6.3. Chancen für die Zivilgesellschaft
7 Herausforderungen und Risiken einer digitalen Transformation
7.1. Herausforderungen und Aufgaben
7.2. Risiken der Digitalisierung
8 Diskussion: Der Weg ist das Ziel
9 Ergebnisse und Ausblick
Literatu
Budget Tourism
Get PDFBausch, Thomas: Budget holidays in the German travel market : Not just a question of money
Butzmann, Elias: Budget trips and sustainable tourism : A contradiction in terms?
Pfaffenberger, Ulrich: “Low Cost” demands “High Brain” : Marketing for budget-conscious travellers demands “visibility
Fresh start
Get PDFScuttari, Anna: (Im)mobilities during and after the COVID-19 pandemic : The changing face of tourism after corona
Pillmayer, Markus: Between Destination Recovery and Destination Resilience : Challenges for a responsible tourism policy in Bavaria
Bösl, Sabine & Chang, Celine: So what’s next after short-time work? : The corona crisis as a job crisis in tourism
Bödinger, Anja & Imhof, Dennis: Corona in Figures : Analysing the hotel market in Germany and Europe
Rauscher, Marion: Travelling into the Past : Cultural travel experiences using virtual-reality technolog
The relevance of intentional communities to overcoming current societal pathologies : An investigation into the community Schloss Tempelhof
Get PDFJonathan Thiel, 2022: Die Bedeutung intentionaler Gemeinschaften für die Überwindung gegenwärtiger gesellschaftlicher Pathologien : Eine Forschung über die Gemeinschaft Schloss Tempelho
Modelling Precipitation Intensities from X-Band Radar Measurements Using Artificial Neural Networks : A Feasibility Study for the Bavarian Oberland Region
Get PDFRadar data may potentially provide valuable information for precipitation quantification, especially in regions with a sparse network of in situ observations or in regions with complex topography. Therefore, our aim is to conduct a feasibility study to quantify precipitation intensities based on radar measurements and additional meteorological variables. Beyond the well-established Z–R relationship for the quantification, this study employs Artificial Neural Networks (ANNs) in different settings and analyses their performance. For this purpose, the radar data of a station in Upper Bavaria (Germany) is used and analysed for its performance in quantifying in situ observations. More specifically, the effects of time resolution, time offsets in the input data, and meteorological factors on the performance of the ANNs are investigated. It is found that ANNs that use actual reflectivity as only input are outperforming the standard Z–R relationship in reproducing ground precipitation. This is reflected by an increase in correlation between modelled and observed data from 0.67 (Z–R) to 0.78 (ANN) for hourly and 0.61 to 0.86, respectively, for 10 min time resolution. However, the focus of this study was to investigate if model accuracy benefits from additional input features. It is shown that an expansion of the input feature space by using time-lagged reflectivity with lags up to two and additional meteorological variables such as temperature, relative humidity, and sunshine duration significantly increases model performance. Thus, overall, it is shown that a systematic predictor screening and the correspondent extension of the input feature space substantially improves the performance of a simple Neural Network model. For instance, air temperature and relative humidity provide valuable additional input information. It is concluded that model performance is dependent on all three ingredients: time resolution, time lagged information, and additional meteorological input features. Taking all of these into account, the model performance can be optimized to a correlation of 0.9 and minimum model bias of 0.002 between observed and modelled precipitation data even with a simple ANN architecture
Classification of Tree Species and Standing Dead Trees with Lidar Point Clouds Using Two Deep Neural Networks : PointCNN and 3DmFV-Net
Get PDFKnowledge about tree species distribution is important for forest management and for modeling and protecting biodiversity in forests. Methods based on images are inherently limited to the forest canopy. Airborne lidar data provide information about the trees’ geometric structure, as well as trees beneath the upper canopy layer. In this paper, the potential of two deep learning architectures (PointCNN, 3DmFV-Net) for classification of four different tree classes is evaluated using a lidar dataset acquired at the Bavarian Forest National Park (BFNP) in a leaf-on situation with a maximum point density of about 80 pts/m 2. Especially in the case of BFNP, dead wood plays a key role in forest biodiversity. Thus, the presented approaches are applied to the combined classification of living and dead trees. A total of 2721 single trees were delineated in advance using a normalized cut segmentation. The trees were manually labeled into four tree classes ( coniferous , deciduous , standing dead tree with crown, and snag ). Moreover, a multispectral orthophoto provided additional features, namely the Normalized Difference Vegetation Index. PointCNN with 3D points, laser intensity, and multispectral features resulted in a test accuracy of up to 87.0%. This highlights the potential of deep learning on point clouds in forestry. In contrast, 3DmFV-Net achieved a test accuracy of 73.2% for the same dataset using only the 3D coordinates of the laser points. The results show that the data fusion of lidar and multispectral data is invaluable for differentiation of the tree classes. Classification accuracy increases by up to 16.3% points when adding features generated from the multispectral orthophoto.Klassifizierung von Baumarten und abgestorbenen Bäumen anhand von Lidar-Punktwolken mit zwei tiefen neuronalen Netzen: PointCNN und 3DmFV-Net . Das Wissen über Baummerkmale wie Standort und Baumarten ist wichtig für die Waldbewirtschaftung und den Schutz der biologischen Vielfalt in Wäldern. Die auf Bildern basierenden Methoden sind von Natur aus auf das Kronendach des Waldes beschränkt. Luftgestützte Lidar-Daten liefern Informationen über die geometrische Struktur der Bäume, sowie über die Bäume, welche unter der oberen Baumkronenschicht stehen. In dieser Arbeit wird das Potenzial von zwei Deep-Learning-Architekturen (PointCNN, 3DmFV-Net) für die Klassifizierung von vier verschiedenen Baumklassen anhand eines Lidar-Datensatzes evaluiert, der im Nationalpark Bayerischer Wald (BFNP) im belaubten Zustand mit einer maximalen Punktdichte von ca. 80 Punkten/m 2aufgenommen wurde. Gerade im BFNP spielt das Totholz eine wichtige Rolle für die Biodiversität im Wald. Daher werden die vorgestellten Ansätze auf die kombinierte Klassifizierung von lebenden und toten Bäumen angewandt. Insgesamt wurden 2.721 Einzelbäume vorab mit einer Normalized Cut Segmentierung deliniert. Anschließend wurden die Bäume manuell in vier Baumklassen eingeteilt (Nadelbäume, Laubbäume, stehende tote Bäume mit Krone und Nadelbäume). Darüber hinaus lieferte ein multispektrales Orthofoto zusätzliche Merkmale wie den Normalized Difference Vegetation Index (NDVI). Bei der Verwendung von PointCNN mit 3D-Punkten, Laserintensität und multispektralen Merkmalen wurde eine Testgenauigkeit von bis zu 87,0% erreicht, was das Potenzial dieses Deep-Learning-Ansatzes für die Forstwirtschaft zeigt. Im Gegensatz dazu erreichte das 3DmFV-Net eine Testgenauigkeit von 73,2% für denselben Datensatz, indem es nur die 3D-Koordinaten der Laserpunkte verwendete. Die Ergebnisse zeigen, dass die Datenfusion von Lidar- und Multispektraldaten entscheidend für die Differenzierung der Baumklassen ist. Die Klassifizierungsgenauigkeit steigt um bis zu 16,3 Prozentpunkte, wenn aus dem multispektralen Orthofoto generierte Merkmale hinzugefügt werden
Sequence effect of as-welded and HFMI-treated transverse attachments under variable loading with linear spectrum
Get PDFIt has been shown in several studies that methods to improve the fatigue strength of welded structures, such as high-frequency impact treatment (HFMI), can increase the fatigue life of welded joints [1–6]. The results of these investigations led to current guidelines and recommendations for the fatigue assessment of HFMI-treated welded joints. Nevertheless, in practice, there are reservations regarding the efficiency of HFMI-treated welded steel joints under variable amplitude loading. Recent results [7] from studies on transverse attachments of the material S355 and S700 under variable amplitude loading show that the fatigue strength increasing effect of the HFMI-treatment is maintained compared to the as-welded state. The aim of this study is to analyse the sequence effect on the fatigue strength of HFMI-treated transverse attachments and to validate the applicability of linear damage accumulation hypotheses for the design of as-welded and HFMI-treated welded details. In this paper, fatigue test results with random variable amplitude loading (VAL) and high-low VAL and low–high VAL with linear spectrum for the two states as-welded (AW) and HFMI-treated joints will be presented
Identifying standing dead trees in forest areas based on 3D single tree detection from full waveform LiDAR data
No full textIn forest ecology, a snag refers to a standing, partly or completely dead tree, often missing a top or most of the smaller branches. The accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, and forest management. Therefore, an understanding of its availability and spatial distribution is required. So far, LiDAR remote sensing has been successfully used to assess live trees and their biomass, but studies focusing on dead trees are rare. The paper develops a methodology for retrieving individual dead trees in a mixed mountain forest using features that are derived from small-footprint airborne full waveform LIDAR data. First, 3D coordinates of the laser beam reflections, the pulse intensity and width are extracted by waveform decomposition. Secondly, 3D single trees are detected by an integrated approach, which delineates both dominate tree crowns and understory small trees in the canopy height model (CHM) using the watershed algorithm followed by applying normalized cuts segmentation to merged watershed areas. Thus, single trees can be obtained as 3D point segments associated with waveform-specific features per point. Furthermore, the tree segments are delivered to feature definition process to derive geometric and reflectional features at single tree level, e.g. volume and maximal diameter of crown, mean intensity, gap fraction, etc. Finally, the spanned feature space for the tree segments is forwarded to a binary classifier using support vector machine (SVM) in order to discriminate dead trees from the living ones. The methodology is applied to datasets that have been captured with the Riegl LMSQ560 laser scanner at a point density of 25 points/m2 in the Bavarian Forest National Park, Germany, respectively under leaf-on and leaf-off conditions for Norway spruces, European beeches and Sycamore maples. The classification experiments lead in the best case to an overall accuracy of 73% in a leaf-on situation and 71% in a leaf-off situation, if we assess the classification results using 5-fold cross-validation method with the help of reference data acquired by the field surveying