Introduction In healthcare organizations, saving patients’ lives while maintaining the staff’s wellbeing, performance and competencies were challenging during the COVID-19 pandemic. Although the complexity of healthcare settings is widely recognized, the pandemic evidenced the necessity of attending to the employees’ wellbeing in such a sector. This research aims to examine the effect of sustainable leadership on wellbeing of healthcare personnel. Furthermore, we also evaluate whether procedural knowledge and compassion act as mediators in such a relationship. Methods The hypothesized model was tested in healthcare organizations in a South Asian country, and the data were collected during the pandemic crisis. A total of 366 health personnel (physicians and nurses) participated in this research. With Hayes’ PROCESS macro, we examined all the direct and indirect paths, including sequential mediation. Results The findings confirm the impact of sustainable leadership on wellbeing and this relationship is also mediated by procedural knowledge and compassion. Discussion/conclusion Sustainable leadership fosters wellbeing among healthcare workers via the sequential mediation of procedural knowledge and compassion. Study findings suggest that sustainable leaders can trigger procedural knowledge among employees which in turn crafts the state of compassion in them that leads to their wellbeing. Theoretical and practical implications are discussed in light of study findings.
Weed management is of crucial importance in precision agriculture to improve productivity and reduce herbicide pollution. In this regard, showing promising results, deep learning algorithms have increasingly gained attention for crop and weed segmentation in agricultural fields. In this paper, the U-Net++ network, a state-of-the-art convolutional neural network (CNN) algorithm, which has rarely been used in precision agriculture, was implemented for the semantic segmentation of weed images. Then, we compared the model performance to that of the U-Net algorithm based on various criteria.The results show that the U-Net++ outperforms traditional U-Net in terms of overall accuracy, intersection over union (IoU), recall, and F1-Score metrics. Furthermore, the U-Net++ model provided weed IoU of 65%, whereas the U-Net gave weed IoU of 56%. In addition, the results indicate that the U-Net++ is quite capable of detecting small weeds, suggesting that this architecture is more desirable for identifying weeds in the early growing season.
Aerial laser scanners find rapidly growing interest in photogrammetry and remote sensing as an efficient tool for reliable three-dimensional extraction and modelling of forest inventory information. In addition to interactive measurements in 3D point clouds, techniques for automatic extraction of objects and determination of geometric parameters form a high and important research issues (Maas, Bienert et al. 2008). This paper presents a novel approach on the extraction and modelling of individual trees from the Idaho National Forest in the USA and calculation of the statistical estimation for each extracted segment for future analysing. Lidar point cloud contains three-dimensional structure information which is used to estimate the statistical information for each tree segments. In this study, we worked on the raster surface made directly from the LiDAR point cloud and two main models, namely the digital terrain model (DTM) and the digital surface model (DSM), are generated when the point clouds are processed by the filtering method. Then we have used the segmentation techniques to extract the tree segments which is a triggering process that facilitates the extraction of statistical information such as crown diameter, eccentricity, and other additional attributes. The proposed individual tree segmentation method results in 73% correctness, 92% completeness and 81% F1-score.
Der Transfer von Ideen und Forschungsergebnissen zu Innovationen am Markt stellt Unternehmen und Forschungsinstitutionen immer wieder vor Herausforderungen. Im Rahmen dieses Buchkapitels wird aufgezeigt wie Makerspaces als Orte des Ideen- und Wissensaustauschs den Transfer von Forschungsergebnissen in die Wirtschaft unterstützen können. Hierfür werden zunächst aktuelle Herausforderungen beim Forschungstransfer dargestellt sowie das Konzept von Makerspaces erläutert. Dabei wird auch auf aktuelle Forschungsergebnisse in Bezug auf die Wirkung von Makerspaces auf das Innovationsgeschehen eingegangen. Anhand einer Case Study aus dem Bereich Medical Photonics wird verdeutlicht in welchen Phasen eines Innovationstransfers Makerspaces besonders relevant sind. Es zeigt sich, dass die Anforderungen jedes Innovationstransfers unterschiedlich und Makerspaces insbesondere in der Prototypingphase relevant sind. Die Barrieren zum Transfer werden durch die Makerspace Infrastruktur herabgesetzt. Es kann daher festgehalten werden, dass Makerspaces großes Potenzial zur Unterstützung des Innovationstransfers bergen.
Geomorphology generally aims to describe and investigate the processes that lead to the formation of landscapes, while geochronology is needed to detect their timing and duration. Due to restrictions on exporting geological samples from Egypt, modern geoscientific studies in the Nile Delta lack the possibility of dating the investigated sediments and geological features by standard techniques such as OSL or AMS 14 C; therefore, this study aims to validate a new approach using machine-learning algorithms on portable X-ray fluorescence (pXRF) data. Archaeologically dated sediments from the archaeological excavations of Buto (Tell el-Fara'in; on-site) that pXRF analyses have geochemically characterized serve as training data for running and comparing Neural Nets, Random Forests, and single-decision trees. The established pXRF fingerprints are transferred via machine-learning algorithms to set up a chronology for undated sediments from sediment cores (i.e., the test data) of the nearby surroundings (off-site). Neural Nets and Random Forests work fine in dating sediments and deliver the best classification results compared with single-decision trees, which struggle with outliers and tend to overfit the training data. Furthermore, Random Forests can be modeled faster and are easier to understand than the complex, less transparent Neural Nets. Therefore, Random Forests provide the best algorithm for studies like this. Furthermore, river features east of Kom el-Gir are dated to pre-Ptolemaic times (before 332 B.C.) when Kom el-Gir had possibly not yet been settled. The research in this paper shows the success of close interactions from various scientific disciplines (Geoinformatics, Physical Geography, Archaeology, Ancient History) to decipher landscape evolution in the long-term-settled Nile Delta's environs using machine learning. With the approach's design and the possibility of integrating many other geographical/sedimentological methods, this study demonstrates the potential of the methodological approach to be applied in other geoscientific fields.
Digitale Lehrmaterialien werden seit mehreren Jahren in den Hochschulen eingesetzt und eröffnen ganz neue Wege zur Vermittlung des Lehrstoffs. Die Erstellung dieser Lehrmaterialien kann allerdings je nach Art und Qualität sehr zeitintensiv sein und für Lehrende einen großen Mehraufwand bedeuten. Im Rahmen eines Kooperationsprojekts zur Erstellung von Lehrvideos für geotechnische Feld‐ und Laborversuche haben die Autoren dieses Beitrags allerdings die Erfahrung gemacht, dass das gemeinsame, hochschulübergreifende Erstellen von Lehrmaterialien viele Vorteile mit sich bringt. Dadurch inspiriert führten die Autoren dieses Berichts eine Umfrage unter den deutschsprachigen Geotechnik‐Lehrstühlen der (Technischen) Universitäten und (Fach‐) Hochschulen durch. Nach drei Semestern, in denen Lehrveranstaltungen an den Hochschulen aufgrund der Corona‐Pandemie überwiegend digital durchgeführt werden mussten, war es ein Ziel dieser Umfrage, den Bestand und den Einsatz digitaler Lehrmaterialien im Fachgebiet Geotechnik zu erheben. Ein weiteres Ziel war die Initiierung eines Netzwerks, in dem sich Geotechnik‐Professorinnen und ‐Professoren zu Lehrthemen austauschen können und gemeinsam (digitale) Lehrmaterialien erstellen und nutzen. Der vorliegende Beitrag stellt das gemeinsame Lehrprojekt der Autoren vor, präsentiert die Ergebnisse der durchgeführten Umfrage und berichtet über die ersten Aktivitäten des neuen Netzwerks. Digital education for geotechnical engineering: current status and further developments Digital teaching tools and materials have been used at universities (of applied sciences) for several years and open up new ways for higher education. However, depending on the type and intended quality, the creation of these teaching materials can be very time‐consuming and mean a lot of extra work for teachers. In a collaborative project to produce teaching videos for geotechnical field and laboratory experiments, the authors of this paper have experienced that a collaborative approach across university borders for the production of teaching materials has many advantages. Inspired by this collaboration, the authors of this report conducted a survey among German‐speaking geotechnical engineering chairs at (technical) universities and universities of applied sciences. After three semesters of mostly digital or hybrid teaching due to the Corona pandemic, one aim of this survey was to capture the use of digital teaching materials in the field of geotechnical engineering. Another goal was to initiate a network in which geotechnical engineering professors can exchange information on teaching topics and jointly create and use (digital) teaching materials. This article presents the authors' collaborative teaching project and the results of the survey conducted. Moreover, it reports on the first activities of the new network.
The rapid growth of geospatial data (at least 20% every year) makes spatial data increasingly heterogeneous. With the emergence of Semantic Web technologies, more and more approaches are trying to group these data in knowledge graphs, allowing to link data together and to facilitate their sharing, use and maintenance. These approaches face the problem of homogenisation of these data, which are not unified in the structure of the data on the one hand and on the other hand have a vocabulary that varies greatly depending on the application domain for which the data are dedicated and the language in which they are described. In order to solve this problem of homogenisation, we present in this paper the foundations of a framework allowing to group efficiently heterogeneous spatial data in a knowledge base. This knowledge base is based on an ontology linked to Schema.org and DCAT-AP, and provides a data structure compatible with GeoSPARQL. This framework allows the integration of geospatial data independently of their original language by translating them using Neural Machine Translation.
This paper uses various machine learning methods which explore the combination of multiple sensors for quality improvement. It is known that a reliable occupancy estimation can help in many different cases and applications. For the containment of the SARS-CoV-2 virus, in particular, room occupancy is a major factor. The estimation can benefit visitor management systems in real time, but can also be predictive of room reservation strategies. By using different terminal and non-terminal sensors in different premises of varying sizes, this paper aims to estimate room occupancy. In the process, the proposed models are trained with different combinations of rooms in training and testing datasets to examine distinctions in the infrastructure of the considered building. The results indicate that the estimation benefits from a combination of different sensors. Additionally, it is found that a model should be trained with data from every room in a building and cannot be transferred to other rooms.
For over a decade augmented reality (AR) technology has been discussed as educational tool. Latest technical advancements such as voice control, hand-tracking and moving virtual objects in augmented interaction using AR head-mounted-displays (HMDs) widen the opportunities for educational use even further. Still, the question remains how AR headsets can contribute to learning. As an example, we will present a use case in vocational education in which students shall practice the reading of hydraulic diagrams using advanced AR headsets. We developed a first prototype and examine its feasibility for a learning concept. In particular, we tested a) use of hand-tracking to select and move technical components, and b) use of voice commands and the gaze cursor to highlight the technical components by their names. Based on the experiences, we will analyse the strengths, weaknesses, opportunities and threats of our learning concept in a SWOT analysis. Finally, advantages and disadvantages of implementing advanced AR headsets in vocational education practice will be discussed.KeywordsARHMDsHoloLens2Technical subjectsVocational educationMetal engineeringElectrical engineering
Mit der BaSeTaLK-App wird eine Tablet-gestützte Biographiearbeit für institutionalisierte ältere Menschen zur Steigerung der Lebensqualität ermöglicht. Um Gelingensbedingungen für die Erprobung und die mögliche Implementierung der Maßnahme zu bestimmen, wurde die Perspektive von Mitarbeitenden einer Pflegeeinrichtung eingeholt. Insbesondere eine umfassende Informationsvermittlung und eine flexible, enge Zusammenarbeit mit den Forschenden wurden hervorgehoben. Supplementary Information: Zusatzmaterial online: Zu diesem Beitrag sind unter 10.1007/s41906-022-1922-4 für autorisierte Leser zusätzliche Dateien abrufbar.
The current market of timber products shows a clearly identifiable dominance of rectangular cross sections in modern timber buildings. To promote roundwood as addition to commonly used wood and wood products, this paper presents experimental investigations serving for axial connectors in roundwood truss structures. Glued-in threaded rods, bonded parallel to grain into Douglas fir (Pseudotsuga menziesii) softwood, represent the basis of this research. To increase the load-carrying capacity and usability of this connection technique in roundwood, a resin-bonded polymer concrete (PC) with an increased bondline thickness and contact surface to timber was investigated. The pull out strength and embedment stiffness of the new modified bonded-in rod connection was studied on Douglas fir roundwood specimen exposed to different service conditions. The study also determined the influence of wood defects and moisture content before testing. The results show a larger single-fastener capacity, also for higher moisture content applications, compared to traditional glued-in rods. The quality of the adhesive bondline, for example bonding length, hardening process and homogeneous adhesive bond could be verified by visual inspection.
The combination of a geodetic total station with a digital camera opens up the possibilities of digital image analysis of the captured images together with angle measurement. In general, such a combination is called image-assisted total station (IATS). The prototype of an IATS called MoDiTa (Modular Digital Imaging Total Station) developed at i3mainz is designed in such a way that an existing total station or a tachymeter can be extended by an industrial camera in a few simple steps. The ad hoc conversion of the measuring system opens up further areas of application for existing commercial measuring systems, such as high-frequency aiming, autocollimation tasks or tracking of moving targets. MoDiTa is calibrated directly on site using image-processing and adjustment methods. The crosshair plane is captured for each image and provides identical points in the camera image as well as in the reference image. However, since the camera is not precisely coaxially mounted and movement of the camera cannot be ruled out, the camera is continuously observed during the entire measurement. Various image-processing algorithms determine the crosshairs in the image and compare the results to detect movement. In the following, we explain the self-calibration and the methods of crosshair detection as well as the necessary matching. We use exemplary results to show to what extent the parameters of self-calibration remain valid even if the distance and thus the focus between instrument and target object changes. Through this, one calibration is applicable for different distances and eliminates the need for repeated, time-consuming calibrations during typical applications.
Brand scholars have devoted attention to different brand management schools, and recently, the co-creative school has been much in focus of brand research. This study aims to explore whether the brand schools distinguished in research are also perceived by brand practitioners, and if a turn towards co-creative paradigms is discernible there. In particular, four thematic complexes are used to narrow the field under examination: relevance of brand school taxonomies for managers, their knowledge about the co-creative school, ideas about a changing role for internal brand management, and their engagements to keep up with recent developments in branding theory. A qualitative study of 20 marketing professionals was undertaken to explore the issues. The results generally support other findings on existing theory-practise gaps. The data indicate that there is no awareness about different brand management schools within the group of managers. The co-creative school has not been appreciated by professionals, accordingly. However, internal brand management is seen as a field of pivotal relevance. Regarding their individual lifelong learning, brand managers do not report any systematic initiatives to connect to brand theory or research; managers do neither have any expectations about learning from current brand research. Implications for further research and management are discussed.
We aim to advance the theory of sustainable supply chains by investigating politically induced structural transitions for sustainability using the case of the palm oil supply chain. We elaborate sustainability transitions mechanisms resulting from key supply chain stakeholders’ efforts to augment their agency for influence in response to political tensions from states and corporations. We find that political tensions render latent influence gaps salient as key supply chain stakeholders feel pressured to respond to criticisms either to fill the institutional gaps or to advance transparency regimes. Our findings contribute to theory, practice, and policy associated with the governance of sustainability transitions in supply chains considering its political economy.
Because of the non-linearity inherent in energy commodity prices, traditional mono-scale smoothing methodologies cannot accommodate their unique properties. From this viewpoint, we propose an extended mode decomposition method useful for the time-frequency analysis, which can adapt to various non-stationarity signals relevant for enhancing forecasting performance in the era of big data. To this extent, we employ variants of mode decomposition-based extreme learning machines namely: (i) Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-based ELM Model (CEEMDAN-ELM), (ii) Ensemble Empirical Mode Decomposition-based ELM Model (EEMD-ELM) and (iii) Empirical Mode Decomposition Based ELM Model (EMD-ELM), which cut-across soft computing and artificial intelligence to analyze multi-commodity time series data by decomposing them into seven independent intrinsic modes and one residual with varying frequencies that depict some interesting characterization of price volatility. Our findings show that in terms of the model-specific forecast accuracy measures different dynamics in the two scenarios namely the (non) COVID periods. However, the introduction of a benchmark, namely the autoregressive integrated moving average model (ARIMA) reveals a slight change in the earlier dynamics, where ARIMA outperform our proposed models in the Japan gas and the US gas markets. To check the superiority of our models, we apply the model-confidence set (MCS) and the Kolmogorov-Smirnov Predictive Ability test (KSPA) with more preference for the former in a multi-commodity framework, which reveals that in the pre-COVID era, CEEMDAN-ELM shows persistence and superiority in accurately forecasting Crude oil, Japan gas, and US gas. Nonetheless, this paradigm changed during the COVID-era, where CEEMDAN-ELM favored Japan gas, US gas, and coal market with different rankings via the Model confidence set evaluation methods. Overall, our numerical experiment indicates that all decomposition-based extreme learning machines are superior to the benchmark model.
The dataset consists of 3D scans of one cuneiform tablet from Haft Tappeh Iran and one cuneiform tablet of the Hilprecht Collection as well as 3D annotations on these 3Dmeshes, including metadata. The 3D annotations were created with the annotation software Annotorious2 on 2D renderings and reprojected to the original 3D model. Therespective 2D renderings and annotations in 2D are also part of this data publication.The annotations might be used in machine learning tasks for character recognition,linguistic studies, or visualization in Assyriology. We publish these data in differentformats and give guidance on how to use them in different usage scenarios and withseveral software applications. The data serve as the basis for a detailed description, reasoning, and elaboration of a recommendation for the state-of-the-art handling of3D data in cuneiform research. The data is stored as an archive on Zenodo and mayserve as an example for replication by similar 3D scanning.
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