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Challenging Dataset: 360° video during rain and snow created with the invisible UAV
No full textThe dataset is used for 3D environment modeling, i.e. for the generation of dense 3D point clouds and 3D models with PatchMatch algorithm and neural networks. Difficult for the modeling algorithm are the reflections of rain, water and snow, as well as windows and vehicle surface. In addition, lighting conditions are constantly changing
Turbulence Modeling for Physics-Informed Neural Networks: Comparison of Different RANS Models for the Backward-Facing Step Flow
No full text
The Influence of the Layer Height and the Filament Color on the Dimensional Accuracy and the Tensile Strength of FDM-Printed PLA Specimens
No full textAmong the FDM process variables, one of the less addressed in previous research is the filament color. Moreover, if not explicitly targeted, the filament color is usually not even mentioned.
Aiming to point out if, and to what extent, the color of the PLA filaments influences the dimensional precision and the mechanical strength of FDM prints, the authors of the present research carried out experiments on tensile specimens. The variable parameters were the layer height (0.05 mm, 0.10 mm, 0.15 mm, 0.20 mm) and the material color (natural, black, red, grey). The experimental results clearly showed that the filament color is an influential factor for the dimensional accuracy as well as for the tensile strength of the FDM printed PLA parts. Moreover, the two way ANOVA test performed revealed that the strongest effect on the tensile strength was exerted by the PLA color (� 2 = 97.3%), followed by the layer height (� 2 = 85.5%) and the interaction between the PLA color and the layer height (� 2 = 80.0%). Under the same printing conditions, the best dimensional accuracy was ensured by the black PLA (0.17% width deviations, respectively 5.48% height deviations), whilst the grey PLA showed the highest ultimate tensile strength values (between 57.10 MPa and 59.82 MPa)
How Sensory Processing Sensitivity Shapes Employee Reactions to Core Job Characteristics
No full textAbstract
Sensory processing sensitivity (SPS) is a personality trait characterized by a high sensitivity to sensory stimuli (Aron & Aron, 1997). On the basis of environmental sensitivity theory (Pluess & Boniwell, 2015) as well as the job characteristics model (Hackman & Oldham, 1976), we investigated the moderating impact of SPS (HSP Scale; Aron & Aron, 1997; Konrad & Herzberg, 2019) on the relationship between job characteristics (Work Design Questionnaire; Morgeson & Humphrey, 2006; Stegmann et al., 2010) and organizational citizenship behavior (OCB Scale; Podsakoff et al., 1990). The results of our two-wave survey study with 199 employees from a broad range of industries and students indicate that SPS strengthens the relationship between feedback as well as task significance and OCB, but SPS weakens the relationship between autonomy (work methods) as well as task variety and OCB.Wie Hochsensibilität die Auswirkungen von Arbeitsmerkmalen auf das Verhalten von Mitarbeitenden beeinflusst
Zusammenfassung:
Hochsensibilität bzw. Sensory processing sensitivity (HPS bzw. SPS) ist ein Persönlichkeitsmerkmal, welches sich durch eine besonders ausgeprägte Empfindsamkeit für sensorische Reize auszeichnet (Aron & Aron, 1997). Basierend auf der Environmental Sensitivity Theory (Pluess & Boniwell, 2015) und dem Job-Characteristics Model (Hackman & Oldham, 1976), untersuchen wir den moderierenden Einfluss von Hochsensibilität (HSP-Skala, Aron & Aron, 1997; Konrad & Herzberg, 2019) auf die Beziehung zwischen Aufgabenmerkmalen (WDQ, Morgeson & Humphrey, 2006, Stegmann et al., 2010) und OCB (OCB-Skala, Podsakoff et al., 1990). Die Ergebnisse unserer zweiwelligen schriftlichen Befragung an N = 199 deutschen Angestellten verschiedener Branchen und Studierenden deuten darauf hin, dass die Ausprägung der Hochsensibilität eines Mitarbeitenden die Auswirkungen von Autonomie, Feedback, Aufgabenvielfalt und Bedeutsamkeit der Aufgabe auf OCB moderiert. Hochsensiblere Mitarbeitende steigern durch Feedback und die Bedeutsamkeit ihrer Tätigkeit ihr extraproduktives Verhalten noch stärker als weniger sensible Mitarbeitende, während die Gestaltungsmerkmale Autonomie und Aufgabenvielfalt ihr extraproduktives Verhalten abschwächen
A Global Intercultural Project Experience (Gipe): Reflections on combining online and onsite project-based learning across four continents
Get PDFThe concept of “Internationalisation at Home“ has gained momentum with the increasing digitalization of education and limitations on mobility. Collaborative Online International Learning (COIL) is an innovative, cost-effective instructional method that promotes intercul-tural learning through online collaboration between faculty and students from different countries or locations. The benefits of using COIL courses have been widely recognized, with learners developing intercultural competencies, digital skills, international education experi-ence, and global awareness.
However, multicultural communication in project environments can be complex and demand awareness of cultural variations . The creation and development of effective cross-cultural collectivism, trust, communication, and empathy in leadership is an important ingredient for remote project collaborations success. This is an area that has been least explored in re-search on communication in virtual teams.
The GIPE projects are mainly carried out as so-called Collaborative Online International Learning (COIL) events. However, to gain a “real world“ experience abroad in an intercultural team, students from all partner universities can participate in the Spring School being held for two weeks in Germany and the Germany students present and hand-over the results in the country of the partner university. The main objective of this research was to examine the experiences of students participating in the GIPE project and to evaluate the effectiveness of the project in enhancing intercultural competencies and fostering collaboration among stu-dents from different continents. This paper will also explore the implications of the GIPE project for Education 2.0 considering the COVID-19 pandemic and the future of education delivery and administration transformation
Guiding Principle 20: Tracking Business Human Rights Responses
No full textThis chapter is a commentary on Principle 20 of the United Nations Guiding Principles on Business and Human Rights (UNGPs). The UNGPs, endorsed by the United Nations Human Rights Council in 2011, are the first universally accepted framework for addressing business responsibilities for human rights. They outline State obligations to protect human rights, businesses’ responsibility to respect human rights, and the importance of both States and businesses offering adequate remedies for human rights breaches
German rescue robotic center captured by the DJI Avata & Insta360
No full textAt the integration sprint of the E-DRZ consortium in march 2023 we improve the information captured by the human spotter (of the fire brigade) by extending him through a 360° drone i.e. the DJI Avata with an Insta360 on top of it. The UAV needs 3 minutes to capture the outdoor scenario and the hall from inside and outside. The hall ist about 70 x 20 meters. When the drone is landed we have all information in 360° degree at 5.7k as you can see it in the video. Furthermore it is a perfect documentation of the deployment scenario. In the next video we will show how to spatial localize the 360° video and how to generate a 3D point cloud from it
On the Similarity of Web Measurements Under Different Experimental Setups
No full textMeasurement studies are essential for research and industry alike to understand the Web’s inner workings better and help quantify specific phenomena. Performing such stud� ies is demanding due to the dynamic nature and size of the Web. An experiment’s careful design and setup are complex, and many factors might affect the results. However, while several works have independently observed differences in
the outcome of an experiment (e.g., the number of observed trackers) based on the measurement setup, it is unclear what causes such deviations. This work investigates the reasons for these differences by visiting 1.7M webpages with five different measurement setups. Based on this, we build ‘de� pendency trees’ for each page and cross-compare the nodes in the trees. The results show that the measured trees differ considerably, that the cause of differences can be attributed to specific nodes, and that even identical measurement setups can produce different results