While forests are known to increase turbulence and fatigue loads on wind turbines, the impact of smaller-scale vegetation such as tree rows has received limited attention. This study investigates speed-up effects caused by a tree row and their influence on wind turbine power and blade loads using Detached Eddy Simulation (DES) and OpenFAST. A realistically modeled tree row at the Risø campus is considered, including seasonal variations in leaf area index (LAI). The simulations reveal a speed-up region above the canopy, leading to a relative power increase of approximately 6% for the high-LAI case and about half that value for the low-LAI case. Aeroelastic simulations with the NREL 5 MW wind turbine confirm the power increase and show a rise in flapwise blade root bending loads. Second-order statistics within the rotor plane remain largely unchanged, indicating that the load increase is driven by mean flow acceleration rather than turbulence. These results demonstrate that tree rows can increase power and highlight the influence of local vegetation in wind turbine siting.

Background:
While border screening measures were widely adopted by countries during the COVID-19 pandemic, a lack of consensus on the utility of border screening created a gap in best practice for its implementation. As such, countries adopted a diversity of approaches, providing an opportunity to evaluate the configuration and evolution of border screening systems. The
article addresses three questions: (i) how did countries configure their border screeningsystems for COVID-19? (ii) In what contexts did countries rely on public or private providers of these services? (iii) what do policies and narratives reveal about the perceived role of border screening in global public health? The article contributes to long-standing debates over the
private sector’s role in public health and the perceived value of border screening measures.

Methods:
This article presents results from an international comparative study based on tracking the organisation of border screening in eight countries. Secondary data was collected between  July 2021 – June 2022 from official government websites and policy publications, private sector sources where relevant, and trusted media sources in each study country. The
countries included are Australia, Canada, Germany, Ireland, South Africa, South Korea, Spain, and the United Kingdom.

Results:
All study countries used private provision for pre-departure diagnostic testing for international travellers. In contrast, screening of arriving travellers was more diverse. Countries that opted for private sector post-arrival screening saw governance challenges around accreditation and monitoring of providers, while public service provision saw challenges in capacity and high
resource costs. Travel was often framed as a ‘luxury,’ allowing states to shift responsibility for obtaining tests onto individuals; especially in the context of individuals travelling from low income to high income countries.

Conclusions:
The different approaches countries followed for screening of departing and incoming travellers suggests wealthy countries were more oriented towards defending their populations against disease importation, rather protecting the international community from disease exportation. These findings provide an opportunity to reflect on the purpose and
implementation of border screening. We emphasise a need for further discussion on the efficacy of border screening from both perspectives, given the tendency for countries to rely on these measures

In today’s fast-paced digital environment, it is essential for marketing professionals to allocate advertising budgets efficiently and maximize return on investment. By leveraging machine learning, particularly regression trees, key success metrics such as cost per click in online advertising campaigns can be predicted and optimized, enabling more effective budgeting decisions in marketing performance optimization. Showcasing a practical use case with a synthetic data set, this study outlines the relevant phases in applying ML to campaign optimization and demonstrates that the advantages of regression trees lie in their transparency and interpretability. Compared to more complex AI models, the results of regression trees are easier for managers to understand and translate into concrete actions. For instance, the models help managers to understand ad effectiveness of specific ad design and campaign decisions before they run costly live tests on ad platforms. Furthermore, the models allow for lean analysis of ad effectiveness across platforms. Finally, they allow capturing nonlinear effects of ad design and campaign decisions on ad effectiveness, thus avoiding overly simplistic analysis results. Therefore, this study shows the application of regression trees, as step-by-step guide for managers and actionable implications, especially for small- and medium sized enterprises adopting AI in marketing.

Künstliche Intelligenz (KI) ist unverzichtbar im modernen Marketing und dient zunehmend als strategischer Wettbewerbsvorteil in innovationsgetriebenen Branchen. Das Interesse an generativer KI wächst auch in der Marketinginnovation. Marketer nutzen zunehmend Sprachmodelle (Large Language Models, LLMs) wie ChatGPT, um Kreativität, Geschwindigkeit und Effizienz zu steigern. Die Forschung zur Schnittstelle von generativer KI und Marketing ist bislang fragmentiert und meist auf spezifische Anwendungsfälle fokussiert. Es fehlt ein ganzheitlicher Ansatz, der den Einsatz generativer KI über den gesamten Marketinginnovationsprozess systematisch analysiert. In einem systematischem Literaturüberblick zeigt dieser Beitrag Potenziale und Herausforderungen auf und demonstriert, dass KI in allen Innovationsphasen einsetzbar ist und dabei drei Rolle einnehmen kann: als Unterstützer, als Erweiterung oder als eigenständiger Akteur, mit entscheidendem Einfluss auf Innovationsprozesse.

Abstract

Alkoholkonsum zählt zu den bedeutendsten gesundheitsbezogenen Risikoverhaltensweisen, insbesondere unter jungen Erwachsenen. Die Wirksamkeit von Präventions- und Interventionsmaßnahmen hängt jedoch maßgeblich davon ab, die Zielgruppe und deren Motivation zu verstehen. Der vorliegende Beitrag identifiziert Mechanismen, die junge Erwachsene bei der Umsetzung einer bewussten Verhaltensänderung ihres Alkoholkonsums beeinflussen. Eine quantitative Studie unter 102 18-34-Jährigen zeigt, das die Problemrealisation und intrinsische Gründe zur Konsumänderung die kritische Selbstreflexion fördern, während extrinsische Gründe hemmend wirken. Negative Erlebnisse aufgrund von Alkoholkonsum wie Erinnerungslücken oder Kontrollverlust verstärken die Reflexion, während Schuldgefühle sie abschwächen. Die Befunde bestätigen, dass das Erkennen problematischer Trinkgewohnheiten eine kritische Selbstreflexion fördert, die wiederum mit einer aktiven Verhaltensänderung verbunden ist. Gleichsam zeigt sich, dass emotionale Reaktionen wie Schuldgefühle den Reflexionsprozess eher behindern als fördern können. Damit ist eine Differenzierung kognitiver und emotionaler Mechanismen nötig, um zu verstehen, wie Problembewusstsein für ungesunde Verhaltensweisen in aktive Verhaltensänderung überführt wird. Die Befunde liefern Implikationen für zielgruppenorientierte Präventionskampagnen und weisen auf die Bedeutung intrapersonaler Prozesse bei der Alkoholreduktion junger Erwachsener hin.

Immer mehr Menschen nutzen Sport-Tracking-Apps zur Überwachung ihres Trainings und zum sozialen Vergleich mit anderen. Sowohl leistungsbezogene Vergleichsprozesse als auch die User Experience der App können die Sportmotivation jedoch ambivalent beeinflussen: Während einige Nutzende durch Vernetzung und Feedback zusätzliche Motivation erfahren, erleben andere sozialen Druck und Demotivation. Diese widersprüchlichen Effekte sind für die nachhaltige Aufrechterhaltung sportlicher Aktivität von zentraler Bedeutung. Der vorliegende Beitrag untersucht den Einfluss von Selbstwirksamkeit und sozialem Vergleich sowie von UX-induziertem Kompetenz- und Beziehungserleben auf intrinsische und extrinsische Sportmotivation und deren Zusammenhang mit subjektivem Wohlbefinden. Eine quantitative Studie mit 101 Nutzenden von Sport-Tracking-Apps zeigt, dass soziale Vergleiche vor allem extrinsische Motivation fördern, während intrinsische Motivation unbeeinflusst bleibt. Selbstwirksamkeit hingegen stärkt intrinsische Motivation und reduziert extrinsische Regulation. Auch ein positives Kompetenzerleben im Nutzungskontext wirkt motivationsfördernd. Die Ergebnisse verdeutlichen die differenzierten Effekte psychologischer und UX-bezogener Faktoren und zeigen, dass intrapersonelle Ressourcen einen größeren Beitrag zur Sportmotivation leisten als gestalterische App-Merkmale. Damit wird deutlich, dass für digitale Gesundheitsanwendungen nicht allein das Interface-Design, sondern insbesondere individuelle Dispositionen entscheidend für sportbezogene Verhaltensintentionen sind.

German universities of Applied Sciences face declining student numbers and a growing shortage of skilled professionals in technical fields, prompting institutions in Bavaria to increasingly recruit international students, many of them from India. While this strategy stabilizes enrolment and supports national goals for securing a future workforce, it also introduces substantial challenges for teaching staff, administration, and students alike. Many incoming students bring competency profiles shaped by non-European higher-education systems, often marked by a strong emphasis on reproductive learning and limited experience with independent research, critical reflection, and scientific writing. High expectations of German master’s programs, combined with linguistic and cultural barriers, contribute to lower retention rates among international students and create additional burdens on teaching staff.

Drawing on experiences from four international master’s programs at Ansbach University of Applied Sciences, the paper analyses typical difficulties and evaluates measures designed to improve integration, academic success, and study conditions. Programs composed almost exclusively of Indian students tend to reproduce culturally homogeneous learning environments, which limit intercultural exchange, hinder discursive teaching formats, and reinforce established learning habits. In contrast, heterogeneously composed cohorts show better interaction, stronger language development, and improved academic performance. To counteract homogeneity, targeted interventions such as international poster sessions, mixed laboratory groups, and joint courses between German- and English-taught programs were introduced with positive but context-dependent outcomes.

Major challenges arise in the areas of scientific practice, rule compliance, and the unreflected use of AI tools, often rooted in a lack of prior exposure to principles of good scientific practice. The university responded with measures such as training units on academic integrity, adapted assessment formats emphasizing transfer performance, and workshops on literature research and academic writing. Additional structural factors such as particularly limited access to affordable local housing, negatively affect class attendance. Attempts to mitigate this included schedule adaptations to public transport and the introduction of block courses with mandatory practical components.

The paper concludes that successful internationalization requires more than English-language programs. It demands comprehensive support structures, including language training, intercultural competence development for staff, and institutional services tailored to international students’ needs. Given the intensive advising workload, smaller learning groups and recognition of international teaching efforts in workload models are essential. Only through sustained institutional commitment, adequate resources, and openness on all sides can internationalization efforts translate into improved learning outcomes and long-term integration.

Accurate wind flow prediction is essential for various applications, including the placement of wind turbines and a multitude of environmental assessments. Traditionally this can be achieved by using time-consuming computational fluid dynamics (CFD) simulations on reanalysis data. This study explores the performance of an autoencoder (AE) and a variational autoencoder (VAE) in approximating downscaled wind speed and direction using real-world reanalysis data and reference geo- and vegetation data. The AE model was trained for 2000 epochs and demonstrates the ability to replicate wind patterns with a mean absolute error (MAE) of approximately −0.9. However, the AE model exhibited a consistent underestimation of wind speeds and a directional shift of approximately 10 degrees compared to CFD reference simulations. The VAE model produced visually improved results, capturing complex wind flow structures more accurately than the AE model. It mainly achieves better local accuracy and a reduced variance of the results. The overall result suggests that while autoencoders can approximate wind flow patterns, challenges remain in capturing the full variability of wind speeds and directions with sufficient precision. The study highlights the importance of balancing reconstruction accuracy and latent space regularization in VAE models. Future work should focus on optimizing model architecture and training strategies to enhance accuracy, prediction reliability and generalizability across diverse wind conditions and various locations.

This paper explores the association of anthropomorphism and cognitive load with respect to the influence of negative attitudes towards robots. The study consists in a cooperative pick-and-place task, where participants are required to repeatedly and alternatingly put a Lego brick onto one of two trays to be picked up and returned by a robot arm. The task is varied by whether or not participants had to remember an 8-digit number inducing extraneous cognitive load (within-subjects factor). Results show significant correlations of some dimensions of anthropomorphism and perceived cognitive load. However, dividing participants in groups with different negative attitudes towards robots, a significant difference of this association is found. This finding puts prior results on the dependency of anthropomorphism of robots and cognitive load into perspective and more research on the underlying cognitive processes is suggested.

Digital communication has played an increasingly important role in schools around the world, especially since the COVID-19 pandemic. For professional communica tion and collaboration in particular, digital tools have provided teachers with the op portunity to collaborate location-independently and to easily exchange information and teaching materials. Hence, this study aimed to explore how teachers communi cate and collaborate digitally by examining differences in the use of instant messag ing and videoconferencing and the attitudes of teachers towards these technologies. Therefore, an online survey was conducted with primary and secondary school teachers from Germany (N = 250, 72.0% female). The analysis showed that mes sengers were used significantly more than videoconferences and that they differed in their usefulness for occasions of communication and collaboration. Structural equation modeling indicated that the self-assessed digital communication compe tence of teachers is both a significant predictor for the behavioral intention to use messengers as well as videoconferences, although the behavioral intention to use messengers in the future was significantly higher than for videoconferences. While a higher threshold to use videoconferences might be a reason for the differences that were identified in this study, further research into the communication and col laboration among teachers is still needed to understand the reasons for the differ ences in use.

AI-powered social robots are increasingly discussed as a way to improve the in-store customer experience, but the effects of similarity-based personalization remain unclear. This study examines whether language-based similarity features in a retail recommendation dialogue improve user comfort, trust, and intention to use. In a controlled laboratory experiment, participants interacted with the social robot Furhat either in a basic “good sales assistant” or in an adaptive, similarity-based variant (speech rate, volume, pitch and similarity statements). Questionnaire data and continuous electrodermal activity (EDA) were combined to capture both self-reported and physiological responses. Using robust mean comparisons (Yuen tests), structural equation modeling (SEM) and EDA analyses, no significant differences between conditions in terms of comfort, trust or intention to use were found. However, SEM revealed stable internal mechanisms: rapport strongly predicted comfort, usefulness strongly predicted trust and comfort predicted intention to use. These results suggest that general similarity matching in early social robot interactions in retail is not reliably beneficial. In retail sales consulting, it seems sensible to leave robot behavior unadjusted and to base it on a generally good sales consultant. Improvement through personalization is not generally advantageous but it depends heavily on the context.