In this work, the Theory of Porous Media (TPM) is employed to model percutaneous vertebroplasty, a medical procedure in which acrylic cement is injected into cancellous vertebral bone. Previously, isothermal macroscale models have been derived to describe this material injection and the arising mechanical interactions. However, the temperature of the injected cement is typically below the human body temperature, necessitating the extension of these existing models to the non-isothermal case. Following the modelling principles of the TPM and considering local thermal non-equilibrium conditions, our model introduces three energy balances as well as constitutive relations for thermal conduction and heat transfer. If restricted to local thermal equilibrium conditions, our model equations are in agreement with other TPM-based models. We observe that our model elicits physically reasonable behaviour in numerical simulations that employ parameter values and initial and boundary conditions relevant for our application. We claim our model to be thermodynamically consistent despite the employment of the Coleman and Noll procedure.

Selective exposure in online news is often attributed to personalization mechanisms and user modeling. Recent work proposes interface-level interventions that visualize reading balance or frame cross-cutting exposure as progress. However, we lack empirical evidence on whether alternative representations of user-model feedback meaningfully influence engagement with belief-opposing content. We conducted a between-subject experiment (N = 84) in a controlled news environment comparing two representations of diversity feedback: (1) an analytic bias visualization summarizing viewpoint balance and (2) a metaphorical growth visualization framing cross-cutting exposure as personal development. Across behavioral and attitudinal measures of open-minded engagement, neither feedback representation increased engagement relative to control, and the two designs did not differ reliably. Our results suggest that lightweight representations of diversity signals, without adaptive personalization or structural changes to recommendations, may be insufficient to alter selective exposure in single-session settings. We discuss implications for designing user-model feedback and depolarization objectives in recommender systems.

Empirische Rechtswissenschaft im Fokus: Dieses Buch bietet einen fundierten Überblick über Stand und Perspektiven interdisziplinärer Rechtswissenschaften. Von Verfassungs-, Europa-, Kartell- und Strafrecht über richterliches Entscheidungsverhalten bis hin zu Fragen von Politikberatung, Arbeitsmärkten und Gemeinschaftsgütern entfalten die Beiträge ein breites thematisches Spektrum. Zugleich zeigen sie, wie Verhaltenswissenschaften, datengetriebene Methoden und Künstliche Intelligenz unser Verständnis des Rechts vertiefen und verändern. Sie zeichnen dabei auch das Wirken von Christoph Engel, Direktor am Max-Planck-Institut zur Erforschung von Gemeinschaftsgütern, nach. 

This paper explores whether cognitive load affects intervention behavior of a driver in terms of taking control of the driving situation in autonomous driving. A video of a simulated autonomous driving scenario from a driver's perspective including critical situations is presented to the participants. In parallel, extraneous cognitive load is induced by letting participants mentally solve arithmetic problems as a secondary task. Whenever participants feel that they want to intervene and take control of the driving situation, they are required to press the button of a stopwatch. In contrast to expectations of increased impulsive behavior, results reveal an inverse association of cognitive load and counts of interventions paired with increased trust in the autonomous vehicle, but reduced perceived safety and situation awareness. This behavior may be explained with impaired switching of attentional focus and lack of possibility of internalization of the primary task under depletion of cognitive resources. The observed effects are suggested to be accounted for towards shared autonomy concepts in order to promote the design of safe autonomous vehicles and their integration in society.

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.

This paper experimentally examines how different greenfluencer message types (emotional, informational, social, and neutral) on Instagram affect purchase intention and attitudes toward sustainable fashion, with a particular focus on non-sustainable consumers. In an online experiment, 202 participants were randomly assigned to one of four fictitious Instagram posts and completed pre- and post-measures of attitudes and purchase intention. The results show that none of the message types improved purchase intention or attitudes; instead, most effects were negative. However, attitudes remained positively associated with purchase intention. The findings suggest that greenfluencer communication does not automatically promote sustainable consumer responses and is not more effective among non-sustainable consumers.

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

According to the latest reports, selective soldering plays a critical role in the manufacturing of printed circuit boards (PCBs), especially for through-hole technology (THT) components, where molten solder is dispensed from a nozzle to create a continuous 360^° wave around the part. The consistency of this wave is crucial for reliable electrical and mechanical connections. However, repeated exposure to high temperatures (up to 300^°C) and molten solder (e.g., Sn-Pb or SAC alloys) causes nozzle oxidation, leading to impurity buildup and irregular waves. This degradation results in defective solder joints, compromising product quality, increasing rework costs, and potentially causing production downtime. Current monitoring relies on manual visual inspections, which are subjective and timeconsuming, or rule-based image algorithms that are prone to noise and require frequent recalibration. To overcome these limitations, this paper investigates autoencoders-a type of neural network for unsupervised anomaly detection-that reconstruct input images and flag deviations via reconstruction errors. Data was collected from camera-monitored nozzles under controlled conditions. Preprocessing included grayscale conversion, Gaussian blurring, normalization, and resizing. A Flat (fully connected baseline) was evaluated. Models trained on operable images using mean squared error (MSE) loss; performance assessed with MSE distributions and receiver operating characteristic (ROC) curves, using area under the curve (AUC) as the primary metric. Results show the flat autoencoder achieving  ∼ 96 % AUC with a MSE separation (oxidized > 0.02). This is the first application of autoencoders to real-time nozzle oxidation detection in selective soldering, filling a gap in AI for tool-specific degradation. It enables predictive maintenance, reducing defects.

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.

Human Motion Simulation (HMS) predicts and visualizes how virtual agents move and behave in various scenarios, aiding decision-making. However, it does not communicate the experience behind agents' behavior. To address this, we present Embodied Spatial Simulation (ESS), a concept combining augmented reality(AR) and HMS to support in-situ decision-making. It enables an XR user to create “what-if” scenarios, simulate virtual agents' behavior in situ, and experience the scenarios from their perspectives through embodiment. To demonstrate ESS, we developed a proof-of-concept prototype featuring two scenarios (e.g., fire evacuation planning). From expert walkthroughs (N=4) and a survey (N=38) with different professions, we found that incorporating embodiment into simulations can change users' intentions to use simulation outcomes. Rather than directly obtaining solutions from the results, users showed more interest in understanding the experience behind simulated behavior and using this insight to shape decisions. We discuss the potential of ESS to support future human-centered design practices.

What characterizes individuals who stand up and take action against violations of fundamental moral principles, even in the face of personal risk and adversity? Due to methodological and ethical challenges, we are limited in our understanding of personality dispositions that predict who acts morally courageously in situations of severe wrongdoings and considerable risk. In Germany and Austria, we recruited recipients of public awards for outstanding moral courage (n = 54) and individuals who nominated themselves to have acted morally courageously (n = 48). We contrasted these morally courageous individuals with a demographically matched reference group of people who reported not to have acted morally courageously before (n = 323), and with normative samples of the German population. Results showed three key patterns: First, among the HEXACO personality factors, heightened extraversion consistently distinguished the morally courageous groups from the reference group. Second, membership in the morally courageous groups was predicted by heightened moral attentiveness and anger proneness, and lowered endorsement of loyalty and authority, indicating exceptional moral functioning of the morally courageous. Third, the morally courageous were characterized by diminished risk avoidance and social anxiety, suggesting that they encounter a lower psychological barrier posed by fear of negative social evaluation.

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.

zusätzlich: https://kronachleuchtet.com/portfolio-archive/11-2026-video-kunst-frisch-von-der-hochschule-i/