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/

Human–Robot Interaction (HRI) education has largely emphasized technical skills, while psychological and socio-cognitive dimensions of interaction remain underrepresented. Yet contemporary HRI research increasingly views robots as social actors that shape perception, behavior, and decision-making. To address this gap, this paper introduces the Scientific HRI Didactic Framework (SHRI-DiF), a didactical approach that integrates research-based learning with the 5E cycle (Engage–Explore–Explain–Elaborate–Evaluate). The framework positions students as active researchers who conduct the full empirical process, from identifying research questions to evaluating findings. Social robots serve as epistemic tools and experimental agents to investigate social and psychological constructs such as trust, perceived control, and acceptance. The framework is demonstrated in a 12-week undergraduate seminar in business and media psychology using the Furhat robot for dialog strategies in HRI. Results indicate that authentic HRI research settings foster scientific reasoning, methodological competence, and interdisciplinary thinking, offering a transferable model for research-based higher education.

Abstract

As generative Artificial Intelligence (AI) becomes increasingly embedded and utilized for digital design, it presents both opportunities and risks. One major concern is its potential to facilitate and incorporate deceptive design patterns into computing technologies, which could manipulate or mislead users to their disadvantage. Similar to the concept of precedent-based design, a common approach in design theory that suggests reapplying previous design solutions to similar or identical problems, generative AI can integrate deceptive design patterns included in the training data a model has seen before. Our workshop explores how generative AI suggests and enacts deceptive design patterns in digital design. The goal of the workshop is to explore the ethical challenges of utilizing generative AI models and develop strategies to detect or prevent manipulative practices, thereby creating more transparent and equitable AI-generated experiences.

Deceptive patterns in visual interfaces - subtle nudges that persuade or manipulate users to take a certain action - have been researched extensively. Yet, little is known about how these patterns can be translated to the other human senses and what other forms of deception could be implemented for them. This meet-up aims to bring together researchers and practitioners with an interest in studying multi-sensory manipulative interface design, exploring how deceptive patterns for touch, sound, taste, and smell, but also lesser-known senses such as balance, pain, or interoception, can be created, embedded, and mitigated in and with current and future technologies. By facilitating the discussion of potential opportunities and risks across senses, we want to foster new collaborations to advance the field of responsible multisensory deceptive pattern research. In the long term, we hope to raise awareness of potential threats before they arise so we can better safeguard end users.

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.

This open access book offers a comprehensive exploration of digital health and care, addressing the profound transformations driven by digital technologies in the healthcare sector. It serves as a vital resource for understanding the evolution, impact and future directions of digital health innovations, providing readers with in-depth knowledge of regulatory frameworks, adoption strategies and patient-centric approaches. Drawing on perspectives from hospital care, ambulatory services, and long-term care, the book offers practical insights into implementation strategies, organizational change, and sector-specific challenges.

Digital health technologies are advancing rapidly and are beginning to reshape how care is accessed, delivered, and financed, creating new possibilities for more timely, patient-centered, and resilient health systems. At the same time, their large-scale adoption is constrained by persistent barriers such as unequal access and digital literacy, unresolved questions around data privacy, security and ownership, limited interoperability between fragmented systems, cultural resistance within organizations, and uncertain reimbursement and sustainability models. This chapter offers a concise map of this landscape by outlining key challenges, including the digital divide, governance of health data, integration and interoperability, change management, “pilotitis,” and ethical risks, as well as major opportunities, from AI-enabled decision support and personalized medicine to real-time analytics, value-based care, and gamified approaches to engagement. It also highlights how these issues intersect across regulatory frameworks, implementation strategies, equity considerations, and emerging innovation paradigms discussed in later chapters. It argues that the same technologies that can deepen fragmentation and inequity, if poorly governed, can also be harnessed through deliberate policy, design, and business models to generate durable, system-wide benefits.

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.