Natürlich sollte man nichts verkomplizieren. Oft sind ja einfache Lösungen durchaus sinnvoll. Aber angesichts der Komplexität unserer Umwelt neigen wir offenbar dazu, Probleme auf vermeintlich eindeutige Ursachen zurückzuführen. Dieser „Simplification Bias“ bestimmt zunehmend den gesellschaftlichen Diskurs, führt aber auch in Managementfragen zu schlechten Entscheidungen.

Loyalty Marketing hat sich in den letzten Jahren stark gewandelt. Unternehmen müssen heute weit mehr tun, als nur Rabattkarten auszustellen oder Treuepunkte zu vergeben, um ihre Kunden langfristig zu binden. Künstliche Intelligenz (KI) spielt dabei zunehmend eine zentrale Rolle, insbesondere bei der Datenanalyse, Automatisierung und Personalisierung.

Background

The impact of the infecting SARS-CoV-2 variant of concern (VOC) and the vaccination status was determined on the magnitude, breadth, and durability of the neutralizing antibody (nAb) profile in a longitudinal multicentre cohort study.

Methods

173 vaccinated and 56 non-vaccinated individuals were enrolled after SARS-CoV-2 Alpha, Delta, or Omicron infection and visited four times within 6 months and nAbs were measured for D614G, Alpha, Delta, BA.1, BA.2, BA.5, BQ.1.1, XBB.1.5 and JN.1.

Findings

Magnitude-breadth-analysis showed enhanced neutralization capacity in vaccinated individuals against multiple VOCs. Longitudinal analysis revealed sustained neutralization magnitude-breadth after antigenically distant Delta or Omicron breakthrough infection (BTI), with triple-vaccinated individuals showing significantly elevated titres and improved breadth. Antigenic mapping and antibody landscaping revealed initial boosting of vaccine-induced WT-specific responses after BTI, a shift in neutralization towards infecting VOCs at peak responses and an immune imprinted bias towards dominating WT immunity in the long-term. Despite that bias, machine-learning models confirmed a sustained shift of the immune-profiles following BTI.

Interpretation

In summary, our longitudinal analysis revealed delayed and short lived nAb shifts towards the infecting VOC, but an immune imprinted bias towards long-term vaccine induced immunity after BTI.

Funding

This work was funded by the Bavarian State Ministry of Science and the Arts for the CoVaKo study and the ForCovid project. The funders had no influence on the study design, data analysis or data interpretation.

Der Rückgriff auf ChatGPT & Co. vereinfacht vieles im Alltag. Es ist unkompliziert und naheliegend, sich insbesondere Texte durch generative KI schreiben zu lassen oder auch Zusammenfassungen von komplexen und langen Artikeln damit zu erstellen, gerade wenn Zeit und Aufmerksamkeit begrenzt sind. Aber werden wir damit nicht zu bequem? Lassen wir unsere grauen Zellen damit verkümmern?Oder verkennen wir mit solchen Fragen das Potenzial der Technologie? Und welche Kompetenzen brauchen wir dann überhaupt in Zukunft noch?

This paper investigates the comparative effectiveness of model-to-model transformations generated by an LLM based upon user prompts versus those created with dedicated model transformation languages, using a standard benchmark. The emergence of Generative AI offers a novel approach, allowing developers to specify transformations in natural language rather than learning specialized languages. However, our findings suggest that, in its current state, generative AI does not yet pose a threat to dedicated model transformation languages. While AI-assisted approaches promise to provide flexibility and accessibility, dedicated model transformation languages still offer structured advantages crucial for complex transformations, especially when bidirectionality and incrementality are mandatory requirements. This research contributes to the ongoing discourse on the role of AI in software engineering, highlighting its potential and current limitations in enhancing model transformation processes.

Being able to maintain the consistency between various different, but related models is an important enabler for model-based software engineering. Research on bidirectional transformations (bx) addresses this issue and has resulted in various and diverse formal foundations, approaches, tools, and application scenarios. In order to understand and compare different bx approaches, we have developed benchmarx, a benchmarking framework specifically for bx. Up until now, however, benchmarx has been limited to one-sided model synchronisation tasks, where only one of two related models can be changed at a time.As the more general case of concurrent model synchronisation is crucial for many practical applications of bx, we propose in this paper an extension to our bx benchmarking framework to support concurrent model synchronisation tasks, where two related models can both be changed concurrently and must then be synchronised to restore consistency. To evaluate our new extensions we present an update of an existing benchmarx example, families-to-persons, to include new test cases requiring concurrent synchronisation. We discuss some of the challenges involved in defining such a benchmark including handling conflicts, defining the expected behavior of the bx tool under test, and providing bx tools with enough freedom to reject some of the changes to either model. We also present a solution to the updated families-to-persons benchmarx example implemented using BXtend as a bx tool.

We are presenting our approach for interactive cultural heritage storytelling in WebXR. Therefore, we are describing our scenes’ structure consisting of (stylized) photospheres of the historic locations, 3D models of 3D-scanned historic artifacts and animated 2D textures of historic characters generated with a machine learning toolset. The result is a platform-independent web-application in an immersive interactive WebXR environment running in browsers on PCs, tablets, phones and XR headsets thanks to the underlying software based on the open-source framework A-Frame. Our pa- per describes the process, the results and the limitations in detail. The resulting application, designed for the Fichtelgebirge region in Upper Franconia, Germany, offers users an immersive digital time travel experience in the virtual space and within a museum setting connecting real artifacts and virtual stories.

Roboter, die autonom und flexibel arbeiten, könnten in Zukunft im Haushalt helfen. Um ihre Schritte zu planen, brauchen sie künstliche Intelligenz. Generative Sprachmodelle sollen dafür nicht nur Sätze oder Programmcode schreiben, sondern die Abläufe auch strukturieren.

Traditional spatial hypertext systems, predominantly limited to two-dimensional (2D) interfaces, offer limited support for addressing long debated inherent problems such as orientation difficulties and navigation in large information spaces. In this context, we present opportunities from interdisciplinary fields such as immersive analytics (IA) and embodied cognition that may mitigate some of these challenges. However, while some research has explored the extension of spatial hypertext to three dimensions, there is a lack of discussion on recent advances in virtual reality technologies and related fields, and their potential impact on immersive spatial hypertext systems. This paper addresses this gap by exploring the integration of immersive technologies into spatial hypertext systems, proposing a novel approach to enhance user engagement and comprehension through three-dimensional (3D) environments and multisensory interaction.

This paper explores the integration of hypertext structures within Virtual Reality (VR) environments, differentiating between two distinct design philosophies: VR as a native framework for 3D embodiment-enabled spaces similar to traditional 2D spatial hypertext, and utilizing hypertext to enhance VR experiences. Focusing on the latter approach, we propose an abstract knowledge layer that bridges typical VR systems and human thinking, thus facilitating the integration of human cognitive capabilities. Finally, we explore ethical implications of VR systems that arise in the presented context and propose hypertext as a paradigm to address some of these concerns.