Der Einsatz von Blockchain und Smart Contracts ermöglicht auch den Aufbau und Betrieb dezentraler autonomer Organisationen, sogenannter DAOs, die auf Grundlage eines zuvor fixierten Regelsystems automatisiert Entscheidungen treffen und an den Märkten als eigenständige Institution agieren können. Während DAOs in anderen Bereichen – in der Finanzwirtschaft und in der Creator-Economy – bereits regelmäßig zum Einsatz kommen, ist im industriellen Umfeld eine entsprechende Nutzbarmachung bislang kaum zu verzeichnen. Dabei bietet gerade die Vernetzung durch das „Internet der Dinge“ hierfür sinnvolle Anknüpfungspunkte. Der Beitrag befasst sich mit den möglichen wirtschaftlichen Adaptionsansätzen und lotet potenzielle Geschäftsmodelle aus, die sich aus der Errichtung von DAOs in der „Industrie 4.0“ ergeben könnten.

Background

Rare diseases affect a small percentage of the population, leading to challenges such as delayed diagnoses and limited treatment options. Mobile health technologies offer solutions to improve patient outcomes, yet their application in rare diseases remains underexplored. The German citizen science project SelEe created a customizable app for the self-management of rare diseases through a co-creation process that involved patients with such conditions.

Methods

The project consisted of three phases. In Phase 1, 9 to 68 patients or relatives of patients participated in workshops to define research topics and app requirements. Phase 2 involved a core research team of nine patients and researchers who iteratively developed the app, released in March 2023. Phase 3 focused on evaluating the app’s usage and usability through an in-app survey conducted from March 2023 to February 2024. We utilized descriptive statistics to evaluate app usage and employed the mHealth App Usability Questionnaire to assess usability.

Results

The SelEe app offers the possibility to create and store data in a personalized health diary. Patients can create their own templates or use templates which were defined by the core research team. Users can record findings (e.g. blood test results) and export data using different graphs and formats. Furthermore, the app supports blind users. The app was downloaded 3040 times and 1456 users registered, with 1967 unique diseases entered. 50.7% of the diseases were rare, 30.5% non-rare, and 18.8% were classified as suspected, undefined, or symptoms. A total of 1223 valid user profiles were analyzed for app usage and demographics. Furthermore, 432 users qualified for the in-app survey by making at least one health diary entry, and 117 participated. The app was rated with an overall usability score of 5.19 out of 7. While the app’s health diary function was frequently used, other functionalities like findings and data export were less utilized. Feedback highlighted the need for improved usability and additional features.

Conclusions

The study highlights active patient engagement in developing a mobile health app for individuals with rare diseases. Although improvements are necessary for broader acceptance, the app is promising for the management of rare diseases.

Es ist nahezu unmöglich, sich dem Hype um das Thema Künstliche Intelligenz (KI) zu entziehen. Insbesondere das Aufkommen der generativen KI, von Instrumenten wie ChatGPT oder Midjourney und der zugrundeliegenden großen Sprachmodelle, hat die Spielregeln für das Marketing neu definiert. Auch in der Mediaplanung, bei der Entwicklung und Umsetzung von Werbestrategien, entstehen neue Handlungsfelder, Chancen und Herausforderungen.

We introduce WireTrust, a VPN architecture for ARMv8-A devices that leverages ARM TrustZone to mitigate OS-level vulnerabilities. Contrary to commodity VPNs, WireTrust does not rely on the security of the OS, its network stack, or its routing tables to provide a secure VPN full tunnel. WireTrust operates transparently to applications on the device and enforces that all IP traffic is routed exclusively through the VPN tunnel, blocking attempts to bypass it even if the OS has been compromised. WireTrust ensures that packets outside the tunnel are discarded before they reach the OS, significantly reducing the device’s attack surface that is exposed to the public internet. Extending the WireGuard VPN, we implement a proof of concept on real hardware, show that WireTrust's additions to the trusted computing base account for 6.61%, and measure a performance penalty of 2.12% - 5.50% on TCP throughput and 1.40% on latency compared to stock WireGuard.

KI im Marketing, Methoden des maschinellen Lernens, Anwendungsmöglichkeiten von KI im Marketing, insbesondere auch von generativer KI

This paper introduces a novel benchmark dataset designed to evaluate the capabilities of Vision Language Models (VLMs) on tasks that combine visual reasoning with subject-specific background knowledge in the German language. In contrast to widely used English-language benchmarks that often rely on artificially difficult or decontextualized problems, this dataset draws from real middle school curricula across nine domains including mathematics, history, biology, and religion. The benchmark includes over 2,000 open-ended questions grounded in 486 images, ensuring that models must integrate visual interpretation with factual reasoning rather than rely on superficial textual cues. We evaluate thirteen state-of-the-art open-weight VLMs across multiple dimensions, including domain-specific accuracy and performance on adversarial crafted questions. Our findings reveal that even the strongest models achieve less than 45% overall accuracy, with particularly poor performance in music, mathematics, and adversarial settings. Furthermore, the results indicate significant discrepancies between success on popular benchmarks and real-world multimodal understanding. We conclude that middle school-level tasks offer a meaningful and underutilized avenue for stress-testing VLMs, especially in non-English contexts. The dataset and evaluation protocol serve as a rigorous testbed to better understand and improve the visual and linguistic reasoning capabilities of future AI systems.