This paper investigates the physiological responses of individuals driving both on a real
route and within a vehicle simulator designed as a digital twin of that route. The analysis
of observed data patterns in stress response bio signals provides sufficient evidence of
similarity to validating the driving simulation digital twin as a reliable replacement for
real-world experiences in controlled and consistent settings, or when overall trends of
physiological variables, rather than specific variable levels, are of interest. The findings also
stress the need for optimizing the precision of digital twins in complex settings. This study
introduces a time-series-based validation approach for driving digital twins by comparing
continuous physiological trajectories between real and simulated driving

Successful innovation processes involve multiple actors with distinct objectives, resources,
and competences across different stages of value creation. Close collaboration among relevant
stakeholders is therefore essential, yet it also entails substantial coordination and alignment
challenges. Drawing on a multi-method research design, this study examines the development and
market introduction of an advanced driver assistance system (ADAS) for passenger vehicles across
multiple market stages.
The findings conceptually and empirically demonstrate how stakeholders’ heterogeneous goal
structures and value assessments influence the innovation process across stages. In particular, the
results reveal that the absence of a multi-stage marketing (MSM) perspective can lead to
misalignments that adversely affect the progression and market success of the innovation. The
study further provides insights into how innovation processes can be systematically designed
across market stages to ensure end-user orientation and economic viability.
By integrating a multi-stage marketing perspective into innovation research, this paper
contributes to a more comprehensive understanding of innovation processes in complex, multi-
actor environments.

At a time when vehicle automation is becoming increasingly important, there
is a growing need for greater consumer centricity. However, the importance of
effectively deriving functional product specifications appears to be diminishing.
The case of Automated Parking Systems (APS) demonstrates that the automotive
industry often employs a top-down approach, in contrast to a more customer-
-centric method in the development process. To assess the effectiveness of this
top-down approach, we conducted a field study and a mixed-method online
survey to explore user expectations of APS functionality. Our findings indicate
that drivers strongly dislike excessive parking maneuvers caused by overly re-
strictive product specifications. Moreover, user demands are less stringent
than the development requirements set by OEMs. Based on these insights, we
recommend adopting a more user-centered approach. This shift could enable
companies to reduce development costs and time investments, while accelerat-
ing the adoption of their innovations.

Scents influence emotions, cognition and behavior by activating memories, enhanc-
ing mood and modulating mental processes. Perception of scents is shaped by both
stimulus-related dimensions, including familiarity, pleasantness and intensity and
individual factors – such as mood, sensory sensitivity and personality traits. The
present study systematically examined the relative impact of these determinants in
a quasi-experimental design involving 51 participants. Seven scents were evaluated
along perceptual dimensions (recognizability, pleasantness, familiarity, intensity)
and related to participants’ individual characteristics. Findings emphasize the inter-
play of stimulus-related dimensions and personality traits but highlight the need
for refined measures of (sensory-specific) personality traits in olfactory process-
ing for multisensory marketing and immersive applications.

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.

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.