An interdisciplinary competence profile for AI in engineering

The use of Artificial Intelligence (AI) in engineering is on the rise and comes with the promise of cost reductions and efficiency gains. However, classical engineers often lack the necessary skills to implement data-driven solutions. At the same time, computer scientists lack the required understanding of engineering systems. Thus, we need to extend the current set of competencies of engineers across the boundaries of disciplines to include competencies of Artificial Intelligence as well as skills necessary for interdisciplinary work. In this paper, we propose a competence profile of a so-called AI Engineer that combines the expertise of AI systems in the context of engineering. Based on perspectives from literature and interviews with experts from industry and research, we highlight the most important set of competencies across the professional, methodological, social, and selfcompetencies. The contributions of our paper can act as a reference point for developing and advancing future engineering curricula. Furthermore, it serves as a guide for professional self-development

The unique status of first-in-human studies: strengthening the social value requirement

For clinical research to be ethical, risks need to be balanced by anticipated benefits. This is challenging for first-in-human (FIH) studies as participants are not expected to benefit directly, and risks are potentially high. We argue that this differentiates FIH studies from other clinical trials to the extent that they should be given unique status in international research ethics guidelines. As there is a general positive attitude regarding the benefits of science, it is important to establish a more systematic method to assess anticipated social value to safeguard participants not only from enrolling in risky, but also in futile trials. Here, we provide some of necessary steps needed to assess the anticipated social value of the intervention

Quantum oscillations in the parent pnictide BaFe2_2As2_2 : itinerant electrons in the reconstructed state

We report quantum oscillation measurements that enable the direct observation of the Fermi surface of the low temperature ground state of \ba122. From these measurements we characterize the low energy excitations, revealing that the Fermi surface is reconstructed in the antiferromagnetic state, but leaving itinerant electrons in its wake. The present measurements are consistent with a conventional band folding picture of the antiferromagnetic ground state, placing important limits on the topology and size of the Fermi surface.Comment: 5 pages, 3 figure

Applying the mixed-blessings model and labeling theory to stigma in inclusive education: an experimental study of student and trainee teachers’ perceptions of pupils with ADHD, DLD, and intellectual disability

Institutional and individual stigmatization represent major barriers that prevent children with disabilities from accessing education. It can be presumed that children with disabilities are labeled as such even in inclusive educational settings and that teachers’ attitudes toward inclusive education and children with disabilities play a crucial role in this context. Against this background, the present study aims to (a) apply and conceptualize the mixed-blessings model in the context of stigma-related reactions to children’s disability labels in inclusive education and (b) shed light on the causal attributions of teachers that underlie stigma-related attitudes toward children with various disabilities. A 3 × 2 × 2 × 2 × 2 online experiment examined the ways in which disability-specific causes and symptoms, the type of disability in question, the children’s sex, and efficacy cues regarding educational efforts affect future teachers’ attitudes toward and expectations of inclusive education as well as their social distance toward children with disabilities. The participants in this experiment were N = 605 German student and trainee teachers representing different types of teaching professions. A multivariate analysis of variance (MANOVA) revealed that, in particular, the cause attributed to the disability, the depicted type of disability and the probability of learning success led to changes in attitudes. Respondents’ teaching self-efficacy and their status as students or trainees emerged as moderators of the effect of pupils’ type of disability. As a result, teacher education and training as well as communication regarding pupils with disabilities require a high degree of sensitivity to disability-specific and efficacy-related cues to prevent (accidental) professional or institutional stigmatization

Mobility choices - an instrument for precise automatized travel behavior detection & analysis

Within the Mobility Choices (MC) project we have developed an app that allows users to record their travel behavior and encourages them to try out new means of transportation that may better fit their preferences. Tracks explicitly released by the users are anonymized and can be analyzed by authorized institutions. For recorded tracks, the freely available app automatically determines the segments with their transportation mode; analyzes the track according to the criteria environment, health, costs, and time; and indicates alternative connections that better fit the criteria, which can individually be configured by the user. In the second step, the users can edit their tracks and release them for further analysis by authorized institutions. The system is complemented by a Web-based analysis program that helps authorized institutions carry out specific evaluations of traffic flows based on the released tracks of the app users. The automatic transportation mode detection of the system reaches an accuracy of 97%. This requires only minimal corrections by the user, which can easily be done directly in the app before releasing a track. All this enables significantly more accurate surveys of transport behavior than the usual time-consuming manual (non-automated) approaches, based on questionnaires

Reduced Expression of Emotion: A Red Flag Signalling Conversion to Psychosis in Clinical High Risk for Psychosis (CHR-P) Populations

Objective: In this hypothesis-testing study, which is based on findings from a previous atheoretical machine-learning study, we test the predictive power of baseline “reduced expression of emotion” for psychosis. Method: Study participants (N = 96, mean age 16.55 years) were recruited from the Prevention of Psychosis Study in Rogaland, Norway. The Structured Interview for Prodromal Syndromes (SIPS) was conducted 13 times over two years. Reduced expression of emotion was added to positive symptoms at baseline (P1–P5) as a predictor of psychosis onset over a two-year period using logistic regression. Results: Participants with a score above zero on expression of emotion had over eight times the odds of conversion (OR = 8.69, p < .001). Data indicated a significant dose–response association. A model including reduced expression of emotion at baseline together with the positive symptoms of the SIPS rendered the latter statistically insignificant. Conclusions: The study findings confirm findings from the previous machine-learning study, indicating that observing reduced expression of emotion may serve two purposes: first, it may add predictive value to psychosis conversion, and second, it is readily observable. This may facilitate detection of those most at risk within the clinical high risk of psychosis population, as well as those at clinical high risk. A next step could be including this symptom within current high-risk criteria. Future research should consolidate these findings.publishedVersio