The 10 most recently published documents
AI-driven risk estimation: a GPT-based approach to news monitoring for manufacturing resilience
(2026)
In today’s rapidly evolving commercial landscape, manufacturing enterprises face significant challenges in maintaining resilience amid disruptions such as pandemics, natural disasters, and geopolitical conflicts. To address these challenges, we introduce a novel GPT-based early detection tool designed for real-time supply chain risk assessment. This system integrates proprietary company data, including supply chain portfolios, with publicly available information, such as news articles, to estimate risk scores for respective supply chains, thereby enhancing decision-making processes. Leveraging advanced machine learning techniques–Generative Pretrained Transformers (GPT), zero-shot learning, and structured outputs–the tool operates locally to ensure data privacy and minimize information leakage. Utilizing the "news-please" crawler and the "Llama 3.1" GPT model, the system continuously monitors selected media sources, providing timely risk assessments. Our research demonstrates the tool’s potential to enhance proactive risk management in supply chains, validated through testing on both real and augmented datasets. By evaluating four exemplary supply chains, we characterize the tool’s capability to support decision-making in unpredictable global environments. The results indicate that, while the system occasionally exhibits oversensitivity, it consistently aids in identifying critical events that may impact supply chain operations. Future developments will focus on refining the tool’s accuracy and expanding its applications, particularly in monitoring regulatory changes.
Background: The use of generative AI, as represented by ChatGPT, holds promising potential for nursing education. This manifests itself in various areas, including personalized learning, simulation training and teaching process support. However, its integration requires careful consideration of ethical implications, adaptation of curricula and a high level of digital competence on the part of teachers. Only in this way can potential risks, such as the distortion of knowledge, bias and educational inequalities, be avoided.
Methods: Relevant publications were identified between 2019 and 2025 as part of a comprehensive literature search in the specialist databases PubMed, Embase, CINAHL and Scopus. The search was conducted using combined search terms that included the terms “generative AI”, “ChatGPT” and “nursing”. After removing duplicates and screening (PRISMA-guided), 140 full texts were analysed and divided into two publications. This rapid overview focuses on the topic of generative AI in nursing education.
Results: As part of the analysis of the included studies, five thematic areas were identified, which were divided into the categories of nursing education, competence development and nursing skills, implementation possibilities, examination quality and ethical considerations, and evaluated. A key theme is the dual potential of this technology: it can enrich learning through features such as virtual tutors and improved exam preparation, but it also requires critical consideration of ethical issues such as plagiarism, data bias and the need for human oversight.
Outlook: In this context, the conclusion emphasises the urgent need to adapt curricula and provide targeted further training for teachers so that GenAI can be used responsibly and effectively—rather than, as is often the case at present, by banning it altogether.
Hochschulbericht 2019 | 2020
(2021)
Hochschulbericht 2021 | 2022
(2023)
Hochschulbericht 2023 | 2024
(2025)
Presentamos aquí una crónica de la interpretación entre el español y la lengua de señas de Venezuela (LSV). Nuestros datos provienen de entrevistas y encuestas con intérpretes de LSV (ILSV) y otros profesionales vinculados al área. Esos datos fueron contrastados con documentos institucionales. Se propone una periodización de cuatro fases, delimitadas por diversos hitos. La primera, la “prehistoria” de la ILSV, comprende el largo período previo a la fundación de la primera escuela de sordos (1935), un período sólo reconstruible especulativamente. La segunda se extiende entre 1935 y 1985. En ella surgen comunidades sordas organizadas y aparecen las primeras referencias a la LSV y a los ILSV. La tercera se inicia en 1985, con el modelo bilingüe en las escuelas de sordos, que empodera a la comunidad sorda y demarca el oficio de ILSV. La cuarta fase se define con la fundación, en 2006, de la asociación nacional de los ILSV y su progresiva profesionalización. En los últimos años de esta fase, especialmente desde 2016, la crisis social y económica del país impone cambios dramáticos en las condiciones de vida y trabajo de los ILSV que podrían ser considerados el inicio de una quinta fase.
In this paper the application of Active Disturbance Rejection Control (ADRC) for discrete-time field-oriented PMSM current control is discussed. ADRC offers an interesting alternative to established current control algorithms due to the considerably simplified control engineering process. After introducing the ADRC approach and the plant model, the possibilities for tuning the controller and the comprised state space observer are examined in detail. In particular, the adequate consideration of the modulator delay in the ADRC’s extended state observer is investigated, leading to a new ADRC current control algorithm. This allows a parameterization for very fast dynamics, so that almost deadbeat behavior is achievable. All investigations are validated by means of practical measurements.
