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Aim: The use of artificial intelligence in nursing has become increasingly important in recent years. In particular, generative artificial intelligence (GenAI) such as ChatGPT offers the potential to improve care processes, support decision-making, and reduce workload. The aim of this paper is to provide an overview of the current state of research on the use of GenAI in nursing and clinical practice.
Subject and methods: A systematic literature search was conducted in the PubMed, Embase, CINAHL, and Scopus databases. Studies from the last 5 years (2019–2024) dealing with the use of GenAI in professional nursing and the improvement of nursing skills through AI were included. Studies on machine learning, deep learning, and specific disease contexts were excluded. A total of 13 studies were included in the analysis.
Results: GenAI in nursing and clinical practice can increase the efficiency of tasks such as scheduling and care planning, but there are currently significant gaps in decision accuracy and reliability. Studies show potential to reduce workload, but also point to the need for further research and technical improvements.
Conclusion: Although GenAI in nursing is promising, there are still significant limitations. Future developments and regulatory measures are needed to ensure the safe and effective use of GenAI in nursing practice.
The paper investigates the change of the structural dynamic behavior caused by embedding dry carbon fibers into beam-shaped specimens. It was assumed that a significant part of the vibration energy is dissipated due to friction between the dry fibers. To verify this, and to separate the effects of mass and stiffness changes, three different types of specimens – with and without dry fibers – were designed, manufactured and dynamically characterized through a bending resonance test. The results show a significant increase in damping due to the embedding of dry carbon fibers. Contrary to expectations, the natural frequency of this type of specimen increased along with the damping. However, the reason for this increase in damping and natural frequency remains unclear, as the decay curves, for example, do not exhibit a friction-typical characteristic.
This study focuses on the application of the Green Wave in a real-world context, namely in the city of Duisburg; using Lisa in planning and Vissim simulation modeling and following the German traffic signal guidelines (Rilsa). The aim is to redesign a series of signalized intersections, the transition from fixed-time to vehicle-actuated signal controllers, and integrate the Green Wave under safe and functional conditions. Finally, an analytical evaluation of the developed design is conducted, in order to shed light on the performance and advocate for a model; that in addition to being technically efficient, it promotes safety, environmental, and economic impacts.
Based on Welzl's algorithm for smallest circles and spheres we develop a simple linear time algorithm for finding the smallest circle enclosing a point cloud on a sphere. The algorithm yields correct results as long as the point cloud is contained in a hemisphere, but the hemisphere does not have to be known in advance and the algorithm automatically detects whether the hemisphere assumption is met. For the full-sphere case, that is, if the point cloud is not contained in a hemisphere, we provide hints on how to adapt existing linearithmic time algorithms for spherical Voronoi diagrams to find the smallest enclosing circle.
This paper examines how the Balanced Scorecard (BSC) and Digital Maturity Models (DMM) can be integrated as strategic management tools for Digital Transformation (DT) in manufacturing companies. The paper first presents the theoretical foundations of both models and then demonstrates their application in the context of digitalization. The BSC approach is enhanced with specific digital Key Performance Indicators (KPIs) and perspectives, while the acatech Industry 4.0 Maturity Index serves as an assessment framework for digital maturity. The integration of both models is presented in a matrix that links strategic objectives with operational development stages. This combined methodology provides companies with a structured framework to plan, implement, and monitor their Digital Transformation, considering both strategic alignment and operational maturity.
