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Highly vulnerable people, such as those on permanent ventilation, are heavily
dependent on medical, nursing and social support. The aim of the treatment of these
patients is to make them independent of the ventilators - insofar as there is a po-
tential for weaning from ventilation. This requires a considerable amount of time,
especially for people with multimorbid diseases. In a clinical setting, the necessary
resources for this are not available.
In a newly created transitional living form for out-of-hospital ventilation wean-
ing, patients are closely supervised and supported in the weaning process by a
multidisciplinary team in a homely environment. The transitional housing form is
supported by research carried out by the Ostbayerische Technische Hochschule Re-
gensburg. The evaluation of self-determination in a complex disease situation is
one aspect of this research.
This paper is based on a study that included 54 guideline-based, semi-stan-
dardised interviews which were conducted with the patients themselves and/or rel-
atives covering the aspects of medical and therapeutic care as well as housing and
self-determination. The interviews were evaluated using content analysis. In a first
step, the categories of the analysis of self-determination are described. Further-
more, the results are exemplified with a case study.
Results of the study show that the perception of quality of life is closely linked
to the possibilities for self-determination. The comprehensive medical, nursing and
therapeutic support contributes to the well-being of the sick people, who should be
informed about and involved in the treatment. The case study illustrates by way of
example how self-determination can be achieved in the case of severely diseased
patients (e.g. referring to their mobilisation, visits, spare time activities) and how
compliance can be increased. It became evident that even in the case of lethal dis-
eases whose progression cannot be influenced, a self-determined life is still possi-
ble.
Between 51% and 72% of a bituminous roofing membrane used for structural water-proofing consists of organic material, predominantly bitumen - a derivative of crude oil refining - highlighting the strong dependence of this product on fossil resources. Considering that several tonnes of these membranes must be replaced every 30 to 50 years, substantial potential exists for emission reduction through the establishment of circular material systems. This study investigates this potential by analysing 26 Environmental Product Declarations (EPDs) and life cycle datasets from across Europe covering the period from 2007 to 2023. To ensure comparability, all data were normalised to a declared unit of 1 kg of roofing membrane. The reinforcement layers were categorised into glass and polyester & glass composites, and their differences were examined using Welch’s t-tests.
Correlative analyses and linear as well as multiple regression models were then applied to explore relationships between environmental indicators and the shares of organic and mineral mass fractions. The findings reveal that renewable energy sources, although currently representing only a small share of total production energy, provide a major lever for
reducing nearly all environmental impact categories. The type of reinforcement layer was also found to influence the demand for fossil resources, both materially and energetically.
For most environmental indicators, only multiple regression models can explain at least 30% of the variance based on the proportions of organic and mineral inputs. Overall, the study underscores the crucial importance of high-quality, transparently documented product data for accurately assessing the sustainability of building products. It further demonstrates that substituting fossil energy carriers with renewable sources and optimising material efficiency can substantially reduce environmental burdens, provided that methodological consistency and clarity of indicator definitions are maintained.
Artificial intelligence (AI) is not only a ubiquitous topic in scholarly debates but in recent years has also received increased media and political attention. Discussions, initially strongly influenced by autonomous vehicles but currently dominated by expectations concerning generative AI such as ChatGPT, have contributed to public awareness. There are debate cycles, often characterized by a significant decline in public attention after a certain period of time, and the issue to a large extent or completely disappears from mass media’s agenda; also, they are often specific to certain countries. In this paper, a cursory cross-country overview concerning studies on media representations and the public perception of AI is provided. The use of AI in healthcare will be particularly highlighted, as there are currently high expectations regarding the benefits of AI, but other areas of application will also be considered. It will be tentatively concluded that in many countries, public perception of and public attitudes toward AI often is based on superficial knowledge and even prejudices.
Der vorliegende Beitrag diskutiert ausgewählte Schlaglichter eines hybriden Lehr- und Lernprojektes, welches mithilfe von EMPAMOS als Analyse-, Gestaltungs- und Reflexionsinstrument entwickelt und begleitet wurde. Das Lehrprojekt verfolgte das Ziel, hybride und digitale Lehre in einem Kurs zur mathematischen Statistik durch Gamification motivational ansprechend zu gestalten. Hierfür wurde das hybride Lehr- und Lernlabor als ein veränderliches Spielfeld verstanden, mit welchem es umzugehen galt, um stets handlungsfähig zu bleiben und Studierende adäquat auf das Bestehen des Leistungsnachweises vorzubereiten. Dabei konnte die kooperative Spielform als ein zentrales Spielelement identifiziert werden, mit dem sich dieser Auftrag umsetzen ließ. Bei den Studierenden hatte jedoch das immanente motivationale Bedürfnis nach Kompetenzerleben die höchste Priorität.
Additively processed materials are increasingly used to manufacture customized parts, e.g. medical implants. Implant surfaces often require a smooth finish, which can be achieved by post-processing and well-defined process parameters. In this study, the effects of electropolishing of metal parts produced by laser powder bed fusion are investigated using Hull cell experiments and a three-electrode setup. Current density voltage curves were measured with the three-electrode setup to identify the regimes for electropolishing. Subsequently different constant-currents were applied and Hull cell experiments were conducted. The surface roughness (Sz, Sa) and the mass removal were analysed. Surface morphologies were assessed using laser scanning and scanning electron microscopy. A reduction of the initial surface roughness of more than 90% to Sa < 0.3 μm has been achieved. Considering the passed electrical charge during electropolishing, results from Hull cell experiments are systematically correlated with current-controlled electropolishing. This approach enables the precise tailoring of polishing parameters to achieve surfaces with defined roughness. Furthermore, the study demonstrates the suitability of Hull cells in determining electropolishing parameters for additive materials and highlights their contribution to post-processing in additive manufacturing.
This paper presents the architecture and functionality of a simulation-based Digital Twin designed to support decision-making in the control of internal transport systems. The developed Digital Twin leverages a wide range of data from operational information systems to predict future system loads. Specific methods for forecasting transport orders have been developed. An integrated simulation framework enables analysis of potential control decisions, such as resource allocation or the timing of order releases. The simulation results are used for multi-criteria evaluation of the available options for preventive action, thus, providing users with concrete recommendations to support their operational decision-making processes.
Simulation can support early-stage production system planning by enabling
efficient evaluation of concept variants. However, detailed models are often
impractical in concept phases due to limited data and high modelling effort. This paper presents a structured three-stage simulation approach for supporting the concept planning of water electrolyzer stack production systems . The approach leverages automatic model generation from formal process descriptions and gradually increases model detail across stages, carried out through a custom-developed simulation framework. This enables early validation of the throughput capability, resource planning, and the analysis of stochastically occurring technical malfunction. The method aims to reduce planning time while improving decision quality.