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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.
In a data set of non-metallic inclusion sizes in samples from engineering steel, common order statistics fail to serve as a suitable model for ascendingly ordered measurements within single samples. Therefore, a flexible model of ordered random variables is proposed, which allows for changes of distributions described by model parameters. Joint maximum likelihood estimation of these parameters and the shape parameter of an underlying left-truncated Weibull distribution is considered, and a model test is developed for the null-hypothesis of common order statistics being an adequate model. To overcome small data situations, a link-function approach is examined in order to reduce the number of involved model parameters as well as to propose to use a link-function parameter as a material indicator. An asymptotic test is provided to check for the presence of a linear link function, and tests for hypotheses about two link-function parameters are studied. Moreover, the construction of simultaneous confidence regions for the link-function parameters as well as of confidence bands for the entire graph of the link function are presented. Throughout, the findings are applied to the real metallurgical data set. Similar problems and data structures arise in other fields of material science and applications such as geology.
Hintergrund und Zielsetzung
Zunehmend werden digitale Anamneseverfahren, wie z. B. Chatbots, entwickelt und evaluiert, sind jedoch in Arztpraxen in Deutschland noch nicht flächendeckend implementiert. Ziel dieser Arbeit ist die Erhebung der Akzeptanz der Bevölkerung in Deutschland gegenüber KI-gestützten Verfahren im Anamneseprozess, durch die es möglich wird, Rückschlüsse auf die Nutzung der Verfahren und ihre Eignung als Bestandteil von Digitalisierungsstrategien der Praxen zu ziehen.
Methode
Die Studie basiert auf einer Onlinebefragung der Bevölkerung in Deutschland (Feldphase 18.-24.11.2024, n = 1.000). Zurückgehend auf die „Unified Theory of Acceptance and Use of Technology“ (UTAUT) wurden drei zentrale Hypothesen zur Nutzung von Künstlicher Intelligenz (KI) im Anamneseprozess aufgestellt und mit linearen Regressionsmodellen getestet.
Ergebnisse
Die Akzeptanz für die Nutzung von KI-gestützten Systemen fällt in beiden untersuchten Szenarien, der Nutzung eines Chatbots zu Hause und der Nutzung eines Chatbots in einer Arztpraxis, hoch aus. Dabei bestätigen sich die erwarteten Zusammenhänge von Leistungserwartung, Aufwandserwartung und erwartetem Sozialem Einfluss mit der Verhaltensabsicht, einen Chatbot in der Arztpraxis oder vor dem Besuch einer Arztpraxis zu nutzen. Die Einflüsse von Alter, Geschlecht oder Erfahrung mit der Technologie zeigen jedoch nicht die vermuteten Zusammenhänge.
Diskussion und Schlussfolgerung
Die Praktikabilität ist von entscheidender Bedeutung für eine positive Einstellung gegenüber der Nutzung. Dabei zeigt sich kein Unterschied zwischen den Geschlechtern. Bei der Implementierung von Chatbots im Anamneseprozess sollten die Aspekte Praktikabilität, Datenschutz und Datensicherheit eine wesentliche Rolle spielen. Angesichts der Tatsache, dass ein großer Teil der älteren Bevölkerung in Deutschland nach wie vor über begrenzte Erfahrung mit digitalen Technologien verfügt, ist Gebrauchstauglichkeit für diese Bevölkerungsgruppe sowie für Menschen mit Beeinträchtigungen von hoher Relevanz.
Intra-abdominal pressure is a significant physiological parameter influencing spinal stability and pelvic floor health. This study investigates the potential of a transformer encoder model to predict IAP using motion capture data and musculoskeletal modeling. Data from 211 subjects performing walking, fast walking, and running were used to train a transformer encoder. The model showed promising results with an overall Mean Absolute Percentage Error of 13.5% and a Pearson correlation coefficient of 0.85. Predictions for fast walking and running proved to be more challenging compared to walking, which was attributed to the greater variability and complexity of faster movements.
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.
Background: Group treatments and interdisciplinary collaboration are recommended in evidence-based guidelines for neurorehabilitation, including physical and speech-language therapy. Evidence suggests that activating overlapping neural networks for upper extremity motor control and speechlanguage processing produces synergistic effects during therapy. This systematic
review aims to overview and appraise the efficacy of group treatments in traditional rehabilitation and telerehabilitation. In addition to summarizing evidence on monodisciplinary approaches in physical and speech-language therapy, it seeks data on integrative approaches involving one or both disciplines to inform further interdisciplinary collaboration.
Methods: The review was registered with PROSPERO (CRD42021288012) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Systematic searches were conducted in PubMed, CINAHL, and the Cochrane Library. Two reviewers independently screened studies, extracted data, and assessed quality using AMSTAR 2, the Physiotherapy Evidence Database (PEDro) scale, or the Joanna Briggs Institute (JBI) Checklist, as appropriate. The evidence was summarized in a systematic narrative synthesis and its certainty rated based on the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach.
Results: A total of 29 studies were included: 16 on speech-language therapy (861 participants) and 13 on physical therapy (1757 participants). No studies addressed interdisciplinary group interventions, and only two evaluated group telerehabilitation. Outcome domains and measures varied across studies and the certainty of evidence was predominantly low. However, moderate-certainty evidence supports that group speech-language therapy improves quality of life,
communication, and language in stroke survivors, especially when interventions emphasize verbal production in communicative settings with multimodal materials and cueing. In physical therapy, circuit class training may be more effective than other group approaches for enhancing quality of life and mobility.
Conclusion: Group treatments in neurorehabilitation show some benefits, but further research is needed – especially regarding interdisciplinary approaches and telerehabilitation.
Purpose
Recognizing previously unseen classes with neural networks is a significant challenge due to their limited generalization capabilities. This issue is particularly critical in safety-critical domains such as medical applications, where accurate classification is essential for reliability and patient safety. Zero-shot learning methods address this challenge by utilizing additional semantic data, with their performance relying heavily on the quality of the generated embeddings.
Methods
This work investigates the use of full descriptive sentences, generated by a Sentence-BERT model, as class representations, compared to simpler category-based word embeddings derived from a BERT model. Additionally, the impact of z-score normalization as a post-processing step on these embeddings is explored. The proposed approach is evaluated on a multi-label generalized zero-shot learning task, focusing on the recognition of surgical instruments in endoscopic images from minimally invasive cholecystectomies.
Results
The results demonstrate that combining sentence embeddings and z-score normalization significantly improves model performance. For unseen classes, the AUROC improves from 43.9% to 64.9%, and the multi-label accuracy from 26.1% to 79.5%. Overall performance measured across both seen and unseen classes improves from 49.3% to 64.9% in AUROC and from 37.3% to 65.1% in multi-label accuracy, highlighting the effectiveness of our approach.
Conclusion
These findings demonstrate that sentence embeddings and z-score normalization can substantially enhance the generalization performance of zero-shot learning models. However, as the study is based on a single dataset, future work should validate the method across diverse datasets and application domains to establish its robustness and broader applicability.
Planning modern facade systems is complex, requiring optimization across multiple domains.This paper proposes an AI-enhanced workflow for facade planning, harnessing computer vision and human input via a Large Language Model.A generative AI system then guides a parametric model to produce 3D facade designs. Automated checks provide feedback to a Reinforcement Learning system, to iteratively determine optimal solutions.These solutions are verified and finalized by human expertise, ensuring improved outcomes with reduce planning time and effort.The approach illustrates how combining advanced AI methods with human expertise can address the multifactorial challenges of facade design within current industry practices.
The early design phase of deep-drawn structural components involves time-consuming iterative development. Traditional drawability assessments rely on finite element simulations, which are computationally expensive and slow the design process. Alternative machine learning (ML) approaches show promise in accelerating this process but face challenges with existing methods. Existing low-dimensional ML models only provide global predictions without identifying specific geometric regions prone to failure. High-dimensional models provide local predictions but require significant amounts of training data. We propose a data-driven approach leveraging graph neural networks (GNNs) for face-wise drawability prediction of sheet metal components in their computer-aided design (CAD) representation. Our method aims to bridge the gap between the computational efficiency of ML and the spatial resolution of simulation by providing face-wise insight into potential failure regions. This study utilises a dataset of parametric U-channel geometries with variability in both geometry and topology. Ground-truth labels are generated using inverse analysis simulations. Geometric entities are represented through the use of UV parameterisations, whereby 3D surfaces are mapped into 2D space to facilitate geometric encoding. Concurrently, the topological relationships are captured using a face adjacency graph. To address data scarcity, we evaluate how different amounts of training data affect model performance and perform ablation studies to analyse the impact of different CAD representation features. Our results show that the proposed approach achieves high accuracy even with limited training data. In addition, the ablation studies provide insights into the most critical CAD features, guiding future research. These results highlight the potential of our GNN to predict face-wise drawability in the early design phase.
PV and wind systems with PEM electrolysis offer great potential for producing hydrogen with low emissions. Our research has identified the ecologically optimal size of PEM in relation to fixed PV/wind capacities. We calculate efficiencies and production volumes for PEM with 240 capacity and site variations. We analyse the global warming potential of all systems and draw conclusions about the optimal system design. The lowest GWP is achieved at the site with the highest full load hours with 1.32 kg CO2-eq/kg H2 (Wind, 28 MW electrolysis) and 4.24 kg CO2-eq/kg H2 (PV, 23 MW electrolysis). We have identified a clear trend: increasing PV/wind full load hours leads to higher ideal PEM capacities. However, there is a significant discrepancy between the ideal economic and ecological capacity. Furthermore, higher electrolysis capacities can achieve lower emissions as they increasingly operate at a more efficient partial load.