Publikationsserver der Ostbayerischen Technischen Hochschule Regensburg
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Photoacoustic trace-analysis of breath isoprene and acetone via interband- and Quantum Cascade Lasers
No full textThis research presents two laser-based photoacoustic approaches for analyzing exhaled breath isoprene and acetone. The integration of a PTR-ToF-MS as a reference device ensured the reliability and accuracy of the photoacoustic systems that is based on an ICL for isoprene and a QCL for acetone detection. The calibration yielded limits of detection of 26.9 ppbV and 1.7 ppbV, respectively, and corresponding normalized noise equivalent absorption coefficients (NNEAs) of 5.0E-9 Wcm 1Hz 0.5 and 4.9E-9 Wcm 1Hz 0.5. Laboratory as well as real breath sample measurements from alveolar breath revealed a robust system performance, with only one outlier within the static isoprene measurements. However, discrepancies emerged under dynamic breath sampling conditions, emphasizing the need for further optimization. Especially by knowing the dynamic nature and endogenous origin of exhaled isoprene our findings highlight the potential of breath analysis for non-invasive
physio-metabolic and pathophysiological monitoring towards point-of-care device
Real-time hardware-based processing of high-precision detector signals for surface plasmon resonance spectroscopy
No full textSurface plasmon resonance (SPR) is limited by small-signal detectability and drift when subtraction occurs in software after digitization. We introduce an SPR detector that performs on-detector amplification and analog differential readout, eliminating moving parts and software-heavy correction. The hardware-native subtraction boosts the usable ADC range and suppresses illumination and environmental noise. In fixed-angle refractive-index steps (NaCl), the platform resolves Δn_min ≈ 1.8 × 10⁻⁷ RIU compared to 4.6–7.2 × 10⁻⁶ RIU on a commercial comparator and improves small-signal SNR by up to ∼5,000-fold, while remaining competitive at high signal levels. In a model IgG–BSA assay, the detector’s low noise floor clarifies early binding and equilibrium transitions. By generating inherently clean raw signals, this hardware-native approach dramatically enhances sensitivity and long-term stability for label-free biosensing and inline process analytics while rendering AI-based or complex post-processing entirely unnecessary. The concept generalizes across platforms and opens a compact route to robust, high-fidelity SPR in complex environments, with a clear path toward multi-wavelength and arrayed detectors for high-throughput chemical monitoring
Is the Finnish grip tight enough? A manometric study of two manual perineal protection techniques
No full textPossibilities for Reducing the Environmental Impact in the Construction Industry Using the Example of a 3D Printed Staircase
No full textThe construction industry faces numerous challenges, including environmental sustainability, high material costs, and a shortage of skilled labor. Modern technologies enabling digital fabrication present opportunities to reduce raw material consumption and waste generation. Among these, 3D printing technologies offer distinct advantages over traditional construction methods, particularly in handling complex geometries. However, the significant environmental impact of cement in 3D printed concrete, due to its high rheological and printability requirements, remains a concern. This study introduces a novel application of 3D printed permanent formwork in the construction of a winder staircase, assessed through an environmental Life Cycle Assessment (LCA) from cradle to gate. By comparing the environmental impacts of various construction materials and processes, the study highlights the comparative advantages and disadvantages of conventional methods versus 3D printing. The LCA results reveal that traditional production methods, particularly those using plywood formwork, exhibit higher environmental impacts. In contrast, timber formwork performs better than most 3D printed mixtures in terms of Global Warming Potential (GWP), Acidification Potential (AP), and Abiotic Depletion Potential (ADP). The findings of this study underscore the potential of additive manufacturing for sustainable construction, particularly through the use of low clinker cement in 3D printed formwork, offering a promising pathway towards reducing the environmental footprint of construction activities
Exploring the Potential of BIM Models for Deriving Synthetic Training Data for Machine Learning Applications, Montreal
No full textTo increase the efficiency and quality of design and construction tasks, the use of Artificial Intelligence (AI) and Machine Learning (ML) offers a way to automate both repetitive and complex tasks. Many of these ML models rely heavily on large amounts of suitable, machine-readable, and labeled training data. Therefore, a variety of conceivable use cases for ML in the Architecture, Engineering and Construction (AEC) industry are difficult to implement due to a lack of freely and directly usable training data. The process of manually structuring and labeling existing data is time-consuming and needs in some cases skilled personnel to ensure the quality of the labeled data. Due to these factors, approaches for utilizing artificially generated data, referred to as synthetic data, are becoming more prevalent. Since Building Information Models contain a large amount of information, deriving training data from these models presents an obvious route for generation of this data. There are many ML applications whose implementation is inhibited due to a lack of training data, for which model-based synthetic data offer a possible solution approach. The Industry Foundation Classes (IFC) standard provides a powerful exchange format for models independently of their authoring software. Parametric and generative approaches to model creation enable the generation of numerous different building models within a short period of time and with low effort. This paper presents a workflow for automated derivation of synthetic training data from rule-based or parametrically generated models combined with existing IFC datasets as a multimodal data repository. The method is validated by testing automated synthetically labeled image data for a plan detection task, which is carried out with the Object Detection Framework YOLOv8. The suggested workflow has the potential to enhance data accessibility, thereby contributing to the implementation of ML applications in the AEC industry
Datenschutzsorgen aus Sicht der Bevölkerung im Kontext der Einführung von Smart-Metern
No full textDer Beitrag stellt die Ergebnisse einer repräsentativen Bevölkerungsumfrage zur Akzeptanz von Smart-Metern in Privathaushalten vor. Bedenken gegenüber der Einführung von Smart-Metern hat knapp die Hälfte der Befragten. Anonymisierte Zugriffe auf ERnergieverbrauchsdaten werden anstelle der Weitergabe personenbezogener Daten favorisiert. Informationen über den Datenschutz haben für deutsche Privathaushalte offenbar die höchste Priorität
Self-supervised 3D Vision Transformer Pre-training for Robust Brain Tumor Classification
No full textBrain tumors pose significant challenges in neurology, making precise classification crucial for prognosis and treatment planning. This work investigates the effectiveness of a self-supervised learning approach–masked autoencoding (MAE)–to pre-train a vision transformer (ViT) model for brain tumor classification. Our method uses non-domain specific data, leveraging the ADNI and OASIS-3 MRI datasets, which primarily focus on degenerative diseases, for pretraining. The model is subsequently fine-tuned and evaluated on the BraTS glioma and meningioma datasets, representing a novel use of these datasets for tumor classification. The pre-trained MAE ViT model achieves an average F1 score of 0.91 in a 5-fold cross-validation setting, outperforming the nnU-Net encoder trained from scratch, particularly under limited data conditions. These findings highlight the potential of self-supervised MAE in enhancing brain tumor classification accuracy, even with restricted labeled data
iRBSM: A Deep Implicit 3D Breast Shape Model
No full textWe present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the need for computationally demanding non-rigid registration, a task that is particularly difficult for feature-less breast shapes. The resulting model, dubbed iRBSM, captures detailed surface geometry including fine structures such as nipples and belly buttons, is highly expressive, and outperforms the RBSM on different surface reconstruction tasks. Finally, leveraging the iRBSM, we present a prototype application to 3D reconstruct breast shapes from just a single image. Model and code publicly available at https://rbsm.re-mic.de/implicit
Second-Order Dynamic Friction Model (FrD2) in a Nutshell
No full textThe well-known LuGre friction model generates dynamic friction forces. This force results from the approximation of the dynamics of a massless fictitious bristle. However, it has several drawbacks and fails to reproduce predefined friction characteristics. The second-order dynamic friction model (FrD2) avoids these drawbacks and accurately reproduces friction characteristics. The FrD2 model is based on a fictitious bristle whose mass automatically adapts to visco-elastic bristle properties. The FrD2 model describes friction characteristics using piecewise-defined analytical functions and applies shifted regularization, which allows for smooth handling of stick-slip transitions. FrD2 parameters can easily be derived from LuGre model parameters
Second-Order Dynamic Friction Model Goes Bi-Dimensional
No full textDynamic friction models can handle not only slip-stick-slip transitions but also stick as long as the external load does not exceed the friction limit. The recently developed second-order dynamic friction model (FrD2) uses two internal states. It models standard friction characteristics by a smooth analytical function, which includes the Stribeck effect and also a viscous component. A horizontal shift of the regularized friction characteristics provides non-vanishing friction forces required to keep stick. Unlike the well-known LuGre model, the FrD2 model reproduces predefined friction characteristics very accurately and shows no drift under pulsating loads. This paper shows how to extend FrD2 to its bi-dimensional version FrD2bd