Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (48) (remove)
Has Fulltext
- yes (48) (remove)
Document Type
- Article (48) (remove)
Keywords
- Einspielen <Werkstoff> (7)
- FEM (4)
- Finite-Elemente-Methode (4)
- Bauingenieurwesen (2)
- CAD (2)
- Capacitive field-effect sensor (2)
- Einspielanalyse (2)
- Label-free detection (2)
- Shakedown analysis (2)
- Traglastanalyse (2)
The importance of the availability of stored blood or blood cells, respectively, for urgent transfusion cannot be overestimated. Nowadays, blood storage becomes even more important since blood products are used for epidemiological studies, bio-technical research or banked for transfusion purposes. Thus blood samples must not only be processed, stored, and shipped to preserve their efficacy and safety, but also all parameters of storage must be recorded and reported for Quality Assurance. Therefore, blood banks and clinical research facilities are seeking more accurate, automated means for blood storage and blood processing.
The growing body of political texts opens up new opportunities for rich insights into political dynamics and ideologies but also increases the workload for manual analysis. Automated speaker attribution, which detects who said what to whom in a speech event and is closely related to semantic role labeling, is an important processing step for computational text analysis. We study the potential of the large language model family Llama 2 to automate speaker attribution in German parliamentary debates from 2017-2021. We fine-tune Llama 2 with QLoRA, an efficient training strategy, and observe our approach to achieve competitive performance in the GermEval 2023 Shared Task On Speaker Attribution in German News Articles and Parliamentary Debates. Our results shed light on the capabilities of large language models in automating speaker attribution, revealing a promising avenue for computational analysis of political discourse and the development of semantic role labeling systems.
In this work, the effects of carbon sources and culture media on the production and structural properties of bacterial cellulose (BC) synthesized by Medusomyces gisevii have been studied. The culture medium was composed of different initial concentrations of glucose or sucrose dissolved in 0.4% extract of plain green tea. Parameters of the culture media (titratable acidity, substrate conversion degree etc.) were monitored daily for 20 days of cultivation. The BC pellicles produced on different carbon sources were characterized in terms of biomass yield, crystallinity and morphology by field emission scanning electron microscopy (FE-SEM), atomic force microscopy and X-ray diffraction. Our results showed that Medusomyces gisevii had higher BC yields in media with sugar concentrations close to 10 g L−1 after a 18–20 days incubation period. Glucose in general lead to a higher BC yield (173 g L−1) compared to sucrose (163.5 g L−1). The BC crystallinity degree and surface roughness were higher in the samples synthetized from sucrose. Obtained FE-SEM micrographs show that the BC pellicles synthesized in the sucrose media contained densely packed tangles of cellulose fibrils whereas the BC produced in the glucose media displayed rather linear geometry of the BC fibrils without noticeable aggregates.
Diese Studie beschäftigte sich mit der Dämpfungswirkung von Schienbeinschonern, wie sie beim Fußball zum Einsatz kommen. Sie wurde mit Hilfe eines Pendelhammers durchgeführt, der verschiedene Aufschlagkräfte auf die Schoner ermöglichte. Dabei wurde deutlich, dass Schienbeinschoner die beste Wirkung bei Maximalkräften unterhalb von 5kN erreichen können, dass bei größerer Belastung allerdings Verbesserungsbedarf besteht. Hierfür konnte, u.a. durch den Einsatz neuer Materialien, ein guter Ansatzpunkt im „adäquaten Zusammenspiel von Schale und Polsterung“ der Schoner gefunden werden. Die Untersuchung hat weiterhin gezeigt, dass zumindest teilweise eine deutliche Verbesserung der Dämpfungswirkung der Schienbeinschoner in den letzten Jahren erreicht werden konnte.
A method for detecting and approximating fault lines or surfaces, respectively, or decision curves in two and three dimensions with guaranteed accuracy is presented. Reformulated as a classification problem, our method starts from a set of scattered points along with the corresponding classification algorithm to construct a representation of a decision curve by points with prescribed maximal distance to the true decision curve. Hereby, our algorithm ensures that the representing point set covers the decision curve in its entire extent and features local refinement based on the geometric properties of the decision curve. We demonstrate applications of our method to problems related to the detection of faults, to multi-criteria decision aid and, in combination with Kirsch’s factorization method, to solving an inverse acoustic scattering problem. In all applications we considered in this work, our method requires significantly less pointwise classifications than previously employed algorithms.
Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network.
We consider a binary multivariate regression model where the conditional expectation of a binary variable given a higher-dimensional input variable belongs to a parametric family. Based on this, we introduce a model-based bootstrap (MBB) for higher-dimensional input variables. This test can be used to check whether a sequence of independent and identically distributed observations belongs to such a parametric family. The approach is based on the empirical residual process introduced by Stute (Ann Statist 25:613–641, 1997). In contrast to Stute and Zhu’s approach (2002) Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), a transformation is not required. Thus, any problems associated with non-parametric regression estimation are avoided. As a result, the MBB method is much easier for users to implement. To illustrate the power of the MBB based tests, a small simulation study is performed. Compared to the approach of Stute & Zhu (Scandinavian J Statist 29:535–545, 2002), the simulations indicate a slightly improved power of the MBB based method. Finally, both methods are applied to a real data set.
An acetoin biosensor based on a capacitive electrolyte–insulator–semiconductor (EIS) structure modified with the enzyme acetoin reductase, also known as butane-2,3-diol dehydrogenase (Bacillus clausii DSM 8716ᵀ), is applied for acetoin detection in beer, red wine, and fermentation broth samples for the first time. The EIS sensor consists of an Al/p-Si/SiO₂/Ta₂O₅ layer structure with immobilized acetoin reductase on top of the Ta₂O₅ transducer layer by means of crosslinking via glutaraldehyde. The unmodified and enzyme-modified sensors are electrochemically characterized by means of leakage current, capacitance–voltage, and constant capacitance methods, respectively.
Plant viruses are major contributors to crop losses and induce high economic costs worldwide. For reliable, on-site and early detection of plant viral diseases, portable biosensors are of great interest. In this study, a field-effect SiO2-gate electrolyte-insulator-semiconductor (EIS) sensor was utilized for the label-free electrostatic detection of tobacco mosaic virus (TMV) particles as a model plant pathogen. The capacitive EIS sensor has been characterized regarding its TMV sensitivity by means of constant-capacitance method. The EIS sensor was able to detect biotinylated TMV particles from a solution with a TMV concentration as low as 0.025 nM. A good correlation between the registered EIS sensor signal and the density of adsorbed TMV particles assessed from scanning electron microscopy images of the SiO2-gate chip surface was observed. Additionally, the isoelectric point of the biotinylated TMV particles was determined via zeta potential measurements and the influence of ionic strength of the measurement solution on the TMV-modified EIS sensor signal has been studied.