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Traditional and newly developed testing methods were used for extensive application-related characterization of transdermal therapeutic systems (TTS) and pressure sensitive adhesives (PSA). Large amplitude oscillatory shear tests of PSAs were correlated to the material behavior during the patient’s motion and showed that all PSAs were located close to the gel point. Furthermore, an increasing strain amplitude results in stretching and yielding of the PSA´s microstructure causing a consolidation of the network and a release with increasing strain amplitude. RheoTack approach was developed to allow for an advanced tack characterization of TTS with visual inspection. The results showed a clear resin content and rod geometry dependent behavior, and displays the PSA´s viscoelasticity resulting in either high tack and long stretched fibrils or non-adhesion and brittle behavior. Moreover, diffusion of water / sweat during TTS´s application might influence its performance. Therefore, a dielectric analysis based evaluation method displayed occurring water diffusion into the PSA from which the diffusion coefficient can be determined, and showed clear material and resin content dependent behavior. All methods allow for an advanced product-oriented material testing that can be utilized within further TTS development.
Force field (FF) based molecular modeling is an often used method to investigate and study structural and dynamic properties of (bio-)chemical substances and systems. When such a system is modeled or refined, the force field parameters need to be adjusted. This force field parameter optimization can be a tedious task and is always a trade-off in terms of errors regarding the targeted properties. To better control the balance of various properties’ errors, in this study we introduce weighting factors for the optimization objectives. Different weighting strategies are compared to fine-tune the balance between bulk-phase density and relative conformational energies (RCE), using n-octane as a representative system. Additionally, a non-linear projection of the individual property-specific parts of the optimized loss function is deployed to further improve the balance between them. The results show that the overall error is reduced. One interesting outcome is a large variety in the resulting optimized force field parameters (FFParams) and corresponding errors, suggesting that the optimization landscape is multi-modal and very dependent on the weighting factor setup. We conclude that adjusting the weighting factors can be a very important feature to lower the overall error in the FF optimization procedure, giving researchers the possibility to fine-tune their FFs.
In vision tasks, a larger effective receptive field (ERF) is associated with better performance. While attention natively supports global context, convolution requires multiple stacked layers and a hierarchical structure for large context. In this work, we extend Hyena, a convolution-based attention replacement, from causal sequences to the non-causal two-dimensional image space. We scale the Hyena convolution kernels beyond the feature map size up to 191$\times$191 to maximize the ERF while maintaining sub-quadratic complexity in the number of pixels. We integrate our two-dimensional Hyena, HyenaPixel, and bidirectional Hyena into the MetaFormer framework. For image categorization, HyenaPixel and bidirectional Hyena achieve a competitive ImageNet-1k top-1 accuracy of 83.0% and 83.5%, respectively, while outperforming other large-kernel networks. Combining HyenaPixel with attention further increases accuracy to 83.6%. We attribute the success of attention to the lack of spatial bias in later stages and support this finding with bidirectional Hyena.
Die nationale Politik- und Forschungsstrategie Bioökonomie sieht eine Transformation der Wirtschaft vor, bei der die Verwendung fossiler Rohstoffe zunehmend durch den Einsatz nachwachsender Rohstoffe ersetzt wird. Der Einsatz biobasierter Kunststoffe soll dabei gefördert werden. Erste Analysen der Berichterstattung zu Biokunststoffen im Rahmen einer Pilotstudie ergaben, dass der Grundgedanke biologisch abbaubarer Kunststoffe breite Zustimmung im öffentlichen Diskurs erfährt. Abseits der soziopolitischen Diskursebene entwickelt sich jedoch eine medial geführte Diskussion um erhebliche Probleme mit den Stoffen in der Abfallwirtschaft. Die Gefahr besteht nun, dass diese Haltung verbreitet durch die Massenmedien auf die öffentliche Meinung abfärbt. Mangelnde öffentliche Akzeptanz könnte den Erfolg von innovativen Biokunststoff-Produkten gefährden.
This paper addresses the classification of Arabic text data in the field of Natural Language Processing (NLP), with a particular focus on Natural Language Inference (NLI) and Contradiction Detection (CD). Arabic is considered a resource-poor language, meaning that there are few data sets available, which leads to limited availability of NLP methods. To overcome this limitation, we create a dedicated data set from publicly available resources. Subsequently, transformer-based machine learning models are being trained and evaluated. We find that a language-specific model (AraBERT) performs competitively with state-of-the-art multilingual approaches, when we apply linguistically informed pre-training methods such as Named Entity Recognition (NER). To our knowledge, this is the first large-scale evaluation for this task in Arabic, as well as the first application of multi-task pre-training in this context.