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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.
The Potential of Sustainable Antimicrobial Additives for Food Packaging from Native Plants in Benin
(2019)
The representation, or encoding, utilized in evolutionary algorithms has a substantial effect on their performance. Examination of the suitability of widely used representations for quality diversity optimization (QD) in robotic domains has yielded inconsistent results regarding the most appropriate encoding method. Given the domain-dependent nature of QD, additional evidence from other domains is necessary. This study compares the impact of several representations, including direct encoding, a dictionary-based representation, parametric encoding, compositional pattern producing networks, and cellular automata, on the generation of voxelized meshes in an architecture setting. The results reveal that some indirect encodings outperform direct encodings and can generate more diverse solution sets, especially when considering full phenotypic diversity. The paper introduces a multi-encoding QD approach that incorporates all evaluated representations in the same archive. Species of encodings compete on the basis of phenotypic features, leading to an approach that demonstrates similar performance to the best single-encoding QD approach. This is noteworthy, as it does not always require the contribution of the best-performing single encoding.
Transdermal therapeutic systems (TTS) represent an up-to-day medication applied to human skin, which consists of a drug-containing pressure-sensitive adhesive (PSA) and a flexible backing layer. The development of a reliable TTS requires precise knowledge of the viscoelastic tack behavior of PSA in terms of adhesion and detaching. Tailoring of a PSA can be achieved by altering the resin content or modifying the chemical properties of the macromolecules. In this study, three different resin content of two silicone-based PSA – non-amine compatible, and less tack, amine-compatible – were investigated with the help of recently developed RheoTack method to characterize the retraction speed dependent tack behavior for various geometries of the testing rods. The obtained force-retraction displacement-curves clearly depict the effect of the chemical structure as well as the resin content. Decreasing the resin content shifts the start of fibril fracture to larger deformations states and significantly enhances the stretchability of the fibrils. To compare various rod geometries precisely, the force-retraction displacement curves were normalized to account for effective contact areas. The flat and spherical rods led to completely different failure and tack behaviors. Furthermore, the adhesion formation between TTS with flexible backing layers and rods during the dwell phase happens in a different manner compared to rigid plates, in particular for flat rods, where maximum compression stresses occur at the edges and not uniformly over the cross-section. Thus, the approach to follow ASTM D2949 has to be reconsidered for tests of these materials.
Im Rahmen dieser Arbeit wurden zunächst neuartige ionische Agarosederivate synthetisiert und anschließend umfassend charakterisiert. Anionische Agarosesulfate mit einer regioselektiven Derivatisierung in Position G6 wurden durch homogene Umsetzung in ionischer Flüssigkeit erhalten. Kationische Agarosecarbamate mit einstellbarem Funktionalisierungsgrad waren durch einen zweistufigen Syntheseansatz zugänglich. Hierzu wurden zunächst Agarosephenylcarbonate in einer homogenen Synthese hergestellt, im Anschluss folgte eine Aminolyse zu den gewünschten funktionalen Agarosederivaten. Die ionischen Agarosederivate waren bereits bei geringen Funktionalisierungsgraden vollständig löslich in Wasser. Damit war es möglich, Alginatmikrokapseln polyelektrolytisch zu beschichten und diese als Träger für eine kontrollierte Wirkstofffreisetzung zu verwenden. Ebenfalls konnten Kompositgele aus Agarose, Hydroxyapatit und Agarosederivaten hergestellt und charakterisiert werden. Im zweiten Teil wurden sowohl die Kompositträgermaterialien als auch die Alginatmikrokapseln mit vier verschiedenen Modellwirkstoffen (ATP, Suramin, Methylenblau und A740003) beladen und die Wirkstofffreisetzung über einen Zeitraum von zwei Wochen untersucht. Für die ionischen Modellwirkstoffe erwiesen sich Kompositträgermaterialien mit ionischem Agarosederivat, die beschichteten Mikrokapseln sowie die Kombination aus Komposit und Kapseln als effektiv, um die Freisetzung auf bis zu 40% zu verlangsamen. Für die schlecht wasserlösliche Substanz A740003, ein Rezeptorligand für die osteogene Differenzierung von Stammzellen, wurde eine stark verzögerte Freisetzung aus Polyelektrolytemikrokapseln festgestellt. Mithilfe von literaturbekannten und neu entwickelten Anpassungsmodellen gelang es, die Diffusion als Hauptmechanismus der Wirkstofffreisetzung zu identifizieren und die Freisetzungskurven mathematisch akkurat zu beschreiben und daraus Rückschlüsse über die einzelnen Phasen der Freisetzung zu ziehen.
Trueness and precision of milled and 3D printed root-analogue implants: A comparative in vitro study
(2023)
In this paper, modeling of piston and generic type gas compressors for a globally convergent algorithm for solving stationary gas transport problems is carried out. A theoretical analysis of the simulation stability, its practical implementation and verification of convergence on a realistic gas network have been carried out. The relevance of the paper for the topics of the conference is defined by a significance of gas transport networks as an advanced application of simulation and modeling, including the development of novel mathematical and numerical algorithms and methods.
In this paper, the electrochemical alkaline methanol oxidation process, which is relevant for the design of efficient fuel cells, is considered. An algorithm for reconstructing the reaction constants for this process from the experimentally measured polarization curve is presented. The approach combines statistical and principal component analysis and determination of the trust region for a linearized model. It is shown that this experiment does not allow one to determine accurately the reaction constants, but only some of their linear combinations. The possibilities of extending the method to additional experiments, including dynamic cyclic voltammetry and variations in the concentration of the main reagents, are discussed.
Alkaline methanol oxidation is an important electrochemical process in the design of efficient fuel cells. Typically, a system of ordinary differential equations is used to model the kinetics of this process. The fitting of the parameters of the underlying mathematical model is performed on the basis of different types of experiments, characterizing the fuel cell. In this paper, we describe generic methods for creation of a mathematical model of electrochemical kinetics from a given reaction network, as well as for identification of parameters of this model. We also describe methods for model reduction, based on a combination of steady-state and dynamical descriptions of the process. The methods are tested on a range of experiments, including different concentrations of the reagents and different voltage range.
The general method of topological reduction for the network problems is presented on example of gas transport networks. The method is based on a contraction of series, parallel and tree-like subgraphs for the element equations of quadratic, power law and general monotone dependencies. The method allows to reduce significantly the complexity of the graph and to accelerate the solution procedure for stationary network problems. The method has been tested on a large set of realistic network scenarios. Possible extensions of the method have been described, including triangulated element equations, continuation of the equations at infinity, providing uniqueness of solution, a choice of Newtonian stabilizer for nearly degenerated systems. The method is applicable for various sectors in the field of energetics, including gas networks, water networks, electric networks, as well as for coupling of different sectors.
Fatigue strength estimation is a costly manual material characterization process in which state-of-the-art approaches follow a standardized experiment and analysis procedure. In this paper, we examine a modular, Machine Learning-based approach for fatigue strength estimation that is likely to reduce the number of experiments and, thus, the overall experimental costs. Despite its high potential, deployment of a new approach in a real-life lab requires more than the theoretical definition and simulation. Therefore, we study the robustness of the approach against misspecification of the prior and discretization of the specified loads. We identify its applicability and its advantageous behavior over the state-of-the-art methods, potentially reducing the number of costly experiments.
The cube in cube approach was used by Paul and Ishai-Cohen to model and derive formulas for filler content dependent Young´s moduli of particle filled composites assuming perfect filler matrix adhesion. Their formulas were chosen because of their simplicity, recalculated using an elementary volume approach which transforms spherical inclusions to cubic inclusions. The EV approach led to expression for the composites moduli that allow for introducing an adhesion factor kadh ranging from 0 and 1 to take into account none perfect reduced filler matrix adhesion. This adhesion factor scales the edge length of the cubic inclusions, thus, reducing the stress transfer area between matrix and filler. Fitting the experimental data with the modified Paul model provides reasonable kadh for PA66, PBT, PP, PE-LD and BR which are in line with their surface energies. Further analysis showed that stiffening only occurs if kadh exceeds <span class="math-tex">\( { \ \sqrt{E^M/E^F} \ }\) and depends on the ratio of matrix modulus and filler modulus. The modified model allows for a quick calculation of any particle filled composites for known matrix modulus EM, filler modulus EF, filler volume content vF and adhesion factor kadh. Thus, finite element analysis (FEA) simulations of any particle filled polymer parts as well as materials selection are significantly eased. FEA of cubic and hexagonal EV arrangements show that stress distributions within the EV exhibit more shear stresses if one deviates from the cubic arrangement. At high filler contents the assumption that the property of the EV is representative for the whole composite, holds only for filler volume contents up to 15 or 20 % (corresponding to 30 to 40 weight %). Thus, for vast majority of commercially available particulate composites, the modified model can be applied. Furthermore, this indicates that the cube in cube approach reaches two limits: i) the occurrence of increasing shear stresses at filler contents above 20 % due to deviations of EV arrangements or spatial filler distribution from cubic arrangements (singular), and ii) increasing interaction between particles with the formation of particle network within the matrix violating the EV assumption of their homogeneous dispersion.
Künstliche Intelligenz (KI) ist aus der heutigen Gesellschaft kaum noch wegzudenken. Auch im Sport haben Methoden der KI in den letzten Jahren mehr und mehr Einzug gehalten. Ob und inwieweit dabei allerdings die derzeitigen Potenziale der KI tatsächlich ausgeschöpft werden, ist bislang nicht untersucht worden. Der Nutzen von Methoden der KI im Sport ist unbestritten, jedoch treten bei der Umsetzung in die Praxis gravierende Probleme auf, was den Zugang zu Ressourcen, die Verfügbarkeit von Experten und den Umgang mit den Methoden und Daten betrifft. Die Ursache für die, verglichen mit anderen Anwendungsgebieten, langsame An- bzw. Übernahme von Methoden der KI in den Spitzensport ist nach Hypothese des Autorenteams auf mehrere Mismatches zwischen dem Anwendungsfeld und den KI-Methoden zurückzuführen. Diese Mismatches sind methodischer, struktureller und auch kommunikativer Art. In der vorliegenden Expertise werden Vorschläge abgeleitet, die zur Auflösung der Mismatches führen können und zugleich neue Transfer- und Synergiemöglichkeiten aufzeigen. Außerdem wurden drei Use Cases zu Trainingssteuerung, Leistungsdiagnostik und Wettkampfdiagnostik exemplarisch umgesetzt. Dies erfolgte in Form entsprechender Projektbeschreibungen. Dabei zeigt die Ausarbeitung, auf welche Art und Weise Probleme, die heute noch bei der Verbindung zwischen KI und Sport bestehen, möglichst ausgeräumt werden können. Eine empirische Umsetzung des Use Case Trainingssteuerung erfolgte im Radsport, weshalb dieser ausführlicher dargestellt wird.
Bionik
(2022)
Wie machen die das… kann angesichts der erstaunlichen Fähigkeiten mancher Lebewesen gefragt werden. Die Bionik fragt noch weiter …und wie kann man das nachmachen? Hier liegt ein Schwerpunkt dieses Lehrbuches, das die Bionik nicht nur an zahlreichen Beispielen erklärt, sondern auch eine Vorgehensweise für die Identifizierung biologischer Lösungen und deren Übertragung auf technische Anwendungen vermittelt. Basisinformationen der Biologie und Grundlagen der Konstruktionstechnik gewährleisten einen leichten Zugang zum Stoff. Mit dem 3D-Druck als Schlüsseltechnologie und der Thematisierung der Nachhaltigkeit geht das Buch zudem auf aktuelle Entwicklungen ein. Dieser ganzheitliche Blick auf die Bionik soll den Leser zur Durchführung bionischer Projekte befähigen und motivieren. Die vorliegende Auflage wurde überarbeitet und um aktuelle Forschungserkenntnisse und Entwicklungen ergänzt. (Verlagsangaben)
Sie sind im Bereich Qualitätsmanagement tätig und haben die Aufgabe bekommen, ein Problem systematisch zu untersuchen und methodisch zu lösen? Sie haben zu viele Aufgaben und wissen nicht, wie Sie diese priorisieren sollen? Oder haben Sie zu begrenzte Ressourcen, um alle Reklamationen gleichzeitig bearbeiten zu können? Oder wissen nicht, wie Sie einen bestimmten Prozess in seinen Grenzen zielführend verbessern können?
In this paper, an analysis of the error ellipsoid in the space of solutions of stationary gas transport problems is carried out. For this purpose, a Principal Component Analysis of the solution set has been performed. The presence of unstable directions is shown associated with the marginal fulfillment of the resistivity conditions for the equations of compressors and other control elements in gas networks. Practically, the instabilities occur when multiple compressors or regulators try to control pressures or flows in the same part of the network. Such problems can occur, in particular, when the compressors or regulators reach their working limits. Possible ways of resolving instabilities are considered.
Introduction of Matrix-Filler Adhesion to Modelling of Elastic Moduli of Particulate Composites
(2022)
Cube in cube elementary volume (EV) concept serves to predict a filler-content dependent Young´s moduli of particle filled composites using moduli of a matrix EM and a filler EF. Paul and Ishai-Cohen derived formulas for composites moduli considering different load transfer boundaries in the EV assuming a complete filler-matrix adhesion. In this paper it is confirmed that their models represent the upper and lower bounds, respectively, with the respect to the experimental data. However, in vast majority of composites a filler-matrix adhesion is not complete. Therefore, an adhesion factor kadh gaining values between 0 and 1 was introduced into Paul´s model to consider the reduced adhesion as the reduction of the filler-matrix contact area for glass beads filled in polar and unpolar thermoplastic matrices as well as elastomer. The evaluation of these composite systems provides reasonable adhesion coefficients of PA66 > PBT > PP > PE-LD >> BR. It was also found that stiffening only occurs if kadh exceeds the minimum value adhesion of root square of E(M) divided by E(F). The determined kadh correspond to scanning electron microscopy observations of the composites fracture surfaces. Additionally, finite element analysis of the cubic and hexagonal arrangements of the EV show that the stress distributions are different, but they affect the calculated moduli only for the filler volume contents exceeding 20 %. The introduction of the filler-matrix adhesion provides more reliable predictions of Young´s moduli of particulate composites.
This dataset contains data from two measurement campaigns in autumn 2018 and summer 2019 that were part of the BMWi project "MetPVNet", and serve as a supplement to the paper "Dynamic model of photovoltaic module temperature as a function of atmospheric conditions", published in the special edition of "Advances in Science and Research", the proceedings of the 19th EMS Annual Meeting: European Conference for Applied Meteorology and Climatology 2019.
Data are resampled to one minute, and include:
PV module temperature
Ambient temperature
Plane-of-array irradiance
Windspeed
Atmospheric thermal emission
The data were used for the dynamic temperature model, as presented in the paper
Typically, plastic packaging materials are produced using additives, like e.g. stabilisers, to introduce specific desired properties into the material or, in case of stabilisers, to prolong the shelf life of such packaging materials. However, those stabilisers are typically fossil-based and can pose risks to both environmental and human health. Therefore, the present study presents more sustainable alternatives based on regional renewable resources which show the relevant antioxidant, antimicrobial and UV absorbing properties to successfully serve as a plastic stabiliser. In the study, all plants are extracted and characterised with regard to not only antioxidant, antimicrobial and UV absorbing effects, but also with regard to additional relevant information like chemical constituents, molar mass distribution, absorbance in the visible range et cetera. The extraction process is furthermore optimised and, where applicable, reasonable opportunities for waste valorisation are explored and analysed. Furthermore, interactions between analysed plant extracts are described and model films based on Poly-Lactic Acid are prepared, incorporating analysed plant extracts. Based on those model films, formulation tests and migration analysis according to EU legislation is conducted.
The well-known aromatic and medicinal plant thyme (Thymus vulgaris L.) includes phenolic terpenoids like thymol and carvacrol which have strong antioxidant, antimicrobial and UV absorbing effects. Analyses show that those effects can be used in both lipophilic and hydrophilic surroundings, that the variant Varico 3 is a more potent cultivar than other analysed thyme variants, and that a passive extraction setup can be used for extract preparation while distillation of the Essential Oils can be a more efficient approach.
Macromolecular antioxidant polyphenols, particularly proanthocyanidins, have been found in the seed coats of the European horse chestnut (Aesculus hippocastanum L.) which are regularly discarded in phytopharmaceutical industry. In this study, such effects and compounds have been reported for the first time while a valorisation of waste materials has been analysed successfully. Furthermore, a passive extraction setup for waste materials and whole seeds has been developed. In extracts of snowdrops, precisely Galanthus elwesii HOOK.F., high concentrations of tocopherol have been found which promote a particularly high antioxidant capacity in lipophilic surroundings. Different coniferous woods (Abies div., Picea div.) which are in use as Christmas trees are extracted after separating the biomass in leafs and wood parts before being analysed regarding extraction optimisation and drought resistance of active substances. Antioxidant and UV absorbing proanthocyanidins are found even in dried biomasses, allowing the circular use of already used Christmas trees as bio-based stabilisers and the production of sustainable paper as a byproduct.
Fundamentals of Energy Meteorology - Influence of atmospheric parameters on solar energy production
(2015)
Comparing Armature Windings for a 10 MW Fully Superconducting Synchronous Wind Turbine Generator
(2022)
How self-reliant Peer Teaching can be set up to augment learning outcomes for university learners
(2022)
In this thesis it is posed that the central object of preference discovery is a co-creative process in which the Other can be represented by a machine. It explores efficient methods to enhance introverted intuition using extraverted intuition's communication lines. Possible implementations of such processes are presented using novel algorithms that perform divergent search to feed the users' intuition with many examples of high quality solutions, allowing them to take influence interactively. The machine feeds and reflects upon human intuition, combining both what is possible and preferred. The machine model and the divergent optimization algorithms are the motor behind this co-creative process, in which machine and users co-create and interactively choose branches of an ad hoc hierarchical decomposition of the solution space.
The proposed co-creative process consists of several elements: a formal model for interactive co-creative processes, evolutionary divergent search, diversity and similarity, data-driven methods to discover diversity, limitations of artificial creative agents, matters of efficiency in behavioral and morphological modeling, visualization, a connection to prototype theory, and methods to allow users to influence artificial creative agents. This thesis helps putting the human back into the design loop in generative AI and optimization.
Background & Objective: Due to the policy goals for sustainable energy production, renewable energy plants such as photovoltaics are increasingly in use. The energy production from solar radiation depends strongly on atmospheric conditions. As the weather mostly changes, electrical power generation fluctuates, making technical planning and control of power grids to a complex problem. Due to used materials (semiconductors e.g. silicon, gallium arsenide, cadmium telluride) the photovoltaic cells are spectrally selective. It means that only radiation of certain wavelengths converts into electrical energy. A material property called spectral response characterizes a certain degree of conversion of solar radiation into the electric current for each wavelength of solar light.