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From One Polymer to Many Functions: A PVA-Based Toolbox for Tissue Engineering
No full textIntroduction
Tissue regeneration needs biomaterials that combine controlled structure and function with biological relevance. Natural biomaterials offer inherent bioactivity and cell adhesion motifs, but suffer from batch variability and low tunability, limiting reproducibility. Shifting to synthetic materials offers precise control over chemistry and physical properties. FDA-approved Polyvinyl alcohol (PVA) is widely used in biomedical applications. Its functionalisation with norbornene (NB) groups allows rapid UV crosslinking into hydrogels with tunable crosslinking degree and mechanical properties.
Materials & Methods
Norbornene functionalised PVA (PVA-NB) was used as the hydrogel precursor and was crosslinked under UV irradiation with a dithiolated crosslinker, in presence of a photoinitiator. Hydrogels were biofunctionalised using peptides or molecules to afford different bioactive materials. These bio interactive scaffolds were studied in terms of physical properties (mechanical properties, swelling, degradation) and also cell behaviour (biocompatibility, adhesion). Alternatively, the PVA-NB hydrogel precursor was applied in an electrospinning setup to afford fibrous scaffolds. These matrices were then studied in terms of fibre morphology (diameter, porosity).
Results & Discussion
To demonstrate the versatility of this material platform, three modification approaches were investigated addressing biochemical and structural control. Functionalisation of PVA-NB with different bioactive motifs improved the hydrogel’s physical properties. Scaffolds reached higher storage moduli after biofunctionalisation, while stability studies showed that the hydrogels remained stable after over 1 month in physiological conditions. Also, biofunctionalisation enabled integration of bioactive cues, which is essential for tissue regeneration applications. In parallel, PVA-NB was explored in the electrospinning setup, enabling the development of ECM like structures without limiting PVA-NB’s processability.
Conclusions
The presented modification approaches illustrate how a single material system can be adapted across biochemical and architectural length scales. Combining biofunctionalisation and electrospinning, PVA-NB can be used as a versatile toolbox for various tissue regeneration applications
Strategies to Stabilize Inductively Heating Fe Nanoparticles for Catalyzing the CO₂ Conversion to Syngas via Reverse Water Gas Shift
No full textFe-based solid materials are studied here as self-heating catalysts that can convert CO₂ into CO via a reverse water–gas shift (RWGS), a key reaction for carbon utilization. We describe the scalable synthesis of Fe nanoparticles capable to be inductively heated to 500–800 °C while catalytically active in RWGS. For iron species to develop a dual function as heaters and catalysts, the formation of nonferromagnetic Fe phases under reaction conditions must be avoided. Such phases lead to rapid cooldown of the reactor and the deactivation of the catalyst. Here, the stabilization of the self-heating RWGS catalysts was achieved by adjusting the nanoparticle properties via mesoporous supports, the addition of Co as a promoter, and redox pretreatments. Through extensive operando and in situ characterization, we show the dynamics of self-heating catalytic nanoparticles under reactive atmospheres, as those verified during RWGS reaction and pave the way for other applications in energy-intensive catalytic processes
Vergleichende Evaluierung von Business Analytics und Visualisierungswerkzeugen und -anwendungen
Full text linkIn den letzten Jahren ist die visuelle Analytik ein entscheidender Faktor für die datengetriebene Entscheidungsfindung in vielen Branchen geworden. Je mehr Daten zur strategischen Steuerung genutzt werden, desto wichtiger wird es, komplexe Informationen klar zu visualisieren. Eine Vielzahl von Werkzeugen und Bibliotheken, die unterschiedliche Nutzergruppen und Kompetenzniveaus bedienen, ist als Ergebnis dieses Trends entstanden. Anwendungen mit benutzerfreundlichen Drag-and-Drop-Funktionalitäten wie Microsoft Power BI, Tableau und Excel werden immer beliebter. Ohne dass man programmieren können muss, erlauben sie es, aussagekräftige Visualisierungen zu erstellen; deshalb sind sie im Bereich der Business Intelligence für Anwender ohne technische Ausbildung besonders interessant. Im Gegensatz dazu sind Bibliotheken wie Plotly, GGPlot und D3 für Entwickler gedacht und bieten fortgeschrittene Funktionen für statistische Analysen, komplexe Datenmanipulationen und hochgradige Anpassungen von Visualisierungen. Frühere Untersuchungen haben Unterschiede zwischen diesen Werkzeugen aufgezeigt, vor allem in Bezug auf die Unterstützung explorativer Analysen im Vergleich zur Ergebnispräsentation. Aber es gibt inzwischen Lösungen, die leistungsfähiger sind und mehr Funktionen bieten, um den neuen Anforderungen der Nutzer gerecht zu werden. Die vorliegende Untersuchung hat das Ziel, eine vergleichende Analyse von ausgewählten, gängigen visuellen Analytikwerkzeugen zu bieten. Entscheidende Kriterien wie Benutzerfreundlichkeit, Anforderungen an die Datenaufbereitung und weitere relevante Eigenschaften werden bewertet. Ein Clustering-Ansatz, der funktionale Ähnlichkeiten berücksichtigt, wird zur Klassifikation der Werkzeuge angewandt. Um eine ausgewogene und umfassende Betrachtung zu garantieren, schließt die Analyse sowohl innovative, neuere Ansätze als auch etablierte Plattformen ein. Basierend auf den Ergebnissen sollen Empfehlungen für Praktiker und Wissenschaftler formuliert und Forschungsfragen identifiziert werden, die zur Weiterentwicklung von visuellen Analytiktechnologien beitragen – vor allem im Hinblick auf die Herausforderungen durch großskalige Datenmengen.In recent years, Visual Analytics has transformed how we approach data-driven decision making across many industries. As organizations increasingly turn to data to guide their strategies, being able to clearly visualize complex information has become more important than ever. This rising demand has led to the creation of a wide variety of tools and libraries, each designed to suit different users and skill levels. Because of their easy drag-and-drop functionality, programs like Microsoft Power BI, Tableau, and Excel are growing in popularity. Since they can generate insightful visuals without any programming, which is gaining a lot of attention in the field of Business Intelligence, these applications are especially helpful for professionals who lack technical skills. In contrast, Plotly, GGPlot and D3 are charting libraries that cater to programmers by offering options for more sophisticated statistical analysis, advanced levels of data manipulation, and even greater customization for sophisticated visualizations. Prior conducted researches marked disparities among these tools, particularly with respect to how well their design supports data exploration in comparison to data presentation. Nevertheless, and as the field progresses, today’s tools for Visual Analytics are more comprehensive and robust than in the past, providing new features and enhancements to keep pace with user demand.This thesis sets out to carry out a detailed comparative analysis of some of the most widely used visualization tools in the field today. The goal is to evaluate their core strengths, limitations, and overall performance in the context of data visualization. Primary visualization criteria, including usability, data preparation requirements, and other pertinent aspects, will be the main focus of the evaluation. A clustering approach will be used to analyze the features of the tools, classifying them according to similarities in their features. To make sure the overview is thorough and balanced, both more recent, cutting-edge solutions and older, more established platforms will be covered. The goal of this thesis is, through these findings, provide guidance to the users regarding the tools that best address their specific requirements. Additionally, the analysis is anticipated to highlight questions that need further exploration and identify solutions aimed at improving visualization technologies in general, particularly as these technologies face new challenges posed by large-scale data
Efficient Validation of UML Sequence Diagrams against UML State Machines via Kronecker Algebra
No full textDiese Arbeit stellt einen Ansatz zur Validierung von UML-Sequenzdiagrammen gegenüber Zustandsautomaten unter Verwendung von Kronecker Algebra vor. Aufbauend auf früheren Arbeiten zur Konsistenzprüfung zwischen Sequenz- und Zustandsdiagrammen erweitern wir bestehende Techniken, um UML-spezifische Kontrollflussstrukturen wie alt-Blöcke und Schleifen zu unterstützen.Wir definieren formal, wie Sequenzdiagramme mithilfe von Kronecker Algebra zu endlichen Zustandsautomaten (Finite State Machines, FSMs) synthetisiert werden können, wobei besonderes Augenmerk auf die Nachrichtenreihenfolge und Abhängigkeiten unter Berücksichtigung von Alternativen und Schleifen gelegt wird.Die vorgeschlagenen Ansätze werden anhand praktischer Beispiele demonstriert. Im Rahmen dieser Arbeit wurde eine C++-Implementierung für Kronecker Algebra entwickelt, um diese Beispiele zu konstruieren und zu verifizieren. Darüber hinaus stellen wir Algorithmen vor, die für Kronecker Algebra Operationen optimiert sind und es der Implementierung ermöglichen, für unsere Anwendungsfälle bestehende Bibliotheken zur Matrixberechnung zu übertreffen.Unsere Ergebnisse zeigen, dass sich Kronecker Algebra zur Modellierung von Kontrollflussstrukturen in UML-Sequenzdiagrammen eignet und dass sogar Verzweigungsbedingungen in bestimmten Szenarien verifiziert werden können. Dadurch wird es möglich, unerreichbare Abschnitte in Sequenzdiagrammen sowie Inkonsistenzen zwischen Sequenz- und Zustandsdiagrammen zu identifizieren.This thesis presents an approach to validate UML sequence diagrams against state machines using Kronecker Algebra. Building upon prior work in consistency checking between sequence and state machine diagrams, we extend the existing techniques to support UML-specific control flow structures such as alt-blocks and loops. We formally define how sequence diagrams can be synthesized into finite state machines (FSMs) through Kronecker Algebra, with special attention to message ordering and dependencies in the presence of alternatives and loops. The proposed techniques are demonstrated through practical examples. A C++ implementation of Kronecker Algebra was developed as part of this work to construct and verify these examples. In addition, we provide algorithms optimized for Kronecker Algebra operations, enabling the implementation to outperform existing matrix computation libraries.Our results show that Kronecker Algebra is suitable for modeling control flow structures in UML sequence diagrams and even branching conditions can be respected in certain settings. Enabling us to identify unreachable sections in sequence diagrams and inconsistencies between sequences and state machines
Chirality Switching in 1T-TaS2 by Highly Charged Ion Irradiation
No full textIn layered materials, charge density waves can occur in distinct chiral phases, which can be switched. We use Xe8+ ions at a kinetic energy of 22.5 keV to switch the commensurate charge density wave chirality on the nanoscale in 1T-TaS2 at 50 K. Changes in spectral weight, density of states, and band structure are monitored in situ by angle-resolved photoemission spectroscopy. We find that changes in the band structure are most pronounced at the charge density wave gaps and that chirality switches gradually with ion fluence, saturating to near-full handedness reversal at ≳4000 ions/μm2. We discuss a scenario for ion-induced chiral switching within the framework of intense, spatially confined electronic excitations, which induce a phase transition and defect-stabilized grain boundaries
Entwicklung von Biogaskraftwerksmodellen basierend auf statistischen Methoden
No full textUm die angestrebten Klimaschutzziele zu erreichen, ist ein diversifiziertes und robustes Energieversorgungssystem unerlässlich. Biogasanlagen können hierzu einen bedeutenden Beitrag leisten, indem sie organische Substrate unter kontrollierten anaeroben Bedingungen in ein methanhaltiges Gasgemisch überführen, das anschließend in Blockheizkraftwerken zur gekoppelten Strom- und Wärmeerzeugung genutzt werden kann. Die Funktionsweise solcher Anlagen lässt sich jedoch nicht in das konventionelle energetische Wandlungsparadigma einordnen, das durch direkte Umwandlungsprozesse - wie etwa bei Brennstoffzellen oder Batteriespeichern - charakterisiert ist. Die Biogaserzeugung beruht vielmehr auf einem komplexen biochemischen Prozess, der derzeit üblicherweise mittels hochdetaillierter und rechenintensiver anaerober Vergärungsmodelle oder durch Verfahren des maschinellen Lernens beschrieben wird. Letztere neigen jedoch dazu, physikalische Zusammenhänge und betriebsrelevante Qualitätsindikatoren zu abstrahieren, was die Vorhersage des Anlagenverhaltens unter variablen und strategisch angepassten Betriebsbedingungen erheblich erschwert. Diese Arbeit befasst sich mit dem oben genannten Problem, indem sie Methoden einführt, die auf numerischen und statistischen Überlegungen beruhen, um die Korrelationen und wechselseitigen Auswirkungen der verschiedenen Komponenten von Biogaskraftwerken zu visualisieren und mathematisch zu erfassen. Dies wird durch die Anwendung von verzögerten Kreuzkorrelationstests und Binning erreicht. Anschließend werden physikalisch basierte Modelle vorgestellt, die sowohl die Biogasproduktion als auch die Biogasentnahme in Abhängigkeit von einstellbaren externen Parametern, d.h. elektrischer Energie und Fütterung, erfassen. Die automatisierte Leistungsregelung der Anlage wird ebenfalls berücksichtigt. Dabei werden Nachschlagetabellen verwendet, um ein geschlossenes Regelkreissystem zu etablieren, das vollständig durch die Anpassung der Menge, Qualität und des Zeitpunkts der organischen Zuführungen steuerbar ist. Die Biogasproduktionsrate verwendet den bewährten Ansatz der modifizierten Gompertz-Funktion, um eine datengesteuerte Parameteranpassung durchzuführen. Dabei wird der mittlere quadratische Fehler zwischen den vorverarbeiteten Rohmessdaten und der Simulationsvorhersage minimiert. Die praktischen Auswirkungen der Datenqualität werden ebenfalls erörtert, ebenso wie das Potential davon, Daten mit größerer Genauigkeit zu erfassen. Die Simulationsergebnisse werden mit Daten aus einem realen Kraftwerk im burgenländischen Weingraben verglichen.To achieve the targeted climate protection goals, a diversified and robust energy supply system is indispensable. Biogas plants can make a significant contribution to this objective by converting organic substrates under controlled anaerobic conditions into a methane-rich gas mixture, which can subsequently be used in combined heat and power units for the cogeneration of electricity and heat. However, the operational principles of such facilities cannot be readily classified within the conventional paradigm of energy conversion, which is characterized by direct transformation processes, as is the case for fuel cells or battery storage systems. Instead, biogas production is based on a complex biochemical process that is typically described either by highly detailed and computationally intensive anaerobic digestion models or by methods of machine learning. The latter, however, tend to abstract physical relationships and operationally relevant quality indicators, which significantly complicates the prediction of plant behavior under variable and strategically adapted operating conditions. This thesis addresses the aforementioned issue by introducing methods based on numerical and statistical considerations to visualise and mathematically capture the correlations and mutual effects of the different components of biogas power plants. This is achieved by applying lagged crosscorrelation tests and binning. Subsequently, physics-based models are presented that capture both biogas production and outtake depending on adjustable external parameters, namely electric power and feeding. The automated set-point power strategy of the plant is also considered, using look-up tables to establish a closed feedback-loop system that is fully controllable by adjusting the quantity, quality, and timing of organic feed instances. The biogas production rate uses the well-established approach of the modified Gompertz function to perform data-driven parameter fitting. This involves minimising the mean square error between the pre-processed raw measurement data and the simulation prediction. The practical effects of data quality are also discussed, as is the potential for sampling data with greater accuracy. The simulation outputs are compared with data from a real power plant in Weingraben, Burgenland
Impact of atomic couples and pairs on quenched-in vacancies in Al-Mg-Si-Cu alloys
Full text linkAfter solutionizing and quenching of Al-Mg-Si-Cu alloys at cooling rates differing by a factor of about 200, the quenched-in vacancy concentration difference as measured by positron annihilation lifetime spectrometry is only about 30 times. This contradicts the expected ~200 times difference predicted by the recently developed FSAK model for vacancy generation and annihilation at dislocation jogs and grain boundaries. To address this discrepancy, we investigate the influence of atomic couples and pairs (C&Ps) on the quenched-in vacancy concentration. A combined theoretical framework incorporating C&Ps formation kinetics and vacancy trapping is developed and applied to an Al-Mg-Si-Cu alloy. The results indicate that Si-Si pairs in Al-Mg-Si-Cu alloys act as primary vacancy traps during quenching, capturing significantly more vacancies than isolated solutes. The simulations satisfactorily explain the experimentally measured ~30 times difference in quenched-in vacancy concentration compared to the much larger difference predicted in the absence of vacancy trapping on C&Ps
Empowering Pupils to Explore Urban Environments. Identifying contexts for environmental sensing using non-functional prototyping.
No full textPhysiological energy demand modeling for cycling and network graphs
No full textWhile the health benefits of cycling are well established, its role as a primary mode of sustainable daily transport and how to promote it remains less explored. Route selection and energy expenditure of cyclists are influenced by factors such as network availability, terrain, traffic, and environmental conditions. We propose the physiological energy demand model for cycling (PEDMC) that links network conditions with human energy effort by considering real-world urban cycling conditions, including slope, wind, and stop-and-go traffic. The PEDMC is grounded in interpretable physical relations without the need for data-driven calibration. It enables detailed analysis of the physiological energy consumption of cycling and allows comparisons with traditional routing algorithms based on time or distance. Applied to a cycling network in Vienna, the PEDMC demonstrates that using physiological energy consumption as a path search criterion leads to significantly different route selection compared to distance- or time-based route planning approaches. Employing the PEDMC reduces cycling effort by up to 6 % in flat areas and up to 30 % in hilly terrain. By evaluating cycling routes based on physiological energy expenditure, the PEDMC not only supports energy-efficient mobility planning but also contributes to the development of health-supportive, inclusive, and accessible cycling infrastructure. As such, the proposed model offers a valuable tool for advancing active travel strategies and promoting sustainable and healthy urban transport systems. It supports designing clean, healthy, and inclusive urban mobility systems
Semantic-aware query answering with Large Language Models
No full textIn the modern data-driven world, answering queries over heterogeneous and semantically inconsistent data remains a significant challenge. Modern datasets originate from diverse sources, such as relational databases, semi-structured repositories, and unstructured documents, leading to substantial variability in schemas, terminologies, and data formats. Traditional systems, constrained by rigid syntactic matching and strict data binding, struggle to capture critical semantic connections and schema ambiguities, failing to meet the growing demand among data scientists for advanced forms of flexibility and context-awareness in query answering. In parallel, the advent of Large Language Models (LLMs) has introduced new capabilities in natural language interpretation, making them highly promising for addressing such challenges. However, LLMs alone lack the systematic rigor and explainability required for robust query processing and decision-making in high-stakes domains. In this paper, we propose Soft Query Answering (Soft QA), a novel hybrid approach that integrates LLMs as an intermediate semantic layer within the query processing pipeline. Soft QA enhances query answering adaptability and flexibility by injecting semantic understanding through context-aware, schema-informed prompts, and leverages LLMs to semantically link entities, resolve ambiguities, and deliver accurate query results in complex settings. We demonstrate its practical effectiveness through real-world examples, highlighting its ability to resolve semantic mismatches and improve query outcomes without requiring extensive data cleaning or restructuring