OPUS Online Publikationen der Universität Stuttgart
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Comparison of actual hybrid-electric flights with a digital twin in a preliminary aircraft design environment
No full textTo tackle climate change, aircraft designers envision new aircraft concepts which promise to reduce greenhouse gas emissions and enable greener flights. One option is hybrid-electric propulsion architectures. The University of Stuttgart has built and operates such an aircraft, called the e-Genius. This paper aims to demonstrate how far a digital twin is able to replicate a real-world flight using a simplified mission definition and to estimate the range limit for a high-performance hybrid-electric aircraft, lifting the operational constraints faced in the real-world environment. First a digital twin is built and compared to actual flight data to calibrate the model. Next, a comparison with a full flight is performed, using a long-range flight of 2000 km for this purpose. Due to the duration of this flight, weather conditions like wind need to be considered. Validation is performed by comparison to two additional missions, one 500 km mission flown at faster speed and a 1000 km mission flown at a similar speed. To estimate the maximum range based on this calibrated model, operational constraints like daylight and maximum flight time are lifted to see the further potential of the aircraft. This allows the aircraft to fly more slowly, at best cruise speed, and thus estimate the maximum range of the aircraft. Results show good agreement with flight tests for fuel burnt, highlighting however a need to measure additional parameters in future flights. Overall, the model allows us to plan future flights and assess the feasibility of new projects
Reduction of graph isomorphism to isomorphism and conjugacy of permutation subgroups
No full textThis paper provides polynomial time reductions from Graph Isomorphism to isomorphism and conjugacy of two permutation subgroups, in both cases input as a set of generating elements. It also covers a reduction from conjugacy to isomorphism for the case where the conjugating permutation may be from the whole symmetric group
Accelerator support for GPRat : a task-based Gaussian process library in Python
No full textGaussian Processes (GPs) are a powerful tool for regression tasks, but their computational complexity is a limiting factor. In particular, it must calculate the Cholesky decomposition of a large covariance matrix, which results in a lower triangular matrix and is used for making predictions and calculating the uncertainty of those predictions. This step has a cubic time complexity of and is the prevalent part of the computation. Therefore, we use task-based parallelism with the High Performance ParalleX (HPX) library and tiled algorithms that view the covariance matrix as smaller tiles of equal size. The tiled algorithm executes parallel tasks that each perform a Basic Linear Algebra Subprograms (BLAS) operation on those tiles to speed up the computation. This led to the creation of the Gaussian Process Regression using Asynchronous Tasks (GPRat) Python library, which implements GPs based on this approach with the HPX runtime system. GPRat aims to provide a scalable and efficient implementation of GPs for large datasets with an implementation in C++ while leveraging the simplicity of Python for ease of use and seamless interaction with other Python libraries. It utilizes Intel oneAPI Math Kernel Library (oneMKL) to perform the BLAS operations on the Central Processing Unit (CPU). Graphics Processing Units (GPUs) are well-optimized for parallel processing, in particular for BLAS operations that operate on large matrices. This thesis extends the GPRat library with accelerator support for NVIDIA GPUs to further speed up the Cholesky decomposition and the other GP operations that utilize its result. The CUDA library includes the cuBLAS library and cuSolver library which provide the necessary Linear Algebra PACKage (LAPACK) and BLAS operations for executing computations on GPUs.
This thesis presents the addition of CUDA support to the GPRat library. In particular, it describes the implementation of the tiled Cholesky decomposition on GPUs and also the other GP operations that utilize its result and therefore perform its additional operations on the GPU as well.
We then compare the performance of the GPU implementation to the existing CPU implementation on a single node including a dual-socket AMD EPYC 9274F CPU and an NVIDIA A30 24 GB GPU. The results show that the GPU implementation is faster when working with at least 128 training samples and fewer but larger tiles. We also compared the performance of the GPRat library in computing the full covariance matrix prediction to the CPU implementation, but also to the GPyTorch and GPflow libraries on both CPU and GPU, which are two popular and highly efficient GP libraries for Python. We find that the GPRat library is the fastest on the CPU, competitive with the other libraries on the GPU, and scales well with the number of training samples. However, GPflow is slightly faster than GPRat on the GPU for higher numbers of training samples, and the optimal number of matrix tiles gives hardly any performance benefit on a single node.Gauß'sche Prozesse (GPs) sind ein leistungsfähiges Werkzeug für Regressionsaufgaben, jedoch ist ihre Zeitkomplexität ein begrenzender Faktor. Insbesondere muss die Cholesky-Zerlegung einer großen Kovarianzmatrix berechnet werden, welche in einer unteren Dreiecksmatrix resultiert, zur Erstellung von Vorhersagen und zur Berechnung der Unsicherheit dieser Vorhersagen verwendet wird. Dieser Schritt hat eine kubische Zeitkomplexität von und ist der am aufwendigste Teil der Berechnung für Vorhersagen. Daher verwenden wir aufgabenbasierte Parallelität mit der High Performance ParalleX (HPX)-Bibliothek und einen Kachelalgorithmus, der die Kovarianzmatrix als kleinere Kacheln gleicher Größe betrachtet. Der Kachelalgorithmus führt parallele Aufgaben aus, die jeweils eine BLAS-Operation (Basic Linear Algebra Subprograms) auf diesen Kacheln ausführen, um die Berechnung zu beschleunigen. Dies führte zur Erstellung der Python-Bibliothek Gaussian Process Regression using Asynchronous Tasks (GPRat), die GPs auf der Grundlage dieses Ansatzes mit dem HPX-Laufzeitsystem implementiert. GPRat zielt darauf ab, eine skalierbare und effiziente Implementierung von GPs für große Datensätze mit einer Implementierung in C++ bereitzustellen und gleichzeitig die Einfachheit von Python für eine einfache Nutzung und nahtlose Interaktion mit anderen Python-Bibliotheken bereitzustellen. Es nutzt die Intel oneAPI Math Kernel Library (oneMKL), um die BLAS-Operationen auf der Central Processing Unit (CPU) auszuführen. Grafikprozessoren (GPUs) sind für die parallele Verarbeitung gut optimiert, insbesondere für BLAS-Operationen, die auf großen Matrizen arbeiten. Diese Arbeit erweitert die GPRat-Bibliothek um eine Beschleunigerunterstützung für NVIDIA-GPUs erweitert, um die Cholesky-Zerlegung und die anderen GP-Operationen, die ihr Ergebnis verwenden, weiter zu beschleunigen. Die CUDA-Bibliothek umfasst die cuBLAS-Bibliothek und die cuSolver-Bibliothek, die die notwendigen Linear Algebra PACKage (LAPACK) und BLAS Operationen für die Ausführung von Berechnungen auf GPUs bereitstellen.
Diese Arbeit stellt die Erweiterung der GPRat-Bibliothek um CUDA-Unterstützung vor. Insbesondere wird die Implementierung der gekachelten Cholesky-Zerlegung auf GPUs beschrieben und auch die anderen GP-Operationen, die das Ergebnis nutzen und daher ihre zusätzlichen Operationen auch auf der GPU ausführen.
Anschließend vergleichen wir die Leistung der GPU-Implementierung mit der bestehenden CPU-Implementierung auf einem einzelnen Knoten mit einer AMD EPYC 9274F CPU mit zwei Sockeln und einer NVIDIA A30 24 GB GPU. Die Ergebnisse zeigen, dass die GPU-Implementierung schneller ist, wenn mit mindestens 128 Trainingsdaten und weniger, aber größeren Kacheln gearbeitet wird. Wir haben außerdem die Leistung der GPRat-Bibliothek bei der Berechnung der vollständigen Kovarianzmatrix-Vorhersage mit der CPU Implementierung sowie mit den GPyTorch- und GPflow-Bibliotheken auf CPU und GPU verglichen, zwei beliebten und hocheffizienten GP-Bibliotheken für Python. Wir stellen fest, dass die GPRat-Bibliothek auf der CPU am schnellsten ist, mit den anderen Bibliotheken auf der GPU konkurrieren kann und gut mit der Anzahl an Trainingsdaten skaliert. Allerdings ist GPflow auf der GPU bei einer höheren Anzahl von Trainingsproben etwas schneller als GPRat, und die optimale Anzahl von Matrixkacheln bringt auf einem einzelnen Knoten kaum einen Leistungsvorteil
Evaluation of the potential of FDM, MJF, and SLS printing technologies to manufacture reproducible and high-cycle fatigue resistant polymer springs
No full textElastic structures from polymer are a promising alternative to conventional metal springs under challenging conditions like in magnet resonance imaging (MRI) environments, where ferromagnetic materials cannot be used. The suitability of additive manufacturing to manufacture polymeric springs was assessed within the paper with a dedicated focus on stiffness reproducibility and fatigue behavior. Multi-Jet Fusion (MJF), Selective Laser Sintering (SLS), and Fused Deposition Modeling (FDM) were evaluated as manufacturing technologies. Three spring designs were conceived based on a heuristic approach, taking into account the constraint of anisotropic material behavior. MJF and SLS were used to print all three designs, respectively. Use of FDM was limited to print one design, the others were not appropriate for FDM due to high complexity. The anisotropy in mechanical characteristics of SLS and MJF printing technologies was assessed to estimate its possible influence on spring stiffness. MJF-printed tensile specimens show more anisotropic material behavior compared to SLS-printed ones. Overall suitability of FDM to print springs was shown to be limited due to design constraints and manufacturing limitations like warping and in-plane delaminations between deposited polymer strands, as well as very limited applicable cycles during fatigue testing. MJF-printed springs showed higher variability in geometric dimensions compared to SLS. Slight variances in geometric dimensions were shown to crucially influence spring stiffness, thus lowering the reliability of the MJF technology for reproducible springs. Fatigue life of either SLS or MJF samples was shown to be appropriate as all springs survived 100,000 load cycles with moderate loss of reaction force below 16% for MJF- and below 10% for SLS-printed springs. SLS was shown to be the most promising of the three evaluated printing technologies for small-scale series manufacturing of springs considering reproducibility as well as fatigue behavior
Analyse verschiedener zkSNARK-Instanziierungen für Gültigkeitsbeweise von Stimmzetteln
No full textAs e-voting schemes become increasingly prevalent, ensuring their security is essential. In many cases, Zero Knowledge Proofs (ZKPs) are used to allow users to prove the validity of their (encrypted) votes without revealing them. One class of ZKPs that can be used to prove (essentially) arbitrary valid statements are General Purpose ZKPs (GPZKPs) with Zero-Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) being one type of such. zk-SNARKs are a good choice for our use case since voters are usually resource constrained in practice and zk-SNARKs typically offer small proofs and efficient proof generation algorithms.
A typical form of encrypting ballots in e-voting schemes is Exponential ElGamal (EEG) encryption. The feasability of GPZKPs for showing validity of ballots that are component-wise encrypted using EEG ciphertexts in voting systems with arbitrary election types and ballot formats using Groth16-SNARKs has been shown by Huber et al. To demonstrate the versatility of GPZKPs, the authors implemented ballot validity relations for the different election types Single Vote, Multi-Vote, Line-Vote, Multi-Vote with Rules, the ranked election types Pointlist-Borda and Borda Tournament Style, Condorcet methods and Majority Judgment in libsnark and benchmarked their performance.
We extend on this research by implementing ballot validity relations for the same election types in Circom and generating the ZKPs using snarkjs. First, we follow the same approach as Huber et al. for our implementation, using EEG encryption instantiated with elliptic curves in Montgomery form for the encryption of individual ballot entries.
Then, as our main contribution, we introduce a novel variant of computing EEG ciphertexts: Precomputed Powers EEG (PPEEG). By providing some precomputed powers of group elements to the encryption, we reduce the computations needed for ZKPs of ballot validity. Additionally, we instantiate PPEEG encryption with elliptic curves in Twisted Edwards form rather than elliptic curves in Montgomery form, reducing the computational effort further.
Furthermore, we improve the performance of the election type specific computations in the ballot validity proofs for several election types, most notably for Condorcet methods. However, these improvements are negligible compared to the reduction of computational effort in the encryption part in most cases.
Compared to the results by Huber et. al, we reduce the computational effort for the ballot validity proofs accross all election types by 35% to 82% depending on the metric. Moreover, we can compute ballot validity proofs for ballots with up to 1000 votes on a standard PC which is sufficient for almost all real-world applications of any of the covered election types.Mit der zunehmenden Verbreitung von E-Voting-Systemen wird es immer wichtiger, die Sicherheit in diesen Sytemen zu gewährleisten. In vielen Fällen werden Zero Knowledge Proofs (ZKPs) verwendet, um Wählern die Möglichkeit zu geben, die Gültigkeit ihrer (verschlüsselten) Stimmen zu beweisen, ohne sie preiszugeben. Eine Klasse von ZKPs, die zum Beweis von (fast) beliebigen gültigen Aussagen verwendet werden können, sind General Purpose ZKPs (GPZKPs), wobei eine Subform dieser Zero Knowledge Succinct Non-interactive Arguments of Knowledge (zk-SNARKs) sind. zk-SNARKs sind eine gute Option im E-Voting Kontext, da Wähler in der Regel nur über begrenzte Ressourcen verfügen und zk-SNARKs kleine Beweise und in der Regel effiziente Beweiser haben.
Eine typische Form der Verschlüsselung von Stimmzetteln in E-Voting-Systemen ist Exponential ElGamal (EEG) Verschlüsselung.
Huber et al. haben mittels des Groth16-SNARKs gezeigt, dass GPZKPs eine valide Option für den Nachweis der Gültigkeit von Stimmzetteln sind, die komponentenweise als EEG-Chiffretexte verschlüsselt sind. Hierbei haben die Autoren in der Praxis nachgewiesen, dass sich die Beweise der Stimmzettelgültigkeit leicht für viele Wahltypen und Stimmzettelformate implementieren lassen. Um die Vielseitigkeit von GPZKPs zu demonstrieren, implementierten die Autoren Relationen in libsnark, um die Gültigkeit von Stimmzetteln für die verschiedenen Wahltypen zu zeigen: Single-Vote, Multi-Vote, Line-Vote, Multi-Vote with Rules, die Ranglisten-Wahltypen Pointlist-Borda und Borda Tournament Style, Condorcet-Methoden und Majority Judgment. Anschließend, vergleichen sie die Performance der ZKP-Berechnung zwischen den verschiedenen Wahltypen.
Wir bauen auf dieser Forschung auf, indem wir die Relationen für Stimmzettelgültigkeit für dieselben Wahltypen in Circom implementieren und ZKPs mit snarkjs erzeugen. Zunächst folgen wir hier demselben Ansatz wie Huber et al. und verwenden Elliptische Kurven in Montgomery Form für die EEG-Verschlüsselung der einzelnen Stimmzettel.
Als Hauptbeitrag unserer Arbeit führen wir eine neue Variante zur Berechnung von EEG-Chiffretexten ein: Precomputed Powers EEG (PPEEG). Hierbei verringern wir die Berechnungen, die für ZKPs der Stimmzettelgültigkeit erforderlich sind, indem wir der Verschlüsselung einige vorberechnete Potenzen von Gruppenelementen zur Verfügung stellen. Außerdem verwenden wir bei der PPEEG-Verschlüsselung Elliptische Kurven in Twisted-Edwards Form statt in Montgomery-Form, was den Rechenaufwand weiter verringert.
Darüber hinaus reduzieren wir die wahltypspezifischen Berechnungen in den Beweisen der Stimmzettelgültigkeit für mehrere Wahltypen, insbesondere für Condorcet-Verfahren. Diese Verbesserungen sind jedoch in den meisten Fällen vernachlässigbar im Vergleich zu der Verringerung des Rechenaufwands im Verschlüsselungsteil.
Im Vergleich zu den Ergebnissen von Huber et al. reduzieren wir den Rechenaufwand für den Beweis der Stimmzettelgültigkeit, abhängig von der Metrik, über alle Wahltypen hinweg um 35% bis 82%. Darüber hinaus können wir Beweise für Stimmzettelgültigkeit mit bis zu 1000 Stimmen auf einem Standard-PC berechnen, was für fast alle realen Anwendungen der behandelten Wahltypen ausreichend ist
Reproduzierbare Modellierung und Unsicherheitsquantifizierung von sparsen und variablen Daten
No full textSystems biology and systems medicine aim to understand complex relationships in living systems. These living systems exhibit considerable variability due to genetic predispositions, epigenetic modifications, and stochastic and environmental effects. Further, observations are sparse in many biomedical areas due to complex measurements, costs, and ethics. Addressing system complexity paired with sparsity and data variability requires reliable modeling and uncertainty quantification methods. Here, modeling enables understanding and testing hypotheses about systems. At the same time, uncertainty quantification sheds light on the reliability of model predictions and the prediction variability for a certain credibility level.
This thesis contributes to a systematic biomedical understanding and sound statistical analysis in three major aspects: (i) The Bayesian workflow BayModTS was developed to process and compare sparse and variable time series data and applied to three hepatic datasets. BayModTS is a Findable, Accessible, Interoperable, and Reproducible (FAIR) workflow utilizing the retarded transient functions of \citet{Kreutz.2020} as a universal simulation model. It can be used to statistically test whether different dynamics stem from the same data-generating process and to process discrete and variable time series data into continuous, uncertainty-equipped functions. (ii) Using sparse data, deterministic and stochastic modeling is used to characterize the tumor-suppressor protein DLC1 in the Epithelial-Mesenchymal Transition (EMT). We showed that loss of DLC1 increases EMT plasticity, a crucial feature in cancer metastasis. The modeling results are confirmed by experimental data, showing a partial EMT phenotype in DLC1-depleted cells. Further, narrow posterior marginals validate our model and the uniqueness of the estimated parameters. (iii) Bayesian estimation shows that papers with reproducible systems biology models get more citations. This confirms that reproducibility is valuable, as ten years after introducing the Systems Biology Markup Language (SBML), papers with reproducible models get more citations than papers with non-reproducible models. A heatmap visualization scheme for Bayesian estimation multi-group comparisons was developed to assist in visual interpretation and pattern identification of the results. The heatmap contains the credibility of group differences, thereby capturing the uncertainty in the data. We applied the heatmap visualization to verify an increased citation count for sub-periods until 2020.
All methods and models are developed reproducibly, using state-of-the-art formats and providing all code in public repositories with detailed reproduction instructions. In short, statistical methods are paired with FAIR modeling approaches to gain insight and provide trustworthy predictions into processes where only limited data is available
Modelling multifunctionality of agricultural product systems in the bioeconomy and assessment in the context of the sustainable development goals
No full textOver the last decade, there has been a noticeable increase in the adoption of bioeconomy policy strategies on a global scale. Through the lens of resource economics, this trend can be seen as a carbon transition complementing the ongoing energy transition. While the energy transition primarily focuses on decarbonisation by phasing out fossil fuels, bioeconomies are centred on a shift from non-renewable to renewable carbon, i.e., biomass. In any case, ex-ante modelling assessments of biomass supply and use are required to understand its potential impacts on and contributions to the Sustainable Development Goals (SDGs). Agricultural biomass poses a particular challenge in the modelling of product systems (PS) because of its multifunctional nature and the resulting competition in the markets known as the 4 Fs: Food, Feed, Fibre (material), and Fuel (energy). Current approaches have primarily been divided into modelling of food systems, or of industrial applications of biomass (material and energy), or of cross-sectoral modelling concerning bioenergy and food security.
The objective of this thesis is to develop a harmonized modelling approach among all aspects of multifunctionality of agricultural PS along its life cycle, i.e., the potential use of agricultural biomass in the 4 Fs, and the variety of co-products generated throughout its end-of-life. In this sense, this thesis explores the systemic consequences of multifunctionality at each stage of agricultural life cycles by introducing the C-Trans-LCA methodology. The C-Trans-LCA combines material flow analysis (MFA) and consequential life cycle assessment (LCA). A novel feature of the methodology is the introduction of archetypes to define the functional unit based on the most direct utilization of an agricultural resource in the 4 Fs in a specific region. Furthermore, the developed methodology enlarges the understanding of multifunctionality of agricultural PS by defining the classes resource, land, process, and product multifunctionality. It is demonstrated that consequential life cycle inventory modelling is effective in harmonizing the representation of these multifunctionality classes. Moreover, the 4 Fs evolve to the 4 Fs+ modelling the function of carbon dioxide removal via soil organic carbon (SOC) increase when climate smart practices are in place. For the assessment, the SDGs were operationalised for agricultural PS (micro level of bioeconomy) in a set of five life-cycle indicators. The importance of SDG 2 Zero hunger, SDG 12 Responsible consumption and production, and SDG 13 Climate action is highlighted as fundamental requirements for a sustainable bioeconomy. A graphical representation is provided by the “Bioeconomy Compass”.
The case study “Grain maize to 4 Fs+ in Baden-Württemberg” showed that for sweetcorn (Food), consumer actions can importantly reduce the carbon footprint by minimizing food waste, whilst for polylactide (PLA, Fibre) optimising production processes to reduce material losses is more effective. Regarding dried grain maize (Feed), the sensitivity analysis resulted in a substantial variation depending on whether the substituted soybean meal came from Brazil or the United States. This result is also relevant for distiller’s dried grains with solubles, a co-product of bioethanol production (Fuel). In a carbon transition scenario 2030, the climate benefits of SOC increase may offset the greenhouse gas emissions of the Food and Feed PS, except when the larger area needed for organic cropping is diverted from PLA production. This occurs because PLA’s marginal supplier (MS) is assumed to be still a fossil counterpart (polyethylene terephthalate, PET) in the middle-term. Alternatively, where the larger area is diverted from bioethanol production, the carbon footprint would be lower. This is due to the fact that bioethanol MS is a combination of a fossil counterpart (gasoline) and electric vehicles. It is assumed that these electric vehicles are supplied by a less carbon-intensive electricity mix by 2030. The lower revenues resulting from lower yields of organic cropping may be compensated by processing grain maize to products of higher added-value. Such a potential is higher for Food rather than Feed products, which are usually directly consumed.
The C-Trans-LCA methodology evidenced that resource multifunctionality leads to “competing” functions across the 4 Fs, while “complementary” functions resulting from dependent co-products from multi-output processes and from the cascade and circular use of products increase the palette of co-products of a PS. It was shown that both aspects of multifunctionality can be modelled with a harmonized consequential approach. The identification of MS of dependent co-products and diverted areas proves to be of paramount importance in the modelling given the global behaviour of agricultural markets. Such a finding has implications for contentious debates like "Food vs. Fuel." While using agricultural resources for energy purposes may displace its use as food, in the case study it was evident that a "food vs. food" dynamic exists. This means that reducing food waste could free up productive areas, easing the strain on food security. The modelling of changes in SOC as a complementary function of agricultural PS shall be encouraged to support the participation of producers in voluntary carbon markets and diversify their income.
The C-Trans-LCA methodology provides the opportunity to formulate tailored strategies for utilizing the same resource across different markets, leveraging the multifunctionality of renewable carbon in a sustainable carbon transition. This involves preserving it as a resource, mitigating its impact as a greenhouse gas emission, and capitalizing on its potential as a marketable product. This resource economics perspective serves as an overarching umbrella, enabling the modelling and assessment of PS. Under this umbrella, broader bioeconomy visions can seamlessly integrate: biotechnological processes can be modelled in life cycle studies, and biological knowledge and principles can support the definition of indicators for impact assessment. Adopting this life cycle perspective on carbon, strengths the effectiveness of bioeconomies in truly contributing to societal well-being
Crystal structure of 3-O-benzy 1-6,8-di-O-benzylidene-5,7-dideoxy-1,2-di-O-isopropylidene-5-nitro-L-glycero-D-galacto-octitol, (C6H5)(C4H6O2)[C3H3(NO2)(0H)(OCH2C6H5)][(C3H3O2)(CH3)2]
No full textC25H31NO8, orthorhombic, P212121 (No. 19), a = 10.137(2) Å, b = 28.990(4) Å, c = 8.190(2) Å, V= 2406.8 Å3, Ζ = 4, Rgt(F) = 0.071, Rw(F) = 0.046, T= 293 K.Fonds der Chemischen Industri
Abschlussbericht der Evaluation des Schulversuchs „Zentrum für Digitalisierung und nachhaltige Berufs- und Studienorientierung“ (DIGIMINT)
No full textIm Mittelpunkt des vorliegenden Abschlussberichts steht das Schulversuchsprojekt DIGIMINT („Zentrum für Digitalisierung und nachhaltige Berufs- und Studienorientierung“), das am Otto-Hahn-Gymnasium Nagold in Zusammenarbeit mit dem Jugendforschungszentrum Nagold, lokalen Unternehmen, Hochschulen sowie der Agentur für Arbeit durchgeführt wird. Zielgruppe von DIGIMINT sind Oberstufenschüler\*innen der Klassen 10 bis 12, die sich durch ein besonderes Interesse an MINT-Themen sowie durch entsprechende schulische Leistungen in diesen Fächern auszeichnen. Das Projekt intendiert, die Schüler\*innen im Bereich digitalisierungsbezogener Kompetenzen und MINT-fachlicher Methoden zu fördern und ihnen eine praxisorientierte Berufs- und Studienorientierung im Bezugsfeld zu ermöglichen. Ein besonderer Fokus liegt auf der Förderung von motivierten und MINT-interessierten Schüler\*innen, um diese für technische und naturwissenschaftliche Bereiche zu begeistern. Im Unterricht werden hierzu neben den etablierten Themen des Bildungsplans aktuelle Themen aus Wissenschaft und Wirtschaft integriert. Dabei stehen eigenständiges Lernen, forschendes Arbeiten und Projekte in Zusammenarbeit mit den Partnerinstitutionen im Vordergrund. DIGIMINT verfolgt zudem das Ziel, die regionale MINT-Bildung zu stärken, digitale und technische Zukunftsthemen aufzugreifen und durch die enge Vernetzung von Schule, Wirtschaft und Wissenschaft den Schüler\*innen einen Ansatz für nachhaltige regionale berufliche Orientierung zu bieten.
Die wissenschaftliche Begleitung des Schulversuchsprojekts DIGIMINT erfolgte durch die Abteilung Berufspädagogik mit Schwerpunkt Technikdidaktik (BPT) im Institut für Erziehungswissenschaft der Universität Stuttgart. Im Rahmen der mehrjährigen Begleitevaluation wurden die Interessen und Vorkenntnisse der Jugendlichen beim Projektstart, ihre Motivation, Kompetenzentwicklung und berufliche Orientierung analysiert.
Mit der wissenschaftlichen Untersuchung wurden systematische anwendungsorientierte Erkenntnisse erstens zu Motivation, Interessen und MINT-Leistungsentwicklung der am Schulversuch beteiligten Schüler\*innen generiert, zweitens zur inhaltlichen und methodisch-didaktischen Gestaltung des Unterrichts in den Klassenstufen 10 bis 12 eruiert und drittens zur Optimierung des Schulversuchs bereitgestellt. Das Ziel ist dabei, Bedingungen zu identifizieren, die für die Implementation und den weiteren Entwicklungsprozess der institutionellen Zusammenarbeit der einzelnen Akteure des Schulversuchs förderlich scheinen. Die Untersuchung folgt einem Mixed-Methods-Ansatz, der qualitative mit quantitativen Methoden kombiniert, um formative und summative Erkenntnisse im Sinne eines Design-Based-Research-Ansatzes zu generieren.
Die vorliegenden Ergebnisse der Begleitforschung zeigen, dass der Einsatz digitaler Werkzeuge, kooperativer Lernarrangements sowie anwendungsorientierter Projekte zur Steigerung von Interesse, Motivation und zur beruflichen Orientierung der beteiligten Schüler\*innen im MINT-Bereich beiträgt. Die Integration aktueller wissenschaftlicher und wirtschaftlicher Themen in den Unterricht fördert nicht nur die Zukunftsrelevanz der behandelten Themen, sondern begünstigt auch die Entwicklung einer Entscheidungssicherheit der Schüler\*innen im Hinblick auf ihre zukünftigen Studien- und Berufswege. Darüber hinaus geben die empirischen Befunde Hinweise auf projektspezifische Optimierungspotenziale, um die Effektivität und Attraktivität innovativer Unterrichtsmodelle im regionalen MINT-Kontext weiterzuentwickeln
Transport phenomena in fractionalized quantum materials
No full textThe quantum effects in a many-body system can induce novel behavior on a system with fractionalized degrees of freedom, whose study is not only interesting from the perspective of fundamental research but also in view of potential applications to quantum technologies such as quantum computers. In this regard, quantum spin liquids are systems of particular interest. They are an exotic phase of matter characterized by the presence of fractionalized excitation (spinons) and emergent gauge fields. The efforts in the community have not yet succeeded in asserting the existence of quantum spin liquids beyond any reasonable doubt.
The technological advances in material synthesis provide us with two-dimensional samples of candidate quantum spin liquid materials like 1T-TaS2, 1T-TaSe2, and αRuCl3, which might avoid the problem of the disruptive effects of the interlayer interactions in candidate materials. Experimental techniques like neutron scattering, aimed at measuring the bulk properties of a sample, are not applicable in the case of two-dimensional samples. One of the difficulties in probing experimentally a QSL phase comes from the fact that the spinons do not carry an electric charge, ruling out the possibility of using conventional electrical probes. Going beyond conventional transport, we propose two setups of electric probes to characterize a QSL phase. First, we analyze a setup in which a QSL layer is interposed between two metallic layers. In this setup, we apply a current in the first metallic layer and measure the induced voltage on the second one. The momentum transfer is affected by the non-trivial behavior of momentum-carrying spinons and results in a response that carries information about the dynamic of the spinons and will potentially be helpful for the future characterization of candidate QSL materials. The second probe we propose is a scanning tunneling microscopy (STM) experiment on a Kondo lattice with the addition of an antiferromagnetic interaction between the localized magnetic moments. We calculate the STM response in each of the phase configurations of this system allowing also for the possibility for the conduction electrons and for the spinons to form a superconducting phase and present our derivation of the mean field equations in a Kondo lattice system.
This last setup might find a concrete realization in materials such as TaS2, TaSe2, and NbSe2 in the 1T, 2H, and in the 4Hb crystallographic phases