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Die Angabe von Unsicherheiten bei zertifizierten Werten von Referenzmaterialien ist von entscheidender Bedeutung. Die korrekte Einbindung der Unsicherheiten zur Berechnung von Verfahrensmessunsicherheiten ist wesentlich für die Gewährleistung der Genauigkeit und Zuverlässigkeit von Messungen. In diesem Vortrag werden die verschiedenen Einflussfaktoren auf die Unsicherheit zertifizierter Werte gemäß ISO Guide 35 dargestellt. Dabei werden insbesondere die Charakterisierung, Homogenität und Stabilität als entscheidende Faktoren für die Bestimmung der Unsicherheit eines Referenzmaterials betrachtet. Abschließend wird das Konzept anhand eines konkreten Beispiels veranschaulicht, um die praktische Anwendung und die Auswirkungen auf die Berechnung von Verfahrensmessunsicherheiten zu verdeutlichen.
The BAM Data Store
(2023)
As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities.
The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis.
Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.
Angesichts der zunehmenden Digitalisierung und dem Einsatz datenintensiver Methodiken in der Wissenschaft stehen Forschende vor der Herausforderung, stetig wachsende Datenmengen nachvollziehbar zu dokumentieren, langfristig zu speichern und für Dritte nachnutzbar zu machen. Um diesen Anforderungen gerecht zu werden, bietet sich die Nutzung von Software-Lösungen an, welche Forschungsdatenmanagement mit der digitalen Dokumentation von Laborinventar und Experimenten in elektronischen Laborbüchern (engl. electronic lab notebooks (ELN)) verknüpfen.
This document constitutes the Strategic Research Agenda (SRA) for the European Metrology Network for Mathematics and Statistics in Metrology (EMN Mathmet). The EMN Mathmet is an alliance of European National Metrology Institutes (NMIs), Designated Institutes (DIs) and an EMN Partner that aims to strengthen research and cooperation in the field. The SRA has been developed within a European project (EMPIR 18NET05 MATHMET) to promote and support the network. The SRA was developed based on a consultation process with stakeholders and the strategies of individual NMIs and DIs, and in alignment with the EURAMET 2030 strategy.
As a key result, the SRA defines a long-term research goal: the EMN Mathmet will coordinate research to strengthen the trust in algorithms, software tools and data to underpin digital transformation. For this purpose, new emerging research topics where algorithms, software tools and data play a significant role were identified: (i) Artificial Intelligence and Machine Learning, and (ii) Computational Modelling and Virtual Metrology. The foundation for the development of these new topics is given by the traditional focus on (iii) Data Analysis and Uncertainty Evaluation. The SRA characterises the future needs and challenges in the field of mathematics and statistics in metrology and provides an outline of how the EMN Mathmet can meet these new emerging requirements.
The deployment of machine learning (ML) and deep learning (DL) in structural health monitoring (SHM) faces multiple challenges. Foremost among these is the insufficient availability of extensive high-quality data sets essential for robust training. Within SHM, high-quality data is defined by its accuracy, relevance, and fidelity in representing real-world structural scenarios (pristine as well as damaged). Although methods like data augmentation and creating synthetic data can add to datasets, they frequently sacrifice the authenticity and true representation of the data. Sharing real-world data encapsulating true structural and anomalous scenarios offers promise. However, entities are often reluctant to share raw data, given the potential extraction of sensitive information, leading to trust issues among collaborating entities.
Our study introduces a novel methodology leveraging Federated Learning (FL) to navigate these challenges. Within the FL framework, models are trained in a decentralized manner across different entities, preserving data privacy. In our research, we simulated several scenarios and compared them to traditional local training methods. Employing guided wave (GW) datasets, we distributed the data among different parties (clients) using IID (independent, identically distributed or in other words, statistically identical) mini batches of dataset, as well as non-IID configurations. This approach mirrors real-world data distribution among varied entities, such as hydrogen refueling stations.
In our methodology, the initial round involves individualized training for each client using their unique datasets . Subsequently, the model parameters are sent to the FL server, where they are averaged to construct a global model. In the second round, this global model is disseminated back to the clients to aid in predictive tasks. This iterative process continues for several rounds to reach convergence.
Our findings distinctly highlight the advantages of FL over localized training, evidenced by a marked improvement in prediction accuracy . This research underscores the potential of FL in GW-based SHM, offering a remedy to similar challenges tied to data scarcity in other SHM approaches and paving the way for a new era of collaborative, data-centric monitoring systems.
Harmonized and interoperable national Quality Infrastructure (QI) systems are essential for fostering cooperation, promoting mutual trust, and facilitating trade. The true potential of the QI is realized when its elements and actors are seamlessly integrated into a cohesive digital QI ecosystem. Recent developments towards industrial international data spaces enable such an ecosystem but require the integration of QI principles. Recognizing the lack of such a platform, Quality-X aims at setting the stage for the implementation of a QI ecosystem in international data spaces (IDS), GAIA-X and related German and European projects dedicated to secure data sharing. Quality-X is not about the construction of a platform; it is the creation of an inclusive QI ecosystem with harmonized interfaces. Instead of imposing rigid data structures, it prioritizes interoperability. Through the utilization of Decentralized Identifiers (DIDs), Verifiable Credentials, and Identity Hubs, Quality-X seeks seamless interactions across diverse service provider systems.
This white paper introduces the concept and vision of Quality-X and discusses the general prerequisites for integrating QI processes within data spaces. Further on, we introduce existing testbeds, which will serve as an experimental proving ground for exploring various use cases related to the implementation of the vision of a QI-Digital.
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Harmonisierte und interoperable nationale Qualitätsinfrastrukturen (QI) sind für die Förderung der Zusammenarbeit, des gegenseitigen Vertrauens und der Erleichterung des Handels unerlässlich. Das wahre Potenzial der QI kommt zum Tragen, wenn ihre Elemente und Akteure nahtlos in ein kohärentes digitales QI-Ökosystem integriert werden. Die jüngsten Entwicklungen hin zu industriellen internationalen Datenräumen ermöglichen ein solches Ökosystem, erfordern jedoch die Integration von QI-Prinzipien. Angesichts des Fehlens einer solchen Plattform zielt Quality-X darauf ab, die Voraussetzungen für die Umsetzung eines QI-Ökosystems in internationalen Datenräumen (IDS), GAIA-X und verwandten deutschen und europäischen Projekten zum sicheren Datenaustausch zu schaffen. Bei Quality-X geht es nicht um den Aufbau einer Plattform, sondern um die Schaffung eines umfassenden QI-Ökosystems mit harmonisierten Schnittstellen. Anstatt starre Datenstrukturen aufzuerlegen, steht die Interoperabilität im Vordergrund. Durch die Verwendung von dezentralen Identifikatoren (DIDs), überprüfbaren Berechtigungsnachweisen und Identitäts-Hubs strebt Quality-X eine nahtlose Interaktion zwischen verschiedenen Systemen von Dienstleistern an.
Dieses Whitepaper stellt das Konzept und die Vision von Quality-X vor und erörtert die allgemeinen Voraussetzungen für die Integration von QI-Prozessen in Datenräumen. Darüber hinaus stellen wir bestehende Testbeds vor, die als experimentelles Versuchsfeld für die Erforschung verschiedener Anwendungsfälle im Zusammenhang mit der Umsetzung der Vision einer QI-Digital dienen sollen.
The statistical tool eCerto was developed for the evaluation of measurement data to assign property values and associated uncertainties of reference materials. The analysis is based on collaborative studies of expert laboratories and was implemented using the R software environment. Emphasis was put on comparability of eCerto with SoftCRM, a statistical tool based on the certification strategy of the former Community Bureau of Reference. Additionally, special attention was directed towards easy usability from data collection through processing, archiving, and reporting. While the effects of outlier removal can be flexibly explored, eCerto always retains the original data set and any manipulation such as outlier removal is (graphically and tabularly) documented adequately in the report. As a major reference materials producer, the Bundesanstalt für Materialforschung und -prüfung (BAM) developed and will maintain a tool to meet the needs of modern data processing, documentation requirements, and emerging fields of RM activity. The main features of eCerto are discussed using previously certified reference materials.
Conceptually, high-precision manufacturing is a sequence of production and measurement steps, where both kinds of steps require to use non-deterministic models to represent production and measurement tolerances. This paper demonstrates how to effectively represent these manufacturing processes as Partially Observable Markov Decision Processes (POMDP) and derive an offline strategy with state-of-the-art Monte Carlo Tree Search (MCTS) approaches. In doing so, we face two challenges: a continuous observation space and explainability requirements from the side of the process engineers. As a result, we find that a tradeoff between the quantitative performance of the solution and its explainability is required. In a nutshell, the paper elucidates the entire process of explainable production planning: We design and validate a white-box simulation from expert knowledge, examine state-of-the-art POMDP solvers, and discuss our results from both the perspective of machine learning research and as an illustration for high-precision manufacturing practitioners.
After entry of a quarantine/regulated pathogen, infected plants shall be destroyed, and the cultivated area (e.g., greenhouse) shall be disinfected. Therefore, the selection of an effective disinfectant plays an important role. With the availability of different methods for virus quantification, we investigated the application of quantitative ELISA (qELISA), RT-qPCR (reverse transcription-quantitative polymerase chain reaction), and bioassays for the quantification of disinfectant efficacy. Therefore, we estimated the titer reduction in tomato brown rugose fruit virus (ToBRFV), a regulated pathogen, in plant sap and on germ carriers after treatment with MENNO Florades 4% for 16 h. The virus load before and after the treatment was measured with the mentioned methods. The RT-qPCR and qELISA methods showed very low efficacy in the presence of the disinfectant. Although bioassays are time-consuming, need purified particles for establishing the quantification models, and are less sensitive than RT-qPCR, they were able to quantify the differences in virus titer in the presence/absence of disinfectant. Interestingly, the bioassays reached at least the lower limit sensitivity of a qELISA. By being less sensitive to the presence of the disinfectant, bioassays proved to be the only technique for the determination of the disinfectant efficacy against ToBRFV on different germ carriers as well as on virus-infected plant sap.
In this study, a wound dressing composed of an alginate dialdehyde−gelatin (ADA-GEL) hydrogel incorporated by astaxanthin (ASX) and 70B (70:30 B2O3/CaO in mol %) borate bioactive glass (BBG) microparticles was developed through 3D printing. ASX and BBG particles sti.ened the composite hydrogel construct and delayed its in vitro degradation compared to the pristine hydrogel construct, mainly due to their cross-linking role, likely arising from hydrogen bonding between the ASX/BBG particles and ADA-GEL chains. Additionally, the composite hydrogel construct could hold and deliver ASX steadily. The composite hydrogel constructs codelivered biologically active ions (Ca and B) and ASX, which should lead to a faster, more e.ective wound-healing process. As shown through in vitro tests, the ASX-containing composite hydrogel promoted fibroblast (NIH 3T3) cell adhesion, proliferation, and vascular endothelial growth factor expression, as well as keratinocyte (HaCaT) migration, thanks to the antioxidant activity of ASX, the release of cell-supportive Ca2+ and B3+ ions, and the biocompatibility of ADA-GEL. Taken together, the results show that the ADA-GEL/BBG/ASX composite is an attractive biomaterial to develop multipurposed wound-healing constructs through 3D printing.