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As the number of electronic control units (ECUs) in vehicles continues to grow, exchanging physical components between original equipment manufacturers (OEMs) and ECU suppliers presents logistical challenges, impeding the pace of development and leading to accumulation of costs. In this work, we introduce a conceptual framework that enables remote testing of geographically dispersed ECUs over the Internet. Pertinent to the Internet of Things (IoT) sphere, where interconnection of distributed components is hampered by the inherent challenges of latency, we propose a hybrid synchronous methodology that combines asynchronous test management with time synchronization mechanisms to mitigate the delay impact within the distributed environment. Additionally, we discuss challenges and prospects associated with this approach.

The ongoing digitization of the industrial sector has reached a pivotal juncture with the emergence of Digital Twins, offering a digital representation of physical assets and processes. One key aspect of these digital representations is the utilization of simulation models, enabling a deeper insight into the asset’s current state and its characteristics. This paper asserts that the next evolutionary step in this digitization journey involves the integration of intelligent linkages between diverse simulation models within the Digital Twin framework. Crucially, for the Digital Twin to be a scalable and cost-effective solution, there is a pressing need for automated adaption, (re-)configuration, and generation of simulation models. Recognizing the inherent challenges in achieving such automation, this paper analyses the utilization of knowledge graphs as a potentially very suitable technological solution. Knowledge graphs, acting as interconnected and interrelated databases, provide a means of seamlessly integrating different data sources, facilitating the efficient integration and automated adaption of data and (simulation) models in the Digital Twin. We conducted a comprehensive literature review to analyze the current landscape of knowledge graphs in the context of Digital Twins with a focus on simulation models. By addressing the challenges associated with scalability and maintenance, this research contributes to the effective adoption of Digital Twins in the industrial sector, paving the way for enhanced efficiency, adaptability, and resilience in the face of evolving technological landscapes.

This research introduces a novel approach for achieving semantic interoperability in digital twins and assisting the creation of Asset Administration Shell (AAS) as digital twin model within the context of Industry 4.0. The foundational idea of our research is that the communication based on semantics and the generation of meaningful textual data are directly linked, and we posit that these processes are equivalent if the exchanged information can be serialized in text form. Based on this, we construct a “semantic node” data structure in our research to capture the semantic essence of textual data. Then, a system powered by large language models is designed and implemented to process the “semantic node” and generate standardized digital twin models (AAS instance models in the context of Industry 4.0) from raw textual data collected from datasheets describing technical assets. Our evaluation demonstrates an effective generation rate of 62-79%, indicating a substantial proportion of the information from the source text can be translated error-free to the target digital twin instance model with the generative capability of large language models. This result has a direct application in the context of Industry 4.0, and the designed system is implemented as a data model generation tool for reducing the manual effort in creating AAS model by automatically translating unstructured textual data into a standardized AAS model. The generated AAS model can be integrated into AAS-compliant digital twin software for seamless information exchange and communication. In our evaluation, a comparative analysis of different LLMs and an in-depth ablation study of Retrieval-Augmented Generation (RAG) mechanisms provide insights into the effectiveness of LLM systems for interpreting technical concepts and translating data. Our findings emphasize LLMs’ capability to automate AAS instance creation and contribute to the broader field of semantic interoperability for digital twins in industrial applications. The prototype implementation and evaluation results are presented on our GitHub Repository: https://github.com/YuchenXia/AASbyLLM.

The domain of industrial automation faces challenges, such as shortened product life cycles, shortage of skilled labor, and increased complexity. Addressing these issues necessitates innovative solutions, one of which is the Digital Twin, being a virtual counterpart of a physical asset. Central to the quality of a Digital Twin is the data it harnesses. While current Digital Twins primarly draw data from their corresponding physical assets, future interconnected production environments promise an influx of additional data from external devices. However, it remains uncertain how existing Digital Twins incorporate and leverage such data. In this systematic literature review, drawing from a pool of 1107 unique publications, we analyzed 141 works to shed light on data utilization in industrial Digital Twins. We categorized these publications based on Digital Twin types and classified them according to various criteria regarding different characteristics of data. Our findings reveal that the majority of Digital Twins predominantly rely on structured data sourced directly from their associated assets, often employing proprietary integration methods. Facing the trends towards agile and interconnected production ecosystems, as well as an increasing amount of unstructured data, we assert that current Digital Twins are not equipped to meet forthcoming demands in the industrial domain. Consequently, we propose necessary adaptations to fully unleash the potential of Digital Twins and outline future research fields, including automated data integration and evaluation.

Dosage of immunosuppressive drugs after transplantation critically determines rejection and infection episodes. In this study, a global immune function assay was characterized among controls, dialysis-patients, and transplant-recipients to evaluate its utility for pharmacodynamic monitoring of immunosuppressive drugs and for predicting infections. Whole-blood samples were stimulated with anti-CD3/toll-like-receptor (TLR7/8)-agonist in the presence or absence of drugs and IFN-γ secretion was measured by ELISA. Additional stimulation-induced cytokines were characterized among T-, B-, and NK-cells using flow-cytometry. Cytokine-secretion was dominated by IFN-γ, and mainly observed in CD4, CD8, and NK-cells. Intra-assay variability was low (CV = 10.4 ± 6.2%), whereas variability over time was high, even in the absence of clinical events (CV = 65.0 ± 35.7%). Cyclosporine A, tacrolimus and steroids dose-dependently inhibited IFN-γ secretion, and reactivity was further reduced when calcineurin inhibitors were combined with steroids. Moreover, IFN-γ levels significantly differed between controls, dialysis-patients, and transplant-recipients, with lowest IFN-γ levels early after transplantation (p < 0.001). However, a single test had limited ability to predict infectious episodes. In conclusion, the assay may have potential for basic pharmacodynamic characterization of immunosuppressive drugs and their combinations, and for assessing loss of global immunocompetence after transplantation, but its application to guide drug-dosing and to predict infectious on an individual basis is limited.

W oparciu o autonomiczne agenty programowe zdolne do obliczania indywidualnych numerycznych problemów pola, przedstawiono rozproszoną metodę rozwiązywania stanów przejściowych pola. Agenty programowe działają na zasobach rozproszonych połączonych za pośrednictwem sieci i reprezentują środowisko obliczeń dynamicznych. Komunikacja i wymiana danych między wieloma agentami umożliwia ich współpracę i pozwala podejmować decyzje w oparciu o rozproszoną wiedzę ogólną. Jako unikalna charakterystyką jest fakt, że żadna jednostka centralna nie wpływa w żadnym momencie na proces rozwiązania. Przedstawiony przykład symulacji i jej oszacowany proces obliczeniowy dowodzi, że metoda umożliwia korzystanie z nadmiarowych zasobów.

Human protein kinase CK2 is an emerging target for neoplastic diseases. Potent lead structures for human CK2 inhibitors are derived from dibenzofuranones. Two new derivatives, 7,9-dichloro-1,2-dihydro-8-hydroxy-4-[(4-methoxyphenylamino)-methylene]dibenzo[b,d]furan-3(2H)-one (4a) and (E)-1,3-dichloro-6-[(4-methoxyphenylimino)-methyl]dibenzo[b,d]furan-2,7-diol (5) were tested for inhibition of CK2 and induction of apoptosis in LNCaP cells. Both turned out to be tight binding inhibitors, with IC50 values of 7 nM (4a) and 5 nM (5) and an apparent Ki value of 0.4 nM for both. Compounds 4a and 5 reduced cellular CK2 activity, indicating cell permeability. Cell viability was substantially impaired in LNCaP cells, as well as apoptosis was induced, which was not appearing in non-neoplastic ARPE-19 cells. Co-crystallization of 4a and 5 revealed an unexpected π-halogen bond of the chloro substituent at C9 with the gatekeeper amino acid Phe113, leading to an inverted binding mode in comparison to parent compound 4b, with the Cl at C6 instead, which was co-crystallized as a control. This indicates that the position of the chloro substituent on ring A of the dibenzofuran scaffold is responsible for an inversion of the binding mode that enhances potency.