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An uncertain elite
No full textThis is an open-access article distributed under the terms of the Creative Commons
Attribution Licence (CC BY) 4.0 https://creativecommons.org/licenses/by/4.0/).The digital transformation of industries has given rise to new categories of tech workers, such as software engineers and UX/UI designers, who now work alongside traditional engineers. This study explores the evolving relationship between these groups, focusing on work processes, status perceptions and professional interactions. The research questions addressed include: how has digitalisation affected these two groups’ work processes? what strategies do they use to maintain or improve their career paths? and how do their roles converge or diverge? Using qualitative data from interviews and workshops in a German automotive company undergoing a digital and electric mobility transformation, the study finds both competition and cooperation between engineers and IT professionals, with the former adopting some IT work methods and the latter adjusting to the highly structured processes of the industrial sector. Despite growing overlaps, distinct professional identities nevertheless remain.Vo
Transmissions, decisions, discourses - replication data
No full textThis is the replication data set for the publication "Transmissions, decisions, discourses : a methodological framework for measuring and comparing democratic innovations’ policy impact(s)" by PD Dr. Dannica Fleuß and Christoph Deppe.N
Modelling crack initiation and propagation in heterogeneous solid microstructures with interfaces
No full textIn Structural Health Monitoring (SHM) of large concrete structures like bridges, optimal sensor placement is crucial due to high installation costs and the challenges of managing large data volumes generated by sensors. The FE2 framework provides an effective solution to address this issue by simulating bridge responses under various load conditions. This enables precise identification of critical sensor locations and enhances the cost-efficiency of SHM by reducing the number of sensors required. The FE2 framework requires small-scale simulations of concrete’s heterogeneous microstructure, which consists of at least three solid phases and involves non-linear, complex fracture dynamics. Consequently, the development of a modeling framework that requires the least computational cost is a major step in the FE2 framework. To this end, a numerical study was conducted to determine the best numerical framework for fracture simulation of concrete microstructures in terms of computational cost and implementation complexity. Among various frameworks, the Cohesive Zone Model (CZM), the Phase-field Fracture Model (PFM), and a hybrid of these approaches were analyzed. Specifically, the intrinsic CZM was selected from the CZM approach, while the standard and cohesive phasefield fracture approaches were chosen from the phase-field fracture framework. Within the hybrid model, the CZM is used for interface debonding, while the Cohesive Phase-field Fracture Model (CPFM) is employed for matrix cracking. The numerical study revealed that the computational cost of a complete mesoscale finite element fracture simulation of concrete was lower in CPFM simulations than in CZM simulations when the same resources were utilized. While CPFM simulations showed initial promise, a full simulation still required an average of 3.5 hours using five CPUs, each capable of 2.43 × 108 FLOPS (floating-point operations). This computational demand remains excessively high for the FE2 framework, indicating that its use in the SHM of large structures, such as bridges, remains impractical. These computationally intensive simulations exceed current resources, making FE2 application to large structures like bridges infeasible and hindering optimal sensor placement. Advancements in computational power, especially with modern GPUs, have enabled deep learning-based surrogate models to address computationally intensive problems in mechanical engineering and materials science. These surrogate models can predict physical phenomena in a fraction of a second instead of hours, bringing the realization of an online FE2 framework closer to feasibility. To this end, the CPFM model is employed to generate the required data to train deep learning-based surrogate models. In the first attempt, data-driven surrogate models were developed to predict fracture in concrete microstructures. Following the successful training and testing of the data-driven surrogate model, the computational cost was reduced by a factor of 3315, with each fracture simulation taking just 3.8 seconds. This substantial reduction in computational cost indeed brings the FE2 framework closer to practical implementation. However, a major limitation of data-driven models is their reliance on training data on conditions that is generated on, limiting their generalization to unrepresented boundary conditions. This necessitates generating new training data and retraining the model, making their use in SHM of bridges under the FE2 framework impractical. To address this limitation, the Neural Operator (NO) framework has been proposed. NOs learn operators that map functional parametric dependencies to their solutions, enabling generalization across various functional spaces, including different initial and boundary conditions. This overcomes the dependency issues of data-driven models and provides system responses in fractions of a second, bringing an online FE2 framework for bridges closer to realization. It should be noted that no neural operator models have been developed or implemented in this dissertation. Instead, they are suggested as a future research direction to address the challenges associated with data-driven models.Vo
AISHIP: an ontology for extended vessel representation and multimodal data integration
No full textCopyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).The maritime sector generates vast amounts of heterogeneous data due to dense global vessel traffic. This data offers significant potential for improving operational efficiency and ensuring safety at sea. However, its effective use is hindered by a lack of semantic alignment between diverse information sources. To address this challenge, we present AISHIP, an ontology designed to unify and semantically enrich maritime data. AISHIP extends the existing VesselAI ontology by incorporating enhanced vessel characteristics, additional trajectory information, multimodal vessel representations, and a detailed conceptualization of propulsion systems. The primary objective of AISHIP is to serve as a semantic interface that facilitates the integration of heterogeneous maritime datasets, enabling consistent annotation, querying, and reasoning across systems.
By providing a shared semantic foundation, AISHIP supports a range of maritime applications, including vessel behavior analysis, fleet management, and search and rescue operations. These tasks benefit from harmonized data representations, which improve analytical precision and operational decision-making. We discuss relevant use cases to illustrate the ontology’s practical value and evaluate its design to demonstrate its quality. AISHIP represents an extensible and reusable resource, aligning with FAIR principles, and offers strong potential for adoption in future maritime analytics and digital twin frameworks.Vo
MicroLabVR: a VR-based platform for interactive 3D microbiome data visualization
No full textThis is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (https://creativecommons.org/licenses/by/4.0/).Microbiome data are inherently complex, high-dimensional, and often challenging to interpret using conventional two-dimensional visualization techniques. To address this limitation, we present MicroLabVR, a proof-of-concept virtual reality (VR) application designed for the immersive visualization and exploration of simulated microbiome data. MicroLabVR supports the interactive inspection of spatial population dynamics and substance concentration distributions in two and three dimensions, offering a more intuitive understanding of microbial behavior. The application integrates data exported from BacArena, a hybrid constraintand agent-based modeling tool, and visualizes metabolic fluxes and explores spatiotemporal changes within a simulated Petri dish environment. Unlike existing microbiome visualization tools, MicroLabVR enables real-time exploration of dynamic, spatially resolved simulation data in an immersive 3D environment. It provides a novel interface that enhances the interpretation of microbiome simulations by allowing users to interactively examine spatial patterns, temporal changes, and metabolic activities. Future developments will focus on volumetric representations, integration of metabolic flux directions, and streamlined data transfer from simulation tools. MicroLabVR extends the capabilities of existing desktop platforms and highlights VR’s potential in microbiome research.Vo
MQTT4SSN: an ontology for the MQTT message protocol
No full textCopyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/).In the Web of Things community, the SSN/SOSA ontology, a W3C recommendation, has established itself as a foundational framework for representing semantic sensor networks, modeling sensors, observations, and actuations. However, an explicit representation of message protocols is missing, which limits semantic interoperability in machine-to-machine communication scenarios. To close this gap, we propose MQTT4SSN as an extension to SSN/SOSA that semantically models the MQTT protocol. Furthermore, the ontology addresses use cases without SSN/SOSA implementations by exclusively describing the MQTT messaging protocol. MQTT4SSN represents MQTT entities such as brokers, clients, control packets, topics, and payload metadata, linking them to SSN/SOSA concepts to enable end-to-end traceability between sensing semantics and communication semantics. The ontology captures heterogeneous payload formats, encodings, and transport metadata, enabling machine-interpretable description and integration of transmitted content. MQTT4SSN represents an extensible and reusable resource that is accessible according to the FAIR principles and documented as an Ontology Specification Draft.
Ontology: https://doernern.github.io/MQTT4SSNOntology/MQTT4SSN.owl
Documentation: https://doernern.github.io/MQTT4SSNOntology/documentation/index-en.html
WebVOWL: https://doernern.github.io/MQTT4SSNOntology/documentation/webvowl/index.html
OOPS!: https://doernern.github.io/MQTT4SSNOntology/documentation/OOPSevaluation/oopsEval.html
GitHub: https://github.com/doernern/MQTT4SSNOntology
License: CC BY-NC-SA 4.0
DOI: 10.5281/zenodo.16704302Vo