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DMN Fire Class Decisions – Choose the Right Fire Extinguisher Automatically
No full textThis video demonstrates how practical decision-making scenarios—such as selecting the appropriate fire extinguisher based on fire class—can be formalized using DMN (Decision Model and Notation). By framing firefighting response as a structured decision problem, the tutorial shows how domain knowledge about fire classifications and extinguisher suitability can be captured in decision tables.
The walkthrough introduces fire classes, develops corresponding DMN tables, and integrates these decisions into a BPMN process using Business Rule Tasks. This illustrates how operational decisions can be separated from process flow and managed declaratively, enabling clearer models and easier automation.
Overall, the video provides an applied example of decision automation, highlighting how DMN can support consistent, software-executable decision logic within broader business process automation architectures.
This is Video #62 of the BPMN Series.
You can find supplementary material on GitHub: https://github.com/ahense/bpmn
Link to the DMN editor: https://sandbox.kie.or
Recent Developments in the Design of Photoactivated Metal Oxide Gas Sensors and Application of Plasmonic Nanoparticles in Hydrogen‐Sensing Devices
No full textRecent advancements in photoactivated metal oxide (MOX) gas sensors and the application of plasmonic nanoparticles (NPs) in hydrogen sensing have demonstrated significant potential in enhancing sensor performance. Hydrogen, as a high-energy, carbon-free alternative to fossil fuels, requires reliable detection methods due to its storage and handling risks. Traditional MOX gas sensors, while cost-effective and versatile, face challenges such as high operating temperatures and limited selectivity. In this review, innovative photonic methods are explored to overcome these limitations, focusing on photoactivation and plasmonic effects. Photonic activation improves sensitivity, response time, and recovery time at room temperature, mitigating the safety risks associated with high-temperature operations. Additionally, the integration of plasmonic NPs, made from gold, palladium, or other less noble metals, into MOX gas sensors enhances catalytic activity and sensor response through localized surface plasmon resonance. In this review, also the synergistic effects of noble metal decoration and photonic enhancement are covered, providing a comprehensive overview of the current state and possible future directions in hydrogen-sensing technology. These advancements promise safer and more efficient hydrogen detection, crucial for the expanding hydrogen infrastructure and its role in a sustainable energy future
COFFAIL: A Dataset of Successful and Anomalous Robot Skill Executions in the Context of Coffee Preparation
No full textThis dataset includes both successful and anomalous execution episodes of the Jessie robot (https://www.h-brs.de/en/a2s/robots) performing seven different skills in the context of preparing coffee. All episodes were collected by demonstration (either by executing hand-coded scripts that produce the desired behaviour or using kinesthetic teaching)
Molecular Dynamics Simulation of Indentation Behavior Tests on Silicon Carbide
No full textSilicon carbide (SiC) is widely used in high-temperature and high-hardness applications. Knowledge of its mechanical response is essential for understanding deformation mechanisms at the atomic scale. In this study, molecular dynamics simulations were performed to investigate the indentation behavior of three SiC polytypes (3C-, 4H-, and 6H-SiC). Indenter size and crystallographic orientation were varied to examine their influence on elastic and plastic deformation. At small indentation depths (5% - 10%), all polytypes exhibit primarily elastic behavior, with minor changes in energy, low root mean squared displacements (RMSD), stable pair correlation functions, and only minor changes in the frequency spectra. With increasing indentation, irreversible structural changes appear, including bond breaking, dislocation activity, and local amorphization. The hexagonal polytypes (4H- and 6H-SiC) show pronounced anisotropy, with direction-dependent energy evolution, RMSD, pair distribution functions and shifts of the high-frequency peaks in the vibration spectra. In particular, 6H-SiC exhibits non-monotonic RMSD behavior and splitting of coordination peaks at high indentation depths, indicating the formation of metastable atomic configurations and increased amorphization. These findings demonstrate that the mechanical response of SiC is governed by the interplay of elastic, plastic, and amorphization processes, and that lattice anisotropy plays a key role in determining the deformation pathways
Tabular Data Adapters: Pseudo-Labeling Unlabeled Private Tabular Data for Outlier Detection
No full textThe remarkable success of Deep Learning approaches is often demonstrated on large public datasets. However, when applying such approaches to internal, private datasets, one frequently faces challenges arising from structural differences in the datasets, domain shift, and the lack of labels. This leads practitioners to face a cold-start problem: they cannot determine which model or configuration is reliable without labels. Alternatives such as manual annotation, heuristic thresholding, or blindly applying models are either costly or unreliable. In this work, we introduce Tabular Data Adapters (TDA), a method for generating pseudo-labels for unlabeled tabular data in outlier detection (OD) tasks. By identifying statistically similar public datasets and transforming private data (based on a shared autoencoder) into a format compatible with state-of-the-art public models, our approach enables the generation of weak labels. These labels provide a starting point for training, tuning, and calibrating OD models in label-scarce scenarios. It can thereby help to mitigate the cold start problem of labeling by basing on existing outlier detection models for public datasets. In experiments on 50 tabular datasets across different domains, we demonstrate that our method is able to provide more accurate annotations than baseline approaches while reducing computational time. Our approach offers a scalable, efficient, and cost-effective solution to bridge the gap between public research models and real-world industrial applications
scisciuro/shimadzu-qgd2csv: v0.2.4
No full textChanged the file handling, integrated CLI usage and added batch conversion.
Zenodo integratio
