Hochschule Bonn-Rhein-Sieg
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Creating high-quality coaching conversations: a video-based analysis of executive coach behaviour in initial coach–client interactions
No full textFeasibility Study of Combining Data from Different Sources Within Artificial Intelligence Models to Reduce the Need for Constant Velocity Joint Test Rig Runs
No full textWithin this paper, the feasibility of reducing test rig runs in constant velocity joint (CVJ) development by combining data from different sources (simulation and test rig) for artificial intelligence (AI) models has been investigated. Therefore, a case study on CVJ efficiency prediction using a random forest regressor, a decision-tree-based algorithm, was conducted using a data set of 95,798 points derived from both test rigs (52,486 points) and multi-body simulations (43,312 points). The amount of test rig data in the training data set of the regression model was iteratively reduced from 100% to 12.5% to investigate the need of expensive test rig data. Additionally, clustering models related to KMeans-algorithm were performed, to achieve further improvements of the AI models and more information about the data. Furthermore, regression and clustering models were performed with data dimensionally reduced by principal component analysis (PCA) to improve model complexity and performance. The number of principal components for the regression model was reduced from 65 to 5 components to investigate their influence on the models predictions. The study showed that combining data from different sources has a positive impact on the predictions of AI models and the confidence of their results, even though the R2-Score of the trained regression models did not change significantly, ranging from 0.927% to 0.9497%
BPMN Token Cannon – The Modeling Mistake That Breaks Processes
No full textThis video introduces the concept of a “token cannon” in BPMN and explains why it represents a serious modeling hazard. A token cannon describes a control-flow construct that unintentionally generates an unbounded number of tokens, potentially overwhelming the process engine or leading to incorrect and unpredictable execution behavior.
Using multiple illustrative examples, the tutorial demonstrates how common BPMN elements—such as AND-splits, OR-splits, boundary events, and implicit joins—can interact in ways that silently multiply tokens. Each case is analyzed using token-based reasoning to show how seemingly harmless modeling choices can escalate into uncontrolled process execution.
The discussion concludes by emphasizing the practical relevance of token cannons for both modelers and tool users, highlighting the importance of careful gateway design and event placement. Overall, the video aims to raise awareness of this subtle but critical BPMN antipattern and provides guidance for avoiding it in robust process models.
This is Video #48 of the BPMN Series.
Link to a related video where a token cannon can have a meaningful interpretation: https://youtu.be/BuwLJl6Cpy
Ionic agarose derivatives as polyelectrolytic additives for drug release
No full textPolysaccharides are being used as hydrogels for drug release in biomedical applications due to their inherent properties and biocompatibility. In this study, the synthesis, characterization, and application of ionic agarose derivatives are presented. Synthesis pathways for both anionic and cationic agarose derivatives with tunable degrees of substitution (DS) were established through homogeneous derivatization under mild conditions in ionic liquid. These derivatives partly retain their thermoreversible gelling behavior and can be used for polyelectrolyte systems. Agarose sulfates (AS) were synthesized in a one-step synthesis, while ammonium-bearing cationic agarose carbamates (AC) were synthesized via agarose phenylcarbonates (APC) with subsequent aminolysis. Both AS and AC were used in composite scaffolds of agarose and hydroxyapatite for sustained drug release into aqueous media. Layer-by-layer-coated alginate microbeads were prepared using oppositely charged agarose derivatives. Incorporating ionic agarose derivatives into these materials proved to effectively reduce the burst release and sustain the release of adenosine triphosphate (ATP), suramin, methylene blue, and A740003 over a time of 14 days. The release curves were evaluated using established models as well as two novel Langmuir-like models. Especially the latter two offered additional insight into the release and yielded higher scores in Akaike’s Information Criterion rankings than commonly used models
