TY - JOUR A1 - Cazares, Adrián A1 - García-Contreras, Rodolfo A1 - Pérez-Velázquez, Judith T1 - Eco-evolutionary effects of bacterial cooperation on phage therapy BT - an unknown risk? JF - Frontiers in Microbiology N2 - If there is something we have learned from the antibiotic era, it is that indiscriminate use of a therapeutic agent without a clear understanding of its long-term evolutionary impact can have enormous health repercussions. This knowledge is particularly relevant when the therapeutic agents are remarkably adaptable and diverse biological entities capable of a plethora of interactions, most of which remain largely unexplored. Although phage therapy (PT) undoubtedly holds the potential to save lives, its current efficacy in case studies recalls the golden era of antibiotics, when these compounds were highly effective and the possibility of them becoming ineffective seemed remote. Safe PT schemes depend on our understanding of how phages interact with, and evolve in, highly complex environments. Here, we summarize and review emerging evidence in a commonly overlooked theme in PT: bacteria-phage interactions. In particular, we discuss the influence of quorum sensing (QS) on phage susceptibility, the consequent role of phages in modulating bacterial cooperation, and the potential implications of this relationship in PT, including how we can use this knowledge to inform PT strategies. We highlight that the influence of QS on phage susceptibility seems to be widespread but can have contrasting outcomes depending on the bacterial host, underscoring the need to thoroughly characterize this link in various bacterial models. Furthermore, we encourage researchers to exploit competition experiments, experimental evolution, and mathematical modeling to explore this relationship further in relevant infection models. Finally, we emphasize that long-term PT success requires research on phage ecology and evolution to inform the design of optimal therapeutic schemes. UR - https://doi.org/10.3389/fmicb.2020.590294 KW - phages KW - phage therapy KW - quorum sensing KW - bacteria-phage interactions KW - cheaters KW - anti-phage defense Y1 - 2020 UR - https://doi.org/10.3389/fmicb.2020.590294 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9408 SN - 1664-302X VL - 11 PB - Frontiers Media CY - Lausanne ER - TY - JOUR A1 - Theissler, Andreas A1 - Pérez-Velázquez, Judith A1 - Kettelgerdes, Marcel A1 - Elger, Gordon T1 - Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry JF - Reliability Engineering & System Safety N2 - Recent developments in maintenance modelling fuelled by data-based approaches such as machine learning (ML), have enabled a broad range of applications. In the automotive industry, ensuring the functional safety over the product life cycle while limiting maintenance costs has become a major challenge. One crucial approach to achieve this, is predictive maintenance (PdM). Since modern vehicles come with an enormous amount of operating data, ML is an ideal candidate for PdM. While PdM and ML for automotive systems have both been covered in numerous review papers, there is no current survey on ML-based PdM for automotive systems. The number of publications in this field is increasing — underlining the need for such a survey. Consequently, we survey and categorize papers and analyse them from an application and ML perspective. Following that, we identify open challenges and discuss possible research directions. We conclude that (a) publicly available data would lead to a boost in research activities, (b) the majority of papers rely on supervised methods requiring labelled data, (c) combining multiple data sources can improve accuracies, (d) the use of deep learning methods will further increase but requires efficient and interpretable methods and the availability of large amounts of (labelled) data. UR - https://doi.org/10.1016/j.ress.2021.107864 KW - predictive maintenance KW - artificial intelligence KW - machine learning KW - deep learning KW - vehicle KW - automotive KW - reliability KW - lifetime prediction KW - condition monitoring Y1 - 2021 UR - https://doi.org/10.1016/j.ress.2021.107864 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-9673 SN - 0951-8320 VL - 2021 IS - 215 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Zippelius, Andreas A1 - Hanss, Alexander A1 - Liu, E A1 - Schmid, Maximilian A1 - Pérez-Velázquez, Judith A1 - Elger, Gordon T1 - Comparing prediction methods for LED failure measured with Transient Thermal Analysis T2 - 2020 21st International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems (EuroSimE) UR - https://doi.org/10.1109/EuroSimE48426.2020.9152657 KW - Temperature measurement KW - Artificial neural networks KW - Light emitting diodes KW - Aging KW - Current measurement KW - Training KW - Heating systems Y1 - 2020 UR - https://doi.org/10.1109/EuroSimE48426.2020.9152657 SN - 978-1-7281-6049-8 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Zippelius, Andreas A1 - Hanss, Alexander A1 - Schmid, Maximilian A1 - Pérez-Velázquez, Judith A1 - Elger, Gordon T1 - Reliability analysis and condition monitoring of SAC+ solder joints under high thermomechanical stress conditions using neuronal networks JF - Microelectronics Reliability N2 - The thermo-mechanical fatigue of different SAC+ solders is investigated using transient thermal analysis (TTA) and predicted using artificial neural networks (ANN). TTA measures the thermal impedance and allows detection of solder cracks and delamination of material interfaces. LEDs soldered to printed circuit boards using seven different solders were aged within passive air-to-air temperature shock tests with TTA measurements every 50 cycles with the increase of the thermal resistance as failure criterium. A SnAgCuSb solder showed the best performance improvement over the SAC305 reference under the test conditions. In addition to standard evaluation by the cumulative failure-curve and Weibull plot, new approaches for reliability assessment are investigated to assess the reliability of the solder joint of the individual LEDs. A hybrid approach to predict failures in the solder joints of the individual LEDs during accelerated stress testing is set-up which processes the TTA data using artificial neural networks with memory, specifically LSTM, where the memory allows full use of the measurement history. Two ANN approaches, regression and classification, are used. Both approaches are shown to be quite accurate. The greater information gained from the regression approach requires more processing using external knowledge of the problem requirements, whereas the categorical approach can be more directly implemented. The results demonstrate the advantages of integrated approaches for assessment of the remaining useful life of solder joints. UR - https://doi.org/10.1016/j.microrel.2021.114461 KW - Artificial neural networks KW - LSTM KW - Prediction KW - Reliability KW - Solder joints KW - Transient thermal analysis Y1 - 2022 UR - https://doi.org/10.1016/j.microrel.2021.114461 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-19719 SN - 0026-2714 VL - 2022 IS - 129 PB - Elsevier CY - Amsterdam ER -