PDPK: A Framework to Synthesise Process Data and Corresponding Procedural Knowledge for Manufacturing

Procedural knowledge describes how to accomplish tasks and mitigate problems. Such knowledge is commonly held by domain experts, e.g. operators in manufacturing who adjust parameters to achieve quality targets. To the best of our knowledge, no real-world datasets containing process data and corresponding procedural knowledge are publicly available, possibly due to corporate apprehensions regarding the loss of knowledge advances. Therefore, we provide a framework to generate synthetic datasets that can be adapted to different domains. The design choices are inspired by two real-world datasets of procedural knowledge we have access to. Apart from containing representations of procedural knowledge in Resource Description Framework (RDF)-compliant knowledge graphs, the framework simulates parametrisation processes and provides consistent process data. We compare established embedding methods on the resulting knowledge graphs, detailing which out-of-the-box methods have the potential to represent procedural knowledge. This provides a baseline which can be used to increase the comparability of future work. Furthermore, we validate the overall characteristics of a synthesised dataset by comparing the results to those achievable on a real-world dataset. The framework and evaluation code, as well as the dataset used in the evaluation, are available open source

Towards robustness of production planning and control against supply chain disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

Towards Robustness Of Production Planning And Control Against Supply Chain Disruptions

Just-in-time supply chains have become increasingly popular in past decades. However, these are particularly vulnerable when logistic routes are blocked, manufacturing capacities are limited or customs are under strain, as has been seen in the last few years. The principle of just-in-time delivery requires a coordinated production and material flow along the entire supply chain. Challenges in the supply chain can lead to various disruptions, so that certain manufacturing jobs must be changed, postponed or cancelled, which will then impact supply down the line up to the consumer. Nowadays, many planning and control processes in the event of a disturbance are based on the procedural knowledge of employees and undertaken manually by those. The procedures to mitigate the negative effects of disturbances are often quite complex and time-critical, making disturbance management highly challenging. In this paper, we introduce a real-world use case where we automate the currently manual reschedule of a production plan containing unavailable jobs. First, we analyse existing literature regarding the classification of disturbances encountered in similar use cases. We show how we automate existing manual disturbance management and argue that employing stochastic optimization allows us to not only promote future jobs but to on-the-fly create entirely new plans that are optimized regarding throughput, energy consumption, material waste and operator productivity. Building on this routine, we propose to create a Bayesian estimator to determine the probabilities of delivery times whose predictions we can then reintegrate into our optimizer to create less fragile schedules. Overall, the goals of this approach are to increase robustness in production planning and control

RELSA—A multidimensional procedure for the comparative assessment of well-being and the quantitative determination of severity in experimental procedures

Good science in translational research requires good animal welfare according to the principles of 3Rs. In many countries, determining animal welfare is a mandatory legal requirement, implying a categorization of animal suffering, traditionally dominated by subjective scorings. However, how such methods can be objectified and refined to compare impairments between animals, subgroups, and animal models remained unclear. Therefore, we developed the RELative Severity Assessment (RELSA) procedure to establish an evidence-based method based on quantitative outcome measures such as body weight, burrowing behavior, heart rate, heart rate variability, temperature, and activity to obtain a relative metric for severity comparisons. The RELSA procedure provided the necessary framework to get severity gradings in TM-implanted mice, yielding four distinct RELSA thresholds L1<0.27, L2<0.59, L3<0.79, and L4<3.45. We show further that severity patterns in the contributing variables are time and model-specific and use this information to obtain contextualized between animal-model and subgroup comparisons with the severity of sepsis > surgery > restraint stress > colitis. The bootstrapped 95% confidence intervals reliably show that RELSA estimates are conditionally invariant against missing information but precise in ranking the quantitative severity information to the moderate context of the transmitter-implantation model. In conclusion, we propose the RELSA as a validated tool for an objective, computational approach to comparative and quantitative severity assessment and grading. The RELSA procedure will fundamentally improve animal welfare, data quality, and reproducibility. It is also the first step toward translational risk assessment in biomedical research

Moderne Behandlung von Kraniosynostosen

Hintergrund Bei 13–48 % aller Lebendgeborenen treten Auffälligkeiten der Schädelform auf. Meistens ist ein lagerungsbedingter Plagiozephalus ursächlich. Bei vorzeitigem Verschluss von Schädelnähten resultieren pathognomische Deformitäten durch das kompensatorische Wachstum der umgebenden Schädelknochen. Es muss zwischen Einzelnahtsynostosen und Kraniosynostosen im Rahmen syndromaler Erkrankungen unterschieden werden. Ziel Diese Arbeit soll einen Überblick über Diagnostik, konservative und chirurgische Therapie von Kraniosynostosen geben. Methoden Narratives Review. Ergebnis Bei Verdacht auf eine Kraniosynostose erfolgt zunächst die klinische Beurteilung und Einschätzung durch erfahrene Untersucher. Die pathognomische Schädelform ergibt die Arbeitsdiagnose. Bestätigt wird diese durch Verfahren wie 3D-Stereofotografie und Sonographie. In komplexen Fällen können CT oder MRT notwendig sein. Die Indikation für eine Therapie ergibt sich aus ästhetischen Gesichtspunkten und der Vorbeugung psychosozialer Folgen. Bei syndromalen Formen besteht diese insbesondere zur Vermeidung möglicher Folgen eines erhöhten Hirndrucks. Besteht die Indikation zur Operation muss zwischen endoskopischer und offener Technik unterschieden werden. Unterschiede bestehen hier hinsichtlich Invasivität und möglichem Korrekturausmaß. Im Anschluss an die operative Behandlung schließt sich häufig eine Helmtherapie an, um das bestmögliche Ergebnis zu erreichen. Die anschließende Follow-up-Periode erstreckt sich mindestens bis zum 12. Lebensjahr. Insgesamt sollte die Behandlung im Team mit Neurochirurgen, Mund‑, Kiefer‑, Gesichtschirurgen, Kinderärzten, Augenärzten und Humangenetikern stattfinden

New Insights into the Evolution of Wolbachia Infections in Filarial Nematodes Inferred from a Large Range of Screened Species

Wolbachia are intriguing symbiotic endobacteria with a peculiar host range that includes arthropods and a single nematode family, the Onchocercidae encompassing agents of filariases. This raises the question of the origin of infection in filariae. Wolbachia infect the female germline and the hypodermis. Some evidences lead to the theory that Wolbachia act as mutualist and coevolved with filariae from one infection event: their removal sterilizes female filariae; all the specimens of a positive species are infected; Wolbachia are vertically inherited; a few species lost the symbiont. However, most data on Wolbachia and filaria relationships derive from studies on few species of Onchocercinae and Dirofilariinae, from mammals.We investigated the Wolbachia distribution testing 35 filarial species, including 28 species and 7 genera and/or subgenera newly screened, using PCR, immunohistochemical staining, whole mount fluorescent analysis, and cocladogenesis analysis. (i) Among the newly screened Onchocercinae from mammals eight species harbour Wolbachia but for some of them, bacteria are absent in the hypodermis, or in variable density. (ii) Wolbachia are not detected in the pathological model Monanema martini and in 8, upon 9, species of Cercopithifilaria. (iii) Supergroup F Wolbachia is identified in two newly screened Mansonella species and in Cercopithifilaria japonica. (iv) Type F Wolbachia infect the intestinal cells and somatic female genital tract. (v) Among Oswaldofilariinae, Waltonellinae and Splendidofilariinae, from saurian, anuran and bird respectively, Wolbachia are not detected.The absence of Wolbachia in 63% of onchocercids, notably in the ancestral Oswaldofilariinae estimated 140 mya old, the diverse tissues or specimens distribution, and a recent lateral transfer in supergroup F Wolbachia, modify the current view on the role and evolution of the endosymbiont and their hosts. Further genomic analyses on some of the newly sampled species are welcomed to decipher the open questions

BacHBerry: BACterial Hosts for production of Bioactive phenolics from bERRY fruits

BACterial Hosts for production of Bioactive phenolics from bERRY fruits (BacHBerry) was a 3-year project funded by the Seventh Framework Programme (FP7) of the European Union that ran between November 2013 and October 2016. The overall aim of the project was to establish a sustainable and economically-feasible strategy for the production of novel high-value phenolic compounds isolated from berry fruits using bacterial platforms. The project aimed at covering all stages of the discovery and pre-commercialization process, including berry collection, screening and characterization of their bioactive components, identification and functional characterization of the corresponding biosynthetic pathways, and construction of Gram-positive bacterial cell factories producing phenolic compounds. Further activities included optimization of polyphenol extraction methods from bacterial cultures, scale-up of production by fermentation up to pilot scale, as well as societal and economic analyses of the processes. This review article summarizes some of the key findings obtained throughout the duration of the project