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The demand of replacements for inoperable organs exceeds the amount of available organ transplants. Therefore, tissue engineering developed as a multidisciplinary field of research for autologous in-vitro organs. Such three dimensional tissue constructs request the application of a bioreactor. The UREPLACE bioreactor is used to grow cells on tubular collagen scaffolds OPTIMAIX Sponge 1 with a maximal length of 7 cm, in order to culture in vitro an adequate ureter replacement. With a rotating unit, (urothelial) cells can be placed homogeneously on the inner scaffold surface. Furthermore, a stimulation is combined with this bioreactor resulting in an orientation of muscle cells. These culturing methods request a precise control of several parameters and actuators. A combination of a LabBox and the suitable software LabVision is used to set and conduct parameters like rotation angles, velocities, pressures and other important cell culture values. The bioreactor was tested waterproof successfully. Furthermore, the temperature controlling was adjusted to 37 °C and the CO2 - concentration regulated to 5 %. Additionally, the pH step responses of several substances showed a perfect functioning of the designed flow chamber. All used software was tested and remained stable for several days.
In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem.
Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ.
Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible.
In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production.
Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data.
Companies often build their businesses based on product information and therefore try to automate the process of information extraction (IE). Since the information source is usually heterogeneous and non-standardized, classic extract, transform, load techniques reach their limits. Hence, companies must implement the newest findings from research to tackle the challenges of process automation. They require a flexible and robust system that is extendable and ensures the optimal processing of the different document types. This paper provides a distributed microservice architecture pattern that enables the automated generation of IE pipelines. Since their optimal design is individual for each input document, the system ensures the ad-hoc generation of pipelines depending on specific document characteristics at runtime. Furthermore, it introduces the automated quality determination of each available pipeline and controls the integration of new microservices based on their impact on the business value. The introduced system enables fast prototyping of the newest approaches from research and supports companies in automating their IE processes. Based on the automated quality determination, it ensures that the generated pipelines always meet defined business requirements when they come into productive use.
Miniaturised reference electrodes for field-effect sensors compatible to silicon chip technology
(2005)