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Is part of the Bibliography
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Software Stories Guide
(2017)
Die Steiff Spielwarenfabrik in Giengen / Brenz : Ein unbekanntes Meisterwerk der frühen Moderne
(2017)
Cyber-physical systems are ever more common in manufacturing industries. Increasing their autonomy has been declared an explicit goal, for example, as part of the Industry 4.0 vision. To achieve this system intelligence, principled and software-driven methods are required to analyze sensing data, make goal-directed decisions, and eventually execute and monitor chosen tasks. In this chapter, we present a number of knowledge-based approaches to these problems and case studies with in-depth evaluation results of several different implementations for groups of autonomous mobile robots performing in-house logistics in a smart factory. We focus on knowledge-based systems because besides providing expressive languages and capable reasoning techniques, they also allow for explaining how a particular sequence of actions came about, for example, in the case of a failure.
In the future, we expect manufacturing companies to follow a new paradigm that mandates more automation and autonomy in production processes. Such smart factories will offer a variety of production technologies as services that can be combined ad hoc to produce a large number of different product types and variants cost-effectively even in small lot sizes. This is enabled by cyber-physical systems that feature flexible automated planning methods for production scheduling, execution control, and in-factory logistics.
During development, testbeds are required to determine the applicability of integrated systems in such scenarios. Furthermore, benchmarks are needed to quantify and compare system performance in these industry-inspired scenarios at a comprehensible and manageable size which is, at the same time, complex enough to yield meaningful results.
In this chapter, based on our experience in the RoboCup Logistics League (RCLL) as a specific example, we derive a generic blueprint for how a holistic benchmark can be developed, which combines a specific scenario with a set of key performance indicators as metrics to evaluate the overall integrated system and its components.
The gene encoding a putative (R,R)-butane-2,3-diol dehydrogenase (bdhA) from Bacillus clausii DSM 8716T was isolated, sequenced and expressed in Escherichia coli. The amino acid sequence of the encoded protein is only distantly related to previously studied enzymes (identity 33–43%) and exhibited some uncharted peculiarities. An N-terminally StrepII-tagged enzyme variant was purified and initially characterized. The isolated enzyme catalyzed the (R)-specific oxidation of (R,R)- and meso-butane-2,3-diol to (R)- and (S)-acetoin with specific activities of 12 U/mg and 23 U/mg, respectively. Likewise, racemic acetoin was reduced with a specific activity of up to 115 U/mg yielding a mixture of (R,R)- and meso-butane-2,3-diol, while the enzyme reduced butane-2,3-dione (Vmax 74 U/mg) solely to (R,R)-butane-2,3-diol via (R)-acetoin. For these reactions only activity with the co-substrates NADH/NAD+ was observed. The enzyme accepted a selection of vicinal diketones, α-hydroxy ketones and vicinal diols as alternative substrates. Although the physiological function of the enzyme in B. clausii remains elusive, the data presented herein clearly demonstrates that the encoded enzyme is a genuine (R,R)-butane-2,3-diol dehydrogenase with potential for applications in biocatalysis and sensor development.
The incorporation of nanomaterials that are biocompatible with different types of biological compounds has allowed the development of a new generation of biosensors applied especially in the biomedical field. In particular, the integration of film-based nanomaterials employed in field-effect devices can be interesting to develop biosensors with enhanced properties. In this paper, we studied the fabrication of sensitive nanofilms combining ZnO nanocrystals and carbon nanotubes (CNTs), prepared by means of the layer-by-layer (LbL) technique, in a capacitive electrolyte-insulator-semiconductor (EIS) structure for detecting glucose and urea. The ZnO nanocrystals were incorporated in a polymeric matrix of poly(allylamine) hydrochloride (PAH), and arranged with multi-walled CNTs in a LbL PAH-ZnO/CNTs film architecture onto EIS chips. The electrochemical characterizations were performed by capacitance–voltage and constant capacitance measurements, while the morphology of the films was characterized by atomic force microscopy. The enzymes glucose oxidase and urease were immobilized on film’s surface for detection of glucose and urea, respectively. In order to obtain glucose and urea biosensors with optimized amount of sensitive films, we investigated the ideal number of bilayers for each detection system. The glucose biosensor showed better sensitivity and output signal for an LbL PAH-ZnO/CNTs nanofilm with 10 bilayers. On the other hand, the urea biosensor presented enhanced properties even for the first bilayer, exhibiting high sensitivity and output signal. The presence of the LbL PAH-ZnO/CNTs films led to biosensors with better sensitivity and enhanced response signal, demonstrating that the adequate use of nanostructured films is feasible for proof-of-concept biosensors with improved properties that may be employed for biomedical applications.