A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

In the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.The present work was developed under the EUREKA–ITEA3 Project CyberFactory#1 (ITEA-17032), co-funded by Project CyberFactory#1PT (ANI|P2020 40124), from FEDER Funds through NORTE2020 program and from National Funds through FCT under the project UID/EEA/00760/2019 and by the Federal Ministry of Education and Research (BMBF, Germany, funding No. 01IS18061C).info:eu-repo/semantics/publishedVersio

Set-base dynamical parameter estimation and model invalidation for biochemical reaction networks

<p>Abstract</p> <p>Background</p> <p>Mathematical modeling and analysis have become, for the study of biological and cellular processes, an important complement to experimental research. However, the structural and quantitative knowledge available for such processes is frequently limited, and measurements are often subject to inherent and possibly large uncertainties. This results in competing model hypotheses, whose kinetic parameters may not be experimentally determinable. Discriminating among these alternatives and estimating their kinetic parameters is crucial to improve the understanding of the considered process, and to benefit from the analytical tools at hand.</p> <p>Results</p> <p>In this work we present a set-based framework that allows to discriminate between competing model hypotheses and to provide guaranteed outer estimates on the model parameters that are consistent with the (possibly sparse and uncertain) experimental measurements. This is obtained by means of exact proofs of model invalidity that exploit the polynomial/rational structure of biochemical reaction networks, and by making use of an efficient strategy to balance solution accuracy and computational effort.</p> <p>Conclusions</p> <p>The practicability of our approach is illustrated with two case studies. The first study shows that our approach allows to conclusively rule out wrong model hypotheses. The second study focuses on parameter estimation, and shows that the proposed method allows to evaluate the global influence of measurement sparsity, uncertainty, and prior knowledge on the parameter estimates. This can help in designing further experiments leading to improved parameter estimates.</p

Graph problems arising from parameter identification of discrete dynamical systems

This paper focuses on combinatorial feasibility and optimization problems that arise in the context of parameter identification of discrete dynamical systems. Given a candidate parametric model for a physical system and a set of experimental observations, the objective of parameter identification is to provide estimates of the parameter values for which the model can reproduce the experiments. To this end, we define a finite graph corresponding to the model, to each arc of which a set of parameters is associated. Paths in this graph are regarded as feasible only if the sets of parameters corresponding to the arcs of the path have nonempty intersection. We study feasibility and optimization problems on such feasible paths, focusing on computational complexity. We show that, under certain restrictions on the sets of parameters, some of the problems become tractable, whereas others are NP-hard. In a similar vein, we define and study some graph problems for experimental design, whose goal is to support the scientist in optimally designing new experiment

Trust in the Sharing Economy: An Experimental Framework

Peer-to-peer platforms in the realm of the sharing economy, such as Airbnb or BlaBlaCar, have heavily rattled the electronic commerce landscape and are expected to further impact consumer behavior in the future. While trust between the parties involved is of utmost importance in such platform economies, experimental research on this aspect is scarce. In this conceptual paper, we first present an experimental framework for targeting trust in the sharing economy based on experimental economics and the trust game in particular. In doing so, we sketch out a path to complement existing Information Systems research on the sharing economy by experimental methods. Second, we apply the framework to a specific use case, by developing a research model and experimental design to explore the role of user representation for trust on sharing economy platforms. We therefore set the stage for controlled (laboratory) experiments to enrich research on trust in the sharing economy

Metric Based Dynamic Control Charts for Edge Anomaly Detection in Factory Logistics

The optimization of transport logistics in production environments is a holistic task for the factory of the future. Autonomous guided vehicles that perform transport jobs in factories are facing this challenge and have to detect, react and prepare to unforeseen changes and anomalies in the production system. Due to data protection concerns, details like production plans are often not available for an external transportation system. Hence the anomaly detection has to be based on self-collected and observed data of the transport system like occurred transport needs or the evolution of internal metrics. In this paper we infused a production system with manufacturing process anomalies and demonstrate a detection based on the observation of transport needs to overcome the gab caused by restricted information. For that detection we extended classic control charts to work with expected values based on learned dynamic production characteristics. The system sets a tolerance field as narrow as possible around dynamically determined values, resulting in an average precision of 95% for detection unusual number of transport jobs

A viologen polymer and a compact ferrocene: Comparison of solution viscosities and their performance in a redox flow battery with a size exclusion membrane

In this work, the synthesis and characterization of a compact, ferrocene tetramer and a linear viologen polymer is reported. The latter material is a new, 4,4′‐bipyridine containing, organo‐soluble polymer. As aimed for solubility in nonpolar solvents, a 2‐ethylhexyl‐moiety to promote organosolubility and 4‐vinylbenzyl serving as a polymerizable group are introduced to a 4,4′‐bipyridine. The halide anions of the monomer cation are exchanged to bis(trifluoromethansulfon)imide, which further enhances organosolubility. The monomer is subsequently copolymerized with styrene by free radical polymerization. In addition, a four‐ferrocene‐containing compact structure, based on pentaerythritol, is synthesized via the straightforward radical thiol‐ene reaction. The polymer solutions are thoroughly characterized hydrodynamically. Subsequently, propylene carbonate‐based solutions of both materials are prepared to allow an assessment for future energy storage applications. This is done by testing battery characteristics in a custom‐made flow‐cell with a simple dialysis membrane for physical separation of the active materials. The capability of energy storage is verified by leaving the charged materials in solution in an open circuit for 24 h. Here, more than 99% of the stored charges can be recovered. Cycling the battery for 100 times reveals the remarkable stability of the materials of only 0.2% capacity loss per day in the battery setup

Aqueous Redox Flow Battery Suitable for High Temperature Applications Based on a Tailor‐Made Ferrocene Copolymer

Abstract Water‐soluble, and ferrocene‐containing methacrylamide copolymers with different comonomer ratios of the solubility‐promoting comonomer [2‐(methacryloyloxy)‐ethyl]‐trimethylammonium chloride (METAC) are synthesized in order to obtain a novel, temperature‐stable electrolyte for aqueous redox flow batteries. The electrochemical properties of one chosen polymer are studied in detail by cyclic voltammetry and rotating disc electrode (RDE) investigations. Additionally, the diffusion coefficient and the charge transfer rate are obtained from these measurements. The diffusion coefficient from RDE is compared to the value from synthetic boundary experiments at battery concentrations, using an analytical ultracentrifuge, yielding diffusion coefficients of a similar order of magnitude. The polymer is further tested in a redox flow battery setup. While performing charge and discharge experiments against the well‐established bis ‐(trimethylammoniumpropyl)‐viologen, the polymer reveals high columbic efficiencies of >99.8% and desirable apparent capacity retention, both at room temperature as well as at 60 °C. Further experiments are conducted to verify the stability of the active compounds under these conditions in both charge states. Lastly, the electrochemical behavior is linked to the characteristics of the polymers concerning absolute values of the molar mass and diffusion coefficients.A new ferrocene containing monomer is synthesized and its copolymerization with a water‐solubility promoting comonomer is investigated. The electrochemical and solution characteristics of a corresponding polymer are studied in detail. With a coulombic efficiency of >99.8% in an aqueous redox flow battery setup at 60 °C, a cheap, robust system for use at elevated temperatures is presented. imag

Detecting and Processing Anomalies in a Factory of the Future

Production systems are changing in many aspects on the way to a Factory of the Future, including the level of automation and communication between components. Besides all benefits, this evolution raises the amount, effect and type of anomalies and unforeseen behavior to a new level of complexity. Thus, new detection and mitigation concepts are required. Based on a use-case dealing with a distributed transportation system for production environments, this paper describes the different sources of possible anomalies with the same effect, anomaly detection methods and related mitigation techniques. Depending on the identified anomaly, the FoF should react accordingly, such as fleet or AGV reconfiguration, strong authentication and access control or a deletion of adversarial noises. In this paper, different types of mitigation actions are described that support the fleet in overcoming the effect of the anomaly or preventing them in the future. A concept to select the most appreciate mitigation method is presented, where the detection of the correct source of the anomaly is key. This paper shows how various techniques can work together to gain a holistic view on anomalies in the Factory of the Future for selecting the most appropriate mitigation technique

Nitration of the Egg-Allergen Ovalbumin Enhances Protein Allergenicity but Reduces the Risk for Oral Sensitization in a Murine Model of Food Allergy

Nitration of proteins on tyrosine residues, which can occur due to polluted air under "summer smog" conditions, has been shown to increase the allergic potential of allergens. Since nitration of tyrosine residues is also observed during inflammatory responses, this modification could directly influence protein immunogenicity and might therefore contribute to food allergy induction. In the current study we have analyzed the impact of protein nitration on sensitization via the oral route.BALB/c mice were immunized intragastrically by feeding untreated ovalbumin (OVA), sham-nitrated ovalbumin (snOVA) or nitrated ovalbumin (nOVA) with or without concomitant acid-suppression. To analyze the impact of the sensitization route, the allergens were also injected intraperitoneally. Animals being fed OVA or snOVA under acid-suppressive medication developed significantly elevated levels of IgE, and increased titers of specific IgG1 and IgG2a antibodies. Interestingly, oral immunizations of nOVA under anti-acid treatment did not result in IgG and IgE formation. In contrast, intraperitoneal immunization induced high levels of OVA specific IgE, which were significantly increased in the group that received nOVA by injection. Furthermore, nOVA triggered significantly enhanced mediator release from RBL cells passively sensitized with sera from allergic mice. Gastric digestion experiments demonstrated protein nitration to interfere with protein stability as nOVA was easily degraded, whereas OVA and snOVA remained stable up to 120 min. Additionally, HPLC-chip-MS/MS analysis showed that one tyrosine residue (Y(107)) being very efficiently nitrated is part of an ovalbumin epitope recognized exclusively after oral sensitization.These data indicated that despite the enhanced triggering capacity in existing allergy, nitration of OVA may be associated with a reduced de novo sensitizing capability via the oral route due to enhanced protein digestibility and/or changes in antibody epitopes