Coupled Physical and Numerical Simulation to Optimize Thermo‐mechanical Metal Forming Processes

The numeric simulation of thermo‐mechanical hot forming processes and the modelling of the structure development and the mechanical properties of complex metallic materials serve for both the optimization of forming processes and the increase of process reliability. To meet close process windows, predictions of the formability of the material as function of the process conditions and material history are required as well. Lifetime evaluation of the tools is made possible from coupled computation of workpiece and tool loads. Material and damage models in use must be adapted on the basis of laboratory experiments and advanced analysis methods.     

Energieinformatik

Due to the increasing importance of producing and consuming energy more sustainably, Energy Informatics (EI) has evolved into a thriving research area within the CS/IS community. The article attempts to characterize this young and highly dynamic field of research by describing current EI research topics and methods and provides an outlook of how the field might evolve in the future. It is shown that two general research questions have received the most attention so far and are likely to dominate the EI research agenda in the coming years: How to leverage information and communication technology (ICT) to (1) improve energy efficiency, and (2) to integrate decentralized renewable energy sources into the power grid. Selected EI streams are reviewed, highlighting how the respective research questions are broken down into specific research projects and how EI researchers have made contributions based on their individual academic background.     

Think,feel, bid: the impact of environmental conditions on the role of bidders’ cognitive and affective processes in auction bidding

Environmental conditions and the interplay of cognitive and affective processes both exert influences on bidding behavior. This paper brings the above together, considering how the (external) auction environment determines the impact of (internal) cognitive and affective processes on bidding behavior, assessed in comparison to the optimal bid. Two aspects of the auction environment were considered, namely auction dynamics (low: first-price sealed-bid auction, high: Dutch auction) and value uncertainty (low, high). In a laboratory experiment, we assess bidders’ cognitive workload and emotional arousal through physiological measurements. We find that higher auction dynamics increase the impact of emotional arousal on bid deviations, but not that of cognitive workload. Higher value uncertainty, conversely, increases the impact of cognitive workload on bid deviations, but not that of emotional arousal. Taken together, the auction environment is a critical factor in understanding the nature of the underlying decision process and its impact on bids.     

Toward the Proteome of the Human Peripheral Blood Eosinophil

Eosinophils (EOSs) are granular leukocytes that have significant roles in many inflammatory and immunoregulatory responses, especially asthma and allergic diseases. We have undertaken a fairly comprehensive proteomic analysis of purified peripheral blood EOSs from normal human donors primarily employing 2‐DE with protein spot identification by MALDI‐MS. Protein subfractionation methods employed included IEF (Zoom^® Fractionator) and subcellular fractionation using differential protein solubilization. We have identified 3141 proteins, which had Mascot expectation scores of 10^?3 or less. Of these 426 were unique and non‐redundant of which 231 were novel proteins not previously reported to occur in EOSs. Ingenuity Pathway Analysis showed that some 70% of the non‐redundant proteins could be subdivided into categories that are clearly related to currently known EOS biological activities. Cytoskeletal and associated proteins predominated among the proteins identified. Extensive protein posttranslational modifications were evident, many of which have not been previously reported that reflected the dynamic character of the EOS. This data set of eosinophilic proteins will prove valuable in comparative studies of disease versus normal states and for studies of gender differences and polymorphic variation among individuals.     

Metal-Organic Framework Thin Films Grown on Functionalized Graphene as Solid-State Ion-Gated FETs

The unique properties of 2D-materials like graphene are exploited in various electronic devices. In sensor applications, graphene shows a very high sensitivity, but only a low specificity. This shortcoming can be mastered by using heterostructures, where graphene is combined with materials exhibiting high analyte selectivities. Herein, this study demonstrates the precise deposition of nanoporous metal-organic frameworks (MOFs) on graphene, yielding bilayers with excellent specificity while the sensitivity remains large. The key for the successful layer-by-layer deposition of the MOF films (SURMOFs) is the use of planar polyaromatic anchors. Then, the MOF pores are loaded with ionic liquid (IL). For functioning sensor devices, the IL@MOF films are grown on graphene field-effect transistors (GFETs). Adding a top-gate electrode yields an ion-gated GFET. Analysis of the transistor characteristics reveals a clear Dirac point at low gate voltages, good on-off ratios, and decent charge mobilities and densities in the graphene channel. The GFET-sensor reveals a strong and selective response. Compared to other ion-gated-FET devices, the IL@MOF material is relatively hard, allowing the manufacturing of ultrathin devices. The new MOF-anchoring strategy offers a novel approach generally applicable for the functionalization of 2D-materials, where MOF/2D-material hetero-bilayers carry a huge potential for a wide variety of applications.     

Automated bidding in computational markets: an application in market-based allocation of computing services

Autonomous agents are widely applied to automate interactions in robotics, e.g. for selling and purchasing goods on eBay, and in financial markets, e.g. in the form of quote machines and algorithmic traders. Current research investigates efficient economic mechanisms that fully automate the provisioning and usage processes of Grid-based services. On the one hand, consumers want to allocate resources on demand for their various applications, e.g. data sharing, stream processing, email, business applications and simulations. On the other hand, providers of Grid services want to govern business policies to meet their utilization and profit goals. The above-mentioned processes are not manually manageable, however, because decisions need to be taken within milliseconds. Therefore, such processes have to be automated to minimize human interactions. Hence, market mechanisms and strategic behavior play important roles when it comes to achieving automated and efficient allocation of Grid services. The paper begins by presenting a framework for automated bidding, providing a methodology for the design and implementation of configurable bidding strategies. Second, it presents a novel bidding strategy based on a reinforcement learning technique. This strategy is designed to automate the bid generation processes of consumers and providers in various market mechanisms. Third, the behavior and convergence of the strategy is evaluated in a centralized Continuous Double Auction and a decentralized on-line machine scheduling mechanism against selected benchmark bidding strategies. Fourth, we define a bidding language for communicating consumer and provider preferences to the market as well as report back the match of the market-based allocation process.     

Koordination in Service Value Networks

The fundamental paradigm shift from traditional value chains to agile service value networks implies new economic and organizational challenges. As coordination mechanisms, auctions have proven to perform quite well in situations where intangible and heterogeneous goods are traded. Nevertheless, traditional approaches in the area of multidimensional combinatorial auctions are not quite suitable to enable the trade of composite services. A flawless service execution and therefore the requester’s valuation highly depends on the accurate sequence of the functional parts of the composition, meaning that in contrary to service bundles, composite services only generate value through a valid order of their components. The authors present an abstract model as a formalization of service value networks. The model comprehends a graph-based mechanism implementation to allocate multidimensional service offers within the network, to impose penalties for non-performance and to determine prices for complex services. The mechanism and the bidding language support various types of QoS attributes and their (semantic) aggregation. It is analytically shown that this variant is incentive compatible with respect to all dimensions of the service offer (quality and price). Based on these results, the authors numerically analyze strategic behavior of participating service providers regarding possible collusion strategies.