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Now showing 1 - 10 of 37
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    Graphite modified epoxy-based adhesive for joining of aluminium and PP/graphite composites
    (New York, NY [u.a.] : Taylor & Francis, 2020) Rzeczkowski, P.; Pötschke, Petra; Fischer, M.; Kühnert, I.; Krause, Beate
    A graphite-modified adhesive was developed in order to simultaneously enhance the thermal conductivity and the strength of an adhesive joint. The thermal conductivity through the joint was investigated by using highly filled PP/graphite composite substrates, which were joined with an epoxy adhesive of different layer thicknesses. Similar measurements were carried out with a constant adhesive layer thickness, whilst applying an epoxy adhesive modified with expanded graphite (EG) (6, 10, and 20 wt%). By reducing the adhesive layer thickness or modifying the adhesive with conductive fillers, a significant increase of the thermal conductivity through the joint was achieved. The examination of the mechanical properties of the modified adhesives was carried out by tensile tests (adhesive only), lap-shear tests, and fracture energy tests (mode 1) with aluminium substrates. Modification of the adhesive with EG led to an increase of the tensile lap-shear strength and the adhesive fracture energy (mode 1) of the joint. In addition, burst pressure tests were performed to determine the strength of the joint in a complex component. The strength of the joint increased with the graphite content in the PP substrate and in the epoxy adhesive.
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    Compact representations for efficient storage of semantic sensor data
    (Dordrecht : Springer Science + Business Media B.V, 2021) Karim, Farah; Vidal, Maria-Esther; Auer, Sören
    Nowadays, there is a rapid increase in the number of sensor data generated by a wide variety of sensors and devices. Data semantics facilitate information exchange, adaptability, and interoperability among several sensors and devices. Sensor data and their meaning can be described using ontologies, e.g., the Semantic Sensor Network (SSN) Ontology. Notwithstanding, semantically enriched, the size of semantic sensor data is substantially larger than raw sensor data. Moreover, some measurement values can be observed by sensors several times, and a huge number of repeated facts about sensor data can be produced. We propose a compact or factorized representation of semantic sensor data, where repeated measurement values are described only once. Furthermore, these compact representations are able to enhance the storage and processing of semantic sensor data. To scale up to large datasets, factorization based, tabular representations are exploited to store and manage factorized semantic sensor data using Big Data technologies. We empirically study the effectiveness of a semantic sensor’s proposed compact representations and their impact on query processing. Additionally, we evaluate the effects of storing the proposed representations on diverse RDF implementations. Results suggest that the proposed compact representations empower the storage and query processing of sensor data over diverse RDF implementations, and up to two orders of magnitude can reduce query execution time.
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    Concept for Setting up an LTA Working Group in the NFDI Section "Common Infrastructures"
    (Zenodo, 2022-04-12) Bach, Felix; Degkwitz, Andreas; Horstmann, Wolfram; Leinen, Peter; Puchta, Michael; Stäcker, Thomas
    NFDI consortia have a variety of disparate and distributed information infrastructures, many of which are as yet only loosely or poorly connected. A major goal is to create a Research Data Commons (RDC) . The RDC concept1 includes, for example, shared cloud services, an application layer with access to high-performance computing (HPC), collaborative workspaces, terminology services, and a common authentication and authorization infrastructure (AAI). The necessary interoperability of services requires, in particular, agreement on protocols and standards, the specification of workflows and interfaces, and the definition of long-term sustainable responsibilities for overarching services and deliverables. Infrastructure components are often well-tested in NFDI on a domain-specific basis, but are quite heterogeneous and diverse between domains. LTA for digital resources has been a recurring problem for well over 30 years and has not been conclusively solved to date, getting urgency with the exponential growth of research data, whether it involves demands from funders - the DFG requires 10 years of retention - or digital artifacts that must be preserved indefinitely as digital cultural heritage. Against this background, the integration of the LTA into the RDC of the NFDI is an urgent desideratum in order to be able to guarantee the permanent usability of research data. A distinction must be2 made between the archiving of the digital objects as bitstreams (this can be numeric or textual data or complex objects such as models), which represents a first step towards long-term usability, and the archiving of the semantic and software-technical context of the digital original objects, which entails far more effort. Beyond the technical embedding of the LTA in the system environment of a multi-cloud-based infrastructure, a number of technically differentiated requirements of the NFDI's subject consortia are part of the development of a basic service for the LTA and for the re-use of research data.3 The need for funding for the development of a basic LTA service for the NFDI consortia results primarily from the additional costs associated with the technical and organizational development of a cross-NFDI, decentralized network structure for LTA and the sustainable subsequent use of research data. It is imperative that the technical actors are able to act within the network as a technology-oriented community, and that they can provide their own services as part of the support for also within a federated infrastructure. The working group "Long Term Archiving" (LTA) is to develop the requirements of the technical consortia for LTA and, on this basis, strategic approaches for the implementation of a basic service LTA. The working group consists of members of various NFDI consortia covering the humanities, natural science and engineering disciplines and experts from a variety of pertinent infrastructures with strong overall connections to the nestor long-term archiving competence network. The close linkage of NFDI consortia with experienced4 partners in the field of LTA ensures that a) the relevant technical state-of-the-art is present in the group and b) the knowledge of data producers about contexts of origin and data users interact directly. This composition enables the team to take an overarching view that spans the requirements of the disciplines and consortia, also takes into account interdisciplinary needs, and at the same time brings in the existing know-how in the infrastructure sector.
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    Simultaneous statistical inference for epigenetic data
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2015) Schildknecht, Konstantin; Olek, Sven; Dickhaus, Thorsten
    Epigenetic research leads to complex data structures. Since parametric model assumptions for the distribution of epigenetic data are hard to verify we introduce in the present work a nonparametric statistical framework for two-group comparisons. Furthermore, epigenetic analyses are often performed at various genetic loci simultaneously. Hence, in order to be able to draw valid conclusions for specific loci, an appropriate multiple testing correction is necessary. Finally, with technologies available for the simultaneous assessment of many interrelated biological parameters (such as gene arrays), statistical approaches also need to deal with a possibly unknown dependency structure in the data. Our statistical approach to the nonparametric comparison of two samples with independent multivariate observables is based on recently developed multivariate multiple permutation tests. We adapt their theory in order to cope with families of hypotheses regarding relative effects. Our results indicate that the multivariate multiple permutation test keeps the pre-assigned type I error level for the global null hypothesis. In combination with the closure principle, the family-wise error rate for the simultaneous test of the corresponding locus/parameter-specific null hypotheses can be controlled. In applications we demonstrate that group differences in epigenetic data can be detected reliably with our methodology.
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    Robust homoclinic orbits in planar systems with Preisach hysteresis operator
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2014) Pimenov, Alexander; Rachinskii, Dmitrii
    We construct examples of robust homoclinic orbits for systems of ordinary differential equations coupled with the Preisach hysteresis operator. Existence of such orbits is demonstrated for the first time. We discuss a generic mechanism that creates robust homoclinic orbits and a method for finding them. An example of a homoclinic orbit in a population dynamics model with hysteretic response of the prey to variations of the predator is studied numerically.
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    Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV
    (Amsterdam : IOS Press, 2020) Chaves-Fraga, David; Ruckhaus, Edna; Priyatna, Freddy; Vidal, Maria-Esther; Corchio, Oscar
    Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets, either by materializing integrated data into RDF or by performing on-the fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented; thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with a benchmark using the GTFS dataset from the Madrid subway; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of MorphCSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.
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    Minimization of a fractional perimeter-Dirichlet integral functional
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2013) Caffarelli, Luis; Savin, Ovidiu; Valdinoci, Enrico
    We consider a minimization problem that combines the Dirichlet energy with the nonlocal perimeter of a level set. We obtain regularity results for the minimizers and for their free boundaries using blow-up analysis, density estimates, monotonicity formulas, Euler-Lagrange equations and extension problems.
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    Improving accuracy and temporal resolution of learning curve estimation for within- and across-session analysis
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2015) Deliano, Matthias; Tabelow, Karsten; König, Reinhard; Polzehl, Jörg
    Estimation of learning curves is ubiquitously based on proportions of correct responses within moving trial windows. In this approach, it is tacitly assumed that learning performance is constant within the moving windows, which, however, is often not the case. In the present study we demonstrate that violations of this assumption lead to systematic errors in the analysis of learning curves, and we explored the dependency of these errors on window size, different statistical models, and learning phase. To reduce these errors for single subjects as well as on the population level, we propose adequate statistical methods for the estimation of learning curves and the construction of confidence intervals, trial by trial. Applied to data from a shuttle-box avoidance experiment with Mongolian gerbils, our approach revealed performance changes occurring at multiple temporal scales within and across training sessions which were otherwise obscured in the conventional analysis. The proper assessment of the behavioral dynamics of learning at a high temporal resolution clarified and extended current descriptions of the process of avoidance learning. It further disambiguated the interpretation of neurophysiological signal changes recorded during training in relation to learning.
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    Electrical, mechanical, and glass transition behavior of polycarbonate-based nanocomposites with different multi-walled carbon nanotubes
    (Oxford : Elsevier Science, 2011) Castillo, Frank Yepez; Socher, Robert; Krause, Beate; Headrick, Robert; Grady, Brian P.; Prada-Silvy, Ricardo; Pötschke, Petra
    Five commercially available multi-walled carbon nanotubes (MWNTs), with different characteristics, were melt mixed with polycarbonate (PC) in a twin-screw micro compounder to obtain nanocomposites containing 0.25-3.0 wt.% MWNT. The electrical properties of the composites were assessed using bulk electrical conductivity measurements, the mechanical properties of the composites were evaluated using tensile tests and dynamic mechanical analysis (DMA), and the thermal properties of the composites were investigated using differential scanning calorimetry (DSC). Electrical percolation thresholds (pcs) were observed between 0.28 wt.% and 0.60 wt.%, which are comparable with other well-dispersed melt mixed materials. Based on measurements of diameter and length distributions of unprocessed tubes it was found that nanotubes with high aspect ratios exhibited lower pcs, although one sample did show higher pc than expected (based on aspect ratio) which was attributed to poorer dispersion achieved during mixing. The stress-strain behavior of the composites is only slightly altered with CNT addition; however, the strain at break is decreased even at low loadings. DMA tests suggest the formation of a combined polymer-CNT continuous network evidenced by measurable storage moduli at temperatures above the glass transition temperature (T g), consistent with a mild reinforcement effect. The composites showed lower glass transition temperatures than that of pure PC. Lowering of the height of the tanδ peak from DMA and reductions in the heat capacity change at the glass transition from DSC indicate that MWNTs reduced the amount of polymer material that participates in the glass transition of the composites, consistent with immobilization of polymer at the nanotube interface. © 2011 Elsevier Ltd. All rights reserved.
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    Influence of cell shape, inhomogeneities and diffusion barriers in cell polarization models
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2014) Giese, Wolfgang; Eigel, Martin; Westerheide, Sebastian; Engwer, Christian; Klipp, Edda
    In silico experiments bear the potential to further the understanding of biological transport processes by allowing a systematic modification of any spatial property and providing immediate simulation results for the chosen models. We consider cell polarization and spatial reorganization of membrane proteins which are fundamental for cell division, chemotaxis and morphogenesis. Our computational study is motivated by mating and budding processes of S. cerevisiae. In these processes a key player during the initial phase of polarization is the GTPase Cdc42 which occurs in an active membrane-bound form and an inactive cytosolic form. We use partial differential equations to describe the membrane-cytosol shuttling of Cdc42 during budding as well as mating of yeast. The membrane is modeled as a thin layer that only allows lateral diffusion and the cytosol is modeled as a volume. We investigate how cell shape and diffusion barriers like septin structures or bud scars influence Cdc42 cluster formation and subsequent polarization of the yeast cell. Since the details of the binding kinetics of cytosolic proteins to the membrane are still controversial, we employ two conceptual models which assume different binding kinetics. An extensive set of in silico experiments with different modeling hypotheses illustrate the qualitative dependence of cell polarization on local membrane curvature, cell size and inhomogeneities on the membrane and in the cytosol. We examine that spatial inhomogenities essentially determine the location of Cdc42 cluster formation and spatial properties are crucial for the realistic description of the polarization process in cells. In particular, our computer simulations suggest that diffusion barriers are essential for the yeast cell to grow a protrusion.