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- ItemThe effect of branched carbon nanotubes as reinforcing nano-filler in polymer nanocomposites(London : Elsevier, 2022) Thompson, S.M.; Talò, M.; Krause, Beate; Janke, A.; Lanzerotti, M.; Capps, J.; Lanzara, G.; Lacarbonara, W.This work discusses the mechanical and dissipative properties of nanocomposite materials made of a high-performance thermoplastic polymer (polybutylene terephthalate, PBT) integrated with branched carbon nanotubes (bCNTs) as nanofiller. The storage and loss moduli as well as the loss factor/damping ratio of the nanocomposites are experimentally characterized for increasing bCNT weight fractions (wt% bCNT) upon variations of the input cyclic strain amplitude and of the input frequency, respectively. The trends obtained for the nanocomposites mechanical properties indicate improvements both in storage and loss modulus by increasing the bCNT weight fraction from 0.5% to 2%. The striking differences between the damping capacities exhibited by CNT/polymer and bCNT/polymer nanocomposites are discussed to shed light onto the different underlined mechanics of the nanocomposites. Due to the stick–slip relative sliding motion of the polymer chains with respect to the straight CNTs, CNT/PBT nanocomposites are known to exhibit a peak in the damping vs. strain amplitude curves, past which, the damping capacity shows a monotonically increasing trend due to the conjectured sliding of the polymer crystals. On the other hand, we show for the first time that bCNT/PBT nanocomposites do not exhibit a peak in the damping capacity but rather a plateau after an initial drop at low strains. This behavior is attributed to the much reduced mobility of the branched CNTs and the lack of formation of crystalline structures around the bCNTs.
- ItemRobust homoclinic orbits in planar systems with Preisach hysteresis operator(Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2014) Pimenov, Alexander; Rachinskii, DmitriiWe 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.
- ItemTowards Customizable Chart Visualizations of Tabular Data Using Knowledge Graphs(Cham : Springer, 2020) Wiens, Vitalis; Stocker, Markus; Auer, Sören; Ishita, Emi; Pang, Natalie Lee San; Zhou, LihongScientific articles are typically published as PDF documents, thus rendering the extraction and analysis of results a cumbersome, error-prone, and often manual effort. New initiatives, such as ORKG, focus on transforming the content and results of scientific articles into structured, machine-readable representations using Semantic Web technologies. In this article, we focus on tabular data of scientific articles, which provide an organized and compressed representation of information. However, chart visualizations can additionally facilitate their comprehension. We present an approach that employs a human-in-the-loop paradigm during the data acquisition phase to define additional semantics for tabular data. The additional semantics guide the creation of chart visualizations for meaningful representations of tabular data. Our approach organizes tabular data into different information groups which are analyzed for the selection of suitable visualizations. The set of suitable visualizations serves as a user-driven selection of visual representations. Additionally, customization for visual representations provides the means for facilitating the understanding and sense-making of information.
- ItemCompact representations for efficient storage of semantic sensor data(Dordrecht : Springer Science + Business Media B.V, 2021) Karim, Farah; Vidal, Maria-Esther; Auer, SörenNowadays, 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.
- ItemOntology Design for Pharmaceutical Research Outcomes(Cham : Springer, 2020) Say, Zeynep; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören; Hall, Mark; Merčun, Tanja; Risse, Thomas; Duchateau, FabienThe network of scholarly publishing involves generating and exchanging ideas, certifying research, publishing in order to disseminate findings, and preserving outputs. Despite enormous efforts in providing support for each of those steps in scholarly communication, identifying knowledge fragments is still a big challenge. This is due to the heterogeneous nature of the scholarly data and the current paradigm of distribution by publishing (mostly document-based) over journal articles, numerous repositories, and libraries. Therefore, transforming this paradigm to knowledge-based representation is expected to reform the knowledge sharing in the scholarly world. Although many movements have been initiated in recent years, non-technical scientific communities suffer from transforming document-based publishing to knowledge-based publishing. In this paper, we present a model (PharmSci) for scholarly publishing in the pharmaceutical research domain with the goal of facilitating knowledge discovery through effective ontology-based data integration. PharmSci provides machine-interpretable information to the knowledge discovery process. The principles and guidelines of the ontological engineering have been followed. Reasoning-based techniques are also presented in the design of the ontology to improve the quality of targeted tasks for data integration. The developed ontology is evaluated with a validation process and also a quality verification method.
- ItemAudio Ontologies for Intangible Cultural Heritage(Bramhall, Stockport ; EasyChair Ltd., 2022-04-12) Tan, Mary Ann; Posthumus, Etienne; Sack, HaraldCultural heritage portals often contain intangible objects digitized as audio files. This paper presents and discusses the adaptation of existing audio ontologies intended for non-cultural heritage applications. The resulting alignment of the German Digital Library-Europeana Data Model (DDB-EDM) with Music Ontology (MO) and Audio Commons Ontology (ACO) is presented.
- ItemUnveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings(Cham : Springer, 2020) Rivas, Ariam; Grangel-González, Irlán; Collarana, Diego; Lehmann, Jens; Vidal, Maria-Esther; Hartmann, Sven; Küng, Josef; Kotsis, Gabriele; Tjoa, A Min; Khalil, IsmailIndustry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans∗ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.
- ItemGraphite 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, BeateA 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.
- ItemImproving 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, JörgEstimation 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.
- ItemCreating a Scholarly Knowledge Graph from Survey Article Tables(Cham : Springer, 2020) Oelen, Allard; Stocker, Markus; Auer, Sören; Ishita, Emi; Pang, Natalie Lee San; Zhou, LihongDue to the lack of structure, scholarly knowledge remains hardly accessible for machines. Scholarly knowledge graphs have been proposed as a solution. Creating such a knowledge graph requires manual effort and domain experts, and is therefore time-consuming and cumbersome. In this work, we present a human-in-the-loop methodology used to build a scholarly knowledge graph leveraging literature survey articles. Survey articles often contain manually curated and high-quality tabular information that summarizes findings published in the scientific literature. Consequently, survey articles are an excellent resource for generating a scholarly knowledge graph. The presented methodology consists of five steps, in which tables and references are extracted from PDF articles, tables are formatted and finally ingested into the knowledge graph. To evaluate the methodology, 92 survey articles, containing 160 survey tables, have been imported in the graph. In total, 2626 papers have been added to the knowledge graph using the presented methodology. The results demonstrate the feasibility of our approach, but also indicate that manual effort is required and thus underscore the important role of human experts.