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search.filters.applied.f.access: openAccess × End date: 2020 × Start date: 2018 × Version: acceptedVersion ×

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Now showing 1 - 10 of 25
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    Sperm Micromotors for Cargo Delivery through Flowing Blood
    (Washington, DC : American Chemical Society, 2020) Xu, Haifeng; Medina-Sánchez, Mariana; Maitz, Manfred F.; Werner, Carsten; Schmidt, Oliver G.
    Micromotors are recognized as promising candidates for untethered micromanipulation and targeted cargo delivery in complex biological environments. However, their feasibility in the circulatory system has been limited due to the low thrust force exhibited by many of the reported synthetic micromotors, which is not sufficient to overcome the high flow and complex composition of blood. Here we present a hybrid sperm micromotor that can actively swim against flowing blood (continuous and pulsatile) and perform the function of heparin cargo delivery. In this biohybrid system, the sperm flagellum provides a high propulsion force while the synthetic microstructure serves for magnetic guidance and cargo transport. Moreover, single sperm micromotors can assemble into a train-like carrier after magnetization, allowing the transport of multiple sperm or medical cargoes to the area of interest, serving as potential anticoagulant agents to treat blood clots or other diseases in the circulatory system.
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    An OER Recommender System Supporting Accessibility Requirements
    (New York : Association for Computing Machinery, 2020) Elias, Mirette; Tavakoli, Mohammadreza; Lohmann, Steffen; Kismihok, Gabor; Auer, Sören; Gurreiro, Tiago; Nicolau, Hugo; Moffatt, Karyn
    Open Educational Resources are becoming a significant source of learning that are widely used for various educational purposes and levels. Learners have diverse backgrounds and needs, especially when it comes to learners with accessibility requirements. Persons with disabilities have significantly lower employment rates partly due to the lack of access to education and vocational rehabilitation and training. It is not surprising therefore, that providing high quality OERs that facilitate the self-development towards specific jobs and skills on the labor market in the light of special preferences of learners with disabilities is difficult. In this paper, we introduce a personalized OER recommeder system that considers skills, occupations, and accessibility properties of learners to retrieve the most adequate and high-quality OERs. This is done by: 1) describing the profile of learners with disabilities, 2) collecting and analysing more than 1,500 OERs, 3) filtering OERs based on their accessibility features and predicted quality, and 4) providing personalised OER recommendations for learners according to their accessibility needs. As a result, the OERs retrieved by our method proved to satisfy more accessibility checks than other OERs. Moreover, we evaluated our results with five experts in educating people with visual and cognitive impairments. The evaluation showed that our recommendations are potentially helpful for learners with accessibility needs.
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    Melt mixed composites of polypropylene with singlewalled carbon nanotubes for thermoelectric applications: Switching from p- to n-type behavior by additive addition
    (Melville, NY : AIP, 2019) Pötschke; Petra; Krause, Beate; Luo, Jinji
    Composites were prepared with polypropylene (PP) as the matrix and singlewalled CNTs (SWCNTs) of the type TUBALL from OCSiAl Ltd. as the conducting component by melt processing in a small-scale twin-screw compounder. In order to switch the typical p-type behavior of such composites from positive Seebeck coefficients (S) into n-type behavior with negative Seebeck coefficients, a non-ionic surfactant polyoxyethylene 20 cetyl ether (Brij58) was used and compared with a PEG additive, which was shown previously to be able to induce such switching. For PP-2 wt% SWCNT composites Brij58 is shown to result in n-type composites. The negative S values (up to −48.2 µV/K) are not as high as in the case of previous results using PEG (−56.6 µV/K). However, due to the more pronounced effect of Brij58 on the electrical conductivity, the achieved power factors are higher and reach a maximum of 0.144 µW/(m·K2) compared to previous 0.078 µW/(m·K2) with PEG. Dispersion improvement depends on the type of SWCNTs obtained by using varied synthesis/treatment conditions. Solution prepared composites of PEG with SWCNTs also have negative S values, indicating the donation of electrons from PEG to the SWCNTs. However, such composites are brittle and not suitable as thermoelectric materials.
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    Accessibility and Personalization in OpenCourseWare : An Inclusive Development Approach
    (Piscataway, NJ : IEEE, 2020) Elias, Mirette; Ruckhaus, Edna; Draffan, E.A.; James, Abi; Suárez-Figueroa, Mari Carmen; Lohmann, Steffen; Khiat, Abderrahmane; Auer, Sören; Chang, Maiga; Sampson, Demetrios G.; Huang, Ronghuai; Hooshyar, Danial; Chen, Nian-Shing; Kinshuk; Pedaste, Margus
    OpenCourseWare (OCW) has become a desirable source for sharing free educational resources which means there will always be users with differing needs. It is therefore the responsibility of OCW platform developers to consider accessibility as one of their prioritized requirements to ensure ease of use for all, including those with disabilities. However, the main challenge when creating an accessible platform is the ability to address all the different types of barriers that might affect those with a wide range of physical, sensory and cognitive impairments. This article discusses accessibility and personalization strategies and their realisation in the SlideWiki platform, in order to facilitate the development of accessible OCW. Previously, accessibility was seen as a complementary feature that can be tackled in the implementation phase. However, a meaningful integration of accessibility features requires thoughtful consideration during all project phases with active involvement of related stakeholders. The evaluation results and lessons learned from the SlideWiki development process have the potential to assist in the development of other systems that aim for an inclusive approach. © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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    Quality Prediction of Open Educational Resources A Metadata-based Approach
    (Piscataway, NJ : IEEE, 2020) Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören; Chang, Maiga; Sampson, Demetrios G.; Huang, Ronghuai; Hooshyar, Danial; Chen, Nian-Shing; Kinshuk; Pedaste, Margus
    In the recent decade, online learning environments have accumulated millions of Open Educational Resources (OERs). However, for learners, finding relevant and high quality OERs is a complicated and time-consuming activity. Furthermore, metadata play a key role in offering high quality services such as recommendation and search. Metadata can also be used for automatic OER quality control as, in the light of the continuously increasing number of OERs, manual quality control is getting more and more difficult. In this work, we collected the metadata of 8,887 OERs to perform an exploratory data analysis to observe the effect of quality control on metadata quality. Subsequently, we propose an OER metadata scoring model, and build a metadata-based prediction model to anticipate the quality of OERs. Based on our data and model, we were able to detect high-quality OERs with the F1 score of 94.6%. © 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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    Toward Representing Research Contributions in Scholarly Knowledge Graphs Using Knowledge Graph Cells
    (New York City, NY : Association for Computing Machinery, 2020) Vogt, Lars; D'Souza, Jennifer; Stocker, Markus; Auer, Sören
    There is currently a gap between the natural language expression of scholarly publications and their structured semantic content modeling to enable intelligent content search. With the volume of research growing exponentially every year, a search feature operating over semantically structured content is compelling. Toward this end, in this work, we propose a novel semantic data model for modeling the contribution of scientific investigations. Our model, i.e. the Research Contribution Model (RCM), includes a schema of pertinent concepts highlighting six core information units, viz. Objective, Method, Activity, Agent, Material, and Result, on which the contribution hinges. It comprises bottom-up design considerations made from three scientific domains, viz. Medicine, Computer Science, and Agriculture, which we highlight as case studies. For its implementation in a knowledge graph application we introduce the idea of building blocks called Knowledge Graph Cells (KGC), which provide the following characteristics: (1) they limit the expressibility of ontologies to what is relevant in a knowledge graph regarding specific concepts on the theme of research contributions; (2) they are expressible via ABox and TBox expressions; (3) they enforce a certain level of data consistency by ensuring that a uniform modeling scheme is followed through rules and input controls; (4) they organize the knowledge graph into named graphs; (5) they provide information for the front end for displaying the knowledge graph in a human-readable form such as HTML pages; and (6) they can be seamlessly integrated into any existing publishing process thatsupports form-based input abstracting its semantic technicalities including RDF semantification from the user. Thus RCM joins the trend of existing work toward enhanced digitalization of scholarly publication enabled by an RDF semantification as a knowledge graph fostering the evolution of the scholarly publications beyond written text.
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    Influence of a supplemental filler in twin-screw extruded PP/CNT composites using masterbatch dilution
    (Melville, NY : AIP, 2019) Müller, Michael Thomas; Krause, Beate; Kretzschmar, Bernd; Pötschke, Petra
    In this study commercially available multiwalled carbon nanotubes (2-8 wt.%) were incorporated in polypropylene (PP) by direct powder feeding or by a masterbatch dilution procedure using a twin-screw extruder. The influence of a supplemental, electrical non-conductive talc or electrically conductive carbon black (CB), filler on the resulting composite properties was investigated. In comparison to the direct carbon nanotube (CNT) incorporation the masterbatch dilution step resulted in improved CNT macro dispersion. The use of the supplemental fillers CB or talc does not show a significant influence on the CNT dispersion state. When compared to direct CNT incorporation, the second compounding process involved in masterbatch dilution leads to higher electrical resistivity of injection molded samples. On the other hand, the supplemental fillers talc or CB decreased the electrical resistivity values. With the addition of talc or CB an increase of the Young’s modulus due to the reinforcing effect of the second filler was achieved. However, no synergistic effect between the used supplemental fillers and the CNT on the mechanical properties was obtained.
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    SDM-RDFizer: An RML Interpreter for the Efficient Creation of RDF Knowledge Graphs
    (New York City, NY : Association for Computing Machinery, 2020) Iglesias, Enrique; Jozashoori, Samaneh; Chaves-Fraga, David; Collarana, Diego; Vidal, Maria-Esther
    In recent years, the amount of data has increased exponentially, and knowledge graphs have gained attention as data structures to integrate data and knowledge harvested from myriad data sources. However, data complexity issues like large volume, high-duplicate rate, and heterogeneity usually characterize these data sources, being required data management tools able to address the negative impact of these issues on the knowledge graph creation process. In this paper, we propose the SDM-RDFizer, an interpreter of the RDF Mapping Language (RML), to transform raw data in various formats into an RDF knowledge graph. SDM-RDFizer implements novel algorithms to execute the logical operators between mappings in RML, allowing thus to scale up to complex scenarios where data is not only broad but has a high-duplication rate. We empirically evaluate the SDM-RDFizer performance against diverse testbeds with diverse configurations of data volume, duplicates, and heterogeneity. The observed results indicate that SDM-RDFizer is two orders of magnitude faster than state of the art, thus, meaning that SDM-RDFizer an interoperable and scalable solution for knowledge graph creation. SDM-RDFizer is publicly available as a resource through a Github repository and a DOI.
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    Generate FAIR Literature Surveys with Scholarly Knowledge Graphs
    (New York City, NY : Association for Computing Machinery, 2020) Oelen, Allard; Jaradeh, Mohamad Yaser; Stocker, Markus; Auer, Sören
    Reviewing scientific literature is a cumbersome, time consuming but crucial activity in research. Leveraging a scholarly knowledge graph, we present a methodology and a system for comparing scholarly literature, in particular research contributions describing the addressed problem, utilized materials, employed methods and yielded results. The system can be used by researchers to quickly get familiar with existing work in a specific research domain (e.g., a concrete research question or hypothesis). Additionally, it can be used to publish literature surveys following the FAIR Data Principles. The methodology to create a research contribution comparison consists of multiple tasks, specifically: (a) finding similar contributions, (b) aligning contribution descriptions, (c) visualizing and finally (d) publishing the comparison. The methodology is implemented within the Open Research Knowledge Graph (ORKG), a scholarly infrastructure that enables researchers to collaboratively describe, find and compare research contributions. We evaluate the implementation using data extracted from published review articles. The evaluation also addresses the FAIRness of comparisons published with the ORKG.
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    Towards the semantic formalization of science
    (New York City, NY : Association for Computing Machinery, 2020) Fathalla, Said; Auer, Sören; Lange, Christoph
    The past decades have witnessed a huge growth in scholarly information published on the Web, mostly in unstructured or semi-structured formats, which hampers scientific literature exploration and scientometric studies. Past studies on ontologies for structuring scholarly information focused on describing scholarly articles' components, such as document structure, metadata and bibliographies, rather than the scientific work itself. Over the past four years, we have been developing the Science Knowledge Graph Ontologies (SKGO), a set of ontologies for modeling the research findings in various fields of modern science resulting in a knowledge graph. Here, we introduce this ontology suite and discuss the design considerations taken into account during its development. We deem that within the next years, a science knowledge graph is likely to become a crucial component for organizing and exploring scientific work.