Using digital humanity approaches to visualize and evaluate the cultural heritage ontology

Purpose This study applies digital humanity tools (Gephi and Protégé) for establishing and visualizing ontologies in the cultural heritage domain. According to that, this study aims to develop a novel evaluation approach using five ontology indicators (data overview, visual presentation, highlight links, scalability and querying) to evaluate the knowledge structure presentation of cultural heritage ontology. Design/methodology/approach The researchers collected and organized 824 pieces of government’s open data (GOD), converted GOD into the resource description framework format, applied Protégé and Gephi to establish and visualize cultural heritage ontology. After ontology is built, this study recruited 60 ontology participants (30 from information and communications technology background; 30 from cultural heritage background) to operate this ontology and gather their different perspectives of visual ontology. Findings Based on the ontology participant’s feedback, this study discovered that Gephi is more supporting than Protégé when visualizing ontology. Especially in data overview, visual presentation and highlight links dimensions, which is supported visualization and demonstrated ontology class hierarchy and property relation, facilitated the wider application of ontology. Originality/value This study offers two contributions. First, the researchers analyzed data on East Asian architecture with novel digital humanities tools to visualize ontology for cultural heritage. Second, the study collected participant’s feedback regarding the visualized ontology to enhance its design, which can serve as a reference for future ontological development.

A system approach to the study of the city in the social understanding of its phenomenon for modern society

В данной статье рассматривается системный подход различных учёных к философскому пониманию феномена городского пространства, даётся обоснование его эвристичности. Современная социальная наука изучает феномен городского пространства не единолично, а находясь в тесной взаимосвязи с другими науками, такими как социология, психология, логистика, политология, география, культурология, практическими разделами архитектуры и т.д. Перечисленные науки изучают город с практической и эмпирической стороны, но теоретическое изучение происходит посредством использования устоявшихся методов философской науки. Отмечается, что применение социологической методологии в изучении городского локуса значительно расширяет возможности социогуманитарных наук в определении взаимозависимости пространственных и популяционных факторов развития городской среды (Т. Парсонс, Н. Луман). На основе обобщения системно-семиотического метода А.И. Уёмова город рассматривается через триаду «вещь», «свойство», «отношение». Антитезой в понимании города выступает концепция А.Э. Гутнова, объясняющего его сущностные характеристики посредством воссоздания пространственной параметрики. Делается вывод о том, что существующие теоретико-методологические подходы к исследованию городского пространства обладают значительным эвристическим потенциалом в экспликации города как целостного (интегрального) социокультурного феномена. Understanding of the phenomenon of urban space provides a justification for its heuristicity. Modern social science studies the phenomenon of urban space not alone, but being in close relationship with other sciences, such as sociology, psychology, logistics, political science, geography, cultural studies, practical sections of architecture, etc. The listed sciences study the city from the practical and empirical side, but theoretical study takes place through the established methods of philosophical science. The application of sociological methodology in the study of the urban locus significantly expands the possibilities of socio-humanities in determining the interdependence of spatial and population factors for the development of the urban environment (T. Parsons, N. Luhmann). Based on the generalization of the system-semiotic method of A.I. Uyomov, the city is considered through the triad "thing," "property," "relation". Antithesis in the understanding of the city is the concept of A.E. Gutnov, explaining its essential characteristics by recreating spatial parametrics. It is concluded that the existing theoretical and methodological approaches to the study of urban space have significant heuristic potential in the explication of the city as a holistic (integral) socio-cultural phenomenon.

Ontological Categories

The chapter advances a preliminary account of categories of what there is. We find ourselves surrounded by objects that interact in various ways. Refining this idea points to three seemingly indispensable categories: substance, property, relation. Properties are ways particular substances are. Substance and property are correlative categories: every substance must be some way or other and every way must be a way some substance is. The possibility that relations are ‘founded’ is introduced and explicated by reference to truthmaking: truthmakers for relational truths could turn out to be nonrelational features of the universe. In the same vein, truthmakers for truths invoking traditionally important categories including universal, attribute, and modality might be fully particular propertied substances.

Microstructure–property relation and machine learning prediction of hole expansion capacity of high-strength steels

The relationship between microstructure features and mechanical properties plays an important role in the design of materials and improvement of properties. Hole expansion capacity plays a fundamental role in defining the formability of metal sheets. Due to the complexity of the experimental procedure of testing hole expansion capacity, where many influencing factors contribute to the resulting values, the relationship between microstructure features and hole expansion capacity and the complexity of this relation is not yet fully understood. In the present study, an experimental dataset containing the phase constituents of 55 microstructures as well as corresponding properties, such as hole expansion capacity and yield strength, is collected from the literature. Statistical analysis of these data is conducted with the focus on hole expansion capacity in relation to individual phases, combinations of phases and number of phases. In addition, different machine learning methods contribute to the prediction of hole expansion capacity based on both phase fractions and chemical content. Deep learning gives the best prediction accuracy of hole expansion capacity based on phase fractions and chemical composition. Meanwhile, the influence of different microstructure features on hole expansion capacity is revealed. Graphical abstract

Machine learning on properties of multiscale multisource hydroxyapatite nanoparticles datasets with different morphologies and sizes

Machine learning models for exploring structure-property relation for hydroxyapatite nanoparticles (HANPs) are still lacking. A multiscale multisource dataset is presented, including both experimental data (TEM/SEM, XRD/crystallinity, ROS, anti-tumor effects, and zeta potential) and computation results (containing 41,976 data samples with up to 9768 atoms) of nanoparticles with different sizes and morphologies at density functional theory (DFT), semi-empirical DFTB, and force field, respectively. Three geometric descriptors are set for the explainable machine learning methods to predict surface energies and surface stress of HANPs with satisfactory performance. To avoid the pre-determination of features, we also developed a predictive deep learning model within the framework of graph convolution neural network with good generalizability. Energies with DFT accuracy are achievable for large-sized nanoparticles from the learned correlations and scale functions for mapping different theoretical levels and particle sizes. The simulated XRD spectra and crystallinity values are in good agreement with experiments.

Direct insight into the structure-property relation of interfaces from constrained crystal structure prediction

A major issue that prevents a full understanding of heterogeneous materials is the lack of systematic first-principles methods to consistently predict energetics and electronic properties of reconstructed interfaces. In this work we address this problem with an efficient and accurate computational scheme. We extend the minima-hopping method implementing constraints crafted for two-dimensional atomic relaxation and enabling variations of the atomic density close to the interface. A combination of density-functional and accurate density-functional tight-binding calculations supply energy and forces to structure prediction. We demonstrate the power of this method by applying it to extract structure-property relations for a large and varied family of symmetric and asymmetric tilt boundaries in polycrystalline silicon. We find a rich polymorphism in the interface reconstructions, with recurring bonding patterns that we classify in increasing energetic order. Finally, a clear relation between bonding patterns and electrically active grain boundary states is unveiled and discussed.

Structure-Property Relation in Varieties of Millets Grown in Karnataka

Aim: This study aims to establish structure-property relation of the varieties of millets grown in Karnataka. Study Design: Seven different varieties of millets were collected from the farms in Chitradurga district from the state of Karnataka in India. Place and Duration of Study: This study was conducted between January and April 2020 at the Vijnana Bhavan, University with Potential for excellence, University of Mysore, Karnataka Methodology: Magnetic property and characterization for seven out of the nine varieties of millets grown in Chitradurga, Hiriyur and Khandenahalli of Karnataka were carried out using X-ray diffraction studies (XRD), Energy Dispersive X-ray analysis (EDAX), Raman spectroscopy, SEM and Xplore AC magnetic techniques to understand the physical properties of these samples and to find out the structure-property relation in these millets. Results: The Foxtail millet is unique in terms of crystallites size, elemental distribution and magnetic properties. The structure-property relation of all the millets is determined. Conclusion: It is evident from these studies that all the millets are diamagnetic in nature, crystalline like order is less and the major component in all these millets is cellulose. Also the Foxtail millet has excellent structure-property relation.