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Influential spreaders are the crucial nodes in a complex network that can act as a controller or a maximizer of a spreading process. For example, we can control the virus propagation in an epidemiological network by controlling the behavior of such influential nodes, and amplify the information propagation in a social network by using them as a maximizer. Many indexing methods have been proposed in the literature to identify the influential spreaders in a network. Nevertheless, we have notice that each individual network holds different connectivity structures that we classify as complete, incomplete, or in-between based on their components and density. These affect the accuracy of existing indexing methods in the identification of the best influential spreaders. Thus, no single indexing strategy is sufficient from all varieties of network connectivity structures. This article proposes a new indexing method Network Global Structure-based Centrality ( ngsc ) which intelligently combines existing kshell and sum of neighbors’ degree methods with knowledge of the network’s global structural properties, such as the giant component, average degree, and percolation threshold. The experimental results show that our proposed method yields a better spreading performance of the seed spreaders over a large variety of network connectivity structures, and correlates well with ranking based on an SIR model used as ground truth. It also out-performs contemporary techniques and is competitive with more sophisticated approaches that are computationally cost.

Herein we report the first example of the catalytic aerobic partial oxidation of allyl ether to its acrylate ester derivative. Many partial oxidations often need an expensive oxidant such as peroxides or other species to drive such reactions. In addition, selective generation of esters using porous catalysts has been elusive. This reaction is catalyzed by a Li ion promoted mesoporous manganese oxide (meso-Mn 2 O 3 ) under mild conditions with no precious metals, a reusable heterogeneous catalyst, and easy isolation. This process is very attractive for the oxidation of allyl ethers. We report on the catalytic activity, selectivity, and scope of the reaction. In the best cases presented, almost complete conversion of allyl ether with near complete chemo-selectivity towards acrylate ester derivatives is observed. Based on results from controlled experiments, we propose a possible reaction mechanism for the case in which N -hydroxyphthalimide (NHPI) is used in combination with trichloroacetonitrile (CCl 3 CN). Acrylics and acrylates have become important building blocks for the chemical industry, but their efficient synthesis remains a challenge. Here, the authors report the first example of the catalytic aerobic partial oxidation of allyl ether to its acrylate ester derivative using a Li ion promoted mesoporous manganese oxide under mild conditions.

The concept of quasi-particles forms the theoretical basis of our microscopic understanding of emergent phenomena associated with quantum-mechanical many-body interactions. However, the quasi-particle theory in disordered materials has proven difficult, resulting in the predominance of mean-field solutions. Here, we report first-principles phonon calculations and inelastic X-ray and neutron-scattering measurements on equiatomic alloys (NiCo, NiFe, AgPd, and NiFeCo) with force-constant dominant disorder—confronting a key 50-year-old assumption in the Hamiltonian of all mean-field quasi-particle solutions for off-diagonal disorder. Our results have revealed the presence of a large, and heretofore unrecognized, impact of local chemical environments on the distribution of the species-pair-resolved force-constant disorder that can dominate phonon scattering. This discovery not only identifies a critical analysis issue that has broad implications for other elementary excitations, such as magnons and skyrmions in magnetic alloys, but also provides an important tool for the design of materials with ultralow thermal conductivities.

In today’s data-driven world, integrating diverse healthcare data sources into a unified framework is essential. The COVID-19 pandemic has underscored the critical need for extracting meaningful insights from fragmented clinical data, particularly in areas such as treatment efficacy, risk factor identification, and drug interactions. To address these challenges, we propose the COVID-19 Drug and Risk Ontology (COViDRO)-a formally developed OWL-DL ontology designed to model and integrate COVID-19 treatment options aligned with the “PRADiCT” framework (Patient Risk factors, Adverse effects, Drug interaction, Clinical findings, and Treatment procedure). We hypothesize that this ontology will assist healthcare professionals in discovering and recommending COVID-19 therapeutics tailored to individual patients by considering risk factors, underlying health conditions, ongoing medications, potential drug interactions, and adverse effects. To validate its reliability and effectiveness, COViDRO underwent a multi-tier evaluation process: (1) Quality-based assessment using the Ontology Pitfall Scanner (OOPS!) to detect and resolve modeling errors, benchmarking COViDRO against related ontologies based on structural, functional, and usability dimensions; (2) Structural and logical validation using OntoDebug for structural integrity checks and the Pellet reasoner for logical consistency verification; (3) Quantitative evaluation using the OntoMetrics framework to assess ontology metrics such as attribute richness, relation richness, and knowledge base complexity, comparing with related ontologies; and (4) Query-based evaluation using SPARQL to assess the ontology’s reasoning and retrieval capacity. The evaluation results confirm that COViDRO effectively organizes 135 classes, 32 object properties, and 15 data properties, enabling structured clinical reasoning. SPARQL queries successfully demonstrate its ability to retrieve patient-specific therapeutic recommendations, assess risk factors, and generate drug interaction alerts, validating its practical utility in healthcare settings. As a formal, DL-enabled ontology, COViDRO can contribute to automated inference of treatment options, thereby enhancing decision support systems and knowledge-based applications. Its structured and extensible approach makes it a valuable resource not only for COVID-19 but also for future pandemics and infectious disease management, reinforcing its significance in healthcare informatics.

Purpose Ontology and Linked Data (LD) are the two prominent web technologies that have emerged in the recent past. Both of them are at the center of Semantic Web and its applications. Researchers and developers from both academia and business are actively working in these areas. The increasing interest in these technologies promoted the growth of LD sets and ontologies on the web. The purpose of this paper is to investigate the possible relationships between them. The effort is to investigate the possible roles that ontologies may play in further empowering the LD. In a similar fashion, the author also studies the possible roles that LD may play to empower ontologies. Design/methodology/approach The work is mainly carried out by exploring the ontology- and LD-based real-world systems, and by reviewing the existing literature. Findings The current work reveals, in general, that both the technologies are interdependent and have lots to offer to each other for their faster growth and meaningful development. Specifically, anything that we can do with LD, we can do more by adding an ontology to it. Practical implications The author envisions that the current work, in the one hand, will help in boosting the successful implementation and the delivery of semantic applications; on the other hand, it will also become a food for the future researchers in further investigating the relationships between the ontologies and LD. Originality/value So far, as per the author’s knowledge, there are very little works that have attempted in exploring the relationships between the ontologies and LD. In this work, the author illustrates the real-world systems that are based on ontology and LD, discusses the issues and challenges and finally illustrates their interdependency discussing some of the ongoing research works.

We have investigated twin boundaries in double-lattice hexagonal close-packed metallic materials, focusing on their atomic geometry. Combining accurate ab-initio methods and large-scale atomistic simulations we address the following two fundamental questions: (i) What are the possible intrinsic twin boundary structures in hcp crystals? (ii) Are these structures stable against small distortions? In order to help end a decade-long controversy over the experimental observations of the atomic structures of twin boundaries, we have determined the energetics, spectra, and transition mechanisms of the twin boundaries. Our results confirm that the mechanical stability controls structures which are observed. Structural materials: at the boundary between twins A group of atomic defects that are critical to the mechanical properties of common metals is investigated by researchers in Germany and the Czech Republic. Zongrui Pei from the Max-Planck-Institut für Eisenforschung and co-workers identify the types of structural aberration that can exist in materials such as magnesium, zirconium and titanium. A twin boundary occurs where the regular atomic structure in one region becomes misaligned from that in the next. For one specific atomic arrangement, known as a hexagonal close-packed structure, the atomic structures of such defects are not very well understood. Pei et al. use ab-initio methods and large-scale atomistic simulations to show that two types of twin boundaries can occur in magnesium: glide twin boundaries and reflection twin boundaries. They show that mechanical instability makes the former difficult to see experimentally.

In today’s data-driven world, integrating diverse healthcare data sources into a unified framework is essential. The COVID-19 pandemic has underscored the critical need for extracting meaningful insights from fragmented clinical data, particularly in areas such as treatment efficacy, risk factor identification, and drug interactions. To address these challenges, we propose the COVID-19 Drug and Risk Ontology (COViDRO)-a formally developed OWL-DL ontology designed to model and integrate COVID-19 treatment options aligned with the “PRADiCT” framework (Patient Risk factors, Adverse effects, Drug interaction, Clinical findings, and Treatment procedure). We hypothesize that this ontology will assist healthcare professionals in discovering and recommending COVID-19 therapeutics tailored to individual patients by considering risk factors, underlying health conditions, ongoing medications, potential drug interactions, and adverse effects. To validate its reliability and effectiveness, COViDRO underwent a multi-tier evaluation process: (1) Quality-based assessment using the Ontology Pitfall Scanner (OOPS!) to detect and resolve modeling errors, benchmarking COViDRO against related ontologies based on structural, functional, and usability dimensions; (2) Structural and logical validation using OntoDebug for structural integrity checks and the Pellet reasoner for logical consistency verification; (3) Quantitative evaluation using the OntoMetrics framework to assess ontology metrics such as attribute richness, relation richness, and knowledge base complexity, comparing with related ontologies; and (4) Query-based evaluation using SPARQL to assess the ontology’s reasoning and retrieval capacity. The evaluation results confirm that COViDRO effectively organizes 135 classes, 32 object properties, and 15 data properties, enabling structured clinical reasoning. SPARQL queries successfully demonstrate its ability to retrieve patient-specific therapeutic recommendations, assess risk factors, and generate drug interaction alerts, validating its practical utility in healthcare settings. As a formal, DL-enabled ontology, COViDRO can contribute to automated inference of treatment options, thereby enhancing decision support systems and knowledge-based applications. Its structured and extensible approach makes it a valuable resource not only for COVID-19 but also for future pandemics and infectious disease management, reinforcing its significance in healthcare informatics.

Purpose The purpose of this paper is to describe the CODO ontology (COviD-19 Ontology) that captures epidemiological data about the COVID-19 pandemic in a knowledge graph that follows the FAIR principles. This study took information from spreadsheets and integrated it into a knowledge graph that could be queried with SPARQL and visualized with the Gruff tool in AllegroGraph. Design/methodology/approach The knowledge graph was designed with the Web Ontology Language. The methodology was a hybrid approach integrating the YAMO methodology for ontology design and Agile methods to define iterations and approach to requirements, testing and implementation. Findings The hybrid approach demonstrated that Agile can bring the same benefits to knowledge graph projects as it has to other projects. The two-person team went from an ontology to a large knowledge graph with approximately 5 M triples in a few months. The authors gathered useful real-world experience on how to most effectively transform “from strings to things.” Originality/value This study is the only FAIR model (to the best of the authors’ knowledge) to address epidemiology data for the COVID-19 pandemic. It also brought to light several practical issues that generalize to other studies wishing to go from an ontology to a large knowledge graph. This study is one of the first studies to document how the Agile approach can be used for knowledge graph development.

From the literature study, it was observed that there are significantly fewer studies that review ontology- based narrative models. This motivates the current work. A parametric approach was adopted to report the existing ontology-driven models for narrative information. The work considers the narrative and ontology components as parameters. This study hopes to encompass the relevant literature and ontology models together. The work adopts a systematic literature review methodology for an extensive literature selection. The models were selected from the literature using a stratified random sampling technique. The findings illustrate an overview of the narrative models across domains. The study identifies the differences and similarities of knowledge representation in ontology-based narrative information models. This paper will explore the basic concepts and top-level concepts in the models. Besides, this study provides a study of the narrative theories in the context of ongoing research. It also identifies the state-of-the-art literature for ontology-based narrative information. Keywords: ontologies, narrative information, modelling

In the process of searching for a particular course on e-learning platforms, it is required to browse through different platforms, and it becomes a time-consuming process. To resolve the issue, an ontology has been developed that can provide single-point access to all the e-learning platforms. The modelled ONline Course Ontology (ONCO) is based on YAMO, METHONTOLOGY and IDEF5 and built on the Protégé ontology editing tool. ONCO is integrated with sample data and later evaluated using pre-defined competency questions. Complex SPARQL queries are executed to identify the effectiveness of the constructed ontology. The modelled ontology is able to retrieve all the sampled queries. The ONCO has been developed for the efficient retrieval of similar courses from massive open online course (MOOC) platforms.