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Intimate partner violence (IPV) is a social issue that continues to haunt humans globally. Despite the magnitude of research that has been conducted, the Sustainable Developmental Goals target 5.2, and the South African proposed National Strategic Plan on Gender-Based Violence and Femicide, South Africa experiences high incidences of IPV. In heterosexual couples, violence incidences are a concern that requires further research by scholars because cohabiting relationships are an increasing phenomenon within the African context. This study attempts to theorize from an African philosophical stance, focusing particularly on the African psychological perspective. In this chapter, The authors illuminate the nature and forms of violence that manifest in cohabiting relationships. This research explores participants’ experiences of IPV in cohabiting relationships.

This enquiry has been conceptualized using a qualitative constructivism paradigm with in-depth, unstructured one-on-one interviews. Interviews were conducted with 10 participants between the ages of 18 and 24 years recruited from the Thohoyandou Victim Empowerment Programme in Vhembe District in Limpopo Province, South Africa. Thematic analysis was used to generate themes while narrative analysis was used for the participants’ stories. Participants shared their self-reflections on their IPV experiences, deciding to leave their relationships, and threats from their partners when they tried to leave the relationships.

This chapter analyzes the process of surplus generation in Latin America based on the Penn World Tables, also with a comparison with the United States. The reference period is 1950–2019, revealing long-run evolution as well as certain differences between a State-driven industrialization strategy, the turn toward neoliberalism beginning in the 1980s, and a neo-developmentalist period in the twenty-first century. The research shows a steep decline in the rate of profit in Latin America until the early 1990s, with stabilization thereafter but without reversal of the downward trend. However, the turning point in terms of capital accumulation, surplus generation, and productivity indicators occurred in the early 1980s. In addition, divergence vis-à-vis the United States has been growing in the capacity to generate surplus, labor productivity, and GDP per capita.

The purpose of this study is to analyse the recommendations on knowledge organisation from guidelines, policies and procedure manuals of a sample of institutional repositories and networks within the Latin American area and observe the level of follow-up of international guidelines.

Presented is an exploratory and descriptive study of repositories’ professional documents. This study comprised four steps: definition of convenience sample; development of data codebook; coding of data; and analysis of data and conclusions drawing. The convenience sample includes representative sources at three levels: local institutional repositories, national aggregators and international network and aggregators. The codebook gathers information from the repositories’ sample, such as institutional rules and procedure manuals openly available, or recommendations on the use of controlled vocabularies.

The results indicate that at the local repository level, the use of controlled vocabularies is not regulated, leaving the choice of terms to the authors’ discretion. It results in a set of unstructured keywords, not standardised terms, mixing subject terms with other authorities on persons, institutions or places. National aggregators do not regulate these issues either and limit to pointing to international guidelines and policies, which simply recommend the use of controlled vocabularies, using URIs to facilitate interoperability.

The originality of this study lies in identifying how the principles of knowledge organisation are effectively applied by institutional repositories, at local, national and international levels.

In the last few years, the size of Linked Open Data (LOD) describing artworks, in general or domain-specific Knowledge Graphs (KGs), is gradually increasing. This provides (art-)historians and Cultural Heritage professionals with a wealth of information to explore. Specifically, structured data about iconographical and iconological (icon) aspects, i.e. information about the subjects, concepts and meanings of artworks, are extremely valuable for the state-of-the-art of computational tools, e.g. content recognition through computer vision. Nevertheless, a data quality evaluation for art domains, fundamental for data reuse, is still missing. The purpose of this study is filling this gap with an overview of art-historical data quality in current KGs with a focus on the icon aspects.

This study’s analyses are based on established KG evaluation methodologies, adapted to the domain by addressing requirements from art historians’ theories. The authors first select several KGs according to Semantic Web principles. Then, the authors evaluate (1) their structures’ suitability to describe icon information through quantitative and qualitative assessment and (2) their content, qualitatively assessed in terms of correctness and completeness.

This study’s results reveal several issues on the current expression of icon information in KGs. The content evaluation shows that these domain-specific statements are generally correct but often not complete. The incompleteness is confirmed by the structure evaluation, which highlights the unsuitability of the KG schemas to describe icon information with the required granularity.

The main contribution of this work is an overview of the actual landscape of the icon information expressed in LOD. Therefore, it is valuable to cultural institutions by providing them a first domain-specific data quality evaluation. Since this study’s results suggest that the selected domain information is underrepresented in Semantic Web datasets, the authors highlight the need for the creation and fostering of such information to provide a more thorough art-historical dimension to LOD.

This paper aims to delve into the complexities of terminology mapping and annotation, particularly within the context of the COVID-19 pandemic. It underscores the criticality of harmonizing clinical knowledge organization systems (KOS) through a cohesive clinical knowledge representation approach. Central to the study is the pursuit of a novel method for integrating emerging COVID-19-specific vocabularies with existing systems, focusing on simplicity, adaptability and minimal human intervention.

A design science research (DSR) methodology is used to guide the development of a terminology mapping and annotation workflow. The KNIME data analytics platform is used to implement and test the mapping and annotation techniques, leveraging its powerful data processing and analytics capabilities. The study incorporates specific ontologies relevant to COVID-19, evaluates mapping accuracy and tests performance against a gold standard.

The study demonstrates the potential of the developed solution to map and annotate specific KOS efficiently. This method effectively addresses the limitations of previous approaches by providing a user-friendly interface and streamlined process that minimizes the need for human intervention. Additionally, the paper proposes a reusable workflow tool that can streamline the mapping process. It offers insights into semantic interoperability issues in health care as well as recommendations for work in this space.

The originality of this study lies in its use of the KNIME data analytics platform to address the unique challenges posed by the COVID-19 pandemic in terminology mapping and annotation. The novel workflow developed in this study addresses known challenges by combining mapping and annotation processes specifically for COVID-19-related vocabularies. The use of DSR methodology and relevant ontologies with the KNIME tool further contribute to the study’s originality, setting it apart from previous research in the terminology mapping and annotation field.

Quality 4.0 (Q4.0) leverages new emerging technologies to achieve operational excellence and enhance performance. Implementing Q4.0 in digital manufacturing can bring about reliable, flexible and decentralized manufacturing. Emerging technologies such as Non-Fungible Tokens (NFTs), Blockchain and Interplanetary File Storage (IPFS) can all be utilized to realize Q4.0 in digital manufacturing. NFTs, for instance, can provide traceability and property ownership management and protection. Blockchain provides secure and verifiable transactions in a manner that is trusted, immutable and tamper-proof. This research paper aims to explore the concept of Q4.0 within digital manufacturing systems and provide a novel solution based on Blockchain and NFTs for implementing Q4.0 in digital manufacturing.

This study reviews the relevant literature and presents a detailed system architecture, along with a sequence diagram that demonstrates the interactions between the various participants. To implement a prototype of the authors' system, the authors next develop multiple Ethereum smart contracts and test the algorithms designed. Then, the efficacy of the proposed system is validated through an evaluation of its cost-effectiveness and security parameters. Finally, this research provides other potential applications and scenarios across diverse industries.

The proposed solution's smart contracts governing the transactions among the participants were implemented successfully. Furthermore, the authors' analysis indicates that the authors' solution is cost-effective and resilient against commonly known security attacks.

This study represents a pioneering endeavor in the exploration of the potential applications of NFTs and blockchain in the attainment of a comprehensive quality framework (Q4.0) in digital manufacturing. Presently, the body of research on quality control or assurance in digital manufacturing is limited in scope, primarily focusing on the products and production processes themselves. However, this study examines the other vital elements, including management, leadership and intra- and inter-organizational relationships, which are essential for manufacturers to achieve superior performance and optimal manufacturing outcomes.

To facilitate the achievement of Q4.0 and empower manufacturers to attain outstanding quality and gain significant competitive advantages, the authors propose the integration of Blockchain and NFTs into the digital manufacturing framework, with all related processes aligned with an organization's strategic and leadership objectives.

This study represents a pioneering endeavor in the exploration of the potential applications of NFTs and blockchain in the attainment of a comprehensive quality framework (Quality 4.0) in digital manufacturing. Presently, the body of research on quality control or assurance in digital manufacturing is limited in scope, primarily focusing on the products and production processes themselves. However, this study examines the other vital elements, including management, leadership and intra- and inter-organizational relationships, which are essential for manufacturers to achieve superior performance and optimal manufacturing outcomes.

Vehicle companion is one of the most common companion patterns in daily life, which has great value to accident investigation, group tracking, carpooling recommendation and road planning. Due to the complexity and large scale of vehicle sensor streaming data, existing work were difficult to ensure the efficiency and effectiveness of real-time vehicle companion discovery (VCD). This paper aims to provide a high-quality and low-cost method to discover vehicle companions in real time.

This paper provides a real-time VCD method based on pro-active data service collaboration. This study makes use of dynamic service collaboration to selectively process data produced by relative sensors, and relax the temporal and spatial constraints of vehicle companion pattern for discovering more potential companion vehicles.

Experiments based on real and simulated data show that the method can discover 67% more companion vehicles, with 62% less response time comparing with centralized method.

To reduce the amount of processing streaming data, this study provides a Service Collaboration-based Vehicle Companion Discovery method based on proactive data service model. And this study provides a new definition of vehicle companion through relaxing the temporal and spatial constraints for discover companion vehicles as many as possible.

This study aims to examine the role of different forms of corporate social advocacy (CSA) in shaping individuals’ attitudinal and behavioral intentions towards companies taking their public stand on controversial socio-political issues. With an online experiment as the research method, this study tests whether depicting nonpolitical or political behaviors in CSA messages increases individuals’ positive behavioral intentions.

This study uses a single factor between subject online experiment. A total of 135 US young adults were recruited through a Qualtrics online panel. Three social media mockups were created to manipulate three levels of actions in CSA messages (no action, nonpolitical action and political action). Participants viewed one of those social media posts depicting presented actions to counter anti-LGBTQ + legislation in the USA and answered questions about values-driven motives behind CSA, brand preference and positive word-of-mouth (WOM) intention.

Participants displayed higher levels of brand preference when they viewed CSA messages depicting the company’s political action intended to repel anti-LGBTQ + legislation. Participants showed more positive WOM intentions towards the company when they perceived its political actions as more values-driven.

The findings of this study offer practical insights to companies when designing CSA messages and strategies. The results of this study indicate that the presence of political actions in CSA communication increases individuals’ positive behaviors towards companies. The results also suggest that depicting altruistic motives behind CSA leads individuals to talk about companies more in positive ways.

This study is one of the early studies investigating the impact of various forms of CSA on individuals’ attitudinal and behavioral intentions to companies practicing CSA. This study provides practical implications on how to effectively appeal individuals’ favorable attitudes and behaviors towards CSA. In particular, this research presents the importance of action aspects in individuals’ attitudes toward corporations’ CSA messages.

The purpose of this work is to explore the new possibilities enabled by the recent introduction of RDF-star, an extension that allows for statements about statements within the Resource Description Framework (RDF). Alongside Named Graphs, this approach offers opportunities to leverage a meta-level for data modeling and data applications.

In this extended paper, the authors build onto three modeling use cases published in a previous paper: (1) provide provenance information, (2) maintain backwards compatibility for existing models, and (3) reduce the complexity of a data model. The authors present two scenarios where they implement the use of the meta-level to extend a data model with meta-information.

The authors present three abstract patterns for actively using the meta-level in data modeling. The authors showcase the implementation of the meta-level through two scenarios from our research project: (1) the authors introduce a workflow for triple annotation that uses the meta-level to enable users to comment on individual statements, such as for reporting errors or adding supplementary information. (2) The authors demonstrate how adding meta-information to a data model can accommodate highly specialized data while maintaining the simplicity of the underlying model.

Through the formulation of data modeling patterns with RDF-star and the demonstration of their application in two scenarios, the authors advocate for data modelers to embrace the meta-level.

With RDF-star being a very new extension to RDF, to the best of the authors’ knowledge, they are among the first to relate it to other meta-level approaches and demonstrate its application in real-world scenarios.

This study aims to investigate the contribution of blockchain technology to supply chain risk management and its impact on performance among Indian manufacturing companies.

Drawing on a resource-based view, dynamic capability and system of systems theory, this study examines the direct relationships between blockchain, supply chain risk management and supply chain performance. The authors validate the mediating effects of three supply chain risk management components, namely supply risk management, demand risk management and cyber security management, on financial transaction reliability and information reliability. Data were collected from 204 Indian manufacturing companies that have adopted blockchain technology.

The results demonstrate that companies adopting blockchain technology have experienced positive outcomes in managing supply chain-related risks, financial transaction reliability and information reliability. These findings provide valuable guidance to managers, highlighting blockchain as a competitive advantage for supply chain management.

To the best of the authors’ knowledge, no previous research on blockchain-based risk management capabilities has been conducted.