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Multipath serially-mediating mechanisms of employee attitudes and behaviors: Socially intelligent leadership and organizational performance
No full textThis research employs the social exchange theory within a macro-external context to conduct a theoretical and empirical examination of the relationship between socially intelligent leadership and organizational performance. It explores, through multipath serially mediating mechanisms, the employee attitudes (reflected on employee motivation and organizational commitment) and employee behaviors (reflected on work engagement and organizational citizen behavior). The hypotheses of the study were tested using structural equation modelling based on data collected from 657 employees within 99 SMEs located in Greece. The analysis confirms the positive yet varying effects of the associated partial and serial mediating mechanisms. Specifically, the mediating mechanism through organizational commitment and work engagement demonstrates a stronger impact of socially intelligent leadership on organizational performance compared to the other mediating pathways. The research and practical implications are also discussed.122Leveraging AI and Emotional Intelligence in Contemporary Business Organization
Towards exploiting BPMN and DMN in public service modeling
No full textModeling of Public Services (PSs) is used by a large number of public authorities worldwide to improve their quality. The resulting diagrams are useful in the case of simple PSs however they can become very complex in the case of complex PSs. To address process complexity, the integration of Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN) has been proposed. DMN is applied to model the decision logic of a process and has proven to provide promising results in the private sector. However, the integration of BPMN and DMN has not been studied in the case of PSs. The aim of this paper is twofold. First, to investigate the integration of BPMN and DMN when applied to complex PSs. Second, to identify a set of steps and principles for integrating BPMN and DMN to model complex PSs. For this purpose, a literature review is conducted leading to the result that there is limited research on the use of BPMN to model complex PS while research on the use of DMN is missing. In addition, a PS is selected and modeled based on selected criteria and finally, a set of steps and principles is proposed.211216Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and Informatic
Towards Optimizing Fuel Consumption in Railways: A Generic Greedy Approach in Continuous Space
No full textThis paper addresses the challenge of producing fuel-optimal speed profiles for trains by transforming time-optimal profiles. In a timeoptimal speed profile, the train travels over a specific path with various speed limits in the shortest time. Our approach aims to adjust the time-optimal speed profile by extending its total duration to produce a fuel-optimal speed profile within that extended time frame. The proposed method greedily modifies parts of the given speed profile in a gradient descent manner, trying to maximize the
ratio of fuel savings to time increase. Our analysis concluded that it is most effective to adjust either "Acceleration and Deceleration"or "Acceleration, Cruising and Deceleration" throttle sequences, with the former always being the best option.216222Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatic
A Neutrosophic Model for Measuring Evolution, Involution, and Indeterminacy in Species: Integrating Common and Uncommon Traits in Environmental Adaptation
No full textIn 2017, Professor F. Smarandache introduced the Neutrosophic Theory of Evolution, Involution, and Indeterminacy (or Neutrality) (NToEIaI) for the first time. He concluded that every theory of evolution is characterized by a certain degree of truth, indeterminacy, and untruth, as in neutrosophic logic. In this perspective, he raised a number of open questions on evolution, neutrality, and involution that required further research effort. Very recently, in 2024, Smarandache conducted research, from a soft sciences/philosophical viewpoint, on identifying and studying common parts in uncommon things and uncommon parts in common things emphasizing the complexity and interconnectedness of concepts within the context of neutrosophy. In this article, we propose a neutrosophic model that incorporates the ideas of finding common parts in uncommon things and uncommon parts in common things within the realms of NToEIaI. We attempt to provide a proper answer on how to measure the degree of evolution, involution, and indeterminacy (neutrality) of a species in a given environment and specific timespan. By employing our approach, we can explain how a species adapts, regresses, or remains neutral within a specific environment and timespan. This method acknowledges not only the clear changes in traits but also the uncertainty and ambiguity that may arise during the process of evolution or involution.231233
Enhanced and Combined Representations in Extended Reality through Creative Industries
No full textThe urgent need for research and study with nondestructive and noninvasive methods and the preservation of cultural heritage led to the development and application of methodologies for the multi-level digitization of cultural elements. Photogrammetry and three-dimensional scanning offer photorealistic and accurate digital representations, while X-rays and computed tomography reveal properties and characteristics of the internal and invisible structure of objects. However, the investigation of and access to these datasets are, in several cases, limited due to the increased computing resources and the special knowledge required for their processing and analysis. The evolution of immersive technologies and the creative industry of video games offers unique user experiences. Game engines are the ideal platform to host the development of easy-to-use applications that combine heterogeneous data while simultaneously integrating immersive and emerging technologies. This article seeks to shed light on how heterogeneous digital representations of 3D imaging and tomography can be harmoniously combined in a virtual space and, through simple interactions, provide holistic knowledge and enhanced experience to end users. This research builds on previous experience concerning the virtual museum for the Antikythera Mechanism and describes a conceptual framework for the design and development of an affordable and easy-to-use display tool for combined representations of heterogeneous datasets in the virtual space. Our solution was validated by 62 users who participated in tests and evaluations. The results show that the proposed methodology met its objectives. Apart from cultural heritage, the specific methodology could be easily extended and adapted for training purposes in a wide field of application, such as in education, health, engineering, industry, and more.745
The Effect of Sustainable Development Goals and Subjecting Well-Being on Art Nascent Entrepreneurship: The Moderating Role of Entrepreneurship Education
No full textThis study explores the impact of the perceived well-being of students; the degree to which they perceive that their university enhances, facilitates, and supports six sustainable development goals (SDGs); and entrepreneurship education on nascent entrepreneurship. Moving beyond main effects, our research uses entrepreneurship education as a moderator, offering nuanced insights into nascent entrepreneurship, particularly among art students—an under-researched group. Utilizing data from a large sample within the established research project GUESSS, our findings show a very small statistically significant positive relationship between subjective well-being and nascent entrepreneurship among art students. However, the anticipated impact of university enhancement, facilitation, and support of SDGs on nascent entrepreneurship is not evident for these students. Notably, entrepreneurship education is important for the cultivation of the future generation of art entrepreneurs. These results have important theoretical and practical implications.14549
A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda
No full textDespite the increased adoption of Artificial Intelligence in Education (AIED), several concerns are still associated with it. This has motivated researchers to conduct (systematic) reviews aiming at synthesizing the AIED findings in the literature. However, these AIED reviews are diversified in terms of focus, stakeholders, educational level and region, and so on. This has made the understanding of the overall landscape of AIED challenging. To address this research gap, this study proceeds one step forward by systematically meta-synthesizing the AIED literature reviews. Specifically, 143 literature reviews were included and analyzed according to the technology-based learning model. It is worth noting that most of the AIED research has been from China and the U.S. Additionally, when discussing AIED, strong focus was on higher education, where less attention is paid to special education. The results also reveal that AI is used mostly to support teachers and students in education with less focus on other educational stakeholders (e.g. school leaders or administrators). The study provides a possible roadmap for future research agenda on AIED, facilitating the implementation of effective and safe AIED.1115
Explainable Graph Neural Networks: An Application to Open Statistics Knowledge Graphs for Estimating House Prices
No full textIn the rapidly evolving field of real estate economics, the prediction of house prices continues to be a complex challenge, intricately tied to a multitude of socio-economic factors. Traditional predictive models often overlook spatial interdependencies that significantly influence housing prices. The objective of this study is to leverage Graph Neural Networks (GNNs) on open statistics knowledge graphs to model these spatial dependencies and predict house prices across Scotland’s 2011 data zones. The methodology involves retrieving integrated statistical indicators from the official Scottish Open Government Data portal and applying three representative GNN algorithms: ChebNet, GCN, and GraphSAGE. These GNNs are compared against traditional models, including the tabular-based XGBoost and a simple Multi-Layer Perceptron (MLP), demonstrating superior prediction accuracy. Innovative contributions of this study include the use of GNNs to model spatial dependencies in real estate economics and the application of local and global explainability techniques to enhance transparency and trust in the predictions. The global feature importance is determined by a logistic regression surrogate model while the local, region-level understanding of the GNN predictions is achieved through the use of GNNExplainer. Explainability results are compared with those from a previous work that applied the XGBoost machine learning algorithm and the SHapley Additive exPlanations (SHAP) explainability framework on the same dataset. Interestingly, both the global surrogate model and the SHAP approach underscored the comparative illness factor, a health indicator, and the ratio of detached dwellings as the most crucial features in the global explainability. In the case of local explanations, while both methods showed similar results, the GNN approach provided a richer, more comprehensive understanding of the predictions for two specific data zones.12812
Using the EU Big Data Test Infrastructure to Publish MITOS Public Service Descriptions as Linked Open Data
No full textThe provision of public services is one of the main goals of public authorities worldwide. In this frame the availability of structured public service descriptions openly is very important since it can improve transparency and trust. The CPSV-AP specification has been proposed as a standard model to publish public service descriptions as Linked Open Data (LOD) and thus enhance their interoperability and integration with other data sources. The aim of this project is publish public service descriptions of the National Registry of Administrative Procedures (MITOS) in Greece as LOD based on CPSV-AP by leveraging the EU Big Data Test Infrastructure (BDTI). The methodology involves understanding the context, modelling public service descriptions, generating LOD, and publishing the data. The project architecture utilizes Apache Airflow for orchestration and Open Link Virtuoso for data storage and retrieval. Through various usage scenarios, the paper highlights the potential benefits of publishing public service descriptions as LOD.3737Proceedings EGOV-CeDEM-ePart conferenc
Artificial Intelligence Reinventing Materials Engineering: A Bibliometric Review
No full textThe use of artificial intelligence (AI) is revolutionizing many professions and research fields. Thus, the present study focuses on the implications that AI is having on research in materials science and engineering (MSE). To this end, a bibliometric review has been conducted to analyze the advances that AI is generating in MSE. Although expectations for AI advances in the field of MSE are high, the results of this study indicate that we are still at a preliminary stage of development. It is worth highlighting that despite the progress made, the potential of AI in MSE has not been fully exploited and numerous challenges remain to be overcome to achieve effective and widespread implementation. It should be noted that the subarea “Materials structure, processing, and properties” is the one that currently presents the largest number of research works linked to AI. It appears that the United States and China are currently the countries with the greatest involvement in the use of AI in the field of MSE. The emerging themes and thematic map of the topic are revealed, and future research directions are provided.1418814