• Digital Humanities and Cultural Heritage
• 2013-2022close
• QA close
• Hal-Diderot close

This review aims to summarize the fundamental classes of antioxidants, the utilization of the supplement with antioxidants as ergogenic benefits in exercise and sport, the efficiency, and the consequence of their chronic uses. Antioxidants have been used for a long time in different sports disciplines and at all degrees of rivalry. The supplement with antioxidants anticipates muscle damage by reducing immune dysfunction, inflammation, and fatigue syndrome for athletes. Subsequently, antioxidant supplementation will improve the physical performances and health of athletes. Polyphenol compounds as incredible antioxidants that are abundant in numerous plants. The consumption of antioxidants could be hazardous whenever taken in high dosages. Researchers found that overdoses may cause undesirable symptoms and might grow the risk of death.

The rapid advancement of artificial intelligence (AI) offers exciting opportunities for marketing practice and academic research. In this study, through the application of natural language processing, machine learning, and statistical algorithms, we examine extant literature in terms of its dominant topics, diversity, evolution over time, and dynamics to map the existing knowledge base. Ten salient research themes emerge: (1) understanding consumer sentiments, (2) industrial opportunities of AI, (3) analyzing customer satisfaction, (4) electronic word-of-mouth–based insights, (5) improving market performance, (6) using AI for brand management, (7) measuring and enhancing customer loyalty and trust, (8) AI and novel services, (9) using AI to improve customer relationships, and (10) AI and strategic marketing. The scientometric analyses reveal key concepts, keyword co-occurrences, authorship networks, top research themes, landmark publications, and the evolution of the research field over time. With the insights as a foundation, this article closes with a proposed agenda for further research. International audience

Citizenship by investment (CBI) programs have recently garnered significant academic and media attention. Turkey introduced such a program in 2017 that offers citizenship in exchange for investment in residential property. Eventually, thousands of foreigners, mainly from the Middle East and Asia have purchased houses, particularly in Istanbul. Foreigners' share in total houses sold in Istanbul almost sextupled and exceeded 10 percent of total sales. This study estimates the short-run impact of relatively wealthy foreigners on the residential property prices in Istanbul to buy a Turkish passport. It finds that the Turkish CBI program positively impacts house prices by two percent in the districts, which are likely to be favored most by immigrant investors.

Museums and cultural institutions, in general, are in a constant challenge of adding more value to their collections. The attractiveness of assets is practically tightly related to their value obeying the offer and demand law. New digital visualization technologies are found to give more excitements, especially to the younger generation as it is proven by multiple studies. Nowadays, museums around the world are currently trying to promote their collections through new multimedia and digital technologies such as 3D modeling, virtual reality (VR), augmented reality (AR), and serious games. However, the difficulty and the resources required to implement such technologies present a real challenge. Through this paper, we propose a 3D acquisition and visualization framework aiming mostly at increasing the value of cultural collections. This framework preserves cost-effectiveness and time constraints while still introducing new ways of visualization and interaction with high-quality 3D models of cultural objects. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021. Acknowledgements. This publication was made possible by NPRP grant 9-181-1-036 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors (www.ceproqha.qa). Scopus

Purpose Digital tools have been used to document cultural heritage with high-quality imaging and metadata. However, some of the historical assets are totally or partially unlabeled and some are physically damaged, which decreases their attractiveness and induces loss of value. This paper introduces a new framework that aims at tackling the cultural data enrichment challenge using machine learning. Design/methodology/approach This framework focuses on the automatic annotation and metadata completion through new deep learning classification and annotation methods. It also addresses issues related to physically damaged heritage objects through a new image reconstruction approach based on supervised and unsupervised learning. Findings The authors evaluate approaches on a data set of cultural objects collected from various cultural institutions around the world. For annotation and classification part of this study, the authors proposed and implemented a hierarchical multimodal classifier that improves the quality of annotation and increases the accuracy of the model, thanks to the introduction of multitask multimodal learning. Regarding cultural data visual reconstruction, the proposed clustering-based method, which combines supervised and unsupervised learning is found to yield better quality completion than existing inpainting frameworks. Originality/value This research work is original in sense that it proposes new approaches for the cultural data enrichment, and to the authors' knowledge, none of the existing enrichment approaches focus on providing an integrated framework based on machine learning to solve current challenges in cultural heritage. These challenges, which are identified by the authors are related to metadata annotation and visual reconstruction. International audience

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Through this paper, we aim at investigating the impact of artificial intelligence technologies on cultural heritage promotion and long-term preservation in terms of digitization effectiveness, attractiveness of the assets, and value empowering. Digital tools have been validated to yield sustainable and yet effective preservation for multiple types of content. For cultural data, however, there are multiple challenges in order to achieve sustainable preservation using these digital tools due to the specificities and the high-quality requirements imposed by cultural institutions. With the rise of machine learning and data science technologies, many researchers and heritage organizations are nowadays searching for techniques and methods to value and increase the reliability of cultural heritage digitization through machine learning. The present study investigates some of these initiatives highlighting their added value and potential future improvements. We mostly cover the aspects related to our context which is the long-term cost-effective digital preservation of the Qatari cultural heritage through the CEPROQHA project. International audience

Through this paper, we aim at investigating the impact of using deep learning-based technologies such as super-resolution on Holoscopic 3D (H3D) images. Holoscopic 3D imaging is a technology that aims at providing cost-effective alternatives for 3D content viewing and consumption without requiring a special headgear or posture. The technique is using a special lens array fitted to standard DSLR or mirrorless cameras to generate or capture 3D content. The output is a Holoscopic 3D image that can be displayed in lightfield displays or Multiview displays following a post-processing procedure. The main advantage of this technique is its cost-effectiveness in viewing and interacting with 3D content. However, one of its drawbacks is the low spatial density of the commercial cameras CMOS sensors and the lens induced imperfections. The latter can be fixed in software using some distortion correction techniques. However, the former is still challenging in terms of techniques that result in naturally looking output. Mitigating such issues with hardware will lead to higher costs and the technique loses its main advantage. Our approach consists of designing a framework that leverages software tools in order to upscale the output of H3D cameras whilst solving the low spatial density problem of H3D images. We also investigate the impact of deep learning-based video motion interpolation on the output quality of the cultural H3D imaging framework. International audience