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  1. 6201

    Investing in an Olympic agenda: from Rio to Tokyo and beyond by Eduardo Russo, Ariane Roder Figueira, Leonardo Jose Mataruna-Dos-Santos

    Published 2025-01-01
    “…ContextThis study was inspired by the considerable risks and diminishing enthusiasm among societies to invest in Olympic agendas, which traditionally involve billions of dollars, various opportunities, and complexities for host countries.PurposeThe objective of this study was to evaluate the risks and benefits of long-term equity investments for companies and governments engaged in the Olympic movement.MethodQualitative methodologies were employed for this research, utilizing a multi-case approach that included 38 comprehensive interviews with companies and entities impacted by the Rio 2016 and Tokyo 2020 Olympic Games.ResultsConsequently, a theoretical framework titled “Risks and Opportunities Related to Olympic Investments” is presented to elucidate the dynamics of investment flows, competitive pressures in specific sectors, and future threats and trends for the Olympic movement.FindingsThe research revealed that the gigantism of past editions exerted significant pressure on organizers and companies to adopt new management practices and enhance investment planning, striving for minimal environmental impact and long-term economic sustainability. …”
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    Role of platelet indices and vitamin D in forecasting deterioration of glycemic control and vascular complications in type 2 diabetes by Maryam Ahmed Obaid Al Ali, Khalid Abdelsamea Mohamedahmed, Asaad Ma Babker

    Published 2025-01-01
    “…The findings suggest a potential predictive role of platelet indices and vitamin D in the deterioration of glycemic control and the development of vascular complications in T2DM patients attending Thumbay Hospital, Ajman. …”
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  5. 6205

    QAR Data Imputation Using Generative Adversarial Network with Self-Attention Mechanism by Jingqi Zhao, Chuitian Rong, Xin Dang, Huabo Sun

    Published 2024-03-01
    “…Therefore, we innovatively propose the imputation models A-AEGAN (“A” denotes attention mechanism, “AE” denotes autoencoder, and “GAN” denotes generative adversarial network) and SA-AEGAN (“SA” denotes self-attentive mechanism) for missing values of QAR data, which can be effectively applied to QAR data. …”
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    Empirically validating a computational model of automatic behavior shaping. by Vincent Berardi, Tian Lan, Ariane Guirguis, Uri Maoz

    Published 2025-01-01
    “…In previous work, a computational model was built to investigate how to best arrange automatic behavior shaping procedures, but the degree to which this model reflects actual human behavior is not known.…”
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    Les Plantagenêts vus par un abbé du xve siècle : l’article « Anglia » du Granarium de John Wheathampstead by Élisa Mantienne

    Published 2021-06-01
    “…This paper deals with the image of Plantagenet kings as represented in a 15th century encyclopedia : the Granarium, composed by John Wheathampstead the Benedictine abbot of the monastery of St Albans. More precisely, we will study the short text called « Anglia », which is a narrative of the reigns of the English kings from the mythical origins of England to king Henry V’s death. …”
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