Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2025/r/bayburt/16189
  Artificial Intelligence and the Metaverse in Sport: Emerging Trends and Future Directions from a Bibliometric Analysis

  Esmer, Yusuf

  In recent years, artificial intelligence (AI) and metaverse technologies have found applications in areas such as athlete performance analysis, fan engagement and virtual event management, leading to a growing volume of research in these areas. The aim of this study is to reveal the bibliometric profile of scientific research on AI and metaverse in sports and to increase the knowledge at the intersection of these disciplines by analyzing scientific production and trends in this field. For this purpose, 255 publications on AI and metaverse in sports between 1992-2025 in the Web of Science (WoS) core collection in the period of October 2024 were examined by bibliometric analysis technique. As a result of the analysis, it was concluded that scientific studies on AI and metaverse in sports have increased over the years and attracted more and more attention. Especially the high scientific production in 2022 and the high citation rate in 2013 revealed the potential and importance of these fields. It is possible to say that this study is important in terms of identifying new research/application areas, emphasizing international collaborations, providing important information/data for academia/the sports industry, encouraging interdisciplinary approaches, guiding future studies and contributing to the relevant literature. In future studies, it is considered necessary to conduct more research on AI and metaverse in sports and to develop practical projects. In this context, it is recommended to conduct research to explain the effects of AI and metaverse technologies on sports and how these technologies can be used in sports organizations.

  AtıfOpenAlex2·TR Dizin1

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• MakaleAçık Erişim2021/r/bayburt/14686
  The Effect of Using Different Exhaustion Intervals and Mathematical Models on Critical Power Estimations

  Balcı, Görkem Aybars; Ozkaya, Ozgur; Norouzi, Mahdi; Çabuk, Refık; As, Hakan

  Predicted critical power (CP) varies up to 5-20% depending on the preferred mathematical model and different time to exhaustion intervals. Those differentiation rates related to CP estimations cause some contradictory results. The aim of this study was to evaluate the relationship between CP predictions obtained from three different exhaustion approaches (short: 2-10 minutes; medium: 2-15 minutes; long: 2-20 minutes) using five mathematical models (linear total work (CP1), linear 1/time (CP2), nonlinear 2-parameter (CP3), nonlinear 3-parameter (CP4) and exponential (CP5)), and other indices such as maximal lactate steady-state (MLSS), ventilatory threshold (VT), respiratory compensation point (RCP) and critical threshold (CT). 10 well trained male cyclists voluntarily participated in the study. VT and RCP levels of the athletes were determined by incremental ramp tests. Constant work rate exercises were applied on different days to determine maximal oxygen uptake, peak power output, MLSS, CT and CP. Obtained data were tested by validity analysis. As mathematical models and exhaustion intervals changed, the CP predictions varied up to 20%. Except the CP4, other CP estimations were higher than the work rates corresponding to the MLSS and VT (p<0.05). The CP5, which was estimated by short exhaustion interval, corresponded to the work rates belonging to the CT and RCP (p>0.05; standard error of estimate ~4% and r>0.95). Regardless of the preferred exhaustion interval, CP predictions obtained from the other mathematical models were insufficient to estimate any of anaerobic threshold indices (p<0.05). As a result, the CP5 estimated by short exhaustion interval can be used to predict the work rates corresponded to the CT and RCP. It was not appropriate to estimate the other threshold intensities by the CP. 

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  AtıfOpenAlex2·TR Dizin1

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• MakaleAçık Erişim2019/r/bayburt/15037
  Tek Bir Seansta Zirve Oksijen Kullanım Düzeyini Veren En Yüksek Güç Çıktısını Tahmin Etmenin Basit Bir Yöntemi

  Balcı, Görkem Aybars; Ozkaya, Ozgur; Norouzi, Mahdi; Çabuk, Refık; As, Hakan

  Zirve O2 tüketim düzeyi (𝑉 O2pik) veren en yüksek güç çıktısı (aerobik limit güç; ALG)şiddetli ve aşırı egzersiz alanlarını ayıran önemli bir eşiktir ve sporcularda bireysel birALG belirleyebilmek için çok sayıda sabit yüklü tüketici test yapmak gerekmektedir.Bu çalışmanın amacı; sporcuların ALG’lerini tek bir seansta belirleyebilecek biryaklaşım önermektir. Araştırmaya iyi antrene 12 bisiklet sporcusu katılmıştır (𝑉 O2maks:64,7±4,2 mL∙dk-1∙kg-1; Zirve Güç (ZG): 374,1±65,7 W). 𝑉 O2pik düzeyinin belirlenmesiiçin lineer yük artışlı (ramp) testler uygulatılmıştır (+1 W·2 s-1) ve protokollerinsonunda ulaşılan final güç üretim değerleri (FG) ortalama yanıt zamanı (mean responsetime: MRT) düzeltmesi uygulanmadan (FGMRT) ve uygulanarak (FGMRT)değerlendirilmiştir. Sporcuların 𝑉 O2maks’ı ve bu değeri veren ZG değerleri, doğrulamatestleri uygulanarak bulunmuştur. Devamında sporcuların ALG (𝑉 O2maks’a %5’tendaha yakın 𝑉 O2 değeri veren en yüksek güç çıktısı) değerleri, 15 W’lık intervallerlebitkinlikle sonlanan sabit yüklü testler yoluyla ayrı günlerde taranmıştır. FGMRT ileALG değerleri arasındaki fark anlamlı değildir (FGMRT: 435,2±50,8; ALG: 435,4±62,5W, p=0,968). Bland-Altman sonuçları yüksek uyum göstermiştir (Bias=0,20±17,6 W;p=0,968). FGMRT, gerçek ZG’nin %117,5±8,8’ine karşılık gelirken, belirlenen ALGdeğeri ZG’nin %117,1±7,37’sine karşılık gelmiştir (p=0,759). Diğer yandan ramptestinden elde edilen en yüksek 15-s 𝑉 O2 ortalamaları da ALG yükündeki 𝑉 O2yanıtlarıyla benzerdir ( Ramp Test: 62,3±4,9 ml∙dk-1∙kg-1, %96,2; ALG: 61,5±4,3ml∙dk-1∙kg-1, %95; p=0,119 ). Sonuç olarak, ALG’yi belirleyebilmek için ayrı günlerdeçok sayıda bitkinlikle sonlanan test yapmak yerine, tek seansta bir ramp testuygulamasının 𝑉 O2maks’ı düşük tahmin ettiği ancak ALG’yi ve bu değere karşılık gelen𝑉 O2 yanıtını belirleyebilmenin oldukça pratik bir yöntemi olabileceği bulunmuştur

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  AtıfTR Dizin1

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