The purpose of this study is to investigate the effect of regular CrossFit training on some force and jump parameters. 32 healthy wrestling men participated in the study, 16 experimental and 16 control groups. For the experimental group, CrossFit training, known as Cindy, was practiced three times a week for 8 weeks. The training consisted of 5 bars, 10 push-ups and 15 squats for 20 minutes. The control group continued the classical wrestling practice. Myotest accelerometric system was used for measurements of participants" values. For the analysis of the data, repeated measure ANOVA was used. According to the results, as a result of CrossFit training, athletes' squat jump heights increased (Wilks' Lambda = .541, F (1,30) = 25, p = .00). The mean post-training leap values (33.778 ± 5.48) were higher than the pre-training leap values (32.169 ± 4.95) (p <0.05). It can be concluded that Cindy CrossFit studies improve jumping and strength ability.

Object ve: COVID-19 is linked with significant mortality and morbidity. To curb the spread of the pandemic, curfews and lockdowns were imposed in many countries, leading to reduced physical activity (PA) irrespective of race, ethnicity, or income level. Although some papers documented how PA level was affected by COVID-19 in children and elderly in some countries, no similar data is available in Turkey during the pandemic. Therefore, we aimed to document the changes in step count in Turkey following the first reported case.Mater als and Methods: A total of 1427 participants were included in the study (male: n=242, female: n=1185), and were asked to fill out an online survey with questions on demographic information, working conditions, medical history, and average daily step count for two months before (January-February) and after (March-April) the outbreak of COVID-19 (10 March) in Turkey. Two-way repeated measure variance analysis and independentsample t-tests were used to analyze the data.Results: Data revealed that step count/day decreased by 43.5% (pre: 6564 ± 3615 steps/day vs. during: 3707 ± 3006 steps/day; p<0.05) during the pandemic compared to the pre-pandemic, with no difference between males (32.9%) and females (45.9%) (p>0.05). A similar significant reduction (p>0.05) in step count was observed in the working (pre: 6795 ± 3832 steps/day vs. post: 4027 ± 3223 steps/day) and unemployed adults (pre: 6337 ± 3374 steps/day vs. post: 3390 ± 2742 steps/day) (p<0.001). Conclus on:Compared with the pre-pandemic, step count markedly decreased in all groups during the pandemic in Turkey, regardless of gender and medical condition. This study provides preliminary data on how the COVID-19 pandemic has impacted step count in Turkey.

For many practical problems and applications, it is not feasible to create a vast and accurately labeled dataset, which restricts the application of deep learning in many areas. Semi-supervised learning algorithms intend to improve performance by also leveraging unlabeled data. This is very valuable for 2D-pose estimation task where data labeling requires substantial time and is subject to noise. This work aims to investigate if semi-supervised learning techniques can achieve acceptable performance level that makes using these algorithms during training justifiable. To this end, a lightweight network architecture is introduced and mean teacher, virtual adversarial training and pseudo-labeling algorithms are evaluated on 2D-pose estimation for surgical instruments. For the applicability of pseudo-labelling algorithm, we propose a novel confidence measure, total variation. Experimental results show that utilization of semi-supervised learning improves the performance on unseen geometries drastically while maintaining high accuracy for seen geometries. For RMIT benchmark, our lightweight architecture outperforms state-of-the-art with supervised learning. For Endovis benchmark, pseudolabelling algorithm improves the supervised baseline achieving the new state-of-the-art performance.

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