489 research outputs found

    Excessive Exercise Habits in Marathoners as Novel Indicators of Masked Hypertension

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    Background. Excessive exercise such as marathon running increases the risk of cardiovascular events that may be related to myocardial infarction and sudden death. We aimed to investigate that the exercise characteristics can be used as a novel indicator of masked hypertension. Methods. A total of 571 middle-aged recreational male marathoners were assigned to a high blood pressure group (HBPG; = 214) or a normal blood pressure group (NBPG; = 357). A graded exercise test was used to examine the hemodynamic response and cardiac events, and the personal exercise characteristics were recorded. Results. Systolic blood pressure and diastolic blood pressure were higher in the HBPG than in the NBPG ( < 0.05, all). The marathon history, exercise intensity, and time were longer and higher, whereas the marathon completion duration was shorter in the HBPG than in NBPG ( < 0.05, all). HBPG showed a higher frequency of alcohol consumption than NBPG ( < 0.05). Conclusion. More excessive exercise characteristics than the normative individuals. If the individuals exhibit high blood pressure during rest as well as exercise, the exercise characteristics could be used as a novel indicator for masked hypertension

    Retrieval of Melt Ponds on Arctic Multiyear Sea Ice in Summer from TerraSAR-X Dual-Polarization Data Using Machine Learning Approaches: A Case Study in the Chukchi Sea with Mid-Incidence Angle Data

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    Melt ponds, a common feature on Arctic sea ice, absorb most of the incoming solar radiation and have a large effect on the melting rate of sea ice, which significantly influences climate change. Therefore, it is very important to monitor melt ponds in order to better understand the sea ice-climate interaction. In this study, melt pond retrieval models were developed using the TerraSAR-X dual-polarization synthetic aperture radar (SAR) data with mid-incidence angle obtained in a summer multiyear sea ice area in the Chukchi Sea, the Western Arctic, based on two rule-based machine learning approachesdecision trees (DT) and random forest (RF)in order to derive melt pond statistics at high spatial resolution and to identify key polarimetric parameters for melt pond detection. Melt ponds, sea ice and open water were delineated from the airborne SAR images (0.3-m resolution), which were used as a reference dataset. A total of eight polarimetric parameters (HH and VV backscattering coefficients, co-polarization ratio, co-polarization phase difference, co-polarization correlation coefficient, alpha angle, entropy and anisotropy) were derived from the TerraSAR-X dual-polarization data and then used as input variables for the machine learning models. The DT and RF models could not effectively discriminate melt ponds from open water when using only the polarimetric parameters. This is because melt ponds showed similar polarimetric signatures to open water. The average and standard deviation of the polarimetric parameters based on a 15 x 15 pixel window were supplemented to the input variables in order to consider the difference between the spatial texture of melt ponds and open water. Both the DT and RF models using the polarimetric parameters and their texture features produced improved performance for the retrieval of melt ponds, and RF was superior to DT. The HH backscattering coefficient was identified as the variable contributing the most, and its spatial standard deviation was the next most contributing one to the classification of open water, sea ice and melt ponds in the RF model. The average of the co-polarization phase difference and the alpha angle in a mid-incidence angle were also identified as the important variables in the RF model. The melt pond fraction and sea ice concentration retrieved from the RF-derived melt pond map showed root mean square deviations of 2.4% and 4.9%, respectively, compared to those from the reference melt pond maps. This indicates that there is potential to accurately monitor melt ponds on multiyear sea ice in the summer season at a local scale using high-resolution dual-polarization SAR data.open

    Automatic White Balancing via Gray Surface Identification

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    The key to automatic white balancing of digital imagery is to estimate accurately the color of the overall scene illumination. Many methods for estimating the illuminationā€™s color have been proposed [1-6]. Although not the most accurate, one of the simplest and quite widely used methods is the gray world algorithm [6]. Borrowing on some of the strengths and simplicity of the gray world algorithm, we introduce a modification of it that significantly improves on its performance while adding little to its complexity
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