Engineering with Computers - Aerated flow characterized by complex mass transfer processes with multiple hydraulic properties is a common enviro-hydraulics phenomenon, which have a variety of... 相似文献
The Journal of Supercomputing - The performance of XPath query is the key factor to the capacity of XML processing. It is an important way to improve the performance of XPath by making full use of... 相似文献
The purpose is to study the applicability of digital and intelligent real-time Image Processing (IP) in fitness motion detection under the environment of the Internet of Things (IoT). Given the absence of real-time training standards and possible workout injury problems during fitness activities, an intelligent fitness real-time IP system based on Deep Learning (DL) is implemented. Specifically, the keyframes of the real-time images are collected from the fitness monitoring video, and the DL algorithm is introduced to analyze the fitness motions. Afterward, the performance of the proposed system is evaluated through simulation. Subsequently, the Noise Reduction (NR) performance of the proposed algorithm is evaluated from the Peak Signal-to-Noise Ratio (PSNR), which remains above 20 dB for seriously noisy images (with a noise density reaching up to 90%). By comparison, the PSNR of the Standard Median Filter (SMF) and Ranked-order Based Adaptive Median Filter (RAMF) algorithms are not higher than 10 dB. Meanwhile, the proposed algorithm outperforms other DL algorithms by over 2.24% with a detection accuracy of 97.80%; the proposed system can adaptively detect the fitness motion, with a transmission delay no larger than 1 s given a maximum of 750 keyframes. Therefore, the proposed DL-based intelligent fitness real-time IP algorithm has strong robustness, high detection accuracy, and excellent real-time image diagnosis and processing effect, thus providing an experimental reference for sports digitalization and intellectualization.
Introduction: The aim of this study was to analyze the selected psychosocial aspects of chronic kidney disease in children treated with hemodialysis (HD). Methods: The study included 25 children treated with HD aged 2 to 18 years and their parents. Data concerning the illness and socio‐demographic parameters was collected. We used the Paediatric Quality of Life Inventory (PedsQL) for patients and for their parents the PedsQL‐proxy version, General Health Questionnaire (GHQ‐12), Berlin Social Support Scales (BSSS), and the Caregivers Burden Scale (CBS) to evaluate health‐related quality of life (QoL) of HD children and their primary caregivers. Findings: In the PedsQL test, the QoL of HD children was lower than in healthy children. Children treated with HD assessed their QoL on the PedsQL questionnaire higher than the primary caregivers, on all subscales as well as an overall health‐related QoL. Scoring below 2 on the GHQ‐12 test was reported in 56% of mothers, which may indicate that psychological symptoms have intensified. There was no correlation between BSSS, CBS, and GHQ‐12. Discussion: The assessment of QoL in pediatric patients would allow for the earliest possible identification of their nonsomatic problems and irregularities. This could, consequently, contribute to improving QoL in both children with chronic kidney disease and their families. 相似文献