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https://www.selleckchem.com/products/gsk2578215a.html The installation of solar plants everywhere in the world increases year by year. Automated diagnostic methods are needed to inspect the solar plants and to identify anomalies within these photovoltaic panels. The inspection is usually carried out by unmanned aerial vehicles (UAVs) using thermal imaging sensors. The first step in the whole process is to detect the solar panels in those images. However, standard image processing techniques fail in case of low-contrast images or images with complex backgrounds. Moreover, the shades of power lines or structures similar to solar panels impede the automated detection process. In this research, two self-developed methods are compared for the detection of panels in this context, one based on classical techniques and another one based on deep learning, both with a common post-processing step. The first method is based on edge detection and classification, in contrast to the second method is based on training a region based convolutional neural networks to identify a preaches a precision of 0.996, a recall of 0.981 and a F1 score of 0.989. The two panel detection methods are highly effective in the presence of complex backgrounds.The aim of this study was to evaluate the effect of additional motivational enhancement through telephone-based counseling on short- and long-term smoking abstinence among Korean adolescents. A comparative retrospective study was conducted based on the longitudinal follow up in Quitline from 2010 to 2017. A total of 533 and 178 adolescent smokers voluntarily participated in the 1-year quitting counseling only (group A, who were ready to quit) and the additional 4-week motivational interviewing before 1-year quitting counseling (group B, who were ambivalent about quitting), respectively. The outcomes were self-reported continuous abstinence at 30-day, 6-month, and 1-year follow up. Logistic regression was applied to estimate the effect of potential

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