RSCM: Region Selection and Concurrency Model for Multi-Class Weather Recognition

2017 ◽  
Vol 26 (9) ◽  
pp. 4154-4167 ◽  
Author(s):  
Di Lin ◽  
Cewu Lu ◽  
Hui Huang ◽  
Jiaya Jia
Keyword(s):  
Region Selection ◽  
Concurrency Model

Селекция и семеноводство картофеля в Камчатском крае

2018 ◽  
Author(s):  
N.I. Ryakhovskaya ◽  
T.P. Sherstyukova ◽  
M.L. Gamolina
Keyword(s):  
Russian Federation ◽  
Potato Yield ◽  
The State ◽  
Limiting Factors ◽  
State Register ◽  
Region Selection ◽  
New Varieties ◽  
The Russian Federation ◽  
Selection Of

Рассмотрены агроклиматические условия Камчатского края и лимитирующие факторы, сдерживающие рост урожайности картофеля. Приведены характеристики новых сортов картофеля селекции Камчатского НИИСХ созданных в соответствии с приоритетными для региона направлениями селекции, включенных в Государственный реестр селекционных достижений РФ и охраняемых патентами.Agroclimatic conditions of the Kamchatka Krai and limiting factors that restrain the growth of potato yield are considered. The characteristics of new varieties of potatoes of the selection of the Kamchatsky RIA are created in accordance with the priority for the region selection areas included in the State Register of Selection Achievements of the Russian Federation and protected by patents.

Image restoring method based on region selection network and its application in computational imaging

2021 ◽  
Vol 29 (4) ◽  
pp. 864-876
Author(s):  
Xiao-tian WU ◽  
◽  
Hang YANG ◽  
Xing-long SUN ◽  
Keyword(s):  
Computational Imaging ◽  
Region Selection

scholarly journals Prototyping a Concurrency Model

2013 ◽  
Author(s):  
Benjamin Morandi ◽  
Mischael Schill ◽  
Sebastian Nanz ◽  
Bertrand Meyer
Keyword(s):  
Concurrency Model

Improvement of Control Law Derivation and Region Selection for ${D}{-}\Sigma$ Digital Control

2015 ◽  
Vol 62 (10) ◽  
pp. 6042-6050 ◽  
Author(s):  
Tsai-Fu Wu ◽  
Hui-Chung Hsieh ◽  
Chih-Hao Chang ◽  
Li-Chiun Lin ◽  
Yung-Ruei Chang
Keyword(s):  
Digital Control ◽  
Control Law ◽  
Region Selection ◽  
Selection For

Euclidean Distance Based Region Selection for Fundus Images

2020 ◽  
pp. 1659-1664
Author(s):  
Ramakrishnan Sundaram ◽  
K. S. Ravichandran ◽  
Premaladha Jayaraman
Keyword(s):  
Euclidean Distance ◽  
Fundus Images ◽  
Region Selection ◽  
Selection For

Region Selection Model with Saliency Constraint for Fine-Grained Recognition

2019 ◽  
pp. 365-376
Author(s):  
Shaoxiong Zhou ◽  
Shengrong Gong ◽  
Shan Zhong ◽  
Wei Pan ◽  
Wenhao Ying
Keyword(s):  
Selection Model ◽  
Fine Grained ◽  
Region Selection

scholarly journals Influence of the background activity region selection on the measurement of glomerular filtration rate using the Gates method

2008 ◽  
Vol 65 (10) ◽  
pp. 729-732 ◽  
Author(s):  
Marija Dopudja ◽  
Boris Ajdinovic ◽  
Ljiljana Jaukovic ◽  
Marijana Petrovic ◽  
Zoran Jankovic
Keyword(s):  
Glomerular Filtration Rate ◽  
Glomerular Filtration ◽  
Filtration Rate ◽  
Background Activity ◽  
Accurate Method ◽  
Lateral Side ◽  
Lower Pole ◽  
Region Selection ◽  
Significant Difference ◽  
51Cr Edta

Background/Aim. The most frequent method for the assessment of glomerular filtration rate GFR) in clinical practice is clearance of creatinine, clearance of chromium-51 radiolabelled ethylene diamine tetraacetic acid (51Cr-EDTA) and clearance of technetium-99m radiolabelled diethylene triamine pentaacetic acid (99mTc-DTPA). The Gates method for glomerular filtration rate assessment is based on distribution of 99mTc-DTPA in the kidney 2-3 minute after its applying. Calculation of GFR is corrected for the background and depth of the kidney and finally expressed as a percentage of the net injected counts. This value of GFR highly correlates with biexponential model as the most accurate method for the assessment of GFR. The aim of this study was to determine the influence of different background sites on GFR rate assessment using the Gates method. Methods. We analysed 50 patients who were divided into two groups: the group of healthy subjects (21 subjects, mean age 47.38 yrs) and the group of subjects with unilateral kidney damage (29 subjects, mean age 39.79 yrs). Three different background activity regions were chosen: a region drawn below the lower pole of each kidney, region drawn by the lateral side of each kidney and region drawn around the whole kidney. Results. Gromerular filtration rate calculated by the use of a region under the lower pole is statistically significantly higher than GFR calculated using a region by the lateral side and around the whole kidney (p < 0.0001). Glomerular filtration rate calculated using the region by the lateral side and region around the whole kidney did not show statistically significant difference (p > 0.05). Conclusion. The selection of background activity region has a significant influence on GFR rate measured by the Gates method. It is recommended to use only one method for choosing the region of background activity.

scholarly journals Macroscopy and Microscopy Study of Scopraia dulcis L.

2018 ◽  
Vol 23 (1) ◽  
pp. 56
Author(s):  
Djoko Santosa ◽  
Subagus Wahyuono ◽  
Sugeng Riyanto ◽  
SM Widyastuti
Keyword(s):  
Glandular Trichomes ◽  
Principal Component ◽  
Morphological Diversity ◽  
Microscopy Study ◽  
Sampling Location ◽  
Scoparia Dulcis ◽  
Region Selection ◽  
Microscopy Analysis ◽  
Palisade Cells ◽  
Selection Of

A morphological diversity analysis of Scoparia dulcis by macroscopy and microscopy has been conducted. The aim of this research is to determine the morphological and anatomical character of S. dulcis which grow in Opak watershed, Special Region of Yogyakarta. Samples were taken from Sleman (Cangkringan, Ngemplak Kalasan, and Prambanan) and Bantul (Imogiri and Pundong) region. Selection of sampling location is based on altitude difference. The samples are the third leaf order from the tip of the stem. They were measured in length and width. Microscopy analysis was performed by observing the number of glandular trichomes, palisade cells in the epidermis and stomata in the upper and lower epidermis. The data obtained were analyzed using principal component analysis (PCA). The results show that based on morphological and anatomical assessment, there were found 3 clusters of S. dulcis, i.e. Cangkringan-Ngemplak-Prambanan, Kalasan, and Imogiri-Pundong cluster.

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