Density-reduction-oriented layer assignment for rectangle escape routing

Author(s):  
Jin-Tai Yan ◽  
Jun-Min Chung ◽  
Zhi-Wei Chen
Integration ◽  
2012 ◽  
Vol 45 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Jin-Tai Yan ◽  
Zhi-Wei Chen

2015 ◽  
pp. 50-58
Author(s):  
Thi Dung Nguyen ◽  
Tam Vo

Background: The patients on hemodialysis have a significantly decreased quality of life. One of many problems which reduce the quality of life and increase the mortality in these patients is osteoporosis and osteoporosis associated fractures. Objectives: To assess the bone density of those on hemodialysis by dual energy X ray absorptiometry and to examine the risk factors of bone density reduction in these patients. Patients and Method: This is a cross-sectional study, including 93 patients on chronic hemodialysis at the department of Hemodialysis at Cho Ray Hospital. Results: Mean bone densities at the region of interest (ROI) neck, trochanter, Ward triangle, intertrochanter and total neck are 0.603 ± 0.105; 0.583 ± 0.121; 0.811 ± 0.166; 0.489 ± 0.146; 0.723 ± 0.138 g/cm2 respectively. The prevalences of osteoporosis at those ROI are 39.8%, 15.1%; 28%; 38.7%; and 26.9% respectively. The prevalences of osteopenia at those ROI are 54.8%; 46.3%; 60.2%; 45.2% and 62.7% respectively. The prevalence of osteopososis in at least one ROI is 52.7% and the prevalence of osteopenia in at least one ROI is 47.3%. There are relations between the bone density at the neck and the gender of the patient and the albuminemia. Bone density at the trochanter is influenced by gender, albuminemia, calcemia and phosphoremia. Bone density at the intertrochanter is affected by the gender. Bone density at the Ward triangle is influenced by age and albuminemia. Total neck bone density is influenced by gender, albuminemia and phosphoremia. Conclusion: Osteoporosis in patients on chronic hemodialysis is an issue that requires our attention. There are many interventionable risk factors of bone density decrease in these patients. Key words: Osteoporosis, DEXA, chronic renal failure, chronic hemodialysis


2013 ◽  
Vol 33 (6) ◽  
pp. 1548-1552
Author(s):  
Wenchao GAO ◽  
Qiang ZHOU ◽  
Xu QIAN ◽  
Yici CAI

2021 ◽  
Vol 13 (11) ◽  
pp. 2135
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Henrique Lorenzo ◽  
Adrián Meijide-Rodríguez

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m.


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