Visualization analysis of side-edge flow phenomena in different thickness/width rectangular cavities using a rotary runner exchange system

2011 ◽  
Vol 51 (4) ◽  
pp. 721-729 ◽  
Author(s):  
Yoshinori Kanetoh ◽  
Hidetoshi Yokoi
Keyword(s):  
Exchange System ◽  
Visualization Analysis ◽  
Side Edge ◽  
Flow Phenomena ◽  
Different Thickness ◽  
Edge Flow

Detailed measurements of a flap side-edge flow field

1998 ◽  
Author(s):  
Ronald Radeztsky, Jr. ◽  
Bart Singer ◽  
Mehdi Khorrami
Keyword(s):  
Flow Field ◽  
Side Edge ◽  
Edge Flow

The Ultrastructure of Monkey Gingival Epithelium

1967 ◽  
Vol 25 ◽  
pp. 16-17
Author(s):  
C.N. Sun
Keyword(s):  
Epithelial Cells ◽  
Macaca Nemestrina ◽  
Stratum Granulosum ◽  
Solution Ph ◽  
Gingival Epithelial Cells ◽  
Stratum Spinosum ◽  
Cell Layers ◽  
Gingival Epithelium ◽  
The Individual ◽  
Different Thickness

The present study demonstrates the ultrastructure of the gingival epithelium of the pig tail monkey (Macaca nemestrina). Specimens were taken from lingual and facial gingival surfaces and fixed in Dalton's chrome osmium solution (pH 7.6) for 1 hr, dehydrated, and then embedded in Epon 812.Tonofibrils are variable in number and structure according to the different region or location of the gingival epithelial cells, the main orientation of which is parallel to the long axis of the cells. The cytoplasm of the basal epithelial cells contains a great number of tonofilaments and numerous mitochondria. The basement membrane is 300 to 400 A thick. In the cells of stratum spinosum, the tonofibrils are densely packed and increased in number (fig. 1 and 3). They seem to take on a somewhat concentric arrangement around the nucleus. The filaments may occur scattered as thin fibrils in the cytoplasm or they may be arranged in bundles of different thickness. The filaments have a diameter about 50 A. In the stratum granulosum, the cells gradually become flatted, the tonofibrils are usually thin, and the individual tonofilaments are clearly distinguishable (fig. 2). The mitochondria and endoplasmic reticulum are seldom seen in these superficial cell layers.

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Absolute Point ◽  
Language Analysis ◽  
Search Patterns ◽  
Language Data ◽  
Report Accuracy ◽  
Point To Point ◽  
Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

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