scholarly journals Simulative Global Warming Negatively Affects Cotton Fiber Length through Shortening Fiber Rapid Elongation Duration

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Yanjiao Dai ◽  
Jiashuo Yang ◽  
Wei Hu ◽  
Rizwan Zahoor ◽  
Binglin Chen ◽  
...  
Keyword(s):  
Global Warming ◽  
Cotton Fiber ◽  
Fiber Length ◽  
Rapid Elongation

Influence of Cotton Fiber Strength and Fineness on Fiber Damage during Lint Cleaning

1988 ◽  
Vol 58 (8) ◽  
pp. 433-438 ◽  
Author(s):  
J. K. Dever ◽  
J. R. Gannaway ◽  
R. V. Baker
Keyword(s):  
Significant Relationship ◽  
Cotton Fiber ◽  
Fiber Length ◽  
Fiber Strength ◽  
Fiber Breakage ◽  
Fiber Damage ◽  
Fiber Properties ◽  
Fiber Fineness ◽  
Wide Range ◽  
Induced Changes

Seven sources of cotton representing a wide range of fiber properties were roller ginned, saw ginned, or saw ginned plus processed through tandem saw lint cleaners or through an aggressive carding-type cleaner (Cottonmaster1). Lint cleaner induced changes in fiber length and nep count were compared to fiber property measurements from roller ginned samples. Fiber length deterioration from saw ginning was negatively correlated with fiber strength. Fiber breakage in lint cleaning was positively correlated with fiber fineness. Resistance to fiber length damage in ginning was explained best by fiber strength and fineness, or an estimate of individual fiber strength. Initial and final nep level were related to fineness, nonlint content, and upper quartile length, but an increase in neps due to lint cleaning had no significant relationship to fiber properties.

Generation of cotton fiber length probability density function with length measures

2012 ◽  
Vol 103 (2) ◽  
pp. 225-230 ◽  
Author(s):  
Qian Lin ◽  
Mingjie Xing ◽  
William Oxenham ◽  
Chongwen Yu
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Cotton Fiber ◽  
Fiber Length

Competition of Four Morningglory(Ipomoeaspp.) Species with Cotton(Gossypium hirsutum)

Weed Science ◽  
1978 ◽  
Vol 26 (5) ◽  
pp. 484-488 ◽  
Author(s):  
R. H. Crowley ◽  
G. A. Buchanan
Keyword(s):  
Gossypium Hirsutum ◽  
Cotton Fiber ◽  
Fiber Length ◽  
Competitive Ability ◽  
The Other ◽  
Lint Percentage ◽  
Ipomoea Purpurea ◽  
Pitted Morningglory ◽  
Harvesting Efficiency

Tall morningglory [Ipomoea purpurea(L.) Roth.], pitted morningglory(I. lacunosaL.), ivyleaf morningglory [I. hederacea(L.) Jacq.] and entireleaf morningglory(I. hederaceavar.integruisculaGray) were compared for their competitive ability with cotton(Gossypium hirsutumL.) at weed densities of 4, 8, 16 and 32 weeds/15 m of row. Tall morningglory was the most competitive with yield reductions of as much as 88% at 32 weeds/15 m. The higher populations of tall morningglory also significantly reduced cotton populations, mechanical harvesting efficiency and lint percentage below that in the controls. Pitted morningglory densities twice that of tall morningglory were required to produce cotton responses similar to those induced by tall morningglory. Ivyleaf morningglory and entireleaf morningglory were almost identical in their competitive potentials and were less competitive than the other species evaluated. Their competitive ability was greatly reduced by disease. Competition from morningglories did not consistently affect cotton fiber micronaire, fiber length or strength.

A comparative study of the effects of cotton fiber length parameters on modeling yarn properties

2013 ◽  
Vol 83 (9) ◽  
pp. 961-970 ◽  
Author(s):  
Yiyun Cai ◽  
Xiaoliang Cui ◽  
James Rodgers ◽  
Devron Thibodeaux ◽  
Vikki Martin ◽  
...  
Keyword(s):  
Comparative Study ◽  
Cotton Fiber ◽  
Fiber Length ◽  
Yarn Properties

Bias Reduction in Kernel Estimation of the Density Function of Cotton Fiber Length

2012 ◽  
Vol 627 ◽  
pp. 23-28
Author(s):  
Yu Heng Su ◽  
Guang Song Yan
Keyword(s):  
Density Function ◽  
Cotton Fiber ◽  
Fiber Length ◽  
Length Distribution ◽  
Estimation Method ◽  
Kernel Estimation ◽  
Short Fiber ◽  
Bias Reduction ◽  
Theoretical Research ◽  
Fiber Length Distribution

Abstract. The non-parameter kernel estimation has become a dramatic method on fitting the distribution density function of cotton fiber length in theoretical research on fiber length. It can get a differentiable and integrable density function of cotton length distribution, and make the probability approach more effective on analysis and prediction of yarn performance. But, due to the requirements of the fitting smoothness, there is a bias between calculational index and measured value, especially to the short fiber content. This research uses the power function to fit the distribution of short fibers, then according to the principle of mixed distribution, revises the density function gotten by kernel estimation method, and gives a precise estimation of density function. The revised algorithm is more exact to fit the density function of fiber length. This approach is a new way to study the fiber length distribution and its effect on yarn properties both theoretically and practically.

Study on the Characterization of Cotton Fiber Length Distribution

2013 ◽  
Vol 821-822 ◽  
pp. 398-401 ◽  
Author(s):  
Xue Qin Kuang ◽  
Jian Ping Yang ◽  
Chong Wen Yu
Keyword(s):  
Probability Density Function ◽  
Weibull Distribution ◽  
Cotton Fiber ◽  
Fiber Length ◽  
Mixed Model ◽  
Length Distribution ◽  
Length Frequency ◽  
Fiber Length Distribution ◽  
Two Component

In order to characterize cotton fiber length distribution, the probability density function with parameters were used to describe the fiber length frequency histograms. In this paper, four cotton fiber samples (bale, carded sliver, combed sliver and finisher sliver) were selected, and the fiber histograms by weight were measured by USTER AFIS Pro. Two- and three-component mixed Weibull distributions were adopted by us to fit these histograms, and the relevant fiber length measures were calculated. The results showed that mixed model could well describe the entire fiber length distribution of different cotton fiber samples, and two-component mixed Weibull distribution, rather than three-component one, fitted these histograms better.

scholarly journals Study of the influence of cotton fiber indicators on yarn quality

2021 ◽  
Vol 304 ◽  
pp. 03036
Author(s):  
Umida Yusupalieva ◽  
Saypila Matismailov ◽  
Alisher Yuldashev
Keyword(s):  
Tensile Strength ◽  
Coefficient Of Variation ◽  
Cotton Fiber ◽  
Fiber Length ◽  
Optimal Parameter ◽  
Regression Equation ◽  
Mathematical Planning ◽  
Fiber Properties ◽  
Yarn Quality ◽  
The Relationship

In order to determine the relationship between the fiber properties of the yarn quality in the article, experiments were conducted using a passive method of mathematical planning and a regression equation was obtained for each optimal parameter. Based on the experiments, it was found that a decrease in fiber micronaire, an increase in tensile strength, fiber length and uniformity, leads to an increase in the specific tensile strength of yarn, a decrease in the coefficient of variation in tensile strength and the number of breaks in the yarn.

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