Evaluating the softness of animal fibers

Softness is an important property of textile fibers, and animal fibers in particular. At present, there is no reliable method for objectively evaluating fiber softness. This paper examines a simple technique of such evaluations by pulling a bundle of parallel fibers through a series of pins. Softer fibers with lower bending rigidities and smoother surfaces should have lower pulling forces. Alpaca and wool fibers are used in this study to validate this technique, and the results suggest that pulling force measurements can reflect differences in fiber softness.

A comparative study on the felting propensity of animal fibers

The felting propensity of different animal fibers, particularly alpaca and wool, has been examined. The Aachen felting test method was employed. 1 g of each type of fiber was soaked in 50 ml of wetting solution and agitated in a dyeing machine to make felt balls. The diameter of each ball was measured in nine directions and the ball density was calculated in g/cm3; the higher the density value of the ball, the higher the feltability of the fibers. The effects of fiber diameter and fiber length on the felting propensity of these fibers were investigated. The results show that the alpaca fibers felt to a higher degree than wool fibers, and short and fine cashmere fibers have lower felting propensity than wool fibers at a similar diameter range. There is a higher tendency of felting for bleached and dyed alpaca fibers than for untreated fibers. Fiber length has a remarkable influence on the propensity of fiber felting. Cotton and nylon fibers were also tested for felting propensity to verify the mechanism responsible for the different fiber felting behavior.

Intelligent animal fibre identification and classification

To develop an objective and repeatable method of identification and classification of animal fibres, two different integrated systems were developed to mimic the human brain's ability to undertake feature extraction and discrimination of animal fibres. Both integrated systems are basically composed of an image processing system and an artificial neural network system.

Identification of animal fibers with wavelet texture analysis

This paper presents the use of the wavelet transform to extract fiber surface texture features for classifying cashmere and superfine merino wool fibers. Extracting features from brightness variations caused by the cuticular scale height, shape and interval provides an effective way for characterizing different animal fibers and subsequently classifying them. This may enable the development of a completely automated and objective system for animal fiber identification.

Wool statistics, including mohair and other animal fibers.

Cover-title.

Weibull analysis of the tensile behavior of fibers with geometrical irregularities

This paper further develops the conventional Weibull/weakest-link model by incorporating the within-fiber diameter variation. This is necessary for fibers with considerable geometrical irregularities, such as the wool and other animal fibers. The strength of wool fibers has been verified to follow this modified Weibull/weakest-link distribution. In addition, the modified Weibull model can predict the gauge length effect more accurately than the conventional model.

Intelligent animal fiber classification with artificial neural networks

Artificial neural networks (ANN) are increasingly used to solve many problems related to pattern recognition and object classification. In this paper, we report on a study using artificial neural networks to classify two kinds of animal fibers: merino and mohair. We have developed two different models, one extracting nine scale parameters with image processing, and the other using an unsupervised artificial neural network to extract features automatically, which are determined in accordance with the complexity of the scale structure and the accuracy of the model. Although the first model can achieve higher accuracy, it requires more effort for image processing and more prior knowledge, since the accuracy of the ANN largely depends on the parameters selected. The second model is more robust than the first, since only raw images are used. Because only ordinary optical images taken with a microscope are employed, we can use the approach for many textile applications without expensive equipment such as scanning electron microscopy.

Diameter variations of irregular fibers under different tensions

The cross-section area of animal fibers varies along the fiber length, and this geometrical irregularity has a major impact on the mechanical properties of those fibers. In practice fibers are often subjected to tensile stresses during processing and application, which may change fiber cross-section area. It is thus necessary to examine geometrical irregularity of fibers under tension. In this study, scoured animal fibers were subjected to different tensile loading using a Single Fiber Analyzer (SIFAN) instrument. The 3D images of the fiber specimens were first constructed, and then along-fiber diameter irregularities of the specimens were analyzed for different levels of tensile loading. The changes in effective fineness of the fiber specimens were also discussed. The results indicate that for the wool fibers examined, there is considerable discrepancy in the fiber diameter results obtained from the commonly used single scan along fiber length and that from multiple scans at different rotational angles, and that the diameter variation along fiber length increases as fiber tension increases. The results also show that when diameter reduction treatments are applied to wool by stretching, the reduced average fiber diameter is associated with an increase in both within-fiber and between-fiber diameter variations. So in terms of effective fineness, the change is much smaller than the difference between the average diameters of the parent and treated wool. These results have significant implications for improving the accuracy of fiber diameter measurement and evaluation.

Testing the immunocompetence handicap hypothesis : a review of the evidence

The immunocompetence handicap hypothesis was formulated 12 years ago in an attempt to offer a proximate mechanism by which female choice of males could be explained by endocrine control of honest signalling. The hypothesis suggested that testosterone has a dual effect in males of controlling the development of sexual signals while causing immunosuppression. Our purpose in this review is to examine the empirical evidence to date that has attempted to test the hypothesis, and to conduct a meta-analysis on two of the assumptions of the hypothesis, that testosterone reduces immunocompetence and increases parasitism, to ascertain any statistical trend in the data. There is some evidence to suggest that testosterone is responsible for the magnitude of trait expression or development of sexual traits, but this is by no means conclusive. The results of many studies attempting to find evidence for the supposed immunosuppressive qualities of testosterone are difficult to interpret since they are observational rather than experimental. Of the experimental studies, the data obtained are ambiguous, and this is reflected in the result of the meta-analysis. Overall, the meta-analysis found a significant suppressive effect of testosterone on immunity, in support of the hypothesis, but this effect disappeared when we controlled for multiple studies on the same species. There was no effect of testosterone on direct measures of immunity, but it did increase ectoparasite abundance in several studies, in particular in reptiles. A funnel analysis indicated that the results were robust to a publication bias. Alternative substances that interact with testosterone, such as glucocorticoids, may be important. Ultimately, a greater understanding is required of the complex relationships that exist both within and between the endocrine and immune systems and their consequences for mate choice decision making.

Feature extraction for animal fiber identification

Fiber identification has been a very important task in many industries such as wool growing, textile processing, archaeology, histochernical engineering, and zoology. Over the years, animal fibers have been identified using physical and chemical approaches. Recently, objective identification of animal fibers has been developed based on the cuticular information of fibers. Effective and accurate extraction of representative features is essential to animal fiber identification and classification. In the current work, two different strategies are developed for this purpose. In the first method, explicit features are extracted using image processing. However, only implicit features are used in the second method with an unsupervised artificial neural network. It is found that the use of explicit features increases the accuracy of fiber identification but requires more effort on processing images and solid knowledge of what features are representative ones.

Morpho-physical Recording of Bovine Conceptus (Bos indicus) and Placenta from Days 20 to 70 of Pregnancy

The study is based on 141 pregnant Bos indicus cows, from days 20 to 70 post-insemination. First, special attention was given to the macroscopically observable phenomena of attachment of the conceptus to the uterus, i.e. the implantation, from about days 20 to 30 post-insemination up to day 70, and placentome development by growth, vascularization and increase in the number of cotyledons opposite to the endometrial caruncles. Secondly, as for the conceptuses, semiquantitative, statistical analyses were performed of the lengths of chorio-allantois, amnion and yolk sac; and the different parts of the centre and two extremes of the yolk sacs were also analysed. Thirdly, the embryos/foetuses corresponding to their membranes were measured by their greatest length and by weight, and described by the appearance of external developmental phenomena during the investigated period like neurulation, somites, branchial arcs, brain vesicles, limb buds, C-form, pigmented eye and facial grooves. In conclusion, all the data collected in this study from days 20 to 70 of bovine pregnancy were compared extensively with corresponding data of the literature. This resulted in an `embryo/foetal age-scale`, which has extended the data in the literature by covering the first 8 to 70 days of pregnancy. This age-scale of early bovine intrauterine development provides model for studies, even when using slaughtered cows without distinct knowledge of insemination or fertilization time, through macroscopic techniques. This distinctly facilitates research into the cow, which is now being widely used as `an experimental animal` for testing new techniques of reproduction like in vitro fertilization, embryo transfer and cloning.

Image processing and pattern recognition in textiles

Image processing and pattern recognition have been successfully applied in many textile related areas. For example, they have been used in defect detection of cotton fibers and various fabrics. In this work, the application of image processing into animal fiber classification is discussed. Integrated into / with artificial neural networks, the image processing technique has provided a useful tool to solve complex problems in textile technology. Three different approaches are used in this work forfiber classification and pattern recognition: feature extraction with image process, pattern recognition and classification with artificial neural networks, and feature recognition and classification with artificial neural network. All of them yieldssatisfactory results by giving a high level of accuracy in classification.
--Session data-->