Community attitudes to fertility and infertility

Assesses by survey some of the current community attitudes to fertility and infertility with reference to artificial insemination. Indicates that a pronatalist norm is dominant with some reservation in acceptance of artificial insemination by donor.

Determinants of permanent first incisor eruption in grazing Australian Angora goats

Objective: To investigate the effects of live weight, sex and other factors on deciduous (first incisor) loss and permanent first incisor development in Angora goats. Design: Goats were part of a pen study on the effects of energy intake in Angora does during pregnancy and lactation on kid growth and development. The design was three levels of nutrition in mid-pregnancy × two levels of postnatal nutrition in 17 randomised blocks. Methods: Conception times were calculated by using artificial insemination, with ultrasound examination 43 days after insemination. Does were fed different amounts of a formulated diet in their pens. After weaning, goats were grazed in sex groups. Deciduous first incisor loss and permanent first incisor development were recorded at 11 time points from 14 to 20 months of age. Results: For each sex, the time for visible eruption and full development of permanent first incisor declined linearly with increased live weight by 5.9 and 5.4 days/kg live weight, respectively. The time to reach similar development stages for first permanent incisors eruption was 3 months longer for the lightest animals compared with the heaviest animals. Date of birth, birth weight, doe age, growth rates, mid-pregnancy and postnatal nutrition, parity, day of weaning and weaning weight had no detectable effect. Conclusions: The results explain much of the substantial range in reported first permanent incisor eruption dates for small ruminants and have application in ageing of goats, marketing of kids for meat, in the selection of animals for breeding flocks and in educational material.

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.

Predicting worsted spinning performance with an artificial neural network model

For a given fiber spun to pre-determined yarn specifications, the spinning performance of the yarn usually varies from mill to mill. For this reason, it is necessary to develop an empirical model that can encompass all known processing variables that exist in different spinning mills, and then generalize this information and be able to accurately predict yarn quality for an individual mill. This paper reports a method for predicting worsted spinning performance with an artificial neural network (ANN) trained with backpropagation. The applicability of artificial neural networks for predicting spinning performance is first evaluated against a well established prediction and benchmarking tool (Sirolan YarnspecTM). The ANN is then subsequently trained with commercial mill data to assess the feasibility of the method as a mill-specific performance prediction tool. Incorporating mill-specific data results in an improved fit to the commercial mill data set, suggesting that the proposed method has the ability to predict the spinning performance of a specific mill accurately.

Predicting the pilling propensity of fabrics through artificial neural network modeling

Fabric pilling is affected by many interacting factors. This study uses artificial neural networks to model the multi-linear relationships between fiber, yarn and fabric properties and their effect on the pilling propensity of pure wool knitted fabrics. This tool shall enable the user to gauge the expected pilling performance of a fabric from a number of given inputs. It will also provide a means of improving current products by offering alternative material specification and/or selection. In addition to having the capability to predict pilling performance, the model will allow for clarification of major fiber, yarn and fabric attributes affecting fabric pilling.