• Asian-Australasian Journal of Animal Sciences
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• v.28 no.5
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• pp.605-611
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• 2015

A synthetic strain of ducks (Anas platyrhynchos) was developed by introducing genes for long duration of fertility to be used as mother of mule ducklings and a seven-generation selection experiment was conducted to increase the number of fertile eggs after a single artificial insemination (AI) with pooled Muscovy semen. Reciprocal crossbreeding between Brown Tsaiya LRI-2 (with long duration of fertility) and Pekin L-201 (with white plumage mule ducklings) ducks produced the G0. Then G1 were intercrossed to produce G2 and so on for the following generations. Each female duck was inseminated 3 times, at 26, 29, and 32 weeks of age. The eggs were collected for 14 days from day 2 after AI. Individual data regarding the number of incubated eggs (Ie), the number of fertile eggs at candling at day 7 of incubation (F), the total number of dead embryos (M), the maximum duration of fertility (Dm) and the number of hatched mule ducklings (H) with plumage colour were recorded. The selection criterion was the breeding values of the best linear unbiased prediction animal model for F. The results show high percentage of exhibited heterosis in G2 for traits to improve (19.1% for F and 12.9% for H); F with a value of 5.92 (vs 3.74 in the Pekin L-201) was improved in the G2. Heritabilities were found to be low for Ie ($h^2=0.07{\pm}0.03$) and M ($h^2=0.07{\pm}0.01$), moderately low for Dm ($h^2=0.13{\pm}0.02$), of medium values for H ($h^2=0.20{\pm}0.03$) and F ($h^2=0.23{\pm}0.03$). High and favourable genetic correlations existed between F and Dm ($r_g=0.93$), between F and H ($r_g=0.97$) and between Dm and H ($r_g=0.90$). The selection experiment showed a positive trend for phenotypic values of F (6.38 fertile eggs in G10 of synthetic strain vs 5.59 eggs in G4, and 3.74 eggs in Pekin L-201), with correlated response for increasing H (5.73 ducklings in G10 vs 4.86 in G4, and 3.09 ducklings in Pekin L-201) and maximum duration of the fertile period without increasing the embryo mortality rate. The average predicted genetic response for F was 40% of genetic standard deviation per generation of selection. The mule ducklings' feather colour also was improved. It was concluded that this study provided results for a better understanding of the genetics of the duration of fertility traits in the common female duck bred for mule and that the selection of a synthetic strain was effective method of improvement.

• The korean journal of orthodontics
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• v.27 no.1
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• pp.91-103
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• 1997

This paper aimed to study what the influences of orthodontic treatment of pronunciation are. We compared the duration and the acoustic wave patterns of Korean consonants pronounced by a control group with those of a patient who had his four premolars extracted and had been given orthodontic treatment The results were as follows : 1. Compared to the control group, the treatment group had a longer duration time of consonant pronunciation for all consonants but "ㅅ(s)" and "ㅌ($(t^h)$" in CV(consonant-vowel) pairs. Especially in the case of "ㅈ(dz)", "ㅆ$({\varphi}^h)$" for CV-pairs, and "ㄷ(d)" in VCV(vowel-consonant-vowel) clusters, the duration of consonant sound showed a sharp contrast between the control group and the treatment group. 2. There were clear differences in the acoustic wave patterns of "ㅉ(ts)", "ㅆ$({\varphi}^h)$" and "ㅊ$(c^h)$", all of which were in VCV-clusters. The acoustic wave pattern of "ㅉ(ts)", when pronounced by the treatment group, was stronger than the control group's. This phenomenon was most remarkable in the transitive section where the "ㅉ(ts)" sound flowed into the following vowel. When a preceding vowel shifted to the consonant "ㅆ$({\varphi}^h)$", the attack property of the appeared clearly in the acoustic waves of the treament group, while in the control group the starting point of consonart was indistinctive. Consonant duration for the treatment group was longer, and the appearance of a zero crossing point in the acoustic wave was more frequent. In the case of "ㅊ$(c^h)$", the treatment group produced a strong acoustic wave, and the property of aspiration was obvious in it. 3. When the treatment group pronounced "ㄷ(d)" and "ㅈ(dz)" in CV-pairs, the acoustic-wave was similar to that of aspirated "ㅌ$(t^h)$" and "ㅊ$(c^h)$". 4. The aspirated "ㅌ$(t^h)$" and "ㅊ$(c^h)$" pronounced by the treatment group showed the stronger airstream and acoustic wave form.

• Journal of Internet Computing and Services
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• v.25 no.4
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• pp.121-130
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• 2024

As internet and communication technology (ICT) is improved exponentially, types and amount of available data also increase. Even though data analysis including statistics is significant to utilize this large amount of data, there are inevitable limits to process various and complex data in general way. Meanwhile, there are many attempts to apply machine learning (ML) in various fields to solve the problems according to the enhancement in computational performance and increase in demands for autonomous systems. Especially, data processing for the model input and designing the model to solve the objective function are critical to achieve the model performance. Data processing methods according to the type and property have been presented through many studies and the performance of ML highly varies depending on the methods. Nevertheless, there are difficulties in deciding which data processing method for data analysis since the types and characteristics of data have become more diverse. Specifically, multi-variate data processing is essential for solving non-linear problem based on ML. In this paper, we present a multi-variate tabular data processing scheme for ML-aided data analysis by using Titanic dataset from Kaggle including various kinds of data. We present the methods like input variable filtering applying statistical analysis and normalization according to the data property. In addition, we analyze the data structure using visualization. Lastly, we design an ML model and train the model by applying the proposed multi-variate data process. After that, we analyze the passenger's survival prediction performance of the trained model. We expect that the proposed multi-variate data processing and visualization can be extended to various environments for ML based analysis.