• Asian-Australasian Journal of Animal Sciences
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• 제14권8호
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• pp.1149-1156
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• 2001

Previous studies conducted with swine have reported that the mobile nylon bag technique (MNBT) does not always accurately predict in vivo nutrient digestibilities. Therefore, in this study, the MNBT was modified so that nutrient digestibilities would more closely resemble those from conventional (Con) digestibility studies obtained using the indicator method. A total of 19 feeds were tested including five cereal grains, five legumes, three high protein sources and six mixed diets. The principle changes to the MNBT included the use of a fecal collection harness which minimized the number of bags lost. In addition, previous protocols involved pooling of bags within pig while in the present experiment all bags were analyzed separately to increase the precision of the test. Finally, chemical analyses were done using the entire nylon bag plus residue rather than opening.the bags and scraping out the contents. With the exception of the barley sample (p=0.01), dry matter digestibility (DMD) coefficients obtained with the MNBT were not significantly different from those obtained with the indicator method. The linear regression equation relating the MNBT to the indicator method was Con DMD=-O.77+1.02 MNBT DMD ($r^2=0.93$: p<0.0001). There was no significant (p>0.05) difference in gross energy digestibility (GED) coefficients determined using the MNBT or the indicator method for any of the 19 feeds. The regression line equation relating the MNBT to the indicator method was Con GED=-5.68+1.06 MNBT GED ($r^2=0.94$: p<0.0001). The MNBT was less effective in predicting in vivo crude protein digestibility (CPD) than it was in predicting dry matter and energy digestibility. Differences greater than five percentage units were observed for two of the legumes, Kabuli chickpeas (p=0.02) and the extruded pea-canola seed mixture (p=0.01) as well as for three of the mixed diets including the unheated hulled barley-based diet (p=0.01), the unheated hulless-barley based diet (p=0.08) and the barley-soybean meal based diet (p=0.008). The regression equation relating the MNBT to the indicator method was Con CPD=5.75 + 0.90 MNBT CPO ($r^2=0.76$; p<0.0001). This study indicates that the modified MNBT can be used for the rapid determination of dry matter and energy digestibility in a wide variety of ingredients. For the measurement of crude protein digestibility, the technique produces results similar to conventional digestibility studies for cereal grains and high protein feeds but tends to overestimate protein digestibility for legumes and mixed diets.

• Asian-Australasian Journal of Animal Sciences
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• 제21권5호
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• pp.677-684
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• 2008

Accurate estimation of dry matter intake (DMI) is a prerequisite to meet animal performance targets without penalizing animal health and the environment. The objective of the current study was to evaluate some of the existing models in order to predict DMI when lactating dairy cows were offered a total mixed ration containing a high level of concentrates and locally produced agricultural by-products. Six popular models were chosen for DMI prediction (Brown et al., 1977; Rayburn and Fox, 1993; Agriculture Forestry and Fisheries Research Council Secretariat, 1999; National Research Council (NRC), 2001; Cornell Net Carbohydrate and Protein System (CNCPS), Fox et al., 2003; Fuentes-Pila et al., 2003). Databases for DMI comparison were constructed from two different sources: i) 12 commercial farm investigations and ii) a controlled dairy cow experiment. The model evaluation was performed using two different methods: i) linear regression analysis and ii) mean square error prediction analysis. In the commercial farm investigation, DMI predicted by Fuentes-Pila et al. (2003) was the most accurate when compared with the actual mean DMI, whilst the CNCPS prediction showed larger mean bias (difference between mean predicted and mean observed values). Similar results were observed in the controlled dairy cow experiment where the mean bias by Fuentes-Pila et al. (2003) was the smallest of all six chosen models. The more accurate prediction by Fuentes-Pila et al. (2003) could be attributed to the inclusion of dietary factors, particularly fiber as these factors were not considered in some models (i.e. NRC, 2001; CNCPS (Fox et al., 2003)). Linear regression analysis had little meaningful biological significance when evaluating models for prediction of DMI in this study. Further research is required to improve the accuracy of the models, and may recommend more mechanistic approaches to investigate feedstuffs (common to the Asian region), animal genotype, environmental conditions and their interaction, as the majority of the models employed are based on empirical approaches.

• Asian-Australasian Journal of Animal Sciences
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• 제24권1호
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• pp.17-27
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• 2011

A selection experiment for reduced residual feed intake (RFI) in Yorkshire pigs consisted of a line selected for lower RFI (LRFI) and a random control line (CTRL). Longitudinal measurements of daily feed intake (DFI) and body weight (BW) from generation 5 of this experiment were used. The objectives of this study were to evaluate the use of random regression (RR) and nonlinear mixed models to predict DFI and BW for individual pigs, accounting for the substantial missing information that characterizes these data, and to evaluate the effect of selection for RFI on BW and DFI curves. Forty RR models with different-order polynomials of age as fixed and random effects, and with homogeneous or heterogeneous residual variance by month of age, were fitted for both DFI and BW. Based on predicted residual sum of squares (PRESS) and residual diagnostics, the quadratic polynomial RR model was identified to be best, but with heterogeneous residual variance for DFI and homogeneous residual variance for BW. Compared to the simple quadratic and linear regression models for individual pigs, these RR models decreased PRESS by 1% and 2% for DFI and by 42% and 36% for BW on boars and gilts, respectively. Given the same number of random effects as the polynomial RR models, i.e., two for BW and one for DFI, the non-linear Gompertz model predicted better than the polynomial RR models but not as good as higher order polynomial RR models. After five generations of selection for reduced RFI, the LRFI line had a lower population curve for DFI and BW than the CTRL line, especially towards the end of the growth period.

• 한국도로학회논문집
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• 제20권2호
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• pp.75-85
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• 2018

PURPOSES : This study aimed to analyze traffic accidents at circular intersections, and discuss accident reduction strategies based on land use and vehicle type. METHODS : Traffic accident data from 2010 to 2014 were collected from the "traffic accident analysis system" (TAAS) data set of the Road Traffic Authority. To develop the accident rate model, a multiple linear regression model was used. Explanatory variables such as geometry and traffic volume were used to develop the models. RESULTS : The main results of the study are as follows. First, it was found that the null hypotheses that land use and vehicle type do not affect the accident rate should be rejected. Second, 16 accident rate models, which are statistically significant (with high $R^2$ values), were developed. Finally, the area of the central island, number of speed humps, entry lane width, circulatory roadway width, bus stops, and pedestrian crossings were analyzed to determine their effect on accidents according to the type of land use and vehicle. CONCLUSIONS : Through the developed accident rate models, it was revealed that the accident factors at circular intersections changed depending on land use and vehicle type. Thus, selecting the appropriate location of bus stops for trucks, widening entry lanes for cars, and installing splitter islands and optimal lighting for motorcycles were determined to be important for reducing the accident rate. Additionally, the evaluation showed that commercial and mixed land use had a weaker effect on accidents than residential land use.

• Ocean and Polar Research
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• 제27권1호
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• pp.1-13
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• 2005

The samples for organic carbon analysis were collected between $5^{\circ}\;and\;17^{\circ}N$ along $131.5^{\circ}W$ in the northeast Pacific KODOS (Korea Deep Ocean Study) area. The mean concentration of total organic carbon (TOC) in the surface mixed layer $({\sim}50 m)$ was $100.13{\pm}2.05{\mu}M-C$, while the mean concentration of TOC in the lower 500m of the water column was $50.19{\pm}4.23{\mu}M-C$. A strong linear regression between TOC and temperature $(r^2=0.70)$ showed that TOC distribution was controlled by physical process. Results from the linear regression between chlorophyll-a and TOC, and between chlorophyll-a and particulate organic carbon (POC), decreasing of dissolved organic carbon (DOC) in the surface layer caused by non-biological photo-oxidation process. Below the surface layer, biological production and consumption occurred. DOC accumulation dominated in the depth range of $30{\sim}50m$ and DOC consumption occurred in the depth range of $50{\sim}200m$. TOC was inversely correlated with apparent oxygen utilization (AOU) and TOC/AOU molar ratios ranged from -0.077 to -0.21. These ratios indicated that TOC oxidation was responsible fur $10.9{\sim}30.1%$ (mean 20.2%) of oxygen consumption in the NE Pacific KODOS area. In the euphotic zone, distributions of dissolved and particulate organic matter were controlled by photo-chemical, chemical, biological and physical processes.

• 말소리와 음성과학
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• 제8권1호
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• pp.45-51
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• 2016

This study examined the deletion of onset consonant in the word-internal structure in spontaneous Seoul Korean speech. It used the dataset of speakers in their 20s extracted from the Korean Corpus of Spontaneous Speech (Yun et al., 2015). The proportion of deletion of word-internal onset consonants was analyzed using the linear mixed-effects regression model. The factors that promoted the deletion of onsets were primarily the types of consonants and their phonetic contexts. The results showed that onset deletion was more likely to occur for a lenis velar stop [k] than the other consonants, and in the phonetic contexts, when the preceding vowel was a low central vowel [a]. Moreover, some speakers tended to more frequently delete onset consonants (e.g., [k] and [n]) than other speakers, which reflected individual differences. This study implies that word-internal onsets undergo a process of gradient reduction within individuals' articulatory strategies.

• Communications for Statistical Applications and Methods
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• 제26권3호
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• pp.315-323
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• 2019

Panel data sets have recently been developed in various areas, and many recent studies have analyzed panel, or longitudinal data sets. Often a dichotomous dependent variable occur in survival analysis, biomedical and epidemiological studies that is analyzed by a generalized linear mixed effects model (GLMM). The most common estimation method for the binary panel data may be the maximum likelihood (ML). Many statistical packages provide ML estimates; however, the estimates are computed from numerically approximated likelihood function. For instance, R packages, pglm (Croissant, 2017) approximate the likelihood function by the Gauss-Hermite quadratures, while Rchoice (Sarrias, Journal of Statistical Software, 74, 1-31, 2016) use a Monte Carlo integration method for the approximation. As a result, it can be observed that different packages give different results because of different numerical computation methods. In this note, we discuss the pros and cons of numerical methods compared with the exact computation method.

• Current Optics and Photonics
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• 제4권6호
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• pp.530-539
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• 2020

Hyperspectral images feature a relatively narrow band and are easily disturbed by noise. Accurate estimation of the types and parameters of noise in hyperspectral images can provide prior knowledge for subsequent image processing. Existing hyperspectral-noise estimation methods often pay more attention to the use of spectral information while ignoring the spatial information of hyperspectral images. To evaluate the noise in hyperspectral images more accurately, we have proposed a spectral-spatial cooperative noise-evaluation method. First, the feature of spatial information was extracted by Gabor-filter and K-means algorithms. Then, texture edges were extracted by the Otsu threshold algorithm, and homogeneous image blocks were automatically separated. After that, signal and noise values for each pixel in homogeneous blocks were split with a multiple-linear-regression model. By experiments with both simulated and real hyperspectral images, the proposed method was demonstrated to be effective and accurate, and the composition of the hyperspectral image was verified.

• Asian-Australasian Journal of Animal Sciences
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• 제29권6호
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• pp.775-781
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• 2016

A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM) considering different order of Legendre polynomial for the additive genetic effect (4th order) and the permanent environmental effect (5th order). Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11) to 0.99 (TD-4 and TD-5). The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.

• Advances in Computational Design
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• 제5권2호
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• pp.195-207
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• 2020

The present study focuses on the application of artificial neural network (ANN) and Multiple linear Regression (MLR) analysis for developing a model to predict the unconfined compressive strength (UCS) and split tensile strength (STS) of the fiber reinforced clay stabilized with grass ash, fly ash and lime. Unconfined compressive strength and Split tensile strength are the nonlinear functions and becomes difficult for developing a predicting model. Artificial neural networks are the efficient tools for predicting models possessing non linearity and are used in the present study along with regression analysis for predicting both UCS and STS. The data required for the model was obtained by systematic experiments performed on only Kaolin clay, clay mixed with varying percentages of fly ash, grass ash, polypropylene fibers and lime as between 10-20%, 1-4%, 0-1.5% and 0-8% respectively. Further, the optimum values of the various stabilizing materials were determined from the experiments. The effect of stabilization is observed by performing compaction tests, split tensile tests and unconfined compression tests. ANN models are trained using the inputs and targets obtained from the experiments. Performance of ANN and Regression analysis is checked with statistical error of correlation coefficient (R) and both the methods predict the UCS and STS values quite well; but it is observed that ANN can predict both the values of UCS as well as STS simultaneously whereas MLR predicts the values separately. It is also observed that only STS values can be predicted efficiently by MLR.