• Asian Pacific Journal of Cancer Prevention
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• v.16 no.13
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• pp.5273-5278
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• 2015

Background: Chronic myeloid leukemia (CML) is a stem cell disorder characterized by the fusion of two oncogenes namely BCR and ABL with their aberrant expression. Autophosphorylation of BCR-ABL oncogenes results in proliferation of CML. The study deals with estimation of rate constant involved in each step of the cellular autophosphorylation process, which are consequently playing important roles in the proliferation of cancerous cells. Materials and Methods: A mathematical model was proposed for autophosphorylation of BCR-ABL oncogenes utilizing ordinary differential equations to enumerate the rate of change of each responsible system component. The major difficulty to model this process is the lack of experimental data, which are needed to estimate unknown model parameters. Initial concentration data of each substrate and product for BCR-ABL systems were collected from the reported literature. All parameters were optimized through time interval simulation using the fminsearch algorithm. Results: The rate of change versus time was estimated to indicate the role of each state variable that are crucial for the systems. The time wise change in concentration of substrate shows the convergence of each parameter in autophosphorylation process. Conclusions: The role of each constituent parameter and their relative time dependent variations in autophosphorylation process could be inferred.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.6
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• pp.47-53
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• 2007

True motion estimation is necessary for deinterlacing, frame-rate conversion, and film judder compensation. There have been several block-based approaches to find true motion vectors by tracing minimum sum-of-absolute-difference (SAD) values by considering spatial and temporal consistency. However, the algorithms cannot find robust motion vectors when the texture of objects is changed. To find the robust motion vectors in the region, a recursive vector selection scheme and an adaptive weighting parameter are proposed. Previous frame vectors are recursively averaged to be utilized for motion error region. The weighting parameter controls fidelity to input vectors and the recursively averaged ones, where the input vectors come from the conventional estimators. If the input vectors are not reliable, then the mean vectors of the previous frame are used for temporal consistency. Experimental results show more robust motion vectors than those of the conventional methods in time-varying texture objects.

• Animal Bioscience
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• v.34 no.6
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• pp.949-956
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• 2021

Objective: This study was focused on the estimation of parameters of Wood's model and description of the lactation curve using the cows which were lactated over 24 months on the first lactation. Methods: The database included 1,333 pure-bred dairy Simmental primiparous cows which lactated for 24 months (732 days). The initial dataset entering the procedure of assessment of parameters of Wood's function included 35,826 milk yield records. Milk yield was recorded throughout lactation, with the earliest record taken on day 6 and the latest on day 1,348 of lactation. This dataset was used for the assessment of parameters a, b, c of Wood's model using the non-linear statistical procedure. These parameters were estimated for different length of lactation. The assessed parameters were used for calculation of some characteristics of lactation curves. Results: The lowest value of a parameter (15.2317) of Wood's model of lactation curve was found out in lactations up to 305 days long, contrary to b and c parameters which were highest in those lactations (0.1029 and 0.0015, respectively). The maximum value of a parameter (17.4329) was found out in lactations up to 640 days long, unlike b and c parameters which were minimal in those lactations (0.0603 and 0.0010, respectively). Conclusion: It can be concluded that the parameters of Wood's model and the shape of lactation curve are changing with the growing number of milk yield records. Also, the assessed parameters revealed a significant milk production potential after 305 days of lactation.

• Animal Bioscience
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• v.35 no.11
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• pp.1649-1655
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• 2022

Objective: The primary objective of this study was to determine the genetic parameters for reproductive traits among Large White pigs, including the following traits: total number born (TNB), number born alive (NBA), litter birth weight (LBW), average birth weight (ABW), gestation length (GL), age at first service (AFS) and age at first farrowing (AFF). Methods: The dataset consisted of 19,036 reproductive records from 4,986 sows, and a multi-trait animal model was used to estimate genetic variance components of seven reproductive traits. Results: The heritability estimates for these reproductive traits ranged from 0.09 to 0.26, with the highest heritability for GL and AFF, and the lowest heritability for NBA. The repeatabilities for TNB, NBA, LWB, ABW, and GL were ranged from 0.16 to 0.34. Genetic and phenotypic correlations ranged from -0.41 to 0.99, and -0.34 to 0.98, respectively. In particular, the correlations between TNB, NBA and LBW, between AFS and AFF, exhibited a strong positive correlation. Furthermore, for TNB, NBA, LBW, ABW, and GL, genetic correlations of the same trait between different parities were moderately to strongly correlated (0.32 to 0.97), and the correlations of adjacent parities were higher than those of nonadjacent parities. Conclusion: All the results in the present study can be used as a basis for the genetic assessment of the target population. In the formulation of dam line selection index, AFS or AFF can be considered to combine with TNB in a multiple trait swine breeding value estimation system. Moreover, breeders are encouraged to increase the proportion of sows at parity 3-5 and reinforce the management of sows at parity 1 and parity ≥8.

• The Korean Journal of Applied Statistics
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• v.37 no.1
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• pp.103-118
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• 2024

The horseshoe prior is notably one of the most popular priors in sparse regression models, where only a small fraction of coefficients are nonzero. The parameter space of the horseshoe prior is much smaller than that of the spike and slab prior, so it enables us to efficiently explore the parameter space even in high-dimensions. However, on the other hand, the horseshoe prior has a high computational cost for each iteration in the Gibbs sampler. To overcome this issue, various MCMC algorithms for the horseshoe prior have been proposed to reduce the computational burden. Especially, Johndrow et al. (2020) recently proposes an approximate algorithm that can significantly improve the mixing and speed of the MCMC algorithm. In this paper, we compare (1) the traditional MCMC algorithm, (2) the approximate MCMC algorithm proposed by Johndrow et al. (2020) and (3) its variant in terms of computing times, estimation and variable selection performance. For the variable selection, we adopt the sequential clustering-based method suggested by Li and Pati (2017). Practical performances of the MCMC methods are demonstrated via numerical studies.

• Steel and Composite Structures
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• v.51 no.5
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• pp.509-527
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• 2024

Bonding carbon fiber-reinforced polymer (CFRP) laminates have been extensively employed in the restoration of steel constructions. In addition to the mechanical properties of the CFRP, the bond strength (P_U) between the CFRP and steel is often important in the eventual strengthened performance. Nonetheless, the bond behavior of the CFRP-steel (CS) interface is exceedingly complicated, with multiple failure causes, giving the P_U challenging to forecast, and the CFRP-enhanced steel structure is unsteady. In just this case, appropriate methods were established by hybridized Random Forests (RF) and support vector regression (SVR) approaches on assembled CS single-shear experiment data to foresee the P_U of CS, in which a recently established optimization algorithm named Aquila optimizer (AO) was used to tune the RF and SVR hyperparameters. In summary, the practical novelty of the article lies in its development of a reliable and efficient method for predicting bond strength at the CS interface, which has significant implications for structural rehabilitation, design optimization, risk mitigation, cost savings, and decision support in engineering practice. Moreover, the Fourier Amplitude Sensitivity Test was performed to depict each parameter's impact on the target. The order of parameter importance was t_c> L_c > E_A > t_A > E_c > b_c > f_c > f_A from largest to smallest by 0.9345 > 0.8562 > 0.79354 > 0.7289 > 0.6531 > 0.5718 > 0.4307 > 0.3657. In three training, testing, and all data phases, the superiority of AO - RF with respect to AO - SVR and MARS was obvious. In the training stage, the values of R² and VAF were slightly similar with a tiny superiority of AO - RF compared to AO - SVR with R² equal to 0.9977 and VAF equal to 99.772, but large differences with results of MARS.

• Journal of Korea Water Resources Association
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• v.46 no.9
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• pp.933-946
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• 2013

The quantitative analysis of evapotranspiration (ET) is a key component in hydrological studies and the establishment of water resources planning. Generally, the quantitative analysis of ET is performed by the estimation method of potential or reference ET based on meteorological factors such as air temperature, wind speed, etc. Hargreaves equation is one of empirical methods for reference ET using air temperature data. In this study, in order to estimate more exact reference ET considering climatological characteristics in Korea, parameter regionalization of Hargreaves equation is carried out. Firstly, modified Hargreaves equation is presented after the analysis of the relationship between solar radiation and temperature. Secondly, parameter ($K_{ET}$) optimization of Hargreaves equation is performed using Penman-Monteith method and modified equation at 71 weather stations. Lastly, the equation for calculating $K_{ET}$ using temperature data is proposed and verified. As a result, reference ET from original Hargreaves equation is overestimated or underestimated compared with Penman-Monteith method. But modified equation in this study is more accurate in the climatic conditions of Korea. In addition, the applicability of the equation between $K_{ET}$ and temperature is confirmed.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.733-745
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• 2017

Local composite quantile regression is a useful non-parametric regression method widely used for its high efficiency. Data smoothing methods using kernel are typically used in the estimation process with performances that rely largely on the smoothing parameter rather than the kernel. However, $L_2$-norm is generally used as criterion to estimate the performance of the regression function. In addition, many studies have been conducted on the selection of smoothing parameters that minimize mean square error (MSE) or mean integrated square error (MISE). In this paper, we explored the optimality of selecting smoothing parameters that determine the performance of non-parametric regression models using local linear composite quantile regression. As evaluation criteria for the choice of smoothing parameter, we used mean absolute error (MAE) and mean integrated absolute error (MIAE), which have not been researched extensively due to mathematical difficulties. We proved the uniqueness of the optimal smoothing parameter based on MAE and MIAE. Furthermore, we compared the optimal smoothing parameter based on the proposed criteria (MAE and MIAE) with existing criteria (MSE and MISE). In this process, the properties of the proposed method were investigated through simulation studies in various situations.

• Journal of the Korean Wood Science and Technology
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• v.43 no.4
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• pp.456-462
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• 2015

365 pieces of domestic $38{\times}140{\times}3600mm$ Red pine structural lumber were machine graded conforming to a softwood structural lumber standard (KS F 3020). The allowable bending stresses calculated for each grade were compared with the values currently tabulated in the standard. Four calculation methods for lower $5^{th}$ percentile bending stress were non-parametric estimation with 75% confidence level, 2-parameter and 3-parameter Weibull distribution fit, and bending modulus of rupture (MOR)-modulus of elasticity (MOE) regression based method. Only the data set of Grades E8, E9, and E10 were statistically eligible for the $5^{th}$ percentile calculation. The MOR-MOE regression based method only was able to estimate the lower $5^{th}$ percentile values theoretically for the full range of grades. The results showed that all allowable bending stresses calculated were lower than the design values tabulated in the standard. This implies that the current machine grading system has the pitfall of structural safety. Improvement in current machine grading system could be achieved by introducing the bending strength and stiffness combination grade system.

• Spatial Information Research
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• v.20 no.3
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• pp.27-38
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• 2012

As the necessity of forest conservation and management has been increased, various forest studies using LIDAR data have been actively performed. These studies often utilize the tree height as an important parameter to measure the forest quantitatively. This study thus attempt to apply two representative methods to estimate tree height from airborne LIDAR data and compare the results. The first method based on the detection of the individual trees using a local maximum filter estimates the number of trees, the position and heights of the individual trees, and the mean tree height. The other method estimates the maximum and mean tree height, and the crown mean height for each grid cell or the entire area from the canopy height model (CHM) and height histogram. In comparison with the field measurements, 76.6% of the individual trees are detected correctly; and the estimated heights of all trees and only conifer trees show the RMSE of 1.91m and 0.75m, respectively. The tree mean heights estimated from CHM retain about 1~2m RMSE, and the histogram method underestimates the tree mean height with about 0.6m. For more accurate derivation of diverse forest information, we should select and integrate the complimentary methods appropriate to the tree types and estimation parameters.