• Health Policy and Management
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• v.9 no.3
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• pp.70-94
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• 1999

This paper was performed for a cost-effectiveness analysis of pharmacologic treatment of hypercholesterolemia. Agents modeled were cholestyramine, gemfibrozil. bezafibrate, lovastatin, pravastatin, simvastatin. Pharmacologic effectiveness was estimated by regression from reported clinical trials. Pharmacologic effects were expressed as the percent change of blood cholesterol level. Cost estimates included patients' travel expenses and time loss as well as resource consumption in the health care sector. Bezafibrate was the most efficient agent for reducing total cholesterol levels, having an cost over 1 year of ￦31.400 per percent reduction in total cholesterol. Simvastatin (10mg/d) was also efficient(￦33,100 per percent reduction). Chole styramine(8g/d) was least efficient at ￦90,200. For low-density lipoprotein cholesterol. simvastatin(10mg/d) was most efficient, at ￦23,200 per percent reduction, followed by lovastatin(20mg/d) at ￦28,000. Gemfibrozil was least efficient at ￦77,800 per percent reduction. For high-density lipoprotein cholesterol. bezafibrate(400mg/d) was most efficient at ￦39,300 per percent increase of high-density lipoprotein cholesterol. Cholestyramine was least efficient at ￦514,700. Analyses combining low-density lipoprotein cholesterol and high-density cholesterol effects suggest that bezafibrate(600mg/d) and simvastatin (10mg/d) were most efficient for reducing cardiovascular risk. The cost-effectiveness analysis results show that both simvastatin and bezafibrate could be efficient treatment. Simvastatin provide more effective treatment at higher cost, whereas bezafibrate is more cost-effective, as it may be less effective, at lower cost. Therefore, clinicians should choose reasonable treatment according to the patient's needs This pharmacoeconimc analysis will provide a guideline for efficient pharmacologic treatment and also be reference data for pricing new drugs.

• Journal of the Korea Society of Computer and Information
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• v.17 no.7
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• pp.45-52
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• 2012

In this paper, an crowd density in elevator estimation method based on multi-class Adaboost classifier is proposed. The SOM (Self-Organizing Map) based conventional methods have shown insufficient performance in practical scenarios and have weakness for low reproducibility. The proposed method estimates the crowd density using multi-class Adaboost classifier with texture features, namely, GLDM(Grey-Level Dependency Matrix) or GGDM(Grey-Gradient Dependency Matrix). In order to classify into multi-label, weak classifier which have better performance is generated by modifying a weight update equation of general Adaboost algorithm. The crowd density is classified into four categories depending on the number of persons in the crowd, which can be 0 person, 1-2 people, 3-4 people, and 5 or more people. The experimental results under indoor environment show the proposed method improves detection rate by about 20% compared to that of the conventional method.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.6
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• pp.208-221
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• 2020

The objective of this study is to estimate and analyze the traffic density of continuous flow using the trajectory of individual vehicles and the headway of sample probe vehicles-front vehicles obtained from ADAS (Advanced Driver Assitance System) installed in sample probe vehicles. In the past, traffic density of continuous traffic flow was mainly estimated by processing data such as traffic volume, speed, and share collected from Vehicle Detection System, or by counting the number of vehicles directly using video information such as CCTV. This method showed the limitation of spatial limitations in estimating traffic density, and low reliability of estimation in the event of traffic congestion. To overcome the limitations of prior research, In this study, individual vehicle trajectory data and vehicle headway information collected from ADAS are used to detect the space on the road and to estimate the spatiotemporal traffic density using the Generalized Density formula. As a result, an analysis of the accuracy of the traffic density estimates according to the sampling rate of ADAS vehicles showed that the expected sampling rate of 30% was approximately 90% consistent with the actual traffic density. This study contribute to efficient traffic operation management by estimating reliable traffic density in road situations where ADAS and autonomous vehicles are mixed.

• The Bulletin of The Korean Astronomical Society
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• v.39 no.2
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• pp.54-54
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• 2014

We use dense redshift surveys of nine galaxy clusters at z ~ 0.2 to compare the galaxy distribution in each system with the projected matter distribution from weak lensing. By combining 2087 new MMT/Hectospec redshifts and the data in the literature, we construct spectroscopic samples within the region of weak-lensing maps of high (70-89%) and uniform completeness. With these dense redshift surveys, we construct galaxy number density maps using several galaxy subsamples. The shape of the main cluster concentration in the weak-lensing maps is similar to the global morphology of the number density maps based on cluster members alone, mainly dominated by red members. We cross correlate the galaxy number density maps with the weak-lensing maps. The cross correlation signal when we include foreground and background galaxies at 0.5zcl < z < 2 zcl is 10 - 23% larger than for cluster members alone at the cluster virial radius. The excess can be as high as 30% depending on the cluster. Cross correlating the galaxy number density and weak-lensing maps suggests that superimposed structures close to the cluster in redshift space contribute more significantly to the excess cross correlation signal than unrelated large-scale structure along the line of sight. Interestingly, the weak-lensing mass profiles are not well constrained for the clusters with the largest cross correlation signal excesses (>20% for A383, A689 and A750). The fractional excess in the cross correlation signal including foreground and background structures could be a useful proxy for assessing the reliability of weak-lensing cluster mass estimates.

• Journal of Forest and Environmental Science
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• v.34 no.3
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• pp.209-223
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• 2018

We investigated tree composition, stand characteristics, biomass allocation pattern and carbon storage variability in Sal forests (Shorea robusta Garten.) under two forest management regimes (Sal forest and Sal plantation) in Tripura, Northeast India. The results revealed higher species richness (29 species), stand density of $1060.00{\pm}11.12stems\;ha^{-1}$ and diversity index ($1.90{\pm}0.08$) in Sal forest. and lower species richness (4 species), stand density of $ 230.00{\pm}37.22stems\;ha^{-1}$ and diversity index ($0.38{\pm}0.15$) in Sal plantation. The total basal cover $33.02{\pm}4.87m^2ha^{-1}$) and dominance ($0.76{\pm}0.08$) were found higher in Sal plantation than the Sal forest ($22.53{\pm}0.38m^2ha^{-1}$ and $0.23{\pm}0.02$ respectively). The total vegetation carbon density was recorded higher in Sal plantation ($219.68{\pm}19.65Mg\;ha^{-1}$) than the Sal forest ($167.64{\pm}16.73Mg\;ha^{-1}$). The carbon density estimates acquired in this study suggest that Sal plantation in Tripura has the potentiality to store a large amount of atmospheric carbon inspite of a very low species diversity. However, Sal forests has also an impending sink of carbon due to presence of large number of young trees.

• Asian-Australasian Journal of Animal Sciences
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• v.24 no.2
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• pp.162-172
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• 2011

In this paper, simulation was used to determine accuracies of genomic breeding values for polygenic traits associated with many thousands of markers obtained from high density genome scans. The statistical approach was based upon stochastically simulating a pedigree with a specified base population and a specified set of population parameters including the effective and noneffective marker distances and generation time. For this population, marker and quantitative trait locus (QTL) genotypes were generated using either a single linkage group or multiple linkage group model. Single nucleotide polymorphism (SNP) was simulated for an entire bovine genome (except for the sex chromosome, n = 29) including linkage and recombination. Individuals drawn from the simulated population with specified marker and QTL genotypes were randomly mated to establish appropriate levels of linkage disequilibrium for ten generations. Phenotype and genomic SNP data sets were obtained from individuals starting after two generations. Genetic prediction was accomplished by statistically modeling the genomic relationship matrix and standard BLUP methods. The effect of the number of linkage groups was also investigated to determine its influence on the accuracy of breeding values for genomic selection. When using high density scan data (0.08 cM marker distance), accuracies of breeding values on juveniles were obtained of 0.60 and 0.82, for a low heritable trait (0.10) and high heritable trait (0.50), respectively, in the single linkage group model. Estimates of 0.38 and 0.60 were obtained for the same cases in the multiple linkage group models. Unexpectedly, use of BLUP regression methods across many chromosomes was found to give rise to reduced accuracy in breeding value determination. The reasons for this remain a target for further research, but the role of Mendelian sampling may play a fundamental role in producing this effect.

• Ocean Systems Engineering
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• v.9 no.3
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• pp.287-328
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• 2019

Comprehensive understanding of the flood risk assessments via frequency analysis often demands multivariate designs under the different notations of return periods. Flood is a tri-variate random consequence, which often pointing the unreliability of univariate return period and demands for the joint dependency construction by accounting its multiple intercorrelated flood vectors i.e., flood peak, volume & durations. Selecting the most parsimonious probability functions for demonstrating univariate flood marginals distributions is often a mandatory pre-processing desire before the establishment of joint dependency. Especially under copulas methodology, which often allows the practitioner to model univariate marginals separately from their joint constructions. Parametric density approximations often hypothesized that the random samples must follow some specific or predefine probability density functions, which usually defines different estimates especially in the tail of distributions. Concentrations of the upper tail often seem interesting during flood modelling also, no evidence exhibited in favours of any fixed distributions, which often characterized through the trial and error procedure based on goodness-of-fit measures. On another side, model performance evaluations and selections of best-fitted distributions often demand precise investigations via comparing the relative sample reproducing capabilities otherwise, inconsistencies might reveal uncertainty. Also, the strength & weakness of different fitness statistics usually vary and having different extent during demonstrating gaps and dispensary among fitted distributions. In this literature, selections efforts of marginal distributions of flood variables are incorporated by employing an interactive set of parametric functions for event-based (or Block annual maxima) samples over the 50-years continuously-distributed streamflow characteristics for the Kelantan River basin at Gulliemard Bridge, Malaysia. Model fitness criteria are examined based on the degree of agreements between cumulative empirical and theoretical probabilities. Both the analytical as well as graphically visual inspections are undertaken to strengthen much decisive evidence in favour of best-fitted probability density.

• The Bulletin of The Korean Astronomical Society
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• v.40 no.2
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• pp.41.3-41.3
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• 2015

As part of the Herschel key program "Water in Star-forming Regions with Herschel (WISH)", PACS imaging spectroscopy data have been taken toward ten massive young stellar objects (YSOs): four high mass protostellar objects (HMPOs), two hot molecular cores (HMCs), and four ultracompact HII regions (UCHIIs). The spectra cover a broad range of wavelengths (55 to 210 micron) presenting various atomic and molecular lines as well as excellent dust thermal continua. By fitting the continua utilizing a modified black-body formula we estimate mass-weighted temperature and column density distributions of warm dust and find that UCHII regions are warmer and HMCs are more deeply embedded than the other types. We also estimate rotational temperature and column density distributions of warm CO gas using the rotational diagram analysis. In addition, based on the comparison of high J CO line fluxes to the RATRAN estimates of central heating envelope models, we find that majority of warm CO is originated from bipolar outflow shocks.

• Environmental and Resource Economics Review
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• v.18 no.1
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• pp.105-138
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• 2009

This study investigates the spatial distribution of the degree of water pollutants in Han River using data obtained by the water pollution observation stations. This study estimates a non -parametric kernel density function for each water pollutants, and tests a significant difference between two estimated distribution functions. Next, Generalized Entropy inequality indices are evaluated and this research tests difference of inequality indices between two years using bootstrapping method. Lastly in a dynamic of view, it is analyzed that there are significant changes in the ranking of water pollution level. Estimation results show that the degree of inequality in spatial distribution of water pollution tends to be stable or decreasing for last 15 years in a great part of water pollutants, and ranking of water pollution level hardly changes in Han River.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.121-130
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• 2014

Essential ideas, successes, and difficulties of Areal Density Analysis (ADA) for color-magnitude diagrams (CMD's) of resolved stellar populations are examined, with explanation of various algorithms and strategies for optimal performance. A CMD-generation program computes theoretical datasets with simulated observational error and a solution program inverts the problem by the method of Differential Corrections (DC) so as to compute parameter values from observed magnitudes and colors, with standard error estimates and correlation coefficients. ADA promises not only impersonal results, but also significant saving of labor, especially where a given dataset is analyzed with several evolution models. Observational errors and multiple star systems, along with various single star characteristics and phenomena, are modeled directly via the Functional Statistics Algorithm (FSA). Unlike Monte Carlo, FSA is not dependent on a random number generator. Discussions include difficulties and overall requirements, such as need for fast evolutionary computation and realization of goals within machine memory limits. Degradation of results due to influence of pixelization on derivatives, Initial Mass Function (IMF) quantization, IMF steepness, low Areal Densities ($\mathcal{A}$), and large variation in $\mathcal{A}$ are reduced or eliminated through a variety of schemes that are explained sufficiently for general application. The Levenberg-Marquardt and MMS algorithms for improvement of solution convergence are contained within the DC program. An example of convergence, which typically is very good, is shown in tabular form. A number of theoretical and practical solution issues are discussed, as are prospects for further development.