• Journal of the Semiconductor & Display Technology
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• v.22 no.4
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• pp.13-18
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• 2023

A subway station is a prominent multi-purpose facility where the quantitative management of fine dust, generated by various factors, is conducted. Recently, eco-friendly air purification methods using air-purifying plants are being discussed, with the focus on biofiltration through vegetation. Previous research in this field has confirmed the reduction effects of transition metals such as Fe, which have been identified as harmful to human health. This study aimed to identify the sources of fine dust dispersion within subway stations and derive an efficient operational strategy for air-purifying plants that takes into account the behavior characteristics of fine dust within multi-purpose facilities. The experiment monitored regional fine dust levels through IAQ stations established based on prior research. Also, the data was analyzed through time-series and correlation analyses by linking it with passenger counts at subway stations and the frequency of train stops. Furthermore, to consider energy efficiency, we conducted component-specific power consumption monitoring. Through this study, we were able to derive the optimal operational strategy for air-purifying plants based on time-series comprehensive analysis data and confirm significant energy efficiency.

• Journal of Korean Society of Transportation
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• v.25 no.1 s.94
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• pp.121-132
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• 2007

Up to now Permanent traffic volumes have been counted by Automatic Vehicle Classification (AVC) on National Highways. When counted data have missing items or errors, the data must be revised to stay statistically reliable This study was carried out to estimate correct data based on outoregression and seasonal AutoRegressive Integrated Moving Average (ARIMA). As a result of verification through seasonal ARIMA, the longer the missed period is, the greater the error. Autoregression results in better verification results than seasonal ARIMA. Traffic data is affected by the present state mote than past patterns. However. autoregression can be applied only to the cases where data include similar neighborhood patterns and even in this case. the data cannot be corrected when data are missing due to low qualify or errors Therefore, these data shoo)d be corrected using past patterns and seasonal ARIMA when the missing data occurs in short periods.

• Journal of Preventive Medicine and Public Health
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• v.57 no.2
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• pp.120-127
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• 2024

Objectives: The coronavirus disease 2019 (COVID-19) pandemic led to increased mortality rates. To assess this impact, this ecological study aimed to estimate the excess death counts in southern Iran. Methods: The study obtained weekly death counts by linking the National Death Registry and Medical Care Monitoring Center repositories. The P-score was initially estimated using a simple method that involved calculating the difference between the observed and expected death counts. The interrupted time series analysis was then used to calculate the mean relative risk (RR) of death during the first year of the pandemic. Results: Our study found that there were 5571 excess deaths from all causes (P-score=33.29%) during the first year of the COVID-19 pandemic, with 48.03% of these deaths directly related to COVID-19. The pandemic was found to increase the risk of death from all causes (RR, 1.26; 95% confidence interval [CI], 1.19 to 1.33), as well as in specific age groups such as those aged 35-49 (RR, 1.21; 95% CI, 1.12 to 1.32), 50-64 (RR, 1.38; 95% CI, 1.28 to 1.49), and ≥65 (RR, 1.29; 95% CI, 1.12 to 1.32) years old. Furthermore, there was an increased risk of death from cardiovascular diseases (RR, 1.17; 95% CI, 1.11 to 1.22). Conclusions: There was a 26% increase in the death count in southern Iran during the COVID-19 pandemic. More than half of these excess deaths were not directly related to COVID-19, but rather other causes, with cardiovascular diseases being a major contributor.

• Communications for Statistical Applications and Methods
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• v.17 no.1
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• pp.27-37
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• 2010

This article concerns the forecasting in binomial AR(p) models which is proposed by Wei$\ss$ (2009b) for time series of binomial counts. Our method extends to binomial AR(p) models a recent result by Jung and Tremayne (2006) for integer-valued autoregressive model of second order, INAR(2), with simple Poisson innovations. Forecasts are produced by conditional median which gives 'coherent' forecasts, and we estimate the forecast distributions of future values of binomial AR(p) models by means of a Monte Carlo method allowing for parameter uncertainty. Model parameters are estimated by the method of moments and estimated standard errors are calculated by means of block of block bootstrap. The method is fitted to log data set used in Wei$\ss$ (2009b).

• International Journal of Highway Engineering
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• v.9 no.3
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• pp.21-28
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• 2007

We suggest a new spatial linear interpolation method to substitute linear interpolation method which widely used in transportation engineering to impute the missing daily traffic volume. We layout daily traffic volume which is time series data over the virtual lattice space to consider the spatial correlation. We used Moran Index to evaluate the spatial correlations among daily traffic volume in same week and same date traffic volume by week considering the circularity of daily traffic volume. For real application, we used daily traffic volume on November, 2004 provided by Korea Institute of Construction Technology(KICT) and transformed daily traffic volume to 4 times 7 virtual lattice space to reflect the spatial correlation. Finally we showed that the spatial linear interpolation method has good performance for missing data imputation based on MAPE, RMSE, and Theil's U criteria.

• Journal of Environmental Health Sciences
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• v.20 no.3
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• pp.61-77
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• 1994

Sprague Dawley rats were exposed to electric fields (6,000 V, 10 kV/m, 30 min/day, 6 days per week) and injected strontium 90 (681 kbq/rat, one time) through abdominal cavity (strontium 90 and electric field complexed exposure group). In parallel, series with the electric field exposure only, strontium 90 injection only and control groups were run. Every group was consisted of 110 rats (55 male and 55 female). This animal experiment was performed from May to December in 1993. This results were conducted to investigate the effect of electric field for 11 weeks. The results are summarized as follows: 1. Ornithine decarboxylase (ODC) activity in rat's bone marrow cells: The ODC values was significantly increased in Sr$^{90}$ injection group and Sr$^{90}$ and electric field complexed exposure group as compared with that of control group (p<0.05). The ODC value was significantly decreased in electric field and Sr$^{90}$ complexed exposure group in comparison with Sr$^{90}$ injection group (p<0.05). The ODC values of electric field only exposure group was not different to that of control group (p>0.05). 2. The amount of Sr$^{90}$ accumulation in the femur, kidney and spleen:The accumulation amount of Sr$^{90}$ in the femur of Sr$^{90}$ injection group represented higher value than that of electric field and Sr$^{90}$ complexed exposure group (p<0.05). In the kidney and spleen, the difference between electric field and Sr$^{90}$ complexed exposure group and Sr$^{90}$ injection group wasn't observed. 3. The counts of white cells in blood of Sr9?injection group was decreased as compared with the value from control group and electric field and Sr$^{90}$ complexed exposure group (p<0.05). The rat's body weight, red blood cell counts and the weight data of liver, kidney and spleen did not show differences among four groups.

• Journal of Chest Surgery
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• v.2 no.1
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• pp.115-132
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• 1969

The normal physiological values of Korean mongrel dogs were studied, comparing them with foreign references, on the basis of control physiological data measured on 110 cases out of the dogs submitted to the experiments in our department. The hemodynamic measurements varied widely between the both limits of reported normal control data, with the mean value of heart rate $140.4{\pm}26.6$/min., and both the systolic and diastolic arterial blood pressures $137.2{\pm}31.7$mmHg and $104.7{\pm}14.4$ mmHg, as well as the venous pressure of $9.11{\pm}2.18$ cm.$H_2O$. Hematologically, the number of red blood cells $4,571,000{\pm}767,000$per cu.mm., the amount of hemoglobin $11.57{\pm}3.74$ gm/dl and the hematocrit $37.3{\pm}7.2$ per cent, were equally the values a little lower than the reported normal means. However, the white blood cells were within the reported normal limits both in number, $10,384{\pm}4,877$ per cu. mm and their differential counts with slightly broader ranges of variation. The platelet count was $149,800{\pm}47,000 $per cu. mm and was also far below the normal, while the coagulation time $9.03{\pm}2.69$min. and the prothrombin time $13.17{\pm}6.52$sec were within normal limits, though a little prolonged. The serum electrolytes, Na $146.6{\pm}10.44$mEq/L.,K $4.46{\pm}0.84$mEq/L., CI $118.3{\pm}7.88$mEq/L. and Ca $11.45{\pm}5.62$mg./dl, and the blood glucose level of $94.9{\pm}31.79$mg./dl were essentially not different from the reported normal values. The serum proteins, total protein $7.15{\pm}1.41$gm/dl., albumin $4.09{\pm}0.77$gm./dl. and globulin $3.18{\pm}0.88$gm/dl. were included near the higher limits of the reported normal levels. The thymol turbidity 0.1-3.04 units were normal in 10 cases, and the cephaline flocculation was within normal range except 2 cases out of 26 dogs, showing two positive in 24 hours. And the nitrogen series, NPN $34.61{\pm}10.29$mg/dl. and BUN $12.77{\pm}6.37$mg./dl. were normal. It may be concluded that from the point of view of hereby measured physiological data compared with the foreign references, the Korean mongrel dogs have a compatible laboratory data with only the special regards to their tendency toward anemia in red blood cell series.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.2
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• pp.274-281
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• 2019

Objective: The objective of this study was to estimate the risk of Campylobacter jejuni (C. jejuni) infection from various jerky products in Korea. Methods: For the exposure assessment, the prevalence and predictive models of C. jejuni in the jerky and the temperature and time of the distribution and storage were investigated. In addition, the consumption amounts and frequencies of the products were also investigated. The data for C. jejuni for the prevalence, distribution temperature, distribution time, consumption amount, and consumption frequency were fitted with the @RISK fitting program to obtain appropriate probabilistic distributions. Subsequently, the dose-response models for Campylobacter were researched in the literature. Eventually, the distributions, predictive model, and dose-response model were used to make a simulation model with @RISK to estimate the risk of C. jejuni foodborne illness from the intake of jerky. Results: Among 275 jerky samples, there were no C. jejuni positive samples, and thus, the initial contamination level was statistically predicted with the RiskUniform distribution [RiskUniform (-2, 0.48)]. To describe the changes in the C. jejuni cell counts during distribution and storage, the developed predictive models with the Weibull model (primary model) and polynomial model (secondary model) were utilized. The appropriate probabilistic distribution was the BetaGeneral distribution, and it showed that the average jerky consumption was 51.83 g/d with a frequency of 0.61%. The developed simulation model from this data series and the dose-response model (Beta Poisson model) showed that the risk of C. jejuni foodborne illness per day per person from jerky consumption was $1.56{\times}10^{-12}$. Conclusion: This result suggests that the risk of C. jejuni in jerky could be considered low in Korea.

• Korean Journal of Remote Sensing
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• v.30 no.4
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• pp.417-430
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• 2014

We have found an error in the operational OMI HCHO columns, and corrected it by applying a background parameterization derived on a 4th order polynomial fit to the time series of monthly average OMI HCHO data. The corrected OMI HCHO agrees with this understanding as well as with the other sensors measurements and has no unrealistic trends. A new scientific approach, statistical analyses with EOF and SVD, was adapted to reanalyze the consistency of the corrected OMI HCHO with other satellite measurements of HCHO, CO, $NO_2$, and fire counts over Africa. The EOF and SVD analyses with MOPITT CO, OMI $NO_2$, SCIAMAHCY, and OMI HCHO show the overall spatial and temporal pattern consistent with those of biomass burning over these regions. However, some discrepancies were observed from OMI HCHO over northern equatorial Africa during the northern biomass burning seasons: The maximum HCHO was found further downwind from where maximum fire counts occur and the minimum was found in January when biomass burning is strongest. The statistical analysis revealed that the influence of biogenic activity on HCHO wasn't strong enough to cause the discrepancies, but it is caused by the error in OMI HCHO from using the wrong Air Mass Factor (AMF) associated with biomass burning aerosol. If the error is properly taken into consideration, the biomass burning is the strongest source of HCHO seasonality over the regions. This study suggested that the statistical tools are a very efficient method for evaluating satellite data.

• Near Infrared Analysis
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• v.2 no.1
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• pp.59-66
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• 2001

Nowadays, medical diagnostics is efficiently supported by clinical chemistry and near infrared spectroscopy is becoming a new dimension, which has shown high potential to provide valuable information for diagnosis. The investigation was carried out to study the influence of mammary gland inflammation, called mastitis, on cow´s milk spectra and milk composition measured by near infrared spectroscopy (NIRS). Milk somatic cell counts (SCC) in milk were used as a measure of mammary gland inflammation. Naturally occurred variations with milk composition within lactation and in the process of milking were included in the experimental design of this study. Time series of unhomogenized, raw milk spectral data were collected from 3 cow along morning and evening milking, for 5 consecutive months, within their second lactation. In the time of the trial, the investigated cows had periods with mammary gland inflammation. Transmittance spectra of 258 milk samples were obtained by NIRSystem 6500 spectrophotometer in 1100-2400 nm region. Calibration equations for the examined milk components were developed by PLS regression using 3 different sets of samples: samples with low somatic cell count (SCC), samples with high SCC and combined data set. The NIR calibration and prediction of individual cow´s milk fat, protein, and lactose were highly influenced by the presence of mil samples from animals with mammary gland inflammation in the data set. The best accuracy of prediction (i.e. the lower SEP and the higher correlation coefficient) for fat, protein and lactose was obtained for equations, developed when using only “healthy” samples, with low SCC. The standard error of prediction increased and correlation coefficient decreased significantly when equations for low SCC milk were used to predict examined components in “mastitis” samples with high SCC, and vice versa. Combined data set that included samples from healthy and mastitis animals could be used to build up regression models for screening. Further use of separate model for healthy samples improved milk composition measurement. Regression vectors for NIR mild protein measurement obtained for “healthy” and “mastitic” group were compared and revealed differences in 1390-1450 nm, 1500-1740 nm and 1900-2200 nm regions and thus illustrated post-secretory breakdown of milk proteins by hydrolytic enzymes that occurred with mastitis. For the first time it has been found that monitoring the spectral differences in water bands at 1440 nm and 1912 nm could provide valuable information for inflammation diagnosis.