• Journal of Preventive Medicine and Public Health
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• v.45 no.2
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• pp.105-112
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• 2012

Objectives: Cigarette smoking is a modifiable risk factor for cardiovascular disease. Bilirubin is a potent antioxidant and its concentration decreases in smokers. However, studies about the association between cigarette smoking and bilirubin are scarce and most are limited to total bilirubin. Additionally, bilirubin is highly related to hemoglobin. Therefore, this study evaluates the association between bilirubin subtypes and cigarette smoking in healthy Korean men independently of hemoglobin. Methods: This study included 48 040 Korean men aged 30 to 87 years who visited the Korea Medical Institute for routine health examinations from January to December, 2007. The association of smoking with total, direct, and indirect bilirubin was assessed by logistic regression analysis taking into consideration differences in subjects and smoking characteristics. Results: Current smokers had lower bilirubin concentrations than never-smokers and ex-smokers. Smoking amount and duration were inversely significantly associated with total, direct, and indirect bilirubin. In a multivariable adjusted model, compared to never-smokers, the odds ratios (ORs) and 95% confidence intervals (CIs) of current smokers with the highest number of pack-years were 1.7 (1.6 to 1.9) for total, 1.5 (1.4 to 1.6) for direct, and 1.7 (1.6 to 1.9) for indirect bilirubin. After further adjustment for hemoglobin, this association became stronger (OR [95% CI], 2.1 [1.9 to 2.2] for total; 1.9 [1.8 to 2.0] for direct; 2.0 [1.9 to 2.2] for indirect bilirubin). Conclusions: In this study, bilirubin subtypes are inversely associated with smoking status, smoking amount, and smoking duration in healthy Korean men independently of hemoglobin. Further studies are needed to investigate this association in healthy Korean women.

• Tribology and Lubricants
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• v.22 no.3
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• pp.155-163
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• 2006

Ring, groove and cylinder bore wear may not be a problem in most current automotive engines. However, a small change in ring face, groove geometry and cylinder bore diameter can significantly affect the lubrication characteristics and ring axial motion. This in turn can cause to change inter-ring pressure, blowby and oil consumption in an engine. Therefore, by predicting the wear of piston ring face, ring groove and cylinder bore altogether, the changed ring end gap and the changed volume of gas reservoir can be calculated. Then the excessive oil consumption can be predicted. Being based on the calculation of gas flow amount by the theory of piston ring dynamics and gas flow, and the calculation of oil film thickness and friction force by the analysis of piston ring lubrication, the calculation theory of oil amount through top ring gap into combustion chamber will be set. This is estimated as engine oil consumption. Furthermore, the wear theories of ring, groove and cylinder bore are included. Then the each amount of wear is to be obtained. The changed oil consumption caused by the new end gap and the new volume of oil reservoir around second land, can be calculated at some engine running interval. Meanwhile, the wear amount and oil consumption occurred during engine durability cycle are compared with the calculated values. Next, the calculated amount of oil consumption and wear are compared with the guideline of each part's wear and oil consumption. So, the timing of part repair and engine life cycle can be predicted in advance without performing engine durability test. The wear data of rings, grooves and cylinder bore are obtained from three engines before and after engine durability test. The calculated wear data of each part are turn out to be around the band of averaged test values or a little below.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.E1
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• pp.1-12
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• 2002

One technique for determining dry acid deposition fluxes involves measurement of time - averaged ambient concentrations of dry acid deposition species using filter packs (FP) coupled with estimates of mean deposition velocities for the exposure period. A critical problem associated with filter pack data comparisons between various field sampling networks is the use of diverse sampling flow rates and duration protocols. Field experiments were conducted to evaluate the effects of varying sampling flow rates, from 1.5 to 10 standard liters per minute, on total nitrate and sulfate measurements of specific dry acid deposition species . Collocated FP samplers were used to determine sampling and analysis data reproducibility and representativeness . Ambient air samples were simultaneously collected using groups of filter packs operated at various flow rates over identical 7 day periods. The species measured were sulfur dioxide, particulate sulfate , nitric acid and particulate nitrate. Statistical results (ANOVA; alpha level 5%) showed that neither the low nor high sampling flow rates caused a significant difference in the measurements of total sulfate and adjusted total nitrate (ATN) . However, it was concluded that for high flow rate sampling measurements, total nitrate (TN) could be affected during extended sampling durations because of potential nitric acid overloading and breakthrough. Although the previous workers (Costello, 1990; Quillian, 1990) used much higher sampling flow rates (~ 17 sLpm) than employed here, it was assumed that for a high loading (> 50$\mu\textrm{g}$ HNO$_3$) of nitric acid on the Nylon filters, a significant fraction (~10%) of nitric acid could pass through the Nylon filters and be collected on the carbonate impregnated filters. It was concluded that even at the highest sampling flow rate employed (10 sLpm) at the Cary Forest site, nitric acid breakthrough was less than 10% of the total HNO$_3$ collected. However, for a heavily polluted urban airshed or with longer sampling times , higher filter loadings could result in substantial nitric acid breakthrough and HNO$_3$concentrations would be underestimated.

• Asian Pacific Journal of Cancer Prevention
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• v.13 no.6
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• pp.2663-2667
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• 2012

Objectives: An attempt was made to understand the nicotine dependence of smokers selected for an ongoing smoking cessation intervention programme in rural Kerala, India. Methods: Data were collected from resident males in the age group of 18 to 60 years from 4 randomly allocated community development blocks of rural Thiruvananthapuram district (2 intervention and 2 control groups). Trained accredited social health activist workers were utilised to collect data from all groups through face to face interview. Nicotine dependence among participants was assessed by means of the six-item Fagerstrom Test for Nicotine Dependence (FTND) translated into the local language. The internal consistency of FTND was computed using Cronbach's alpha coefficient. Criterion validity (concurrent) was assessed by correlations of nicotine dependence scores with age at initiation of smoking and cumulative smoking volume in pack-years. Results: Among the 928 smokers identified, 474 subjects were in the intervention area (mean age = 44.6 years, SD = 9.66 years) and 454 in the control area (mean age = 44.5 years, SD = 10.30 years). The overall FTND score among current daily smokers was 5.04 (SD: 5.05). FTND scores in the control and intervention areas were 4.75 (SD: 2.57) and 4.92 (SD: 2.51) respectively. The FTND scores increased with age and decreased with higher literacy and socioeconomic status. The average FTND score was high among smokers using both bidi and cigarettes (mean 6.10, SD 2.17). Internal consistency analysis yielded a Cronbach's alpha coefficient of 0.70 in a subsample of 150 subjects, a moderate result. The association of the scale was strongest, with the number of pack-years smoked (rho = 0.677, p < 0.001). Conclusion: A moderate level of nicotine dependence was observed among smokers in the current study. Tobacco cessation strategies could be made more cost effective and productive if a baseline assessment of nicotine dependence is completed before any intervention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.8
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• pp.1-7
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• 2017

This study numerically evaluates the heat transfer characteristics of a 360-Wh Li-ion battery pack. The analysis was done in ANSYS CFX using different cell arrangements, cell holders, and case materials for a personal mobility device program. A total of four cases of cell arrangements were considered, along with various materials for both the cell holder and the case, such as polypropylene, aluminum, and magnesium alloy. Out of the four cell arrangements, model 2 showed the best heat transfer performance, while aluminum showed the best heat transfer performance for the cell holder and case.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1889-1894
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• 2017

Global automobile manufacturers are developing electric vehicles (EVs) to eliminate the pollutant emissions from internal combustion vehicles and to minimize fossil fuel consumptions for the future generations. However, EVs have a disadvantage of shorter traveling distance than that of conventional vehicles. To answer this shortfall, more batteries are installed in the EV to satisfy the consumer expectation for the driving range. However, as the energy capacity of the battery mounted in the EV increases, the amount of heat generated by each cell also increases. Naturally, a better battery thermal management system (BTMS) is required to control the temperature of the cells efficiently because the appropriate thermal environment of the cells greatly affects the power output from the battery pack. Typically, the BTMS is divided into an active and a passive system depending on the energy usage of the thermal management system. Heat exchange materials usually include gas and liquid, semiconductor devices and phase change material (PCM). In this study, an application of PCM for a BTMS was investigated to maintain an optimal battery operating temperature range by utilizing characteristics of a PCM, which can accumulate large amounts of latent heat. The system was modeled using Dymola from Dassault Systems, a multi-physics simulation tool. In order to compare the relative performance, the BTMS with the PCM and without the PCM were modeled and the same battery charge/discharge scenarios were simulated. Number of analysis were conducted to compare the battery cooling performance between the model with the aluminum case and PCM and the model with the aluminum case only.

• Journal of Preventive Medicine and Public Health
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• v.43 no.3
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• pp.193-204
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• 2010

Objectives: The objective of this study was to analyze the causal relationship between smoking and depression using longitudinal data. Methods: Two waves of the Korea Welfare Panel collected in 2006 and 2007 were used. The sample consisted of 14 426 in 2006 and 13 052 in 2007 who were aged 20 and older. Smoking was measured by smoking amount (none/$\geq$ two packs). Depression was defined when the summated CESD (center for epidemiological studies depression)-11 score was greater than or equal to 16. The causal relationship between smoking and depression was tested using logistic regression. In order to test the causal effect of smoking on depression, depression at year 2 was regressed on smoking status at year 1 only using the sample without depression at year 1. Likewise, smoking status at year 2 was regressed on depression at year 1 only using those who were not smoking at year 1 in order to test the causal effect of depression on smoking. The statistical package used was Stata 10.0. Sampling weights were applied to obtain the population estimation. Results: The logistic regression testing for the causal relationship between smoking and depression showed that smoking at year 1 was significantly related to depression at year 2. Smoking amounts associated with depression were different among age groups. On the other hand, the results from the logistic regression testing for the opposite direction of the relationship between smoking and depression found no significant association regardless of age group. Conclusions: The study results showed some evidence that smoking caused depression but not the other way around.

• Korean journal of applied entomology
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• v.60 no.2
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• pp.203-214
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• 2021

This study aimed at establishing a packaging standardization method that allows the maintenance of biological control agents product quality during delivery. First, based on the results of distribution status analysis of biological control agent products from four manufacturers, we confirmed that the mean temperature was maintained at 25.6℃ (minimum and maximum temperatures.: 18.1 and 30.7℃, respectively) inside the packaging box of each company for 36-48 h. To establish the optimal packaging method for each season, seven external temperature conditions were set ranging between 0℃ and 30℃ at intervals of 5℃. In addition, we evaluated internal temperature maintenance performance for each coolant pack handling method and determined 14 packaging combinations. A packaging combination that maintained a temperature of 3℃ - 9.9℃ at each external temperature conditions was considered efficient. This temperature range is close to a lower developmental threshold at which the biological control agents can survive with minimum energy for 12 h (direct delivery time), or 36-48 h (general delivery time) after packaging.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.12
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• pp.819-828
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• 2022

Sensitivity of aircraft sizing depending on battery performance was studied for a generic quad tilt rotor type electric vertical takeoff and landing vehicle. The mission requirements proposed by Uber Elevate and NASA were used for initial sizing, and the calculated gross weight is ranged between 5,000lb and 11,000lb for battery specific energy range of 200-400Wh/kg in pack level and continuous discharge rate range of 4-5C. For the assumed gross weight of 7,000lb, the required battery performance was calculated with two different criteria: available power and energy, and the effects of battery specific energy and discharge rate are analyzed. The maximum discharge rate is also recommended considering failure cases such as one battery pack inoperative and one prop rotor inoperative.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.