• Environmental Analysis Health and Toxicology
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• v.31
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• pp.6.1-6.10
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• 2016

Objectives This study examined the spatial distribution and the extent of soil contamination by heavy metals resulting from primitive, unconventional informal electronic waste recycling in the Agbogbloshie e-waste processing site (AEPS) in Ghana. Methods A total of 132 samples were collected at 100 m intervals, with a handheld global position system used in taking the location data of the soil sample points. Observing all procedural and quality assurance measures, the samples were analyzed for barium (Ba), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), and zinc (Zn), using X-ray fluorescence. Using environmental risk indices of contamination factor and degree of contamination ($C_{deg}$), we analyzed the individual contribution of each heavy metal contamination and the overall $C_{deg}$. We further used geostatistical techniques of spatial autocorrelation and variability to examine spatial distribution and extent of heavy metal contamination. Results Results from soil analysis showed that heavy metal concentrations were significantly higher than the Canadian Environmental Protection Agency and Dutch environmental standards. In an increasing order, Pb>Cd>Hg>Cu>Zn>Cr>Co>Ba>Ni contributed significantly to the overall $C_{deg}$. Contamination was highest in the main working areas of burning and dismantling sites, indicating the influence of recycling activities. Geostatistical analysis also revealed that heavy metal contamination spreads beyond the main working areas to residential, recreational, farming, and commercial areas. Conclusions Our results show that the studied heavy metals are ubiquitous within AEPS and the significantly high concentration of these metals reflect the contamination factor and $C_{deg}$, indicating soil contamination in AEPS with the nine heavy metals studied.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.2
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• pp.117-127
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• 2008

Korea has a large number of landslides due to localized torrential downpours and typhoons in summer, causing great human damage and economic losses. In particular, most roads in the Gangwon area are located in mountains, making them expose to a great risk of landslide. Therefore, it is urgent to prepare countermeasures to prevent these landslides. Necessary for that are various slope investigation and high-tech observation techniques for slope maintenance. Recently there have been slope observation techniques using optical fiber sensors, GPS, CCD cameras, Total Station and satellite images; however, these are not used much due to poor economic feasibility, low accuracy and efficiency. This study evaluated accuracy of displacement extraction of model slopes using terrestrial LiDAR to determine its application to landslide monitoring. As a result, it can measure several mm of minute displacement with high accuracy and help to rapidly obtain geographical features of slope.

• Journal of Korean Neurosurgical Society
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• v.58 no.6
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• pp.571-577
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• 2015

Direct removal of beak-type ossification of posterior longitudinal ligament at thoracic spine (T-OPLL) is a challenging surgical technique due to the potential risk of neural injury. Slipping off the cutting surface of a high-speed drill may result in entrapment in neural structures, leading to serious complications. Removal of T-OPLL with an ultrasonic osteotome, utilizing back and forth micro-motion of a blade rather than rotatory-motion of drill, may reduce such complications. We have applied the ultrasonic osteotome for posterior circumferential decompression of T-OPLL for three consecutive patients with beak-type OPLL and have described the surgical techniques and patient outcomes. The preoperative chief complaint was gait disturbance in all patients. Japanese orthopedic association scores (JOA) was used for functional assessment. Scores measured 2/11, 5/11, 2/11, and 4/11 for each patient. The ventral T-OPLL mass was exposed after posterior midline approach, laminotomy and transeversectomy. The T-OPLL mass was directly removed with an ultrasonic osteotome and instrumented segmental fixation was performed. The surgeries were uneventful. Detailed surgical techniques were presented. Gait disturbance was improved in all patients. Dural tear occurred in one patient without squeal. Postoperative JOA was 6/11, 10/11, 8/11, and 8/11 (recovery rate; 44%, 83%, 67%, and 43%) respectively at 18, 18, 10, and 1 months postoperative. T-OPLL was completely removed in all patients as confirmed with computed tomography scan. We hope that surgical difficulties in direct removal of T-OPLL might be reduced by utilizing ultrasonic osteotome.

• Journal of the Korean Society for Railway
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• v.12 no.5
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• pp.793-799
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• 2009

Using transportations like railway, aviations and roads have been increasing continuously. Traffic accidents have been increasing as well. To prevent or lessen these accidents become a big issue to solve. Therefore, more enforced safety levels are being required to meet. In railway field, continuous efforts of railway safety are being executed to prevent traffic accidents. In present, railway accidents have been decreasing since subway accident happened in Daegu Subway in 2003. However, safety levels in railway field have not yet achieved as much as in advanced countries. Basic concept for enforced safety level in railway system is to prevent or minimize accidents by managing systems like related facilities, management method, organization, education. This is the same concept to minimize error rate in six sigma theory. In this paper, we are to apply six sigma techniques used in manufacturing industry to railway safety and go through ways to make more efficient railway safety system.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.137-144
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• 2023

One of the methods being suggested as a way to address social issues is the utilization of big data analysis techniques. In this study, we utilized keyword network analysis and CONCOR analysis techniques to analyze the research trends on work-related musculoskeletal disorders. The findings of this study are as follows: Firstly, the number of papers on work-related musculoskeletal disorders has been consistently increasing, with an average of over 33 articles published per year since the investigation of musculoskeletal risk factors in 2003. The publication rate showed an increase from 2007 to 2009. Secondly, the frequency of the top keywords identified through text mining were as follows: work (4,940), musculoskeletal disorders (2,197), symptoms (1,836), related (1,769), musculoskeletal system (1,421). Thirdly, the CONCOR analysis resulted in the formation of four clusters: ' Musculoskeletal disorder treatment', 'Occupational health and safety management', 'Work environment assessment', and ' Workplace environment measurement'. It is expected that this study will contribute to the development of research on musculoskeletal disorders and provide various directions for future studies.

• The Korean Journal of Pain
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• v.28 no.4
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• pp.244-253
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• 2015

Background: Although circumcision for phimosis in children is a minor surgical procedure, it is followed by pain and carries the risk of increased postoperative anxiety. This study examined predictive factors of postoperative pain and anxiety in children undergoing circumcision. Methods: We conducted a prospective cohort study of children scheduled for elective circumcision. Circumcision was performed applying one of the following surgical techniques: sutureless prepuceplasty (SP), preputial plasty technique (PP), and conventional circumcision (CC). Demographics and base-line clinical characteristics were collected, and assessment of the level of preoperative anxiety was performed. Subsequently, a statistical model was designed in order to examine predictive factors of postoperative pain and postoperative anxiety. Assessment of postoperative pain was performed using the Faces Pain Scale (FPS). The Post Hospitalization Behavior Questionnaire study was used to assess negative behavioral manifestations. Results: A total of 301 children with a mean age of $7.56{\pm}2.61$ years were included in the study. Predictive factors of postoperative pain measured with the FPS included a) the type of surgical technique, b) the absence of siblings, and c) the presence of postoperative complications. Predictive factors of postoperative anxiety included a) the type of surgical technique, b) the level of education of mothers, c) the presence of preoperative anxiety, and d) a history of previous surgery. Conclusions: Although our study was not without its limitations, it expands current knowledge by adding new predictive factors of postoperative pain and postoperative anxiety. Clearly, further randomized controlled studies are needed to confirm its results.

• Journal of Environmental Health Sciences
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• v.45 no.5
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• pp.405-425
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• 2019

Objective: This study aimed to review the methodologies for evaluation of consumer spray products containing engineered nanomaterials (ENM), particularly focusing on inhalation exposure. Method: Literature on the evaluation methods for aerosolized ENM exposure from consumer spray products were collected through academic web searching. Common methodologies used in the literature, including research reports and academic articles, were also introduced. Results: The number of ENM-containing products have shown a considerable increase over recent years, from 54 in 2005 to 1,827 in 2018. Currently there is still discussion over the existing regulations with regard to product safety. Analysis of both ENM suspensions in the products and their aerosols is important for risk assessment. Comparison between the phases suggests how the size and concentration of particles change during the spray process. To analyze the ENM suspensions, dynamic light scattering, electron microscopy techniques, and inductively coupled plasma with mass spectrometry were used. In the aerosol monitoring, direct-reading instruments have been used to monitor the aerosols and conventional active sampling is used together to supplement the lack of real-time monitoring. There are also some models for estimating inhalation exposure. These models may be used to estimate mass exposure to nanomaterials contained in consumer products. Conclusion: Although there is no standardized method to evaluate ENM exposure from consumer products, many concerns about ENM have emerged. Every potential measure to reduce exposure to ENM from spray product use should be implemented through a precautionary recognition.

• Korean Journal of Environmental Agriculture
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• v.30 no.1
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• pp.43-51
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• 2011

BACKGROUND: This study was conducted to investigate the arsenic (As) contaminations in polished rice cultivated nearby abandoned mine areas, and to estimate the potential health risk through dietary intake of As-enriched polished rice in each age-gender population. METHODS AND RESULTS: The As contents in polished rice grown fifteen abandoned mine areas were analyzed. The average daily intake (ADD) as well as probabilistic health risk were estimated by assuming probability distribution of exposure parameters. The average total As concentration in polished rice was $0.09{\pm}0.06$ mg/kg with a range of 0.02~0.35 mg/kg. For health risk assessment, the ADD values in all age-gender populations did not exceed the provisional tolerable daily intake (PTDI) of 2.1 ${\mu}g/kg$ b.w./day for inorganic As. Cancer risk probability (R) values were $2.45{\sim}3.28{\times}10^{-4}$ and $2.51{\sim}5.75{\times}10^{-4}$ for all age population and gender population, respectively. Particularly, the R value, $5.75{\times}10^{-4}$, for children less than six years old were estimated to be high. Hazard quotient (HQ) values were 0.23~0.31 and 0.11~0.33 for general population and age-gender population, respectively. CONCLUSION(s): The average R values assessed via intake of polished rice cultivated in abandoned mine areas exceeded the acceptable cancer risk of $10^{-6}{\sim}10^{-4}$ for regulatory purpose. Considering the HQ values smaller than 1.0, potential non-cancer toxic effects may not be caused by the long-time exposure through intake of As-contaminated polished rice.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Korean Journal of Agricultural and Forest Meteorology
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• v.15 no.4
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• pp.320-331
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• 2013

The National Center for AgroMeteorology (NCAM) has designed a risk management solution for individual farms threatened by the climate change and variability. The new service produces weather risk indices tailored to the crop species and phenology by using site-specific weather forecasts and analysis derived from digital products of the Korea Meteorological Administration (KMA). If the risk is high enough to cause any damage to the crops, agrometeorological warnings or watches are delivered to the growers' cellular phones with relevant countermeasures to help protect their crops against the potential damage. Core techniques such as scaling down of weather data to individual farm level and the crop specific risk assessment for operational service were developed and integrated into a cloud based service system. The system was employed and implemented in a rural catchment of 50 $km^2$ with diverse agricultural activities and 230 volunteer farmers are participating in this project to get the user-specific weather information from and to feed their evaluations back to NCAM. The experience obtained through this project will be useful in planning and developing the nation-wide early warning service in agricultural sector exposed to the climate and weather extremes under climate change and climate variability.