• Journal of the Korean Orthopaedic Association
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• v.55 no.4
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• pp.281-293
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• 2020

An anterior cruciate ligament (ACL) reconstruction is one of the most frequent surgical procedures in the knee joint, but despite the better understanding of anatomy and biomechanics, surgical reconstruction procedures still fail to restore rotational stability in 7%-16% of patients. Hence, many studies have attempted to identify the factors for rotational laxity, including the anterolateral ligament (ALL), but still showed controversies. Descriptions of the ALL anatomy are also confused by overlapping nomenclature, but it is usually known as a distinctive fiber running in an anteroinferior and oblique direction from the lateral epicondyle of the femur to the proximal anterolateral tibia, between the fibular head and Gerdy's tubercle. The importance of the ALL as a secondary restraint in the knee has been emphasized for successful ACL reconstructions that can restore rotational stability, but there is still some controversy. Some studies reported that the ALL could be a restraint to the tibial rotation, but not to anterior tibial translation. On the other hand, some studies reported that the role of ALL in rotational stability would be limited as a secondary structure because it bears loads only beyond normal biomechanical motion. The diagnosis of an ALL injury can be performed by a physical examination, radiology examination, and magnetic resonance imaging, but it should be assessed using a multimodal approach. Recently, ALL was considered one of the anterolateral complex structures, as well as the Kaplan fiber in the iliotibial band. Many studies have introduced many indications and treatment options, but there is still some debate. The treatment methods are introduced mainly as ALL reconstructions or lateral extra-articular tenodesis, which can achieve additional benefit to the knee stability. Further studies will be needed on the indications and proper surgical methods of ALL treatment.

• 한국노년학
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• v.41 no.2
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• pp.197-215
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• 2021

This study aims to evaluate Person Centered Care practice and characteristics of care services in Korean long-term care facilities using Dementia Care Mapping as a tool. DCM, systematic observational evaluation tool for measuring dementia patients' QOL, was transformed into self-report rating scale. The process of transforming DCM into a scale of 34 items involves operationalization of DCM concepts and it's adaptation into Korean long-term care practices. Review by research team of Bradford university was added to maintain DCM concept and meaning in this scale. The scale with Cronbach alpha of .88 was surveyed on 343 care workers. Survey result shows PCC value practiced by them is 3.77(of 5 likert scale) and values on each categories of PCC reveal the characteristics of care in Korean facilities; attachment(4.02), comfort(3.95), inclusion(3.89), identity(3.67) and occupation(3.41). Dementia care in Korean facilities focuses on recipients'safety, comfort but lacks individualistic care and the meaningful and fulfilling occupation for patients. Looking at the organizational and individual factors influencing DCM values, the small facilities showed higher PCC values and there are no significant difference in PCC values between public and private facilities. Managers and care workers with career of 1~2 years showed higher PCC values compared to other career ranks and lengthes. This study suggests care practice should be centered on personhood of patients in long-term care facilities, for which introduction of unit care and education of PCC for service providers including support personnel are needed. DCM and Korean DCM scale developed in this study are suggested for the PCC-based assessment on care quality.

• Journal of the Korean Institute of Landscape Architecture
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• v.50 no.3
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• pp.19-34
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• 2022

This study was conducted to provide basic data that can be used when establishing Net Zero policies and implementation plans for non-urban settlements by quantitatively analyzing the Net Zero contribution to green infrastructure in rural areas corresponding to non-urban settlements. The main purpose is to first, systematize green infrastructure in rural areas, secondly derive basic units for each element of green infrastructure, and thirdly quantify and present the impact on Net Zero in Korea using these. In this study, CVR(Content Validity Ration) analysis was performed to verify the adequacy of green infrastructure elements in rural areas derived through research and analysis of previous studies, is as follows. First, Hubs of Green infrastructure in rural area include village forests, wetlands, farm land, and smart farms with a CVR value of .500 or higher. And Links of Green infrastructure in rural area include streams, village green areas, and LID (rainwater recycling). Second, the basic unit for each green infrastructure element was presented by classifying it into minimum, maximum, and median values using the results of previous studies so that it could be used for spatial planning and design for Net Zero. Third, when Green infrastructure in rural areas is applied to non-urban settlements in Korea, it is analyzed that it has the effect of indirectly reducing CO₂ by at least 70.76 million tons and up to 141.16 million tons. This is 3.4 to 6.7 times the amount of CO₂ emission from the agricultural sector in 2019, and it can be seen that the contribution to Net Zero is very high. It is expected to greatly contribute to the transformation of the ecosystem. This study quantitatively presented the carbon-neutral contribution to settlements located in non-urban areas, and by deriving the carbon reduction unit for each element of green infrastructure in rural areas, it can be used in spatial planning and design for carbon-neutral at the village level. It has significance as a basic research. In particular, the basic unit of carbon reduction for each green infrastructure factors will be usable for Net Zero policy at the village level, presenting a quantitative target when establishing a plan, and checking whether or not it has been achieved. In addition, based on this, it will be possible to expand and apply Net Zero at regional and city units such as cities, counties, and districts.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.2
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• pp.69-77
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• 2022

The 3D spatial model is an analysis framework for solving urban problems and is used in various fields such as urban planning, environment, land and housing management, and disaster simulation. The utilization of drones that can capture 3D images in a short time at a low cost is increasing for the construction of 3D spatial model. In terms of building a virtual city and utilizing simulation modules, high location accuracy of aerial survey and precision of 3D spatial model function as important factors, so a method to increase the accuracy has been proposed. This study analyzed location accuracy of aerial survey and precision of 3D spatial model by each condition of aerial survey for urban areas where buildings are densely located. We selected Daerim 2-dong, Yeongdeungpo-gu, Seoul as a target area and applied shooting angle, shooting altitude, and overlap rate as conditions for the aerial survey. In this study, we calculated the location accuracy of aerial survey by analyzing the difference between an actual survey value of CPs and a predicted value of 3D spatial Model. Also, We calculated the precision of 3D spatial Model by analyzing the difference between the position of Point cloud and the 3D spatial Model (3D Mesh). As a result of this study, the location accuracy tended to be high at a relatively high rate of overlap, but the higher the rate of overlap, the lower the precision of 3D spatial model and the higher the shooting angle, the higher precision. Also, there was no significant relationship with precision. In terms of baseline-height ratio, the precision tended to be improved as the baseline-height ratio increased.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1095-1105
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• 2021

The advancement of dam operation is further required due to the upcoming rainy season, typhoons, or torrential rains. Besides, physical models based on specific rules may sometimes have limitations in controlling the release discharge of dam due to inherent uncertainty and complex factors. This study aims to forecast the water level of the nearest station to the dam multi-timestep-ahead and evaluate the availability when it makes a decision for a release discharge of dam based on LSTM (Long Short-Term Memory) of deep learning. The LSTM model was trained and tested on eight data sets with a 1-hour temporal resolution, including primary data used in the dam operation and downstream water level station data about 13 years (2009~2021). The trained model forecasted the water level time series divided by the six lead times: 1, 3, 6, 9, 12, 18-hours, and compared and analyzed with the observed data. As a result, the prediction results of the 1-hour ahead exhibited the best performance for all cases with an average accuracy of MAE of 0.01m, RMSE of 0.015 m, and NSE of 0.99, respectively. In addition, as the lead time increases, the predictive performance of the model tends to decrease slightly. The model may similarly estimate and reliably predicts the temporal pattern of the observed water level. Thus, it is judged that the LSTM model could produce predictive data by extracting the characteristics of complex hydrological non-linear data and can be used to determine the amount of release discharge from the dam when simulating the operation of the dam.

• The Journal of the Korea Contents Association
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• v.21 no.11
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• pp.565-575
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• 2021

The purpose of this study was to identify the amount of alcohol and drinking frequency among high school students based on social alcohol drinking supply. The data was on 161 drinkers aged between 16 and 19 from 21 high schools across the country using a questionnaire of the International Alcohol Control(IAC) Study, which was developed in 2012. Results show that the higher the number of social suppliers offering alcoholic beverages to high school students, the greater the consumption of alcohol per episode(59.433 g for one person, 113.40 g for two, and 133.56 g for three or more people). On the other hand, alcohol consumption among 'Honsul' people, a group that drinks alone without a social drinking supplier, was 167.84 grams, higher than that of groups that receive social drinking services. As a social drinking supplier for teenagers, drinking was the highest by their father (29.3 %), while friends (25.0 %) and mothers (20.7 %) were the main drinking suppliers. In particular, the provision of drinking due to father(𝛽=-.32, t=3.55, p<.01) and mother(𝛽=.22, t=2.71, p<.01) showed statistical significance as a factor in increasing the frequency of providing social drinking in adolescents. On the other hand, partner/boy or girl friend (𝛽=-.23, t=-2.73, p<.01) was a factor in reducing the frequency of alcohol provision. Friends(𝛽=.24, t=3.02, p<.01) and senior-junior schoolmates(𝛽=.16, t=2.04, p<.05) were the factors that increase the total alcohol intake of adolescents. This is due to the increase in the frequency of alcohol provision. This suggests that alcohol harm education should be expanded from students to parents, considering the role of parents as a social drinking supplier and the link between high alcohol intake among teenagers due to senior-junior friends and schoolmates.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.723-736
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• 2022

In this study, monthly precipitation forecasting models that can predict up to 12 months in advance were constructed for the Geum River basin, and two statistical techniques, multiple linear regression (MLR) and artificial neural network (ANN), were applied to the model construction. As predictor candidates, a total of 47 climate indices were used, including 39 global climate patterns provided by the National Oceanic and Atmospheric Administration (NOAA) and 8 meteorological factors for the basin. Forecast models were constructed by using climate indices with high correlation by analyzing the teleconnection between the monthly precipitation and each climate index for the past 40 years based on the forecast month. In the goodness-of-fit test results for the average value of forecasts of each month for 1991 to 2021, the MLR models showed -3.3 to -0.1% for the percent bias (PBIAS), 0.45 to 0.50 for the Nash-Sutcliffe efficiency (NSE), and 0.69 to 0.70 for the Pearson correlation coefficient (r), whereas, the ANN models showed PBIAS -5.0~+0.5%, NSE 0.35~0.47, and r 0.64~0.70. The mean values predicted by the MLR models were found to be closer to the observation than the ANN models. The probability of including observations within the forecast range for each month was 57.5 to 83.6% (average 72.9%) for the MLR models, and 71.5 to 88.7% (average 81.1%) for the ANN models, indicating that the ANN models showed better results. The tercile probability by month was 25.9 to 41.9% (average 34.6%) for the MLR models, and 30.3 to 39.1% (average 34.7%) for the ANN models. Both models showed long-term predictability of monthly precipitation with an average of 33.3% or more in tercile probability. In conclusion, the difference in predictability between the two models was found to be relatively small. However, when judging from the hit rate for the prediction range or the tercile probability, the monthly deviation for predictability was found to be relatively small for the ANN models.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.627-646
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• 2022

Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.

• Journal of the Korean Society of Radiology
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• v.16 no.5
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• pp.567-574
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• 2022

Coronary artery disease (CAD) and atrial fibrillation (AF) are known to share many risk factors. In particular, in the case of acute coronary syndrome, it may be difficult to clearly determine the diameter of the vessel due to complete occlusion of the vessel and thrombus. Thus, the relationship between the diameter of the coronary arteries was evaluated to be used as a reference data before the treatment of coronary arteries and drug selection in patients with AF. From January 2020 to August 2022, images of coronary angiography (CAG) with AF and normal sinus rhythm (NSR) on electrocardiography were target. In both subjects, images of normal coronary artery without lesions as a result of CAG were used. For all vessels, the diameters of the vessels were measured by dividing them into proximal, middle, and distal parts, and the measured diameters were divided by the average for evaluation. As a result of analyzing the left anterior descending artery diameter, the vessel diameter of the AF patient was 2.24±0.26 mm, which was smaller than that of the NSR patient, 2.86±0.38 mm, and was statistically significant. (p<0.001) As a result of analyzing the left circumflex artery diameter, the vessel diameter of the AF patient was 2.34±0.28 mm, which was smaller than the vessel diameter of the NSR patient, 2.87±0.29 mm, and was statistically significant. (p<0.001) As a result of analyzing the diameter of the right coronary artery, the vessel diameter of the AF patient was 2.68±0.5 mm, which was smaller than the vessel diameter of the NSR patient, 3.35±0.4 mm, and was statistically significant. (p<0.001) Considering that the coronary artery size of AF patients is significantly smaller than the coronary vessel size of NSR patients, it is considered as a useful study to be used as a reference for evaluating coronary artery diameter when the arrhythmia is AF. In particular, it is considered to be a study that can be helpful in diagnosing lesions, using drugs before and after surgery, and choosing to use auxiliary devices such as intravascular ultrasound.

• Journal of agricultural medicine and community health
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• v.47 no.3
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• pp.181-188
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• 2022

Objectives: The prevalence of diabetes mellitus was approximately 16% in populations of over age 30 years, and deaths from diabetes mellitus became the sixth most prevalent cause of death by disease. To assess the relationship between HbA1c and heavy metal level in blood and urine, targeted residents were evaluated in a vast steel industrial complex. Methods: We selected 414 subjects for analysis after applying the following exclusion criterion: 18 persons with diabetes mellitus. They took part in a questionnaire survey and underwent blood and urinary assessments. HbA1c and lead (Pb) level were measured in blood and, cadmium (Cd), inorganic arsenic (iAs) and mercury (Hg) were evaluated in urine. Two subgroups were divided by HbA1c 6.5%. Each subgroup was divided by 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th and 90th percentile levels of biological exposure index of the heavy metals for logistic regression. Results: Odd ratios have a tendency to increase as they go from the 90th to the 10th percentile of cadmium. However, lead, arsenic and mercury did not have significant relationships with HbA1c. In correction of age, region, gender and smoking history, a higher distribution in the subgroup with cadmium above 0.8318 ㎍/g creatinine (30th percentile) was demonstrated in the subgroup with HbA1c levels above the 6.5%, with an odds ratio of 5.26 (95% C.I. ; 1.44~19.17). Conclusion: This study found a significant correlation between urinary levels of cadmium and HbA1c in correction of several factors. It is meaningful that this outcome may be used as a basis for a study to establish the acceptable limit of urinary cadmium in Korea.