• Journal of Preventive Medicine and Public Health
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• 제57권2호
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• pp.185-196
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• 2024

Objectives: Excess mortality associated with long-term exposure to fine particulate matter (PM_2.5) has been documented. However, research on the disease burden following short-term exposure is scarce. We investigated the cause-specific mortality burden of short-term exposure to PM_2.5 by considering the potential non-linear concentration-response relationship in Korea. Methods: Daily cause-specific mortality rates and PM_2.5 exposure levels from 2010 to 2019 were collected for 8 Korean cities and 9 provinces. A generalized additive mixed model was employed to estimate the non-linear relationship between PM_2.5 exposure and cause-specific mortality levels. We assumed no detrimental health effects of PM_2.5 concentrations below 15 ㎍/m³. Overall deaths attributable to short-term PM_2.5 exposure were estimated by summing the daily numbers of excess deaths associated with ambient PM_2.5 exposure. Results: Of the 2 749 704 recorded deaths, 2 453 686 (89.2%) were non-accidental, 591 267 (21.5%) were cardiovascular, and 141 066 (5.1%) were respiratory in nature. A non-linear relationship was observed between all-cause mortality and exposure to PM_2.5 at lag0, whereas linear associations were evident for cause-specific mortalities. Overall, 10 814 all-cause, 7855 non-accidental, 1642 cardiovascular, and 708 respiratory deaths were attributed to short-term exposure to PM_2.5. The estimated number of all-cause excess deaths due to short-term PM_2.5 exposure in 2019 was 1039 (95% confidence interval, 604 to 1472). Conclusions: Our findings indicate an association between short-term PM_2.5 exposure and various mortality rates (all-cause, non-accidental, cardiovascular, and respiratory) in Korea over the period from 2010 to 2019. Consequently, action plans should be developed to reduce deaths attributable to short-term exposure to PM_2.5.

• Clinical and Experimental Reproductive Medicine
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• 제47권4호
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• pp.312-318
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• 2020

Objective: The objective of the study was to compare the effects of long-term and short-term embryo culture to assess whether there is a correlation between culture duration and clinical outcomes. Methods: Embryos were divided into two study groups depending on whether their post-warming culture period was long-term (20-24 hours) or short-term (2-4 hours). Embryo morphology was analyzed with a time-lapse monitoring device to estimate the appropriate timing and parameters for evaluating embryos with high implantation potency in both groups. Propensity score matching was performed to adjust the confounding factors across groups. The grades of embryos and blastocoels, morphokinetic parameters, implantation rate, and ongoing pregnancy rate were compared. Results: No significant differences were observed in the implantation rate or ongoing pregnancy rate between the two groups (long-term culture group vs. short-term culture group: 56.3% vs. 67.9%, p=0.182; 47.3% vs. 53.6%, p=0.513). After warming, there were more expanded and hatching/hatched blastocysts in the long-term culture group than in the short-term culture group, but there was no significant between-group difference in embryo grade. Regarding pregnancy outcomes, the time to complete blastocyst re-expansion after warming is shorter in women who became pregnant than in those who did not in both culture groups (long-term: 2.19±0.63 vs. 4.11±0.81 hours, p=0.003; short-term: 1.17±0.29 vs. 1.94±0.76 hours, p=0.018, respectively). Conclusion: The outcomes of short-term culture and long-term culture were not significantly different in vitrified-warmed blastocyst transfer. Regardless of the post-warming culture time, the degree of blastocyst re-expansion 3-4 hours after warming is an important marker for embryo selection.

• 한국물환경학회지
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• 제39권1호
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• pp.46-60
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• 2023

Climate change causes fluctuations in water quality in the aquatic environment, which can cause changes in water circulation patterns and severe adverse effects on aquatic ecosystems in the future. Therefore, research is needed to predict and respond to water quality changes caused by climate change in advance. In this study, we tried to predict the dissolved oxygen (DO), chlorophyll-a, and turbidity of the Paldang reservoir for about two weeks using long short-term memory (LSTM) and gated recurrent units (GRU), which are deep learning algorithms based on recurrent neural networks. The model was built based on real-time water quality data and meteorological data. The observation period was set from July to September in the summer of 2021 (Period 1) and from March to May in the spring of 2022 (Period 2). We tried to select an algorithm with optimal predictive power for each water quality parameter. In addition, to improve the predictive power of the model, an important variable extraction technique using random forest was used to select only the important variables as input variables. In both Periods 1 and 2, the predictive power after extracting important variables was further improved. Except for DO in Period 2, GRU was selected as the best model in all water quality parameters. This methodology can be useful for preventive water quality management by identifying the variability of water quality in advance and predicting water quality in a short period.

• Tuberculosis and Respiratory Diseases
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• 제65권2호
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• pp.125-130
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• 2008

저자 등은 수술 후 단기간의 항생제 치료로 완치된 기관지 방선균증 2예를 경험하였기에 보고하는 바이다.

• Environmental Engineering Research
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• 제24권3호
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• pp.404-411
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• 2019

In this study, we designed a data-driven model to predict chlorophyll-a using M5P model tree and extreme learning machine (ELM). The Juksan weir in the Youngsan River has high chlorophyll-a, which is the primary indicator of algal bloom every year. Short-term algal bloom prediction is important for environmental management and ecological assessment. Two models were developed and evaluated for short-term algal bloom prediction. M5P is a classification and regression-analysis-based method, and ELM is a feed-forward neural network with fast learning using the least square estimate for regression. The dataset used in this study includes water temperature, rainfall, solar radiation, total nitrogen, total phosphorus, N/P ratio, and chlorophyll-a, which were collected on a daily basis from January 2013 to December 2016. The M5P model showed that the prediction model after one day had the highest performance power and dropped off rapidly starting with predictions after three days. Comparing the performance power of the ELM model with the M5P model, it was found that the performance power of the 1-7 d chlorophyll-a prediction model was higher. Moreover, in a period of rapidly increasing algal blooms, the ELM model showed higher accuracy than the M5P model.

• 아동학회지
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• 제37권1호
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• pp.73-82
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• 2016

Objective: This two-wave study examined stability in kindergarteners' friendship patterns over 5 months. Methods: Participants were 501 five-year-old children (262 girls and 239 boys) attending kindergartens in Seoul, Incheon, and Kyounggi provinces in Korea. Each child nominated three individuals as his/her friends in July, and again in December of 2013. Depending on the presence/absence of friendships and the mutuality of identifying friends, the children's friendship patterns were categorized into five groups: stable, fluid, loss, gain, and friendless. The data were analyzed by descriptive statistics, and chi-square tests. Results: Results revealed stability, as well as changes in friendship patterns, among kindergarteners over the 5-month period. The stable friendships, those that maintained the same friend(s) in both waves, was 43.7%, the fluid friendships, those that changed friends over the 5 month period was 18%, the gain friendships, those who had newly developed friends in wave 2 was 17%, and the loss friendships, those who had friends at wave 1 but lost friends at wave 2, was 9.8%. The friendless, those that had no friends in both waves, was 11.5%. Conclusion: Results showed that kindergarteners were capable of maintaining and making new friends over a 5-month period.

• 품질경영학회지
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• 제47권3호
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• pp.497-508
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• 2019

Purpose: The purpose of this study is to overcome limitations of conventional studies that to predict Baltic Dry Index (BDI). The study proposed applications of Artificial Neural Network (ANN) named Long Short-Term Memory (LSTM) to predict BDI. Methods: The BDI time-series prediction was carried out through eight variables related to the dry bulk market. The prediction was conducted in two steps. First, identifying the goodness of fitness for the BDI time-series of specific ANN models and determining the network structures to be used in the next step. While using ANN's generalization capability, the structures determined in the previous steps were used in the empirical prediction step, and the sliding-window method was applied to make a daily (one-day ahead) prediction. Results: At the empirical prediction step, it was possible to predict variable y(BDI time series) at point of time t by 8 variables (related to the dry bulk market) of x at point of time (t-1). LSTM, known to be good at learning over a long period of time, showed the best performance with higher predictive accuracy compared to Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). Conclusion: Applying this study to real business would require long-term predictions by applying more detailed forecasting techniques. I hope that the research can provide a point of reference in the dry bulk market, and furthermore in the decision-making and investment in the future of the shipping business as a whole.

• 전기의세계
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• 제22권2호
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• pp.28-32
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• 1973

System modeling is applied in developing a probabilistic linear estimator for the load of an electric power system for the purpose of short term load forecasting. The model assumer that the load in given by the suns of a periodic discrete time serier with a period of 24 hour and a residual term such that the output of a discrete time dynamical linear system driven by a white random process and a deterministic input. And also we have established the main forecasting algorithms, which are essemtally the Kalman filter-predictor equations.

• Journal of Electrical Engineering and Technology
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• 제9권6호
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• pp.1882-1890
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• 2014

This paper proposes an augmented Lagrange Hopfield network (ALHN) based method for solving multi-objective short term fixed head hydrothermal scheduling problem. The main objective of the problem is to minimize both total power generation cost and emissions of $NO_x$, $SO_2$, and $CO_2$ over a scheduling period of one day while satisfying power balance, hydraulic, and generator operating limits constraints. The ALHN method is a combination of augmented Lagrange relaxation and continuous Hopfield neural network where the augmented Lagrange function is directly used as the energy function of the network. For implementation of the ALHN based method for solving the problem, ALHN is implemented for obtaining non-dominated solutions and fuzzy set theory is applied for obtaining the best compromise solution. The proposed method has been tested on different systems with different analyses and the obtained results have been compared to those from other methods available in the literature. The result comparisons have indicated that the proposed method is very efficient for solving the problem with good optimal solution and fast computational time. Therefore, the proposed ALHN can be a very favorable method for solving the multi-objective short term fixed head hydrothermal scheduling problems.

• 한국기후변화학회지
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• 제7권1호
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• pp.1-8
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• 2016

In this study, we investigated the applicability of a short-term carbon dioxide ($CO_2$) passive sampler using turbidity change in a solution containing barium hydroxide ($Ba(OH)_2$). The mass of $CO_2$ introduced into the $Ba(OH)_2$ aqueous solution was strongly correlated ($r^2=0.9565$) to the change in turbidity caused by its reaction with the solution. The sampling rates calculated for 1 h and 24 h were $42.4{\pm}5.4mL\;min^{-1}$ and $2.3{\pm}0.3mL\;min^{-1}$, respectively. Both unexposed (blank) and exposed samplers remained stable during the storage period of at least two weeks. The detection limits of the passive sampler for $CO_2$ were 81.5 ppm for 1 h and 61.5 ppm for 24 h. Based on the results, the passive sampler using the change of turbidity in the $Ba(OH)_2$ aqueous solution appears to be a suitable tool for measuring short-term atmospheric concentrations of $CO_2$.