Detection of COVID-19 epidemic outbreak using machine learning

BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic

Load Evaluation for Tower Design of Large Floating Offshore Wind Turbine System According to Wave Conditions

This study entailed a load evaluation for the tower design of a large floating offshore wind turbine system in accordance with the wave conditions. The target model includes the IEA 15 MW reference wind turbine and a semi-submersible VolturnUS-S reference floating offshore wind turbine platform from the University of Maine. The OpenFAST, which is an aero-hydro-servo-elastic fully coupled analysis tool, was used for load analysis. The DLC1.2 and 1.6 were used as the design load cases, and the environmental conditions suitable for the design load cases were cited in the VolturnUS-S platform report. Load evaluation was performed according to time series and FFT results. The findings of the study are as follows: first, in the correlation analysis, the tower-top deflection had the highest correlation, and this further affects nacelle acceleration. Second, the tower-base pitch moment increased with the significant wave height. However, the wave peak period increased until it matched the tower-top deflection frequency and decreased thereafter. Third, the comparison between the normal and severe sea state conditions revealed that the tower-base pitch moments for the two conditions are almost similar, despite the conditions wherein the wave spectral energy differs by a factor of 3.5. Fourth, the tower shape is changed while adjusting the diameter of the tower, and the tower-top and tower-base pitch moments are reviewed using a redesigned tower. Even if the mass is the same, adjusting the diameter of the tower reduces only the pitch moment

A Numerical Study on the Performance Evaluation of a Semi-Type Floating Offshore Wind Turbine System According to the Direction of the Incoming Waves

In this study, the performance evaluation of a semi-type floating offshore wind turbine system according to the direction of the incoming waves is investigated. The target model in this this study is a DTU 10 MW reference wind turbine and a LIFES50+ OO-Star Wind Floater Semi 10 MW, which is the semisubmersible platform. Numerical simulation is performed using FAST developed by National Renewable Energy Laboratory (NREL), which is an aero-hydro-servo-elastic fully coupled simulation tool. The analysis condition used in this study is the misalignment condition, which is the wind direction fixed at 0 degree and the wave direction changed at 15 degrees intervals. In this study, two main contents could be confirmed. First, it is confirmed that sway, roll, and yaw motions occur even though the direction of the incoming waves is 0 degree. The cause of the platform’s motion such as sway, roll and yaw is the turbulent wind and gyroscope phenomenon. In addition, the optimal value for the nacelle–yaw angle that maximizes the rotor power and minimizes the tower load is confirmed by solving the multiobjective optimization problem. These results show the conclusion that setting the initial nacelle–yaw angle can reduce the tower load and get a higher generator power. Second, it is confirmed that the platform’s motion and loads may be underestimated depending on the interval angle of incidence of the wind and waves. In particular, through the load diagram results, it is confirmed that most of the results are asymmetric, and the blade and tower loads are especially spiky. Through these results, the importance of examining the interval angle of incidence of the wind and waves is confirmed. Unlike previous studies, this will be a more considerable issue as turbines become larger and platforms become more complex

Incidence of edentulism among older adults using the Korean National Health Insurance Service database, 2013-2018

OBJECTIVES Population aging is rapidly accelerating worldwide. Oral diseases related to aging are also on the rise. This study examined trends in the incidence of edentulism among the older Korean population using data from the Korean National Health Insurance Service (KNHIS). METHODS Data on older adults, aged ≥75 years of age, were obtained from the KNHIS for the period 2013-2018. Edentulism was defined as a treatment history of complete dentures in the KNHIS database. The exclusion criteria consisted of both disease codes and treatment codes related to conservative dental treatment, including periodontal and extraction treatment afterward. Crude incidence rates (CIRs) and age-standardized incidence rates (AIRs) with 95% confidence intervals were calculated and reported per 100,000 person-years by the direct method. Trends were tested by Cochrane Armitage models. RESULTS Statistically significant increasing trends in both CIRs and AIRs were found among the older Korean population registered in the KNHIS (CIRs, 707.92 to 895.92; AIRs, 705.11 to 889.68; p<0.01). The incidence tended to increase in both genders (p<0.01). Both CIRs and AIRs in specific regions also showed slight but significant annual increases except for Jeju Island (p<0.01 or <0.05). The incidence showed increasing trends (p<0.01) in all income quintiles apart from the highest quintile. The edentulism incidence was highest in the lowest income group (the first quintile). CONCLUSIONS Our data showed that the incidence of edentulism among the elderly showed an increasing trend from 2013 to 2018. This result provides a basis for future epidemiological studies on the incidence of edentulism in the older Korean population

Data_Sheet_1_Detection of COVID-19 epidemic outbreak using machine learning.pdf

BackgroundThe coronavirus disease (COVID-19) pandemic has spread rapidly across the world, creating an urgent need for predictive models that can help healthcare providers prepare and respond to outbreaks more quickly and effectively, and ultimately improve patient care. Early detection and warning systems are crucial for preventing and controlling epidemic spread.ObjectiveIn this study, we aimed to propose a machine learning-based method to predict the transmission trend of COVID-19 and a new approach to detect the start time of new outbreaks by analyzing epidemiological data.MethodsWe developed a risk index to measure the change in the transmission trend. We applied machine learning (ML) techniques to predict COVID-19 transmission trends, categorized into three labels: decrease (L0), maintain (L1), and increase (L2). We used Support Vector Machine (SVM), Random Forest (RF), and XGBoost (XGB) as ML models. We employed grid search methods to determine the optimal hyperparameters for these three models. We proposed a new method to detect the start time of new outbreaks based on label 2, which was sustained for at least 14 days (i.e., the duration of maintenance). We compared the performance of different ML models to identify the most accurate approach for outbreak detection. We conducted sensitivity analysis for the duration of maintenance between 7 days and 28 days.ResultsML methods demonstrated high accuracy (over 94%) in estimating the classification of the transmission trends. Our proposed method successfully predicted the start time of new outbreaks, enabling us to detect a total of seven estimated outbreaks, while there were five reported outbreaks between March 2020 and October 2022 in Korea. It means that our method could detect minor outbreaks. Among the ML models, the RF and XGB classifiers exhibited the highest accuracy in outbreak detection.ConclusionThe study highlights the strength of our method in accurately predicting the timing of an outbreak using an interpretable and explainable approach. It could provide a standard for predicting the start time of new outbreaks and detecting future transmission trends. This method can contribute to the development of targeted prevention and control measures and enhance resource management during the pandemic.</p

Highly Luminous and Thermally Stable Mg-Substituted Ca_2–<i>x</i>Mg_<i>x</i>SiO₄:Ce (0 ≤ <i>x</i> ≤ 1) Phosphor for NUV-LEDs

Blue-emitting Ca_2–<i>x</i>Mg_<i>x</i>­SiO₄:Ce (0.0 ≤ <i>x</i> ≤ 1.0) phosphors were successfully synthesized and characterized. Rietveld refinement revealed that four main phases exist within the solid-solution range of CaO-MgO-SiO₂, namely, β-Ca₂SiO₄ (Mg (<i>x</i>) = 0.0), Ca₁₄Mg₂(SiO₄)₈ (Mg (<i>x</i>) = 0.25), Ca₃Mg­(SiO₄)₂ (Mg (<i>x</i>) = 0.5), and CaMgSiO₄ (Mg (<i>x</i>) = 1.0). The variation of the IR modes was more prominent with increasing Mg²⁺ content in the Ca_2<i>–x</i>Mg_<i>x</i>SiO₄ materials. The sharing of O atoms of the SiO₄-tetrahedra by the MgO₆-octahedra induced weakening of the Si–O bonds, which resulted in the red shift of the [SiO₄] internal modes and appearance of a Mg–O stretching vibration at ∼418 cm^–1. Raman measurement revealed that the change of the Ca–O bond lengths because of the Mg²⁺-substitution directly reflected the frequency shift of the Si–O stretching-Raman modes. Notably, the thermal stability of Ca_2<i>–x</i>Mg_<i>x</i>­SiO₄:Ce (Mg (<i>x</i>) > 0.0) phosphors was superior to that of β-Ca₂SiO₄:Ce (Mg (<i>x</i>) = 0.0) as confirmed by temperature-dependent photoluminescence (PL) measurements. This indicated that Mg²⁺ ions play an important role in enhancement of the thermal stability. In combination with the results from PL and electroluminescence (EL), it was elucidated that the luminous efficiency of Ca_2–<i>x</i>Mg_<i>x</i>SiO₄:Ce (Mg (<i>x</i>) = 0.1) was approximately twice as much as β-Ca₂SiO₄:Ce (Mg (<i>x</i>) = 0.00), directly indicating a “Mg²⁺-substitution effect”. The large enhancements of PL, EL, and thermal stability because of Mg²⁺-substitution may provide a platform in the discovery of more efficient phosphors for NUV-LEDs

Additional file 1: of Substitution of ethambutol with linezolid during the intensive phase of treatment of pulmonary tuberculosis: study protocol for a prospective, multicenter, randomized, open-label, phase II trial

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