A Comparative Study on the Homeroom Teachers’ Perception of the School guidance in Korea and Finland

This study has four major purposes. First, it compares school guidance of homeroom teachers in Korea and Finland, in order to understand the reality of education, based on the teachers’ perceptions. Secondly, it also considers the topic within its historical, social, and cultural backgrounds, from a critical standpoint. Thirdly, it investigates the direction of the improvement of school guidance, based on the analysis of similarities and differences between Korea and Finland, with regards to the meaning, practice, and environmental factors of the school guidance. Lastly, the influential factors surrounding the school guidance are noted by analysing empirical data from a microscopic approach, and extending the understanding of it into a social context. As for the methods, it employs thematic analysis approach through 10 homeroom teacher interviews in the lower secondary schools. As a result, firstly, the teachers in both countries assumed similarly, that the role of the teacher was not only to teach the subject, but also to care about every aspects of the students’ development in their school life. In addition, they accepted the fact that school guidance became more significant. However, the school guidance became the top priority for the Korean teachers, while teaching subject is the main task for the Finnish teachers. Secondly, the homeroom teachers in both countries hoped to have a better working environment, to perform school guidance concerning education budget for the resources of school guidance, tight curriculum, and increasing the teachers’ tasks. Thirdly, the school guidance in Korea seemed to be influenced by social expectation and government demand, whereas, the Finnish teachers considered school guidance in more aspects of adjustment and academic motivation, rather than resolving the social problems. Fourthly, the Korean teachers perceived that the trust and respect from the society and home became weakened, also expressing doubts about the educational policies and the attitude of the government with regards to school guidance. On the other hand, the Finnish teachers believed that they were trusted and respected by the society. However, blurred lines in the roles and accountability between the homeroom teachers, home, and the society were also controversial among the teachers in both countries. To sum up, Finland needs to ameliorate the system and conditions for school guidance of the homeroom teachers. The consensus on the role and tasks of Finnish homeroom teachers for school guidance seem to be also necessary. Meanwhile, Korea should improve the social system and social consciousness of the teacher, school guidance, and schooling, preceding the reform of the education system or conditions.Siirretty Doriast

Illinois and Chicago Region 2021: Poverty, Income and Health Insurance (Fact Sheet)

Poverty rates increased and household incomes were stagnant inIllinois from 2019 to 2021. This data reflects 2021, when COVID-19 pandemic-related government assistance provided some relief, suggesting that the financial picture is likely much worse today

Wyner VAE: Joint and Conditional Generation with Succinct Common Representation Learning

A new variational autoencoder (VAE) model is proposed that learns a succinct common representation of two correlated data variables for conditional and joint generation tasks. The proposed Wyner VAE model is based on two information theoretic problems---distributed simulation and channel synthesis---in which Wyner's common information arises as the fundamental limit of the succinctness of the common representation. The Wyner VAE decomposes a pair of correlated data variables into their common representation (e.g., a shared concept) and local representations that capture the remaining randomness (e.g., texture and style) in respective data variables by imposing the mutual information between the data variables and the common representation as a regularization term. The utility of the proposed approach is demonstrated through experiments for joint and conditional generation with and without style control using synthetic data and real images. Experimental results show that learning a succinct common representation achieves better generative performance and that the proposed model outperforms existing VAE variants and the variational information bottleneck method.Comment: 24 pages, 18 figure

TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models

We propose a robust and reliable evaluation metric for generative models by introducing topological and statistical treatments for rigorous support estimation. Existing metrics, such as Inception Score (IS), Frechet Inception Distance (FID), and the variants of Precision and Recall (P&R), heavily rely on supports that are estimated from sample features. However, the reliability of their estimation has not been seriously discussed (and overlooked) even though the quality of the evaluation entirely depends on it. In this paper, we propose Topological Precision and Recall (TopP&R, pronounced 'topper'), which provides a systematic approach to estimating supports, retaining only topologically and statistically important features with a certain level of confidence. This not only makes TopP&R strong for noisy features, but also provides statistical consistency. Our theoretical and experimental results show that TopP&R is robust to outliers and non-independent and identically distributed (Non-IID) perturbations, while accurately capturing the true trend of change in samples. To the best of our knowledge, this is the first evaluation metric focused on the robust estimation of the support and provides its statistical consistency under noise.Comment: Accepted to NeurIPS 202

CARTOS: A Charging-Aware Real-Time Operating System for Intermittent Batteryless Devices

This paper presents CARTOS, a charging-aware real-time operating system designed to enhance the functionality of intermittently-powered batteryless devices (IPDs) for various Internet of Things (IoT) applications. While IPDs offer significant advantages such as extended lifespan and operability in extreme environments, they pose unique challenges, including the need to ensure forward progress of program execution amidst variable energy availability and maintaining reliable real-time time behavior during power disruptions. To address these challenges, CARTOS introduces a mixed-preemption scheduling model that classifies tasks into computational and peripheral tasks, and ensures their efficient and timely execution by adopting just-in-time checkpointing for divisible computation tasks and uninterrupted execution for indivisible peripheral tasks. CARTOS also supports processing chains of tasks with precedence constraints and adapts its scheduling in response to environmental changes to offer continuous execution under diverse conditions. CARTOS is implemented with new APIs and components added to FreeRTOS but is designed for portability to other embedded RTOSs. Through real hardware experiments and simulations, CARTOS exhibits superior performance over state-of-the-art methods, demonstrating that it can serve as a practical platform for developing resilient, real-time sensing applications on IPDs

Detection and monitoring of forest fires using Himawari-8 geostationary satellite data in South Korea

Geostationary satellite remote sensing systems are a useful tool for forest fire detection and monitoring because of their high temporal resolution over large areas. In this study, we propose a combined 3-step forest fire detection algorithm (i.e., thresholding, machine learning-based modeling, and post processing) using Himawari-8 geostationary satellite data over South Korea. This threshold-based algorithm filtered the forest fire candidate pixels using adaptive threshold values considering the diurnal cycle and seasonality of forest fires while allowing a high rate of false alarms. The random forest (RF) machine learning model then effectively removed the false alarms from the results of the threshold-based algorithm (overall accuracy ~99.16%, probability of detection (POD) ~93.08%, probability of false detection (POFD) ~0.07%, and 96% reduction of the false alarmed pixels for validation), and the remaining false alarms were removed through post-processing using the forest map. The proposed algorithm was compared to the two existing methods. The proposed algorithm (POD ~ 93%) successfully detected most forest fires, while the others missed many small-scale forest fires (POD ~ 50-60%). More than half of the detected forest fires were detected within 10 min, which is a promising result when the operational real-time monitoring of forest fires using more advanced geostationary satellite sensor data (i.e., with higher spatial and temporal resolutions) is used for rapid response and management of forest fires

Psychosocial support interventions for women with gestational diabetes mellitus: a systematic review

Purpose This study aimed to analyze the content and effectiveness of psychosocial support interventions for women with gestational diabetes mellitus (GDM). Methods The following databases were searched with no limitation of the time period: Ovid-MEDLINE, Cochrane Library, Ovid-Embase, CINAHL, PsycINFO, NDSL, KoreaMed, RISS, and KISS. Two investigators independently reviewed and selected articles according to the predefined inclusion/exclusion criteria. ROB 2.0 and the RoBANS 2.0 checklist were used to evaluate study quality. Results Based on the 14 selected studies, psychosocial support interventions were provided for the purpose of (1) informational support (including GDM and diabetes mellitus information; how to manage diet, exercise, stress, blood glucose, and weight; postpartum management; and prevention of type 2 diabetes mellitus); (2) self-management motivation (setting goals for diet and exercise management, glucose monitoring, and enhancing positive health behaviors); (3) relaxation (practicing breathing and/or meditation); and (4) emotional support (sharing opinions and support). Psychosocial supportive interventions to women with GDM lead to behavioral change, mostly in the form of self-care behavior; they also reduce depression, anxiety and stress, and have an impact on improving self-efficacy. These interventions contribute to lowering physiological parameters such as fasting plasma glucose, glycated hemoglobin, and 2-hour postprandial glucose levels. Conclusion Psychosocial supportive interventions can indeed positively affect self-care behaviors, lifestyle changes, and physiological parameters in women with GDM. Nurses can play a pivotal role in integrative management and can streamline the care for women with GDM during pregnancy and following birth, especially through psychosocial support interventions

Improved retrievals of aerosol optical depth and fine mode fraction from GOCI geostationary satellite data using machine learning over East Asia

Aerosol Optical Depth (AOD) and Fine Mode Fraction (FMF) are important information for air quality research. Both are mainly obtained from satellite data based on a radiative transfer model, which requires heavy computation and has uncertainties. We proposed machine learning-based models to estimate AOD and FMF directly from Geostationary Ocean Color Imager (GOCI) reflectances over East Asia. Hourly AOD and FMF were estimated for 00-07 UTC at a spatial resolution of 6 km using the GOCI reflectances, their channel differences (with 30-day minimum reflectance), solar and satellite viewing geometry, meteorological data, geographical information, and the Day Of the Year (DOY) as input features. Light Gradient Boosting Machine (LightGBM) and Random Forest (RF) machine learning approaches were applied and evaluated using random, spatial, and temporal 10-fold cross-validation with ground-based observation data. LightGBM (R-2 = 0.89-0.93 and RMSE = 0.071-0.091 for AOD and R-2 = 0.67-0.81 and RMSE = 0.079-0.105 for FMF) and RF (R-2 = 0.88-0.92 and RMSE = 0.080-0.095 for AOD and R-2 = 0.59-0.76 and RMSE = 0.092-0.118 for FMF) agreed well with the in-situ data. The machine learning models showed much smaller errors when compared to GOCI-based Yonsei aerosol retrieval and the Moderate Resolution Imaging Spectroradiometer Dark Target and Deep Blue algorithms. The Shapley Additive exPlanations values (SHAP)-based feature importance result revealed that the 412 nm band (i. e., ch01) contributed most in both AOD and FMF retrievals. Relative humidity and air temperature were also identified as important factors especially for FMF, which suggests that considering meteorological conditions helps improve AOD and FMF estimation. Besides, spatial distribution of AOD and FMF showed that using the channel difference features to indirectly consider surface reflectance was very helpful for AOD retrieval on bright surfaces

Relationship between sleep and obesity among U.S. and South Korean college students

Background Little is known about the relationship between sleep and obesity in young adults, particularly college students. This study examined the relationship between sleep (i.e., sleep duration and quality) and obesity in a large and diverse binational sample of college students. Methods Analyses were based on a 40-item paper survey from 2016/2017 to 2017/2018 academic years, with a 72% response rate. The samples were 1578 college students aged 18–25 years from five universities (two in the U.S. and three in South Korea). Weight and height were measured objectively; other measures (e.g., health behaviors) were self-reported. Multinomial logistic regression was used to assess the association between sleep duration and independent variables (race/nationality, gender, and BMI). Poisson regression was used to examine the relationship between sleep quality and independent variables. Results Overall, blacks had a higher adjusted odds ratio (AOR) of short sleep (\u3c 7 h/night) than whites (AOR = 1.74, P \u3c .01); overweight participants had a higher AOR of short sleep than normal weight participants (AOR = 1.52, P \u3c .01); and obese participants had a higher AORs of both short and long sleep (\u3e 9 h/night) (AOR = 1.67, P \u3c .01; AOR = 1.79, P \u3c .05, respectively). Among men, being black, overweight, and obesity were associated with short sleep (P \u3c .05), whereas only obesity was related to short sleep among women (P \u3c .05). In analyses stratified by race and nationality, overweight and obesity were related to short sleep among blacks only (P \u3c .05). Overall, sleep quality (getting enough sleep to feel rested in the morning in the past 7 days) was worse in blacks and South Koreans than whites (P \u3c .05), worse in women than men (P \u3c .05), and worse in participants with obesity than normal weight participants (P \u3c .05). Conclusions Obesity was associated with both short (\u3c 7 h/night) and long sleep duration (\u3e 9 h/night) and poor sleep quality among all participants. In comparison with whites, blacks were more like to have short sleep, and blacks and South Koreans had worse sleep quality. Further investigations using a larger sample of college students in multiple countries may be helpful to identify target populations who are at a greater risk of obesity and sleep problems

Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea's current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined