We examine associations between social determinants and mental health and assess how the associations vary by race/ethnicity using a large, diverse sample of older adults.A retrospective study of 444,057 older adults responding to the Medicare Health Outcomes Survey in 2015-2017 was conducted. Using a multilevel linear regression, we examined the associations between the self-reported number of unhealthy days due to mental health and social determinants, stratified by race/ethnicity.Health factors were most strongly associated with unhealthy days across all racial/ethnic groups. Strength of other factors varied by race/ethnicity. Social/economic factors had stronger associations among Whites, Asians, and multiracial individuals, while such factors were not significant for American Indians/Alaska Natives and Native Hawaiians/Other Pacific Islanders.We found varying degrees of associations between social determinants and poor mental health by racial/ethnic groups. These results suggest that homogeneous interventions may not meet the mental health needs of all.
오늘날 국제정치에서 지배적인 이슈는 중국의 부상이며 지배적인 담론은 ‘투키디데스의 함정’(Thucydides Trap)이다. 고대 그리스 시대 패권전쟁이었던 펠로폰네소스 전쟁을 ‘불가피’하게 만든 원인이 “(도전국인) 아테네의 힘의 성장과 그에 대한 (지배국인) 스파르타의 두려움”이었다는 투키디데스의 주장에 빗대어 20세기를 지배해 온 미국과 13억의 인구를 단위로 급속히 성장한 중국 사이에 패권전쟁이 ‘불가피’한 지를 둘러싼 담론이다. 그러나 그것을 둘러싼 국제정치학 논쟁은 또 하나의 함정을 만들고 있다. 그 논쟁은 불확정적인 ‘미래’에 대한 ‘예측’의 형태일 수밖에 없는 바, ‘예측’이 자기실현적 또는 자기부정적의 형태로 현실에 반영됨으로써 이론의 진실성이 과장되거나 부정되고, 역사의 전개과정이 왜곡될 수 있는 함정이다. 그리고 그 함정은 국제정치학이 여러 학파(學派)와 유파(流派)로 나뉘어 서로 우위를 다투는 학계의 관행에 의해 더욱 가중되고 있다. 이 논문은 첫째, 중국의 부상을 둘러싼 학계의 논쟁이 현실과 맞물리면서 초래할 수 있는 이론적, 실천적 위험성에 대해 경고하고, 둘째, 투키디데스의 전쟁사를 재검토하여 ‘투키디데스의 함정’이라는 통상적 담론이 가지는 이론적, 실천적 함정의 실체를 밝힘으로써 혼란의 시대에 국제정치 연구의 역할과 방향을 성찰하고자 하는 데 그 목적이 그 목적이 있다.
본 논문은 표준 기반의 인공지능 복합환경제어시스템을 이용하여 온실 내부 환경정보와 작물 영상 데이터를 자동으로 취득하였다. 또한 취득 된 영상 데이터와 환경정보를 이용해 병해 발생 예측 정보와 일사량, 광투과율, 산란광 등의 광 환경 정보를 활용하여, 온실 실증 환경에서 쉽게 검출하기 힘들었던 작물 영상을 분석하였다. 작물 병해 영상 분석 시 영상 변환 증강기법과 파라미터 미세 조절 등 전처리 기술 적용을 통해 기존 실종 환경에서 2020년도에 92.5%였던 병해 검출율을 95.2%로 상향시켰다. 이는 비교 대상이었던 keras를 활용한 딥러닝 병해 예측 모델 검출 정확도 최대치인 89%와 비교했을 경우, 6.2%p 이상 향상됐고, 실험실에서 조절한 환경이 아닌 외부 환경에서 취득한 unseen data를 이용한 것을 감안할 경우(Practical outcome), 매우 높은 병해 분류 정확도를 보여준다고 할 수 있다.
Walking behaviors are one of the most basic transport modes in daily life. As a result, the efforts and concerns on pedestrians are consistently increased. This study tried to reveal significant factors for pedestrian volumes through structural equation models and compare the impacts of the whole time of day, off-peak time, peak time on the pedestrian volumes. The results of the analysis show that commercial business factors, accessibility factors, walking environment factors are the most significant factors that increase pedestrian volumes. Whereas, housing factors do not contribute to increase the pedestrian volumes. In the non-peak time, the weight of commercial business factors is higher than the whole time of day, while the weight housing factors, accessibility factors, walking environment factors are lower. In the peak time, however, the weight of commercial business factors decreases rather than the whole time of day, while the weight of the other factors increase.
The influence of community context and individual socioeconomic status on health is widely recognized. However, the dynamics of how the relationship of neighborhood context on health varies by individual socioeconomic status is less well understood.To examine the relationship between neighborhood context and mortality among older adults and examine how the influence of neighborhood context on mortality differs by individual socioeconomic status, using two measures of income-level and homeownership.A retrospective study of 362,609 Medicare Advantage respondents to the 2014-2015 Medicare Health Outcomes Survey aged 65 and older.Neighborhood context was defined using the deciles of the Area Deprivation Index. Logistic regression was used to analyze mortality with interaction terms between income/homeownership and neighborhood deciles to examine cross-level relationships, controlling for age, gender, race/ethnicity, number of chronic conditions, obese/underweight, difficulties in activities of daily living, smoking status, and survey year. Predicted mortality rates by group were calculated from the logistic model results.Low-income individuals (8.9%) and nonhomeowners (9.1%) had higher mortality rates compared to higher-income individuals (5.3%) and homeowners (5.3%), respectively, and the differences were significant across all neighborhoods even after adjustment. With regression adjustment, older adults residing in less disadvantaged neighborhoods showed lower predicted 2-year mortality among high-income (4.86% in the least disadvantaged neighborhood; 6.06% in the most disadvantaged neighborhood; difference p-value<0.001) or homeowning individuals (4.73% in the least disadvantaged neighborhood; 6.25% in the most disadvantaged neighborhood; difference p-value<0.001). However, this study did not observe a significant difference in predicted mortality rates among low-income individuals by neighborhood (8.7% in the least disadvantaged neighborhood; 8.61% in the most disadvantaged neighborhood; difference p-value = 0.825).Low-income or non-homeowning older adults had a higher risk of mortality regardless of neighborhood socioeconomic status. While living in a less disadvantaged neighborhood provided a protective association for higher-income or homeowning older adults, low-income older adults did not experience an observable benefit.
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