Sustainable ecological development is key to enhancing the life satisfaction of indigenous populations.However,comprehensive studies on the impact of ecological protection policies on life satisfaction from the perspe...Sustainable ecological development is key to enhancing the life satisfaction of indigenous populations.However,comprehensive studies on the impact of ecological protection policies on life satisfaction from the perspective of the indigenous populations of national parks are lacking.This study investigated the impact of national park ecological protection policies on the life satisfaction of 496 indigenous households in the Qilian Mountain National Park through a questionnaire survey conducted in 2021,employing an ordered multicategorical logistic regression model.The results showed that overall life satisfaction was high and 17.34%of indigenous populations are very satisfied with their current standard of living,with the highest satisfaction of herding households,followed by nonfarming households,half-farming and half-herding households,and farming households.Livelihood capital components had different impacts on life satisfaction.Policy satisfaction,perceived importance,and participation willingness had different impacts on life satisfaction.Key ecological policy instruments,such as ecological compensation,livelihood skills training,eco-stewardship positions,specialty town development,and natural grassland/forest conservation,significantly enhanced life satisfaction.Therefore,emphasizing the interests of indigenous populations,enhancing their willingness to participate in ecological policies,and improving their nonagricultural and pastoral employment abilities can help to improve overall life satisfaction.展开更多
Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a c...Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects.However,multiple major eye diseases,such as DR,GLC,and AMD,could not be detected simultaneously by computer-aided systems to date.There were just high-performance-outcome researches on a pair of healthy and eye-diseased group,besides of four categories of fundus image classification.To have a better knowledge of multi-categorical classification of fundus photographs,we used optimal residual deep neural networks and effective image preprocessing techniques,such as shrinking the region of interest,iso-luminance plane contrast-limited adaptive histogram equalization,and data augmentation.Applying these to the classification of three eye diseases from currently available public datasets,we achieved peak and average accuracies of 91.16%and 85.79%,respectively.The specificities for images from the eyes of healthy,GLC,AMD,and DR patients were 90.06%,99.63%,99.82%,and 91.90%,respectively.The better specificity performances may alert patient in an early stage of eye diseases to prevent vision loss.This study presents a possible occurrence of a multi-categorical deep neural network technique that can be deemed as a successful pilot study of classification for the three most-common eye diseases and can be used for future assistive devices in computer-aided clinical applications.展开更多
基金Fujian Provincial Department of Finance,Fujian Provincial Federation of Social Science Circles for its support of this study(Grant No.FJ2023B124)。
文摘Sustainable ecological development is key to enhancing the life satisfaction of indigenous populations.However,comprehensive studies on the impact of ecological protection policies on life satisfaction from the perspective of the indigenous populations of national parks are lacking.This study investigated the impact of national park ecological protection policies on the life satisfaction of 496 indigenous households in the Qilian Mountain National Park through a questionnaire survey conducted in 2021,employing an ordered multicategorical logistic regression model.The results showed that overall life satisfaction was high and 17.34%of indigenous populations are very satisfied with their current standard of living,with the highest satisfaction of herding households,followed by nonfarming households,half-farming and half-herding households,and farming households.Livelihood capital components had different impacts on life satisfaction.Policy satisfaction,perceived importance,and participation willingness had different impacts on life satisfaction.Key ecological policy instruments,such as ecological compensation,livelihood skills training,eco-stewardship positions,specialty town development,and natural grassland/forest conservation,significantly enhanced life satisfaction.Therefore,emphasizing the interests of indigenous populations,enhancing their willingness to participate in ecological policies,and improving their nonagricultural and pastoral employment abilities can help to improve overall life satisfaction.
基金supported by the KIAT(Korea Institute for Advancement of Technology)grant funded by the Korea Government(MOTIE:Ministry of Trade Industry and Energy)(No.P0012724)the Soonchunhyang University Research Fund.
文摘Various techniques to diagnose eye diseases such as diabetic retinopathy(DR),glaucoma(GLC),and age-related macular degeneration(AMD),are possible through deep learning algorithms.A few recent studies have examined a couple of major diseases and compared them with data from healthy subjects.However,multiple major eye diseases,such as DR,GLC,and AMD,could not be detected simultaneously by computer-aided systems to date.There were just high-performance-outcome researches on a pair of healthy and eye-diseased group,besides of four categories of fundus image classification.To have a better knowledge of multi-categorical classification of fundus photographs,we used optimal residual deep neural networks and effective image preprocessing techniques,such as shrinking the region of interest,iso-luminance plane contrast-limited adaptive histogram equalization,and data augmentation.Applying these to the classification of three eye diseases from currently available public datasets,we achieved peak and average accuracies of 91.16%and 85.79%,respectively.The specificities for images from the eyes of healthy,GLC,AMD,and DR patients were 90.06%,99.63%,99.82%,and 91.90%,respectively.The better specificity performances may alert patient in an early stage of eye diseases to prevent vision loss.This study presents a possible occurrence of a multi-categorical deep neural network technique that can be deemed as a successful pilot study of classification for the three most-common eye diseases and can be used for future assistive devices in computer-aided clinical applications.
