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| http://dx.doi.org/10.4103/0301-4738.179728 | DOI Listing |
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Caregiving in Asia: Priority areas for research, policy, and practice to support family caregivers.
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Department of Health Policy and Management School of Public Health, Shanghai Jiao Tong University School of Medicine Shanghai China.
Population aging presents a growing societal challenge and imposes a heavy burden on the healthcare system in many Asian countries. Given the limited availability of formal long-term care (LTC) facilities and personnel, family caregivers play a vital role in providing care for the increasing population of older adults. While awareness of the challenges faced by caregivers is rising, discussions often remain within academic circles, resulting in the lived experiences, well-being, and needs of family caregivers being frequently overlooked.
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Department of Bacteriology, Noguchi Memorial Institute for Medical Research, University of Ghana, Accra, Ghana.
Introduction: Adolescent girls are more sensitive to hormonal imbalance with major impact on their nutritional, reproductive, physical, psychosocial, and academic wellbeing. This study explored adolescent girls' knowledge and perceptions of causes and management of symptoms of hormonal imbalance.
Materials And Methods: Using a qualitative approach, focus group discussions were conducted with 116 assented in-school adolescent girls aged 10-19 years between 3rd and 19th October 2022.
Relic DNA obscures bacterial diversity and interactions in ballast tank sediment.
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Marine College, Shandong University, Weihai, China. Electronic address:
The dark and anoxic environment of ballast tank sediment (BTS) harbors substantial amounts of relic DNA, yet its impact on microbial diversity estimates in BTS management remains poorly understood. This study employed propidium monoazide (PMA) treatment to eliminate relic DNA and used 16S amplicon high-throughput sequencing to characterize both total and viable bacteria. Our findings revealed that relic DNA is abundant in BTS.
View Article and Find Full Text PDFMachine learning combined with geodetector to predict the spatial distribution of soil heavy metals in mining areas.
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Technical Centre for Soil, Agricultural and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China.
An accurate understanding of the spatial distribution of soil heavy metals (HMs) is crucial for the effective prevention of soil pollution and remediation strategies. Traditional machine learning models often overlook the spatially stratified heterogeneity inherent to environmental data, which can impair predictive accuracy. Therefore, we combined the Geodetector model (GDM) with machine learning models.
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Network
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Department of Information Management, Asia Eastern University of Science and Technology, New Taipei, Panchiao, Taiwan.
To improve the calculation accuracy of the Monte Carlo (MC) method and reduce the calculation time. Firstly, CNN and LSTM deep learning networks are introduced for designing nonlinear dynamic systems simulating dam stress. Then, spatial feature mining and sequence information extraction of nonlinear data of dam stress are carried out respectively, and a combined prediction model of dam stress depth (DS-FEM-CNN-LSTM) is proposed.
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