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Study on the evolution of the spatial patterns and driving factors of national agricultural cultural heritage in China.
PLoS One
January 2025
College of Tourism, Hubei University, Wuhan, Hubei, China.
The study analyzed the spatial distribution characteristics, evolution rules, and driving factors of 138 China's national agricultural cultural heritage sites from 2013 to 2021 at the overall and regional levels, using kernel density analysis, Centres for standard deviation ellipse analyses, spatial autocorrelation analysis, and geographical detector analysis.The results showed that: ①From an overall perspective, the spatial pattern of China's national agricultural cultural heritage changed greatly from 2013 to 2021, with a highly uneven spatial distribution, gradually showing a distribution pattern of "widely distributed, locally concentrated". The spatial distribution of China's national agricultural cultural heritage is increasingly evident, and the spatial distribution type has evolved from discrete to clustered.
View Article and Find Full Text PDFAnnotated Checklist of Poroid Hymenochaetoid Fungi in Central Asia: Taxonomic Diversity, Ecological Roles, and Potential Distribution Patterns.
J Fungi (Basel)
January 2025
State Key Laboratory of Mycology, Institute of Microbiology, Chinese Academy of Sciences, Beijing 100101, China.
Central Asia, located at the heart of Eurasia, is renowned for its varied climate and vertical vegetative distribution, which support diverse biomes and position it as a global biodiversity hotspot. Despite this ecological richness, Central Asia's fungal diversity, particularly wood-inhabiting macrofungi, remains largely unexplored. This study investigates the diversity, ecological roles, and potential distribution of poroid Hymenochaetoid fungi in the region.
View Article and Find Full Text PDFEnhancing CO Adsorption on MgO: Insights into Dopant Selection and Mechanistic Pathways.
Biomimetics (Basel)
December 2024
Entropic Interface Group, Engineering Product Development, Singapore University of Technology and Design, 8 Somapah Road, Singapore 487372, Singapore.
Inspired by our recent success in designing CO-phobic and CO-philic domains on nano-MgO for effective CO adsorption, our ongoing efforts focus on incorporating dopants into pristine MgO to further enhance its CO adsorption capabilities. However, a clear set of guidelines for dopant selection and a holistic understanding of the underlying mechanisms is still lacking. In our investigation, we combined first-principles calculations with experimental approaches to explore the crystal and electronic structural changes in MgO doped with high-valence elements (Al, C, Si, and Ti) and their interactions with CO.
View Article and Find Full Text PDFPandemic preparedness in Vietnam: a review of health system resilience and areas for improvement.
J Glob Health
January 2025
SingHealth Duke-NUS Global Health Institute, National University of Singapore, Singapore.
In this viewpoint, we explore Vietnam's health system vulnerabilities and its national response to the COVID-19 pandemic, as well as critical areas of health system resilience, including health financing, workforce distribution, information systems, and governance. While Vietnam achieved early success through strong governance and mass vaccination campaigns, the pandemic revealed weaknesses in resource procurement, workforce imbalance, and limitations of its health information system. There are challenges in ensuring the rapid disbursement of financial resources and reliance on imported medical supplies, which delayed response times.
View Article and Find Full Text PDFAutomatic visual detection of activated sludge microorganisms based on microscopic phase contrast image optimisation and deep learning.
J Microsc
January 2025
Ningbo Key Laboratory of Micro-Nano Motion and Intelligent Control, Ningbo University, Ningbo, PR China.
The types and quantities of microorganisms in activated sludge are directly related to the stability and efficiency of sewage treatment systems. This paper proposes a sludge microorganism detection method based on microscopic phase contrast image optimisation and deep learning. Firstly, a dataset containing eight types of microorganisms is constructed, and an augmentation strategy based on single and multisamples processing is designed to address the issues of sample deficiency and uneven distribution.
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