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Dynamic gridded population data are crucial in fields such as disaster reduction, public health, urban planning, and global change studies. Despite the use of multi-source geospatial data and advanced machine learning models, current frameworks for population spatialization often struggle with spatial non-stationarity, temporal generalizability, and fine temporal resolution. To address these issues, we introduce a framework for dynamic gridded population mapping using open-source geospatial data and machine learning. The framework consists of (i) delineation of human footprint zones, (ii) construction of muliti-scale population prediction models using automated machine learning (AutoML) framework and geographical ensemble learning strategy, and (iii) hierarchical population spatial disaggregation with pycnophylactic constraint-based corrections. Employing this framework, we generated hourly time-series gridded population maps for China in 2016 with a 1-km spatial resolution. The average accuracy evaluated by root mean square deviation (RMSD) is 325, surpassing datasets like LandScan, WorldPop, GPW, and GHSL. The generated seamless maps reveal the temporal dynamic of population distribution at fine spatial scales from hourly to monthly. This framework demonstrates the potential of integrating spatial statistics, machine learning, and geospatial big data in enhancing our understanding of spatio-temporal heterogeneity in population distribution, which is essential for urban planning, environmental management, and public health.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11210339 | PMC |
http://dx.doi.org/10.1016/j.jag.2024.103882 | DOI Listing |
J Neural Eng
December 2024
West China Hospital of Sichuan University, No.37 Guoxue Alley, Wuhou District, Chengdu City, Sichuan Province, Chengdu, Sichuan, 610041, CHINA.
Objective: Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals. This paper introduces a hybrid network that employs a Transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
View Article and Find Full Text PDFJ Int Med Res
December 2024
Department of Hematology, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
Objective: Mobilization and collection of peripheral blood stem cells (PBSCs) are time-intensive and costly. Excessive apheresis sessions can cause physical discomfort for donors and increase the costs associated with collection. Therefore, it is essential to identify key predictive factors for successful harvests to minimize the need for multiple apheresis procedures.
View Article and Find Full Text PDFACS Synth Biol
December 2024
Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California91125, United States.
Sequence-function data provides valuable information about the protein functional landscape but is rarely obtained during directed evolution campaigns. Here, we present Long-read every variant Sequencing (LevSeq), a pipeline that combines a dual barcoding strategy with nanopore sequencing to rapidly generate sequence-function data for entire protein-coding genes. LevSeq integrates into existing protein engineering workflows and comes with open-source software for data analysis and visualization.
View Article and Find Full Text PDFACS Sens
December 2024
York Plasma Institute, School of Physics, Engineering and Technology, University of York, York YO10 5DD, U.K.
Nanogold is an emerging material for enhancing surface-enhanced Raman scattering (SERS), which enables the detection of hazardous analytes at trace levels. This study presents a simple, single-step plasma synthesis method to control the size and yield of Au nanoparticles by using plasma-liquid redox chemistry. The pin-based argon plasma reduces the Au precursor in under 5 min, synthesizing Au spherical particles ranging from ∼20 nm at 0.
View Article and Find Full Text PDFJ Phys Chem B
December 2024
Science and Technology on Aerospace Chemical Power Laboratory, Laboratory of Emergency Safety and Rescue Technology, Hubei Institute of Aerospace Chemotechnology, Xiangyang 441003, China.
Doped semiconductors are often used to improve photocatalytic efficiency and address the challenges of easy recombination of electron-hole pairs and poor photoluminescence. However, the reproducibility and complexity of experimental studies result in time-consuming and less cost-effective studies, and it is difficult to gain insights into the intrinsic properties of doped photocatalysts to control their performance. Introducing a machine learning approach, we constructed a photocatalytic model of transition-metal- and rare earth metal-ion-doped γ-BiMoO.
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