While many studies suggest evidence for the health benefits of nature, there is currently no standardized method to measure time spent in nature or nature contact, nor agreement on how best to define nature contact in research. The purpose of this review is to summarize how nature contact has been measured in recent health research and provide insight into current metrics of exposure to nature at individual and population scales. The most common methods include surrounding greenness, questionnaires, and global positioning systems (GPS) tracking. Several national-level surveys exist, though these are limited by their cross-sectional design, often measuring only a single component of time spent in nature, and poor links to measures of health. In future research, exposure assessment combining the quantifying (e.g., time spent in nature and frequency of visits to nature) and qualifying (e.g., greenness by the normalized difference of vegetation index (NDVI) and ratings on perception by individuals) aspects of current methods and leveraging innovative methods (e.g., experience sampling methods, ecological momentary assessment) will provide a more comprehensive understanding of the health effects of nature exposure and inform health policy and urban planning.
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http://dx.doi.org/10.3390/ijerph18084092 | DOI Listing |
Sci Rep
December 2024
Gansu Provincial Ecological Environment Engineering Assessment Center, Lanzhou, 730000, People's Republic of China.
In this study, polyethylene glycol (PEG) and dextran (Dex) were chemically modified to obtain amino-functionalized PEG (PEG-(NH)) and oxidized dextran (ODex). They were subsequently reacted via -NH and -CHO groups to synthesize a macromolecular Schiff base particle. The structures, morphologies, and thermal properties of the macromolecular Schiff base particle were characterized using Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), and thermogravimetry analysis (TGA).
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December 2024
School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
Microtextured microneedles are tiny needle-like structures with micron-scale microtextures, and the drugs stored in the microtextures can be released after entering the skin to achieve the effect of precise drug delivery. In this study, the skin substitution model of Ogden's hyperelastic model and the microneedle array and microtexture models with different geometrical parameters were selected to simulate and analyse the flow of the microtexture microneedle arrays penetrating the skin by the finite-element method, and the length of the microneedles was determined to be 200 μm, the width 160 μm, and the value of the gaps was determined to be 420 μm. A four-pronged cone was chosen as the shape of microneedles, and a rectangle was chosen as the shape of the drug-carrying microneedle.
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December 2024
Faculté des Sciences et Technologies, LEMTA - Université de Lorraine - CNRS UMR 7563, Boîte Postale 70239, Vandoeuvre les Nancy cedex, 54506, France.
The wetting characteristics of fluids play a crucial role in various fields of interface and surface science. Contact angle serves as a fundamental indicator of wetting behavior. However, accurate quantification of wetting phenomena even at the macroscale often poses challenges, particularly due to the hysteresis between receding and advancing contact angles.
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December 2024
Department of Zoology, Musée National d'Histoire Naturelle, Luxembourg, Luxembourg.
Raccoons (Procyon lotor) originated in North America and have been introduced to Europe. Due to their close contact with human settlements, they are important reservoirs for zoonotic pathogens, such as Baylisascaris procyonis. The relevance and prevalence of vector-borne pathogens have not yet been fully elucidated.
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December 2024
Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, 1 Castle Point Terrace, Hoboken, NJ, 07030, USA. Electronic address:
Phosphate (PO(III)) contamination in water bodies poses significant environmental challenges, necessitating efficient and accurate methods to predict and optimize its removal. The current study addresses this issue by predicting the adsorption capacity of PO(III) ions onto biochar-based materials using five probabilistic machine learning models: eXtreme Gradient Boosting LSS (XGBoostLSS), Natural Gradient Boosting, Bayesian Neural Networks (NN), Probabilistic NN, and Monte-Carlo Dropout NN. Utilizing a dataset of 2952 data points with 16 inputs, XGBoostLSS demonstrated the highest R (0.
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