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Perspiration is an important physiological process that maintains thermal homeostasis and water–salt balance. However, the collection and analysis of perspiration currently rely on microfluidic technology and colorimetric assays. The complexity and high cost of fabricating microfluidic channels and the insecurity of chemical reagents for color reactions should be optimized. In this work, a colorimetry electronic skin (e‐skin) for intelligent perspiration monitoring has been realized. The colorimetry e‐skin system consists of the polyurethane (PU) nanomesh and the object detection algorithm You Only Look Once version 3 (YOLOv3). Due to the 44% porosity of the PU nanomesh and capillary action, the low‐cost PU nanomesh (<1 cent) can be used as the colorimetric indicator. The volume of the PU nanomesh expands to 362.37% as a result of perspiration being absorbed and changes the optical transmittance (up to 277.78%). A finite element model based on capillary action has been proposed to explain the change in optical transmittance. Finally, a database containing 735 images has been built, and the object detection algorithm YOLOv3 is used to analyze the perspiration absorbed by the PU nanomesh. The detection results can identify the perspiration volume with a high accuracy of 97%. These results show that this work has great potential in healthcare field. [ABSTRACT FROM AUTHOR]

Classifying and quantifying small molecules on‐site with a rapid and cost‐effective operation are essential for pharmaceutical manufacturing, healthcare, hazardous risk control, and other applications. However, traditional molecular detection and identification methods like chromatography often involve expensive and bulky equipment and are required to be operated by trained professionals, which severely hinders the further development of small molecule‐based applications. Herein, a novel molecular detection platform is introduced by imaging a spatial gradient metasurface consisting of millions of different unique atoms and following with deep learning modeling to classify and quantify small molecules from mixed solutions accurately. The metasurface has a circular gradient geometry, which changes its transmittance intensity pattern based on the surrounding molecules under narrow‐band illumination. A convolutional neural network trained on the monochromatic images of the metasurface is employed. The results demonstrate a recognition rate of 96.88% for classification and a mean absolute error of 16.23% for quantification. This novel platform enables label‐free, sensitive, and rapid molecular classification and quantification, which opens a new avenue for small molecule classification and quantification and enables possibilities for real‐time, on‐site, and label‐free applications, including environmental monitoring, drug screening, and early diagnosis. [ABSTRACT FROM AUTHOR]

Vernacular architecture is an important artificial habitat landscape that responds to and evolves with specific natural and social contexts. Although abundant evidence highlights the fragility of vernacular architecture in the urbanization process, in-depth discussion of the resilient manifestations represented by its anonymous evolution is lacking. Liangshuixiu , a courtyard-form vernacular building in Southern Hebei , is taken as the example. This study explored its spontaneous evolution mechanism characterized by the courtyard being covered (CBR). Benefiting from the computer vision technology and the remarkable orthophotos of Liangshuaixiu , the distribution characteristics of the CBR phenomenon in 131 townships were identified by high-resolution network. Subsequently, 14 potential influencing factors were selected according to "push–pull" theory, and explanatory variables that affect the development of CBR were identified using redundancy analysis. Spatial autocorrelation analysis of the CBR showed a significant clustering trend of high in the south and low in the north. Redundancy analysis results showed that the combined effects of push–pull factors were the most important aspects to explain the evolution of Liangshuaixiu. Urban pull and rural push made a greater contribution to the development of CBR, with education level being the most critical factor, followed by population decline rate and government revenue. Rural pull and urban push had limited contribution to the development of CBR, in which the aging rate was an important explanatory factor. This study revealed the evolutionary characteristics of specific vernacular buildings in a data-driven manner, thereby providing quantitative insights into how urbanization drives the evolution of vernacular habitats. • The evolution of Liangshuaixiu dwellings was identified by CV technology. • The "push-pull" factors between urban and rural areas have jointly affected the evolution. • Education level is the most critical explanatory variable for the evolution. • A new research paradigm was provided to study the evolution mechanism of vernacular architecture. [ABSTRACT FROM AUTHOR]