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Wyszukujesz frazę "Peng, Cheng" wg kryterium: Autor


Wyświetlanie 1-2 z 2
Tytuł:
Preliminary study to explore gene-$\text{PM}_\text{2.5}$ interactive effects on respiratory system in traffic policemen
Autorzy:
Zhao, Jinzhuo
Bo, Liang
Gong, Changyi
Cheng, Peng
Kan, Haidong
Xie, Yuquan
Song, Weimin
Powiązania:
https://bibliotekanauki.pl/articles/2177102.pdf
Data publikacji:
2015-08-07
Wydawca:
Instytut Medycyny Pracy im. prof. dra Jerzego Nofera w Łodzi
Tematy:
inflammation
Fine Particles
traffic workers
respiratory system
single nucleotide polymorphism
SNP
Opis:
Objectives Traffic-related particulate matter (PM) is one of the major sources of air pollution in metropolitan areas. This study is to observe the interactive effects of gene and fine particles (particles smaller than 2.5 μm – $\text{PM}_\text{2.5}$) on the respiratory system and explore the mechanisms linking $\text{PM}_\text{2.5}$ and pulmonary injury. Material and Methods The participants include 110 traffic policemen and 101 common populations in Shanghai, China. Continuous 24 h individual-level $\text{PM}_\text{2.5}$ is detected and the pulmonary function, high-sensitivity C-reactive protein (hs-CRP), Clara cell protein 16 (CC16) and the polymorphism in CXCL3, NME7 and C5 genes are determined. The multiple linear regression method is used to analyze the association between $\text{PM}_\text{2.5}$ and health effects. Meanwhile, the interactive effects of gene and $\text{PM}_\text{2.5}$ on lung function are analyzed. Results The individual $\text{PM}_\text{2.5}$ exposure for traffic policemen was higher than that in the common population whereas the forced expiratory volume in 1 s (FEV₁), the ratio of FEV₁ to forced vital capacity (FEV₁/FVC) and lymphocytes are lower. In contrast, the hs-CRP level is higher. In the adjusted analysis, $\text{PM}_\text{2.5}$ exposure was associated with the decrease in lymphocytes and the increase in hs-CRP. The allele frequencies for NME7 and C5 have significant differences between FEV₁/FVC ≤ 70% and FEV₁/FVC > 70% participants. The results didn’t find the interaction effects of gene and $\text{PM}_\text{2.5}$ on FEV₁/FVC in all the 3 genes. Conclusions The results indicated that traffic exposure to high levels of $\text{PM}_\text{2.5}$ was associated with systemic inflammatory response and respiratory injury. Traffic policemen represent a high risk group suffering from the respiratory injury.
Źródło:
International Journal of Occupational Medicine and Environmental Health; 2015, 28, 6; 971-983
1232-1087
1896-494X
Pojawia się w:
International Journal of Occupational Medicine and Environmental Health
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A BIM technology-based underwater structure damage identification and management method
Autorzy:
Li, Xiaofei
Su, Rongrong
Cheng, Peng
Sun, Heming
Meng, Qinghang
Song, Taiyi
Wei, Mengpu
Zhang, Chen
Powiązania:
https://bibliotekanauki.pl/articles/2204531.pdf
Data publikacji:
2023
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
building information modeling
underwater structural disease
damage identification
deep learning
modelowanie informacji o budynku
identyfikacja uszkodzeń
uczenie głębokie
uszkodzenie podwodnej konstrukcji
Opis:
With the continuous development of bridge technology, the condition assessment of large bridges has gradually attracted attention. Structural Health Monitoring (SHM) technology provides valuable information about a structure's existing health, keeping it safe and uninterrupted use under various operating conditions by mitigating risks and hazards on time. At the same time, the problem of bridge underwater structure disease is becoming more obvious, affecting the safe operation of the bridge structure. It is necessary to test the bridge’s underwater structure. This paper develops a bridge underwater structure health monitoring system by combining building information modeling (BIM) and an underwater structure damage algorithm. This paper is verified by multiple image recognition networks, and compared with the advantages of different networks, the YOLOV4 network is used as the main body to improve, and a lightweight convolutional neural network (Lite-yolov4) is built. At the same time, the accuracy of disease identification and the performance of each network are tested in various experimental environments, and the reliability of the underwater structure detection link is verified.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2023, 71, 2; art. no. e144602
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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