Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Wenjing Sang"'
Publikováno v:
Viruses, Vol 4, Iss 1, Pp 102-116 (2012)
Porcine reproductive and respiratory syndrome virus (PRRSV) can subvert early innate immunity, which leads to ineffective antimicrobial responses. Overcoming immune subversion is critical for developing vaccines and other measures to control this dev
Externí odkaz:
https://doaj.org/article/b44b9ae65f4242c1853a8fd0379ebdaf
Publikováno v:
Geophysical Journal International. 232:940-957
SUMMARY Porosity characterization is of profound significance for seismic inversion and hydrocarbon prediction. Although semi-supervised learning (SSL) based methods have been used to boost prediction accuracy and lateral continuity of supervised lea
Autor:
Yinzhu Diao, Xiaoxia Wang, Lei Zhou, Yitong Dan, wenjing Sang, Muhammad Usman, Gang Luo, Yalei Zhang
Publikováno v:
Journal of Soils and Sediments. 22:2765-2776
Publikováno v:
Environmental Science and Pollution Research. 29:84675-84689
A series of 60-day soil immobilized incubations were performed to explore the impacts of various factors (incubation time, chitosan modified magnetic sawdust hydrochar (CMSH) dosages, initial pH values, moisture contents, and humic acid (HA)) on CMSH
Publikováno v:
Environmental Science and Pollution Research. 29:71871-71881
The preparation of magnetic biochar from sewage sludge and rice straw for heavy metal contaminated soil remediation has greater application prospects, but its remediation mechanism was rarely considered by combining soil physicochemical properties wi
Publikováno v:
Water, Air, & Soil Pollution. 234
Publikováno v:
GEOPHYSICS. 87:R165-R181
Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, the low-frequency impedance background is from
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 18:1861-1865
Noise attenuation has been a long-standing but still active topic in seismic data processing. The deep convolutional neural networks (CNNs) have been recently adopted to remove the learned random noise from noisy seismic data, but it is still difficu
Publikováno v:
Journal of Applied Geophysics. 213:105040
Publikováno v:
Geophysical Prospecting.