Silicon(Si)is an important element in aquatic ecosystems.Based on the observed data in the Huanghe(Yellow)River Basin,a forward model was used to evaluate the silicate weathering rate in the Huanghe River Basin.The ef...Silicon(Si)is an important element in aquatic ecosystems.Based on the observed data in the Huanghe(Yellow)River Basin,a forward model was used to evaluate the silicate weathering rate in the Huanghe River Basin.The effects of silicate weathering,damming,and land use change on the Si concentration and flux were analyzed.Results show that the dissolved Si(DSi)concentration decreased first and then increased,and was 0.82–2.96 mg/L.As a silicon source in the Huanghe River Basin,the silicate weathering rate in the upper reaches of Lanzhou was high,and a large amount of DSi was transported to the lower reaches.Agricultural irrigation in the middle reaches caused a large amount of DSi loss,and the interception of large-scale cascade reservoirs caused a large amount of DSi retention.The DSi released by sediment re-suspension due to high runoff scouring in the downstream channel also served as a silicon source to supplement the DSi flux transported to the sea.Suspended particulate matter and biogenic Si(BSi)increased first and then decreased in the ranges of 24.1–1590.7 mg/L and 0.08–2.17 mg/L,respectively,due mainly to severe soil erosion in the eastern Loess Plateau,which caused significant amounts of phytoliths to enter the water.展开更多
This paper presents an enhanced version of the correlation-driven dual-branch feature decomposition framework(CDDFuse)for fusing low-and high-exposure images captured by the G400BSI sensor.We introduce a novel neural ...This paper presents an enhanced version of the correlation-driven dual-branch feature decomposition framework(CDDFuse)for fusing low-and high-exposure images captured by the G400BSI sensor.We introduce a novel neural long-term memory(NLM)module into the CDDFuse architecture to improve feature extraction by leveraging persistent global feature representations across image sequences.The proposed method effectively preserves dynamic range and structural details,and is evaluated using a new metric,the ATEF dynamic range preservation index(ATEF-DRPI).Experimental results on a G400BSI dataset demonstrate superior fusion quality,with ATEF-DRPI scores of 0.90,a 12.5%improvement over that of the baseline CDDFuse(0.80),indicating better detail retention in bright and dark regions.This work advances image fusion techniques for extreme lighting conditions,offering improved performance for downstream vision tasks.展开更多
基金Supported by the Joint Fund of National Natural Science Foundation of China(NSFC)and Shandong Province(Nos.U22A20580,U1906210)the Laoshan Laboratory(No.LSKJ202203904)the National Natural Science Foundation of China(No.41876116)。
文摘Silicon(Si)is an important element in aquatic ecosystems.Based on the observed data in the Huanghe(Yellow)River Basin,a forward model was used to evaluate the silicate weathering rate in the Huanghe River Basin.The effects of silicate weathering,damming,and land use change on the Si concentration and flux were analyzed.Results show that the dissolved Si(DSi)concentration decreased first and then increased,and was 0.82–2.96 mg/L.As a silicon source in the Huanghe River Basin,the silicate weathering rate in the upper reaches of Lanzhou was high,and a large amount of DSi was transported to the lower reaches.Agricultural irrigation in the middle reaches caused a large amount of DSi loss,and the interception of large-scale cascade reservoirs caused a large amount of DSi retention.The DSi released by sediment re-suspension due to high runoff scouring in the downstream channel also served as a silicon source to supplement the DSi flux transported to the sea.Suspended particulate matter and biogenic Si(BSi)increased first and then decreased in the ranges of 24.1–1590.7 mg/L and 0.08–2.17 mg/L,respectively,due mainly to severe soil erosion in the eastern Loess Plateau,which caused significant amounts of phytoliths to enter the water.
文摘This paper presents an enhanced version of the correlation-driven dual-branch feature decomposition framework(CDDFuse)for fusing low-and high-exposure images captured by the G400BSI sensor.We introduce a novel neural long-term memory(NLM)module into the CDDFuse architecture to improve feature extraction by leveraging persistent global feature representations across image sequences.The proposed method effectively preserves dynamic range and structural details,and is evaluated using a new metric,the ATEF dynamic range preservation index(ATEF-DRPI).Experimental results on a G400BSI dataset demonstrate superior fusion quality,with ATEF-DRPI scores of 0.90,a 12.5%improvement over that of the baseline CDDFuse(0.80),indicating better detail retention in bright and dark regions.This work advances image fusion techniques for extreme lighting conditions,offering improved performance for downstream vision tasks.
