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Titlebook: Soil Emission of Nitrous Oxide and its Mitigation; David Ussiri,Rattan Lal Book 2013 Springer Science+Business Media Dordrecht 2013 Dentri

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发表于 2025-3-21 19:12:55 | 显示全部楼层 |阅读模式
书目名称Soil Emission of Nitrous Oxide and its Mitigation
编辑David Ussiri,Rattan Lal
视频video
概述This volume which the environmental consequences of enhanced nitrous oxide emission in a multidisciplinary nature including soil science, agriculture, climate change and Oceanography.Easy to understan
图书封面Titlebook: Soil Emission of Nitrous Oxide and its Mitigation;  David Ussiri,Rattan Lal Book 2013 Springer Science+Business Media Dordrecht 2013 Dentri
描述Nitrous oxide gas is a long-lived relatively active greenhouse gas (GHG) with an atmospheric lifetime of approximately 120 years, and heat trapping effects about 310 times more powerful than carbon dioxide per molecule basis. It contributes about 6% of observed global warming. Nitrous oxide is not only a potent GHG, but it also plays a significant role in the depletion of stratospheric ozone. This book describes the anthropogenic sources of N2O with major emphasis on agricultural activities.It summarizes an overview of global cycling of N and the role of nitrous oxide on global warming and ozone depletion, and then focus on major source, soil borne nitrous oxide emissions. The spatial-temporal variation of soil nitrous oxide fluxes and underlying biogeochemical processes are described, as well as approaches to quantify fluxes of N2O from soils. Mitigation strategies to reduce the emissions, especially from agricultural soils, and fertilizer nitrogen sources are described in detail in the latter part of the book.
出版日期Book 2013
关键词Dentrification; Global nitrogen cycle; Global warming potential; Greenhouse gas; Nitrification; climate c
版次1
doihttps://doi.org/10.1007/978-94-007-5364-8
isbn_softcover978-94-007-9880-9
isbn_ebook978-94-007-5364-8
copyrightSpringer Science+Business Media Dordrecht 2013
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发表于 2025-3-21 22:45:54 | 显示全部楼层
David Ussiri,Rattan Lalf irreversible angle-closure glaucoma. Clinical identification of PAS requires indentation gonioscopy, which is patient-contacting and time-consuming. Thus, we aim to design an automatic deep-learning-based method for PAS recognition based on non-contacting anterior segment optical coherence tomogra
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David Ussiri,Rattan Lalysis. Computer-aided nuclei grading aims to improve pathologists’ work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most o
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David Ussiri,Rattan Lalata is very common, especially in ophthalmic image analysis, since manual annotation is time-consuming and labor-intensive. Self-supervised learning (SSL) methods bring huge opportunities for better utilizing unlabeled data, as they do not need massive annotations. With an attempt to use as many as
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David Ussiri,Rattan Laling recent works employ deep neural network (DNN) to regress the probe motion. However, these deep regression-based methods leverage the DNN to overfit on the specific training data, which is naturally lack of generalization ability for the clinical application. In this paper, we are back to general
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