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Fate of ^(15)N Labeled Nitrate and Ammonium Salts Added to an Alpine Meadow in the Qinghai-Xizang Plateau, China 被引量:5
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作者 徐兴良 欧阳华 +1 位作者 裴志永 周才平 《Acta Botanica Sinica》 CSCD 2003年第3期276-281,共6页
To understand the dynamics of added nitrogen (N) in alpine meadow and the role of alpine plants and soil microorganisms in the retention of deposited N, the fate of 15 N labeled nitrate and ammonium salts was... To understand the dynamics of added nitrogen (N) in alpine meadow and the role of alpine plants and soil microorganisms in the retention of deposited N, the fate of 15 N labeled nitrate and ammonium salts was determined in an alpine meadow for two months. Two weeks after 15 N application, total recovery of 15 N from NO - 3_ 15 N was 73.5% while it was 78% from NH + 4_ 15 N. More 15 N was recovered in plants than in soil organic matter or in microbial biomass, irrespective of forms of N added. After one month, 70.6% of added NO - 3_ 15 N and 57.4% of NH + 4_ 15 N were recovered in soils and plants. 15 N recovered in soil organic matter decreased greatly while that recovered in plants varied little, irrespective of the form N. Compared with the results of two weeks after 15 N application, more NO - 3_ 15 N than NH + 4_ 15 N was recovered in microbial biomass. Total recovery was 58.4% (six weeks) and 67% (eight weeks) from NO - 3_ 15 N, and 43.1% and 49% from NH + 4_ 15 N, respectively. Both plants and soil microorganism recovered more NO - 3_ 15 N than NH + 4_ 15 N. But plants recovered more 15 N than soil microorganisms. During the whole experiment plants retained more NO - 3_N and 15 N than soil microorganisms while 15 N recovered in inorganic N pool did not exceed 1% due to lower amount of inorganic N. This indicates that plants play more important roles in the retention of deposited N although microbial biomass can be an important sink for deposited N in early days after N application. 展开更多
关键词 alpine meadow soil microorganisms retention of deposited N percentage of 15 N recovery fate of NO - 3_ 15 N and NH + 4_ 15 N
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Reaction order and neural network approaches for the simulation of COVID-19 spreading kinetic in India
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作者 Sourav Chakraborty Arun Kumar Choudhary +1 位作者 Mausumi Sarma Manuj Kumar Hazarika 《Infectious Disease Modelling》 2020年第1期737-747,共11页
COVID-19 has created a pandemic situation in the whole world.Controlling of COVID-19 spreading rate in the social environment is a challenge for all individuals.In the present study,simulation of the lockdown effect o... COVID-19 has created a pandemic situation in the whole world.Controlling of COVID-19 spreading rate in the social environment is a challenge for all individuals.In the present study,simulation of the lockdown effect on the COVID-19 spreading rate in India and mapping of its recovery percentage(until May 2020)were investigated.Investigation of the lockdown impact dependent on first order reaction kinetics demonstrated higher effect of lockdown 1 on controlling the COVID-19 spreading rate when contrasted with lockdown 2 and 3.Although decreasing trend was followed for the reaction rate constant of different lockdown stages,the distinction between the lockdown 2 and 3 was minimal.Mathematical and feed forward neural network(FFNN)approaches were applied for the simulation of COVID-19 spreading rate.In case of mathematical approach,exponential model indicated adequate performance for the prediction of the spreading rate behavior.For the FFNN based modeling,1-5-1 was selected as the best architecture so as to predict adequate spreading rate for all the cases.The architecture also showed effective performance in order to forecast number of cases for next 14 days.The recovery percentage was modeled as a function of number of days with the assistance of polynomial fitting.Therefore,the investigation recommends proper social distancing and efficient management of corona virus in order to achieve higher decreasing trend of reaction rate constant and required recovery percentage for the stabilization of India. 展开更多
关键词 Lock down effect COVID-19 FFNN recovery percentage
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