[Objective] Through the investigation of weeds in Coffea arabica orchads in NuJiang River basin, this study aimed to provide scientific guidance for the weed control and improving the ecological and economic benefits ...[Objective] Through the investigation of weeds in Coffea arabica orchads in NuJiang River basin, this study aimed to provide scientific guidance for the weed control and improving the ecological and economic benefits of the plantation of Cof- fea arabica. [Method] The types of weeds and the characteristics of weed occur- rence in Coffea arabica orchads in Nujiang River basin were investigated from July to August in 2012. [Result] The results showed that there were 69 types of weeds belonging to 21 families in Coffea arabica orchads. The predominant harmful types were shown as follows： Commelina nudiflora L. ＋ Leptochloa chinensis, Cyperus ro- tundus L. ＋ Bidens pilosa L. ＋ Eleusine indica, Imperata cylindrical ＋ Ageratum conyzoides L. ＋ Eupatorium odoratum L., Ageratum conyzoides L. ＋ Digitaria san- guinalis （L.） Scop. The preponderant weeds consisted mainly of 10 species, namely, Cyperus rotundus L., Commelina nudiflora L., Leptochloa chinensis, Digitaria san- guinalis （L.） Scop, Imperata cylindrical, Bidens pilosa L., Ageratum conyzoides L, E- upatorium odoratum L., Eleusine indica and Chenopodium serotinum L. [Conclusion] The result from this study is of great significance for the plantation of Coffea arabica in Nujiang River basin, as well as the control of weeds.展开更多
By analysing the hydrogeological conditions of this region and the coal mines hereof, together with the water hazards troubled Shennan mine area in recent years, this paper summarized six types of mine water hazards. ...By analysing the hydrogeological conditions of this region and the coal mines hereof, together with the water hazards troubled Shennan mine area in recent years, this paper summarized six types of mine water hazards. As per the basic characteristics, geological distribution, threat degree and difficulty of prevention of various water hazards, along with the practice of water prevention in the mining area, this article proposed effective technical measures for the prevention and control of different water hazards and laid a solid foundation for the safe production in the mining area.展开更多
This article proposed the risk early-warning model of gas hazard based on Rough Set and neural network. The attribute quantity was reduced by Rough Set, the main characteristic attributes were withdrawn, the complexit...This article proposed the risk early-warning model of gas hazard based on Rough Set and neural network. The attribute quantity was reduced by Rough Set, the main characteristic attributes were withdrawn, the complexity of neural network system and the computing time was reduced, as well. Because of fault-tolerant ability, parallel processing ability, anti-jamming ability and processing non-linear problem ability of neural network system, the methods of Rough Set and neural network were combined. The examples research indicate： applying Rough Set and BP neural network to the gas hazard risk early-warning coal mines in coal mine, the BPNN structure is greatly simplified, the network computation quantity is reduced and the convergence rate is speed up.展开更多
令H_n(x)是基于来自密度函数f(x)的容量为n的一个随机样本的hazard函数H(x)=f^(x)/[1-integral from x=∞ to x(f(t)dt)]的一个核形估计.对于非参数hazard估计的积分均方误差integral ((H_n(x)-H(x))~2ω(x)f(x)dx)中心极限定理成立的...令H_n(x)是基于来自密度函数f(x)的容量为n的一个随机样本的hazard函数H(x)=f^(x)/[1-integral from x=∞ to x(f(t)dt)]的一个核形估计.对于非参数hazard估计的积分均方误差integral ((H_n(x)-H(x))~2ω(x)f(x)dx)中心极限定理成立的一个充分条件被给出,这里ω(x)是一个权函数.展开更多
基金Supported by the Special Fund for Agro-scientific Research in the Public Interest,China(200903024-02)~~
文摘[Objective] Through the investigation of weeds in Coffea arabica orchads in NuJiang River basin, this study aimed to provide scientific guidance for the weed control and improving the ecological and economic benefits of the plantation of Cof- fea arabica. [Method] The types of weeds and the characteristics of weed occur- rence in Coffea arabica orchads in Nujiang River basin were investigated from July to August in 2012. [Result] The results showed that there were 69 types of weeds belonging to 21 families in Coffea arabica orchads. The predominant harmful types were shown as follows： Commelina nudiflora L. ＋ Leptochloa chinensis, Cyperus ro- tundus L. ＋ Bidens pilosa L. ＋ Eleusine indica, Imperata cylindrical ＋ Ageratum conyzoides L. ＋ Eupatorium odoratum L., Ageratum conyzoides L. ＋ Digitaria san- guinalis （L.） Scop. The preponderant weeds consisted mainly of 10 species, namely, Cyperus rotundus L., Commelina nudiflora L., Leptochloa chinensis, Digitaria san- guinalis （L.） Scop, Imperata cylindrical, Bidens pilosa L., Ageratum conyzoides L, E- upatorium odoratum L., Eleusine indica and Chenopodium serotinum L. [Conclusion] The result from this study is of great significance for the plantation of Coffea arabica in Nujiang River basin, as well as the control of weeds.
文摘By analysing the hydrogeological conditions of this region and the coal mines hereof, together with the water hazards troubled Shennan mine area in recent years, this paper summarized six types of mine water hazards. As per the basic characteristics, geological distribution, threat degree and difficulty of prevention of various water hazards, along with the practice of water prevention in the mining area, this article proposed effective technical measures for the prevention and control of different water hazards and laid a solid foundation for the safe production in the mining area.
文摘This article proposed the risk early-warning model of gas hazard based on Rough Set and neural network. The attribute quantity was reduced by Rough Set, the main characteristic attributes were withdrawn, the complexity of neural network system and the computing time was reduced, as well. Because of fault-tolerant ability, parallel processing ability, anti-jamming ability and processing non-linear problem ability of neural network system, the methods of Rough Set and neural network were combined. The examples research indicate： applying Rough Set and BP neural network to the gas hazard risk early-warning coal mines in coal mine, the BPNN structure is greatly simplified, the network computation quantity is reduced and the convergence rate is speed up.
文摘令H_n(x)是基于来自密度函数f(x)的容量为n的一个随机样本的hazard函数H(x)=f^(x)/[1-integral from x=∞ to x(f(t)dt)]的一个核形估计.对于非参数hazard估计的积分均方误差integral ((H_n(x)-H(x))~2ω(x)f(x)dx)中心极限定理成立的一个充分条件被给出,这里ω(x)是一个权函数.
