The Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network(CERN),also named the Yanting Agroecological Experimental Station of Purple Soil,Chinese Academy of Science,is situated...The Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network(CERN),also named the Yanting Agroecological Experimental Station of Purple Soil,Chinese Academy of Science,is situated in Yanting County(105°27'E,31°16'N)in the north-central Sichuan Basin—a region characterized by hilly landscapes.The area features exposed purple sand-mudstone strata from the Jurassic and Cretaceous periods.The station represents a typical subtropical farmland ecosystem dominated by purple soils.Established in 1980,the station joined the CERN in 1991.It was designated a National Key Field Research Station in 2005,and in 2007,it was recognized as a Soil and Water Conservation Science and Technology Demonstration Park by the Ministry of Water Resources.It also serves as an observation site for the Global Terrestrial Observation System(GTOS)and is a key field station under the Chinese Ministry of Agriculture.展开更多
Objective To recognize and assess the impact of the South-to-north Water Transfer Project (SNWTP) on the ecological environment of Xiangfan, Hubei Province, situated in the water-out area, and develop sound scientific...Objective To recognize and assess the impact of the South-to-north Water Transfer Project (SNWTP) on the ecological environment of Xiangfan, Hubei Province, situated in the water-out area, and develop sound scientific countermeasures. Methods A three-layer BP network was built to simulate topology and process of the eco-economy system of Xiangfan. Historical data of ecological environmental factors and socio-economic factors as inputs, and corresponding historical data of ecosystem service value (ESV) and GDP as target outputs, were presented to train and test the network. When predicted input data after 2001 were presented to trained network as generalization sets, ESVs and GDPs of 2002, 2003, 2004... till 2050 were simulated as output in succession. Results Up to 2050, the area would have suffered an accumulative total ESV loss of RMB 104.9 billion, which accounted for 37.36% of the present ESV. The coinstantaneous GDP would change asynchronously with ESV, it would go through an up-to-down process and finally lose RMB89.3 billion, which accounted for 18.71% of 2001. Conclusions The simulation indicates that ESV loss means damage to the capability of socio-economic sustainable development, and suggests that artificial neural networks (ANNs) provide a feasible and effective method and have an important potential in ESV modeling.展开更多
The article analyzes the development of the Chinese Ecosystem Research Network, and its mission, mandate, and management mechanisms, with examples of research, demonstration and consultation for policy-setting.
This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G)....This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G).According to the definition of the business ecosystem,the ecosystem structure of mobile network operators is analyzed.As an important hub in the ecosystem,mobile network operators are advised to take a keystone strategy.The key points of the strategy are summarized.Finally,suggestions for Chinese mobile network operators are given based on the analysis.展开更多
Water conservation is one of the most important ecosystem services of terrestrial ecosystems. Identifying the optimization regions of water conservation using Bayesian belief networks not only helps develop a better u...Water conservation is one of the most important ecosystem services of terrestrial ecosystems. Identifying the optimization regions of water conservation using Bayesian belief networks not only helps develop a better understanding of water conservation processes but also increases the rationality of scenario design and pattern optimization. This study establishes a water conservation network model. The model, based on Bayesian belief networks, forecasts the distribution probability of the water conservation projected under different land use scenarios for the year 2050 with the CA-Markov model. A key variable subset method is proposed to optimize the spatial pattern of the water conservation. Three key findings were obtained. First, among the three scenarios, the probability of high water conservation value was the largest under the protection scenario, and the design of this scenario was conducive to the formulation of future land use policies. Second, the key influencing factors impacting the water conservation included precipitation, evapotranspiration and land use, and the state set corresponding to the highest state of water conservation was mainly distributed in areas with high annual average rainfall and evapotranspiration and high vegetation coverage. Third, the regions suitable for optimizing water conservation were mainly distributed in the southern part of Maiji District in Tianshui, southwest of Longxian and south of Weibin District in Baoji, northeast of Xunyi County and northwest of Yongshou County in Xianyang, and west of Yaozhou District in Tongchuan.展开更多
The software industry has evolved to a multiple-product development created on a platform and based on a common architecture integrated to other systems. This integration happens through components and third-party dev...The software industry has evolved to a multiple-product development created on a platform and based on a common architecture integrated to other systems. This integration happens through components and third-party developers networks in Software Ecosystems (SECOs). Since systems and software development processes present challenges beyond the technical side, SECOs have emerged as an approach to improve the Software Engineering (SE) mindset in the industry. This fact changes the software industry as it requires the management of an integrated social-based environment to support a transition from an intra-organizational to an open business model approach towards a SECO approach. In this context, social networks can be important to coordinate a collaborative and distributed environment to develop SECOs platforms. This paper analyses the impact of social networks in SECOs through an integrated framework of the SECO and social network challenges. So, a proposal for a sociotechnical-based architecture to support the SECOs lifecycle is discussed.展开更多
<span style="font-family:Verdana;">The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amen...<span style="font-family:Verdana;">The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).</span>展开更多
文摘The Sichuan Yanting Agro-ecosystem Research Station in Chinese National Ecosystem Research Network(CERN),also named the Yanting Agroecological Experimental Station of Purple Soil,Chinese Academy of Science,is situated in Yanting County(105°27'E,31°16'N)in the north-central Sichuan Basin—a region characterized by hilly landscapes.The area features exposed purple sand-mudstone strata from the Jurassic and Cretaceous periods.The station represents a typical subtropical farmland ecosystem dominated by purple soils.Established in 1980,the station joined the CERN in 1991.It was designated a National Key Field Research Station in 2005,and in 2007,it was recognized as a Soil and Water Conservation Science and Technology Demonstration Park by the Ministry of Water Resources.It also serves as an observation site for the Global Terrestrial Observation System(GTOS)and is a key field station under the Chinese Ministry of Agriculture.
文摘Objective To recognize and assess the impact of the South-to-north Water Transfer Project (SNWTP) on the ecological environment of Xiangfan, Hubei Province, situated in the water-out area, and develop sound scientific countermeasures. Methods A three-layer BP network was built to simulate topology and process of the eco-economy system of Xiangfan. Historical data of ecological environmental factors and socio-economic factors as inputs, and corresponding historical data of ecosystem service value (ESV) and GDP as target outputs, were presented to train and test the network. When predicted input data after 2001 were presented to trained network as generalization sets, ESVs and GDPs of 2002, 2003, 2004... till 2050 were simulated as output in succession. Results Up to 2050, the area would have suffered an accumulative total ESV loss of RMB 104.9 billion, which accounted for 37.36% of the present ESV. The coinstantaneous GDP would change asynchronously with ESV, it would go through an up-to-down process and finally lose RMB89.3 billion, which accounted for 18.71% of 2001. Conclusions The simulation indicates that ESV loss means damage to the capability of socio-economic sustainable development, and suggests that artificial neural networks (ANNs) provide a feasible and effective method and have an important potential in ESV modeling.
文摘The article analyzes the development of the Chinese Ecosystem Research Network, and its mission, mandate, and management mechanisms, with examples of research, demonstration and consultation for policy-setting.
文摘This paper applies the perspective of business ecosystem to mobile communications industry,trying to help mobile network operators improve their strategies in the era of the third generation mobile communications(3G).According to the definition of the business ecosystem,the ecosystem structure of mobile network operators is analyzed.As an important hub in the ecosystem,mobile network operators are advised to take a keystone strategy.The key points of the strategy are summarized.Finally,suggestions for Chinese mobile network operators are given based on the analysis.
基金National Natural Science Foundation of China,No.41771198,No.41771576The Fundamental Research Funds For the Central Universities,Shaanxi Normal University,No.2017CSY011
文摘Water conservation is one of the most important ecosystem services of terrestrial ecosystems. Identifying the optimization regions of water conservation using Bayesian belief networks not only helps develop a better understanding of water conservation processes but also increases the rationality of scenario design and pattern optimization. This study establishes a water conservation network model. The model, based on Bayesian belief networks, forecasts the distribution probability of the water conservation projected under different land use scenarios for the year 2050 with the CA-Markov model. A key variable subset method is proposed to optimize the spatial pattern of the water conservation. Three key findings were obtained. First, among the three scenarios, the probability of high water conservation value was the largest under the protection scenario, and the design of this scenario was conducive to the formulation of future land use policies. Second, the key influencing factors impacting the water conservation included precipitation, evapotranspiration and land use, and the state set corresponding to the highest state of water conservation was mainly distributed in areas with high annual average rainfall and evapotranspiration and high vegetation coverage. Third, the regions suitable for optimizing water conservation were mainly distributed in the southern part of Maiji District in Tianshui, southwest of Longxian and south of Weibin District in Baoji, northeast of Xunyi County and northwest of Yongshou County in Xianyang, and west of Yaozhou District in Tongchuan.
文摘The software industry has evolved to a multiple-product development created on a platform and based on a common architecture integrated to other systems. This integration happens through components and third-party developers networks in Software Ecosystems (SECOs). Since systems and software development processes present challenges beyond the technical side, SECOs have emerged as an approach to improve the Software Engineering (SE) mindset in the industry. This fact changes the software industry as it requires the management of an integrated social-based environment to support a transition from an intra-organizational to an open business model approach towards a SECO approach. In this context, social networks can be important to coordinate a collaborative and distributed environment to develop SECOs platforms. This paper analyses the impact of social networks in SECOs through an integrated framework of the SECO and social network challenges. So, a proposal for a sociotechnical-based architecture to support the SECOs lifecycle is discussed.
文摘<span style="font-family:Verdana;">The eddy covariance technique is an accurate and direct tool to measure the Net Ecosystem Exchange (NEE) of carbon dioxide. However, sometimes conditions are not amenable to measurements using this technique. Thus, different methods have been developed to allow gap-filling and quality assessment of eddy covariance data sets. In this study first, two different Artificial Neural Networks (ANNs) approaches, the Multi-layer Perceptron (MLP) trained by the Back-Propagation (BP) algorithm, and the Radial Basis Function (RBF), were used to fill missing NEE data measured above rain-fed maize at the University of Nebraska-Lincoln Agricultural Research and Development Center near Mead, Nebraska. The gap-filled data were then compared by different statistical indices to gap-filled data obtained with the technique suggested by Suyker and Verma in 2005 [S&V method], and the ANN approach presented by Papale in 2003. The results showed that the RBF network was able to find better fits for missing values compared to the MLP (BP) network and S&V method. In addition, unlike the S&V method, which depends on different gap-filling procedures over the year;the structure of RBF and MLP (BP) networks was constant. However, data analysis indicated Papale’s approach gave better fits than the RBF and MLP (BP) methods. Thus, based on this work, Papale’s approach is the best method to estimate the missing data;though the applied statistical indices, which were used for model evaluation, show little difference between Papale’s approach and the RBF and MLP (BP).</span>
文摘生物多样性是人类赖以生存和发展的重要保障,保护生物多样性对维持地球生态系统的功能至关重要。珍稀濒危植物是生物多样性的重要组成部分,加强我国珍稀濒危植物保护和研究具有紧迫性和重要现实意义。中国生态系统研究网络(Chinese Ecosystem Research Network, CERN)蕴藏着一批珍稀濒危植物,具有重要的科研、经济和社会价值,对研究站点、区域及全国尺度生物多样性及其保护利用具有重大意义。在整理、形成CERN珍稀濒危维管植物名录的基础上,对CERN珍稀濒危植物资源的种类组成、濒危程度、生态站分布等方面进行分析,旨在为CERN开展单站点与跨站点珍稀濒危植物资源的系统研究、有效保护与合理利用提供参考依据。结果显示,(1)CERN有珍稀濒危植物75科140属189种,包括蕨类植物5科5属7种,裸子植物4科7属7种,被子植物66科128属175种;其中,国家一级保护植物2种、国家二级保护植物62种,133种列入《中国生物多样性红色名录》中极危、濒危和易危等级,31种列入《IUCN红色名录等级和标准》中极危、濒危和易危等级,37种列入CITES附录Ⅱ。(2)CERN的珍稀濒危植物分布于14个生态站,其中12个为森林生态系统生态站;珍稀濒危植物种类多的生态站所处地区植物群落物种多样性丰富,分布着一定数量的珍稀濒危植物,而生态站成为就地保存珍稀濒危植物的重要场所,对珍稀濒危野生植物的保护发挥了一定作用。(3)分布于2个以上生态站的珍稀濒危植物有18种,其中分布最广的一种分布在4个生态站;拥有共有珍稀濒危植物的生态站主要分为3个生态站群,每个群中的生态站可开展共有珍稀濒危植物生物学与生态学特性、种群和群落特征、濒危原因和机制、综合性保护、合理利用等方面的合作研究。由于CERN的珍稀濒危植物具有多方面经济价值,建议生态站在加大对野生植株保护管理力度的同时,加强开展科学开发与持续利用方面技术的试验和示范,更好地服务当地的经济建设。
