Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,a...Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.展开更多
Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Proj...Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Project(HLWPRDP) has been implemented since 2004.A back propagation(BP) artificial neural network(ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland.And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project.Particularly,12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs.The output is one layer of ecosystem health index.After training and testing the BP ANNs,an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland.The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP(in 2002) to middle health after the implementation of the HLWPRDP(in 2005).It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.展开更多
[Objective] This research aimed to assess the state of ecosystem health and comprehensive ecological benefit of an artificial wetland in western Jilin Province. [Method] To investigate the effects of reclaimed water f...[Objective] This research aimed to assess the state of ecosystem health and comprehensive ecological benefit of an artificial wetland in western Jilin Province. [Method] To investigate the effects of reclaimed water from Yingtai Oil Production Plant on the wetland ecosystem, a comprehensive ecological assessment index of an artificial wetland in the west of Jilin Province was established to measure the ecological economic and social benefits. The quantitative evaluation on the ecosystem health and comprehensive ecological benefit of the artificial wetland were carried out from 2003 to 2010 and were measured by means of the square difference method. [Result] After eight years of irrigation by reclaimed water, the levels of ecosystem health and benefit of the artificial wetland improved from Grade IV to Grade II, and the ecological environment, economic and social development of the wetland tended to improve. [Conclusion] The results indicated that the use of reclaimed water for irrigation of wetland ecosystems was a suitable way to control drought in arid and semi-arid regions.展开更多
During the 21 st century,artificial intelligence methods have been broadly applied in geosciences to simulate complex dynamic ecosystems,but the use of artificial intelligence(AI)methods to reproduce land-use/cover ch...During the 21 st century,artificial intelligence methods have been broadly applied in geosciences to simulate complex dynamic ecosystems,but the use of artificial intelligence(AI)methods to reproduce land-use/cover change(LUCC)in arid ecosystems remains rare.This paper presents a hybrid modeling approach to understand the complexity in LUCC.Fuzzy logic,equation-based systems,and expert systems are combined to predict LUCC as determined by water resources and other factors.The driving factors of LUCC in this study include climate change,ecological flooding,groundwater conditions,and human activities.The increase of natural flooding was found to be effective in preventing vegetation degradation.LUCCs are sensitive under different climate projections of RCP2.6,RCP4.5,and RCP8.5.Simulation results indicate that the increase of precipitation is not able to compensate for the additional evaporation losses resulting from temperature increases.The results indicate that grassland,shrub,and riparian forest regions will shrink in this study area.The change in grasslands has a strong negative correlation with the change in groundwater salinity,whereas forest change had a strong positive correlation with ecological flooding.The application of artificial intelligence to study LUCC can guide land management policies and make predictions regarding land degradation.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
To build the artificial forest ecosystem is the major eco-economic development model in the watershed of Miyun Reservoir. It is very important to evaluate the benefits of those ecosystems. Emergy theories are very hel...To build the artificial forest ecosystem is the major eco-economic development model in the watershed of Miyun Reservoir. It is very important to evaluate the benefits of those ecosystems. Emergy theories are very helpful for us to establish a science-based assessment framework. Emergy evaluation of the artificial forest ecosystems in the watershed of Miyun Reservoir is used to asses the relative values of several ecological functions (sometimes called ecosystem services) and main ecosystem storages (sometimes called natural capital). The main driving energies, internal processes and storages are evaluated. The main functions, including transpiration, GPP and infiltration, are evaluated, which are 609em$/ha/yr, 6,245em$/ha/yr and 340em$/ha/yr respectively. The total values of major environmental services are 4,683em$/ha/yr in the artificial forest ecosystem. The main storages of natural capital including live biomass, soil moisture, organic matter, underground water and landform are estimated, which are 112,028em$/ha, 9em$/ha, 40,718em$/ha, 34em$/ha and 6,400,514em$/ha respectively. The largest value is landform, which accounts for 97.7% of these calculated total emdollar values. The concept of replacement value is explored using the emergy values of both ecosystem services and natural capital. The total calculated replacement values are 302,160em$/ha.展开更多
Standardization serves as a critical foundation for the large-scale and industrialized development of the AI sector.With the theme that“standards lead the future,AI empowers industries”,the sub-forum on“artificial ...Standardization serves as a critical foundation for the large-scale and industrialized development of the AI sector.With the theme that“standards lead the future,AI empowers industries”,the sub-forum on“artificial intelligence+standardization”action cultivating future industries was held to focus on the needs of AI and future industries,and promote the exchange of international standardization concepts.It aimed to facilitate the joint forces of industry,academia,and research institutes,and accelerate the integration of technologies and standards.The sub-forum deepened international cooperation with standardization to address challenges of technological transformation,boost the high-quality industrial development,and foster an advanced ecosystem for future industries.展开更多
This study evaluates the ecosystem service value of Qingdao Luhaifeng Sea Ranch artificial reef area through the established meta-analysis value transfer model,and the results show that the total value will be 150 mil...This study evaluates the ecosystem service value of Qingdao Luhaifeng Sea Ranch artificial reef area through the established meta-analysis value transfer model,and the results show that the total value will be 150 million yuan in 2022.According to the regression results of the meta-analysis,the types of ecosystem services,the types of pasture sediment,the population density of the pasture area,and the economic level all have a significant impact on the wetland value.The model in this paper passes the validity test,and the conclusions are the same as many current empirical studies.This shows that the benefit transfer method meta-analysis can save assessment labor,time and capital,and the decision-makers can make judgments quickly.Meta-analysis is an effective and fast ex ante evaluation tool.The reliability of the value transfer method of meta-analysis largely depends on the quantity and quality of existing studies and the establishment of mathematical models.It is necessary to expand the number of literature searches.However,the current evaluation method of ecosystem services in China has just started,so the government needs to encourage research institutions to evaluate the value of ecosystem services to provide more effective and abundant research literature.展开更多
The ecological restoration of water quality in Qianling Lake was conducted by artificial wetland, which transformed N and P in wastewater into essential matters in organism tissues, so pollutants discharged into Qiant...The ecological restoration of water quality in Qianling Lake was conducted by artificial wetland, which transformed N and P in wastewater into essential matters in organism tissues, so pollutants discharged into Qianting Lake were reduced for the purpose of restoration.展开更多
Plant root exudates contain various organic and inorganic components that include glucose, citric and oxalic acid. These components affect rhizosphere microbial and microfaunal activities, but the mechanisms are not f...Plant root exudates contain various organic and inorganic components that include glucose, citric and oxalic acid. These components affect rhizosphere microbial and microfaunal activities, but the mechanisms are not fully known. Studies concerned from degraded grassland ecosystems with low soil carbon（C） contents are rare, in spite of the global distribution of grasslands in need of restoration. All these have a high potential for carbon sequestration, with a reduced carbon content due to overutilization. An exudate component that rapidly decomposes will increase soil respiration and CO2 emission, while a component that reduces decomposition of native soil carbon can reduce CO2 emission and actually help sequestering carbon in soil. Therefore, to investigate root exudate effects on rhizosphere activity, citric acid, glucose and oxalic acid（0.6 g C/kg dry soil） were added to soils from three biotopes（grassland, fixed dune and mobile dune） located in Naiman, Horqin Sandy Land, Inner Mongolia, China） and subjected to a 24-day incubation experiment together with a control. The soils were also analyzed for general soil properties. The results show that total respiration without exudate addition was highest in grassland soil, intermediate in fixed dune and lowest in mobile dune soil. However, the proportion of native soil carbon mineralized was highest in mobile dune soil, reflecting the low C/N ratio found there. The exudate effects on CO2-C emissions and other variables differed somewhat between biotopes, but total respiration（including that from the added substrates） was significantly increased in all combinations compared with the control, except for oxalic acid addition to mobile dune soil, which reduced CO2-C emissions from native soil carbon. A small but statistically significant increase in pH by the exudate additions in grassland and fixed dune soil was observed, but there was a major decrease from acid additions to mobile dune soil. In contrast, electrical conductivity decreased in grassland and fixed dune soil and increased in mobile dune. Thus, discrete components of root exudates affected soil environmental conditions differently, and responses to root exudates in soils with low carbon contents can differ from those in normal soils. The results indicate a potential for, e.g., acid root exudates to decrease decomposition rate of soil organic matter in low carbon soils, which is of interest for both soil restoration and carbon sequestration.展开更多
Cognitive Radio(CR)is developed to provide effective spectrum usage.CR is much significant in improving the efficiency of the global internet in applications.The evolutionary measurement technology is utilised to impr...Cognitive Radio(CR)is developed to provide effective spectrum usage.CR is much significant in improving the efficiency of the global internet in applications.The evolutionary measurement technology is utilised to improve the evaluation of channel-state information.The outcome attained very few spectrums sensing in CR for complex mobility.A good optimisation method is needed to improve the accurate channel state prediction in successful channel access.Thus,this paper aims to implement a novel power and channel allocation mechanism with the help of a new Modified Levy Flight-based Artificial Ecosystem Optimisation(MLF-AEO)Optimisation Strategy.This paper achieves the optimal power control and channel allocation mechanism intending to solve the multiple objective functions based on the constraints like Interference among users,Outage Probability,and throughput.The superiority of the proposed algorithm is thoroughly verified by various simulation results.展开更多
This study aims to explore the transformative role of Artificial Intelligence(AI)in deepening Industry-Education Integration(IEI).Facing the rapidly changing skill demands of the AI era,tra-ditional IEI models are bot...This study aims to explore the transformative role of Artificial Intelligence(AI)in deepening Industry-Education Integration(IEI).Facing the rapidly changing skill demands of the AI era,tra-ditional IEI models are bottlenecked by information asymmetry and slow responsiveness.This paper introduces the“Catalytic Mechanism”perspective,arguing that AI is not merely an efficiency tool but rather a force that reshapes the static partnership between industry and education into a self-optimizing,symbiotic ecosystem by reducing transaction costs and accelerating system responsive-ness.This paper constructs a theoretical framework comprising three pillars:(1)AI-based dynamic skills mapping and curriculum reconstruction;(2)personalized learning pathways and career navi-gation;and(3)an intelligent collaborative ecosystem and value co-creation.The explanatory power of this framework is validated through a comparative case analysis of a university-led regional model and a corporate-led global model.The study finds that AI effectively overcomes the structural bar-riers of traditional models.Concurrently,it identifies challenges brought by AI integration,such as data privacy and algorithmic bias,and provides governance references for building a human-centric,AI-driven new paradigm for IEI,combining Chinese contexts with transnational comparative per-spectives.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
基金supported by the National Natural Science Foundation of China(62263014)the Yunnan Provincial Basic Research Project(202301AT070443,202401AT070344).
文摘Accurate identification of unknown internal parameters in photovoltaic(PV)cells is crucial and significantly affects the subsequent system-performance analysis and control.However,noise,insufficient data acquisition,and loss of recorded data can deteriorate the extraction accuracy of unknown parameters.Hence,this study proposes an intelligent parameter-identification strategy that integrates artificial ecosystem optimization(AEO)and a Bayesian neural network(BNN)for PV cell parameter extraction.A BNN is used for data preprocessing,including data denoising and prediction.Furthermore,the AEO algorithm is utilized to identify unknown parameters in the single-diode model(SDM),double-diode model(DDM),and three-diode model(TDM).Nine other metaheuristic algorithms(MhAs)are adopted for an unbiased and comprehensive validation.Simulation results show that BNN-based data preprocessing com-bined with effective MhAs significantly improve the parameter-extraction accuracy and stability compared with methods without data preprocessing.For instance,under denoised data,the accuracies of the SDM,DDM,and TDM increase by 99.69%,99.70%,and 99.69%,respectively,whereas their accuracy improvements increase by 66.71%,59.65%,and 70.36%,respectively.
基金Under the auspices of National Natural Science Foundation of China (No 40871251)Knowledge Innovation Programs of Chinese Academy of Sciences (No KZCX2-YW-141)
文摘Honghu Lake,located in the southeast of Hubei Province,China,has suffered a severe disturbance during the past few decades.To restore the ecosystem,the Honghu Lake Wetland Protection and Restoration Demonstration Project(HLWPRDP) has been implemented since 2004.A back propagation(BP) artificial neural network(ANN) approach was applied to evaluatinig the ecosystem health of the Honghu Lake wetland.And the effectiveness of the HLWPRDP was also assessed by comparing the ecosystem health before and after the project.Particularly,12 ecosystem health indices were used as evaluation parameters to establish a set of three-layer BP ANNs.The output is one layer of ecosystem health index.After training and testing the BP ANNs,an optimal model of BP ANNs was selected to assess the ecosystem health of the Honghu Lake wetland.The result indicates that four stages can be identified based on the change of the ecosystem health from 1990 to 2008 and the ecosystem health index ranges from morbidity before the implementation of HLWPRDP(in 2002) to middle health after the implementation of the HLWPRDP(in 2005).It demonstrates that the HLWPRDP is effective and the BP ANN could be used as a tool for the assessment of ecosystem health.
基金Supported by 2007 Environmental Protection Project of Jilin Provincial Department of Environmental Protection (2007-09)
文摘[Objective] This research aimed to assess the state of ecosystem health and comprehensive ecological benefit of an artificial wetland in western Jilin Province. [Method] To investigate the effects of reclaimed water from Yingtai Oil Production Plant on the wetland ecosystem, a comprehensive ecological assessment index of an artificial wetland in the west of Jilin Province was established to measure the ecological economic and social benefits. The quantitative evaluation on the ecosystem health and comprehensive ecological benefit of the artificial wetland were carried out from 2003 to 2010 and were measured by means of the square difference method. [Result] After eight years of irrigation by reclaimed water, the levels of ecosystem health and benefit of the artificial wetland improved from Grade IV to Grade II, and the ecological environment, economic and social development of the wetland tended to improve. [Conclusion] The results indicated that the use of reclaimed water for irrigation of wetland ecosystems was a suitable way to control drought in arid and semi-arid regions.
基金Chinese Academy of Sciences“Light of West China”Program,No.2018-XBQNXZ-B-017National Natural Science Foundation of China,No.42107084Philosophy and Social Science Major Project funded by the Ministry of Education of the People’s Republic of China,No.20JZD026。
文摘During the 21 st century,artificial intelligence methods have been broadly applied in geosciences to simulate complex dynamic ecosystems,but the use of artificial intelligence(AI)methods to reproduce land-use/cover change(LUCC)in arid ecosystems remains rare.This paper presents a hybrid modeling approach to understand the complexity in LUCC.Fuzzy logic,equation-based systems,and expert systems are combined to predict LUCC as determined by water resources and other factors.The driving factors of LUCC in this study include climate change,ecological flooding,groundwater conditions,and human activities.The increase of natural flooding was found to be effective in preventing vegetation degradation.LUCCs are sensitive under different climate projections of RCP2.6,RCP4.5,and RCP8.5.Simulation results indicate that the increase of precipitation is not able to compensate for the additional evaporation losses resulting from temperature increases.The results indicate that grassland,shrub,and riparian forest regions will shrink in this study area.The change in grasslands has a strong negative correlation with the change in groundwater salinity,whereas forest change had a strong positive correlation with ecological flooding.The application of artificial intelligence to study LUCC can guide land management policies and make predictions regarding land degradation.
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To build the artificial forest ecosystem is the major eco-economic development model in the watershed of Miyun Reservoir. It is very important to evaluate the benefits of those ecosystems. Emergy theories are very helpful for us to establish a science-based assessment framework. Emergy evaluation of the artificial forest ecosystems in the watershed of Miyun Reservoir is used to asses the relative values of several ecological functions (sometimes called ecosystem services) and main ecosystem storages (sometimes called natural capital). The main driving energies, internal processes and storages are evaluated. The main functions, including transpiration, GPP and infiltration, are evaluated, which are 609em$/ha/yr, 6,245em$/ha/yr and 340em$/ha/yr respectively. The total values of major environmental services are 4,683em$/ha/yr in the artificial forest ecosystem. The main storages of natural capital including live biomass, soil moisture, organic matter, underground water and landform are estimated, which are 112,028em$/ha, 9em$/ha, 40,718em$/ha, 34em$/ha and 6,400,514em$/ha respectively. The largest value is landform, which accounts for 97.7% of these calculated total emdollar values. The concept of replacement value is explored using the emergy values of both ecosystem services and natural capital. The total calculated replacement values are 302,160em$/ha.
文摘Standardization serves as a critical foundation for the large-scale and industrialized development of the AI sector.With the theme that“standards lead the future,AI empowers industries”,the sub-forum on“artificial intelligence+standardization”action cultivating future industries was held to focus on the needs of AI and future industries,and promote the exchange of international standardization concepts.It aimed to facilitate the joint forces of industry,academia,and research institutes,and accelerate the integration of technologies and standards.The sub-forum deepened international cooperation with standardization to address challenges of technological transformation,boost the high-quality industrial development,and foster an advanced ecosystem for future industries.
文摘This study evaluates the ecosystem service value of Qingdao Luhaifeng Sea Ranch artificial reef area through the established meta-analysis value transfer model,and the results show that the total value will be 150 million yuan in 2022.According to the regression results of the meta-analysis,the types of ecosystem services,the types of pasture sediment,the population density of the pasture area,and the economic level all have a significant impact on the wetland value.The model in this paper passes the validity test,and the conclusions are the same as many current empirical studies.This shows that the benefit transfer method meta-analysis can save assessment labor,time and capital,and the decision-makers can make judgments quickly.Meta-analysis is an effective and fast ex ante evaluation tool.The reliability of the value transfer method of meta-analysis largely depends on the quantity and quality of existing studies and the establishment of mathematical models.It is necessary to expand the number of literature searches.However,the current evaluation method of ecosystem services in China has just started,so the government needs to encourage research institutions to evaluate the value of ecosystem services to provide more effective and abundant research literature.
基金Supported by National Natural Science Foundation ( 30530150,40673064, 30710103908)Innovation Team Project in Universities of Fujian Province~~
文摘The ecological restoration of water quality in Qianling Lake was conducted by artificial wetland, which transformed N and P in wastewater into essential matters in organism tissues, so pollutants discharged into Qianting Lake were reduced for the purpose of restoration.
基金financially supported by the National Natural Science Foundation of China (41071185, 31170413)the National Basic Research Program of China (2011BAC07B02)Chinese Academy of Sciences has kindly granted Prof. Olof ANDRéN a ‘Professorship for Senior International Scientists’(Y229D91001)
文摘Plant root exudates contain various organic and inorganic components that include glucose, citric and oxalic acid. These components affect rhizosphere microbial and microfaunal activities, but the mechanisms are not fully known. Studies concerned from degraded grassland ecosystems with low soil carbon（C） contents are rare, in spite of the global distribution of grasslands in need of restoration. All these have a high potential for carbon sequestration, with a reduced carbon content due to overutilization. An exudate component that rapidly decomposes will increase soil respiration and CO2 emission, while a component that reduces decomposition of native soil carbon can reduce CO2 emission and actually help sequestering carbon in soil. Therefore, to investigate root exudate effects on rhizosphere activity, citric acid, glucose and oxalic acid（0.6 g C/kg dry soil） were added to soils from three biotopes（grassland, fixed dune and mobile dune） located in Naiman, Horqin Sandy Land, Inner Mongolia, China） and subjected to a 24-day incubation experiment together with a control. The soils were also analyzed for general soil properties. The results show that total respiration without exudate addition was highest in grassland soil, intermediate in fixed dune and lowest in mobile dune soil. However, the proportion of native soil carbon mineralized was highest in mobile dune soil, reflecting the low C/N ratio found there. The exudate effects on CO2-C emissions and other variables differed somewhat between biotopes, but total respiration（including that from the added substrates） was significantly increased in all combinations compared with the control, except for oxalic acid addition to mobile dune soil, which reduced CO2-C emissions from native soil carbon. A small but statistically significant increase in pH by the exudate additions in grassland and fixed dune soil was observed, but there was a major decrease from acid additions to mobile dune soil. In contrast, electrical conductivity decreased in grassland and fixed dune soil and increased in mobile dune. Thus, discrete components of root exudates affected soil environmental conditions differently, and responses to root exudates in soils with low carbon contents can differ from those in normal soils. The results indicate a potential for, e.g., acid root exudates to decrease decomposition rate of soil organic matter in low carbon soils, which is of interest for both soil restoration and carbon sequestration.
文摘Cognitive Radio(CR)is developed to provide effective spectrum usage.CR is much significant in improving the efficiency of the global internet in applications.The evolutionary measurement technology is utilised to improve the evaluation of channel-state information.The outcome attained very few spectrums sensing in CR for complex mobility.A good optimisation method is needed to improve the accurate channel state prediction in successful channel access.Thus,this paper aims to implement a novel power and channel allocation mechanism with the help of a new Modified Levy Flight-based Artificial Ecosystem Optimisation(MLF-AEO)Optimisation Strategy.This paper achieves the optimal power control and channel allocation mechanism intending to solve the multiple objective functions based on the constraints like Interference among users,Outage Probability,and throughput.The superiority of the proposed algorithm is thoroughly verified by various simulation results.
文摘This study aims to explore the transformative role of Artificial Intelligence(AI)in deepening Industry-Education Integration(IEI).Facing the rapidly changing skill demands of the AI era,tra-ditional IEI models are bottlenecked by information asymmetry and slow responsiveness.This paper introduces the“Catalytic Mechanism”perspective,arguing that AI is not merely an efficiency tool but rather a force that reshapes the static partnership between industry and education into a self-optimizing,symbiotic ecosystem by reducing transaction costs and accelerating system responsive-ness.This paper constructs a theoretical framework comprising three pillars:(1)AI-based dynamic skills mapping and curriculum reconstruction;(2)personalized learning pathways and career navi-gation;and(3)an intelligent collaborative ecosystem and value co-creation.The explanatory power of this framework is validated through a comparative case analysis of a university-led regional model and a corporate-led global model.The study finds that AI effectively overcomes the structural bar-riers of traditional models.Concurrently,it identifies challenges brought by AI integration,such as data privacy and algorithmic bias,and provides governance references for building a human-centric,AI-driven new paradigm for IEI,combining Chinese contexts with transnational comparative per-spectives.
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.
