This project was designated as Meritorious of Mathematical Contest inModeling (MCM'94). We have been required tu solve a problem of findins thebest schedule of a file transfer network in order to niake the niaktis...This project was designated as Meritorious of Mathematical Contest inModeling (MCM'94). We have been required tu solve a problem of findins thebest schedule of a file transfer network in order to niake the niaktispan the smallestone. Three situations with展开更多
Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a ...Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.展开更多
The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individu...The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individuals and halt further transmission. X-ray imaging of the lungs is one of the most reliable diagnostic tools. Utilizing deep learning, we can train models to recognize the signs of infection, thus aiding in the identification of COVID-19 cases. For our project, we developed a deep learning model utilizing the ResNet50 architecture, pre-trained with ImageNet and CheXNet datasets. We tackled the challenge of an imbalanced dataset, the CoronaHack Chest X-Ray dataset provided by Kaggle, through both binary and multi-class classification approaches. Additionally, we evaluated the performance impact of using Focal loss versus Cross-entropy loss in our model.展开更多
Patent transfer has been regarded as an important channel for the nations and regions to acquire external technology,and also a direct research object to depict the relationship between supply and demand of technology...Patent transfer has been regarded as an important channel for the nations and regions to acquire external technology,and also a direct research object to depict the relationship between supply and demand of technology flow.Therefore,based on traceable patent transfer data,this article has established a dual-pipeline theoretical framework of transnational-domestic technology transfer from the interaction of the global and local(glocal)perspective,and combines social networks,GIS spatial analysis as well as spatial econometric model to discover the spatial evolution of China’s transnational technology channels and its determinant factors.It is found that:(1)The spatial heterogeneity of the overall network is significant while gradually weakened over time.(2)The eastward shift of the core cities involved in transnational technology channels is accelerating,from the hubs in North America(New York Bay Area,Silicon Valley,Caribbean offshore financial center,etc.)and West Europe(London offshore financial center etc.)to East Asia(Tokyo and Seoul)and Southeast Asia(Singapore),which illustrates China has decreased reliance on the technology from the USA and West Europe.(3)The four major innovation clusters:Beijing-Tianjin-Hebei region(Beijing as the hub),Yangtze River Delta(Shanghai as the hub),The Greater Bay Area(Shenzhen and Hong Kong as the hubs)and north Taiwan(Taipei and Hsinchu as the hubs),are regarded as global technology innovation hubs and China’s distribution centers in transnational technology flow.Among those,Chinese Hong Kong’s betweenness role of technology is strengthened due to linkage of transnational corporations and their branches,and low tax coverage of offshore finance,thus becoming the top city for technology transfer.Meanwhile,Chinese Taiwan’s core position is diminishing.(4)The breadth,intensity,and closeness of domestic technology transfer are conducive to the expansion of transnational technology import channels.Additionally,local economic level has positive effect on transnational technology transfer channels while technology strength and external economic linkage have multifaceted influences.展开更多
In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs a...In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.展开更多
In order to address the shortcoming of feature representation limitation in machine translation(MT)system,this paper presents a feature transfer method in MT.Meta feature transfer of the decoding process considered no...In order to address the shortcoming of feature representation limitation in machine translation(MT)system,this paper presents a feature transfer method in MT.Meta feature transfer of the decoding process considered not only their own translation system,but also transferred knowledge of another translation system.The domain meta feature and the objective function of domain adaptation are used to better model the domain transfer task.In this paper,extensive experiments and comparisons are made.The experiment results show that the proposed model has a significant improvement in domain transfer task.The first model has better performance than baseline system,which improves 3.06 BLEU score on the news test set,improves 3.27 BLEU score on the education test set,and improves 3.93 BLEU score on the law test set;The second model improves 3.16 BLEU score on the news test set,improves 3.54 BLEU score on the education test set,and improves 4.2 BLEU score on the law test set.展开更多
Background: Postnatal transfer (PT) is interhospital transport of care-needing newborns. In 2016, a perinatal network was implemented to facilitate PT in the town of Douala, Cameroon. The network was supposed to impro...Background: Postnatal transfer (PT) is interhospital transport of care-needing newborns. In 2016, a perinatal network was implemented to facilitate PT in the town of Douala, Cameroon. The network was supposed to improve PT-related care standards. This study aimed at determining characteristics of PT five years following the implementation of this network. Methods: A cross-sectional study was carried out from February to May 2021 at neonatology wards of six hospitals in Douala. Medical records of newborns transferred to the hospitals were scrutinized to document their characteristics. Parents were contacted to obtain information on PT route and itinerary. Data were analyzed using Epi Info software and summarized as percentages, mean and odds ratio. Results: In total, 234 of the 1159 newborns admitted were transferred, giving a PT prevalence of 20.2% (95% CI 17.9% - 22.6%). Male-to-female ratio of the transferred newborns was 1.3. Neonatal infection (26.5%), prematurity (23.5%) and respiratory distress (15.4%) were the main reasons for transfer. Only 3% of the PT was medicalized while only 2% of the newborns were transferred through perinatal network. On admission, hypothermia and respiratory distress were found in 31% and 35% of the newborns, respectively. The mortality rate among babies was 20% and these had a two-fold risk of dying (95% CI 1.58 - 3.44, p Conclusion: PT and the perinatal network are lowly organized and implemented in Douala. Sensitization of medical staff on in utero transfer, creating center for coordination of the network, and implementation of neonatal transport system could improve the quality of PT.展开更多
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
In this paper, we address the problem of routing in delay tolerant networks (DTN). In such networks there is no guarantee of finding a complete communication path connecting the source and destination at any time, esp...In this paper, we address the problem of routing in delay tolerant networks (DTN). In such networks there is no guarantee of finding a complete communication path connecting the source and destination at any time, especially when the destination is not in the same region as the source, which makes traditional routing protocols inefficient in that transmission of the messages between nodes. We propose to combine the routing protocol MaxProp and the model of “transfer by delegation” (custody transfer) to improve the routing in DTN networks and to exploit nodes as common carriers of messages between the network partitioned. To implement this approach and assess those improvements and changes we developed a DTN simulator. Simulation examples are illustrated in the article.展开更多
The land cover types in South China are varied, and the terrain is undulating, and the area of different land types is small, and the remote sensing monitoring work was difficult. In order to solve these problems, an ...The land cover types in South China are varied, and the terrain is undulating, and the area of different land types is small, and the remote sensing monitoring work was difficult. In order to solve these problems, an automatic classification method based on transfer learning and convolutional neural network model was established in this paper, with a total classification accuracy of 98.1611%. This paper proposes a land use classification remote sensing method based on deep learning, which improved the automation level and monitoring accuracy of complex land surface remote sensing monitoring in South China, and it provided technical support for the land consolidation work in China.展开更多
Land cover classification provides efficient and accurate information regarding human land-use, which is crucial for monitoring urban development patterns, management of water and other natural resources, and land-use...Land cover classification provides efficient and accurate information regarding human land-use, which is crucial for monitoring urban development patterns, management of water and other natural resources, and land-use planning and regulation. However, land-use classification requires highly trained, complex learning algorithms for accurate classification. Current machine learning techniques already exist to provide accurate image recognition. This research paper develops an image-based land-use classifier using transfer learning with a pre-trained ResNet-18 convolutional neural network. Variations of the resulting approach were compared to show a direct relationship between training dataset size and epoch length to accuracy. Experiment results show that transfer learning is an effective way to create models to classify satellite images of land-use with a predictive performance. This approach would be beneficial to the monitoring and predicting of urban development patterns, management of water and other natural resources, and land-use planning.展开更多
Ectomycorrhizal(EM)networks provide a variety of services to plants and ecosystems include nutrient uptake and transfer,seedling survival,internal cycling of nutrients,plant competition,and so on.To deeply their struc...Ectomycorrhizal(EM)networks provide a variety of services to plants and ecosystems include nutrient uptake and transfer,seedling survival,internal cycling of nutrients,plant competition,and so on.To deeply their structure and function in ecosystems,we investigated the spatial patterns and nitrogen(N)transfer of EM networks usingN labelling technique in a Mongolian scotch pine(Pinus sylvestris var.mongolica Litv.)plantation in Northeastern China.In August 2011,four plots(20 × 20 m)were set up in the plantation.125 ml 5 at.%0.15 mol/LNHNOsolution was injected into soil at the center of each plot.Before and 2,6,30 and 215 days after theN application,needles(current year)of each pine were sampled along four 12 m sampling lines.Needle total N andN concentrations were analyzed.We observed needle N andN concentrations increased significantly over time afterN application,up to 31 and0.42%,respectively.There was no correlation between needle N concentration andN/N ratio(R2=0.40,n=5,P=0.156),while excess needle N concentration and excess needleN/N ratio were positively correlated across different time intervals(R~2=0.89,n=4,P\0.05),but deceased with time interval lengthening.NeedleN/N ratio increased with time,but it was not correlated with distance.NeedleN/N ratio was negative with distance before and 6th day and 30th day,positive with distance at 2nd day,but the trend was considerably weaker,their slop were close to zero.These results demonstrated that EM networks were ubiquitous and uniformly distributed in the Mongolian scotch pine plantation and a random network.We found N transfer efficiency was very high,absorbed N by EM network was transferred as wide as possible,we observed N uptake of plant had strong bias forN andN,namely N fractionation.Understanding the structure and function of EM networks in ecosystems may lead to a deeper understanding of ecological stability and evolution,and thus provide new theoretical approaches to improve conservation practices for the management of the Earth’s ecosystems.展开更多
文摘This project was designated as Meritorious of Mathematical Contest inModeling (MCM'94). We have been required tu solve a problem of findins thebest schedule of a file transfer network in order to niake the niaktispan the smallestone. Three situations with
文摘Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.
文摘The COVID-19 pandemic has had a widespread negative impact globally. It shares symptoms with other respiratory illnesses such as pneumonia and influenza, making rapid and accurate diagnosis essential to treat individuals and halt further transmission. X-ray imaging of the lungs is one of the most reliable diagnostic tools. Utilizing deep learning, we can train models to recognize the signs of infection, thus aiding in the identification of COVID-19 cases. For our project, we developed a deep learning model utilizing the ResNet50 architecture, pre-trained with ImageNet and CheXNet datasets. We tackled the challenge of an imbalanced dataset, the CoronaHack Chest X-Ray dataset provided by Kaggle, through both binary and multi-class classification approaches. Additionally, we evaluated the performance impact of using Focal loss versus Cross-entropy loss in our model.
基金Major Project of National Social Science Foundation of China,No.21ZDA011。
文摘Patent transfer has been regarded as an important channel for the nations and regions to acquire external technology,and also a direct research object to depict the relationship between supply and demand of technology flow.Therefore,based on traceable patent transfer data,this article has established a dual-pipeline theoretical framework of transnational-domestic technology transfer from the interaction of the global and local(glocal)perspective,and combines social networks,GIS spatial analysis as well as spatial econometric model to discover the spatial evolution of China’s transnational technology channels and its determinant factors.It is found that:(1)The spatial heterogeneity of the overall network is significant while gradually weakened over time.(2)The eastward shift of the core cities involved in transnational technology channels is accelerating,from the hubs in North America(New York Bay Area,Silicon Valley,Caribbean offshore financial center,etc.)and West Europe(London offshore financial center etc.)to East Asia(Tokyo and Seoul)and Southeast Asia(Singapore),which illustrates China has decreased reliance on the technology from the USA and West Europe.(3)The four major innovation clusters:Beijing-Tianjin-Hebei region(Beijing as the hub),Yangtze River Delta(Shanghai as the hub),The Greater Bay Area(Shenzhen and Hong Kong as the hubs)and north Taiwan(Taipei and Hsinchu as the hubs),are regarded as global technology innovation hubs and China’s distribution centers in transnational technology flow.Among those,Chinese Hong Kong’s betweenness role of technology is strengthened due to linkage of transnational corporations and their branches,and low tax coverage of offshore finance,thus becoming the top city for technology transfer.Meanwhile,Chinese Taiwan’s core position is diminishing.(4)The breadth,intensity,and closeness of domestic technology transfer are conducive to the expansion of transnational technology import channels.Additionally,local economic level has positive effect on transnational technology transfer channels while technology strength and external economic linkage have multifaceted influences.
文摘In this paper, a hybrid neural-genetic fuzzy system is proposed to control the flow and height of water in the reservoirs of water transfer networks. These controls will avoid probable water wastes in the reservoirs and pressure drops in water distribution networks. The proposed approach combines the artificial neural network, genetic algorithm, and fuzzy inference system to improve the performance of the supervisory control and data acquisition stations through a new control philosophy for instruments and control valves in the reservoirs of the water transfer networks. First, a multi-core artificial neural network model, including a multi-layer perceptron and radial based function, is proposed to forecast the daily consumption of the water in a reservoir. A genetic algorithm is proposed to optimize the parameters of the artificial neural networks. Then, the online height of water in the reservoir and the output of artificial neural networks are used as inputs of a fuzzy inference system to estimate the flow rate of the reservoir inlet. Finally, the estimated inlet flow is translated into the input valve position using a transform control unit supported by a nonlinear autoregressive exogenous model. The proposed approach is applied in the Tehran water transfer network. The results of this study show that the usage of the proposed approach significantly reduces the deviation of the reservoir height from the desired levels.
基金supported by National Natural Science Youth Fund,China(No.61300115)China Postdoctoral Science Foundation(No.2014m561331)Science and Technology Research Project of Heilongjiang Provincial Education Department,China(No.12521073).
文摘In order to address the shortcoming of feature representation limitation in machine translation(MT)system,this paper presents a feature transfer method in MT.Meta feature transfer of the decoding process considered not only their own translation system,but also transferred knowledge of another translation system.The domain meta feature and the objective function of domain adaptation are used to better model the domain transfer task.In this paper,extensive experiments and comparisons are made.The experiment results show that the proposed model has a significant improvement in domain transfer task.The first model has better performance than baseline system,which improves 3.06 BLEU score on the news test set,improves 3.27 BLEU score on the education test set,and improves 3.93 BLEU score on the law test set;The second model improves 3.16 BLEU score on the news test set,improves 3.54 BLEU score on the education test set,and improves 4.2 BLEU score on the law test set.
基金Supported by National Natural Science Foundation of P. R. China （60574083）, Key Laboratory of Process Industry Automation, State Education Ministry of China （PAL200514）
文摘Background: Postnatal transfer (PT) is interhospital transport of care-needing newborns. In 2016, a perinatal network was implemented to facilitate PT in the town of Douala, Cameroon. The network was supposed to improve PT-related care standards. This study aimed at determining characteristics of PT five years following the implementation of this network. Methods: A cross-sectional study was carried out from February to May 2021 at neonatology wards of six hospitals in Douala. Medical records of newborns transferred to the hospitals were scrutinized to document their characteristics. Parents were contacted to obtain information on PT route and itinerary. Data were analyzed using Epi Info software and summarized as percentages, mean and odds ratio. Results: In total, 234 of the 1159 newborns admitted were transferred, giving a PT prevalence of 20.2% (95% CI 17.9% - 22.6%). Male-to-female ratio of the transferred newborns was 1.3. Neonatal infection (26.5%), prematurity (23.5%) and respiratory distress (15.4%) were the main reasons for transfer. Only 3% of the PT was medicalized while only 2% of the newborns were transferred through perinatal network. On admission, hypothermia and respiratory distress were found in 31% and 35% of the newborns, respectively. The mortality rate among babies was 20% and these had a two-fold risk of dying (95% CI 1.58 - 3.44, p Conclusion: PT and the perinatal network are lowly organized and implemented in Douala. Sensitization of medical staff on in utero transfer, creating center for coordination of the network, and implementation of neonatal transport system could improve the quality of PT.
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
文摘In this paper, we address the problem of routing in delay tolerant networks (DTN). In such networks there is no guarantee of finding a complete communication path connecting the source and destination at any time, especially when the destination is not in the same region as the source, which makes traditional routing protocols inefficient in that transmission of the messages between nodes. We propose to combine the routing protocol MaxProp and the model of “transfer by delegation” (custody transfer) to improve the routing in DTN networks and to exploit nodes as common carriers of messages between the network partitioned. To implement this approach and assess those improvements and changes we developed a DTN simulator. Simulation examples are illustrated in the article.
文摘The land cover types in South China are varied, and the terrain is undulating, and the area of different land types is small, and the remote sensing monitoring work was difficult. In order to solve these problems, an automatic classification method based on transfer learning and convolutional neural network model was established in this paper, with a total classification accuracy of 98.1611%. This paper proposes a land use classification remote sensing method based on deep learning, which improved the automation level and monitoring accuracy of complex land surface remote sensing monitoring in South China, and it provided technical support for the land consolidation work in China.
文摘Land cover classification provides efficient and accurate information regarding human land-use, which is crucial for monitoring urban development patterns, management of water and other natural resources, and land-use planning and regulation. However, land-use classification requires highly trained, complex learning algorithms for accurate classification. Current machine learning techniques already exist to provide accurate image recognition. This research paper develops an image-based land-use classifier using transfer learning with a pre-trained ResNet-18 convolutional neural network. Variations of the resulting approach were compared to show a direct relationship between training dataset size and epoch length to accuracy. Experiment results show that transfer learning is an effective way to create models to classify satellite images of land-use with a predictive performance. This approach would be beneficial to the monitoring and predicting of urban development patterns, management of water and other natural resources, and land-use planning.
基金supported by National Natural Science Foundation of China(30830024)
文摘Ectomycorrhizal(EM)networks provide a variety of services to plants and ecosystems include nutrient uptake and transfer,seedling survival,internal cycling of nutrients,plant competition,and so on.To deeply their structure and function in ecosystems,we investigated the spatial patterns and nitrogen(N)transfer of EM networks usingN labelling technique in a Mongolian scotch pine(Pinus sylvestris var.mongolica Litv.)plantation in Northeastern China.In August 2011,four plots(20 × 20 m)were set up in the plantation.125 ml 5 at.%0.15 mol/LNHNOsolution was injected into soil at the center of each plot.Before and 2,6,30 and 215 days after theN application,needles(current year)of each pine were sampled along four 12 m sampling lines.Needle total N andN concentrations were analyzed.We observed needle N andN concentrations increased significantly over time afterN application,up to 31 and0.42%,respectively.There was no correlation between needle N concentration andN/N ratio(R2=0.40,n=5,P=0.156),while excess needle N concentration and excess needleN/N ratio were positively correlated across different time intervals(R~2=0.89,n=4,P\0.05),but deceased with time interval lengthening.NeedleN/N ratio increased with time,but it was not correlated with distance.NeedleN/N ratio was negative with distance before and 6th day and 30th day,positive with distance at 2nd day,but the trend was considerably weaker,their slop were close to zero.These results demonstrated that EM networks were ubiquitous and uniformly distributed in the Mongolian scotch pine plantation and a random network.We found N transfer efficiency was very high,absorbed N by EM network was transferred as wide as possible,we observed N uptake of plant had strong bias forN andN,namely N fractionation.Understanding the structure and function of EM networks in ecosystems may lead to a deeper understanding of ecological stability and evolution,and thus provide new theoretical approaches to improve conservation practices for the management of the Earth’s ecosystems.
