The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fau...The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.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
Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during train...Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during training.However,adversarial networks are usually unstable when training.In this paper,we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects.At the same time,our method improves the stability of training.Moreover,the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent.Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.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 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
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
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
It has been reported that, through the evanescent near fields, the strongly coupled magnetic resonance is able to achieve an efficient mid-range Wireless Power Transfer (WPT) beyond the characteristic size of the reso...It has been reported that, through the evanescent near fields, the strongly coupled magnetic resonance is able to achieve an efficient mid-range Wireless Power Transfer (WPT) beyond the characteristic size of the resonator. Recent studies on of the relay effect of the WPT allow more distant and flexible energy transmission. These new developments hold a promise to construct a fully wireless Body Sensor Network (wBSN) using the new mid-range WPT theory. In this paper, a general optimization strategy for a WPT network is presented by analysis and simulation using the coupled mode theory. Based on the results of theoretical and computational study, two types of thin-film resonators are designed and prototyped for the construction of wBSNs. These resonators and associated electronic components can be integrated into a WPT platform to permit wireless power delivery to multiple wearable sensors and medical implants on the surface and within the human body. Our experiments have demonstrated the feasibility of the WPT approach.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
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
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
A fiber-based, star-shaped joint time and frequency dissemination scheme is demonstrated. By working in cooperation with the existing commercial telecommunication network. Our scheme enables the frequency, time, and d...A fiber-based, star-shaped joint time and frequency dissemination scheme is demonstrated. By working in cooperation with the existing commercial telecommunication network. Our scheme enables the frequency, time, and digital data networks to be integrated together and could represent an ideal option of interconnection among scientific institutions.The compensation functions of the time and frequency transfer scheme are set at the client nodes. The complexity of the central node is thus reduced, and future expansion by the addition of further branches will be accomplished more easily.During a performance test in which the ambient temperature fluctuation is 30℃/day, timing signal dissemination stability is achieved to be approximately ±50 ps along 25-km-long fiber spools. After calibration, a timing signal synchronization accuracy of 100 ps is also realized. The proposed scheme offers an option of the construction of large-scale fiber-based frequency and time transfer networks.展开更多
In this work, we report the construction of three-dimensional(3D) CdS nanosphere/graphene networks by a one-step hydrothermal self-assembly route. The 3D graphene networks not only enhance the light scattering, thanks...In this work, we report the construction of three-dimensional(3D) CdS nanosphere/graphene networks by a one-step hydrothermal self-assembly route. The 3D graphene networks not only enhance the light scattering, thanks to the interconnected 3D architecture, but also improve the crystallinity of deposited CdS nanospheres, and at the same time provide a direct electron pathway to quickly separate the photogenerated electron-hole pairs from CdS, which thus dramatically improve the photocatalytic activity.The optimized 3D CdS nanosphere/graphene networks with 2 wt% of graphene could produce molecular hydrogen at a rate of 2310 μmol gcatalyst^(-1) h^(-1) under visible-light illumination(λ > 400 nm).展开更多
To meet the increasing ：need of fresh water and to improve the water quality of Taihu Lake, water transfer from the Yangtze River was initiated in 2002. This study was performed to investigate the sediment distributi...To meet the increasing ：need of fresh water and to improve the water quality of Taihu Lake, water transfer from the Yangtze River was initiated in 2002. This study was performed to investigate the sediment distribution along the river course following water transfer. A rainfall-runoff model was first built to calculate the runoff of the Taihu Basin in 2003. Then, the flow patterns of river networks were simulated using a one-dimensional river network hydrodynamic model. Based on the boundary conditions of the flow in tributaries of the Wangyu River and the water level in Taihu Lake, a one-dimensional hydrodynamic and sediment transport numerical model of the Wangyu River was built to analyze the influences of the inflow rate of the water transfer and the suspended sediment concentration （SSC） of inflow on the sediment transport. The results show that the water transfer inflow rate and SSC of inflow have significant effects on the sediment distribution. The higher the inflow rate or SSC of inflow is, the higher the SSC value is at certain cross-sections along the ：river course of water transfer. Higher inflow rate and SSC of inflow contribute to higher sediment deposition per kilometer and sediment thickness. It is also concluded that a sharp decrease of the inflow velocity at the entrance of the Wangyu River on the river course of water transfer induces intense sedimentation at the cross-section near the Changshu hydro-junction. With an increasing distance from the Changshu hydro-junction, the sediment deposition and sedimentation thickness decrease gradually along the river course.展开更多
文摘The ammunition loading system manipulator is susceptible to gear failure due to high-frequency,heavyload reciprocating motions and the absence of protective gear components.After a fault occurs,the distribution of fault characteristics under different loads is markedly inconsistent,and data is hard to label,which makes it difficult for the traditional diagnosis method based on single-condition training to generalize to different conditions.To address these issues,the paper proposes a novel transfer discriminant neural network(TDNN)for gear fault diagnosis.Specifically,an optimized joint distribution adaptive mechanism(OJDA)is designed to solve the distribution alignment problem between two domains.To improve the classification effect within the domain and the feature recognition capability for a few labeled data,metric learning is introduced to distinguish features from different fault categories.In addition,TDNN adopts a new pseudo-label training strategy to achieve label replacement by comparing the maximum probability of the pseudo-label with the test result.The proposed TDNN is verified in the experimental data set of the artillery manipulator device,and the diagnosis can achieve 99.5%,significantly outperforming other traditional adaptation methods.
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.
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the Aerospace Science and Technology Foundation(No.20115557007)the National Natural Science Foundation of China(No.61673262)the Military Science and Technology Foundation of China(No.18-H863-03-ZT-001-006-06)
文摘Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during training.However,adversarial networks are usually unstable when training.In this paper,we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects.At the same time,our method improves the stability of training.Moreover,the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent.Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.
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.
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
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.
文摘It has been reported that, through the evanescent near fields, the strongly coupled magnetic resonance is able to achieve an efficient mid-range Wireless Power Transfer (WPT) beyond the characteristic size of the resonator. Recent studies on of the relay effect of the WPT allow more distant and flexible energy transmission. These new developments hold a promise to construct a fully wireless Body Sensor Network (wBSN) using the new mid-range WPT theory. In this paper, a general optimization strategy for a WPT network is presented by analysis and simulation using the coupled mode theory. Based on the results of theoretical and computational study, two types of thin-film resonators are designed and prototyped for the construction of wBSNs. These resonators and associated electronic components can be integrated into a WPT platform to permit wireless power delivery to multiple wearable sensors and medical implants on the surface and within the human body. Our experiments have demonstrated the feasibility of the WPT approach.
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 the National Key Scientific Instrument and Equipment Development Project,China(Grant No.2013YQ09094303)the Program of International Science and Technology Cooperation,China(Grant No.2016YFE0100200)
文摘A fiber-based, star-shaped joint time and frequency dissemination scheme is demonstrated. By working in cooperation with the existing commercial telecommunication network. Our scheme enables the frequency, time, and digital data networks to be integrated together and could represent an ideal option of interconnection among scientific institutions.The compensation functions of the time and frequency transfer scheme are set at the client nodes. The complexity of the central node is thus reduced, and future expansion by the addition of further branches will be accomplished more easily.During a performance test in which the ambient temperature fluctuation is 30℃/day, timing signal dissemination stability is achieved to be approximately ±50 ps along 25-km-long fiber spools. After calibration, a timing signal synchronization accuracy of 100 ps is also realized. The proposed scheme offers an option of the construction of large-scale fiber-based frequency and time transfer networks.
基金supported by the National Natural Science Foundation of China (no. 91545116 and U1510108)Pioneer ‘‘Hundred Talents Program’’ of CAS, Start-Up Grant of Institute of Coal Chemistry (2016SCXQT01)+3 种基金Singapore Agency for Science, Technology and Research (A*Star)Science and Engineering Research Council- Public Sector Funding (PSF): 1421200075Singapore Ministry of Education Academic Research Fund (AcRF) Tier 1: RG10/16 and RG111/15State Key Laboratory of Coal Conversion (J17-18-913-1, J15-16913)
文摘In this work, we report the construction of three-dimensional(3D) CdS nanosphere/graphene networks by a one-step hydrothermal self-assembly route. The 3D graphene networks not only enhance the light scattering, thanks to the interconnected 3D architecture, but also improve the crystallinity of deposited CdS nanospheres, and at the same time provide a direct electron pathway to quickly separate the photogenerated electron-hole pairs from CdS, which thus dramatically improve the photocatalytic activity.The optimized 3D CdS nanosphere/graphene networks with 2 wt% of graphene could produce molecular hydrogen at a rate of 2310 μmol gcatalyst^(-1) h^(-1) under visible-light illumination(λ > 400 nm).
基金supported by State Key Development Program of Basic Research of China (Grant No.2010CB429001)the National Natural Science Foundation of China (Grant No. 51009062)the Special Fund of Hydrology-Water Resources and Hydraulic Engineering (Grant No. 2009586812)
文摘To meet the increasing ：need of fresh water and to improve the water quality of Taihu Lake, water transfer from the Yangtze River was initiated in 2002. This study was performed to investigate the sediment distribution along the river course following water transfer. A rainfall-runoff model was first built to calculate the runoff of the Taihu Basin in 2003. Then, the flow patterns of river networks were simulated using a one-dimensional river network hydrodynamic model. Based on the boundary conditions of the flow in tributaries of the Wangyu River and the water level in Taihu Lake, a one-dimensional hydrodynamic and sediment transport numerical model of the Wangyu River was built to analyze the influences of the inflow rate of the water transfer and the suspended sediment concentration （SSC） of inflow on the sediment transport. The results show that the water transfer inflow rate and SSC of inflow have significant effects on the sediment distribution. The higher the inflow rate or SSC of inflow is, the higher the SSC value is at certain cross-sections along the ：river course of water transfer. Higher inflow rate and SSC of inflow contribute to higher sediment deposition per kilometer and sediment thickness. It is also concluded that a sharp decrease of the inflow velocity at the entrance of the Wangyu River on the river course of water transfer induces intense sedimentation at the cross-section near the Changshu hydro-junction. With an increasing distance from the Changshu hydro-junction, the sediment deposition and sedimentation thickness decrease gradually along the river course.
