[ Objective] The aim was to study the ecological footprint and sustainable development in Karst Area. [ Method] By dint of statistical da- ta of Anshun City in 2008, the ecological footprint of Anshun City was calcula...[ Objective] The aim was to study the ecological footprint and sustainable development in Karst Area. [ Method] By dint of statistical da- ta of Anshun City in 2008, the ecological footprint of Anshun City was calculated. The sustainable development of ecological system in Anshun City was analyzed from the angle of balance of supply and demand. [ Result] The per capita ecological capacity was 0.447 8 hm2/cap in 2008, per capi- ta ecological footprint was 2.309 0 hm2/cap, and ecological surplus of deficit was 1.861 2 hm2/cap. It meant the EF of the present region in terms of human activities had already exceeded the benchmark of system ecological carrying capacity. The supply of natural resources can＇t fully meet people＇s needs, and land use was unsustainable. The sustainable development of Karst area can be realized through changing people＇s production and life consumption model, building resources-saving social productive consumption system, depending scientific and technological development, improving production technology, using new technology, improving resources utilization effect and developing recycle economy. [Condusion] The study provided theoretical basis for sustainable development in Karst area.展开更多
Analytic Hierarchy Process is selected according to the selection method of leading industries by both domestic and foreign scholars. Leading industries which can accelerate the overall economic development of Anshun ...Analytic Hierarchy Process is selected according to the selection method of leading industries by both domestic and foreign scholars. Leading industries which can accelerate the overall economic development of Anshun Experimental District is taken as the target layer; and market demand, efficiency standards and local conditions are taken as the criterion layers, so as to construct the select model of leading industry and to choose the leading industry in Anshun Experimental District. Result shows that the priority order of the leading industry selection in Anshun Experimental District is as follows: tourism > pharmacy > transportation > energy > food processing > characteristic agriculture > package and printing > automobile industry > mining > electric engineering.展开更多
Prediction of the traffic load of cellular networks is important for network planning, load balancing, and operational optimization. In this paper, a comparative study of cellular traffic load prediction models based ...Prediction of the traffic load of cellular networks is important for network planning, load balancing, and operational optimization. In this paper, a comparative study of cellular traffic load prediction models based on deep learning is performed and a prediction method built on a multi-channel Gated Recurrent Unit(GRU) model is proposed. The proposed method uses multiple channels to extract the daily and weekly variation feature as well as the variation feature of the peak period of the BS load and can be used to provide 24-hour ahead predictions.Experimental results obtained from real dataset show that the proposed multi-channel model can effectively capture the temporal-variations of BS load and reduce the prediction error.Compared with conventional prediction algorithms such as Convolutional Neural Network(CNN), Long Short-Term Memory network, GRU and combination of CNN and GRU(CNN-GRU),the proposed model can achieve better prediction accuracies.展开更多
基金Supported by Natural Science Youth Project of Educational Bureau in Guizhou Province(2008085)
文摘[ Objective] The aim was to study the ecological footprint and sustainable development in Karst Area. [ Method] By dint of statistical da- ta of Anshun City in 2008, the ecological footprint of Anshun City was calculated. The sustainable development of ecological system in Anshun City was analyzed from the angle of balance of supply and demand. [ Result] The per capita ecological capacity was 0.447 8 hm2/cap in 2008, per capi- ta ecological footprint was 2.309 0 hm2/cap, and ecological surplus of deficit was 1.861 2 hm2/cap. It meant the EF of the present region in terms of human activities had already exceeded the benchmark of system ecological carrying capacity. The supply of natural resources can＇t fully meet people＇s needs, and land use was unsustainable. The sustainable development of Karst area can be realized through changing people＇s production and life consumption model, building resources-saving social productive consumption system, depending scientific and technological development, improving production technology, using new technology, improving resources utilization effect and developing recycle economy. [Condusion] The study provided theoretical basis for sustainable development in Karst area.
基金Supported by the Humanities and Social Sciences Planning Project of Guizhou Province (2008GH043)
文摘Analytic Hierarchy Process is selected according to the selection method of leading industries by both domestic and foreign scholars. Leading industries which can accelerate the overall economic development of Anshun Experimental District is taken as the target layer; and market demand, efficiency standards and local conditions are taken as the criterion layers, so as to construct the select model of leading industry and to choose the leading industry in Anshun Experimental District. Result shows that the priority order of the leading industry selection in Anshun Experimental District is as follows: tourism > pharmacy > transportation > energy > food processing > characteristic agriculture > package and printing > automobile industry > mining > electric engineering.
基金supported by Zhejiang Provincial Natural Science Foundation of China under Grant No.LZ22F010001。
文摘Prediction of the traffic load of cellular networks is important for network planning, load balancing, and operational optimization. In this paper, a comparative study of cellular traffic load prediction models based on deep learning is performed and a prediction method built on a multi-channel Gated Recurrent Unit(GRU) model is proposed. The proposed method uses multiple channels to extract the daily and weekly variation feature as well as the variation feature of the peak period of the BS load and can be used to provide 24-hour ahead predictions.Experimental results obtained from real dataset show that the proposed multi-channel model can effectively capture the temporal-variations of BS load and reduce the prediction error.Compared with conventional prediction algorithms such as Convolutional Neural Network(CNN), Long Short-Term Memory network, GRU and combination of CNN and GRU(CNN-GRU),the proposed model can achieve better prediction accuracies.
