[Objective] This study was to explore the skin development law of Datong Yak.[Method]Taking Datong yaks at four various growth stages（1 day old,30 days old,180 days old and adult）as experimental materials,we studied...[Objective] This study was to explore the skin development law of Datong Yak.[Method]Taking Datong yaks at four various growth stages（1 day old,30 days old,180 days old and adult）as experimental materials,we studied histological structure of Datong yak skin by employing the histological methods.[Results]The skins of Datong yak at four various growth stages all showed the characteristics of thinner epidermis and thicker hypodermis,and of less capillary vessels in hypodermis and more pigment cells in epidermis.Datong yak at four various growth stages all showed a fluctuating trend in entire skin thickness,in detail thickening from 1 day old to 30 days old,then thinning to 180 days old,again thickening to adult.[Conclusion]The results provided theoretical basis for further understanding the structural characteristics of Datong yak to adapt to the abominable plateau environment.展开更多
Damage caused by underground coal mining is a serious problem in mining areas in China; therefore, studying and obtaining the rules of ground movement and deformation under different geological conditions is of great ...Damage caused by underground coal mining is a serious problem in mining areas in China; therefore, studying and obtaining the rules of ground movement and deformation under different geological conditions is of great importance. The numerical software ANSYS was used in this study to simulate mining processes under two special geological conditions： （1） thick unconsolidated soil layer and thin bedrock; （2） thin soil layer and thick bedrock. The rules for ground movement and deformation for different soil layer to bedrock ratios were obtained. On the basis of these rules, a prediction parameter modified model of the influence function was proposed, which is suitable for different values of unconsolidated soil layer thickness. The prediction results were verified using two sets of typical field data.展开更多
On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., ...On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.展开更多
基金Supported by Science Foundation for the Excellent Youth Scholars of Ministry of Education of China~~
文摘[Objective] This study was to explore the skin development law of Datong Yak.[Method]Taking Datong yaks at four various growth stages（1 day old,30 days old,180 days old and adult）as experimental materials,we studied histological structure of Datong yak skin by employing the histological methods.[Results]The skins of Datong yak at four various growth stages all showed the characteristics of thinner epidermis and thicker hypodermis,and of less capillary vessels in hypodermis and more pigment cells in epidermis.Datong yak at four various growth stages all showed a fluctuating trend in entire skin thickness,in detail thickening from 1 day old to 30 days old,then thinning to 180 days old,again thickening to adult.[Conclusion]The results provided theoretical basis for further understanding the structural characteristics of Datong yak to adapt to the abominable plateau environment.
文摘Damage caused by underground coal mining is a serious problem in mining areas in China; therefore, studying and obtaining the rules of ground movement and deformation under different geological conditions is of great importance. The numerical software ANSYS was used in this study to simulate mining processes under two special geological conditions： （1） thick unconsolidated soil layer and thin bedrock; （2） thin soil layer and thick bedrock. The rules for ground movement and deformation for different soil layer to bedrock ratios were obtained. On the basis of these rules, a prediction parameter modified model of the influence function was proposed, which is suitable for different values of unconsolidated soil layer thickness. The prediction results were verified using two sets of typical field data.
基金Under the auspices of Special Basic Research Fund for Central Public Scientific Research Institutes (No. 2007-03)
文摘On the basis of artificial neural network (ANN) model, this paper presents an algorithm for inversing snow depth with use of AMSR-E (Advanced Microwave Scanning Radiometer-Earth Observing System (EOS)) dataset, i.e., brightness temperature at 18.7 and 36.5GHz in Qinghai-Tibet Plateau during the snow season of 2002-2003. In order to overcome the overfitting problem in ANN modeling, this methodology adopts a Bayesian regularization approach. The experiments are performed to compare the results obtained from the ANN-based algorithm with those obtained from other existing algorithms, i.e., Chang algorithm, spectral polarization difference (SPD) algorithm, and temperature gradient (TG) algorithm. The experimental results show that the presented algorithm has the highest accuracy in estimating snow depth. In addition, the effects of the noises in datasets on model fitting can be decreased due to adopting the Bayesian regularization approach.
