The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing＇an Mountains, in northeast China. The r...The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing＇an Mountains, in northeast China. The response variables were the area burned by lightning- caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log- linear model （log-transformation on both response and predictor variables）, and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R2, AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regressionmodel and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at at = 0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the .main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum rela- tive humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.展开更多
Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system....Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.展开更多
BACKGROUND： Stroke is the leading cause of death and long-term disability. This study was undertaken to investigate the factors influencing daily activities of patients with cerebral infarction so as to take interven...BACKGROUND： Stroke is the leading cause of death and long-term disability. This study was undertaken to investigate the factors influencing daily activities of patients with cerebral infarction so as to take interventional measures earlier to improve their daily activities.METHODS： A total of 149 patients with first-episode cerebral infarction were recruited into this prospective study. They were admitted to the Encephalopathy Center, Department of Neurology, the First Affiliated Hospital of Wenzhou Medical College in Zhejiang Province from August 2008 to December 2008. The baseline characteristics of the patients and cerebral infarction risk factors on the first day of admission were recorded. White blood cell （WBC） count, plasma glucose （PG）, and many others of laboratory targets were collected in the next morning. Barthel index （BI） was calculated at 2 weeks and 3 months respectively after onset of the disease at the outpatient clinic or by telephone call. Lung infection, urinary tract infection and atrial fibrillation if any were recorded on admission. The National Institute of Health Stroke Scale （NIHSS） scores and the GCS scores were recorded within 24 hours on and after admission, at the second week, and at the third month after the onset of cerebral infarction respectively.RESULTS： The factors of BI at 2 weeks and 3 months after onset were the initial PG level, WBC count and initial NIHSS scores. Besides, urinary tract infection on admission was also the factor for BI at 3 months.CONCLUSION： Active measures should be taken to control these factors to improve the daily activities of patients with cerebral infarction.展开更多
This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires.Multiple linear regression was used in combination with nonlinear variable transfo...This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires.Multiple linear regression was used in combination with nonlinear variable transformations to determine relevant nonlinear forest wildfire size functions.Data from the Prague-East District of the Czech Republic was used for model derivation.Individual burned forest area was hypothesized as a function of water vapor pressure,air temperature and wind speed.Wind speed was added to enhance predictions of the size of forest wildfires,and further improvements to the utility of prediction methods were added to the regression equation.The results show that if the air temperature increases,it may contain less water and the fuel will become drier.The size of the burned area then increases.If the relative humidity in the air increases and the wind speed decreases,the size of the burned area is reduced.Our model suggests that changes in the climate factors caused by ongoing climate change could cause significant changes in the size of wildfire in forests.展开更多
Background: Over the last decades interest has grown on how climate change impacts forest resources. However,one of the main constraints is that meteorological stations are riddled with missing climatic data. This stu...Background: Over the last decades interest has grown on how climate change impacts forest resources. However,one of the main constraints is that meteorological stations are riddled with missing climatic data. This study compared five approaches for estimating monthly precipitation records: inverse distance weighting(IDW), a modification of IDW that includes elevation differences between target and neighboring stations(IDW_m), correlation coefficient weighting(CCW), multiple linear regression(MLR) and artificial neural networks(ANN).Methods: A complete series of monthly precipitation records(1995-2012) from twenty meteorological stations located in central Chile were used. Two target stations were selected and their neighboring stations, located within a radius of25 km(3 stations) and 50 km(9 stations), were identified. Cross-validation was used for evaluating the accuracy of the estimation approaches. The performance and predictive capability of the approaches were evaluated using the ratio of the root mean square error to the standard deviation of measured data(RSR), the percent bias(PBIAS), and the NashSutcliffe efficiency(NSE). For testing the main and interactive effects of the radius of influence and estimation approaches,a two-level factorial design considering the target station as the blocking factor was used.Results: ANN and MLR showed the best statistics for all the stations and radius of influence. However, these approaches were not significantly different with IDW_m. Inclusion of elevation differences into IDW significantly improved IDW_m estimates. In terms of precision, similar estimates were obtained when applying ANN, MLR or IDW_m, and the radius of influence had a significant influence on their estimates, we conclude that estimates based on nine neighboring stations located within a radius of 50 km are needed for completing missing monthly precipitation data in regions with complex topography.Conclusions: It is concluded that approaches based on ANN, MLR and IDWm had the best performance in two sectors located in south-central Chile with a complex topography. A radius of influence of 50 km(9 neighboring stations) is recommended for completing monthly precipitation data.展开更多
Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of tre...Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.展开更多
基金funded by Asia-Pacific Forests Net(APFNET/2010/FPF/001)National Natural Science Foundation of China(Grant No.31400552)Forestry industry research special funds for public welfare projects(201404402)
文摘The purpose of this study was to determine a suitable model for investigating the effects of climate factors on the area burned by forest fire in the Tahe forest region, Daxing＇an Mountains, in northeast China. The response variables were the area burned by lightning- caused fire, human-caused fire, and total burned area. The predictor variables were nine climate variables collected from the local weather station. Three regression models were utilized, including multiple linear regression, log- linear model （log-transformation on both response and predictor variables）, and gamma-generalized linear model. The goodness-of-fit of the models were compared based on model fitting statistics such as R2, AIC, and RMSE. The results revealed that the gamma-generalized linear model was generally superior to both multiple linear regressionmodel and log-linear model for fitting the fire data. Further, the best models were selected based on the criteria that the climate variables were statistically significant at at = 0.05. The gamma best models indicated that maximum wind speed, precipitation, and days that rainfall greater than 0.1 mm had significant impacts on the area burned by the lightning-caused fire, while the mean temperature and minimum relative humidity were the .main drivers of the burned area caused by human activities. Overall, the total burned area by forest fire was significantly influenced by days that rainfall greater than 0.1 mm and minimum rela- tive humidity, indicating that the moisture condition of forest stands determine the burned area by forest fire.
文摘Various nodes,logistics,capital flows,and information flows are required to make systematic decisions concerning the operation of an integrated coal supply system. We describe a quantitative analysis of such a system. A dynamic optimization model of the supply chain is developed. It has achieved optimal system profit under conditions guaranteeing a certain level of customer satisfaction. Applying this model to coal production of the Xuzhou coal mines allows recommendations for a more systematic use of washing and processing,transportation and sale resources for commercial coal production to be made. The results show that this model,which is scientific and effective,has an important value for making reasonable decisions related to complex coal enterprises.
文摘BACKGROUND： Stroke is the leading cause of death and long-term disability. This study was undertaken to investigate the factors influencing daily activities of patients with cerebral infarction so as to take interventional measures earlier to improve their daily activities.METHODS： A total of 149 patients with first-episode cerebral infarction were recruited into this prospective study. They were admitted to the Encephalopathy Center, Department of Neurology, the First Affiliated Hospital of Wenzhou Medical College in Zhejiang Province from August 2008 to December 2008. The baseline characteristics of the patients and cerebral infarction risk factors on the first day of admission were recorded. White blood cell （WBC） count, plasma glucose （PG）, and many others of laboratory targets were collected in the next morning. Barthel index （BI） was calculated at 2 weeks and 3 months respectively after onset of the disease at the outpatient clinic or by telephone call. Lung infection, urinary tract infection and atrial fibrillation if any were recorded on admission. The National Institute of Health Stroke Scale （NIHSS） scores and the GCS scores were recorded within 24 hours on and after admission, at the second week, and at the third month after the onset of cerebral infarction respectively.RESULTS： The factors of BI at 2 weeks and 3 months after onset were the initial PG level, WBC count and initial NIHSS scores. Besides, urinary tract infection on admission was also the factor for BI at 3 months.CONCLUSION： Active measures should be taken to control these factors to improve the daily activities of patients with cerebral infarction.
基金funded by grant"EVA4.0",No.CZ.02.1.01/0.0/0.0/16_019/0000803 financed by the Operational Program Research,Development and Education(OP RDE),the Ministry of Education of the Czech Republic。
文摘This paper presents a new approach to identifying the climate variables that influence the size of the area burned by forest wildfires.Multiple linear regression was used in combination with nonlinear variable transformations to determine relevant nonlinear forest wildfire size functions.Data from the Prague-East District of the Czech Republic was used for model derivation.Individual burned forest area was hypothesized as a function of water vapor pressure,air temperature and wind speed.Wind speed was added to enhance predictions of the size of forest wildfires,and further improvements to the utility of prediction methods were added to the regression equation.The results show that if the air temperature increases,it may contain less water and the fuel will become drier.The size of the burned area then increases.If the relative humidity in the air increases and the wind speed decreases,the size of the burned area is reduced.Our model suggests that changes in the climate factors caused by ongoing climate change could cause significant changes in the size of wildfire in forests.
基金supported by the National Fund for Scientific and Technological Development(FONDECYT)[Project 1151050]the first author gratefully acknowledges funding from Chile's Education Ministry through the program MECESUP2 [Project UC00702]
文摘Background: Over the last decades interest has grown on how climate change impacts forest resources. However,one of the main constraints is that meteorological stations are riddled with missing climatic data. This study compared five approaches for estimating monthly precipitation records: inverse distance weighting(IDW), a modification of IDW that includes elevation differences between target and neighboring stations(IDW_m), correlation coefficient weighting(CCW), multiple linear regression(MLR) and artificial neural networks(ANN).Methods: A complete series of monthly precipitation records(1995-2012) from twenty meteorological stations located in central Chile were used. Two target stations were selected and their neighboring stations, located within a radius of25 km(3 stations) and 50 km(9 stations), were identified. Cross-validation was used for evaluating the accuracy of the estimation approaches. The performance and predictive capability of the approaches were evaluated using the ratio of the root mean square error to the standard deviation of measured data(RSR), the percent bias(PBIAS), and the NashSutcliffe efficiency(NSE). For testing the main and interactive effects of the radius of influence and estimation approaches,a two-level factorial design considering the target station as the blocking factor was used.Results: ANN and MLR showed the best statistics for all the stations and radius of influence. However, these approaches were not significantly different with IDW_m. Inclusion of elevation differences into IDW significantly improved IDW_m estimates. In terms of precision, similar estimates were obtained when applying ANN, MLR or IDW_m, and the radius of influence had a significant influence on their estimates, we conclude that estimates based on nine neighboring stations located within a radius of 50 km are needed for completing missing monthly precipitation data in regions with complex topography.Conclusions: It is concluded that approaches based on ANN, MLR and IDWm had the best performance in two sectors located in south-central Chile with a complex topography. A radius of influence of 50 km(9 neighboring stations) is recommended for completing monthly precipitation data.
文摘Spatio-temporal assessment of the above ground biomass （AGB） is a cumbersome task due to the difficulties associated with the measurement of different tree parameters such as girth at breast height and height of trees. The present research was conducted in the campus of Birla Institute of Technology, Mesra, Ranchi, India, which is predomi- nantly covered by Sal （Shorea robusta C. F. Gaertn）. Two methods of regression analysis was employed to determine the potential of remote sensing parameters with the AGB measured in the field such as linear regression analysis between the AGB and the individual bands, principal components （PCs） of the bands, vegetation indices （VI）, and the PCs of the VIs respectively and multiple linear regression （MLR） analysis be- tween the AGB and all the variables in each category of data. From the linear regression analysis, it was found that only the NDVI exhibited regression coefficient value above 0.80 with the remaining parameters showing very low values. On the other hand, the MLR based analysis revealed significantly improved results as evidenced by the occurrence of very high correlation coefficient values of greater than 0.90 determined between the computed AGB from the MLR equations and field-estimated AGB thereby ascertaining their superiority in providing reliable estimates of AGB. The highest correlation coefficient of 0.99 is found with the MLR involving PCs of VIs.
