Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,...Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.展开更多
For the first time, degree of damage of the disease Botrytis cinerea Pers. is founded in decorative colors in the Tashkent region. Chemical preparations are the most effective methods of struggle in the fight against ...For the first time, degree of damage of the disease Botrytis cinerea Pers. is founded in decorative colors in the Tashkent region. Chemical preparations are the most effective methods of struggle in the fight against diseases of ornamental crops.展开更多
A method for strengthening damaged tubular steel T-joints under axial compression by wrapping them with carbon fiber-reinforced polymer(CFRP)sheets was proposed and evaluated.The influence of the CFRP strengthening on...A method for strengthening damaged tubular steel T-joints under axial compression by wrapping them with carbon fiber-reinforced polymer(CFRP)sheets was proposed and evaluated.The influence of the CFRP strengthening on the failure mode and load capacity of T-joints with different degrees of damage was investigated using experiments and finite element analyses.Five T-joints were physically tested:one bare joint to obtain the peak load and corresponding displacement(D1m),two reinforced joints to provide a reference,and two pre-damaged then retrofitted joints to serve as the primary research objects.The ratio of the pre-loaded specimen chord displacement to the value of D1m was considered to be the degree of damage of the two retrofitted joints,and was set to 0.80 and 1.20.The results demonstrate that the maximum capacity of the retrofitted specimen was increased by 0.83%–15.06%over the corresponding unreinforced specimens.However,the capacity of the retrofitted specimen was 2.51%–22.77%lesser compared with that of the directly reinforced specimens.Next,111 numerical analysis models(0.63≤b≤0.76,9.70≤g≤16.92)were established to parametrically evaluate the effects of different geometric and strengthening parameters on the load capacity of strengthened tubular T-joints under different degrees of damage.The numerical analysis results revealed that the development of equivalent plastic strain at the selected measuring points was moderated by strengthening with CFRP wrapping,and indicated the optimal CFRP strengthening thickness and wrapping orientation according to tubular T-joint parameters.Finally,reasonable equations for calculating the load capacity of CFRP-strengthened joints were proposed and demonstrated to provide accurate results.The findings of this study can be used to inform improved CFRP strengthening of damaged tubular steel structures.展开更多
基金supported by National Key Research&Development Program of China“Research on key technologies for prevention and control of major disasters in plantation”(Grant No.2018YFD0600200)Beijing’s Science and Technology Planning Project“Key technologies for prevention and control of major pests in Beijing ecological public welfare forests”(Grant Nos.Z191100008519004 and Z201100008020001).
文摘Background:Anoplophora glabripennis(Motschulsky),commonly known as Asian longhorned beetle(ALB),is a wood-boring insect that can cause lethal infestation to multiple borer leaf trees.In Gansu Province,northwest China,ALB has caused a large number of deaths of a local tree species Populus gansuensis.The damaged area belongs to Gobi desert where every single tree is artificially planted and is extremely difficult to cultivate.Therefore,the monitoring of the ALB infestation at the individual tree level in the landscape is necessary.Moreover,the determination of an abnormal phenotype that can be obtained directly from remote-sensing images to predict the damage degree can greatly reduce the cost of field investigation and management.Methods:Multispectral WorldView-2(WV-2)images and 5 tree physiological factors were collected as experimental materials.One-way ANOVA of the tree’s physiological factors helped in determining the phenotype to predict damage degrees.The original bands of WV-2 and derived vegetation indices were used as reference data to construct the dataset of a prediction model.Variance inflation factor and stepwise regression analyses were used to eliminate collinearity and redundancy.Finally,three machine learning algorithms,i.e.,Random Forest(RF),Support Vector Machine(SVM),Classification And Regression Tree(CART),were applied and compared to find the best classifier for predicting the damage stage of individual P.gansuensis.Results:The confusion matrix of RF achieved the highest overall classification accuracy(86.2%)and the highest Kappa index value(0.804),indicating the potential of using WV-2 imaging to accurately detect damage stages of individual trees.In addition,the canopy color was found to be positively correlated with P.gansuensis’damage stages.Conclusions:A novel method was developed by combining WV-2 and tree physiological index for semi-automatic classification of three damage stages of P.gansuensis infested with ALB.The canopy color was determined as an abnormal phenotype that could be directly assessed using remote-sensing images at the tree level to predict the damage degree.These tools are highly applicable for driving quick and effective measures to reduce damage to pure poplar forests in Gansu Province,China.
文摘For the first time, degree of damage of the disease Botrytis cinerea Pers. is founded in decorative colors in the Tashkent region. Chemical preparations are the most effective methods of struggle in the fight against diseases of ornamental crops.
基金This research work was supported and funded by Shandong Provincial Science and Technology Plan Project(No.J13LG05).
文摘A method for strengthening damaged tubular steel T-joints under axial compression by wrapping them with carbon fiber-reinforced polymer(CFRP)sheets was proposed and evaluated.The influence of the CFRP strengthening on the failure mode and load capacity of T-joints with different degrees of damage was investigated using experiments and finite element analyses.Five T-joints were physically tested:one bare joint to obtain the peak load and corresponding displacement(D1m),two reinforced joints to provide a reference,and two pre-damaged then retrofitted joints to serve as the primary research objects.The ratio of the pre-loaded specimen chord displacement to the value of D1m was considered to be the degree of damage of the two retrofitted joints,and was set to 0.80 and 1.20.The results demonstrate that the maximum capacity of the retrofitted specimen was increased by 0.83%–15.06%over the corresponding unreinforced specimens.However,the capacity of the retrofitted specimen was 2.51%–22.77%lesser compared with that of the directly reinforced specimens.Next,111 numerical analysis models(0.63≤b≤0.76,9.70≤g≤16.92)were established to parametrically evaluate the effects of different geometric and strengthening parameters on the load capacity of strengthened tubular T-joints under different degrees of damage.The numerical analysis results revealed that the development of equivalent plastic strain at the selected measuring points was moderated by strengthening with CFRP wrapping,and indicated the optimal CFRP strengthening thickness and wrapping orientation according to tubular T-joint parameters.Finally,reasonable equations for calculating the load capacity of CFRP-strengthened joints were proposed and demonstrated to provide accurate results.The findings of this study can be used to inform improved CFRP strengthening of damaged tubular steel structures.