Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50...Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.展开更多
The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments ne...The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.e.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.e.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.展开更多
Tropical forest cover change along with increasing fragmentation has detrimental effects on the global biodiversity.In the current study change in both forest cover and fragmentation of Koraput district have been asse...Tropical forest cover change along with increasing fragmentation has detrimental effects on the global biodiversity.In the current study change in both forest cover and fragmentation of Koraput district have been assessed in the past three decades(1987-2017)and future decade(2017-2027),which has been modelled using logistic regression showing a gradual decrease in the forest cover and increase in fragmentation.The long term deforestation rates from 1987 to 2017(current period)and from 1987 to 2027(predicted period)were found to be-0.018 and-0.012,respectively.Out of the total geographical area,2027 number of grids(1 km^(2))out of 8856 grids were found to have shown extinction of forest in the study period.The conversion of forested lands into other land uses has been one of the major causes of deforestation in Koraput,especially because of the increasing mining activities and establishment of three major industries namely National Aluminium Company(NALCO),Damanjodi,Hindustan Aeronautics Limited(HAL),Sunabeda and Ballarpur Industries Limited(BILT).The forest fragmentation reveals a negative trend,recording highest conversion from large core fragments to edge(191.33 km2)and the predicted period has also shown the same trend of negative change,which poses serious danger to the structure of the forests.Out of all the landscape matrices calculated,number of patches will increase to 214 in 2027 from 93 in 1987.In the test between geographically weighted regression(GWR)and ordinary least square regression(OLS),GWR was the better fit model for drawing a spatial relationship between forest cover and fragmentation changes.The study confirmed that the forest cover change has impacted the forest fragmentation in the study area.The programmes like REDD+should be implemented along with the experiences of Community Forest Management and the joint forest management should be intensified at community level in order to develop better management practices to conserve habitats in biodiversity rich areas.展开更多
We investigate the impact of network topology characteristics on focking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network,including the network's hop count features and information...We investigate the impact of network topology characteristics on focking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network,including the network's hop count features and information's successful transmission probability(STP).Specifically,we first propose a distributed communication calculation execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system,where focking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables.Then,we develop a fragmentation prediction model(FPM)to formulate the impact of hop count features on fragmentation for specific focking scenarios.This model identifies the critical system and network features that are associated with fragmentation.Further considering general focking scenarios affected by both hop count features and STP,we formulate the flocking fragmentation probability(FFP)by a data fitting model based on the back propagation neural network,whose input is extracted from the FPM.The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena.Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation,and several guidelines for constructing the multi-robot ad hoc network are concluded.展开更多
Objective Atherosclerosis(AS),a chronic inflammatory disease,is the basis of cardiovascular disease(CVD).Although the treatment has been greatly improved,AS still imposes a large burden on human health and the medical...Objective Atherosclerosis(AS),a chronic inflammatory disease,is the basis of cardiovascular disease(CVD).Although the treatment has been greatly improved,AS still imposes a large burden on human health and the medical system,and we still need to further study its pathogenesis.As a novel biomolecule,transfer RNA-derived fragments(tRFs)play a key role in the progression of various disease.However,whether tRFs contribute to atherosclerosis pathogenesis remains unexplored.Methods With deep sequencing technology,the change of tRFs expression profiles in patients with AS compared to healthy control group was identified.The accuracy of the sequencing data was validated using RT qPCR.Subsequently,we predicted the potential target genes of tRFs by online miRNA target prediction algorithms.The potential functions of tRFs were evaluated with Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses.Results There were 13 tRFs differentially expressed between patients with AS and healthy controls,of which 2 were up-regulated and 11 were down-regulated.Validation by RT-qPCR analysis confirmed the sequencing results,and tRF-Gly-GCC-009 was highly up-regulated in the AS group based on the results of sequencing which was confirmed by RT-qPCR analysis.Furthermore,GO enrichment and KEGG pathway analyses indicated that 10 signaling pathways were related to tRF-Gly-GCC-009.These pathways might be physiopathological fundamentals of AS,mainly involving in Apelin signaling,Notch signaling and calcium signaling.Conclusion The results of our study provide important novel insight into the underlying pathogenesis and demonstrate that tRFs might be potential biomarkers and therapeutic targets for AS in the future.展开更多
基金Foundation item:Project (2006BAB02A02) supported by the National Key Technology R&D Program during the 11th Five-year Plan Period of ChinaProject (CX2011B119) supported by the Graduated Students' Research and Innovation Fund of Hunan Province, ChinaProject (2009ssxt230) supported by the Central South University Innovation Fund,China
文摘Aiming at the problems of the traditional method of assessing distribution of particle size in bench blasting, a support vector machines （SVMs） regression methodology was used to predict the mean particle size （X50） resulting from rock blast fragmentation in various mines based on the statistical learning theory. The data base consisted of blast design parameters, explosive parameters, modulus of elasticity and in-situ block size. The seven input independent variables used for the SVMs model for the prediction of X50 of rock blast fragmentation were the ratio of bench height to drilled burden （H/B）, ratio of spacing to burden （S/B）, ratio of burden to hole diameter （B/D）, ratio of stemming to burden （T/B）, powder factor （Pf）, modulus of elasticity （E） and in-situ block size （XB）. After using the 90 sets of the measured data in various mines and rock formations in the world for training and testing, the model was applied to 12 another blast data for validation of the trained support vector regression （SVR） model. The prediction results of SVR were compared with those of artificial neural network （ANN）, multivariate regression analysis （MVRA） models, conventional Kuznetsov method and the measured X50 values. The proposed method shows promising results and the prediction accuracy of SVMs model is acceptable.
基金funded by the National Natural Science Foundation of China(Grant No.42177164)the Innovation-Driven Project of Central South University(Grant No.2020CX040)supported by China Scholarship Council(Grant No.202006370006)。
文摘The main purpose of blasting operation is to produce desired and optimum mean size rock fragments.Smaller or fine fragments cause the loss of ore during loading and transportation,whereas large or coarser fragments need to be further processed,which enhances production cost.Therefore,accurate prediction of rock fragmentation is crucial in blasting operations.Mean fragment size(MFS) is a crucial index that measures the goodness of blasting designs.Over the past decades,various models have been proposed to evaluate and predict blasting fragmentation.Among these models,artificial intelligence(AI)-based models are becoming more popular due to their outstanding prediction results for multiinfluential factors.In this study,support vector regression(SVR) techniques are adopted as the basic prediction tools,and five types of optimization algorithms,i.e.grid search(GS),grey wolf optimization(GWO),particle swarm optimization(PSO),genetic algorithm(GA) and salp swarm algorithm(SSA),are implemented to improve the prediction performance and optimize the hyper-parameters.The prediction model involves 19 influential factors that constitute a comprehensive blasting MFS evaluation system based on AI techniques.Among all the models,the GWO-v-SVR-based model shows the best comprehensive performance in predicting MFS in blasting operation.Three types of mathematical indices,i.e.mean square error(MSE),coefficient of determination(R^(2)) and variance accounted for(VAF),are utilized for evaluating the performance of different prediction models.The R^(2),MSE and VAF values for the training set are 0.8355,0.00138 and 80.98,respectively,whereas 0.8353,0.00348 and 82.41,respectively for the testing set.Finally,sensitivity analysis is performed to understand the influence of input parameters on MFS.It shows that the most sensitive factor in blasting MFS is the uniaxial compressive strength.
基金the Department of Science and Technology,Govt.of India,DST-INSPIRE for providing fellowship(Sanction No.DST/INSPIRE Fellowship/2015/IF150127 dated 10.04.2015)during the tenure of the research work。
文摘Tropical forest cover change along with increasing fragmentation has detrimental effects on the global biodiversity.In the current study change in both forest cover and fragmentation of Koraput district have been assessed in the past three decades(1987-2017)and future decade(2017-2027),which has been modelled using logistic regression showing a gradual decrease in the forest cover and increase in fragmentation.The long term deforestation rates from 1987 to 2017(current period)and from 1987 to 2027(predicted period)were found to be-0.018 and-0.012,respectively.Out of the total geographical area,2027 number of grids(1 km^(2))out of 8856 grids were found to have shown extinction of forest in the study period.The conversion of forested lands into other land uses has been one of the major causes of deforestation in Koraput,especially because of the increasing mining activities and establishment of three major industries namely National Aluminium Company(NALCO),Damanjodi,Hindustan Aeronautics Limited(HAL),Sunabeda and Ballarpur Industries Limited(BILT).The forest fragmentation reveals a negative trend,recording highest conversion from large core fragments to edge(191.33 km2)and the predicted period has also shown the same trend of negative change,which poses serious danger to the structure of the forests.Out of all the landscape matrices calculated,number of patches will increase to 214 in 2027 from 93 in 1987.In the test between geographically weighted regression(GWR)and ordinary least square regression(OLS),GWR was the better fit model for drawing a spatial relationship between forest cover and fragmentation changes.The study confirmed that the forest cover change has impacted the forest fragmentation in the study area.The programmes like REDD+should be implemented along with the experiences of Community Forest Management and the joint forest management should be intensified at community level in order to develop better management practices to conserve habitats in biodiversity rich areas.
基金supported by the National Key Research and Development Program of China(No.2019YFB1803400)。
文摘We investigate the impact of network topology characteristics on focking fragmentation for a multi-robot system under a multi-hop and lossy ad hoc network,including the network's hop count features and information's successful transmission probability(STP).Specifically,we first propose a distributed communication calculation execution protocol to describe the practical interaction and control process in the ad hoc network based multi-robot system,where focking control is realized by a discrete-time Olfati-Saber model incorporating STP-related variables.Then,we develop a fragmentation prediction model(FPM)to formulate the impact of hop count features on fragmentation for specific focking scenarios.This model identifies the critical system and network features that are associated with fragmentation.Further considering general focking scenarios affected by both hop count features and STP,we formulate the flocking fragmentation probability(FFP)by a data fitting model based on the back propagation neural network,whose input is extracted from the FPM.The FFP formulation quantifies the impact of key network topology characteristics on fragmentation phenomena.Simulation results verify the effectiveness and accuracy of the proposed prediction model and FFP formulation,and several guidelines for constructing the multi-robot ad hoc network are concluded.
基金supported by grants from the National Natural Science Foundation of China(No.82000441)Shandong Provincial Natural Science Foundation,China(No.ZR201911090321)+2 种基金Medicine and Health Science Technology Development Program of Shandong Province(No.2018WS050)Shandong Province Traditional Chinese Medicine Science and Technology Development Plan of Shandong Province(No.2019-0426)Shandong Province Higher Educational Science and Technology Program for Youth Innovation(No.2020KJL004).
文摘Objective Atherosclerosis(AS),a chronic inflammatory disease,is the basis of cardiovascular disease(CVD).Although the treatment has been greatly improved,AS still imposes a large burden on human health and the medical system,and we still need to further study its pathogenesis.As a novel biomolecule,transfer RNA-derived fragments(tRFs)play a key role in the progression of various disease.However,whether tRFs contribute to atherosclerosis pathogenesis remains unexplored.Methods With deep sequencing technology,the change of tRFs expression profiles in patients with AS compared to healthy control group was identified.The accuracy of the sequencing data was validated using RT qPCR.Subsequently,we predicted the potential target genes of tRFs by online miRNA target prediction algorithms.The potential functions of tRFs were evaluated with Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)pathway analyses.Results There were 13 tRFs differentially expressed between patients with AS and healthy controls,of which 2 were up-regulated and 11 were down-regulated.Validation by RT-qPCR analysis confirmed the sequencing results,and tRF-Gly-GCC-009 was highly up-regulated in the AS group based on the results of sequencing which was confirmed by RT-qPCR analysis.Furthermore,GO enrichment and KEGG pathway analyses indicated that 10 signaling pathways were related to tRF-Gly-GCC-009.These pathways might be physiopathological fundamentals of AS,mainly involving in Apelin signaling,Notch signaling and calcium signaling.Conclusion The results of our study provide important novel insight into the underlying pathogenesis and demonstrate that tRFs might be potential biomarkers and therapeutic targets for AS in the future.
