[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in...[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.展开更多
The identification of runoff generating areas (RGAs) within a watershed is a difficult task because of their temporal and spatial behavior. A watershed was selected to investigate the RGAs to determine the factors aff...The identification of runoff generating areas (RGAs) within a watershed is a difficult task because of their temporal and spatial behavior. A watershed was selected to investigate the RGAs to determine the factors affecting spatio-temporally in southern Ontario. The watershed was divided into 8 fields having a Wireless System Network (WSN) and a V-notch weir for flow and soil moisture measurements. The results show that surface runoff is generated by the infiltration excess mechanism in summer and fall, and the saturation excess mechanism in spring. The statistical analysis suggested that the amount of rainfall and rainfall intensity for summer (R2 = 0.63, 0.82) and fall (R2 = 0.74, 0.80), respectively, affected the RGAs. The analysis showed that 15% area generated 85% of surface runoff in summer, 100% of runoff in fall, and 40% of runoff in spring. The methodology developed has potential for identifying RGAs for protecting Ontario’s water resources.展开更多
Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of grid...Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of gridded reanalysis data of sea level pressure (SLP) and 500 hPa geopotential height. The leading EOF mode captures 37.51% of the total variance and the spatial-temporal variability of January precipitation was consistent in the study area. The temporal changes explicate non-periodic interannual variability and some tacit interdecadal variation. The anomalous condition is more prominent along the western bordering mountains and northern high mountainous region than any other region of Pakistan. Based on results the study reveals spatial-temporal variation in January precipitation and possible links with global teleconnections located both in the proximity as well as in the remote areas from the study locus.展开更多
Background：A history of 3 or more concussions is frequently associated with numerous short-and long-term neuropathologies.Impairments in postural control are a known acute consequence of concussion;however,limited ev...Background：A history of 3 or more concussions is frequently associated with numerous short-and long-term neuropathologies.Impairments in postural control are a known acute consequence of concussion;however,limited evidence exists on the effects of multiple concussions on gait.The purpose of this study was to assess gait stepping characteristics in collegiate aged student-athletes based on concussion history.Methods：There were 63 participants divided into 3 even groups based on concussion history：≥3 concussions,1–2 concussions,and 0 concussion.All participants completed 10 trials of gait on a 4.9 m instrumented walkway.The dependent variables of interest included both gait stepping characteristics（step velocity,length,and width,double support time,and the percentage of the gait cycle in stance） and coefficien of variability（CoV） measures（step length,time,and width）.The gait stepping characteristics were compared firs with a MANOVA with follow-up 1-way ANOVAs and Tukey post hoc tests as appropriate.The Co V measures were compared with 1-way ANOVAs and Tukey post hoc tests.Results：There were main effects for group for step velocity,length,width,and double support time.Overall,the 0 concussion group displayed typical healthy young gait parameters and performed significant y better than either concussion group.The 0 concussion group had a significant y greater step length Co V,but there were no differences in the step time or width Co V.Conclusion：This findin provides evidence of subtle impairments in postural control during gait among individuals with prior history of concussion which could be an early indicator of future neurological deficiencies The limited difference in the variability measures is consistent with prior static stance studies and could suggest the individuals constrain their motor systems to reduce variability.Taken together,these findi gs suggest a conservative gait strategy which is adopted by individuals with a history of concussions.展开更多
Methanol-to-olefins,as a promising non-oil pathway for the synthesis of light olefins,has been successfully industrialized.The accurate prediction of process variables can yield significant benefits for advanced proce...Methanol-to-olefins,as a promising non-oil pathway for the synthesis of light olefins,has been successfully industrialized.The accurate prediction of process variables can yield significant benefits for advanced process control and optimization.The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes,such as high nonlinearities,dynamics,and data distribution shift caused by diverse operating conditions.In this paper,we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues.Firstly,a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions.Subsequently,convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns.Meanwhile,a multi-graph convolutional network is leveraged to model the spatial interactions.Afterward,the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction.Finally,the outputs are denormalized to obtain the ultimate results.The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices,making the model more interpretable.Lastly,this model is deployed onto an end-to-end Industrial Internet Platform,which achieves effective practical results.展开更多
Gait deformities are usually assessed by comparing patients'data with healthy subjects.However,the variability among healthy subjects due to age,body mass,height,BMI,and walking speed could influence the baseline ...Gait deformities are usually assessed by comparing patients'data with healthy subjects.However,the variability among healthy subjects due to age,body mass,height,BMI,and walking speed could influence the baseline data and hence rehabilitation measurements.Recent studies reported gait variability among healthy subjects considering age as a variant.The independent effect of other variants such as body mass,height,BMI,and walking speed on variability in the baseline data has remained unknown,hence investigated in this study.The centre of pressure signals collected from 205 male subjects was categorised for each of the mentioned five variants.Gait variability was evaluated in terms of walking stability.The centre of pressure signals was analysed by applying Nyquist and Bode methods to compute walking stability during the gait loading and unloading phases.A statistical comparison(one-way ANOVA,p<0.05)revealed significant variability in the baseline data for variables(age,height,walking speed)during the loading phase and in the anterior-posterior direction.In the medial-lateral direction,these differences were observed as significant during both loading(age,body mass,speed)and unloading phases(age,body mass,height).Overall,the subgroups in respective variants i.e.age:27±0.8yr,body mass:73±0.4 kg,height:177±0.5 cm,and walking speed:1.42±0.01 m/s exhibited relatively higher stability.Further,the Pearson correlation illustrated a signification stability relationship between body mass and other variants.This study provides evidence that the diversity in the healthy subjects’composition can cause uncertainty in diagnostic and rehabilitation measurements,which is important to consider in gait evaluations.展开更多
基金Supported by National Natural Science Foundation of China(40801216/D011002)~~
文摘[Objective] The aim was to discuss the relationship between forest fire and meterological elements （precipitation and temprature） in each region of China.[Method] Firstly,the average precipitation and temperature in forest area of each province in fire season were obtained based on meterological data,forest distribution data,seasonal and monthly data of forest fire in China.Secondly,the relationship among forest fire area,precipitation and temperature was discussed through temporal and correlation analysis.[Result] The changes of precipitation and temperature with time could reflect the annual variation of fire area well.Forest fire area went up with the decrease of precipitation and increase of temprature,and visa versa.Meanwhile,there existed diffirences in the relationship in various regions over time.Correlation analyses revealed that there was positive correlation between forest fire area and temperature,especailly Northwest China （R=0.367,P〈0.01）,Southwest China （R=0.327,P〈0.05）,South China （R=0.33,P〈0.05）,East China （R=0.516,P〈0.01） and Xinjiang （R=0.447,P〈0.05） with obviously positive correlation.At the same time,the correlation between forest fire area and precipitation was significantly positive in Northwest China （R=0.482,P〈0.01）,while it was significantly negaive in South China （R=-0.323,P=0.03）,but there was no significant correlation in other regions.[Conclusion] Relationships between forest fire and meteorological elements （precipitation and temprature） revealed in the study would be useful for fire provention and early warning in China.
文摘The identification of runoff generating areas (RGAs) within a watershed is a difficult task because of their temporal and spatial behavior. A watershed was selected to investigate the RGAs to determine the factors affecting spatio-temporally in southern Ontario. The watershed was divided into 8 fields having a Wireless System Network (WSN) and a V-notch weir for flow and soil moisture measurements. The results show that surface runoff is generated by the infiltration excess mechanism in summer and fall, and the saturation excess mechanism in spring. The statistical analysis suggested that the amount of rainfall and rainfall intensity for summer (R2 = 0.63, 0.82) and fall (R2 = 0.74, 0.80), respectively, affected the RGAs. The analysis showed that 15% area generated 85% of surface runoff in summer, 100% of runoff in fall, and 40% of runoff in spring. The methodology developed has potential for identifying RGAs for protecting Ontario’s water resources.
文摘Empirical Orthogonal Function (EOF) was performed to investigate spatial variation in January precipitation over Pakistan using ground observed mean monthly precipitation data from 1950-2000 with a combination of gridded reanalysis data of sea level pressure (SLP) and 500 hPa geopotential height. The leading EOF mode captures 37.51% of the total variance and the spatial-temporal variability of January precipitation was consistent in the study area. The temporal changes explicate non-periodic interannual variability and some tacit interdecadal variation. The anomalous condition is more prominent along the western bordering mountains and northern high mountainous region than any other region of Pakistan. Based on results the study reveals spatial-temporal variation in January precipitation and possible links with global teleconnections located both in the proximity as well as in the remote areas from the study locus.
基金funded,in part,by an NIH/NINDS grant (1R15NS070744-01A1)a Georgia Southern University Faculty Development grant
文摘Background：A history of 3 or more concussions is frequently associated with numerous short-and long-term neuropathologies.Impairments in postural control are a known acute consequence of concussion;however,limited evidence exists on the effects of multiple concussions on gait.The purpose of this study was to assess gait stepping characteristics in collegiate aged student-athletes based on concussion history.Methods：There were 63 participants divided into 3 even groups based on concussion history：≥3 concussions,1–2 concussions,and 0 concussion.All participants completed 10 trials of gait on a 4.9 m instrumented walkway.The dependent variables of interest included both gait stepping characteristics（step velocity,length,and width,double support time,and the percentage of the gait cycle in stance） and coefficien of variability（CoV） measures（step length,time,and width）.The gait stepping characteristics were compared firs with a MANOVA with follow-up 1-way ANOVAs and Tukey post hoc tests as appropriate.The Co V measures were compared with 1-way ANOVAs and Tukey post hoc tests.Results：There were main effects for group for step velocity,length,width,and double support time.Overall,the 0 concussion group displayed typical healthy young gait parameters and performed significant y better than either concussion group.The 0 concussion group had a significant y greater step length Co V,but there were no differences in the step time or width Co V.Conclusion：This findin provides evidence of subtle impairments in postural control during gait among individuals with prior history of concussion which could be an early indicator of future neurological deficiencies The limited difference in the variability measures is consistent with prior static stance studies and could suggest the individuals constrain their motor systems to reduce variability.Taken together,these findi gs suggest a conservative gait strategy which is adopted by individuals with a history of concussions.
基金the National Natural Science Foundation of China(Grant No.21991093)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDA29050200)+1 种基金the Dalian Institute of Chemical Physics(DICP I202135)the Energy Science and Technology Revolution Project(Grant No.E2010412).
文摘Methanol-to-olefins,as a promising non-oil pathway for the synthesis of light olefins,has been successfully industrialized.The accurate prediction of process variables can yield significant benefits for advanced process control and optimization.The challenge of this task is underscored by the failure of traditional methods in capturing the complex characteristics of industrial processes,such as high nonlinearities,dynamics,and data distribution shift caused by diverse operating conditions.In this paper,we propose a novel hybrid spatial-temporal deep learning prediction model to address these issues.Firstly,a unique data normalization technique called reversible instance normalization is employed to solve the problem of different data distributions.Subsequently,convolutional neural network integrated with the self-attention mechanism are utilized to extract the temporal patterns.Meanwhile,a multi-graph convolutional network is leveraged to model the spatial interactions.Afterward,the extracted temporal and spatial features are fused as input into a fully connected neural network to complete the prediction.Finally,the outputs are denormalized to obtain the ultimate results.The monitoring results of the dynamic trends of process variables in an actual industrial methanol-to-olefins process demonstrate that our model not only achieves superior prediction performance but also can reveal complex spatial-temporal relationships using the learned attention matrices and adjacency matrices,making the model more interpretable.Lastly,this model is deployed onto an end-to-end Industrial Internet Platform,which achieves effective practical results.
文摘Gait deformities are usually assessed by comparing patients'data with healthy subjects.However,the variability among healthy subjects due to age,body mass,height,BMI,and walking speed could influence the baseline data and hence rehabilitation measurements.Recent studies reported gait variability among healthy subjects considering age as a variant.The independent effect of other variants such as body mass,height,BMI,and walking speed on variability in the baseline data has remained unknown,hence investigated in this study.The centre of pressure signals collected from 205 male subjects was categorised for each of the mentioned five variants.Gait variability was evaluated in terms of walking stability.The centre of pressure signals was analysed by applying Nyquist and Bode methods to compute walking stability during the gait loading and unloading phases.A statistical comparison(one-way ANOVA,p<0.05)revealed significant variability in the baseline data for variables(age,height,walking speed)during the loading phase and in the anterior-posterior direction.In the medial-lateral direction,these differences were observed as significant during both loading(age,body mass,speed)and unloading phases(age,body mass,height).Overall,the subgroups in respective variants i.e.age:27±0.8yr,body mass:73±0.4 kg,height:177±0.5 cm,and walking speed:1.42±0.01 m/s exhibited relatively higher stability.Further,the Pearson correlation illustrated a signification stability relationship between body mass and other variants.This study provides evidence that the diversity in the healthy subjects’composition can cause uncertainty in diagnostic and rehabilitation measurements,which is important to consider in gait evaluations.
基金欧盟地平线2020项目H2020-MSCA-RISE-2016:Smart Robot for Fire-fighting(No.:734875)欧盟第七框架计划项目FP7-PEOPLE-2012-IRSES:Real-time adaptive networked control of rescue robots(No.:318902)燕山大学机械工程学院协同创新项目(JX2014-01)
