Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using dail...Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre,we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC.The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains,Tianshan Mountains,Ili River Valley,Tacheng Prefecture,and Altay Prefecture,with the Ili River Valley,Tacheng City,and Altay Mountains exhibiting the most occurrences.Based on the intensity of ROS events,the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains,Ili River Valley,Tacheng City,and Altay Mountains.The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015,mainly influenced by air temperature and the number of rainfall days.However,due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC,snowpack changes exerted slight impact on ROS events,which is a temporary phenomenon.Furthermore,elevation imposed lesser impact on ROS events in the ARNC than other factors.In the ARNC,the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year,while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter.This may lead to more ROS events in winter in the future.These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.展开更多
In recent years,with rapid increases in the number of vehicles in China,the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent.To achieve the precise control of emissions,on-r...In recent years,with rapid increases in the number of vehicles in China,the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent.To achieve the precise control of emissions,on-road remote sensing(RS)technology has been developed and applied for law enforcement and supervision.However,data quality is still an existing issue affecting the development and application of RS.In this study,the RS data from a cross-road RS system used at a single site(from 2012 to 2015)were collected,the data screening process was reviewed,the issues with data quality were summarized,a new method of data screening and calibration was proposed,and the effectiveness of the improved data quality control methods was finally evaluated.The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%,which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles.The annual variability of emission factors of nitric oxide decreases by 60%-on average-eliminating the annual drift of fleet emissions and improving data reliability.展开更多
Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identific...Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.展开更多
Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen ...Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen consumption during the China＇s urbanization process.Results showed that after 1980s,the annual consumption of Chinese urban residents＇ food-nitrogen had a change trend of ＂ increase-decrease-increase＂ and generally presented as a slight increasing trend;With the acceleration of rapid economic development and urbanization process,Chinese urban residents＇ food-nitrogen consumption will still keep a rising trend in future,and also has a large rising space.展开更多
Guided waves based damage detection methods using base signals offer the advantages of simplicity of signal generation and reception,sensitivity to damage,and large area coverage;however,applications of the technology...Guided waves based damage detection methods using base signals offer the advantages of simplicity of signal generation and reception,sensitivity to damage,and large area coverage;however,applications of the technology are limited by the sensitivity to environmental temperature variations.In this paper,a Spearman Damage Index-based damage diagnosis method for structural health condition monitoring under varying temperatures is presented.First,a PZT sensor-based Guided wave propagation model is proposed and employed to analyze the temperature effect.The result of the analysis shows the wave speed of the Guided wave signal has higher temperature sensitivity than the signal fluctuation features.Then,a Spearman rank correlation coefficient-based damage index is presented to identify damage of the structure under varying temperatures.Finally,a damage detection test on a composite plate is conducted to verify the effectiveness of the Spearman Damage Index-based damage diagnosis method.Experimental results show that the proposed damage diagnosis method is capable of detecting the existence of the damage and identify its location under varying temperatures.展开更多
Air is an important condition for human activities and survival,and its quality is closely related to the quality of life and level of health for the people.In recent years,the problem caused by air quality has become...Air is an important condition for human activities and survival,and its quality is closely related to the quality of life and level of health for the people.In recent years,the problem caused by air quality has become one of the main problems that endanger human health and restrict economic development,which has been widely concerned.In this paper,the air quality status and its changing trend were analyzed by using the methods of the comprehensive index of ambient air quality and Spearman s rank correlation coefficient,based on the hourly pollutant concentration data of five national ambient air quality monitoring stations in the central urban area of Liupanshui City,Guizhou Province from January 1,2015 to December 31,2019.The results showed that the concentration of air pollutants in the atmosphere in the past five years showed a downward trend in the central urban area of Liupanshui City.During 2018-2019,the air quality has been up to the standard for two consecutive years,and it was developing to a higher quality direction.The air quality was better in summer half year than in winter half year.In one year,the air quality was the best in June and the worst in February.The air quality was the best at 07:00 and the worst at 21:00 every day.The air quality in the east and the west of the city was better than that in the middle.In most years,the activities,making and burning paper to resemble money as an offering sacrifices to gods or ancestors in Zhongyuan Festival,caused serious pollution.展开更多
Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as w...Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as well as the changes in the trend and the affecting mechanism. Based on statistics and auto-correlation analysis, this paper studied the spatial and temporal distribution of lung cancer mortality in Yuhui District, Bengbu, Huaihe River Basin, from 2017 to 2020. In addition, Spearman’s Rank Correlation Assessment Model and Geographic Detector Model were used to examine the relationship between environmental factors and lung cancer mortality to identify impact factors and their mechanisms. The findings indicated that: 1) from the characteristics of temporal distribution, the number of lung cancer deaths exhibited a linear growth tendency, with the highest mortality in winter;2) from the characteristics of spatial distribution, lung cancer mortality showed a strong spatial agglomeration form, concentrating on two clustering areas, located in the old city and the central city of Bengbu, near the Huaihe River;3) from the point of view of the whole research area, there were 15 impact factors with significant correlation in the built and natural environment factors. The significant impacting factors in the built environment included land use, road traffic, spatial form and blue-green space, which could indirectly affect lung cancer mortality, while air pollution and temperature constituted the significant impacting factors in the natural environment;4) the influence of screened environmental factors on lung cancer mortality was different. Spatial stratified heterogeneity assessment, the interaction among environmental factors demonstrated statistical significance, it was found that the interaction between environmental factors in pairs had a significant enhancement effect on lung cancer mortality. To some extent, urban planning and policies could reduce lung cancer mortality.展开更多
App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app descri...App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentimentanalysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28 % of apps in Wandoujia App Store and 66.68 % of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25 %. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads.展开更多
As weather radar stations require headroom environment to operate,they were mostly built on highlands which are usually unattended.The mains supply is relatively poor,and the risk of radar stoppages due to power outag...As weather radar stations require headroom environment to operate,they were mostly built on highlands which are usually unattended.The mains supply is relatively poor,and the risk of radar stoppages due to power outage is therefore ever-present.As such,the radar construction program is used to build a complementary security video monitoring system.By collecting monitoring images of the regulated power supply in real-time from power supply auto transfer systems in distribution rooms and radar transceiver rooms,using Spearman’s rank correlation coefficient to analyse pixel variation trends,and supplementing statistical analysis of pixel characteristics difference to eliminate misjudgments resulting from low image contrast in special scenarios,a software can be developed through C#.It has the function of automatically monitoring mains supply and alerting staff on duty to handle the power outage in a timely manner via text message so that any potential risk is neutralised before it can cause damage.This monitoring and auto-alerting approach is generally applicable to unattended rooms with large amounts of electronical equipment.展开更多
The commencement of China–Pakistan Economic Corridor has led to the appreciation of Pakistan’s economic outlook from 5.4%to 5.8%by the World Bank.The upgraded outlook is a welcome sign but it is still trivial,essent...The commencement of China–Pakistan Economic Corridor has led to the appreciation of Pakistan’s economic outlook from 5.4%to 5.8%by the World Bank.The upgraded outlook is a welcome sign but it is still trivial,essentially attributable to the electric power crisis,which approximately trims 2%of Pakistan’s economic growth annually.Almost 60%of the CPEC(China–Pakistan Economic Corridor)funds are directed at Pakistan’s energy sector,hence,demanding careful attention of both researchers and policy analysts alike.The study is based upon a meta-analytic review of literature concerning CPEC and Pakistan’s energy sector.The results of the study demonstrate that CPEC is an easing agent for Pakistan’s energy crisis(82.30%).The results also highlight points of concern,including inadequate planning(47%),dilapidated electricity distribution system causing losses(64.7%),and an unsustainable energy mix(64.7%).The study further validates the findings via Spearman’s Rho-Correlation.The rρvalue for the possible“resolution of Pakistan’s energy crisis”is 0.5426 achieving a significance level of 98%and a corresponding p-value of 0.0252.The significant negative rρvalue attained is−0.4894 which establishes the fact that lack of planning can hinder the energy crisis resolution.展开更多
基金funded by the National Natural Science Foundation of China(42171145,42171147)the Gansu Provincial Science and Technology Program(22ZD6FA005)the Key Talent Program of Gansu Province.
文摘Rain-on-snow(ROS)events involve rainfall on snow surfaces,and the occurrence of ROS events can exacerbate water scarcity and ecosystem vulnerability in the arid region of Northwest China(ARNC).In this study,using daily snow depth data and daily meteorological data from 68 meteorological stations provided by the China Meteorological Administration National Meteorological Information Centre,we investigated the spatiotemporal variability of ROS events in the ARNC from 1978 to 2015 and examined the factors affecting these events and possible changes of future ROS events in the ARNC.The results showed that ROS events in the ARNC mainly occurred from October to May of the following year and were largely distributed in the Qilian Mountains,Tianshan Mountains,Ili River Valley,Tacheng Prefecture,and Altay Prefecture,with the Ili River Valley,Tacheng City,and Altay Mountains exhibiting the most occurrences.Based on the intensity of ROS events,the areas with the highest risk of flooding resulting from ROS events in the ARNC were the Tianshan Mountains,Ili River Valley,Tacheng City,and Altay Mountains.The number and intensity of ROS events in the ARNC largely increased from 1978 to 2015,mainly influenced by air temperature and the number of rainfall days.However,due to the snowpack abundance in areas experiencing frequent ROS events in the ARNC,snowpack changes exerted slight impact on ROS events,which is a temporary phenomenon.Furthermore,elevation imposed lesser impact on ROS events in the ARNC than other factors.In the ARNC,the start time of rainfall and the end time of snowpack gradually advanced from the spring of the current year to the winter of the previous year,while the end time of rainfall and the start time of snowpack gradually delayed from autumn to winter.This may lead to more ROS events in winter in the future.These results could provide a sound basis for managing water resources and mitigating related disasters caused by ROS events in the ARNC.
基金supported by National Key R&D Program of China(Nos.2019YFC0214800 and 2017YFC0212100)Beijing Municipal Science&Technology Commission(No.Z181100005418015)。
文摘In recent years,with rapid increases in the number of vehicles in China,the contribution of vehicle exhaust emissions to air pollution has become increasingly prominent.To achieve the precise control of emissions,on-road remote sensing(RS)technology has been developed and applied for law enforcement and supervision.However,data quality is still an existing issue affecting the development and application of RS.In this study,the RS data from a cross-road RS system used at a single site(from 2012 to 2015)were collected,the data screening process was reviewed,the issues with data quality were summarized,a new method of data screening and calibration was proposed,and the effectiveness of the improved data quality control methods was finally evaluated.The results showed that this method reduces the skewness and kurtosis of the data distribution by up to nearly 67%,which restores the actual characteristics of exhaust diffusion and is conducive to the identification of actual clean and high-emission vehicles.The annual variability of emission factors of nitric oxide decreases by 60%-on average-eliminating the annual drift of fleet emissions and improving data reliability.
基金Financial support for carrying out this work was provided by the Shandong Provincial Key Research and Development Program(2018YFJH0802)。
文摘Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.
基金Supported by State Council Special Fund for Pollution Sources Survey (WPXC2007C200)~~
文摘Change trend of Chinese urban residents＇ per capita food-nitrogen annual consumption from 1981 to 2007 was analyzed and predicted by using ARIMA time-series model in order to reveal the change of urban food-nitrogen consumption during the China＇s urbanization process.Results showed that after 1980s,the annual consumption of Chinese urban residents＇ food-nitrogen had a change trend of ＂ increase-decrease-increase＂ and generally presented as a slight increasing trend;With the acceleration of rapid economic development and urbanization process,Chinese urban residents＇ food-nitrogen consumption will still keep a rising trend in future,and also has a large rising space.
基金This work was supported by the National Key Research and Development Program of China(2018YFA0702800)the National Natural Science Foundation of China(51805068).
文摘Guided waves based damage detection methods using base signals offer the advantages of simplicity of signal generation and reception,sensitivity to damage,and large area coverage;however,applications of the technology are limited by the sensitivity to environmental temperature variations.In this paper,a Spearman Damage Index-based damage diagnosis method for structural health condition monitoring under varying temperatures is presented.First,a PZT sensor-based Guided wave propagation model is proposed and employed to analyze the temperature effect.The result of the analysis shows the wave speed of the Guided wave signal has higher temperature sensitivity than the signal fluctuation features.Then,a Spearman rank correlation coefficient-based damage index is presented to identify damage of the structure under varying temperatures.Finally,a damage detection test on a composite plate is conducted to verify the effectiveness of the Spearman Damage Index-based damage diagnosis method.Experimental results show that the proposed damage diagnosis method is capable of detecting the existence of the damage and identify its location under varying temperatures.
基金Supported by the Science and Technology Plan Project of Liupanshui City(52020-2015-30).
文摘Air is an important condition for human activities and survival,and its quality is closely related to the quality of life and level of health for the people.In recent years,the problem caused by air quality has become one of the main problems that endanger human health and restrict economic development,which has been widely concerned.In this paper,the air quality status and its changing trend were analyzed by using the methods of the comprehensive index of ambient air quality and Spearman s rank correlation coefficient,based on the hourly pollutant concentration data of five national ambient air quality monitoring stations in the central urban area of Liupanshui City,Guizhou Province from January 1,2015 to December 31,2019.The results showed that the concentration of air pollutants in the atmosphere in the past five years showed a downward trend in the central urban area of Liupanshui City.During 2018-2019,the air quality has been up to the standard for two consecutive years,and it was developing to a higher quality direction.The air quality was better in summer half year than in winter half year.In one year,the air quality was the best in June and the worst in February.The air quality was the best at 07:00 and the worst at 21:00 every day.The air quality in the east and the west of the city was better than that in the middle.In most years,the activities,making and burning paper to resemble money as an offering sacrifices to gods or ancestors in Zhongyuan Festival,caused serious pollution.
基金Under the auspices of Natural Science Foundation of Anhui Province (No. 2008085ME160)Provincial Natural Science Research Projects in Anhui Province-Postgraduate Projects (No. YJS20210500)。
文摘Among cancers, lung cancer is the most common cause of death in China. For the prevention and control of lung cancer, it is necessary to investigate the spatial and temporal distribution of lung cancer mortality, as well as the changes in the trend and the affecting mechanism. Based on statistics and auto-correlation analysis, this paper studied the spatial and temporal distribution of lung cancer mortality in Yuhui District, Bengbu, Huaihe River Basin, from 2017 to 2020. In addition, Spearman’s Rank Correlation Assessment Model and Geographic Detector Model were used to examine the relationship between environmental factors and lung cancer mortality to identify impact factors and their mechanisms. The findings indicated that: 1) from the characteristics of temporal distribution, the number of lung cancer deaths exhibited a linear growth tendency, with the highest mortality in winter;2) from the characteristics of spatial distribution, lung cancer mortality showed a strong spatial agglomeration form, concentrating on two clustering areas, located in the old city and the central city of Bengbu, near the Huaihe River;3) from the point of view of the whole research area, there were 15 impact factors with significant correlation in the built and natural environment factors. The significant impacting factors in the built environment included land use, road traffic, spatial form and blue-green space, which could indirectly affect lung cancer mortality, while air pollution and temperature constituted the significant impacting factors in the natural environment;4) the influence of screened environmental factors on lung cancer mortality was different. Spatial stratified heterogeneity assessment, the interaction among environmental factors demonstrated statistical significance, it was found that the interaction between environmental factors in pairs had a significant enhancement effect on lung cancer mortality. To some extent, urban planning and policies could reduce lung cancer mortality.
文摘App store provides rich information for software vendors and customers to understand the market of mobile applications. However, app store analysis don’t consider some vital factors such as version number, app description and app name currently. In this paper we propose an approach that App Store Analysis can be used to predict app downloads. We use data mining to extract app name and description and app rank information etc. from the Wandoujia App Store and AppCha App Store. We use questionnaire and sentimentanalysis to quantify some app nonnumeric information. We revealed strong correlations app name score, app rank, app rating with app downloads by Spearman’s rank correlation analysis respectively. Finally, we establish a multiple nonlinear regression model which app downloads defined as dependent variable and three relevant attributes defined as independent variable. On average, 59.28 % of apps in Wandoujia App Store and 66.68 % of apps in AppCha App Store can be predicted accurately within threshold which error rate is 25 %. One can observe the more detailed classification of app store, the more accurate for regression modeling to predict app downloads. Our approach can help app developers to notice and optimize the vital factors which influence app downloads.
基金Supported by Science and Technology Open Research Fund Project of Guizhou Meteorological Bureau(KF[2009]08)。
文摘As weather radar stations require headroom environment to operate,they were mostly built on highlands which are usually unattended.The mains supply is relatively poor,and the risk of radar stoppages due to power outage is therefore ever-present.As such,the radar construction program is used to build a complementary security video monitoring system.By collecting monitoring images of the regulated power supply in real-time from power supply auto transfer systems in distribution rooms and radar transceiver rooms,using Spearman’s rank correlation coefficient to analyse pixel variation trends,and supplementing statistical analysis of pixel characteristics difference to eliminate misjudgments resulting from low image contrast in special scenarios,a software can be developed through C#.It has the function of automatically monitoring mains supply and alerting staff on duty to handle the power outage in a timely manner via text message so that any potential risk is neutralised before it can cause damage.This monitoring and auto-alerting approach is generally applicable to unattended rooms with large amounts of electronical equipment.
文摘The commencement of China–Pakistan Economic Corridor has led to the appreciation of Pakistan’s economic outlook from 5.4%to 5.8%by the World Bank.The upgraded outlook is a welcome sign but it is still trivial,essentially attributable to the electric power crisis,which approximately trims 2%of Pakistan’s economic growth annually.Almost 60%of the CPEC(China–Pakistan Economic Corridor)funds are directed at Pakistan’s energy sector,hence,demanding careful attention of both researchers and policy analysts alike.The study is based upon a meta-analytic review of literature concerning CPEC and Pakistan’s energy sector.The results of the study demonstrate that CPEC is an easing agent for Pakistan’s energy crisis(82.30%).The results also highlight points of concern,including inadequate planning(47%),dilapidated electricity distribution system causing losses(64.7%),and an unsustainable energy mix(64.7%).The study further validates the findings via Spearman’s Rho-Correlation.The rρvalue for the possible“resolution of Pakistan’s energy crisis”is 0.5426 achieving a significance level of 98%and a corresponding p-value of 0.0252.The significant negative rρvalue attained is−0.4894 which establishes the fact that lack of planning can hinder the energy crisis resolution.
