The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML),and which structure determines the long-term mechanism.Based on the panel data from 2001 to 2015,with th...The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML),and which structure determines the long-term mechanism.Based on the panel data from 2001 to 2015,with the dynamic panel model and system GMM estimation method were employed to test the influence of heterogeneous environmental regulation on green mining and its transmission mechanism.The results show that,there is a 'U' type nonlinear relationship between the ERI and GML.The direct effect of command-control-based (CAC) and the market incentive-based (MBI) environmental regulation on green development of mining shows the characteristics of inhibition and promotion.There is a 'U' type of indirectly moderating effect between technological innovation and the energy consumption structure on the GML.The technological innovation promotes the green development of the mining industry only after pass the inflection point of MBI,while the CAC plays a significant guiding role in upgrading of the energy consumption structure.There is an inhibition and promotion effect of MBI on the GML in the southeast coastal area,and the CAC is not significantly.Meanwhile,both of the ERI shows no positive effects in the central and western inland region.展开更多
This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a var...This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a variable to consider, such as how the flow of people changes over time as well as how location interacts with placement. The predicted data is analyzed using Vensim PLE 32 modeling software, GIS Arc Map 10.2.1, and AnyLogic 7.3.1 software regarding the potential placement of temporal service points, taking into consideration the three dynamic constraints and behavioral aspects: a large population, limitation in time, and space. This research proposes appropriate data analyses to ensure the optimal positioning of the service points with limited time and space for large-scale events. The conceptual framework would be the output of this study. Knowledge may be added to the insights based on the technique.展开更多
Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus o...Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available;the link is given in the dataset section.展开更多
To improve surface accuracy of the work-piece and obtain potentially valuable information,a dynamic milling force prediction model was proposed based on data mining.In view of the current dynamic milling force obtaine...To improve surface accuracy of the work-piece and obtain potentially valuable information,a dynamic milling force prediction model was proposed based on data mining.In view of the current dynamic milling force obtained through finite element simulation and analytical calculation,in the finite element modeling,the model built is inevitably different from the actual working conditions,and the analytical calculation is slightly cumbersome and complex,and a dynamic milling force prediction model based on data mining is proposed.The model was established using a combination of regression analysis and Radial Basis Function(RBF) neural network.Using data mining as a means,the internal relationship between milling force,cutting parameters,temperature,vibration and surface quality is deeply analyzed,and the influence of dynamic milling force changes on different situations is extracted and summarized by the methods of cluster analysis and correlation analysis.The results show that the proposed dynamic milling force model has a good prediction effect,ensures the production quality,reduces the occurrence of flutter,improves the surface accuracy of the work-piece,and provides a more accurate basis for the selection of process parameters.展开更多
文摘The intensity of environmental regulation (ERI) affects the short-term effect of the level of green mining (GML),and which structure determines the long-term mechanism.Based on the panel data from 2001 to 2015,with the dynamic panel model and system GMM estimation method were employed to test the influence of heterogeneous environmental regulation on green mining and its transmission mechanism.The results show that,there is a 'U' type nonlinear relationship between the ERI and GML.The direct effect of command-control-based (CAC) and the market incentive-based (MBI) environmental regulation on green development of mining shows the characteristics of inhibition and promotion.There is a 'U' type of indirectly moderating effect between technological innovation and the energy consumption structure on the GML.The technological innovation promotes the green development of the mining industry only after pass the inflection point of MBI,while the CAC plays a significant guiding role in upgrading of the energy consumption structure.There is an inhibition and promotion effect of MBI on the GML in the southeast coastal area,and the CAC is not significantly.Meanwhile,both of the ERI shows no positive effects in the central and western inland region.
文摘This paper offers preliminary work on system dynamics and Data mining tools. It tries to understand the dynamics of carrying out large-scale events, such as Hajj. The study looks at a large, recurring problem as a variable to consider, such as how the flow of people changes over time as well as how location interacts with placement. The predicted data is analyzed using Vensim PLE 32 modeling software, GIS Arc Map 10.2.1, and AnyLogic 7.3.1 software regarding the potential placement of temporal service points, taking into consideration the three dynamic constraints and behavioral aspects: a large population, limitation in time, and space. This research proposes appropriate data analyses to ensure the optimal positioning of the service points with limited time and space for large-scale events. The conceptual framework would be the output of this study. Knowledge may be added to the insights based on the technique.
基金supported by the National Natural Science Foundation of China(grant no.61573328).
文摘Faster internet, IoT, and social media have reformed the conventional web into a collaborative web resulting in enormous user-generated content. Several studies are focused on such content;however, they mainly focus on textual data, thus undermining the importance of metadata. Considering this gap, we provide a temporal pattern mining framework to model and utilize user-generated content's metadata. First, we scrap 2.1 million tweets from Twitter between Nov-2020 to Sep-2021 about 100 hashtag keywords and present these tweets into 100 User-Tweet-Hashtag (UTH) dynamic graphs. Second, we extract and identify four time-series in three timespans (Day, Hour, and Minute) from UTH dynamic graphs. Lastly, we model these four time-series with three machine learning algorithms to mine temporal patterns with the accuracy of 95.89%, 93.17%, 90.97%, and 93.73%, respectively. We demonstrate that user-generated content's metadata contains valuable information, which helps to understand the users' collective behavior and can be beneficial for business and research. Dataset and codes are publicly available;the link is given in the dataset section.
基金Supported by Gansu Science and Technology Program(21YF5GA080)。
文摘To improve surface accuracy of the work-piece and obtain potentially valuable information,a dynamic milling force prediction model was proposed based on data mining.In view of the current dynamic milling force obtained through finite element simulation and analytical calculation,in the finite element modeling,the model built is inevitably different from the actual working conditions,and the analytical calculation is slightly cumbersome and complex,and a dynamic milling force prediction model based on data mining is proposed.The model was established using a combination of regression analysis and Radial Basis Function(RBF) neural network.Using data mining as a means,the internal relationship between milling force,cutting parameters,temperature,vibration and surface quality is deeply analyzed,and the influence of dynamic milling force changes on different situations is extracted and summarized by the methods of cluster analysis and correlation analysis.The results show that the proposed dynamic milling force model has a good prediction effect,ensures the production quality,reduces the occurrence of flutter,improves the surface accuracy of the work-piece,and provides a more accurate basis for the selection of process parameters.
