OBJECTIVE: To apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and a...OBJECTIVE: To apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and analysis of data on 3970 sub-mentally healthy individuals selected from 13385 relevant question naires.METHODS: The strategic tree algorithm was used to identify the main mani festations of the state of sub-mental health. The backpropogation artificial neural network was used to analyze the main mani festations of sub-healthy mental states of three different degrees. A sub-mental health evaluation model was then established to achieve predictive evaluationresults.RESULTS: Using classifications from the Scale of Chinese Sub-healthy State, the main manifestations of sub-mental health selected using the strate gictree were F1101(Do you lack peace of mind?),F1102(Are you easily nervous when something comes up?), and F1002(Do you often sigh?). The relative intensity of manifestations of sub-mental health was highest for F1101, followed by F1102,and then F1002. Through study of the neural network, better differentiation could be made between moderate and severe and between mild and severe states of sub-mental health. The differentiation between mild and moderate sub-mental health states was less apparent. Additionally, the sub-mental health state evaluation model, which could be used to predict states of sub-mental health of different individuals, was established using F1101, F1102, F1002, and the mental self-assessment totals core.CONCLUSION: The main manifestations of the state of sub-mental health can be discovered using data mining methods to research and analyze the latent laws and knowledge hidden in research evidence on the state of sub-mental health. The state of sub-mental health of different individuals can be rapidly predicted using the model established here.This can provide a basis for assessment and intervention for sub-mental health. It can also replace the relatively outdated approaches to research on sub-health in the technical era of information and digitization by combining the study of states of sub-mental health with information techniques and by further quantifying the relevant information.展开更多
Based on the measured beach profile data of Sanzhou Bay from 2015 to 2019,an erosion hotspot was identified along the Shanwei coastline of eastern Guangdong,where the maximum retreat distance of the shoreline exceeded...Based on the measured beach profile data of Sanzhou Bay from 2015 to 2019,an erosion hotspot was identified along the Shanwei coastline of eastern Guangdong,where the maximum retreat distance of the shoreline exceeded 80 m and the erosion rate was more than 20 m/a.To determine the time at which the erosion hotspot started and the potential causes of its formation,this study used 63 Landsat satellite images from 1986 to 2019 to construct a time series of shoreline positions over the past 30 years by extracting their high-tide shorelines.Next,the M-K trend test method was introduced to evaluate the non-linear shoreline behavior based on the single-transect method.The results showed that the time of approximately 2013 marked the start of the erosion hotspot,the erosion hotspot was characterized by erosion rates of more than 2 m/a(a maximum rate of 31.6 m/a),and the affected shoreline more than 4.3 km from 2013 to 2019.Furthermore,this erosion hotspot was proved to be caused by artificial sand mining in the nearshore zone,which destroyed the original beach’s morphodynamic equilibrium.With the aid of storm events,soil cliffs composed of loose sediment on the backshore were sacrificed to achieve a new equilibrium,resulting in an extremely significant retreat parallel to the coast on the west side of the study area,which reflects the combined effect of human and natural processes.This study provides a concrete example of the rapid response of shorelines to artificial sand mining activities,and the associated finding is a stark warning about the cautious development and utilization of coastal zones and the strict regulation of human activities.展开更多
Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has chal...Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.展开更多
One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire min...One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters ha,; been scrutinised based on a published UB predicting artificial neuron network （ANN） model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm （CWA） and profile method （PM） have been applied. As a result of CWA and PM applications, adjusted Qrate （AQ） revealed as the most influential parameter to UB with contribution of 22,40% in CWA and 20,48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.展开更多
基金Supported by Chinese"Disease"Sub-health Medicine Research and Intervention of the Eleventh Five-Year Science and Technology Support Project of China(No.2006BAI13B01)Financial Support Case Studies of Traditional Chinese Medicine Treatment of Disease and Health Management Ideas of Shanghai Health Bureau(No.2010227)+2 种基金Scientific Innovation Research Funds of Shanghai Municipal Education Commission(No.14YZ061)Teacher Academic Community Fund of Shanghai University of Traditional Chinese Medicine(No.2013JXG03)Chinese Culture and Its Core Value System Modernization Transformation of the National Social Science Funds(No.12AZD094)
文摘OBJECTIVE: To apply data mining methods to research on the state of sub-mental health among residents in eight provinces and cities in China and to mine latent knowledge about many conditions through data mining and analysis of data on 3970 sub-mentally healthy individuals selected from 13385 relevant question naires.METHODS: The strategic tree algorithm was used to identify the main mani festations of the state of sub-mental health. The backpropogation artificial neural network was used to analyze the main mani festations of sub-healthy mental states of three different degrees. A sub-mental health evaluation model was then established to achieve predictive evaluationresults.RESULTS: Using classifications from the Scale of Chinese Sub-healthy State, the main manifestations of sub-mental health selected using the strate gictree were F1101(Do you lack peace of mind?),F1102(Are you easily nervous when something comes up?), and F1002(Do you often sigh?). The relative intensity of manifestations of sub-mental health was highest for F1101, followed by F1102,and then F1002. Through study of the neural network, better differentiation could be made between moderate and severe and between mild and severe states of sub-mental health. The differentiation between mild and moderate sub-mental health states was less apparent. Additionally, the sub-mental health state evaluation model, which could be used to predict states of sub-mental health of different individuals, was established using F1101, F1102, F1002, and the mental self-assessment totals core.CONCLUSION: The main manifestations of the state of sub-mental health can be discovered using data mining methods to research and analyze the latent laws and knowledge hidden in research evidence on the state of sub-mental health. The state of sub-mental health of different individuals can be rapidly predicted using the model established here.This can provide a basis for assessment and intervention for sub-mental health. It can also replace the relatively outdated approaches to research on sub-health in the technical era of information and digitization by combining the study of states of sub-mental health with information techniques and by further quantifying the relevant information.
基金The National Natural Science Foundation of China under contract No.42071007the Natural Science Foundation of Hainan Province under contract Nos 421QN0883 and 422RC665the Foundation of Department Budget Projects of Hainan Province in 2022 under contract No.KYL-2022-06.
文摘Based on the measured beach profile data of Sanzhou Bay from 2015 to 2019,an erosion hotspot was identified along the Shanwei coastline of eastern Guangdong,where the maximum retreat distance of the shoreline exceeded 80 m and the erosion rate was more than 20 m/a.To determine the time at which the erosion hotspot started and the potential causes of its formation,this study used 63 Landsat satellite images from 1986 to 2019 to construct a time series of shoreline positions over the past 30 years by extracting their high-tide shorelines.Next,the M-K trend test method was introduced to evaluate the non-linear shoreline behavior based on the single-transect method.The results showed that the time of approximately 2013 marked the start of the erosion hotspot,the erosion hotspot was characterized by erosion rates of more than 2 m/a(a maximum rate of 31.6 m/a),and the affected shoreline more than 4.3 km from 2013 to 2019.Furthermore,this erosion hotspot was proved to be caused by artificial sand mining in the nearshore zone,which destroyed the original beach’s morphodynamic equilibrium.With the aid of storm events,soil cliffs composed of loose sediment on the backshore were sacrificed to achieve a new equilibrium,resulting in an extremely significant retreat parallel to the coast on the west side of the study area,which reflects the combined effect of human and natural processes.This study provides a concrete example of the rapid response of shorelines to artificial sand mining activities,and the associated finding is a stark warning about the cautious development and utilization of coastal zones and the strict regulation of human activities.
文摘Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.
文摘One of the most serious conundrum facing the stope production in underground metalliferous mining is uneven break （UB： unplanned dilution and ore-loss）. Although the UB has a huge economic fallout to the entire mining process, it is practically unavoidable due to the complex causing mechanism. In this study, the contribution of ten major UB causative parameters ha,; been scrutinised based on a published UB predicting artificial neuron network （ANN） model to put UB under the engineering management. Two typical ANN sensitivity analysis methods, i.e., connection weight algorithm （CWA） and profile method （PM） have been applied. As a result of CWA and PM applications, adjusted Qrate （AQ） revealed as the most influential parameter to UB with contribution of 22,40% in CWA and 20,48% in PM respectively. The findings of this study can be used as an important reference in stope design, production, and reconciliation stages on underground stoping mine.
