Industrial structure has been continuously optimized and upgraded with the rapid development of economy in Shandong Province since1980. On the basis of summarizing the changes of industrial structure and employment st...Industrial structure has been continuously optimized and upgraded with the rapid development of economy in Shandong Province since1980. On the basis of summarizing the changes of industrial structure and employment structure in Shandong Province,the coordination between industrial structure and employment structure in Shandong Province was analyzed by using structure deviation degree and coordination coefficient,and some suggestions to promote the coordinated development of industrial structure and employment structure were put forward according to the practical situations of Shandong Province.展开更多
Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an e...Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an evaluation system to measure the smart growth of a city.And the other develops a growth plan.Firstly,coordination coefficient(C value) model is applied to measure the smart degree.To begin with,we divide the indicators into four aspects which involve five parameters.Then,entropy method is used to calculate the weight of every parameter.After normalizing data of indicators,we set up a smart growth indicator evaluation system.Aiming to assessing the detailed performances,we rank the eight cities according to the score of C value which corresponds to our normal cognition.Secondly,based on Salvo combat model and dynamic trend analysis model,We draw up a 20-year growth plan with a period of 5 years for the two cities we choose.The Salvo model is adopted to describe the dynamic process.Dynamic trend analysis model is introduced to gain the optimum solution and the optimal point in every stage.In addition,compared with the point of every stage,we can obtain the proportion of investment in different stages.Thirdly,to evaluate the sensitivity of our model with the OFAT Method,we adjust the parameters k_1,k_2 and O_(ij) approximately.It comes out that the change of k_1,k_2 and O_(ij) has an impact on the C value.But the sensitivity of k_1,k_2 is higher.Lastly,we analyze the influence caused by population growth.To a certain extent,it can be concluded that the plan we made can alleviate the negative impact of population growth through the analysis of the chart.展开更多
The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this...The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this study proposes three convolutional prediction models:CNN-LSTM-Attention,CNN-BiLSTM-Attention,and CNN-GRUAttention.The displacement coordination coefficient(DCC)index and stress curves were employed as input variables to evaluate the performance of each model in discriminating rock stability states under different data structures and input configurations.Furthermore,an innovative methodology for predicting rock failure time utilizing convolutional models was developed.The experimental results demonstrate that the CNN-LSTMAttention model,utilizing a 10×10×2 data structure,exhibits superior performance in rock stability state discrimination,achieving an accuracy of 95.25%on the validation set and a recall rate of 96%for samples in high-risk areas.Furthermore,when the DCC index was used as the input variable,the CNN-LSTM-Attention model achieved recall rates of 95.8%and 86.5%for medium-and high-risk areas,respectively,in the validation set.These findings indicate that the proposed convolutional models can effectively identify rock stability states by leveraging surface deformation characteristics.The CNN-LSTM-Attention model,with the DCC index as the input variable,is capable of predicting the final rock failure time in real-time once the DCC abrupt change exceeds 0.78.For different rocks,the model can predict the failure time within 20 s after the DCC reaches 0.78,with an error rate of less than 9%.The convolutional neural network model,developed based on the DCC index,provides a novel methodological approach for geohazard early warning research,facilitating slope instability monitoring and earthquake precursor identification using GNSS and other displacement measurement techniques.展开更多
基金Supported by the"Double Service"Project of Binzhou University in 2017(BZXYSFW201713,BZXYSFW201719)
文摘Industrial structure has been continuously optimized and upgraded with the rapid development of economy in Shandong Province since1980. On the basis of summarizing the changes of industrial structure and employment structure in Shandong Province,the coordination between industrial structure and employment structure in Shandong Province was analyzed by using structure deviation degree and coordination coefficient,and some suggestions to promote the coordinated development of industrial structure and employment structure were put forward according to the practical situations of Shandong Province.
文摘Smart growth has been gaining increasing attention among academia and practitioners as a new technology-based solution to meet the city disease challenges.In the research,we mainly accomplish two tasks.One builds an evaluation system to measure the smart growth of a city.And the other develops a growth plan.Firstly,coordination coefficient(C value) model is applied to measure the smart degree.To begin with,we divide the indicators into four aspects which involve five parameters.Then,entropy method is used to calculate the weight of every parameter.After normalizing data of indicators,we set up a smart growth indicator evaluation system.Aiming to assessing the detailed performances,we rank the eight cities according to the score of C value which corresponds to our normal cognition.Secondly,based on Salvo combat model and dynamic trend analysis model,We draw up a 20-year growth plan with a period of 5 years for the two cities we choose.The Salvo model is adopted to describe the dynamic process.Dynamic trend analysis model is introduced to gain the optimum solution and the optimal point in every stage.In addition,compared with the point of every stage,we can obtain the proportion of investment in different stages.Thirdly,to evaluate the sensitivity of our model with the OFAT Method,we adjust the parameters k_1,k_2 and O_(ij) approximately.It comes out that the change of k_1,k_2 and O_(ij) has an impact on the C value.But the sensitivity of k_1,k_2 is higher.Lastly,we analyze the influence caused by population growth.To a certain extent,it can be concluded that the plan we made can alleviate the negative impact of population growth through the analysis of the chart.
基金supported by the National Natural Science Foundation of China(No.52474106).
文摘The identification of rock stability and the prediction of failure time are crucial for the early warning and prevention of sudden geological disasters such as landslides and collapses.To address these challenges,this study proposes three convolutional prediction models:CNN-LSTM-Attention,CNN-BiLSTM-Attention,and CNN-GRUAttention.The displacement coordination coefficient(DCC)index and stress curves were employed as input variables to evaluate the performance of each model in discriminating rock stability states under different data structures and input configurations.Furthermore,an innovative methodology for predicting rock failure time utilizing convolutional models was developed.The experimental results demonstrate that the CNN-LSTMAttention model,utilizing a 10×10×2 data structure,exhibits superior performance in rock stability state discrimination,achieving an accuracy of 95.25%on the validation set and a recall rate of 96%for samples in high-risk areas.Furthermore,when the DCC index was used as the input variable,the CNN-LSTM-Attention model achieved recall rates of 95.8%and 86.5%for medium-and high-risk areas,respectively,in the validation set.These findings indicate that the proposed convolutional models can effectively identify rock stability states by leveraging surface deformation characteristics.The CNN-LSTM-Attention model,with the DCC index as the input variable,is capable of predicting the final rock failure time in real-time once the DCC abrupt change exceeds 0.78.For different rocks,the model can predict the failure time within 20 s after the DCC reaches 0.78,with an error rate of less than 9%.The convolutional neural network model,developed based on the DCC index,provides a novel methodological approach for geohazard early warning research,facilitating slope instability monitoring and earthquake precursor identification using GNSS and other displacement measurement techniques.
