Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathem...Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.展开更多
The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy R...The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy Resource Management(SDHRM)algorithm exploiting the resources dynamically and intelligently is proposed with the consideration of tidal traffic.In network-level resource allocation,the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility.In connection-level network selection,based on the above resource allocation and the pre-defined QoS requirement,three typical network selection policies are provided to assign traffic flow to the most appropriate network.Furthermore,based on multidimensional Markov model,we analyse the performance of SDHRM in HWNs with heavy tailed traffic.Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can improve the resource utilization.展开更多
Background The present study investigated the prognostic value of medical comorbidities at admission for 30-day in-hospital mortality in patients with acute myocardial infarction （AMI）. Methods A total of 5161 patie...Background The present study investigated the prognostic value of medical comorbidities at admission for 30-day in-hospital mortality in patients with acute myocardial infarction （AMI）. Methods A total of 5161 patients with AMI were admitted in Chinese PLA General Hospital between January 1, 1993 and December 31, 2007. Medical comorbidities including hypertension, diabetes mellitus, previous myocardial infarction, valvular heart disease, chronic obstructive pulmonary disease （COPD）, renal insufficiency, previous stroke, atrial fibrillation and anemia, were identified at admission. The patients were divided into 4 groups based on the number of medical comorbidities at admission （0, 1, 2, and ≥3）. Cox regression analysis was used to calculate relative risk （RR） and 95% confidence intervals （CI）, with adjustment for age, sex, heart failure and percutaneous coronary intervention （PCI）. Results The mean age of the studied population was 63.9 ± 13.6 years, and 80.1% of the patients were male. In 74.6% of the patients at least one comorbidity were identified. Hypertension （50.7%）, diabetes mellitus （24.0%） and previous myocardial infarction （12%） were the leading common comorbidities at admission. The 30-day in-hospital mortality in patients with 0, 1, 2, and ≥3 comorbidities at admission （7.2%） was 4.9%, 7.2%, 11.1%, and 20.3%, respectively. The presence of 2 or more comorbidities was associated with higher 30-day in-hospital mortality compared with patients without comorbidity （RR： 1.41, 95% CI： 1.13-1.77, P = 0.003, and RR： 1.95, 95% CI： 1.59-2.39, P = 0.000, respectively）. Conclusions Medical comorbidities were frequently found in patients with AMI. AMI patients with more comorbidities had a higher 30-day in-hospital mortality might be predictive of early poor outcome in patients with AMI.展开更多
Objective To identify clinical characteristics associated with the minimum lumen area （MLA） of proximal or middle intermediate lesions in the left anterior descending （LAD） artery, and to develop a model to predic...Objective To identify clinical characteristics associated with the minimum lumen area （MLA） of proximal or middle intermediate lesions in the left anterior descending （LAD） artery, and to develop a model to predict MLA. Methods We retrospectively analyzed demographic data, medical history, and intravascular ultrasound findings for 90 patients with intermediate lesions in the LAD artery. Linear regression was used to identify factors affecting MLA, and multiple regression was used to develop a model for predicting MLA. Results Age, number of lesions, and diabetes mellitus correlated significantly with MLA of proximal or middle intermediate lesions. A regression model for predicting MLA （mm2） was derived from the data： 7.00 - 0.05 × （age） - 0.50 × （number of lesions）. A cut-off value of 3.1 mm2 was proposed for deciding when to perform percutaneous coronary intervention. Conclusion This model for predicting MLA of proximal or middle intermediate lesions in the LAD artery showed high accuracy, sensitivity, and specificity, indicating good diagnostic potential.展开更多
The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the...The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the first time as a way of predicting sandstone thickness in the study area.The model was constructed by analysis and optimization of measured seismic attributes.The distribution of the sedimentary microfacies in the study area was determined from predicted sandstone thickness and an analysis of sedimentary characteristics of the area.The results indicate that sandstone thickness predictions in the study area using an SVM method are good.The distribution of the sedimentary microfacies in the study area has been depicted at a fine scale.展开更多
文摘Uniaxial Compressive Strength (UCS) and modulus of elasticity (E) are the most important rock parameters required and determined for rock mechanical studies in most civil and mining projects. In this study, two mathematical methods, regression analysis and Artificial Neural Networks (ANNs), were used to predict the uniaxial compressive strength and modulus of elasticity. The P-wave velocity, the point load index, the Schmidt hammer rebound number and porosity were used as inputs for both meth-ods. The regression equations show that the relationship between P-wave velocity, point load index, Schmidt hammer rebound number and the porosity input sets with uniaxial compressive strength and modulus of elasticity under conditions of linear rela-tions obtained coefficients of determination of (R2) of 0.64 and 0.56, respectively. ANNs were used to improve the regression re-sults. The generalized regression and feed forward neural networks with two outputs (UCS and E) improved the coefficients of determination to more acceptable levels of 0.86 and 0.92 for UCS and to 0.77 and 0.82 for E. The results show that the proposed ANN methods could be applied as a new acceptable method for the prediction of uniaxial compressive strength and modulus of elasticity of intact rocks.
基金ACKNOWLEDGEMENT This work was supported by the National Na- tural Science Foundation of China under Gra- nts No. 61172079, 61231008, No. 61201141, No. 61301176 the National Basic Research Program of China （973 Program） under Grant No. 2009CB320404+2 种基金 the 111 Project under Gr- ant No. B08038 the National Science and Tec- hnology Major Project under Grant No. 2012- ZX03002009-003, No. 2012ZX03004002-003 and the Shaanxi Province Science and Techno- logy Research and Development Program un- der Grant No. 2011KJXX-40.
文摘The traffic with tidal phenomenon in Heterogeneous Wireless Networks(HWNs)has radically increased the complexity of radio resource management and its performance analysis.In this paper,a Simplified Dynamic Hierarchy Resource Management(SDHRM)algorithm exploiting the resources dynamically and intelligently is proposed with the consideration of tidal traffic.In network-level resource allocation,the proposed algorithm first adopts wavelet neural network to forecast the traffic of each sub-area and then allocates the resources to those sub-areas to maximise the network utility.In connection-level network selection,based on the above resource allocation and the pre-defined QoS requirement,three typical network selection policies are provided to assign traffic flow to the most appropriate network.Furthermore,based on multidimensional Markov model,we analyse the performance of SDHRM in HWNs with heavy tailed traffic.Numerical results show that our theoretical values coincide with the simulation results and the SDHRM can improve the resource utilization.
文摘Background The present study investigated the prognostic value of medical comorbidities at admission for 30-day in-hospital mortality in patients with acute myocardial infarction （AMI）. Methods A total of 5161 patients with AMI were admitted in Chinese PLA General Hospital between January 1, 1993 and December 31, 2007. Medical comorbidities including hypertension, diabetes mellitus, previous myocardial infarction, valvular heart disease, chronic obstructive pulmonary disease （COPD）, renal insufficiency, previous stroke, atrial fibrillation and anemia, were identified at admission. The patients were divided into 4 groups based on the number of medical comorbidities at admission （0, 1, 2, and ≥3）. Cox regression analysis was used to calculate relative risk （RR） and 95% confidence intervals （CI）, with adjustment for age, sex, heart failure and percutaneous coronary intervention （PCI）. Results The mean age of the studied population was 63.9 ± 13.6 years, and 80.1% of the patients were male. In 74.6% of the patients at least one comorbidity were identified. Hypertension （50.7%）, diabetes mellitus （24.0%） and previous myocardial infarction （12%） were the leading common comorbidities at admission. The 30-day in-hospital mortality in patients with 0, 1, 2, and ≥3 comorbidities at admission （7.2%） was 4.9%, 7.2%, 11.1%, and 20.3%, respectively. The presence of 2 or more comorbidities was associated with higher 30-day in-hospital mortality compared with patients without comorbidity （RR： 1.41, 95% CI： 1.13-1.77, P = 0.003, and RR： 1.95, 95% CI： 1.59-2.39, P = 0.000, respectively）. Conclusions Medical comorbidities were frequently found in patients with AMI. AMI patients with more comorbidities had a higher 30-day in-hospital mortality might be predictive of early poor outcome in patients with AMI.
文摘Objective To identify clinical characteristics associated with the minimum lumen area （MLA） of proximal or middle intermediate lesions in the left anterior descending （LAD） artery, and to develop a model to predict MLA. Methods We retrospectively analyzed demographic data, medical history, and intravascular ultrasound findings for 90 patients with intermediate lesions in the LAD artery. Linear regression was used to identify factors affecting MLA, and multiple regression was used to develop a model for predicting MLA. Results Age, number of lesions, and diabetes mellitus correlated significantly with MLA of proximal or middle intermediate lesions. A regression model for predicting MLA （mm2） was derived from the data： 7.00 - 0.05 × （age） - 0.50 × （number of lesions）. A cut-off value of 3.1 mm2 was proposed for deciding when to perform percutaneous coronary intervention. Conclusion This model for predicting MLA of proximal or middle intermediate lesions in the LAD artery showed high accuracy, sensitivity, and specificity, indicating good diagnostic potential.
基金Financial support for this work,provided by the Major National Science and Technology Special Projects(No.2008ZX05008)
文摘The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the first time as a way of predicting sandstone thickness in the study area.The model was constructed by analysis and optimization of measured seismic attributes.The distribution of the sedimentary microfacies in the study area was determined from predicted sandstone thickness and an analysis of sedimentary characteristics of the area.The results indicate that sandstone thickness predictions in the study area using an SVM method are good.The distribution of the sedimentary microfacies in the study area has been depicted at a fine scale.
