Large-gradient deformation monitoring and parameter inversion in a mining area using a method combining a dynamic prediction model and InSAR

2021 ◽  
Vol 12 (9) ◽  
pp. 838-847
Author(s):  
Shuyi Yao ◽  
Keming Yang ◽  
Xianglin Dong ◽  
Xiaoyu Shi ◽  
Jian Wang
2021 ◽  
pp. 014459872098164
Author(s):  
Shenshen Chi ◽  
Lei Wang ◽  
Xuexiang Yu ◽  
Weicai Lv ◽  
Xinjian Fang

In order to improve the accuracy of the surface dynamic prediction model in mining areas with thick unconsolidated layers and improve Knothe time function, the influence coefficient was firstly changed into the coefficient in exponential form, and the influence coefficient of unconsolidated layer was added. Then, a subsidence basin prediction model for mining under thick unconsolidated layers was established. Next, the model was combined with the improved Knothe function, thus constructing a new mining subsidence prediction model. The new subsidence prediction model was applied in 1414 (1) working face in Huainan mining area. The results showed that the integrated model could better reflect the subsidence process, and the prediction values and the measured values agreed well.


BMC Surgery ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Runwen Liu ◽  
Yunqiang Cai ◽  
He Cai ◽  
Yajia Lan ◽  
Lingwei Meng ◽  
...  

Abstract Background With the recent emerge of dynamic prediction model on the use of diabetes, cardiovascular diseases and renal failure, and its advantage of providing timely predicted results according to the fluctuation of the condition of the patients, we aim to develop a dynamic prediction model with its corresponding risk assessment chart for clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by combining baseline factors and postoperative time-relevant drainage fluid amylase level and C-reactive protein-to-albumin ratio. Methods We collected data of 251 patients undergoing LPD at West China Hospital of Sichuan University from January 2016 to April 2019. We extracted preoperative and intraoperative baseline factors and time-window of postoperative drainage fluid amylase and C-reactive protein-to-albumin ratio relevant to clinically relevant pancreatic fistula by performing univariate and multivariate analyses, developing a time-relevant logistic model with the evaluation of its discrimination ability. We also established a risk assessment chart in each time-point. Results The proportion of the patients who developed clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy was 7.6% (19/251); preoperative albumin and creatine levels, as well as drainage fluid amylase and C-reactive protein-to-albumin ratio on postoperative days 2, 3, and 5, were the independent risk factors for clinically relevant postoperative pancreatic fistula. The cut-off points of the prediction value of each time-relevant logistic model were 14.0% (sensitivity: 81.9%, specificity: 86.5%), 8.3% (sensitivity: 85.7%, specificity: 79.1%), and 7.4% (sensitivity: 76.9%, specificity: 85.9%) on postoperative days 2, 3, and 5, respectively, the area under the receiver operating characteristic curve was 0.866 (95% CI 0.737–0.996), 0.896 (95% CI 0.814–0.978), and 0.888 (95% CI 0.806–0.971), respectively. Conclusions The dynamic prediction model for clinically relevant postoperative pancreatic fistula has a good to very good discriminative ability and predictive accuracy. Patients whose predictive values were above 14.0%, 8.3%, and 7.5% on postoperative days 2, 3, and 5 would be very likely to develop clinically relevant postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.


2021 ◽  
Author(s):  
Mistaya Langridge ◽  
Ed McBean ◽  
Hossein Bonakdari ◽  
Bahram Gharabaghi

2020 ◽  
Vol 14 (4) ◽  
pp. 445-453
Author(s):  
Qian Fan ◽  
Yiqun Zhu

AbstractIn order to solve the problem that the moving span of basic local mean decomposition (LMD) method is difficult to choose reasonably, an improved LMD method (ILMD), which uses three cubic spline interpolation to replace the sliding average, is proposed. On this basis, with the help of noise aided calculation, an ensemble improved LMD method (EILMD) is proposed to effectively solve the modal aliasing problem in original LMD. On the basis of using EILMD to effectively decompose the data of GNSS deformation monitoring series, GNSS deformation feature extraction model based on EILMD threshold denoising is given by means of wavelet soft threshold processing mode and threshold setting method in empirical mode decomposition denoising. Through the analysis of simulated data and the actual GNSS monitoring data in the mining area, the results show that denoising effect of the proposed method is better than EILMD, ILMD and LMD direct coercive denoising methods. It is also better than wavelet analysis denoising method, and has good adaptability. This fully demonstrates the feasibility and effectiveness of the proposed method in GNSS feature extraction.


2018 ◽  
Vol 44 (suppl_1) ◽  
pp. S378-S379 ◽  
Author(s):  
Hok Pan Yuen ◽  
Andrew Mackinnon ◽  
Jessica Hartmann ◽  
Paul Amminger ◽  
Connie Markulev ◽  
...  

2020 ◽  
Vol 15 (9) ◽  
Author(s):  
Marieke Welten ◽  
Alet H. Wijga ◽  
Marleen Hamoen ◽  
Ulrike Gehring ◽  
Gerard H. Koppelman ◽  
...  

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