A simple estimation model for 3D braced excavation wall deflection

2017 ◽  
Vol 83 ◽  
pp. 106-113 ◽  
Author(s):  
A.T.C. Goh ◽  
Fan Zhang ◽  
Wengang Zhang ◽  
Yanmei Zhang ◽  
Hanlong Liu
1992 ◽  
Vol 22 (1) ◽  
pp. 93-115 ◽  
Author(s):  
Richard Johnston

This article lays out the elementary logic of age structures in party preference data and proposes a simple estimation model with demographic and historical elements. As voters age their preferences intensify. But they do not intensify much and generational differences in the direction of party preferences are correspondingly weak. The Canadian electorate does not seem all that strongly anchored by the accumulated experience of the individuals that make it up. The major source of long-term electoral change, therefore, is conversion in the existing electorate. Consideration is given to how distinctive the Canadian pattern is.


2015 ◽  
Vol 63 ◽  
pp. 67-72 ◽  
Author(s):  
Wengang Zhang ◽  
Anthony T.C. Goh ◽  
Feng Xuan

2013 ◽  
Vol 19 (2) ◽  
pp. 169-176 ◽  
Author(s):  
Kai Fang ◽  
Zhongmiao Zhang ◽  
Xingwang Liu ◽  
Qianqing Zhang ◽  
Cungang Lin

A special double-row support structure used for braced excavation was modeled numerically using finite element method. The performance of the braced excavation depends on the interaction between the two walls of the support structure. Comprehensive parametric studies were carried out to investigate the influence factors on the performance. It was ascertained that the support structure behavior was largely influenced by overlapping length of two support walls, embedment ratio of inner support wall and spacing between two support walls. Appropriate parameters should be chosen to limit wall deflection and to maintain the stability of the support structure.


Author(s):  
Frank Paulsen ◽  
Jens Bedke ◽  
Daniel Wegener ◽  
Jolanta Marzec ◽  
Peter Martus ◽  
...  

Abstract Purpose The extent of lymphadenectomy and clinical features influence the risk of occult nodes in node-negative prostate cancer. We derived a simple estimation model for the negative predictive value (npv) of histopathologically node-negative prostate cancer patients (pN0) to guide adjuvant treatment. Methods Approximations of sensitivities in detecting lymph node metastasis from current publications depending on the number of removed lymph nodes were used for a theoretical deduction of a simplified formulation of npv assuming a false node positivity of 0. Results A theoretical formula of npv = p(N0IpN0) = (100 − prevalence) / (100 − sensitivity × prevalence) was calculated (sensitivity and preoperative prevalence in %). Depending on the number of removed lymph nodes (nLN), the sensitivity of pN0-staged prostate cancer was derived for three sensitivity levels accordingly: sensitivity = f(nLN) = 9 × nLN /100 for 0 ≤ nLN ≤ 8 and f(nLN) = (nLN + 70) /100 for 9 ≤ nLN ≤ 29 and f(nLN) = 1 for nLN ≥ 30. Conclusion We developed a theoretical formula for estimation of the npv in pN0-staged prostate cancer patients. It is a sine qua non to use the formula in a clinically experienced context before deciding to electively irradiate pelvic lymph nodes or to intensify adjuvant systemic treatment.


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