Probabilistic bolt load distribution analysis of composite single-lap multi-bolt joints considering random bolt-hole clearances and tightening torques

2018 ◽  
Vol 194 ◽  
pp. 12-20 ◽  
Author(s):  
Fengrui Liu ◽  
Meijuan Shan ◽  
Libin Zhao ◽  
Jianyu Zhang
Keyword(s):  
Load Distribution ◽  
Distribution Analysis

An improved 2D finite element model for bolt load distribution analysis of composite multi-bolt single-lap joints

2020 ◽  
Vol 253 ◽  
pp. 112770
Author(s):  
Fengrui Liu ◽  
Wanting Yao ◽  
Libin Zhao ◽  
Hao Wu ◽  
Xi Zhang ◽  
...  
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Load Distribution ◽  
Element Model ◽  
Lap Joints ◽  
Distribution Analysis ◽  
Single Lap Joints

Static load distribution analysis of ball screws with nut position variation

2020 ◽  
Vol 151 ◽  
pp. 103893 ◽  
Author(s):  
Chang Liu ◽  
Chunyu Zhao ◽  
Xianli Meng ◽  
Bangchun Wen
Keyword(s):  
Load Distribution ◽  
Static Load ◽  
Distribution Analysis

scholarly journals Influence of the CO2 emission factor value on the result of the load distribution analysis between the hybrid PV/WT/FC system and the electric power system

2019 ◽  
Vol 108 ◽  
pp. 01016 ◽  
Author(s):  
Bartosz Ceran
Keyword(s):  
Power Systems ◽  
Load Distribution ◽  
Manufacturing Sector ◽  
Multicriteria Analysis ◽  
Electric Power System ◽  
Distribution Analysis ◽  
Generation System ◽  
Hybrid Power ◽  
Electricity System ◽  
Power Generation System

This article presents the results of the multicriteria analysis of loads distribution between hybrid power generation system consisting of PV/WT/FC and the electricity system. The simulations were performed for power systems with different structures of the manufacturing sector, and so with different values of the factor of CO2 emission. A decision-making criteria, which were used to compare the relevant scenarios, have been defined.

Mutational load distribution analysis of colorectal and pancreatic carcinoma by next generation sequencing platform.

2012 ◽  
Vol 30 (30_suppl) ◽  
pp. 57-57
Author(s):  
Adriana Lopez-Doriga ◽  
Daniel Azuara ◽  
Mireia Morell ◽  
Javier de Oca ◽  
Francisco Rodriguez-Moranta ◽  
...  
Keyword(s):  
Pancreatic Carcinoma ◽  
Load Distribution ◽  
Genetic Alterations ◽  
Response To Therapy ◽  
Colorectal Adenomas ◽  
Distribution Analysis ◽  
Mutational Load ◽  
Colorectal Carcinomas ◽  
Normal Pancreas ◽  
Risk Of Cancer

57 Background: It has been suggested that the sequential analysis of the mutational spectra at loci involved in the pathogenesis of tumors (mutational load distribution analysis [MLDA]) may reflect the biology of the tumor and/or the risk of cancer before the tumor emerges. Metrics derived from MLDA could assist quantifying risk, in early diagnosis, and in monitoring response to therapy. The aim of this study was to assess the efficacy of the NGS platform to obtain clinically relevant metrics derived from MLDA. Methods: Two sets of DNA samples were assessed: (i) 6 paired tumor/normal colorectal carcinomas and 8 colorectal adenomas; and (ii) 21 pancreatic carcinoma and 6 non-matched normal pancreas. Thirty amplicons from 6 genes (KRAS, BRAF, EGFR, CDKN2A, TP53, and PI3KCA) were sequenced using the Roche 454 GS FLX-TI platform. Data analysis was carried out by Variant Identification Pipeline (VIP) software and R (computing environment) commands. Total mutational load (TML) was calculated as the sum of all variants frequency for each sample. Results: In all, 18,626 variants were identified. Optimal filters applied to discard false positives were: (1) variants with an absolute frequency < 10x, (2) variants in homopolymer (HP) ≥ 6 nucleotides, (3) insertions and deletions (INDELs) in HP ≥ 5 nucleotides, and (4) INDELs in only one strand. Filtering yielded 870 variants from all samples remained as valid, 92 being unique. After discarding polymorphisms, variants classified as neutral by the Condel software and variants preferentially detected in normal tissues, 46 variants were used to compute TML. TML in colonic carcinomas and adenomas was higher than normal mucosa (0.83 vs. 0.607 vs. 0.128, p = 0.01). In pancreatic disease, TML for pancreatic carcinoma was higher than normal pancreas (0.716 vs. 0.135, p = 0.01). KRAS and TP53 analysis provide most of the information in both tumors, while PI3KCA adds information for colorectal carcinomas exclusively and CDKNA2 is informative in pancreatic samples. Conclusions: NGS provides a robust and quantitative measure of genetic alterations in tumor samples. MLDA is an effective tool for the classification of GI tumors.

Load Distribution Analysis for Weight and Ballistocardiogram Measurements of Heart Failure Patients using a Bed Scale

2021 ◽  
Author(s):  
Isaac S. Chang ◽  
Jennifer Boger ◽  
Susanna Mak ◽  
Sherry L. Grace ◽  
Amaya Arcelus ◽  
...  
Keyword(s):  
Heart Failure ◽  
Load Distribution ◽  
Distribution Analysis ◽  
Heart Failure Patients
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