scholarly journals A multi-factor interaction model (MFIM) for damage initiation and progression

2011 ◽  
Author(s):  
C. C. Chamis
Keyword(s):  
Interaction Model ◽  
Damage Initiation ◽  
Factor Interaction

A Multi-Factor Interaction Model (MFIM) for Damage Initiation and Progression

2001 ◽  
Author(s):  
Christos C. Chamis ◽  
Levon Minnetyan
Keyword(s):  
Damage Tolerance ◽  
Interaction Model ◽  
Single Equation ◽  
Material Behavior ◽  
Damage Initiation ◽  
Fatigue Data ◽  
Large Shell ◽  
Effective Use ◽  
Temperature Time ◽  
Factor Interaction

Abstract A Multi-Factor-Interaction-Model (MFIM) is briefly described to represent complex point material behavior in a single equation. The model is of product form in order to represent coupled interactions and to be computationally effective. The model describes a continuum or surface in space that represents the complex material behavior in terms of the various factors that affect a specified material behavior. The material specified behavior is inclusive of all material properties, mechanical, thermal, physical and effects thereon, such as temperature, time, cyclic loadings, etc. Sample cases results simulated by using MFIM are compared with test data to illustrate its versatility and its relevance to reality. These results show the MFIM can accurately predict metal matrix composite fatigue data and mechanical properties of a steel alloy. MFIM predicted results for the damage tolerance of a large shell made from structural steel subjected to internal pressure. Helpful guidelines for its effective use are also included.

scholarly journals Higher-order clinical risk factor interaction analysis for overall mortality in maintenance hemodialysis patients

2020 ◽  
Vol 11 ◽  
pp. 204062232094906
Author(s):  
Cheng-Hong Yang ◽  
Sin-Hua Moi ◽  
Li-Yeh Chuang ◽  
Jin-Bor Chen
Keyword(s):  
Interaction Analysis ◽  
Interaction Model ◽  
Higher Order ◽  
Survival Outcome ◽  
Maintenance Hemodialysis ◽  
Order Interaction ◽  
Statistical Approaches ◽  
The Impact ◽  
Factor Interaction ◽  
Overall Mortality

Background and Aims: In Taiwan, approximately 90% of patients with end-stage renal disease receive maintenance hemodialysis. Although studies have reported the survival predictability of multiclinical factors, the higher-order interactions among these factors have rarely been discussed. Conventional statistical approaches such as regression analysis are inadequate for detecting higher-order interactions. Therefore, this study integrated receiver operating characteristic, logistic regression, and balancing functions for adjusting the ratio in risk classes and classification errors for imbalanced cases and controls using multifactor-dimensionality reduction (MDR-ER) analyses to examine the impact of interaction effects between multiclinical factors on overall mortality in patients on maintenance hemodialysis. Meterials and Methods: In total, 781 patients who received outpatient hemodialysis dialysis three times per week before 1 January 2009 were included; their baseline clinical factor and mortality outcome data were retrospectively collected using an approved data protocol (201800595B0). Results: Consistent with conventional statistical approaches, the higher-order interaction model could indicate the impact of potential risk combination unique to patients on maintenance hemodialysis on the survival outcome, as described previously. Moreover, the MDR-based higher-order interaction model facilitated higher-order interaction effect detection among multiclinical factors and could determine more detailed mortality risk characteristics combinations. Conclusion: Therefore, higher-order clinical risk interaction analysis is a reasonable strategy for detecting non-traditional risk factor interaction effects on survival outcome unique to patients on maintenance hemodialysis and thus clinically achieving whole-scale patient care.

scholarly journals Genetic polymorphisms of key enzymes in folate metabolism affect the efficacy of folate therapy in patients with hyperhomocysteinaemia

2018 ◽  
Vol 119 (8) ◽  
pp. 887-895 ◽  
Author(s):  
Binghui Du ◽  
Huizi Tian ◽  
Dandan Tian ◽  
Chengda Zhang ◽  
Wenhua Wang ◽  
...  
Keyword(s):  
Folic Acid ◽  
Interaction Model ◽  
Folate Metabolism ◽  
Efficacy Rate ◽  
Environment Interaction ◽  
Optimal Model ◽  
Acid Therapy ◽  
Gene Environment ◽  
History Of ◽  
Factor Interaction

AbstractThe aim of this study is to analyse the efficacy rate of folate for the treatment of hyperhomocysteinaemia (HHcy) and to explore how folate metabolism-related gene polymorphisms change its efficacy. This study also explored the effects of gene–gene and gene–environment interactions on the efficacy of folate. A prospective cohort study enrolling HHcy patients was performed. The subjects were treated with oral folate (5 mg/d) for 90 d. We analysed the efficacy rate of folate for the treatment of HHcy by measuring homocysteine (Hcy) levels after treatment. Unconditioned logistic regression was conducted to analyse the association between SNP and the efficacy of folic acid therapy for HHcy. The efficacy rate of folate therapy for HHcy was 56·41 %. The MTHFR rs1801133 CT genotype, TT genotype and T allele; the MTHFR rs1801131 AC genotype, CC genotype and C allele; the MTRR rs1801394 GA genotype, GG genotype and G allele; and the MTRR rs162036 AG genotype and AG+GG genotypes were associated with the efficacy of folic acid therapy for HHcy (P<0·05). No association was seen between other SNP and the efficacy of folic acid. The optimal model of gene–gene interactions was a two-factor interaction model including rs1801133 and rs1801394. The optimal model of gene–environment interaction was a three-factor interaction model including history of hypertension, history of CHD and rs1801133. Folate supplementation can effectively decrease Hcy level. However, almost half of HHcy patients failed to reach the normal range. The efficacy of folate therapy may be genetically regulated.

scholarly journals Optimization of Bioinsecticides Overproduction by Bacillus thuringiensis subsp. kurstaki Using Linear Regression

2013 ◽  
Vol 62 (3) ◽  
pp. 287-293 ◽  
Author(s):  
KARIM ENNOURI ◽  
HANEN BEN HASSEN ◽  
NABIL ZOUARI
Keyword(s):  
Bacillus Thuringiensis ◽  
Linear Regression ◽  
Interaction Model ◽  
Inoculum Size ◽  
Order Model ◽  
First Order ◽  
Initial Ph ◽  
Proteolytic Activities ◽  
Factor Interaction ◽  
Significant Factors

A multiple linear regression analyses were performed to screen for the significant factors simultaneously influencing production of deltaendotoxin, proteolytic activities and spore formation by a Bacillus thuringiensis kurstaki strain. Investigated factors included: pH of the medium, available oxygen and inoculum size. It was observed that oxygen availability was the most influencing setting on both deltaendotoxins production and spores counts, followed by initial pH of the medium and inoculum size. On other hand, pH of medium was found to be the most significant parameter for proteolytic activity, followed by inoculum size and dissolved oxygen. Our results suggested that the first order with two-factor interaction model seemed to be more satisfactory than simple first order model for optimization of delta-endotoxin overproduction. The coefficients of determination (R') indicated a better adequacy of the second order models to justify the obtained data. Based on results, relationships between delta-endotoxins production, proteolytic activities and spores counts were established. Our results can help to balance delta-endotoxins production and its stability.

scholarly journals Generalized Measure of Departure from No Three-Factor Interaction Model for 2 x 2 x K Contingency Tables

Entropy ◽  
2008 ◽  
Vol 10 (4) ◽  
pp. 776-785
Author(s):  
Kouji Yamamoto ◽  
Yohei Ban ◽  
Sadao Tomizawa
Keyword(s):  
Contingency Tables ◽  
Interaction Model ◽  
Factor Interaction ◽  
Three Factor
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