Analysis of indentation size effect in copper and its alloys

For describing the indentation size effect (ISE), numerous models, which relate the load or hardness to the indent dimensions, have been proposed. Unfortunately, it is still difficult to associate the different parameters involved in such relationships with physical or mechanical properties of the material. This is an unsolved problem since the ISE can be associated with various causes such as workhardening, roughness, piling-up, sinking-in, indenter tip geometry, surface energy, varying composition and crystal anisotropy. For interpreting the change in hardness with indent size, an original approach is proposed on the basis of composite hardness modelling together with the use of a simple model, which allows the determination of the hardness–depth profile. Applied to copper and copper alloys, it is shown that it is possible to determine the maximum hardness value reached at the outer surface of the material and the distance over which both the ISE and the workhardening take place

Size Effect in Fracture: Roughening of Crack Surfaces and Asymptotic Analysis

Recently the scaling laws describing the roughness development of fracture surfaces was proposed to be related to the macroscopic elastic energy released during crack propagation [Mor00]. On this basis, an energy-based asymptotic analysis allows to extend the link to the nominal strength of structures. We show that a Family-Vicsek scaling leads to the classical size effect of linear elastic fracture mechanics. On the contrary, in the case of an anomalous scaling, there is a smooth transition from the case of no size effect, for small structure sizes, to a power law size effect which appears weaker than the linear elastic fracture mechanics one, in the case of large sizes. This prediction is confirmed by fracture experiments on wood.Comment: 9 pages, 6 figures, accepted for publication in Physical Review

Discrepancies in autologous bone marrow stem cell trials and enhancement of ejection fraction (DAMASCENE): weighted regression and meta-analysis

Objective To investigate whether discrepancies in trials of use of bone marrow stem cells in patients with heart disease account for the variation in reported effect size in improvement of left ventricular function. Design Identification and counting of factual discrepancies in trial reports, and sample size weighted regression against therapeutic effect size. Meta-analysis of trials that provided sufficient information. Data sources PubMed and Embase from inception to April 2013. Eligibility for selecting studies Randomised controlled trials evaluating the effect of autologous bone marrow stem cells for heart disease on mean left ventricular ejection fraction. Results There were over 600 discrepancies in 133 reports from 49 trials. There was a significant association between the number of discrepancies and the reported increment in EF with bone marrow stem cell therapy (Spearman’s r=0.4, P=0.005). Trials with no discrepancies were a small minority (five trials) and showed a mean EF effect size of −0.4%. The 24 trials with 1-10 discrepancies showed a mean effect size of 2.1%. The 12 with 11-20 discrepancies showed a mean effect of size 3.0%. The three with 21-30 discrepancies showed a mean effect size of 5.7%. The high discrepancy group, comprising five trials with over 30 discrepancies each, showed a mean effect size of 7.7%. Conclusions Avoiding discrepancies is difficult but is important because discrepancy count is related to effect size. The mechanism is unknown but should be explored in the design of future trials because in the five trials without discrepancies the effect of bone marrow stem cell therapy on ejection fraction is zero

Predictive physiological anticipatory activity preceding seemingly unpredictable stimuli: An update of Mossbridge et al\u2019s meta-analysis

Background: This is an update of the Mossbridge et al\u2019s meta-analysis related to the physiological anticipation preceding seemingly unpredictable stimuli which overall effect size was 0.21; 95% Confidence Intervals: 0.13 - 0.29 Methods: Nineteen new peer and non-peer reviewed studies completed from January 2008 to June 2018 were retrieved describing a total of 27 experiments and 36 associated effect sizes. Results: The overall weighted effect size, estimated with a frequentist multilevel random model, was: 0.28; 95% Confidence Intervals: 0.18-0.38; the overall weighted effect size, estimated with a multilevel Bayesian model, was: 0.28; 95% Credible Intervals: 0.18-0.38. The weighted mean estimate of the effect size of peer reviewed studies was higher than that of non-peer reviewed studies, but with overlapped confidence intervals: Peer reviewed: 0.36; 95% Confidence Intervals: 0.26-0.47; Non-Peer reviewed: 0.22; 95% Confidence Intervals: 0.05-0.39. Similarly, the weighted mean estimate of the effect size of Preregistered studies was higher than that of Non-Preregistered studies: Preregistered: 0.31; 95% Confidence Intervals: 0.18-0.45; No-Preregistered: 0.24; 95% Confidence Intervals: 0.08-0.41. The statistical estimation of the publication bias by using the Copas selection model suggest that the main findings are not contaminated by publication bias. Conclusions: In summary, with this update, the main findings reported in Mossbridge et al\u2019s meta-analysis, are confirmed

Effect Size Estimation and Misclassification Rate Based Variable Selection in Linear Discriminant Analysis

Supervised classifying of biological samples based on genetic information, (e.g. gene expression profiles) is an important problem in biostatistics. In order to find both accurate and interpretable classification rules variable selection is indispensable. This article explores how an assessment of the individual importance of variables (effect size estimation) can be used to perform variable selection. I review recent effect size estimation approaches in the context of linear discriminant analysis (LDA) and propose a new conceptually simple effect size estimation method which is at the same time computationally efficient. I then show how to use effect sizes to perform variable selection based on the misclassification rate which is the data independent expectation of the prediction error. Simulation studies and real data analyses illustrate that the proposed effect size estimation and variable selection methods are competitive. Particularly, they lead to both compact and interpretable feature sets.Comment: 21 pages, 2 figure

A Meta-Analysis of the Robustness of Market Size and Labour Cost Determinants of FDI

This paper applies a meta-regression analysis to systematically summarise, integrate and synthesise the results of empirical studies that include market size and labour costs as determinants of FDI. Random effects panel estimation is employed separately for the sample of primary studies that use OLS estimation to analyse the effect of market size and labour costs on FDI and for the sample of primary studies that employ discrete choice models to estimate the effect of market size and labour costs on FDI. A number of factors related to model specifications, dataset characteristics and methodologies in the primary studies explain the variation in the estimated t-statistics of the effect of market size and labour costs on FDI across the studies. Most tests for publication bias indicate that the empirical literature on the effect of market size on FDI favours positive estimates while empirical literature on the effect of labour costs on FDI favours negative estimates. None of the literature, however, favours statistical significance.meta-regression analysis, foreign direct investment, market size and labour costs, publication bias

Probing inhomogeneities in type II superconductors by means of thermal fluctuations, magnetic fields and isotope effects

Type II superconductors, consisting of superconducting domains embedded in a normal or insulating matrix, undergo a rounded phase transition. Indeed, the correlation length cannot grow beyond the spatial extent of the domains. Accordingly, the thermodynamic properties will exhibit a finite size effect. It is shown that the specific heat and penetration depth data of a variety of type II superconductors, including cuprates, exhibit the characteristic properties of a finite size effect, arising from domains with nanoscale extent. The finite size scaling analysis reveals essential features of the mechanism. Transition temperature and superfluidity increase with reduced domain size. The combined finite size and isotope effects uncover the relevance of local lattice distortionsComment: 9 pages, 5 figur