Dissociation of hydrogen molecules on the clean and hydrogen-preadsorbed Be(0001) surface

Using first-principles calculations, we systematically study the potential energy surfaces and dissociation processes for hydrogen molecules on the clean and hydrogen-preadsorbed Be(0001) surfaces. It is found that the most energetically favored dissociation channel for H2 molecules on the clean Be surface is at the surface top site, with the minimum energy barrier of 0.75 eV. It is further found that after dissociation, hydrogen atoms do not like to cluster with each other, as well as to penetrate into subsurface sites. For the hydrogen-preadsorbed Be(0001) surface, the smallest dissociation energy barrier for H2 molecules is found to be 0.50 eV, which is smaller than the dissociation energy barrier on a clean Be(0001) surface. The critical dependence of the dissociation energy barriers for H2 molecules on their horizontal distances from the preadsorbed hydrogen atom is revealed. Our studies well describe the adsorption behaviors of hydrogen on the Be(0001) surface.Comment: 17 pages, 9 figure

Influences of Al doping on the electronic structure of Mg(0001) and dissociation property of H2

By using the density functional theory method, we systematically study the influences of the doping of an Al atom on the electronic structures of the Mg(0001) surface and dissociation behaviors of H2 molecules. We find that for the Al-doped surfaces, the surface relaxation around the doping layer changes from expansion of a clean Mg(0001) surface to contraction, due to the redistribution of electrons. After doping, the work function is enlarged, and the electronic states around the Fermi energy have a major distribution around the doping layer. For the dissociation of H2 molecules, we find that the energy barrier is enlarged for the doped surfaces. Especially, when the Al atom is doped at the first layer, the energy barrier is enlarged by 0.30 eV. For different doping lengths, however, the dissociation energy barrier decreases slowly to the value on a clean Mg(0001) surface when the doping layer is far away from the top surface. Our results well describe the electronic changes after Al-doping for the Mg(0001) surface, and reveal some possible mechanisms for improving the resistance to corrosion of the Mg(0001) surface by doping of Al atoms

Robust inference for the unification of confidence intervals in meta-analysis

Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In the situation when the number of studies, denoted as K, is large, the cumulative Gaussian approximation errors from each study could make the final estimation unreliable. In the situation when K is small, it is not realistic to assume the random-effect follows Gaussian distribution. In this paper, we present a novel empirical likelihood method for combining confidence intervals under the meta-analysis framework. This method is free of the Gaussian assumption in effect size estimates from individual studies and from the random-effects. We establish the large-sample properties of the non-parametric estimator, and introduce a criterion governing the relationship between the number of studies, K, and the sample size of each study, n_i. Our methodology supersedes conventional meta-analysis techniques in both theoretical robustness and computational efficiency. We assess the performance of our proposed methods using simulation studies, and apply our proposed methods to two examples

Robust inference for the unification of confidence intervals in meta-analysis

Traditional meta-analysis assumes that the effect sizes estimated in individual studies follow a Gaussian distribution. However, this distributional assumption is not always satisfied in practice, leading to potentially biased results. In the situation when the number of studies, denoted as K, is large, the cumulative Gaussian approximation errors from each study could make the final estimation unreliable. In the situation when K is small, it is not realistic to assume the random effect follows Gaussian distribution. In this paper, we present a novel empirical likelihood method for combining confidence intervals under the meta-analysis framework. This method is free of the Gaussian assumption in effect size estimates from individual studies and from the random effects. We establish the large sample properties of the nonparametric estimator and introduce a criterion governing the relationship between the number of studies, K, and the sample size of each study, (Formula presented.). Our methodology supersedes conventional meta-analysis techniques in both theoretical robustness and computational efficiency. We assess the performance of our proposed methods using simulation studies and apply our proposed methods to two examples

Genetic variants in the metzincin metallopeptidase family genes predict melanoma survival

Metzincins are key molecules in the degradation of the extracellular matrix and play an important role in cellular processes such as cell migration, adhesion, and cell fusion of malignant tumors, including cutaneous melanoma (CM). We hypothesized that genetic variants of the metzincin metallopeptidase family genes would be associated with CM-specific survival (CMSS). To test this hypothesis, we first performed Cox proportional hazards regression analysis to evaluate the associations between genetic variants of 75 metzincin metallopeptidase family genes and CMSS using the dataset from the genome-wide association study (GWAS) from The University of Texas MD Anderson Cancer Center (MDACC) which included 858 non-Hispanic white patients with CM, and then validated using the dataset from the Harvard GWAS study which had 409 non-Hispanic white patients with invasive CM. Four independent SNPs (MMP16 rs10090371 C>A, ADAMTS3 rs788935 T>C, TLL2 rs10882807 T>C and MMP9 rs3918251 A>G) were identified as predictors of CMSS, with a variant-allele attributed hazards ratio (HR) of 1.73 (1.32-2.29, 9.68E-05), 1.46 (1.15-1.85, 0.002), 1.68 (1.31-2.14, 3.32E-05) and 0.67 (0.51-0.87, 0.003), respectively, in the meta-analysis of these two GWAS studies. Combined analysis of risk genotypes of these four SNPs revealed a decreased CMSS in a dose-response manner as the number of risk genotypes increased (Ptrend < 0.001). An improvement was observed in the prediction model (area under the curve [AUC] = 81.4% vs. 78.6%), when these risk genotypes were added to the model containing non-genotyping variables. Our findings suggest that these genetic variants may be promising prognostic biomarkers for CMSS

Improved simulation of Antarctic sea ice due to the radiative effects of falling snow

Southern Ocean sea-ice cover exerts critical control on local albedo and Antarctic precipitation, but simulated Antarctic sea-ice concentration commonly disagrees with observations. Here we show that the radiative effects of precipitating ice (falling snow) contribute substantially to this discrepancy. Many models exclude these radiative effects, so they underestimate both shortwave albedo and downward longwave radiation. Using two simulations with the climate model CESM1, we show that including falling-snow radiative effects improves the simulations relative to cloud properties from CloudSat-CALIPSO, radiation from CERES-EBAF and sea-ice concentration from passive microwave sensors. From 50–70°S, the simulated sea-ice-area bias is reduced by 2.12 × 106 km2 (55%) in winter and by 1.17 × 106 km2 (39%) in summer, mainly because increased wintertime longwave heating restricts sea-ice growth and so reduces summer albedo. Improved Antarctic sea-ice simulations will increase confidence in projected Antarctic sea level contributions and changes in global warming driven by long-term changes in Southern Ocean feedbacks

Two-Step Estimation Procedure for Parametric Copula-Based Regression Models for Semi-Competing Risks Data

Non-terminal and terminal events in semi-competing risks data are typically associated and may be influenced by covariates. We employed regression modeling for semi-competing risks data under a copula-based framework to evaluate the effects of covariates on the two events and the association between them. Due to the complexity of the copula structure, we propose a new method that integrates a novel two-step algorithm with the Bound Optimization by Quadratic Approximation (BOBYQA) method. This approach effectively mitigates the influence of initial values and demonstrates greater robustness. The simulations validate the performance of the proposed method. We further applied our proposed method to the Amsterdam Cohort Study (ACS) real data, where some improvements could be found