The influence of natural ageing on the artificial ageing response of Al-Si-Cu-Mg casting alloys

The T6 heat treatment is commonly used to increase the strength of gravity cast Al-Si components containingCu and/or Mg. The artificial ageing response is known to be affected by the thermal history, such as solutiontreatment, quench rate, natural ageing and heating rate to the artificial ageing temperature. The influence ofnatural ageing on the artificial ageing response was investigated for three alloys; Al-8Si-0.4Mg, Al-7Si-3Cu andAl-8Si-3Cu-0.4Mg. Natural ageing had a strong influence on the ageing response of the Al-Si-Mg alloy in theunderaged condition and the strength increase was strongly reduced. Despite this, the time to peak yieldstrength as well as its magnitude were not strongly affected by natural ageing. No clear influence of naturalageing was observed for the Al-Si-Cu alloy. For the Al-Si-Cu-Mg alloy the ageing response seems to depend onthe natural ageing time. Natural ageing of 3 weeks shifted the peak yield strength to shorter ageing times andits magnitude was decreased a little compared to direct ageing after quench. Natural ageing of 1 day gave theleast beneficial properties after artificial ageing

On the precipitation hardening of selective laser melted AlSi10Mg

Precipitation hardening of selective laser melted AlSi10Mg was investigated in terms of solution heat treatment and aging duration. The influence on the microstructure and hardness was established, as was the effect on the size and density of Si particles. Although the hardness changes according to the treatment duration, the maximum hardening effect falls short of the hardness of the as-built parts with their characteristic fine microstructure. This is due to the difference in strengthening mechanisms

Inverse probability of treatment weighting with generalized linear outcome models for doubly robust estimation

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly robust estimator and/or to misuse more complex established doubly robust estimators. A simple alternative, canonical link generalized linear models (GLM) fit via inverse probability of treatment (propensity score) weighted maximum likelihood estimation followed by standardization (the g-formula) for the average causal effect, is a doubly robust estimation method. Our aim is for the reader not just to be able to use this method, which we refer to as IPTW GLM, for doubly robust estimation, but to fully understand why it has the doubly robust property. For this reason, we define clearly, and in multiple ways, all concepts needed to understand the method and why it is doubly robust. In addition, we want to make very clear that the mere combination of propensity score weighting and an adjusted outcome model does not generally result in a doubly robust estimator. Finally, we hope to dispel the misconception that one can adjust for residual confounding remaining after propensity score weighting by adjusting in the outcome model for what remains `unbalanced' even when using doubly robust estimators. We provide R code for our simulations and real open-source data examples that can be followed step-by-step to use and hopefully understand the IPTW GLM method. We also compare to a much better-known but still simple doubly robust estimator

Propensity score weighting plus an adjusted proportional hazards model does not equal doubly robust away from the null

Recently it has become common for applied works to combine commonly used survival analysis modeling methods, such as the multivariable Cox model, and propensity score weighting with the intention of forming a doubly robust estimator that is unbiased in large samples when either the Cox model or the propensity score model is correctly specified. This combination does not, in general, produce a doubly robust estimator, even after regression standardization, when there is truly a causal effect. We demonstrate via simulation this lack of double robustness for the semiparametric Cox model, the Weibull proportional hazards model, and a simple proportional hazards flexible parametric model, with both the latter models fit via maximum likelihood. We provide a novel proof that the combination of propensity score weighting and a proportional hazards survival model, fit either via full or partial likelihood, is consistent under the null of no causal effect of the exposure on the outcome under particular censoring mechanisms if either the propensity score or the outcome model is correctly specified and contains all confounders. Given our results suggesting that double robustness only exists under the null, we outline two simple alternative estimators that are doubly robust for the survival difference at a given time point (in the above sense), provided the censoring mechanism can be correctly modeled, and one doubly robust method of estimation for the full survival curve. We provide R code to use these estimators for estimation and inference in the supplementary materials

Interaction of photons with plasmas and liquid metals: photoabsorption and scattering

Formulas to describe the photoabsorption and the photon scattering by a plasma or a liquid metal are derived in a unified manner with each other. It is shown how the nuclear motion, the free-electron motion and the core-electron behaviour in each ion in the system determine the structure of photoabsorption and scattering in an electron-ion mixture. The absorption cross section in the dipole approximation consists of three terms which represent the absorption caused by the nuclear motion, the absorption owing to the free-electron motion producing optical conductivity or inverse Bremsstrahlung, and the absorption ascribed to the core-electron behaviour in each ion with the Doppler correction. Also, the photon scattering formula provides an analysis method for experiments observing the ion-ion dynamical structure factor (DSF), the electron-electron DSF giving plasma oscillations, and the core-electron DSF yielding the X-ray Raman (Compton) scattering with a clear definition of the background scattering for each experiment, in a unified manner. A formula for anomalous X-ray scattering is also derived for a liquid metal. At the same time, Thomson scattering in plasma physics is discussed from this general point of view.Comment: LaTeX file: 18 pages without figur

Long-term visit-to-visit variability in hemoglobin A1c and kidney-related outcomes in persons with diabetes

Rationale & Objective: To characterize associations between long-term visit-to-visit variability of hemoglobin A(1c) (HbA(1c)) and risk of adverse kidney outcomes in patients with diabetes.Study Design: Observational study.Setting & Participants: 93,598 adults with diabetes undergoing routine care in Stockholm, Sweden.Exposures and Predictors: Categories of baseline and time-varying HbA(1c) variability score (HVS, the percentage of total HbA(1c) measures that vary by >0.5% [5.5 mmol/mol] during a 3-year window): 0-20%, 21%-40%, 41%-60%, 61%-80%, and 81%-100%, with 0-20% as the reference group.Outcome: Chronic kidney disease (CKD) progression (composite of >50% estimated glomerular filtration rate [eGFR] decline and kidney failure), acute kidney disease (AKI by clinical diagnosis or transient creatinine elevations ac-cording to KDIGO criteria), and worsening of albuminuria.Analytical Approach: Multivariable Cox proportional hazards regression.Results: Compared with persons showing low HbA(1c) variability (HVS 0-20%), any increase in variability was associated with a higher risk of adverse kidney outcomes beyond mean HbA(1c). For example, for patients with a baseline HbA(1c) variability of 81%-100%, the adjusted HR was 1.6 (95% CI, 1.47-1.74) for CKD progression, 1.23 [1.16-1.3] for AKI, and 1.28 [1.21-1.3 6] for worsening of albuminuria. The results were consistent across subgroups (diabetes subtypes, baseline eGFR, or albuminuria categories), in time-varying analyses and in sensitivity analyses including time-weighted average HbA(1c) or alternative metrics of variability.Limitations: Observational study, limitations of claims data, lack of information on diet, body mass index, medication changes, and diabetes duration.Conclusions: Higher long-term visit-to-visit HbA1c variability is consistently associated with the risks of CKD progression, AKI, and worsening of albuminuria.Clinical epidemiolog

A genetically informed study of the associations between maternal age at childbearing and adverse perinatal outcomes

We examined associations of maternal age at childbearing (MAC) with gestational age and fetal growth (i.e., birth weight adjusting for gestational age), using two genetically informed designs (cousin and sibling comparisons) and data from two cohorts, a population-based Swedish sample and a nationally representative United States sample. We also conducted sensitivity analyses to test limitations of the designs. The findings were consistent across samples and suggested that, associations observed in the population between younger MAC and shorter gestational age were confounded by shared familial factors; however, associations of advanced MAC with shorter gestational age remained robust after accounting for shared familial factors. In contrast to the gestational age findings, neither early nor advanced MAC was associated with lower fetal growth after accounting for shared familial factors. Given certain assumptions, these findings provide support for a causal association between advanced MAC and shorter gestational age. The results also suggest that there are not causal associations between early MAC and shorter gestational age, between early MAC and lower fetal growth, and between advanced MAC and lower fetal growth.NonePublishe