This study, a nationwide retrospective cohort analysis in Sweden, used national databases to evaluate fracture risk differentiated by the location of a recent (within two years) fracture, a pre-existing fracture (more than two years old), and compared these risks with controls without any fracture. The study encompassed all Swedish citizens aged 50 or over, tracked during the period from 2007 to 2010. Patients with a recent fracture were grouped according to the type of fracture they sustained before, receiving a designation dependent on that previous type. Recent fracture cases were categorized into major osteoporotic fractures (MOF), comprising fractures of the hip, vertebra, proximal humerus, and wrist, or non-MOF fractures. Monitoring of patients extended to the end of 2017 (December 31st). Events such as death and emigration acted as censoring mechanisms. A subsequent analysis was undertaken to assess the risk of both all fractures and hip fractures. The dataset encompasses a study of 3,423,320 people, including 70,254 with a recent MOF, 75,526 with a recent non-MOF, 293,051 with a pre-existing fracture, and 2,984,489 without any prior fractures. The four cohorts' median periods of follow-up were 61 (interquartile range [IQR] 30-88), 72 (56-94), 71 (58-92), and 81 years (74-97), correspondingly. Patients presenting with recent multi-organ failure (MOF), recent non-MOF conditions, and pre-existing fractures demonstrated a substantially increased susceptibility to any future fracture. Adjusted hazard ratios (HRs) accounting for age and sex revealed significant differences, with HRs of 211 (95% CI 208-214) for recent MOF, 224 (95% CI 221-227) for recent non-MOF, and 177 (95% CI 176-178) for prior fractures, respectively, compared to control subjects. The risk of subsequent fractures is heightened by recent fracture occurrences, encompassing those related to metal-organic frameworks (MOFs) and those without, as well as by older fractures. This underlines the necessity of including all recent fractures within fracture liaison programs and possibly warrants proactive strategies for identifying and managing older fracture cases in order to prevent further incidents. Copyright for the works of 2023 is attributed to The Authors. The American Society for Bone and Mineral Research (ASBMR) utilizes Wiley Periodicals LLC to publish its flagship journal, the Journal of Bone and Mineral Research.

For the sustainable development of buildings, it is crucial to utilize functional energy-saving building materials, which are essential for reducing thermal energy consumption and encouraging the use of natural indoor lighting. The utilization of phase-change materials within wood-based materials positions them for thermal energy storage. Conversely, the renewable resource content often falls short, energy storage and mechanical attributes are usually weak, and the long-term sustainability of these resources remains unexplored. We introduce a fully bio-based, transparent wood (TW) biocomposite designed for thermal energy storage, featuring superior heat storage, tunable optical properties, and significant mechanical strength. Limonene acrylate monomer, synthesized, and renewable 1-dodecanol are combined to form a bio-based matrix that is impregnated and polymerized in situ within mesoporous wood substrates. High latent heat (89 J g-1) is a feature of the TW, surpassing commercial gypsum panels' values. This is combined with a thermo-responsive optical transmittance of up to 86% and a mechanical strength of up to 86 MPa. β-Glycerophosphate Compared to transparent polycarbonate panels, bio-based TW shows a 39% lower environmental impact, as evaluated by life cycle assessment. The bio-based TW's potential as a scalable and sustainable transparent heat storage solution is noteworthy.

Efficient hydrogen production is achievable through the coupling of urea oxidation reaction (UOR) and hydrogen evolution reaction (HER). In spite of efforts, developing low-cost and highly effective bifunctional electrocatalysts for total urea electrolysis continues to be a formidable challenge. Employing a one-step electrodeposition approach, this study synthesizes a metastable Cu05Ni05 alloy. A current density of 10 mA cm-2 for UOR and HER is obtainable by applying potentials of 133 mV and -28 mV, respectively. β-Glycerophosphate Superior performance is directly linked to the metastable alloy's properties. Under alkaline conditions, the newly prepared Cu05 Ni05 alloy shows substantial stability towards the hydrogen evolution reaction; conversely, the UOR environment leads to a rapid formation of NiOOH species due to phase segregation in the Cu05 Ni05 alloy. The hydrogen generation system, energy-saving and coupled with hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), requires only 138 V of voltage at a current density of 10 mA cm-2. Furthermore, at a current density of 100 mA cm-2, the applied voltage decreases by 305 mV, compared to the conventional water electrolysis system (HER and OER). Relative to recently described catalysts, the Cu0.5Ni0.5 catalyst possesses superior electrocatalytic activity and impressive durability. This work also presents a straightforward, gentle, and swift method for engineering highly active bifunctional electrocatalysts, thereby facilitating urea-assisted overall water splitting.

To preface this paper, we engage with exchangeability and its implication for the Bayesian perspective. We explore the predictive power of Bayesian models and the inherent symmetry assumptions within the framework of beliefs regarding an underlying exchangeable sequence of observations. We develop a parametric Bayesian bootstrap by examining the Bayesian bootstrap, the parametric bootstrap method proposed by Efron, and a Bayesian inferential perspective stemming from Doob's martingale theory. Fundamental to any discussion of martingales is their role. Illustrations, accompanied by the pertinent theory, are presented. The theme issue 'Bayesian inference challenges, perspectives, and prospects' encompasses this article.

A Bayesian's task of defining the likelihood is equally perplexing as defining the prior. Our investigations delve into situations where the parameter of interest is no longer dependent on the likelihood, but is directly tied to data through the structure of a loss function. A study of the current research regarding Bayesian parametric inference, incorporating Gibbs posteriors, and Bayesian non-parametric inference is undertaken. Subsequent to this, we analyze current bootstrap computational methods for approximating loss-driven posterior distributions. Crucially, we consider implicit bootstrap distributions that are constructed through an underlying push-forward transformation. Our investigation focuses on independent, identically distributed (i.i.d.) samplers from approximate posteriors, with random bootstrap weights being fed into a pre-trained generative network. The simulation cost of these independent and identically distributed samplers is markedly reduced after the deep-learning mapping is trained. Using support vector machines and quantile regression as illustrative examples, we compare the performance of these deep bootstrap samplers to exact bootstrap and MCMC methods. The theoretical insights into bootstrap posteriors that we offer stem from our exploration of the relationships between them and model mis-specification. This article forms a part of the theme issue devoted to 'Bayesian inference challenges, perspectives, and prospects'.

I dissect the benefits of viewing problems through a Bayesian lens (attempting to find Bayesian justifications for methods seemingly unrelated to Bayesian thinking), and the hazards of being overly reliant on a Bayesian framework (rejecting non-Bayesian methods based on philosophical considerations). Scientists seeking to grasp widely used statistical methods, including confidence intervals and p-values, as well as teachers and practitioners, will hopefully find these ideas helpful in avoiding the error of prioritizing philosophy over practical application. Included within the thematic issue 'Bayesian inference challenges, perspectives, and prospects', this article appears.

Within the framework of potential outcomes, this paper presents a critical analysis of the Bayesian stance on causal inference. We examine the causal targets, the method of assignment, the general architecture of Bayesian causal effect estimation, and sensitivity analyses. We delineate the particular challenges of Bayesian causal inference, which involve the propensity score, the rigorous definition of identifiability, and the selection of appropriate prior distributions for both low-dimensional and high-dimensional data. Covariate overlap and the broader design stage are central to Bayesian causal inference, as we emphasize here. We delve deeper into the discussion, exploring two intricate assignment methods: instrumental variables and time-varying treatments. We investigate the positive and negative impacts of a Bayesian perspective in causal inference research. We present examples throughout to showcase the key ideas. The 'Bayesian inference challenges, perspectives, and prospects' theme issue encompasses this article.

In Bayesian statistics and now in many machine learning domains, prediction occupies a central position, in stark contrast to the historical emphasis on inferential methods. β-Glycerophosphate We examine the fundamental concept of random sampling, specifically Bayesian exchangeability, where uncertainty, as reflected in the posterior distribution and credible intervals, can be interpreted through predictive analysis. The posterior law concerning the unknown distribution is centered around the predictive distribution, and we show its asymptotic marginal Gaussianity; the variance is determined by the predictive updates, indicating how the predictive rule incorporates new information as observations become available. Using solely the predictive rule, asymptotic credible intervals can be computed without specifying a model or a prior distribution. This clarifies the connection between frequentist coverage and predictive learning rules, and we believe this represents a novel approach to understanding predictive efficiency, which warrants further investigation.