High temperature ceramic/metal joint structure

A high temperature turbine engine includes a hybrid ceramic/metallic rotor member having ceramic/metal joint structure. The disclosed joint is able to endure higher temperatures than previously possible, and aids in controlling heat transfer in the rotor member

High temperature turbine engine structure

A hybrid ceramic/metallic fastener (bolt) includes a headed ceramic shank carrying a metallic end termination fitting. A conventional cap screw threadably engages the termination fitting to apply tensile force to the fastener

High temperature turbine engine structure

A high temperature ceramic/metallic turbine engine includes a metallic housing which journals a rotor member of the turbine engine. A ceramic disk-like shroud portion of the engine is supported on the metallic housing portion and maintains a close running clearance with the rotor member. A ceramic spacer assembly maintains the close running clearance of the shroud portion and rotor member despite differential thermal movements between the shroud portion and metallic housing portion

Towards a unified approach to formal risk of bias assessments for causal and descriptive inference

Statistics is sometimes described as the science of reasoning under uncertainty. Statistical models provide one view of this uncertainty, but what is frequently neglected is the invisible portion of uncertainty: that assumed not to exist once a model has been fitted to some data. Systematic errors, i.e. bias, in data relative to some model and inferential goal can seriously undermine research conclusions, and qualitative and quantitative techniques have been created across several disciplines to quantify and generally appraise such potential biases. Perhaps best known are so-called risk of bias assessment instruments used to investigate the likely quality of randomised controlled trials in medical research. However, the logic of assessing the risks caused by various types of systematic error to statistical arguments applies far more widely. This logic applies even when statistical adjustment strategies for potential biases are used, as these frequently make assumptions (e.g. data missing at random) that can never be guaranteed in finite samples. Mounting concern about such situations can be seen in the increasing calls for greater consideration of biases caused by nonprobability sampling in descriptive inference (i.e. survey sampling), and the statistical generalisability of in-sample causal effect estimates in causal inference; both of which relate to the consideration of model-based and wider uncertainty when presenting research conclusions from models. Given that model-based adjustments are never perfect, we argue that qualitative risk of bias reporting frameworks for both descriptive and causal inferential arguments should be further developed and made mandatory by journals and funders. It is only through clear statements of the limits to statistical arguments that consumers of research can fully judge their value for any specific application.Comment: 12 page

Topiramate improves neurovascular function, epidermal nerve fiber morphology, and metabolism in patients with type 2 diabetes mellitus

Amanda L Boyd, Patricia M Barlow, Gary L Pittenger, Kathryn F Simmons, Aaron I VinikDepartment of Internal Medicine, Eastern Virginia Medical School, Norfolk, VA, USAPurpose: To assess the effects of topiramate on C-fiber function, nerve fiber morphology, and metabolism (including insulin sensitivity, obesity, and dyslipidemia) in type 2 diabetes.Patients and methods: We conducted an 18-week, open-label trial treating patients with topiramate. Twenty subjects with type 2 diabetes and neuropathy (61.5 ± 1.29 years; 15 male, 5 female) were enrolled and completed the trial. Neuropathy was evaluated by total neuropathy scores, nerve conduction studies, quantitative sensory tests, laser Doppler skin blood flow, and intraepidermal nerve fibers in skin biopsies.Results: Topiramate treatment improved symptoms compatible with C-fiber dysfunction. Weight, blood pressure, and hemoglobin A1c also improved. Laser Doppler skin blood flow improved significantly after 12 weeks of treatment, but returned to baseline at 18 weeks. After 18 weeks of treatment there was a significant increase in intraepidermal nerve fiber length at the forearm, thigh, and proximal leg. Intraepidermal nerve fiber density was significantly increased by topiramate in the proximal leg.Conclusion: This study is the first to demonstrate that it is possible to induce skin intraepidermal nerve fiber regeneration accompanied by enhancement of neurovascular function, translating into improved symptoms as well as sensory nerve function. The simultaneous improvement of selective metabolic indices may play a role in this effect, but this remains to be determined.Keywords: diabetic neuropathy, skin blood flow, skin biopsy, diabete

We need to talk about nonprobability samples

In most circumstances, probability sampling is the only way to ensure unbiased inference about population quantities where a complete census is not possible. As we enter the era of ‘big data’, however, nonprobability samples, whose sampling mechanisms are unknown, are undergoing a renaissance. We explain why the use of nonprobability samples can lead to spurious conclusions, and why seemingly large nonprobability samples can be (effectively) very small. We also review some recent controversies surrounding the use of nonprobability samples in biodiversity monitoring. These points notwithstanding, we argue that nonprobability samples can be useful, provided that their limitations are assessed, mitigated where possible and clearly communicated. Ecologists can learn much from other disciplines on each of these fronts

occAssess: an R package for assessing potential biases in species occurrence data

Species occurrence records from a variety of sources are increasingly aggregated into heterogeneous databases and made available to ecologists for immediate analytical use. However, these data are typically biased, i.e. they are not a probability sample of the target population of interest, meaning that the information they provide may not be an accurate reflection of reality. It is therefore crucial that species occurrence data are properly scrutinised before they are used for research. In this article, we introduce occAssess, an R package that enables straightforward screening of species occurrence data for potential biases. The package contains a number of discrete functions, each of which returns a measure of the potential for bias in one or more of the taxonomic, temporal, spatial, and environmental dimensions. Users can opt to provide a set of time periods into which the data will be split; in this case separate outputs will be provided for each period, making the package particularly useful for assessing the suitability of a dataset for estimating temporal trends in species' distributions. The outputs are provided visually (as ggplot2 objects) and do not include a formal recommendation as to whether data are of sufficient quality for any given inferential use. Instead, they should be used as ancillary information and viewed in the context of the question that is being asked, and the methods that are being used to answer it. We demonstrate the utility of occAssess by applying it to data on two key pollinator taxa in South America: leaf-nosed bats (Phyllostomidae) and hoverflies (Syrphidae). In this worked example, we briefly assess the degree to which various aspects of data coverage appear to have changed over time. We then discuss additional applications of the package, highlight its limitations, and point to future development opportunities

Regulation to create environments conducive to physical activity : understanding the barriers and facilitators at the Australian State Government level

Introduction Policy and regulatory interventions aimed at creating environments more conducive to physical activity (PA) are an important component of strategies to improve population levels of PA. However, many potentially effective policies are not being broadly implemented. This study sought to identify potential policy/regulatory interventions targeting PA environments, and barriers/facilitators to their implementation at the Australian state/territory government level.Methods In-depth interviews were conducted with senior representatives from state/territory governments, statutory authorities and non-government organisations (n = 40) to examine participants\u27: 1) suggestions for regulatory interventions to create environments more conducive to PA; 2) support for preselected regulatory interventions derived from a literature review. Thematic and constant comparative analyses were conducted.Results Policy interventions most commonly suggested by participants fell into two areas: 1) urban planning and provision of infrastructure to promote active travel; 2) discouraging the use of private motorised vehicles. Of the eleven preselected interventions presented to participants, interventions relating to walkability/cycling and PA facilities received greatest support. Interventions involving subsidisation (of public transport, PA-equipment) and the provision of more public transport infrastructure received least support. These were perceived as not economically viable or unlikely to increase PA levels. Dominant barriers were: the powerful &lsquo;road lobby&rsquo;, weaknesses in the planning system and the cost of potential interventions. Facilitators were: the provision of evidence, collaboration across sectors, and synergies with climate change/environment agendas.Conclusion This study points to how difficult it will be to achieve policy change when there is a powerful &lsquo;road lobby&rsquo; and government investment prioritises road infrastructure over PA-promoting infrastructure. It highlights the pivotal role of the planning and transport sectors in implementing PA-promoting policy, however suggests the need for clearer guidelines and responsibilities for state and local government levels in these areas. Health outcomes need to be given more direct consideration and greater priority within non-health sectors.<br /