1,141 research outputs found

    Knowledge discovery for friction stir welding via data driven approaches: Part 2 – multiobjective modelling using fuzzy rule based systems

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    In this final part of this extensive study, a new systematic data-driven fuzzy modelling approach has been developed, taking into account both the modelling accuracy and its interpretability (transparency) as attributes. For the first time, a data-driven modelling framework has been proposed designed and implemented in order to model the intricate FSW behaviours relating to AA5083 aluminium alloy, consisting of the grain size, mechanical properties, as well as internal process properties. As a result, ‘Pareto-optimal’ predictive models have been successfully elicited which, through validations on real data for the aluminium alloy AA5083, have been shown to be accurate, transparent and generic despite the conservative number of data points used for model training and testing. Compared with analytically based methods, the proposed data-driven modelling approach provides a more effective way to construct prediction models for FSW when there is an apparent lack of fundamental process knowledge

    A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

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    In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

    Pollen Nightmare: Elevated Airborne Pollen Levels at Night

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    High airborne pollen concentrations are generally associated with daylight hours when it is sunny and warm and plants release pollen into the air (Alcázar et al. 1999; Dahl et al. 2013). In contrast, cooler night-time periods are usually considered to be the time of low-allergy risk. This opinion is often reflected in pollen allergy avoidance strategies presented by the media, where the most commonly repeated recommendation is to stay indoors during the day and plan outdoor activities for the evening. However, there is evidence to suggest that elevated concentrations of airborne pollen might also occur during the evening (e.g. Norris-Hill and Emberlin 1991). So, is the night really a time of low-allergy risk? We present the results of the comparative analysis of pollen concentrations during daytime and night-time hours for five allergenic pollen types (Burbach et al. 2009), i.e. alder (Alnus sp.), birch (Betula sp.), grasses (Poaceae), mugwort (Artemisia sp.) and ragweed (Ambrosia sp.)

    Impact of cardiometabolic multimorbidity and ethnicity on cardiovascular/renal complications in patients with COVID-19

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    OBJECTIVE: Using a large national database of people hospitalised with COVID-19, we investigated the contribution of cardio-metabolic conditions, multi-morbidity and ethnicity on the risk of in-hospital cardiovascular complications and death. METHODS: A multicentre, prospective cohort study in 302 UK healthcare facilities of adults hospitalised with COVID-19 between 6 February 2020 and 16 March 2021. Logistic models were used to explore associations between baseline patient ethnicity, cardiometabolic conditions and multimorbidity (0, 1, 2, >2 conditions), and in-hospital cardiovascular complications (heart failure, arrhythmia, cardiac ischaemia, cardiac arrest, coagulation complications, stroke), renal injury and death. RESULTS: Of 65 624 patients hospitalised with COVID-19, 44 598 (68.0%) reported at least one cardiometabolic condition on admission. Cardiovascular/renal complications or death occurred in 24 609 (38.0%) patients. Baseline cardiometabolic conditions were independently associated with increased odds of in-hospital complications and this risk increased in the presence of cardiometabolic multimorbidity. For example, compared with having no cardiometabolic conditions, 1, 2 or ≥3 conditions was associated with 1.46 (95% CI 1.39 to 1.54), 2.04 (95% CI 1.93 to 2.15) and 3.10 (95% CI 2.92 to 3.29) times higher odds of any cardiovascular/renal complication, respectively. A similar pattern was observed for all-cause death. Compared with the white group, the South Asian (OR 1.19, 95% CI 1.10 to 1.29) and black (OR 1.53 to 95% CI 1.37 to 1.72) ethnic groups had higher risk of any cardiovascular/renal complication. CONCLUSIONS: In hospitalised patients with COVID-19, cardiovascular complications or death impacts just under half of all patients, with the highest risk in those of South Asian or Black ethnicity and in patients with cardiometabolic multimorbidit

    Physically active academic lessons; Acceptance, barriers and facilitators for implementation

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    Background To improve health and academic learning in schoolchildren, the Active School programme in Stavanger, Norway has introduced physically active academic lessons. This is a teaching method combining physical activity with academic content. The purpose of this paper was to evaluate the response to the physically active lessons and identify facilitators and barriers for implementation of such an intervention. Methods Five school leaders (principals or vice-principals), 13 teachers and 30 children from the five intervention schools were interviewed about their experiences with the 10-month intervention, which consisted of weekly minimum 2 × 45 minutes of physically active academic lessons, and the factors affecting its implementation. All interviews were transcribed and analysed using the qualitative data analysis program NVivo 10 (QSR international, London, UK). In addition, weekly teacher’s intervention delivery logs were collected and analysed. Results On average, the physically active academic lessons in 18 of the 34 weeks (53%) were reported in the teacher logs. The number of delivered physically active academic lessons covered 73% of the schools’ planned activity. Physically active lessons were well received among school leaders, teachers and children. The main facilitators for implementation of the physically active lessons were active leadership and teacher support, high self-efficacy regarding mastering the intervention, ease of organizing physically active lessons, inclusion of physically active lessons into the lesson curricula, and children’s positive reception of the intervention. The main barriers were unclear expectations, lack of knowledge and time to plan the physiclly active lessons, and the length of the physically active lessons (15–20 min lessons were preferred over the 45 min lessons). Conclusion Physically active academic lessons were considered an appropriate pedagogical method for creating positive variation, and were highly appreciated among both teachers and children. Both the principal and the teachers should be actively involved the implementation, which could be strengthened by including physical activity into the school’s strategy. Barriers for implementing physically active lessons in schools could be lowered by increasing implementation clarity and introducing the teachers to high quality and easily organized lessons.publishedVersio

    Star Clusters

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    This review concentrates almost entirely on globular star clusters. It emphasises the increasing realisation that few of the traditional problems of star cluster astronomy can be studied in isolation: the influence of the Galaxy affects dynamical evolution deep in the core, and the spectrum of stellar masses; in turn the evolution of the core determines the highest stellar densities, and the rate of encounters. In this way external tidal effects indirectly influence the formation and evolution of blue stragglers, binary pulsars, X-ray sources, etc. More controversially, the stellar density appears to influence the relative distribution of normal stars. In the opposite sense, the evolution of individual stars governs much of the early dynamics of a globular cluster, and the existence of large numbers of primordial binary stars has changed important details of our picture of the dynamical evolution. New computational tools which will become available in the next few years will help dynamical theorists to address these questions.Comment: 10 pages, 3 figures, Te
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