13,003 research outputs found

    Estimating notch fatigue limits via a machine learning-based approach structured according to the classic Kf formulas

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    This paper deals with the problem of estimating notch fatigue limits via machine learning. The proposed strategy is based on those constitutive elements that were used by the pioneers like Peterson, Neuber, Heywood, and Topper to devise their well-known formulas. The machine learning algorithms being considered were trained and tested using a database containing 238 notch fatigue limits taken from the literature. The outcomes from this study confirm that machine learning is a promising approach for designing notched components against fatigue. In particular, the accuracy in the estimates can easily be increased by simply increasing size and quality of the calibration dataset. Further, since machine learning regression models are highly flexible and can handle high-dimensional datasets with many input features, they can capture complex relationships between input features and the target variable. This means that the accuracy in estimating notch fatigue limit can be increased by including in the analyses further input features like, for instance, grain size or hardness. Finally, machine learning’s generalization ability is crucial for regression tasks where the goal is to predict values for new materials

    COVID-19, deaths at home and end-of-life cancer care

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    During the COVID-19 pandemic there was a period of high excess deaths from cancer at home as opposed to in hospitals or in care homes. In this paper we aim to explore whether healthcare utilisation trajectories of cancer patients in the final months of life during the COVID-19 pandemic reveal any potential unmet healthcare need. We use English hospital records linked to data on all deaths in and out of hospital which identifies the cause and location of death. Our analysis shows that during the periods of peak COVID-19 caseload, patients dying of cancer experienced up to 42% less hospital treatment in their final month of life compared to historical controls. We find reductions in end-of-life hospital care for cancer patients dying in hospitals, care homes/hospices and at home, however the effect is amplified by the shift to more patients dying at home. Through the first year of the pandemic in England, we estimate the number of inpatient bed-days for end-of-life cancer patients in their final month reduced by approximately 282,282, or 25%. For outpatient appointments in the final month of life we find a reduction in face-to-face appointments and an increase in remote appointments which persists through the pandemic year and is not confined only to the periods of peak COVID-19 caseload. Our results suggest reductions in care provision during the COVID-19 pandemic may have led to unmet need, and future emergency reorganisations of health care systems must ensure consistent care provision for vulnerable groups such as cancer patients

    Non-invasive and non-intrusive diagnostic techniques for gas-solid fluidized beds – A review

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    Gas-solid fluidized-bed systems offer great advantages in terms of chemical reaction efficiency and temperature control where other chemical reactor designs fall short. For this reason, they have been widely employed in a range of industrial application where these properties are essential. Nonetheless, the knowledge of such systems and the corresponding design choices, in most cases, rely on a heuristic expertise gained over the years rather than on a deep physical understanding of the phenomena taking place in fluidized beds. This is a huge limiting factor when it comes to the design, the scale-up and the optimization of such complex units. Fortunately, a wide array of diagnostic techniques has enabled researchers to strive in this direction, and, among these, non-invasive and non-intrusive diagnostic techniques stand out thanks to their innate feature of not affecting the flow field, while also avoiding direct contact with the medium under study. This work offers an overview of the non-invasive and non-intrusive diagnostic techniques most commonly applied to fluidized-bed systems, highlighting their capabilities in terms of the quantities they can measure, as well as advantages and limitations of each of them. The latest developments and the likely future trends are also presented. Neither of these methodologies represents a best option on all fronts. The goal of this work is rather to highlight what each technique has to offer and what application are they better suited for

    The infrared structure of perturbative gauge theories

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    Infrared divergences in the perturbative expansion of gauge theory amplitudes and cross sections have been a focus of theoretical investigations for almost a century. New insights still continue to emerge, as higher perturbative orders are explored, and high-precision phenomenological applications demand an ever more refined understanding. This review aims to provide a pedagogical overview of the subject. We briefly cover some of the early historical results, we provide some simple examples of low-order applications in the context of perturbative QCD, and discuss the necessary tools to extend these results to all perturbative orders. Finally, we describe recent developments concerning the calculation of soft anomalous dimensions in multi-particle scattering amplitudes at high orders, and we provide a brief introduction to the very active field of infrared subtraction for the calculation of differential distributions at colliders. © 2022 Elsevier B.V

    Distribution and Mixotrophy of Cryptophyte Phytoplankton in the Northern Gulf of Alaska

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    The Northern Gulf of Alaska (NGA) is a productive subarctic marine ecosystem that supports high abundances of plankton, fishes, seabirds, and mammals. Research has shown that this high productivity is primarily controlled by seasonal and spatial heterogeneity in the lower trophic level food web. Marine cryptophytes are a crucial, yet understudied, phytoplankton group in the NGA. Cryptophytes have the capacity for mixotrophy (acquiring energy through photosynthesis and feeding) which can improve trophic transfer efficiency, increase cellular growth rates, and improve retention of nutrients in the water column. Field samples collected in spring, summer, and fall 2021 surveyed the contribution of marine cryptophytes to the phytoplankton community in the NGA and assessed how natural variability in environmental factors influenced their distribution and mixotrophic capabilities. Our study demonstrated high spatial and temporal variability in cryptophyte biomass and community composition across the NGA. Cryptophytes were found in highest abundances in summer and fall, with smaller cells (3- 10 μm) dominating the cryptophyte community composition during the summer in nearshore waters and larger cells (10-25 μm) playing an important role offshore in the fall. This variability, along with a capacity to live in a wide range of environmental conditions in the NGA, suggests that cryptophytes are versatile protists. Analysis of small cell-dominated phytoplankton communities generated carbon biomass estimates up to 600 μgC/L and carbon to chlorophyll ratios up to 300. These values were higher than previously expected for a small cell community in the NGA and indicated high carbon transfer potential from small cells despite low chlorophyll concentrations in some seasons. Cryptophytes and other nanoeukaryotes consistently made up ~75 % of the total phytoplankton community biomass in summer and fall in the NGA. This research suggests that cryptophytes are a critical component of the lower trophic level food web in the NGA in summer and fall. Despite their diverse environmental range in 2021, time series analyses of four summers (2018-2021) of cryptophyte data showed lower abundance, biomass, and average cell size during 2019, an anomalously warm year in the NGA. Finally, we found empirical evidence for in situ mixotrophy and the mechanisms that regulate cryptophyte mixotrophy in the NGA. Cryptophyte mixotrophy was highest in larger cells (\u3e 10 μm) in summer and fall and had a strong positive relationship with prey (Synechococcus spp.) concentration, a moderate positive relationship with phosphate, and a weak negative relationship with ammonium. Findings from this research improved our understanding of the basic biology and ecology of this important group of primary producers and will provide novel information to integrate estimates of cryptophyte biomass and mixotrophy into our ecosystem food web model

    Systematic analyses with genomic and metabolomic insights reveal a new species, Ophiocordyceps indica sp. nov. from treeline area of Indian Western Himalayan region

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    Ophiocordyceps is a species-rich genus in the order Hypocreales (Sordariomycetes, Ascomycota) depicting a fascinating relationship between microbes and insects. In the present study, a new species, Ophiocordyceps indica sp. nov., is discovered infecting lepidopteran larvae from tree line locations (2,202–2,653 m AMSL) of the Kullu District, Himachal Pradesh, Indian Western Himalayan region, using combinations of morphological and molecular phylogenetic analyses. A phylogeny for Ophiocordyceps based on a combined multigene (nrSSU, nrLSU, tef-1α, and RPB1) dataset is provided, and its taxonomic status within Ophiocordycipitaceae is briefly discussed. Its genome size (~59 Mb) revealed 94% genetic similarity with O. sinensis; however, it differs from other extant Ophiocordyceps species based on morphological characteristics, molecular phylogenetic relationships, and genetic distance. O. indica is identified as the second homothallic species in the family Ophiocordycipitaceae, after O. sinensis. The presence of targeted marker components, viz. nucleosides (2,303.25 μg/g), amino acids (6.15%), mannitol (10.13%), and biological activity data, suggests it to be a new potential source of nutraceutical importance. Data generated around this economically important species will expand our understanding regarding the diversity of Ophiocordyceps-like taxa from new locations, thus providing new research avenues

    Large-Scale Study of Temporal Shift in Health Insurance Claims

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    Most machine learning models for predicting clinical outcomes are developed using historical data. Yet, even if these models are deployed in the near future, dataset shift over time may result in less than ideal performance. To capture this phenomenon, we consider a task--that is, an outcome to be predicted at a particular time point--to be non-stationary if a historical model is no longer optimal for predicting that outcome. We build an algorithm to test for temporal shift either at the population level or within a discovered sub-population. Then, we construct a meta-algorithm to perform a retrospective scan for temporal shift on a large collection of tasks. Our algorithms enable us to perform the first comprehensive evaluation of temporal shift in healthcare to our knowledge. We create 1,010 tasks by evaluating 242 healthcare outcomes for temporal shift from 2015 to 2020 on a health insurance claims dataset. 9.7% of the tasks show temporal shifts at the population level, and 93.0% have some sub-population affected by shifts. We dive into case studies to understand the clinical implications. Our analysis highlights the widespread prevalence of temporal shifts in healthcare.Comment: To appear as an oral spotlight and poster at Conference on Health, Inference, and Learning (CHIL) 202

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Luminosity for laser-electron colliders

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    High intensity laser facilities are expanding their scope from laser and particle-acceleration test beds to user facilities and nuclear physics experiments. A basic goal is to confirm long-standing predictions of strong-field quantum electrodynamics, but with the advent of high-repetition rate laser experiments producing GeV-scale electrons and photons, novel searches for new high-energy particle physics also become possible. The common figure of merit for these facilities is the invariant χ2γeElaser/Ec\chi\simeq 2\gamma_e|\vec E_{\rm laser}|/E_c describing the electric field strength in the electron rest frame relative to the ``critical'' field strength of quantum electrodynamics where the vacuum decays into electron-positron pairs. However, simply achieving large χ\chi is insufficient; discovery or validation requires statistics to distinguish physics from fluctuations. The number of events delivered by the facility is therefore equally important. In high-energy physics, luminosity quantifies the rate at which colliders provide events and data. We adapt the definition of luminosity to high-intensity laser-electron collisions to quantify and thus optimize the rate at which laser facilities can deliver strong-field QED and potentially new physics events. Modeling the pulsed laser field and electron bunch, we find that luminosity is maximized for laser focal spot size equal or slightly larger than the diameter of the dense core of the electron bunch. Several examples show that luminosity can be maximized for parameters different from those maximizing the peak value of χ\chi in the collision. The definition of luminosity for electron-laser collisions is straightforwardly extended to photon-laser collisions and lepton beam-beam collisions
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