37 research outputs found

    New multi-channel electron energy analyzer with cylindrically symmetrical electrostatic field

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    This paper discusses an electron energy analyzer with a cylindrically symmetrical electrostatic field, designed for rapid Auger analysis. The device was designed and built. The best parameters of the analyzer were estimated and then experimentally verified.Comment: 5 pages, 4 figure

    Collection of Backscattered Electrons with a Single Polepiece Lens and a Multiple Detector

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    This paper is a part of a study on the use of a single-polepiece lens as an objective lens of an analytical scanning electron microscope (SEM). The single-polepiece lens has proved to be very suitable for the efficient collection of backscattered electrons (BSE) with a multielement semiconductor detector. For the BSE images in the sum and difference modes the contrast is a non-monotonic function of the excitation of the lens, due to the complicated nature of the BSE trajectories. The use of a six-element semiconductor detector provides a whole variety of BSE signal compositions in the conventional SEM as wel

    Scanning Electron Microscope with a Single-Polepiece Lens

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    The design of an ultra-high vacuum scanning electron microscope (UHV SEM) with a single-pole-piece lens underneath the specimen is described with the possibility to guide backscattered (BSE) and secondary electrons (SE) which originate in the magnetic field of the single-polepiece lens to the detectors. Our new design of the single-pole-piece lens and in-lens deflection coils closely satisfy the condition of a variable axis immersion lens (VAIL), which results in very low deflection aberrations

    Clinical profile of predefined asthma phenotypes in a large cohort of UK primary care patients (Clinical Practice Research Datalink).

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    BACKGROUND: Distinct asthma phenotypes have previously been suggested, including benign asthma, atopic asthma and obese non-eosinophilic asthma. This study aims to establish if these phenotypes can be identified using data recorded in primary care clinical records and reports on patient characteristics and exacerbation frequency. METHODS: A population-based cohort study identified 193,999 asthma patients in UK primary care from 2007 to 2017. We used linked primary and secondary care data from the Clinical Practice Research Datalink, Hospital Episode Statistics and Office for National Statistics. Patients were classified into predefined phenotypes or included in an asthma "not otherwise specified" (NOS) group. We used negative binomial regression to calculate the exacerbation rates and adjusted rate ratios. Rate ratios were further stratified by asthma treatment step. RESULTS: In our cohort, 3.9% of patients were categorized as benign asthma, 28.6% atopic asthma and 4.8% obese non-eosinophilic asthma. About 62.7% of patients were asthma NOS, including asthma NOS without treatment (10.4%), only on short-acting beta agonist (6.1%) and on maintenance treatment (46.2%). Crude severe exacerbation rates per 1,000 person-years were lowest for benign asthma (106.8 [95% CI: 101.2-112.3]) and highest for obese non-eosinophilic asthma (469.0 [451.7-486.2]). Incidence rate ratios for all phenotype groups decreased when stratified by treatment step but remained raised compared to benign asthma. CONCLUSION: Established phenotypes can be identified in a general asthma population, although many patients did not fit into the specific phenotypes which we studied. Phenotyping patients and knowledge of asthma treatment step could help anticipate clinical course and therefore could aid clinical management but is only possible in a minority of primary care patients based on current phenotypes and electronic health records (EHRs)

    On the Use of Unmanned Aerial Systems for Environmental Monitoring

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    [EN] Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small-and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air-and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challenges.The present work has been funded by the COST Action CA16219 "HARMONIOUS-Harmonization of UAS techniques for agricultural and natural ecosystems monitoring". B. Toth acknowledges financial support by the Hungarian National Research, Development and Innovation Office (NRDI) under grant KH124765. J. Millerovd was supported by projects GA17-13998S and RVO67985939. Isabel and Jodo de Lima were supported by project HIRT (PTDC/ECM-HID/4259/2014-POCI-0145-FEDER016668).Manfreda, S.; Mccabe, MF.; Miller, PE.; Lucas, R.; Pajuelo Madrigal, V.; Mallinis, G.; Ben Dor, E.... (2018). On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sensing. 10(4):1-28. https://doi.org/10.3390/rs10040641S12810

    Imaging with STEM Detector, Experiments vs. Simulation

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    Unconventional Imaging with Backscattered Electrons

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    Scanning Electron Microscopy With Slow Electrons

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