70,691 research outputs found

    Location-aided multi-user beamforming for 60 GHz WPAN systems

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    A rapid staining-assisted wood sampling method for PCR-based detection of pine wood nematode Bursaphelenchus xylophilus in Pinus massoniana wood tissue

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    For reasons of unequal distribution of more than one nematode species in wood, and limited availability of wood samples required for the PCR-based method for detecting pinewood nematodes in wood tissue of Pinus massoniana, a rapid staining-assisted wood sampling method aiding PCR-based detection of the pine wood nematode Bursaphelenchus xylophilus (Bx) in small wood samples of P. massoniana was developed in this study. This comprised a series of new techniques: sampling, mass estimations of nematodes using staining techniques, and lowest limit Bx nematode mass determination for PCR detection. The procedure was undertaken on three adjoining 5-mg wood cross-sections, of 0.5 · 0.5 · 0.015 cm dimension, that were cut from a wood sample of 0.5 · 0.5 · 0.5 cm initially, then the larger wood sample was stained by acid fuchsin, from which two 5-mg wood cross-sections (that adjoined the three 5-mg wood cross-sections, mentioned above) were cut. Nematode-staining-spots (NSSs) in each of the two stained sections were counted under a microscope at 100· magnification. If there were eight or more NSSs present, the adjoining three sections were used for PCR assays. The B. xylophilus – specific amplicon of 403 bp (DQ855275) was generated by PCR assay from 100.00% of 5-mg wood cross-sections that contained more than eight Bx NSSs by the PCR assay. The entire sampling procedure took only 10 min indicating that it is suitable for the fast estimation of nematode numbers in the wood of P. massonina as the prelimary sample selections for other more expensive Bx-detection methods such as PCR assay

    Superconductivity in Ti-doped Iron-Arsenide Compound Sr4Cr0.8Ti1.2O6Fe2As2

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    Superconductivity was achieved in Ti-doped iron-arsenide compound Sr4Cr0.8Ti1.2O6Fe2As2 (abbreviated as Cr-FeAs-42622). The x-ray diffraction measurement shows that this material has a layered structure with the space group of \emph{P4/nmm}, and with the lattice constants a = b = 3.9003 A and c = 15.8376 A. Clear diamagnetic signals in ac susceptibility data and zero-resistance in resistivity data were detected at about 6 K, confirming the occurrence of bulk superconductivity. Meanwhile we observed a superconducting transition in the resistive data with the onset transition temperature at 29.2 K, which may be induced by the nonuniform distribution of the Cr/Ti content in the FeAs-42622 phase, or due to some other minority phase.Comment: 3 pages, 3 figure

    LEARNING FROM NOISY SAMPLES FOR MAN-MADE IMPERVIOUS SURFACE MAPPING

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    Abstract. Man-made impervious surfaces, indicating the human footprint on Earth, are an environmental concern because it leads to a chain of events that modifies urban air and water resources. To better map man-made impervious surfaces in any region of interest (ROI), we propose a framework for learning to map impervious areas in any ROIs from Sentinel-2 images with noisy reference data, using a pre-trained fully convolutional network (FCN). The FCN is first trained with reference data only available in Europe, which is able to provide reasonable mapping results even in areas outside of Europe. The proposed framework, aiming to achieve an improvement over the preliminary predictions for a specific ROI, consists of two steps: noisy training data pre-processing and model fine-tuning with robust loss functions. The framework is validated over four test areas located in different continents with a measurable improvement over several baseline results. It has been shown that a better impervious mapping result can be achieved through a simple fine-tuning with noisy training data, and label updating through robust loss functions allows to further enhance the performances. In addition, by analyzing and comparing the mapping results to baselines, it can be highlighted that the improvement is mainly coming from a decreased omission error. This study can also provide insights for similar tasks, such as large-scale land cover/land use classification when accurate reference data is not available for training
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