768 research outputs found

    Relationship between watershed environments and growth of coastal diatoms

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    Effect of watershed environments on river water quality and the subsequent influence of water quality on the growth of diatoms in coastal seawater were studied. Land use in the upper and lower site of the Ohkawa River (O-up and O-low) and the upper site of the Nanakita River (N-up) were dominated by forestry, whereas the lower site of the Nanakita River (N-low) was characterized by urbanization. Seasonal changes in nutrients in the Ohkawa and Nanakita Rivers suggested that the concentrations of NH_4-N, NO_3-N, PO_4-P and acid extractable-Fe were influenced by human activities, while Si concentration reflected geological conditions. The average concentrations of fulvic acid-like Fe (FA-Fe), closely associated with the growth of coastal diatoms, were 1 and 16μgL^ at the O-up and O-low sites, respectively, while those of the Nanakita River were 5μgL^ (N-up) and 53μgL^ (N-low). For each river, FA-Fe concentrations of the lower sites were much higher than the upper sites. Moreover, the concentration of FA-Fe at N-low was much higher than at O-low. Therefore, it was concluded that FA-Fe originates not only from forest vegetation but also from urban activity. The growth of the Skeletonema, a typical diatom of coastal waters, was stimulated by the addition of O-low river water compared to addition of O-up, reflecting the FA-Fe content. Diatom growth stimulation with the addition of lower river water was much more prominent in the Nanakita River, whose watershed is characterized by runoff from Sendai city.Original Pape

    有機太陽電池の高効率化に向けたチアゾロチアゾール系半導体ポリマーの開発

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    広島大学(Hiroshima University)博士(工学)Doctor of Engineeringdoctora

    楕円曲面に対する局所トレリの定理

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    Pre-training strategy using real particle collision data for event classification in collider physics

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    This study aims to improve the performance of event classification in collider physics by introducing a pre-training strategy. Event classification is a typical problem in collider physics, where the goal is to distinguish the signal events of interest from background events as much as possible to search for new phenomena in nature. A pre-training strategy with feasibility to efficiently train the target event classification using a small amount of training data has been proposed. Real particle collision data were used in the pre-training phase as a novelty, where a self-supervised learning technique to handle the unlabeled data was employed. The ability to use real data in the pre-training phase eliminates the need to generate a large amount of training data by simulation and mitigates bias in the choice of physics processes in the training data. Our experiments using CMS open data confirmed that high event classification performance can be achieved by introducing a pre-trained model. This pre-training strategy provides a potential approach to save computational resources for future collider experiments and introduces a foundation model for event classification.Comment: Presented at the Machine Learning and the Physical Sciences Workshop, NeurIPS 202
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