This study reports zircon U–Pb geochronological, geochemical, and zircon Hf–O isotopic data for metavolcanic rocks from the Shitoukoumen and Yongji areas in central Jilin Province, northeast China, to reveal their petrogenesis and tectonic setting. The metavolcanic rocks collected from the Shitoukoumen and Yongji areas are composed of metabasaltic trachyandesite, metatrachyandesite, and metarhyolitic tuff. Zircon U–Pb dating results indicate that the metavolcanic rocks were erupted during 359–355 Ma. Metavolcanic rocks in the Shitoukoumen area can be divided into two groups according to their geochemical characteristics. Group‐I rocks (metabasaltic trachyandesite and metatrachyandesite) show geochemical features similar to those of ocean island basalt (OIB), with slightly lower zircon δ 18 O values (4.06 ± 0.42‰ to 5.16 ± 0.28‰) than those of mantle‐derived zircons, and depleted ε Hf (t) values (7.84–15.4). Group‐II rocks (metabasaltic andesite) show similar geochemical characteristics to those of normal mid‐ocean ridge basalt (N‐MORB). Group‐I rocks may have been derived from partial melting of enriched mantle involving high‐temperature altered oceanic crust, whereas Group‐II rocks originated mainly from partial melting of depleted mantle. Metarhyolitic tuffs from the Yongji area have high SiO 2 and K 2 O contents, as well as high Ga/Al ratios, and show similar geochemical characteristics to those of A‐type rhyolites. The results of the study, together with published data, indicate that the eastern segment of the northern margin of the North China Craton was a passive continental margin setting during the early Carboniferous, and that the Paleo‐Asian Ocean remained open along the Changchun–Yanji suture belt until the early Carboniferous.
This paper works out relationship between visibility and near-surface meteorological factors. The formation of heavy fog is affected by meteorological factors near the ground and fog in the past period. In this paper, we abstract and simplify the problem as a time series problem. First, the airport AWOS observation data is reprocessed, and some missing and incorrect data are supplemented and corrected. Then draw a distribution map of “Visibility-Near-surface Meteorological Factors” to intuitively grasp the correlation between them. Finally, model the classic VARIMAX to fit the mapping relationship between visibility and near-surface meteorological factors. The results show temperature has the greatest impact on visibility index, positively correlated with it; secondly, dew point temperature index negatively correlated with it. The results show that, with the temperature low and the humidity high, the water vapor in the atmosphere is more likely to condense into mist, which is not easy to dissipate, resulting in reduced visibility. The indicators related to air pressure and wind speed are positively correlated with visibility, indicating that the increase in air pressure and the increase in wind speed will promote the dissipation of heavy fog. Generally speaking, the MOR index fits better with near-surface meteorological factors.
Haze obscures remote sensing images, making it difficult to extract valuable information. To address this problem, we propose a fine detail extraction network that aims to restore image details and improve image quality. Specifically, to capture fine details, we design multi-scale and multi-dimensional extraction blocks and then fuse them to optimize feature extraction. The multi-scale extraction block adopts multi-scale pixel attention and channel attention to extract and combine global and local information from the image. Meanwhile, the multi-dimensional extraction block uses depthwise separable convolutional layers to capture additional dimensional information. Additionally, we integrate an atmospheric scattering model unit into the network to enhance both the dehazing effectiveness and stability. Our experiments on the SateHaze1k and HRSD datasets demonstrate that the proposed method efficiently handles remote sensing images with varying levels of haze, successfully recovers fine details, and achieves superior results compared to existing state-of-the-art dehazing techniques.
Generating biomedical hypotheses is a difficult task as it requires uncovering the implicit associations between massive scientific terms from a large body of published literature. A recent line of Hypothesis Generation (HG) approaches - temporal graph-based approaches - have shown great success in modeling temporal evolution of term-pair relationships. However, these approaches model the temporal evolution of each term or term-pair with Recurrent Neural Network (RNN) independently, which neglects the rich covariation among all terms or term-pairs while ignoring direct dependencies between any two timesteps in a temporal sequence. To address this problem, we propose a Spatiotemporal Transformer-based Hypothesis Generation (STHG) method to interleave spatial covariation and temporal progression in a unified framework for constructing direct connections between any two term-pairs while modeling the temporal relevance between any two timesteps. Experiments on three biomedical relationship datasets show that STHG outperforms the state-of-the-art methods.
A new algorithm for edge detection in color images is proposed,which is based on improved gradient operators(IGO) and a new color difference function in the RGB color space.The proposed method firstly uses improved gradient operators(IGO) to get the gradient value for R,G and B color components separately.Then a new color difference function is taken to calculate the distance between the center and neighboring pixels.Finally,the edge information is obtained by a simple judgment algorithm combining the gradient and color difference values.The experimental results show that the proposed algorithm is effective and robust.
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