Atmospheric Pollution and Microecology of Respiratory Tract

This chapter elaborates the source and ingredients of atmospheric pollutants, the microecology of respiratory tract in animals and humans and the effect of atmospheric pollution on it and thus clarifies the relationship between air pollution and microecology of the respiratory tract based on the experiments

SeisCLIP: A seismology foundation model pre-trained by multi-modal data for multi-purpose seismic feature extraction

Training specific deep learning models for particular tasks is common across various domains within seismology. However, this approach encounters two limitations: inadequate labeled data for certain tasks and limited generalization across regions. To address these challenges, we develop SeisCLIP, a seismology foundation model trained through contrastive learning from multi-modal data. It consists of a transformer encoder for extracting crucial features from time-frequency seismic spectrum and an MLP encoder for integrating the phase and source information of the same event. These encoders are jointly pre-trained on a vast dataset and the spectrum encoder is subsequently fine-tuned on smaller datasets for various downstream tasks. Notably, SeisCLIP's performance surpasses that of baseline methods in event classification, localization, and focal mechanism analysis tasks, employing distinct datasets from different regions. In conclusion, SeisCLIP holds significant potential as a foundational model in the field of seismology, paving the way for innovative directions in foundation-model-based seismology research.Comment: 27 pages, 9 figures, 4 table

Seismic Foundation Model (SFM): a new generation deep learning model in geophysics

While computer science has seen remarkable advancements in foundation models, which remain underexplored in geoscience. Addressing this gap, we introduce a workflow to develop geophysical foundation models, including data preparation, model pre-training, and adaption to downstream tasks. From 192 globally collected 3-D seismic volumes, we create a carefully curated dataset of 2,286,422 2-D seismic images. Fully using these unlabeled images, we employ the self-supervised learning to pre-train a Transformer-based Seismic Foundation Model (SFM) for producing all-purpose seismic features that work across various tasks and surveys. Through experiments on seismic facies classification, geobody identification, interpolation, denoising, and inversion, our pre-trained model demonstrates versatility, generalization, scalability, and superior performance over baseline models. Conclusively, we provide a foundation model and vast dataset to advance AI in geophysics, addressing challenges (poor generalization, lacking labels, and repetitive training for task-specified models) of applying AI in geophysics and paving the way for future innovations in geoscience.Comment: 27 pages, 9 figures, and 4 table

TraInterSim: Adaptive and Planning-Aware Hybrid-Driven Traffic Intersection Simulation

Traffic intersections are important scenes that can be seen almost everywhere in the traffic system. Currently, most simulation methods perform well at highways and urban traffic networks. In intersection scenarios, the challenge lies in the lack of clearly defined lanes, where agents with various motion plannings converge in the central area from different directions. Traditional model-based methods are difficult to drive agents to move realistically at intersections without enough predefined lanes, while data-driven methods often require a large amount of high-quality input data. Simultaneously, tedious parameter tuning is inevitable involved to obtain the desired simulation results. In this paper, we present a novel adaptive and planning-aware hybrid-driven method (TraInterSim) to simulate traffic intersection scenarios. Our hybrid-driven method combines an optimization-based data-driven scheme with a velocity continuity model. It guides the agent's movements using real-world data and can generate those behaviors not present in the input data. Our optimization method fully considers velocity continuity, desired speed, direction guidance, and planning-aware collision avoidance. Agents can perceive others' motion planning and relative distance to avoid possible collisions. To preserve the individual flexibility of different agents, the parameters in our method are automatically adjusted during the simulation. TraInterSim can generate realistic behaviors of heterogeneous agents in different traffic intersection scenarios in interactive rates. Through extensive experiments as well as user studies, we validate the effectiveness and rationality of the proposed simulation method.Comment: 13 pages, 12 figure

Genesis of the Wutuogou Ag-Pb-Zn deposit in the Eastern Kunlun Orogenic Belt, NW China: Constraints from calcite U-Pb geochronology, mineral chemistry, and in-situ sulfur isotopes

The Wutuogou Ag-Pb-Zn deposit, a newly discovered vein-type deposit, is located in the Eastern Kunlun Orogenic Belt (EKOB), northwestern China. The vein-type Ag-Pb-Zn ore bodies are hosted in Middle Triassic granodiorite and monzogranite and are characterized by high-grade Ag, Pb, and Zn (average Ag: 293 g/t, Pb: 3.00 %, Zn: 2.85 %). Three paragenetic stages have been recognized: quartz + pyrite (Py-1) + arsenopyrite (stage I), pyrite (Py-2) + sphalerite + chalcopyrite + tetrahedrite + quartz (substage II-1), galena + pyrargyrite + freibergite + freieslebenite + quartz + calcite (substage II-2), and quartz + calcite (stage III). Except for Ag-bearing minerals (pyrargyrite, freibergite, and freieslebenite), invisible silver is also present in pyrite (1.91–165 ppm), sphalerite (3.86–8806 ppm), and galena (up to 0.21 wt%). The calcite is closely associated with sulfides in substage II-2 and yields a U-Pb age of 210 ± 7 Ma (MSWD = 2.7), which represents the ore-forming age (lower limit). Py-1 displays higher As contents and lower Co contents than those of Py-2, indicating a decrease in temperature from stage I to stage II. In addition, the Fe/Zn mass ratios (0.025–0.075) of sphalerite estimate the fluid temperature for substage II-1 of 246–284 ◦C, whereas the Ag/(Ag + Cu) and Zn/(Zn + Fe) mole ratios of freibergite estimate the fluid temperature for substage II-2 of 140–270 ◦C, further indicating the decrease of temperature from stage I through substage II-1 to substage II-2. Mineral assemblages of pyrite-chalcopyrite-tetrahedrite in substage II-1 and Ag-sulfosalts in substage II-2 suggest a decrease in sulfur fugacity (fS2). Both the decrease in fS2 and cooling of the mineralizing fluids facilitate silver precipitation. The heterogeneous compositions of the freibergite and the Ag zonation in sphalerite (Sp-1) resulted from retrograde solid-state reactions that redistributed Ag through microscale exsolution. The δ34S values (+5.49 to +7.78 ‰) of the sulfides and the low Zn/Cd ratios (107–195) of sphalerite indicate a felsic magma source for the ore-forming materials. Therefore, we concluded that the Wutuogou Ag-Pb-Zn deposit corresponds to a medium- to low-temperature magmatic-hydrothermal deposit associated with Late Triassic magmatism in the Eastern Kunlun Orogenic Belt (EKOB)Genesis of the Wutuogou Ag-Pb-Zn deposit in the Eastern Kunlun Orogenic Belt, NW China: Constraints from calcite U-Pb geochronology, mineral chemistry, and in-situ sulfur isotopespublishedVersio