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Spatiotemporal Modeling and Analysis in Marine Science
With the development of earth observation technologies (such as satellite remote sensing, unmanned aerial vehicle, autonomous underwater vehicle, etc.), an era of big data with important and non-negligible spatial/temporal attributes comes. Novel and rigorous spatiotemporal methodologies and models are needed to process and analyze marine big data. Since many marine environmental processes, such as pollutants diffusion, algae distributions etc., vary or evolve across spatiotemporal domains, detecting the distributions and patterns of marine fauna and, particularly in the coastal regions, will improve our understanding of marine systems and can be beneficial in marine environmental management...
Air-Sea Interaction and Oceanic Extremes
Air-sea interactions control the exchange of mass, momentum and heat between the atmosphere and the ocean. Substantially, they significantly affect the development of atmospheric and oceanic systems from submesoscale to global scales. Oceanic extremes, i.e., cyclones, storms, freak waves, polar lows, storm surge, etc., pose a significant hazard to offshore activities and coastal society. Air-sea interaction processes are complex and play a central role in the development of those oceanic extremes. Improved understanding of the air-sea interaction processes and describing them in weather models and Earth System Models can improve their prediction and mitigate the potential damages. The energy distribution affected by air-sea interactions can change the large scale circulation and alter the climatology of the oceanic extremes. Despite an overall improvement in the understanding of air-sea interactions, there are still many knowledge gaps, particularly under extreme conditions.
Deep Learning for Marine Science, volume II
This Research Topic is the second volume of this collection. You can find the original collection via https://www.frontiersin.org/research-topics/45485/deep-learning-for-marine-science Deep learning (DL) is a critical research branch in the fields of artificial intelligence and machine learning, encompassing various technologies such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), Transformer networks and Diffusion models, as well as self-supervised learning (SSL) and reinforcement learning (RL). These technologies have been successfully applied to scientific research and numerous aspects of daily life. With the continuous advancements in oceanographic observation ...
Multi-scale Variability of Ecosystem Functioning in European and Chinese Shelf Seas
Continued global warming and ocean acidification are predicted with high confidence, while the direction and magnitude of changes of other atmospheric drivers (e.g. precipitation, wind) and nutrient loading are of high uncertainty and regionally dependent. Biogeochemical responses of coastal shelf seas to external drivers are often nonlinear, involving feedback that may amplify or dampen a perturbation imposed on the system. Coupled physical-biogeochemical process-based numerical models have proven useful in elucidating the mechanistic interplay and relative importance of the different factors contributing to ecosystem functioning with increasing realism. This research topic aims to understa...
Literary Societies Of Republican China
Literary Societies in Republican China provides a new and comprehensive perspective on the fascinating literary world of the most turbulent period in recent Chinese history: the Republican era of 1911-1949. Wedged between the fall of the Empire and the founding of the Communist state, the Republican period witnessed enormous social, political, and cultural changes. Traditionally the period is seen as one of transition: from the country being partially colonized and occupied to being an independent nation-state, from Confucianism to socialism, from writing in classical Chinese to writing in the everyday vernacular. Modern scholarship, however, has become suspicious of such attempts to analyze...
現代中文文學學報
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