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Research on the Forecast of the Spread of COVID-19With the spreading of COVID-19, various existing machine learning frameworks can be adopted to effectively control the epidemic to help research and predict the spread of the virus before the large-scale application of vaccines. Based on the spatiotemporal graph neural network and mobility data, this paper attempts to offer a novel prediction by building a high-resolution graph with the characteristics such as willingness to wear masks, daily infection, and daily death. This model is different from the time series prediction model. The method learns from the multivariate spatiotemporal graph, the nodes represent the region with daily confirmed cases and death, and edges represent the inter-regional contacts based on mobility. Simultaneously, the transmission model is built by a time margin as the characteristic of the time change. This paper builds the COVID-19 model by using STGNNs and tries to predict and verify the virus's infection. Finally, the model has an absolute Pearson Correlation of 0.9735, far from the expected value of 0.998. The predicted value on the first and second day is close to the real situation, while the value gradually deviates from the actual situation after the second day. It still shows that the graph neural network uses much temporal and spatial information to enable the model to learn complex dynamics. In the future, the model can be improved by tuning hyper-parameter such as modulation numbers of convolution, or construction of graphs that suitable for smaller individuals such as institutions, buildings, and houses, as well as assigning more features to each node. This experiment demonstrates the powerful combination of deep learning and graph neural networks to study the spread and evolution of COVID-19.
Resource availability influences global social network properties in Gunnison’s prairie dogs (Cynomys gunnisoni)Increasingly we are discovering that the interactions between individuals within social groups can be quite complex and flexible. Social network analysis offers a toolkit to describe and quantify social structure, the patterns we observe, and evaluate the social and environmental factors that shape group dynamics. Here, we used 14 Gunnison's prairie dogs networks to evaluate how resource availability and network size influenced four global properties of the networks (centralization, clustering, average path length, small word index). Our results suggest a positive correlation between overall network cohesion and resource availability, such that networks became less centralized and cliquish as biomass/m2 availability decreased. We also discovered that network size modulates the link between social interactions and resource availability and is consistent with a more 'decentralized' group. This study highlights the importance of how individuals modify social cohesions and network connectedness as a way to reduce intragroup competition under different ecological conditions.
Spatial classification of moisture-sensitive pine and larch tree-ring chronologies within Khakass–Minusinsk Depression, South SiberiaKey message: Growth patterns of Scots pine and Siberian larch under water deficit across an intermontane valley in South Siberia depend not only on landscape physiography but on species-specific climatic sensitivity and phenology. Abstract: The wide intermountain Khakass–Minusinsk Depression (KhMD) in southern Siberia presents an ideal setting for studying the potential impacts of a warming climate on forest ecosystems. The Centre of Continental Asia has one of the most intense rates of warming in the Northern Hemisphere, and the KhMD has multiple tree species of proven dendroclimatic value growing in drought-stressed environments. Investigation was aimed at spatial patterns of tree growth and its climate response across the KhMD for two main conifer species of moisture-deficient habitats, Scots pine (Pinus sylvestris L.) and Siberian larch (Larix sibirica Ledeb.). Correlation and cluster analysis were applied to a recently developed network of 15 tree-ring chronologies. Hierarchical classifications were based on the inter-chronology correlation matrix and on correlations of chronologies with monthly climate variables. Results underscore the general influence of hot-dry conditions on reducing growth and suggest a spatial grouping of chronologies governed by physiography and modified by species-dependent ecophysiological response to climate. Both applied classifications agree on the designation of geographically oriented clusters. A purely geographic grouping is broken, however, by species-specific climate dependence and phenology in deciduous Larix and evergreen Pinus. A differential ability to utilize melting snowpack in spring is advanced as a possible explanation for chronologies abandoning physiographically defined clusters. Such inter-species heterogeneity can manifest itself in the intensity of the climate change impact on vegetation, and lead to prospects of significant species composition changes in ecosystems.
Removal of uranium from contaminated groundwater using monorhamnolipids and ion flotationMining of uranium for defense-related purposes has left a substantial legacy of pollution that threatens human and environmental health. Contaminated waters in the arid southwest are of particular concern, as water resource demand and water scarcity issues become more pronounced. The development of remediation strategies to treat uranium impacted waters will become increasingly vital to meet future water needs. Ion flotation is one technology with the potential to address legacy uranium contamination. The green biosurfactant rhamnolipid has been shown to bind uranium and act as an effective collector in ion flotation. In this study, uranium contaminated groundwater (∼440 μg L−1 U) from the Monument Valley processing site in northeast Arizona was used as a model solution to test the uranium removal efficacy of ion flotation with biosynthetic (bio-mRL) and three synthetic monorhamnolipids with varying hydrophobic chain lengths: Rha-C10-C10, Rha-C12-C12, and Rha-C14-C14. At the groundwater's native pH 8, and at an adjusted pH 7, no uranium was removed from solution by any collector. However, at pH 6.5 bio-mRL and Rha-C10-C10 removed 239.2 μg L−1 and 242.4 μg L−1 of uranium, respectively. By further decreasing the pH to 5.5, bio-mRL was able to reduce the uranium concentration to near or below the Environmental Protection Agency maximum contaminant level of 30 μg L−1. For the Rha-C12-C12 and Rha-C14-C14 collector ligands, decreasing the pH to 7 or below reduced the foam stability and quantity, such that these collectors were not suitable for treating this groundwater. To contextualize the results, a geochemical analysis of the groundwater was conducted, and a consideration of uranium speciation is described. Based on this study, the efficacy of monorhamnolipid-based ion flotation in real world groundwater has been demonstrated with suitable solution conditions and collectors identified.
A [C ii] 158 μm emitter associated with an O i absorber at the end of the reionization epochThe physical and chemical properties of the circumgalactic medium at z ≳ 6 have been studied successfully through the absorption in the spectra of background quasi-stellar objects. One of the most crucial questions is to investigate the nature and location of the source galaxies that give rise to these early metal absorbers. Theoretical models suggest that momentum-driven outflows from typical star-forming galaxies can eject metals into the circumgalactic medium and the intergalactic medium at z = 5–6 (refs. 7–9). Deep, dedicated surveys have searched for Lyα emission associated with strong C iv absorbers at z ≈ 6, but only a few Lyα-emitter candidates have been detected. Interpreting these detections is moreover ambiguous because Lyα is a resonant line, raising the need for complementary techniques for detecting absorbers’ host galaxies. Here we report a [C ii] 158 μm emitter detected using the Atacama Large Millimeter Array that is associated with a strong low-ionization absorber, O i, at z = 5.978. The projected impact parameter between O i and [C ii] emitter is 20.0 kpc. The measured [C ii] luminosity is 7.0 × 107 solar luminosities. Further analysis indicates that strong O i absorbers may reside in the circumgalactic medium of massive halos one to two orders of magnitude more massive than expected values.