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The Susceptible-Exposed-Infectious-Recovered ( SEIR ) epidemic model has been commonly used to analyze the spread of infectious diseases. This 4-compartment ( S , E , I and R ) model uses an approximation of temporal homogeneity of individuals in these compartments to calculate the transfer rates of the individuals from compartment E to I to R . Although this SEIR model has been generally adopted, the calculation errors caused by temporal homogeneity approximation have not been quantitatively examined. In this study, a 4-compartment l-i SEIR model considering temporal heterogeneity was developed from a previous epidemic model (Liu X., Results Phys. 2021; 20:103712), and a closed-form solution of the l-i SEIR model was derived. Here, l represents the latent period and i represents the infectious period. Comparing l-i SEIR model with the conventional SEIR model, we are able to examine how individuals move through each corresponding compartment in the two SEIR models to find what information may be missed by the conventional SEIR model and what calculation errors may be introduced by using the temporal homogeneity approximation. Simulations showed that l-i SEIR model could generate propagated curves of infectious cases under the condition of l > i . Similar propagated epidemic curves were reported in literature, but the conventional SEIR model could not generate propagated curves under the same conditions. The theoretical analysis showed that the conventional SEIR model overestimates or underestimates the rate at which individuals move from compartment E to I to R in the rising or falling phase of the number of infectious individuals, respectively. Increasing the rate of change in the number of infectious individuals leads to larger calculation errors in the conventional SEIR model. Simulations from the two SEIR models with assumed parameters or with reported daily COVID-19 cases in the United States and in New York further confirmed the conclusions of the theoretical analysis.

This study presents a global land projection dataset with a 1-km resolution that comprises 20 land types for 2015–2100, adopting the latest IPCC coupling socioeconomic and climate change scenarios, SSP-RCP. This dataset was produced by combining the top-down land demand constraints afforded by the CMIP6 official dataset and a bottom-up spatial simulation executed via cellular automata. Based on the climate data, we further subdivided the simulation products’ land types into 20 plant functional types (PFTs), which well meets the needs of climate models for input data. The results show that our global land simulation yields a satisfactory accuracy (Kappa = 0.864, OA = 0.929 and FoM = 0.102). Furthermore, our dataset well fits the latest climate research based on the SSP-RCP scenarios. Particularly, due to the advantages of fine resolution, latest scenarios and numerous land types, our dataset provides powerful data support for environmental impact assessment and climate research, including but not limited to climate models. Measurement(s) future land cover Technology Type(s) cellular automata • supervised machine learning Factor Type(s) spatial driving factors - socioeconomic (GDP, population, urban centre and road) • spatial driving factors - physical (temperature, precipitation, topography and soil quality)

Urban populations are expected to increase by 2-3 billion by 2050, but we have limited understanding of how future global urban expansion will affect urban heat island (UHI) and hence change the geographic distributions of extreme heat risks. Here we develop spatially explicit probabilistic global projections of UHI intensification due to urban land expansion through 2050. Our projections show that urban land areas are expected to expand by 0.6-1.3 million km2 between 2015 and 2050, an increase of 78%-171% over the urban footprint in 2015. This urban land expansion will result in average summer daytime and nighttime warming in air temperature of 0.5 °C-0.7 °C, up to ∼3 °C in some locations. This warming is on average about half, and sometimes up to two times, as strong as that caused by greenhouse gas (GHG) emissions (multi-model ensemble average projections in Representative Concentration Pathway 4.5). This extra urban expansion-induced warming, presented here, will increase extreme heat risks for about half of the future urban population, primarily in the tropical Global South, where existing forecasts already indicate stronger GHG emissions-warming and lack of adaptive capacity. In these vulnerable urban areas, policy interventions to restrict or redistribute urban expansion and planning strategies to mitigate UHIs are needed to reduce the wide ranges of impacts on human health, energy system, urban ecosystem, and infrastructures.

The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand, to improve computational efficiency, depthwise separable convolutions and convolution factorization are introduced to significantly decrease the number of model parameters. The proposed model is evaluated on the Inria Aerial Image Labeling Dataset and the Wuhan University (WHU) Aerial Building Dataset. The experimental results show that the proposed methods exhibit significant improvements compared with several state-of-the-art FCNs, including SegNet, U-Net, RefineNet, and DeepLab v3+. The proposed model shows promising potential for building detection from remote sensing images on a large scale.

Rapid urban expansion has profound impacts on global biodiversity through habitat conversion, degradation, fragmentation, and species extinction. However, how future urban expansion will affect global biodiversity needs to be better understood. We contribute to filling this knowledge gap by combining spatially explicit projections of urban expansion under shared socioeconomic pathways (SSPs) with datasets on habitat and terrestrial biodiversity (amphibians, mammals, and birds). Overall, future urban expansion will lead to 11–33 million hectares of natural habitat loss by 2100 under the SSP scenarios and will disproportionately cause large natural habitat fragmentation. The urban expansion within the current key biodiversity priority areas is projected to be higher (e.g., 37–44% higher in the WWF’s Global 200) than the global average. Moreover, the urban land conversion will reduce local within-site species richness by 34% and species abundance by 52% per 1 km grid cell, and 7–9 species may be lost per 10 km cell. Our study suggests an urgent need to develop a sustainable urban development pathway to balance urban expansion and biodiversity conservation. Population growth in the coming decades will lead to increasing land conversion to urban areas. Here, the authors use spatially explicit projections of global urban expansion to analyze its effects on habitat changes, and terrestrial mammals, birds and amphibians under the main shared socioeconomic pathways.

Despite its small land coverage, urban land and its expansion have exhibited profound impacts on global environments. Here, we present the scenario projections of global urban land expansion under the framework of the shared socioeconomic pathways (SSPs). Our projections feature a fine spatial resolution of 1 km to preserve spatial details. The projections reveal that although global urban land continues to expand rapidly before the 2040s, China and many other Asian countries are expected to encounter substantial pressure from urban population decline after the 2050s. Approximately 50–63% of the newly expanded urban land is expected to occur on current croplands. Global crop production will decline by approximately 1–4%, corresponding to the annual food needs for a certain crop of 122–1389 million people. These findings stress the importance of governing urban land development as a key measure to mitigate its negative impacts on food production. Shared socioeconomic pathways (SSPs) is a crucial scenario describing the potential of future socio-economic development. The authors here investigate long-term effects of various government policies suggested by different SSPs on urban land and reveal the impact of future urban expansion on other land and food production.

The global urbanization rate is accelerating; however, data limitations have far prevented robust estimations of either global urban expansion or its effects on terrestrial net primary productivity (NPP). Here, using a high resolution dataset of global land use/cover (GlobeLand30), we show that global urban areas expanded by an average of 5694 km 2 per year between 2000 and 2010. The rapid urban expansion in the past decade has in turn reduced global terrestrial NPP, with a net loss of 22.4 Tg Carbon per year (Tg C year −1 ). Although small compared to total terrestrial NPP and fossil fuel carbon emissions worldwide, the urbanization-induced decrease in NPP offset 30% of the climate-driven increase (73.6 Tg C year −1 ) over the same period. Our findings highlight the urgent need for global strategies to address urban expansion, enhance natural carbon sinks, and increase agricultural productivity. Robust estimates of either urban expansion worldwide or the effects of such phenomenon on terrestrial net primary productivity (NPP) are lacking. Here the authors used the new dataset of global land use to show that the global urban areas expanded largely between 2000 and 2010, which in turn reduced terrestrial NPP globally.

Background Using knowledge-based interpretation to analyze omics data can not only obtain essential information regarding various biological processes, but also reflect the current physiological status of cells and tissue. The major challenge to analyze gene expression data, with a large number of genes and small samples, is to extract disease-related information from a massive amount of redundant data and noise. Gene selection, eliminating redundant and irrelevant genes, has been a key step to address this problem. Results The modified method was tested on four benchmark datasets with either two-class phenotypes or multiclass phenotypes, outperforming previous methods, with relatively higher accuracy, true positive rate, false positive rate and reduced runtime. Conclusions This paper proposes an effective feature selection method, combining double RBF-kernels with weighted analysis, to extract feature genes from gene expression data, by exploring its nonlinear mapping ability.

Global land-use and land-cover change (LUCC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LUCC products at a global scale for public use is difficult because of data and technical issues. This article presents a Future Land-Use Simulation (FLUS) system to simulate global LUCC in relation to human-environment interactions, which is built and verified by using remote sensing data. IMAGE has been widely used in environmental studies despite its relatively coarse spatial resolution of 30 arc-min, which is about 55 km at the equator. Recently, an improved model has been developed to simulate global LUCC with a 5-min resolution (about 10 km at the equator). We found that even the 10-km resolution, however, still produced major distortions in land-use patterns, leading urban land areas to be underestimated by 19.77 percent at the global scale and global land change relating to urban growth to be underestimated by 60 to 97 percent, compared with the 1-km resolution model proposed through this article. These distortions occurred because a large percentage of small areas of urban land was merged into other land-use classes. During land-use change simulation, a majority of small urban clusters were also lost using the IMAGE product. Responding to these deficiencies, the 1-km FLUS product developed in this study is able to provide the spatial detail necessary to identify spatial heterogeneous land-use patterns at a global scale. We argue that this new global land-use product has strong potential in radically reducing uncertainty in global environmental modeling.

Carbon emissions are increasing in the world because of human activities associated with the energy consumptions for social and economic development. Thus, attention has been paid towards restraining the growth of carbon emissions and minimizing potential impact on the global climate. Currently there has also been increasing recognition that the urban forms, which refer to the spatial structure of urban land use as well as transport system within a metropolitan area, can have a wide variety of implications for the carbon emissions of a city. However, studies are limited in analyzing quantitatively the impacts of different urban forms on carbon emissions. In this study, we quantify the relationships between urban forms and carbon emissions for the panel of the four fastest-growing cities in China (i.e., Beijing, Shanghai, Tianjin, and Guangzhou) using time series data from 1990 to 2010. Firstly, the spatial distribution data of urban land use and transportation network in each city are obtained from the land use classification of remote sensing images and the digitization of transportation maps. Then, the urban forms are quantified using a series of spatial metrics which further used as explanatory variables in the estimation. Finally, we implement the panel data analysis to estimate the impacts of urban forms on carbon emission. The results show that, (1) in addition to the growth of urban areas that accelerate the carbon emissions, the increase of fragmentation or irregularity of urban forms could also result in more carbon emissions; (2) a compact development pattern of urban land would help reduce carbon emissions; (3) increases in the coupling degree between urban spatial structure and traffic organization can contribute to the reduction of carbon emissions; (4) urban development with a mononuclear pattern may accelerate carbon emissions. In order to reduce carbon emissions, urban forms in China should transform from the pattern of disperse, single-nuclei development to the pattern of compact, multiple-nuclei development.