Two-Stage Spatiotemporal Context Refinement Network for Precipitation Nowcasting

Precipitation nowcasting by radar echo extrapolation using machine learning algorithms is a field worthy of further study, since rainfall prediction is essential in work and life. Current methods of predicting the radar echo images need further improvement in prediction accuracy as well as in presenting the predicted details of the radar echo images. In this paper, we propose a two-stage spatiotemporal context refinement network (2S-STRef) to predict future pixel-level radar echo maps (deterministic output) more accurately and with more distinct details. The first stage is an efficient and concise spatiotemporal prediction network, which uses the spatiotemporal RNN module embedded in an encoder and decoder structure to give a first-stage prediction. The second stage is a proposed detail refinement net, which can preserve the high-frequency detailed feature of the radar echo images by using the multi-scale feature extraction and fusion residual block. We used a real-world radar echo map dataset of South China to evaluate the proposed 2S-STRef model. The experiments showed that compared with the PredRNN++ and ConvLSTM method, our 2S-STRef model performs better on the precipitation nowcasting, as well as at the image quality evaluating index and the forecasting indices. At a given 45 dBZ echo threshold (heavy precipitation) and with a 2 h lead time, the widely used CSI, HSS, and SSIM indices of the proposed 2S-STRef model are found equal to 0.195, 0.312, and 0.665, respectively. In this case, the proposed model outperforms the OpticalFlow method and PredRNN++ model

Interval Number-Based Safety Reasoning Method for Verification of Decentralized Power Systems in High-Speed Trains

Decentralized power systems are commonly used in high-speed trains. However, many parameters in decentralized power systems are uncertain and inevitably have errors. We present a reasoning method based on the interval numbers for decentralized power systems in high-speed trains. Uncertain parameters and their unavoidable errors are quantitatively described by interval numbers. We also define generalized linear equations with interval numbers (LAIs), which can be used to describe the movement of the train. Furthermore, it is proven that the zero sets of LAIs are convex. Therefore, the inside of the fault-tolerance area can be formed by their vertexes and edges and represented by linear inequalities. Consequently, we can judge whether the system is working properly by verifying that the current system state is in the fault-tolerance area. Finally, a fault-tolerance area is obtained, which can be determined by linear equations with an interval number, and we test the correctness of the fault-tolerance area through large-scale random test cases

Spatial Downscaling of NPP-VIIRS Nighttime Light Data Using Multiscale Geographically Weighted Regression and Multi-Source Variables

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has the advantages of high temporal resolution, long coverage time series, and wide spatial range. The spatial resolution of the monthly and annual composite data of NPP-VIIRS NTL is only 500 m, which hinders studies requiring higher resolution. We propose a multi-source spatial variable and Multiscale Geographically Weighted Regression (MGWR)-based method to achieve the downscaling of NPP-VIIRS NTL data. An MGWR downscaling framework was implemented to obtain NTL data at 120 m resolution based on auxiliary data representing socioeconomic or physical geographic attributes. The downscaled NTL data were validated against LuoJia1-01 imagery based on the coefficient of determination (R2) and the root-mean-square error (RMSE). The results suggested that the spatial resolution of the data was enhanced after downscaling, and the MGWR-based downscaling results demonstrated higher R2 (R2 = 0.9141) and lower RMSE than those of Geographically Weighted Regression and Random Forest-based algorithms. Additionally, MGWR can reveal the different relationships between multiple auxiliary and NTL data. Therefore, this study demonstrates that the spatial resolution of NPP-VIIRS NTL data is improved from 500 m to 120 m upon downscaling, thereby facilitating NTL-based applications

Evaluating the Ability of NPP-VIIRS Nighttime Light Data to Estimate the Gross Domestic Product and the Electric Power Consumption of China at Multiple Scales: A Comparison with DMSP-OLS Data

The nighttime light data records artificial light on the Earth’s surface and can be used to estimate the spatial distribution of the gross domestic product (GDP) and the electric power consumption (EPC). In early 2013, the first global NPP-VIIRS nighttime light data were released by the Earth Observation Group of National Oceanic and Atmospheric Administration’s National Geophysical Data Center (NOAA/NGDC). As new-generation data, NPP-VIIRS data have a higher spatial resolution and a wider radiometric detection range than the traditional DMSP-OLS nighttime light data. This study aims to investigate the potential of NPP-VIIRS data in modeling GDP and EPC at multiple scales through a case study of China. A series of preprocessing procedures are proposed to reduce the background noise of original data and to generate corrected NPP-VIIRS nighttime light images. Subsequently, linear regression is used to fit the correlation between the total nighttime light (TNL) (which is extracted from corrected NPP-VIIRS data and DMSP-OLS data) and the GDP and EPC (which is from the country’s statistical data) at provincial- and prefectural-level divisions of mainland China. The result of the linear regression shows that R2 values of TNL from NPP-VIIRS with GDP and EPC at multiple scales are all higher than those from DMSP-OLS data. This study reveals that the NPP-VIIRS data can be a powerful tool for modeling socioeconomic indicators; such as GDP and EPC

Spatial Downscaling of NPP-VIIRS Nighttime Light Data Using Multiscale Geographically Weighted Regression and Multi-Source Variables

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has the advantages of high temporal resolution, long coverage time series, and wide spatial range. The spatial resolution of the monthly and annual composite data of NPP-VIIRS NTL is only 500 m, which hinders studies requiring higher resolution. We propose a multi-source spatial variable and Multiscale Geographically Weighted Regression (MGWR)-based method to achieve the downscaling of NPP-VIIRS NTL data. An MGWR downscaling framework was implemented to obtain NTL data at 120 m resolution based on auxiliary data representing socioeconomic or physical geographic attributes. The downscaled NTL data were validated against LuoJia1-01 imagery based on the coefficient of determination (R2) and the root-mean-square error (RMSE). The results suggested that the spatial resolution of the data was enhanced after downscaling, and the MGWR-based downscaling results demonstrated higher R2 (R2 = 0.9141) and lower RMSE than those of Geographically Weighted Regression and Random Forest-based algorithms. Additionally, MGWR can reveal the different relationships between multiple auxiliary and NTL data. Therefore, this study demonstrates that the spatial resolution of NPP-VIIRS NTL data is improved from 500 m to 120 m upon downscaling, thereby facilitating NTL-based applications

Molecular characterization and analysis of a novel protein disulfide isomerase-like protein of Eimeria tenella.

Protein disulfide isomerase (PDI) and PDI-like proteins are members of the thioredoxin superfamily. They contain thioredoxin-like domains and catalyze the physiological oxidation, reduction and isomerization of protein disulfide bonds, which are involved in cell function and development in prokaryotes and eukaryotes. In this study, EtPDIL, a novel PDI-like gene of Eimeria tenella, was cloned using rapid amplification of cDNA ends (RACE) according to the expressed sequence tag (EST). The EtPDIL cDNA contained 1129 nucleotides encoding 216 amino acids. The deduced EtPDIL protein belonged to thioredoxin-like superfamily and had a single predicted thioredoxin domain with a non-classical thioredoxin-like motif (SXXC). BLAST analysis showed that the EtPDIL protein was 55-59% identical to PDI-like proteins of other apicomplexan parasites. The transcript and protein levels of EtPDIL at different development stages were investigated by real-time quantitative PCR and western blot. The messenger RNA and protein levels of EtPDIL were higher in sporulated oocysts than in unsporulated oocysts, sporozoites or merozoites. Protein expression was barely detectable in unsporulated oocysts. Western blots showed that rabbit antiserum against recombinant EtPDIL recognized only a native 24 kDa protein from parasites. Immunolocalization with EtPDIL antibody showed that EtPDIL had a disperse distribution in the cytoplasm of whole sporozoites and merozoites. After sporozoites were incubated in complete medium, EtPDIL protein concentrated at the anterior of the sporozoites and appeared on the surface of parasites. Specific staining was more intense and mainly located on the parasite surface after merozoites released from mature schizonts invaded DF-1 cells. After development of parasites in DF-1 cells, staining intensified in trophozoites, immature schizonts and mature schizonts. Antibody inhibition of EtPDIL function reduced the ability of E. tenella to invade DF-1 cells. These results suggested that EtPDIL might be involved in sporulation in external environments and in host cell adhesion, invasion and development of E. tenella

Vacuum-Free, All-Solution, and All-Air Processed Organic Photovoltaics with over 11% Efficiency and Promoted Stability Using Layer-by-Layer Codoped Polymeric Electrodes

Nonfullerene organic photovoltaics (OPVs) have achieved a breakthrough in pushing the efficiency beyond 15%. Although this sheds light on OPV commercialization, the high cost associated with the scalable device fabrications remains a giant challenge. Herein, a vacuum-free, all-solution and all-air processed OPV is reported that yields 11.12% efficiency with a fill factor of 0.725, due to the usages of high-merit polymeric electrodes and modified active blends. The design principle toward the high-merit electrodes is to induce heavy acid doping into the matrices for a raised carrier concentration and mobility, make a large removal of insulating components in the whole matrices rather than surfaces, and restrain the formation of large-domain aggregates. A unique layer-by-layer doping is developed to enable the polymeric electrodes with record-high trade-offs between optical transmittance and electrical conductivity. Moreover, solvent vapor annealing is proposed to boost device efficiency and it has the advantages of finely adjusting the active blend morphology and raising the electron mobility. The resulting devices are highly efficient and most (approximate to 91%) of the initial efficiency are maintained in 30 day storage. This work indicates bright future for making cost-effective all-solution processed OPVs in air

Multiple alignment analysis of EtPDIL of <i>Eimera tenella</i> with PDIL from other apicomplexan parasites.

<p>Shown are sequences from <i>Plasmodium cynomolgi</i> (XP_004221713), <i>Plasmodium vivax</i> (XP_001614725), <i>Eimeria mitis</i> (CDJ34317.1). Deduced protein sequences were used in the Clustal W sequence alignment program. Asterisks, identical amino acids.</p

Quantitative real-time RT-PCR of EtPDIL expression in <i>E. tenella</i> developmental stages.

<p>UO, unsporulated oocysts; SO, sporulated oocysts; Spz, sporozoites; Mrz, merozoites. Bars not sharing the same letters were significantly different (P<0.05).</p