Improved simulation of winter wheat yield in North China Plain by using PRYM-Wheat integrated dry matter distribution coefficient

The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China.Presently,most remote sensing process models use the“biomass×harvest index(HI)”method to simulate regional-scale winter wheat yield.However,spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale.Time-series dry matter partition coefficients(Fr)can dynamically reflect the dry matter partition of winter wheat.In this study,Fr equations were fitted for each organ of winter wheat using site-scale data.These equations were then coupled into a process-based and remote sensingdriven crop yield model for wheat(PRYM-Wheat)to improve the regional simulation of winter wheat yield over the North China Plain(NCP).The improved PRYM-Wheat model integrated with the fitted Fr equations(PRYM-Wheat-Fr)was validated using data obtained from provincial yearbooks.A 3-year(2000-2002)averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination(R^(2)=0.55)and lower root mean square error(RMSE=0.94 t ha^(-1))than PRYM-Wheat with a stable HI(abbreviated as PRYM-Wheat-HI),which had R^(2) and RMSE values of 0.30 and 1.62 t ha^(-1),respectively.The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years(2013-2015).In conclusion,the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model,making it a useful tool for the simulation of regional winter wheat yield.

Dynamic analysis of a rotating tapered cantilever Timoshenko beam based on the power series method

The mathematical modeling of a rotating tapered Timoshenko beam with preset and pre-twist angles is constructed. The partial differential equations governing the six degrees, i.e., three displacements in the axial, flapwise, and edgewise directions and three cross-sectional angles of torsion, flapwise bending, and edgewise bending, are obtained by the Euler angle descriptions. The power series method is then used to inves- tigate the natural frequencies and the corresponding complex mode functions. It is found that all the natural frequencies are increased by the centrifugal stiffening except the twist frequency, which is slightly decreased. The tapering ratio increases the first transverse, torsional, and axial frequencies, while decreases the second transverse frequency. Because of the pre-twist, all the directions are gyroscopically coupled with the phase differences among the six degrees.

Slc43a2^(+)T cell metastasis from spleen to brain in RGNNV infected teleost

The origin of T cells in the teleost's brain is unclear.While viewing the central nervous system(CNS)as immune privileged has been widely accepted,previous studies suggest that T cells residing in the thymus but not in the spleen of the teleost play an essential role in communicating with the peripheral organs.Here,we identified nine T cell subpopulations in the thymus and spleen of orange-spotted grouper(Epinephelus coioices)through single-cell RNA-sequencing analysis.After viral CNS infection with red-spotted grouper nervous necrosis virus(RGNNV),the number of slc43a2^(+)T cells synchronously increased in the spleen and brain.During the infection tests in asplenic zebrafish(tlx1^▲zebrafish model),no increase in the number of slc43a2^(+)T cells was observed in the brain.Single-cell transcriptomic analysis indicated that slc43a2^(+)T cells mature and functionally differentiate within the spleen and then migrate into the brain to trigger an immune response.This study suggests a novel route for T cell migration from the spleen to the brain during viral infection in fish.

A cyberGIS-enabled multi-criteria spatial decision support system: A case study on flood emergency management

With the increased frequency of natural hazards and disasters and consequent losses,it is imperative to develop efficient and timely strategies for emergency response and relief operations.In this paper,we propose a cyberGIS-enabled multi-criteria spatial decision support system for supporting rapid decision making during emergency management.It combines a high-performance computing environment(cyberGIS-Jupyter)and multi-criteria decision analysis models(Weighted Sum Model(WSM)and Technique for Order Preference by Similarity to Ideal Solution Model(TOPSIS))with various types of social vulnerability indicators to solve decision problems that contain conflicting evaluation criteria in a flood emergency situation.Social media data(e.g.Twitter data)was used as an additional tool to support the decision-making process.Our case study involves two decision goals generated based on a past flood event in the city of Austin,Texas,U.S.A.As our result shows,WSM produces more diverse values and higher output category estimations than the TOPSIS model.Finally,the model was validated using an innovative questionnaire.This cyberGIS-enabled spatial decision support system allows collaborative problem solving and efficient knowledge transformation between decision makers,where different emergency responders can formulate their decision objectives,select relevant evaluation criteria,and perform interactive weighting and sensitivity analyses.

Latent spatio-temporal activity structures: a new approach to inferring intra-urban functional regions via social media check-in data

This article introduces a novel low rank approximation (LRA)-based model to detect the functional regions with the data from about 15 million social media check-in records during a year-long period in Shanghai, China. We identified a series of latent structures, named latent spatio-temporal activity structures. While interpreting these structures, we can obtain a series of underlying associations between the spatial and temporal activity patterns. Moreover, we can not only reproduce the observed data with a lower dimensional representative, but also project spatio-temporal activity patterns in the same coordinate system. With the K-means clustering algorithm, five significant types of clusters that are directly annotated with a combination of temporal activities can be obtained, providing a clear picture of the correlation between the groups of regions and different activities at different times during a day. Besides the commercial and transportation dominant areas, we also detected two kinds of residential areas, the developed residential areas and the developing residential areas.We further interpret the spatial distribution of these clusters using urban form analytics. The results are highly consistent with the government planning in the same periods, indicating that our model is applicable to infer the functional regions from social media check-in data and can benefit a wide range of fields, such as urban planning, public services, and location-based recommender systems.