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Land cover change has been identified as an important cause or driving force of global climate change and is a significant research topic. Over the past few decades, global land-cover mapping has progressed, however, long time-series global land-cover change monitoring data are still sparse, especially at 30-m resolution. In this study, GLC_FCS30D is described as the first global 30-m land-cover dynamic monitoring dataset, containing 35 land-cover subcategories and covering the period of 1985-2022 with 26 time-steps (maps updated every five years before 2000 and annually after 2000). GLC_FCS30D has been developed using continuous change detection and all available Landsat imagery based on the Google Earth Engine platform. In specific, we first take advantage of the continuous change-detection model and full time-series Landsat observations to capture the time-points of changed pixels and identify the temporally stable areas. Then, we apply a spatiotemporal refinement method to derive the globally distributed and high-confidence training samples from these temporally stable areas. Next, locally adaptive classification models are used to update the land-cover information for the changed pixels, and a temporal-consistency optimization algorithm is adopted to improve their temporal stability and suppress some false changes. Further, the GLC_FCS30D product is validated using 84,526 globally distributed validation samples in 2020 and achieves an overall accuracy of 80.88 % (±0.27 %) for the basic classification system (10 major land-cover types) and 73.24 % (±0.30 %) for the LCCS level-1 validation system (17 LCCS land-cover types). Meanwhile, two third-party time-series validation datasets in the United States and Europe Union are also collected for analyzing accuracy variations, and the results show that the GLC_FCS30D offers significant stability for time-series accuracy variation and achieves the mean accuracies of 79.50 % (±0.50 %) and 81.91 % (±0.09 %) over the two regions. Last, we conclude the global land-cover change information from GLC_FCS30D dataset, namely, the forest and cropland variations dominate global land cover change over past 37 years, and net loss of forests reaches about 2.5 million km2 and net gain in cropland area is approximately 1.3 million km2. Therefore, the novel GLC_FCS30D is an accurate time-series land-cover dynamic monitoring product benefiting from its diverse classification system, high spatial resolution and the long time span of 1985-2022, thus, it will effectively support global climate change research and promote sustainable development analysis. The GLC_FCS30D datasets are available via https://doi.org/10.5281/zenodo.8239305 (Liu et al, 2023). [ABSTRACT FROM AUTHOR]

Though the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) (Hemiptera: Aleyrodidae) was introduced into China more than 60 years ago, the genetic diversity and structure of this exotic insect pest and virus vector have not been studied. To investigate the population genetic characteristics of this invasive species and to identify potential invasion routes, the genetic diversity and population structure of 17 collections of T. vaporariorum from nine provinces in China were analyzed using seven microsatellite loci. The results of the analyses indicated that the genetic diversity for the populations examined from the four provinces: Jilin, Ningxia, Guizhou and Qinghai, was lower than the genetic diversity of populations from the five provinces: Yunnan, Shandong, Shanxi, Liaoning, and Gansu. The T. vaporariorum populations analyzed in this study grouped as two distinct genetic clusters based on the analysis using STRUCTURE, whereas, 8 clusters were identified based on the BAPS analysis. Of the 136 genetic distance (Fst) values, 128 (94%) were associated with a significant exact test. There was a significant relationship between Fst and geographical distance. These results demonstrate that populations of T. vaporariorum in China exhibit significant genetic differentiation, indicating the likelihood that multiple introductions of T. vaporariorum into China have occurred. Also, the populations collected from the provinces of Jilin, Ningxia, Guizhou and Qinghai appear to represent secondary introductions originating from other Chinese provinces. [ABSTRACT FROM AUTHOR]

High-dimensional American option pricing is computationally challenging in both theory and practice. We use stochastic mesh method combined with performance enhancement policy of bias reduction to solve this practical problem in classic Black-Scholes framework. We effectively parallelize this algorithm through splitting the generated mesh by row among processors, use MPI for efficient implementation, and perform large-scale numerical experiments on heterogeneous supercomputer DeepComp7000. Numerical results of parallel simulation demonstrate that parallel simulation has good scalability in different parallel environments of DeepComp7000; large-scale parallel simulation can obtain much better speedup. The convergent performance is also empirically demonstrated. The estimated option value converges with the increase of mesh size; when using smaller mesh size, the stochastic mesh method with bias reduction can underestimate the true American option value. [ABSTRACT FROM AUTHOR]

Femoral neck compression strength index (fCSI), a novel phenotypic parameter that integrates bone density, bone size, and body size, has significant potential to improve hip fracture risk assessment. The genetic factors underlying variations in fCSI, however, remain largely unknown. Given the important roles of the receptor activator of the nuclear factor-kappaB ligand/receptor activator of the nuclear factor-kappaB/osteoprotegerin (RANKL/RANK/OPG) pathway in the regulation of bone remodeling, we tested the associations between RANKL/RANK/OPG polymorphisms and variations in fCSI as well as its components (femoral neck bone mineral density [fBMD], femoral neck width [FNW], and weight). This was accomplished with a sample comprising 1873 subjects from 405 Caucasian nuclear families. Of the 37 total SNPs studied in these three genes, 3 SNPs, namely, rs12585014, rs7988338, and rs2148073, of RANKL were significantly associated with fCSI (P = 0.0007, 0.0007, and 0.0005, respectively) after conservative Bonferroni correction. Moreover, the three SNPs were approximately in complete linkage disequilibrium. Haplotype-based association tests corroborated the single-SNP results since haplotype 1 of block 1 of the RANKL gene achieved an even more significant association with fCSI (P = 0.0003) than any of the individual SNPs. However, we did not detect any significant associations of these genes with fBMD, FNW, or weight. In summary, our findings suggest that the RANKL gene may play an important role in variation in fCSI, independent of fBMD and non-fBMD components. [ABSTRACT FROM AUTHOR]