文章对Zr-Sn系锆合金中间工序管坯采用两种退火工序制备,采用扫描电镜(Scanning Electron Microscope,SEM)对其微观组织形貌进行观察,并对其第二相尺寸进行统计,最后对其腐蚀性能进行分析。SPPs统计结果表明,退火工序的选择对SPPs的尺...文章对Zr-Sn系锆合金中间工序管坯采用两种退火工序制备,采用扫描电镜(Scanning Electron Microscope,SEM)对其微观组织形貌进行观察,并对其第二相尺寸进行统计,最后对其腐蚀性能进行分析。SPPs统计结果表明,退火工序的选择对SPPs的尺寸形貌有显著影响,A样品内析出相的数量明显多于样品B,且尺寸相对较大。腐蚀结果表明,采用A工序退火的TREX管坯更耐均匀腐蚀。展开更多
Recently,Prevotella spp.,a major genus of gram-negative commensal bacteria in humans,have emerged as a key microbial contributor to host metabolism due to its ability to ferment dietary fibers,produce beneficial short...Recently,Prevotella spp.,a major genus of gram-negative commensal bacteria in humans,have emerged as a key microbial contributor to host metabolism due to its ability to ferment dietary fibers,produce beneficial short-chain fatty acids,and influence immune responses.However,their diversity and functional differences have created challenges for their development and therapeutic use.Recent studies have shown that specific Prevotella species,such as P.copri,P.intestinalis,and P.histicola,can strengthen gut barrier integrity and reduce metabolic imbalances.Notably,Prevotella populations can be increased through high-fiber or herbal-based treatments.Traditional herbal medicines,including fiber-rich decoctions,also demonstrate the potential to boost endogenous Prevotella communities,enhance microbial fermentation,and improve glucose and lipid balance.This perspective examines the context-dependent roles of Prevotella spp.,with emphasis on the functional heterogeneity of key species such as P.copri,suggests a framework for combining herbal modulation with species-level microbiota profiling,and outlines a research plan to explore microbe-herb synergy in treating obesity,type 2 diabetes,and related metabolic disorders.This strategy offers a new,ecology-based approach to complement standard metabolic interventions.展开更多
Satellite Precipitation Products(SPPs) face challenges in detecting Extreme Precipitation Events(EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clusterin...Satellite Precipitation Products(SPPs) face challenges in detecting Extreme Precipitation Events(EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clustering-Merging Algorithms(ML-CMAs) to evaluate EPEs using SPPs and Auxiliary Data(AD). Daily precipitation measurements were utilized for training and evaluating EPE estimates over Iran, which is comprised of arid and semi-arid regions. Statistical analysis and evaluation of five SPPs demonstrated that during EPE occurrences, all products face challenges in precipitation estimation, and using these products individually is not recommended. Among the SPPs, Multi-Source Weighted-Ensemble Precipitation(MSWEP) performed best for heavy(>20 mm d–1) and extreme(>40 mm d–1)precipitation events, followed by Global Satellite Mapping of Precipitation(GSMa P), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement(IMERG), Climate Prediction Center morphing technique(CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Dynamic Infrared-Rain Rate(PERSIANN-PDIR). The findings indicate that all proposed methods based on ML-CMAs could estimate precipitation rates more accurately than SPPs and improve statistical indices. The seasonal assessment and spatial analysis of statistical metrics of the overall daily precipitation results for all periods and climates revealed that all methods based on ML-CMAs performed well in all seasons and at nearly all measurement stations. Using unsupervised K-means++ classification for clustering EPEs and Deep Neural Network(DNN) and Convolutional Neural Network(CNN) methods for merging the MLCMAs reduced the error rate of SPPs in EPE estimation by approximately 50%. Therefore, incorporating ML-CMAs along with PWV as AD can significantly improve the performance of SPPs in evaluating EPEs over the study region.展开更多
多系统组合导航下可见卫星的数量显著增加,同时观测数据冗余会使移动终端的定位效率降低,而高效的选星算法能够提高动态导航的实时性.本文根据选星算法的原理,将8种选星算法划分为基于几何精度因子(geometry dilution of precision,GDOP...多系统组合导航下可见卫星的数量显著增加,同时观测数据冗余会使移动终端的定位效率降低,而高效的选星算法能够提高动态导航的实时性.本文根据选星算法的原理,将8种选星算法划分为基于几何精度因子(geometry dilution of precision,GDOP)最小的选星算法和基于空间几何分布的选星算法两类.在介绍各种选星算法原理的基础上,综合分析算法的优缺点,并通过实例验证各选星算法在GNSS四系统组合导航中的性能.展开更多
文摘文章对Zr-Sn系锆合金中间工序管坯采用两种退火工序制备,采用扫描电镜(Scanning Electron Microscope,SEM)对其微观组织形貌进行观察,并对其第二相尺寸进行统计,最后对其腐蚀性能进行分析。SPPs统计结果表明,退火工序的选择对SPPs的尺寸形貌有显著影响,A样品内析出相的数量明显多于样品B,且尺寸相对较大。腐蚀结果表明,采用A工序退火的TREX管坯更耐均匀腐蚀。
基金supported by the National Research Foundation of Korea(2020R1F1A1074155).
文摘Recently,Prevotella spp.,a major genus of gram-negative commensal bacteria in humans,have emerged as a key microbial contributor to host metabolism due to its ability to ferment dietary fibers,produce beneficial short-chain fatty acids,and influence immune responses.However,their diversity and functional differences have created challenges for their development and therapeutic use.Recent studies have shown that specific Prevotella species,such as P.copri,P.intestinalis,and P.histicola,can strengthen gut barrier integrity and reduce metabolic imbalances.Notably,Prevotella populations can be increased through high-fiber or herbal-based treatments.Traditional herbal medicines,including fiber-rich decoctions,also demonstrate the potential to boost endogenous Prevotella communities,enhance microbial fermentation,and improve glucose and lipid balance.This perspective examines the context-dependent roles of Prevotella spp.,with emphasis on the functional heterogeneity of key species such as P.copri,suggests a framework for combining herbal modulation with species-level microbiota profiling,and outlines a research plan to explore microbe-herb synergy in treating obesity,type 2 diabetes,and related metabolic disorders.This strategy offers a new,ecology-based approach to complement standard metabolic interventions.
文摘Satellite Precipitation Products(SPPs) face challenges in detecting Extreme Precipitation Events(EPEs). Hence, the primary objective of this research is to introduce a novel framework termed Machine-Learning Clustering-Merging Algorithms(ML-CMAs) to evaluate EPEs using SPPs and Auxiliary Data(AD). Daily precipitation measurements were utilized for training and evaluating EPE estimates over Iran, which is comprised of arid and semi-arid regions. Statistical analysis and evaluation of five SPPs demonstrated that during EPE occurrences, all products face challenges in precipitation estimation, and using these products individually is not recommended. Among the SPPs, Multi-Source Weighted-Ensemble Precipitation(MSWEP) performed best for heavy(>20 mm d–1) and extreme(>40 mm d–1)precipitation events, followed by Global Satellite Mapping of Precipitation(GSMa P), Integrated Multi-Satellite Retrievals for Global Precipitation Measurement(IMERG), Climate Prediction Center morphing technique(CMORPH), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Dynamic Infrared-Rain Rate(PERSIANN-PDIR). The findings indicate that all proposed methods based on ML-CMAs could estimate precipitation rates more accurately than SPPs and improve statistical indices. The seasonal assessment and spatial analysis of statistical metrics of the overall daily precipitation results for all periods and climates revealed that all methods based on ML-CMAs performed well in all seasons and at nearly all measurement stations. Using unsupervised K-means++ classification for clustering EPEs and Deep Neural Network(DNN) and Convolutional Neural Network(CNN) methods for merging the MLCMAs reduced the error rate of SPPs in EPE estimation by approximately 50%. Therefore, incorporating ML-CMAs along with PWV as AD can significantly improve the performance of SPPs in evaluating EPEs over the study region.
文摘多系统组合导航下可见卫星的数量显著增加,同时观测数据冗余会使移动终端的定位效率降低,而高效的选星算法能够提高动态导航的实时性.本文根据选星算法的原理,将8种选星算法划分为基于几何精度因子(geometry dilution of precision,GDOP)最小的选星算法和基于空间几何分布的选星算法两类.在介绍各种选星算法原理的基础上,综合分析算法的优缺点,并通过实例验证各选星算法在GNSS四系统组合导航中的性能.
