Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultiv...Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.展开更多
传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出...传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出了一种改进的三级去畸变机制,结合基于体素化网格的分层降采样机制,以提高算法的实时性。经过改进的F-LOAM算法在KITTI数据集上的测试表现出色。三级去畸变机制和分层降采样策略不仅有效降低了计算负担,还确保了特征点的有效性和全局地图的精度。展开更多
目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,...目的基于T2^(*)mapping定量分析业余马拉松运动员足踝部关节软骨的T2^(*)值,并分析其与性别、年龄、身体质量指数(body mass index,BMI)、跑龄、跑量之间的相关性。材料与方法于2023年7月份至2023年9月份招募重庆市长跑运动爱好者48名,其中跑量<300 km/月的36例(中低跑量组),跑量≥300 km/月的12例(高跑量组)。所有受试者均进行单侧无症状踝关节的MRI扫描,扫描序列包括T2^(*)mapping多回波自旋回波(spin echo,SE)序列矢状位、质子密度加权成像脂肪抑制(proton density-weighted imaging fat-saturated,PDWI-FS)序列矢状位、冠状位、横轴位以及T1加权脂肪抑制成像(T1-weighted imaging fat-saturated,T1WI-FS)序列横轴位。沿关节软骨轮廓边缘勾画距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨作为感兴趣区(region of interest,ROI),获得相应的T2^(*)值。采用线性回归分析软骨T2^(*)值与年龄、BMI、跑龄的相关性,采用独立样本t检验分析不同跑量及不同性别间的软骨T2^(*)值差异。结果(1)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面及距骨面软骨T2^(*)值在性别上的差异均具有统计学意义(P=0.001、P<0.001、P=0.002、P=0.008、P=0.004);(2)高跑量组的距骨穹窿、后距下关节跟骨面软骨T2^(*)值高于中低跑量组(P=0.014、0.023),不同跑量的跟骰关节跟骨面及骰骨面、后距下关节距骨面软骨T2^(*)值的差异均无统计学意义(P=0.987、0.072、0.724);(3)距骨穹窿、跟骰关节跟骨面及骰骨面、后距下关节跟骨面、距骨面软骨T2^(*)值均与BMI呈正相关(r=0.376、0.384、0.300、0.422、0.455,P=0.005、0.004、0.019、0.001、0.001)。结论在业余马拉松运动员这一跑步群体中,与中低跑量相比,高跑量更有可能导致距骨穹窿、后距下关节跟骨面软骨损伤;而与较低的BMI相比,高BMI增加了距骨穹窿、跟骰关节跟骨面、骰骨面及后距下关节跟骨面、距骨面软骨损伤的风险。展开更多
目的探讨心脏磁共振组织追踪(cardiac magnetic resonance tissue tracing,CMR-TT)技术及T1 mapping技术在2型糖尿病(type 2 diabetes mellitus,T2DM)患者心肌损伤评估中的应用价值。材料与方法前瞻性收集2023年12月至2025年4月在我院...目的探讨心脏磁共振组织追踪(cardiac magnetic resonance tissue tracing,CMR-TT)技术及T1 mapping技术在2型糖尿病(type 2 diabetes mellitus,T2DM)患者心肌损伤评估中的应用价值。材料与方法前瞻性收集2023年12月至2025年4月在我院进行心脏磁共振检查的T2DM患者64例,健康对照(healthy controls,HC)32例。所有心脏磁共振图像数据导入专用软件进行分析,获取全心心肌应变参数、双心室功能参数以及左心室T1 mapping参数,采用t检验,Mann-Whitney U检验及卡方检验对两组间上述参数进行比较,采用Pearson及Spearman相关性分析心肌结构、功能与心肌应变的关联。结果T2DM组左心室心肌质量指数(left ventricular myocardial mass index,LVMI)、左心室重塑指数(left ventricular remodeling index,LVRI)增加(均P<0.001),左心室全局纵向应变(global longitudinal peak strain in the left ventricle,LV GLS)、右心室全局纵向应变降低(均P<0.05),T2DM组周向左心室收缩期峰值应变率(peak systolic strain rate of the left ventricle,LV PSSR)、纵向LV PSSR及左心室舒张期峰值应变率(diastolic peak strain rate of the left ventricle,LV PDSR)绝对值均降低(均P<0.019)。T2DM患者左心房/右心房(leftatrium/rightatrium,LA/RA)储存应变、LA/RA导管应变均降低(均P<0.001)。T2DM患者的细胞外容积(extracellular volume,ECV)较HC组升高(P<0.001)。双心室射血分数、收缩末期容积指数与双心室应变功能均相关(均P<0.003)。LVMI与左心室全局径向应变(global radial strain of the left ventricle,LV GRS)、左心室全局周向应变(global circumferential strain of the left ventricle,LV GCS)、LV GLS、周向LV PSSR、纵向LV PSSR、径向LV PDSR、周向LV PDSR、纵向LV PDSR相关(均P<0.021)。左心室舒张末期容积指数与LV GCS、LV GLS、周向LV PSSR、纵向LV PSSR、周向LV PDSR相关(均P<0.044)。右心室舒张末期容积指数与右心室全局周向应变相关(r=0.331,P=0.007)。LVRI与LV GLS及纵向LV PDSR相关(均P<0.01),且与径向LV PSSR弱相关(r=0.266,P=0.034)。结论T2DM患者全心心肌应变较对照组降低,ECV值升高,双心室心肌结构、功能与心肌应变相互关联,CMR-TT及T1 mapping技术可以有效检测糖尿病心肌损伤。展开更多
This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
基金Under the auspices of the National Natural Science Foundation of China(No.42101342,U2243205)the Third Comprehensive Scientific Expedition to Xinjiang(No.2021XJKK1403)。
文摘Cotton is an important global cash crops that serve as the primary source of natural fiber for textiles.A thorough understand-ing of the long-term variations in cotton cultivation is vital for optimizing cotton cultivation management and promoting the sustainable development of the cotton industry.Xinjiang is the primary cotton-producing region in China.However,long-term data of cotton cultiv-ation areas with high spatial resolution are unavailable for Xinjiang,China.Therefore,this study aimed to identify and map an accurate 30-m cotton cultivation area dataset in Xinjiang from 2000 to 2020 by applying a Random Forest(RF)-based method that integrates Landsat and Moderate Resolution Imaging Spectroradiometer(MODIS)images,and validated the applicability and accuracy of dataset at a large spatial scale.Then,this study analyzed the spatiotemporal variations and influencing factors of cotton cultivation in the study period.The results showed that a high classification accuracy was achieved(overall accuracy>85%,F1>0.80),strongly agreeing with county-level agricultural statistical yearbook data(R2>0.72).Significant spatiotemporal variation in the cotton cultivation areas was found in Xinjiang,with a total increase of 1131.26 kha from 2000 to 2020.Notably,cotton cultivation area in southern Xinjiang expan-ded substantially,with that in Aksu increasing from 20.10%in 2000 to 28.17%in 2020,representing an expansion of 374.29 kha.In northern Xinjiang,the cotton areas in the Tacheng region also exhibited significant increased by almost ten percentage points in the same period.In contrast,cotton cultivation in eastern Xinjiang declined,decreasing from 2.22%in 2000 to merely 0.24%in 2020.Standard deviation ellipse analysis revealed a‘northeast-southwest’spatial distribution,with the centroid consistently located in Aksu and shifting 102.96 km over the 20-yr period.Pearson correlation analysis indicated that socioeconomic factors had a stronger influence on cotton cultivation than climatic factors,with effective irrigation area(r=0.963,P<0.05)and total agricultural machinery power(r=0.823)showing significant positive correlations,whereas climatic variables exhibiting weak associations(r<0.200).These results provide valuable scientific data for informed agricultural management,sustainable development,and policymaking.
文摘传统的快速激光雷达里程计与建图(Fast LiDAR odometry and mapping,F-LOAM)算法虽然对特征点进行了两级去畸变处理,但仅对第1阶段的特征点进行去畸变,第2阶段的去畸变主要用于建图,这导致位姿估计的准确性不高。为了解决这一问题,提出了一种改进的三级去畸变机制,结合基于体素化网格的分层降采样机制,以提高算法的实时性。经过改进的F-LOAM算法在KITTI数据集上的测试表现出色。三级去畸变机制和分层降采样策略不仅有效降低了计算负担,还确保了特征点的有效性和全局地图的精度。
文摘目的探讨心脏磁共振组织追踪(cardiac magnetic resonance tissue tracing,CMR-TT)技术及T1 mapping技术在2型糖尿病(type 2 diabetes mellitus,T2DM)患者心肌损伤评估中的应用价值。材料与方法前瞻性收集2023年12月至2025年4月在我院进行心脏磁共振检查的T2DM患者64例,健康对照(healthy controls,HC)32例。所有心脏磁共振图像数据导入专用软件进行分析,获取全心心肌应变参数、双心室功能参数以及左心室T1 mapping参数,采用t检验,Mann-Whitney U检验及卡方检验对两组间上述参数进行比较,采用Pearson及Spearman相关性分析心肌结构、功能与心肌应变的关联。结果T2DM组左心室心肌质量指数(left ventricular myocardial mass index,LVMI)、左心室重塑指数(left ventricular remodeling index,LVRI)增加(均P<0.001),左心室全局纵向应变(global longitudinal peak strain in the left ventricle,LV GLS)、右心室全局纵向应变降低(均P<0.05),T2DM组周向左心室收缩期峰值应变率(peak systolic strain rate of the left ventricle,LV PSSR)、纵向LV PSSR及左心室舒张期峰值应变率(diastolic peak strain rate of the left ventricle,LV PDSR)绝对值均降低(均P<0.019)。T2DM患者左心房/右心房(leftatrium/rightatrium,LA/RA)储存应变、LA/RA导管应变均降低(均P<0.001)。T2DM患者的细胞外容积(extracellular volume,ECV)较HC组升高(P<0.001)。双心室射血分数、收缩末期容积指数与双心室应变功能均相关(均P<0.003)。LVMI与左心室全局径向应变(global radial strain of the left ventricle,LV GRS)、左心室全局周向应变(global circumferential strain of the left ventricle,LV GCS)、LV GLS、周向LV PSSR、纵向LV PSSR、径向LV PDSR、周向LV PDSR、纵向LV PDSR相关(均P<0.021)。左心室舒张末期容积指数与LV GCS、LV GLS、周向LV PSSR、纵向LV PSSR、周向LV PDSR相关(均P<0.044)。右心室舒张末期容积指数与右心室全局周向应变相关(r=0.331,P=0.007)。LVRI与LV GLS及纵向LV PDSR相关(均P<0.01),且与径向LV PSSR弱相关(r=0.266,P=0.034)。结论T2DM患者全心心肌应变较对照组降低,ECV值升高,双心室心肌结构、功能与心肌应变相互关联,CMR-TT及T1 mapping技术可以有效检测糖尿病心肌损伤。
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
