The wild camel (Camelus ferus) is a critically endangered large ungulate, surviving in just three distinct populations located in the Taklamakan Desert, China;deserts near Lop Nuur, China;and in China and Mongolia wit...The wild camel (Camelus ferus) is a critically endangered large ungulate, surviving in just three distinct populations located in the Taklamakan Desert, China;deserts near Lop Nuur, China;and in China and Mongolia within and adjacent to Mongolia’s Great Gobi Strictly Protected Area (GGSPA). The population surviving in Mongolia remains poorly researched, but as few as 500 individuals may survive, although its distribution has remained relatively constant over the past 30 - 50 years. This study aimed at identifying potentially important environmental factors that influence the distribution of wild camels in Mongolia and predicting seasonal movement. We predicted distribution by season using presence only data and selected environmental predictors, including land surface temperature, normalized difference vegetation indices (NDVI), water sources, vegetation and soil. Model predictions revealed that land surface temperature in summer correlated significantly with wild camel distribution, with camels occurring in cooler areas. Abundance of biomass did not significantly correlate with camel distribution. Camels occurred in areas with intermediate levels of NDVI in most seasons, implying that they may base foraging decisions on forage quantity, not quality. Positive correlations of camel distribution with higher NDVI in summer (P = 0.03) suggests that they may prefer herbaceous species that appear after rainfall. Models indicate distance to water sources may be critical for camel distribution in all seasons. Camel occurrence correlated with areas containing shallow mountain soils in summer. Camels displayed no significant habitat correlations in other seasons, yet ranges differed among all seasons. Camels used a common region in spring, summer and autumn that we believe represents the core of the species’ annual range. Wild camel distribution during winter varied significantly from other seasons. Our modelling led to a predicted distribution range that was consistent with ranges described by previous research, indicating consistency between survey data and satellite tracking data.展开更多
The observation of single-particle surface-enhanced Raman scattering(SERS) has generated considerable interest both in the nanomaterials filed and in the single-particle spectroscopy community.It is a challenge to rea...The observation of single-particle surface-enhanced Raman scattering(SERS) has generated considerable interest both in the nanomaterials filed and in the single-particle spectroscopy community.It is a challenge to realize rapid,facile,and high throughput SERS at single nanoparticle level.Here,without the complex experimental device and difficult experimental operations,a general single-particle SERS technique has been achieved by using dark-field-assisted surface-enhanced Raman spectroscopy(DFSERS).This advanced method provides in-situ characterization of the chemical reaction performance at single gold nanorod.展开更多
We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,an...We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,and then fits axis-aligned planes to the point cloud through an iterative regularization process.The refined planes partition the space of the data into a series of compact cubic cells(candidate boxes)spanning the entire 3D space of the input data.We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation.The objective function is designed to maximize the point cloud coverage and the compactness of the final model.Finally,all selected boxes are merged into a lightweight polygonal mesh model,which is suitable for interactive visualization of large scale urban scenes.Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.展开更多
文摘The wild camel (Camelus ferus) is a critically endangered large ungulate, surviving in just three distinct populations located in the Taklamakan Desert, China;deserts near Lop Nuur, China;and in China and Mongolia within and adjacent to Mongolia’s Great Gobi Strictly Protected Area (GGSPA). The population surviving in Mongolia remains poorly researched, but as few as 500 individuals may survive, although its distribution has remained relatively constant over the past 30 - 50 years. This study aimed at identifying potentially important environmental factors that influence the distribution of wild camels in Mongolia and predicting seasonal movement. We predicted distribution by season using presence only data and selected environmental predictors, including land surface temperature, normalized difference vegetation indices (NDVI), water sources, vegetation and soil. Model predictions revealed that land surface temperature in summer correlated significantly with wild camel distribution, with camels occurring in cooler areas. Abundance of biomass did not significantly correlate with camel distribution. Camels occurred in areas with intermediate levels of NDVI in most seasons, implying that they may base foraging decisions on forage quantity, not quality. Positive correlations of camel distribution with higher NDVI in summer (P = 0.03) suggests that they may prefer herbaceous species that appear after rainfall. Models indicate distance to water sources may be critical for camel distribution in all seasons. Camel occurrence correlated with areas containing shallow mountain soils in summer. Camels displayed no significant habitat correlations in other seasons, yet ranges differed among all seasons. Camels used a common region in spring, summer and autumn that we believe represents the core of the species’ annual range. Wild camel distribution during winter varied significantly from other seasons. Our modelling led to a predicted distribution range that was consistent with ranges described by previous research, indicating consistency between survey data and satellite tracking data.
基金supported by the National Natural Science Foundation of China(Nos.21421004,21834001)sponsored by National Ten Thousand Talent Program for young top-notch talent。
文摘The observation of single-particle surface-enhanced Raman scattering(SERS) has generated considerable interest both in the nanomaterials filed and in the single-particle spectroscopy community.It is a challenge to realize rapid,facile,and high throughput SERS at single nanoparticle level.Here,without the complex experimental device and difficult experimental operations,a general single-particle SERS technique has been achieved by using dark-field-assisted surface-enhanced Raman spectroscopy(DFSERS).This advanced method provides in-situ characterization of the chemical reaction performance at single gold nanorod.
文摘We propose an approach for automatic generation of building models by assembling a set of boxes using a Manhattan-world assumption.The method first aligns the point cloud with a per-building local coordinate system,and then fits axis-aligned planes to the point cloud through an iterative regularization process.The refined planes partition the space of the data into a series of compact cubic cells(candidate boxes)spanning the entire 3D space of the input data.We then choose to approximate the target building by the assembly of a subset of these candidate boxes using a binary linear programming formulation.The objective function is designed to maximize the point cloud coverage and the compactness of the final model.Finally,all selected boxes are merged into a lightweight polygonal mesh model,which is suitable for interactive visualization of large scale urban scenes.Experimental results and a comparison with state-of-the-art methods demonstrate the effectiveness of the proposed framework.
