A High-Fall Scene Reconstruction Method Based on Prominence Calculation

A novel method is proposed for reconstructing high-fall scenes in complex urban environments based on prominence maximization, addressing the limitations of traditional UAV tilt photogrammetry. Initially, UAV’s initial acquisition data is used to generate a Digital Surface Model (DSM). An algorithm is designed to identify local wave peaks and calculate their saliency values, which, combined with linear interpolation, results in a smoother surface elevation model(SDSM). This provides new planning points for the camera’s position, overcoming issues like mismatching shadows in the DSM model. The method was tested in Boston, USA, covering various functional types and traffic scenarios. Results indicate that compared to traditional tilt photogrammetry, UAV tilt photogrammetry based on saliency maximization achieves higher accuracy, effectively capturing local details in the surveyed area while ensuring flight safety. Furthermore, the actual 3D model’s texture information and geometric structure are expressed with high quality

Long-Term Dynamic of Cold Stress during Heading and Flowering Stage and Its Effects on Rice Growth in China

Short episodes of low-temperature stress during reproductive stages can cause significant crop yield losses, but our understanding of the dynamics of extreme cold events and their impact on rice growth and yield in the past and present climate remains limited. In this study, by analyzing historical climate, phenology and yield component data, the spatial and temporal variability of cold stress during the rice heading and flowering stages and its impact on rice growth and yield in China was characterized. The results showed that cold stress was unevenly distributed throughout the study region, with the most severe events observed in the Yunnan Plateau with altitudes higher than 1800 m. With the increasing temperature, a significant decreasing trend in cold stress was observed across most of the three ecoregions after the 1970s. However, the phenological-shift effects with the prolonged growing period during the heading and flowering stages have slowed down the cold stress decreasing trend and led to an underestimation of the magnitude of cold stress events. Meanwhile, cold stress during heading and flowering will still be a potential threat to rice production. The cold stress-induced yield loss is related to both the intensification of extreme cold stress and the contribution of related components to yield in the three regions

Long-Term Dynamic of Cold Stress during Heading and Flowering Stage and Its Effects on Rice Growth in China

Short episodes of low-temperature stress during reproductive stages can cause significant crop yield losses, but our understanding of the dynamics of extreme cold events and their impact on rice growth and yield in the past and present climate remains limited. In this study, by analyzing historical climate, phenology and yield component data, the spatial and temporal variability of cold stress during the rice heading and flowering stages and its impact on rice growth and yield in China was characterized. The results showed that cold stress was unevenly distributed throughout the study region, with the most severe events observed in the Yunnan Plateau with altitudes higher than 1800 m. With the increasing temperature, a significant decreasing trend in cold stress was observed across most of the three ecoregions after the 1970s. However, the phenological-shift effects with the prolonged growing period during the heading and flowering stages have slowed down the cold stress decreasing trend and led to an underestimation of the magnitude of cold stress events. Meanwhile, cold stress during heading and flowering will still be a potential threat to rice production. The cold stress-induced yield loss is related to both the intensification of extreme cold stress and the contribution of related components to yield in the three regions

Estimating Plant Nitrogen Concentration of Rice through Fusing Vegetation Indices and Color Moments Derived from UAV-RGB Images

Estimating plant nitrogen concentration (PNC) has been conducted using vegetation indices (VIs) from UAV-based imagery, but color features have been rarely considered as additional variables. In this study, the VIs and color moments (color feature) were calculated from UAV-based RGB images, then partial least square regression (PLSR) and random forest regression (RF) models were established to estimate PNC through fusing VIs and color moments. The results demonstrated that the fusion of VIs and color moments as inputs yielded higher accuracies of PNC estimation compared to VIs or color moments as input; the RF models based on the combination of VIs and color moments (R2 ranging from 0.69 to 0.91 and NRMSE ranging from 0.07 to 0.13) showed similar performances to the PLSR models (R2 ranging from 0.68 to 0.87 and NRMSE ranging from 0.10 to 0.29); Among the top five important variables in the RF models, there was at least one variable which belonged to the color moments in different datasets, indicating the significant contribution of color moments in improving PNC estimation accuracy. This revealed the great potential of combination of RGB-VIs and color moments for the estimation of rice PNC

A hybrid CPU-GPU accelerated framework for fast mapping of high-resolution human brain connectome.

Recently, a combination of non-invasive neuroimaging techniques and graph theoretical approaches has provided a unique opportunity for understanding the patterns of the structural and functional connectivity of the human brain (referred to as the human brain connectome). Currently, there is a very large amount of brain imaging data that have been collected, and there are very high requirements for the computational capabilities that are used in high-resolution connectome research. In this paper, we propose a hybrid CPU-GPU framework to accelerate the computation of the human brain connectome. We applied this framework to a publicly available resting-state functional MRI dataset from 197 participants. For each subject, we first computed Pearson's Correlation coefficient between any pairs of the time series of gray-matter voxels, and then we constructed unweighted undirected brain networks with 58 k nodes and a sparsity range from 0.02% to 0.17%. Next, graphic properties of the functional brain networks were quantified, analyzed and compared with those of 15 corresponding random networks. With our proposed accelerating framework, the above process for each network cost 80∼150 minutes, depending on the network sparsity. Further analyses revealed that high-resolution functional brain networks have efficient small-world properties, significant modular structure, a power law degree distribution and highly connected nodes in the medial frontal and parietal cortical regions. These results are largely compatible with previous human brain network studies. Taken together, our proposed framework can substantially enhance the applicability and efficacy of high-resolution (voxel-based) brain network analysis, and have the potential to accelerate the mapping of the human brain connectome in normal and disease states

Ultrathin SnO₂ Nanosheets: Oriented Attachment Mechanism, Nonstoichiometric Defects, and Enhanced Lithium-Ion Battery Performances

We successfully synthesized large-scale and highly pure ultrathin SnO₂ nanosheets (NSs), with a minimum thickness in the regime of ca. 2.1 nm as determined by HRTEM and in good agreement with XRD refinements and AFM height profiles. Through TEM and HRTEM observations on time-dependent samples, we found that the as-prepared SnO₂ NSs were assembled by “oriented attachment” of preformed SnO₂ nanoparticles (NPs). Systematic trials showed that well-defined ultrathin SnO₂ NSs could only be obtained under appropriate reaction time, solvent, additive, precursor concentration, and cooling rate. A certain degree of nonstoichiometry appears inevitable in the well-defined SnO₂ NSs sample. However, deviations from the optimal synthetic parameters give rise to severe nonstoichiometry in the products, resulting in the formation of Sn₃O₄ or SnO. This finding may open new accesses to the fundamental investigations of tin oxides as well as their intertransition processes. Finally, we investigated the lithium-ion storage of the SnO₂ NSs as compared to SnO₂ hollow spheres and NPs. The results showed superior performance of SnO₂ NSs sample over its two counterparts. This greatly enhanced Li-ion storage capability of SnO₂ NSs is probably resulting from the ultrathin thicknesses and the unique porous structures: the nanometer-sized networks provide negligible diffusion times of ions thus faster phase transitions, while the “breathable” interior porous structure can effectively buffer the drastic volume changes during lithiation and delithiation reactions

CPU-GPU hybrid hardware platform.

<p>The communication between the host (CPU) and device (GPU) is through a PCI-e bus.</p