Study on coalescence dynamics of unequal-sized microbubbles captive on solid substrate

The dynamics of bubble coalescence are of importance for a number of industrial processes, in which the size inequality of the parent bubbles plays a significant role in mass transport, topological change and overall motion. In this study, coalescence of unequal-sized microbubbles captive on a solid substrate was observed from cross-section view using synchrotron high-speed imaging technique and a microfluidic gas generation device. The bridging neck growth and surface wave propagation at the early stage of coalescence were investigated by experimental and numerical methods. The results show that theoretical half-power-law of neck growth rate is still valid when viscous effect is neglected. However, the inertial-capillary time scale is associated with the initial radius of the smaller parent microbubble. The surface wave propagation rate on the larger parent microbubble is proportional to the inertial-capillary time scale

Evaluating urban land carrying capacity based on the ecological sensitivity analysis : a case study in Hangzhou, China

In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and ecological environmental sensitivity to create reasonable evaluation indicators to analyze urban land carrying capacity based on ecological sensitivity in the rapidly developing megacity of Hangzhou, China. In the study, ecological sensitivity is grouped into four levels: non-sensitive, lightly sensitive, moderately sensitive, and highly sensitive. The results show that the ecological sensitivity increases progressively from the center to the periphery. The results also show that ULCC is determined by ecologically sensitive levels and that the ULCC is categorized into four levels. Even though it is limited by the four levels, the ULCC still has a large margin if compared with the current population numbers. The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future. However, it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China

Evaluating urban land carrying capacity based on the ecological sensitivity analysis: a case study in Hangzhou, China.

In this study, we present the evaluation of urban land carrying capacity (ULCC) based on an ecological sensitivity analysis. Remote sensing data and geographic information system (GIS) technology are employed to analyze topographic conditions, land-use types, the intensity of urban development, and ecological environmental sensitivity to create reasonable evaluation indicators to analyze urban land carrying capacity based on ecological sensitivity in the rapidly developing megacity of Hangzhou, China. In the study, ecological sensitivity is grouped into four levels: non-sensitive, lightly sensitive, moderately sensitive, and highly sensitive. The results show that the ecological sensitivity increases progressively from the center to the periphery. The results also show that ULCC is determined by ecologically sensitive levels and that the ULCC is categorized into four levels. Even though it is limited by the four levels, the ULCC still has a large margin if compared with the current population numbers. The study suggests that the urban ecological environment will continue to sustain the current population size in the short-term future. However, it is necessary to focus on the protection of distinctive natural landscapes so that decision makers can adjust measures for ecological conditions to carry out the sustainable development of populations, natural resources, and the environment in megacities like Hangzhou, China

Understanding Microbubble Coalescence Using High-Speed Imaging and Lattice Boltzmann Method Simulation

poster abstractMicrobubble coalescence is one of the important research areas of bubble dynamics. The purpose of this research is to seek deeper understanding and relative mathematical relation on microbubble coalescence. To fulfill that, we conducted both experiments and simulations. For the part of experiment, we fabricated a microfluidic gas generator with better performance leading corresponding fluidic chemical reaction. After that we utilized ultrafast synchrotron X-ray imaging facility at the Advanced Photon Source of Argonne National Laboratory to capture the gas generating and microbubble merging phenomena using high speed imaging. These experiments show how the microbubbles with the same ratio contact and merge in the reaction channel and different concentration of reactants. As for the part of simulation, we lead the simulation basing on lattice Boltzmann method to simulate microbubble coalescence in water with unequal diameter ratio. Focuses are on the effects of size inequality of parent bubbles on the coalescence geometry and time. The “coalescence preference” of coalesced bubble closer to the larger parent bubble is well captured. A power-law relation between the preferential relative distance and size inequality is consistent to the recent experimental observations. Meanwhile, the coalescence time also exhibits power-law scaling, indicating that unequal bubbles coalesce faster than equal bubbles

Gravitation-Based Edge Detection in Hyperspectral Images

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitational theory, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of four state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

Perovskite light-emitting diodes toward commercial full-colour displays: progress and key technical obstacles

Metal-halide perovskite light-emitting diodes (PeLEDs) possess wide colour gamut, high luminescence efficiency, and low-cost synthesis, making them a promising photonic source for next-generation display applications. Since the first room-temperature emission PeLED was demonstrated in 2014, their performance has improved rapidly within a few years, leading to considerable attention from academia and industry. In this review, we discuss the primary technical bottlenecks of PeLEDs for commercial display applications, including large-area PeLED preparation, patterning strategies, and flexible PeLED devices. We review the technical approaches for achieving these targets and highlight the current challenges while providing an outlook for these perovskite materials and PeLED devices to meet the requirements of the next-generation high-colour-purity full-colour display market

Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor.

The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images

Dynamic post-earthquake image segmentation with an adaptive spectral-spatial descriptor

The region merging algorithm is a widely used segmentation technique for very high resolution (VHR) remote sensing images. However, the segmentation of post-earthquake VHR images is more difficult due to the complexity of these images, especially high intra-class and low inter-class variability among damage objects. Herein two key issues must be resolved: the first is to find an appropriate descriptor to measure the similarity of two adjacent regions since they exhibit high complexity among the diverse damage objects, such as landslides, debris flow, and collapsed buildings. The other is how to solve over-segmentation and under-segmentation problems, which are commonly encountered with conventional merging strategies due to their strong dependence on local information. To tackle these two issues, an adaptive dynamic region merging approach (ADRM) is introduced, which combines an adaptive spectral-spatial descriptor and a dynamic merging strategy to adapt to the changes of merging regions for successfully detecting objects scattered globally in a post-earthquake image. In the new descriptor, the spectral similarity and spatial similarity of any two adjacent regions are automatically combined to measure their similarity. Accordingly, the new descriptor offers adaptive semantic descriptions for geo-objects and thus is capable of characterizing different damage objects. Besides, in the dynamic region merging strategy, the adaptive spectral-spatial descriptor is embedded in the defined testing order and combined with graph models to construct a dynamic merging strategy. The new strategy can find the global optimal merging order and ensures that the most similar regions are merged at first. With combination of the two strategies, ADRM can identify spatially scattered objects and alleviates the phenomenon of over-segmentation and under-segmentation. The performance of ADRM has been evaluated by comparing with four state-of-the-art segmentation methods, including the fractal net evolution approach (FNEA, as implemented in the eCognition software, Trimble Inc., Westminster, CO, USA), the J-value segmentation (JSEG) method, the graph-based segmentation (GSEG) method, and the statistical region merging (SRM) approach. The experiments were conducted on six VHR subarea images captured by RGB sensors mounted on aerial platforms, which were acquired after the 2008 Wenchuan Ms 8.0 earthquake. Quantitative and qualitative assessments demonstrated that the proposed method offers high feasibility and improved accuracy in the segmentation of post-earthquake VHR aerial images

Single-Locus and Multi-Locus Genome-Wide Association Studies in the Genetic Dissection of Fiber Quality Traits in Upland Cotton (Gossypium hirsutum L.)

A major breeding target in Upland cotton (Gossypium hirsutum L.) is to improve the fiber quality. To address this issue, 169 diverse accessions, genotyped by 53,848 high-quality single-nucleotide polymorphisms (SNPs) and phenotyped in four environments, were used to conduct genome-wide association studies (GWASs) for fiber quality traits using three single-locus and three multi-locus models. As a result, 342 quantitative trait nucleotides (QTNs) controlling fiber quality traits were detected. Of the 342 QTNs, 84 were simultaneously detected in at least two environments or by at least two models, which include 29 for fiber length, 22 for fiber strength, 11 for fiber micronaire, 12 for fiber uniformity, and 10 for fiber elongation. Meanwhile, nine QTNs with 10% greater sizes (R2) were simultaneously detected in at least two environments and between single- and multi-locus models, which include TM80185 (D13) for fiber length, TM1386 (A1) and TM14462 (A6) for fiber strength, TM18616 (A7), TM54735 (D3), and TM79518 (D12) for fiber micronaire, TM77489 (D12) and TM81448 (D13) for fiber uniformity, and TM47772 (D1) for fiber elongation. This indicates the possibility of marker-assisted selection in future breeding programs. Among 455 genes within the linkage disequilibrium regions of the nine QTNs, 113 are potential candidate genes and four are promising candidate genes. These findings reveal the genetic control underlying fiber quality traits and provide insights into possible genetic improvements in Upland cotton fiber quality