Optical vortices enabled by structural vortices

The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material comprised of synthesized molecules with large dipole moments that are crystallized into a spherulite structure. The molecules align in an azimuthal direction, resulting in a vortex polarity with spontaneously broken symmetry leading to strong optical anisotropy and nonlinear optical responses. These unique polarization properties of the judiciously designed organic spherulite combined with the symmetry of structured optical beams enable a plethora of new linear and nonlinear light-matter interactions, including the generation of optical vortex beams with complex spin states and on-demand topological charges at the fundamental, doubled, and tripled frequencies. The results of this work are likely to enable numerous applications in areas such as high-dimensional quantum information processing, with large capacity and high security. The demonstrated spherulite crystals facilitate stand-alone micro-scale devices that rely on the unique micro-scale spontaneous vortex polarity that is likely to enable future applications for high-dimensional quantum information processing, spatiotemporal optical vortices, and a novel platform for optical manipulation and trapping

Natural Gas Power Generation to Promote the Construction of Ecological City

As an unsustainable resource, it is of great significance to explore more efficient and low-carbon ways of using energy. With the fast development of China’s economy, most cities are faced with the pressure of ecological environment to varying degrees. The construction of ecological city is not only the need of ecological protection and improvement of natural environment, but also the requirement of urban development. Compared with coal-fired power generation, natural gas power generation has advantages in terms of environmental pollution, energy utilization efficiency and resource occupancy, and is one of the ideal energy sources for eco-city construction. However, at present, there are constraints such as lack of subsidy policy, high fuel price, dependence on import of equipment and high cost of construction and maintenance. In the long run, the development of gas-fired power generation industry should be actively and prudently promoted from the aspects of strengthening the construction of supporting facilities and the guarantee of gas source, perfecting the mechanism of electricity price, and increasing the localization rate of key equipment, so as to achieve the goal of optimizing industrial structure, saving energy and reducing emissions, and facilitating the construction of China’s ecological city

Improvement of Image Stitching Using Binocular Camera Calibration Model

Image stitching is the process of stitching several images that overlap each other into a single, larger image. The traditional image stitching algorithm searches the feature points of the image, performs alignments, and constructs the projection transformation relationship. The traditional algorithm has a strong dependence on feature points; as such, if feature points are sparse or unevenly distributed in the scene, the stitching will be misaligned or even fail completely. In scenes with obvious parallaxes, the global homography projection transformation relationship cannot be used for image alignment. To address these problems, this paper proposes a method of image stitching based on fixed camera positions and a hierarchical projection method based on depth information. The method does not depend on the number and distribution of feature points, so it avoids the complexity of feature point detection. Additionally, the effect of parallax on stitching is eliminated to a certain extent. Our experiments showed that the proposed method based on the camera calibration model can achieve more robust stitching results when a scene has few feature points, uneven feature point distribution, or significant parallax

Improvement of Image Stitching Using Binocular Camera Calibration Model

Image stitching is the process of stitching several images that overlap each other into a single, larger image. The traditional image stitching algorithm searches the feature points of the image, performs alignments, and constructs the projection transformation relationship. The traditional algorithm has a strong dependence on feature points; as such, if feature points are sparse or unevenly distributed in the scene, the stitching will be misaligned or even fail completely. In scenes with obvious parallaxes, the global homography projection transformation relationship cannot be used for image alignment. To address these problems, this paper proposes a method of image stitching based on fixed camera positions and a hierarchical projection method based on depth information. The method does not depend on the number and distribution of feature points, so it avoids the complexity of feature point detection. Additionally, the effect of parallax on stitching is eliminated to a certain extent. Our experiments showed that the proposed method based on the camera calibration model can achieve more robust stitching results when a scene has few feature points, uneven feature point distribution, or significant parallax

Spatiotemporal Evolution and Prediction of AOT in Coal Resource Cities: A Case Study of Shanxi Province, China

As aerosols in the air have a great influence on the health of residents of coal resource-based cities, these municipalities are confronting the dilemma of air pollution that is caused by the increase of suspended particles in the atmosphere and their development process. Aerosol optical thickness could be used to explore the aerosol temporal and spatial variations and to develop accurate prediction models, which is of great significance to the control of air pollution in coal resource-based cities. This paper explored the temporal spatial variation characteristics of aerosols in coal resource-based regions. A total of 11 typical coal-resource prefecture-level cities in the Shanxi Province were studied and inverted the aerosol optical thickness (AOT) among these cities based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and analyzed the significant factors affecting AOT. Through inputting significant correlation factors as the input variables of NARX (nonlinear auto regressive models with exogenous inputs) neural network, the monthly average AOTs in the Shanxi Province were predicted between 2011 and 2019. The results showed that, in terms of time series, AOT increased from January to July and decreased from July to December, the maximum AOT was 0.66 in summer and the minimum was 0.2 in autumn, and it was related to the local monsoon, temperature, and humidity. While as far as the space alignment is concerned, the figure for AOT in Shanxi Province varied significantly. High AOT was mainly concentrated in the centre and south and low AOT was focused on the northwestern part. Among the positively correlated factors, the correlation coefficient of population density and temperature exceeded 0.8, which was highly positive, and among the negatively correlated factors, the correlation coefficient of NDVI exceeded -0.8, which was highly negative. After improving the model by adding the important factors that were mentioned before, the error between the predicted mean value and the actual mean value was no more than 0.06. Considering this charge, the NARX neural network with multiple inputs can contribute to better prediction results

The optimal realized niches of the diatom Paralia sulcata in the Yellow Sea and its environmental implication

The observational and paleo-ecological records on some shelves (e.g., North Sea, Yellow Sea) have indicated that the proportion of Paralia sulcata in the diatom community has been increasing since the 1980s. Because of its heavily-silicified frustules, this species is considered as an important siliceous organism, which can impact the recycling efficiency of biogenic silicon in the ocean. However, the specific environmental factors driving the increase of P. sulcata remain unclear. In this study, based on seasonal in-situ observations and historical literature in the Yellow Sea, we statistically assessed the importance of different environmental factors in determining P. sulcata abundance. Further, we quantified its optimal realized niches, using the maximum entropy and generalized additive models to predict the fate of this species. P. sulcata indicates a preference for environments with cold temperatures and high nutrient levels. The contribution of sea temperature on cell abundance reaches up to 37.2 %, which is higher than that of the nutrient concentrations (∼ 20.5 %) and salinity (∼ 17.1 %). The optimal realized niches are defined as sea temperature at 6.25 ± 3.50 °C, salinity at 32.8 ± 0.52, the concentrations of dissolved inorganic nitrogen, phosphorus, and silicate at 8.38 ± 5.42 μM, 0.42 ± 0.29 μM, and 5.87 ± 3.53 μM, respectively, and ammonium/nitrate ratio at 1.32 ± 0.19. Based on the multivariate statistical methods, the optimal realized niches indicate that the increased sea temperatures in winter and spring, combined with nitrogen enrichment, especially ammonium enrichment, are key environmental drivers for P. sulcata proliferation in the Yellow Sea

Spatiotemporal Evolution and Prediction of AOT in Coal Resource Cities: A Case Study of Shanxi Province, China

As aerosols in the air have a great influence on the health of residents of coal resource-based cities, these municipalities are confronting the dilemma of air pollution that is caused by the increase of suspended particles in the atmosphere and their development process. Aerosol optical thickness could be used to explore the aerosol temporal and spatial variations and to develop accurate prediction models, which is of great significance to the control of air pollution in coal resource-based cities. This paper explored the temporal spatial variation characteristics of aerosols in coal resource-based regions. A total of 11 typical coal-resource prefecture-level cities in the Shanxi Province were studied and inverted the aerosol optical thickness (AOT) among these cities based on MODIS (Moderate Resolution Imaging Spectroradiometer) data and analyzed the significant factors affecting AOT. Through inputting significant correlation factors as the input variables of NARX (nonlinear auto regressive models with exogenous inputs) neural network, the monthly average AOTs in the Shanxi Province were predicted between 2011 and 2019. The results showed that, in terms of time series, AOT increased from January to July and decreased from July to December, the maximum AOT was 0.66 in summer and the minimum was 0.2 in autumn, and it was related to the local monsoon, temperature, and humidity. While as far as the space alignment is concerned, the figure for AOT in Shanxi Province varied significantly. High AOT was mainly concentrated in the centre and south and low AOT was focused on the northwestern part. Among the positively correlated factors, the correlation coefficient of population density and temperature exceeded 0.8, which was highly positive, and among the negatively correlated factors, the correlation coefficient of NDVI exceeded -0.8, which was highly negative. After improving the model by adding the important factors that were mentioned before, the error between the predicted mean value and the actual mean value was no more than 0.06. Considering this charge, the NARX neural network with multiple inputs can contribute to better prediction results