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A novel series of ZnAl 2 O 4 :Eu3+ aerogels (ZAE) and mullite ceramic phase reinforced ZnAl 2 O 4 :Eu3+ aerogels (MZAE) with high fluorescence thermal stability have been firstly synthesized for the encapsulation of high-power optical devices. However, due to the intrinsic structural brittleness of the aerogel, the structure of ZAE tends to collapse during the heat treatment and the fluorescence performance falls short of expectations. To this end, we propose a simple and effective strategy to enhance the structural rigidity of fluorescent aerogels by introducing the mullite ceramic phase into the network structure of ZAE. This can effectively suppress the agglomeration of Eu3+ caused by the collapse of the structure during the heat treatment, thus enhancing the optical properties of the aerogel. Compared with ZAE, MZAE has higher fluorescence thermal stability. The fluorescence intensity of MZAE at 498 K is still 75 % of that at 298 K, and the chromaticity shift is only 22 × 10−3. [ABSTRACT FROM AUTHOR]

The increase in global wind power installations has also increased the wind turbine density in wind farms. This has made the wake interactions between neighbouring turbines more significant and difficult to describe. Understanding wakes is important to predict the energy production and assess their environmental impacts. Although existing wake description methods can predict the average wind turbine wake under a high wind turbine density, they are unable to identify the wake boundary with an acceptable accuracy and computational cost. Consequently, the role of wake boundaries in modern wind farms has become unclear. To deal with this problem, this paper presents a comprehensive discussion on wind turbine wakes, especially the role of boundary identification in the wind farm planning stage. After a review of existing methods, an approach based on a newly derived mathematical formulation of the velocity field is proposed. Lidar-based field measurements and large-eddy simulations along with actuator line model-based numerical simulations were used to compare different wake boundary identification methods. The results show that the new approach is computationally cost-effective with a 5% increase in accuracy. The new approach also offers significant advantages as wake boundaries become increasingly complex. • The importance of wind turbine wake boundary identification is discussed. • Various wake boundary identification methods are compared for different purposes. • A cost-effective method is presented and demonstrated for its advantages. • The more complicated the wakes, the easier the new method's advantage is shown. [ABSTRACT FROM AUTHOR]

Image dehazing can improve image clarity and visual effect, which plays a pivotal role in many computer vision tasks. Existing dehazing methods are mostly based on a single feature stream and tend to ignore the low-frequency characteristics of haze. In this paper, we propose a dual stream network for image dehazing. To enhance the edge information and texture detail of the image, we construct a frequency stream based on attention octave convolution. We decompose the features into high and low-frequency branches in the frequency stream to obtain different structural information. By adding a residual channel attention block, the attention octave convolution can extract frequency features more efficiently and effectively. Due to the lower resolution of low-frequency features in the frequency stream, the frequency stream features alone are insufficient for recovering the overall content of the image. Therefore, a content stream was added to compensate for the information lost in the frequency stream. By fusing the outputs of two feature streams, the network achieves an enhanced dehazing performance. The results show that our method is superior to other state-of-the-art algorithms in quantitative evaluation and visual impact. • A significant difference between hazy and clear images after low-pass filtering. • Prior information indicates that haze is closer to low-frequency information. • Spatial features are decomposed into high and low-frequency components by Att OctConv. • The content stream uses RCAB to filter out redundant low-frequency information. • Our method is also excellent for underwater image restoration. [ABSTRACT FROM AUTHOR]