Heterogeneity can be used as a promising method to improve the electrochemical performance of electrode materials; thus, ZnCo ₂ O ₄ @PPy samples were prepared using a facile hydrothermal route and an electrochemical deposition process.

Rational design of electrode materials plays a significant role in potential applications such as energy storage and conversion. In this work, CoNi₂S₄/Ni₃S₂ nanowires grown on Ni foam were synthesized through a facile hydrothermal approach, revealing a large capacitance of 997.2 F g⁻¹ and cycling stability with 80.3% capacitance retention after 5000 cycles. The device was prepared using CoNi₂S₄/Ni₃S₂//AC as the positive electrode and active carbon as the negative electrode, and delivered an energy density of 0.4 mW h cm⁻³ at a power density of 3.99 mW cm⁻³ and an excellent cycle life with 79.2% capacitance retention after 10 000 cycles. In addition, the hybrid CoNi₂S₄/Ni₃S₂ nanowires demonstrate excellent OER performance with low overpotential of 360 mV at 30 mA cm⁻² and overpotential of 173.8 mV at −10 mA cm⁻² for the HER, a cell voltage of 1.43 V, and excellent cycle stability.

Laser beam transmission in atmospheric turbulence causes image distortion and affects the quality of information transmission in the field of optical communication. The strength of the atmospheric turbulence, which can be characterized by refractive index structure constant $C^{2}_{n}$ , significantly influences the properties of a laser beam. The accurate estimation of $C^{2}_{n}$ is essential for understanding the strength of turbulence. Although multilayer perceptron (MLP) and deep neural network (DNN) has been applied to estimate the atmospheric turbulence strength, the estimation accuracy is sensitive to the strength of the turbulence. In this article, we propose a method based on the convolution neural network (CNN) approach to estimate $C^{2}_{n}$ ranging from $10^{-17}$ to $10^{-13}$ $\text{m}^{-2/3}$ . We experimentally demonstrate that the correlation coefficient ( $\rm R^{2}$ ) of the model is 99.39%. The mean relative error (MRE), root mean square error (RMSE), and mean absolute error (MAE) are 0.0047, 0.0916, and 0.0684, respectively. For the turbulence strength with the same order of refractive index structure constant $C^{2}_{n}$ , the estimation accuracy of the weak turbulence is higher than that of medium and strong turbulence. Moreover, the mix training different levels of turbulence strength improves the estimation accuracy of $C^{2}_{n}$ compared to that with the same order of $C^{2}_{n}$ . Based on the high estimation accuracy of the CNN in the scheme, the proposed method will be able to provide a way of estimating the strength of atmospheric turbulence without the need for additional optical devices.

When a laser carrying image information is transmitted in seawater, the presence of ocean turbulence leads to significant degradation of the received information due to the effect of interference. To address this issue, we propose a deep-learning-based method to retrieve the original information from a degraded pattern. To simulate the propagation of laser beams in ocean turbulence, a model of an ocean turbulence phase screen based on the power spectrum inversion method is used. The degraded images with different turbulence conditions are produced based on the model. A Pix2Pix network architecture is built to acquire the original image information. The results indicate that the network can realize high-fidelity image recovery under various turbulence conditions based on the degraded patterns. However, as turbulence strength and transmission distance increase, the reconstruction accuracy of the Pix2Pix network decreases. To further improve the image reconstruction ability of neural network architectures, we established three networks (U-Net, Pix2Pix, and Deep-Pix2Pix) and compared their performance in retrieving the degraded patterns. Overall, the Pix2Pix network showed the best performance for image reconstruction.

Understanding the transmission of light in ocean turbulence is of great significance for underwater communication, underwater detection, and other fields. The properties of ocean turbulence can affect the transmission characteristics of light beams, therefore it is essential to estimate the ocean turbulence intensity (OTI). In this study, we propose a deep learning-based method for predicting the OTI. Using phase screens to simulate ocean turbulence, we constructed a database of distorted Gaussian beams generated by Gaussian beams passing through ocean turbulence with varying intensities. We built a convolutional neural network and trained it using this database. For the trained network, inputting a distorted beam can accurately predict the corresponding intensity of ocean turbulence. We also compared our designed network with traditional network models such as AlexNet, VGG16, and Xception, and the results showed that our designed network had higher accuracy.