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Modeling of multiple-scattering channels in atmospheric turbulence is essential for the performance analysis of long-distance non-line-of-sight (NLOS) ultraviolet (UV) communications. Existing works on the turbulent channel modeling for NLOS UV communications either ignored the turbulence-induced scattering effect or erroneously estimated the turbulent fluctuation effect, resulting in a contradiction with reported experiments. In this paper, we establish a comprehensive multiple-scattering turbulent channel model for NLOS UV communications considering both the turbulence-induced scattering effect and the turbulent fluctuation effect. We first derive the turbulent scattering coefficient and turbulent phase scattering function based on the Booker-Gordon turbulent power spectral density model. Then an improved estimation method is proposed for both the turbulent fluctuation and the turbulent fading coefficient based on the Monte-Carlo integration approach. Numerical results demonstrate that the turbulence-induced scattering effect can always be ignored for typical UV communication scenarios. Besides, the turbulent fluctuation will increase as either the communication distance, the elevation angle, or the divergence angle increases, which is compatible with existing experimental results. Moreover, we find that the probability density of the equivalent turbulent fading for multiple-scattering turbulent channels can be approximated as a Gaussian distribution., Comment: 29 pages,6 figures

Sepsis requires urgent diagnosis, but research is predominantly focused on Western datasets. In this study, we perform a comparative analysis of two ensemble learning methods, LightGBM and XGBoost, using the public eICU-CRD dataset and a private South Korean St. Mary's Hospital's dataset. Our analysis reveals the effectiveness of these methods in addressing healthcare data imbalance and enhancing sepsis detection. Specifically, LightGBM shows a slight edge in computational efficiency and scalability. The study paves the way for the broader application of machine learning in critical care, thereby expanding the reach of predictive analytics in healthcare globally.

In this paper, we propose the neural Born iterative method (NeuralBIM) for solving 2D inverse scattering problems (ISPs) by drawing on the scheme of physics-informed supervised residual learning (PhiSRL) to emulate the computing process of the traditional Born iterative method (TBIM). NeuralBIM employs independent convolutional neural networks (CNNs) to learn the alternate update rules of two different candidate solutions regarding the residuals. Two different schemes are presented in this paper, including the supervised and unsupervised learning schemes. With the data set generated by the method of moments (MoM), supervised NeuralBIM are trained with the knowledge of total fields and contrasts. Unsupervised NeuralBIM is guided by the physics-embedded objective function founding on the governing equations of ISPs, which results in no requirement of total fields and contrasts for training. Numerical and experimental results further validate the efficacy of NeuralBIM., Comment: This preprint has been published in IEEE Transactions on Antennas and Propagation on 01 November 2022. Please cite the final published version as [T. Shan et al., "Neural Born Iterative Method for Solving Inverse Scattering Problems: 2D Cases," in IEEE Transactions on Antennas and Propagation, vol. 71, no. 1, pp. 818-829, Jan. 2023, doi: 10.1109/TAP.2022.3217333.]

In this work, we investigated the feasibility of applying deep learning techniques to solve Poisson's equation. A deep convolutional neural network is set up to predict the distribution of electric potential in 2D or 3D cases. With proper training data generated from a finite difference solver, the strong approximation capability of the deep convolutional neural network allows it to make correct prediction given information of the source and distribution of permittivity. With applications of L2 regularization, numerical experiments show that the predication error of 2D cases can reach below 1.5\% and the predication of 3D cases can reach below 3\%, with a significant reduction in CPU time compared with the traditional solver based on finite difference methods., Comment: 7 pages, 10 figures