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Due to the complex underwater environment and the selective absorption and scattering effect of water on light waves, underwater images often suffer from issues such as low contrast, color distortion, and blurred details. This paper presents a stable and effective algorithm for enhancing underwater images to address these challenges. Firstly, an improved color correction algorithm based on the gray world and minimum information loss is employed to remove the blue-green bias present in the images. Secondly, a contrast enhancement algorithm is based on the guided filter and wavelet decomposition to make the texture details of the image clearer. Then, the normalized weight map of the image is obtained to carry out multi-scale fusion. Finally, the fused image is applied to perform the multi-scale decomposition. The experimental results show that the algorithm proposed in this paper can correct the image color deviation, improve the image contrast and enhance the image details. [ABSTRACT FROM AUTHOR]

There have been reports of association between psoriasis and psychological illnesses. Nevertheless, it is not easy to draw conclusions with high quality causality from conventional observational studies. The objective of this study was to evaluate the causality of psoriasis and psychological illnesses. All summary-level data from genome-wide association studies for psoriasis and psychological illnesses were collected from Neale Lab and MRC Integrative Epidemiology Unit. Bidirectional Mendelian randomization analysis was performed to determine the random orientation with an independent genetic variation. The main estimated effects are derived using Inverse-Variance Weighted, weighted median, and MR Egger methods. Multiple data sets were validated against each other, and a series of sensitivity studies were conducted to ensure the reliability of the results. Mendelian randomization analysis revealed a causal effect of psoriasis [odds ratio (OR) 1·34, 95% confidence interval (CI) 1·21–1·46, P < 0.001; and OR 1.28, CI 1.17–1.39, P < 0.001] on psychological illnesses, and vice versa (OR 1.23, CI 1.14–1.31, P < 0.001; and OR 1.21, CI 1.11–1.31, P < 0.001). The results were supported by a series of sensitivity analyses. The findings of this Mendelian randomization study support a causal effect between psoriasis and psychological illnesses, and vice versa. [ABSTRACT FROM AUTHOR]

For the bearing fault diagnosis in small sample cases, a new model for signal denoising and entropy feature fusion (EFF) based on the wild horse optimizer (WHO) optimized variational mode decomposition (VMD) and correlation coefficient weight threshold (CCWT) is proposed (WHO–VMD–CCWT–EFF). For signal denoising, we first take the power spectrum entropy as the fitness function, and the WHO is used to optimize VMD parameters. Secondly, IMFs with correlation coefficient values less than 0.2 are removed and the correlation coefficient values as weights are applied to the corresponding IMF components, and then reconstruct them. Then, the refined composite multiscale dispersion entropy (RCMDE), refined composite multiscale fluctuation dispersion entropy (RCMFDE), refined composite multivariate generalized multiscale fuzzy entropy (RCmvMFE), refined composite multivariate generalized multiscale sample entropy (RCmvMSE), and multiscale permutation entropy (MPE) of the signal are calculated and fused. Finally, the Fisher discriminant classifier is used as the model for fault diagnosis. The proposed model achieves an accuracy of over 99% in 12 single working conditions and 30 multiple working conditions experiments using the case western reserve university (CWRU) dataset and the Paderborn dataset. Compared with existing feature fusion methods, the WHO–VMD–CCWT–EFF model only integrates five selected features, and can achieve accurate diagnosis of bearing faults in small sample experiments with 42 different artificial and real damages. This indicates that the model has good generalization ability between different datasets and working conditions. [ABSTRACT FROM AUTHOR]