Lithology identification is vital for reservoir exploration and petroleum engineering. Recently, there has been growing interest in using an intelligent logging approach for lithology classification. Machine learning has emerged as a powerful tool in inferring lithology types with the logging curves. However, well logs are susceptible to logging parameter manual entry, borehole conditions and tool calibrations. Most studies in the field of lithology classification with machine learning approaches have focused only on improving the prediction accuracy of classifiers. Also, a model trained in one location is not reusable in a new location due to different data distributions. In this paper, a unified framework is provided for training a multi-class lithology classification model for a data set with outlier data. In this paper, a coarse-to-fine framework that combines outlier detection, multi-class classification with an extremely randomized tree-based classifier is proposed to solve these issues. An unsupervised learning approach is used to detect the outliers in the data set. Then a coarse-to-fine inference procedure is used to infer the lithology class with an extremely randomized tree classifier. Two real-world data sets of well-logging are used to demonstrate the effectiveness of the proposed framework. Comparisons are conducted with some baseline machine learning classifiers, namely random forest, gradient tree boosting, and xgboosting. Results show that the proposed framework has higher prediction accuracy in sandstones compared with other approaches.

Unmanned Surface Vehicle (USV) has a broad application prospect and autonomous path planning as its crucial technology has developed into a hot research direction in the field of USV research. This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the selection and calculation of the target Q value action to eliminate overestimation. The prioritized experience replay method is adopted to extract experience samples from the experience replay unit, increase the utilization rate of actual samples, and accelerate the training speed of the neural network. Then, the neural network is optimized by introducing a dueling network structure. Finally, the soft update method is used to improve the stability of the algorithm, and the dynamic $ϵ$-greedy method is used to find the optimal strategy. The experiments are first conducted in the Open AI Gym test platform to pre-validate the algorithm for two classical control problems: the Cart pole and Mountain Car problems. The impact of algorithm hyperparameters on the model performance is analyzed in detail. The algorithm is then validated in the Maze environment. The comparative analysis of simulation experiments shows that IPD3QN has a significant improvement in learning performance regarding convergence speed and convergence stability compared with DQN, D3QN, PD2QN, PDQN, PD3QN. Also, USV can plan the optimal path according to the actual navigation environment with the IPD3QN algorithm.

Image degradation technique has been the focus of current research in image super-resolution( SR). The High-order Deterioration Model(HDM) of Real-ESRGAN is more effective than the conventional bicubic kernel interpolation in simulating the degradation of real-world images. However, the images reconstructed by Real-ESRGAN suffer from two notable weaknesses. Firstly, the rebuilt image is overly smooth and suffers from substantial texture information loss, making it worse than classical models like SRGAN and ESRGAN. Moreover, despite the reconstructed images having a better visualization effect, they are entirely different from the original image, violating the principle of image reconstruction. In order to address these two issues, an improved model built on Real-ESRGAN (IRE) is presented in this work. We remove the first-order degradation modeling of HDM and keep its second-order degradation modeling to lessen the degree of visual deterioration. Then we utilized PatchGAN as the fundamental structure of the discriminator, added a channel attention mechanism to the dense block of the generator, and increased the texture detail in the reconstructed images. Finally, we replaced the L1 loss function with the SmoothL1 loss function to improve the convergence speed with better model performance. The ablation study shows that the average training time was decreased by about 28% when employing PatchGAN rather than U-net as the discriminator. In addition, the ablation study presents that the RankIQA measures assessed by IRE on Set5, Set14, and DIV2K100 were decreased by 15.20%, 20.46%, and 10.27%, respectively, and the PI measures considered by IRE+ on BSD100, Urban100 were decreased by 15.76%, 8.67%, respectively, compared to Real-ESRGAN. Also, the comparison experiment illustrates that the NIQE measures assessed by our suggested model on Set5, RealSR-Canon, and RealSR-Nikon were decreased by 12.31%, 3%, and 2.41%, respectively, compared to Real-ESRGAN.

Mosquito-borne flaviviruses include a group of pathogenic viruses that cause serious diseases in humans and animals, including dengue, West Nile, and Japanese encephalitis viruses. These flaviviruses are zoonotic and use animals, including birds, as amplifying and reservoir hosts. Avian Tembusu virus (TMUV) is an emerging mosquito-borne flavivirus that is pathogenic for many avian species and can infect cells derived from mammals and humans in vitro . Although not currently pathogenic for primates, the infection of duck industry workers and the potential risk of TMUV infection in immunocompromised individuals have been highlighted. Thus, the prevention of TMUV in flocks is important for both avian and mammalian health. Our study reveals the escape of TMUV from the first line of the host defense system in the arthropod-borne transmission route of arboviruses, possibly helping to extend our understanding of flavivirus infection in birds and refine the design of anti-TMUV therapeutics.