Under-Actuated Motion Control of Haidou-1 ARV Using Data-Driven, Model-Free Adaptive Sliding Mode Control Method.

We propose a data-driven, model-free adaptive sliding mode control (MFASMC) approach to address the Haidou-1 ARV under-actuated motion control problem with uncertainties, including external disturbances and parameter perturbations. Firstly, we analyzed the two main difficulties in the motion control of Haidou-1 ARV. Secondly, in order to address these problems, a MFASMC control method was introduced. It is combined by a model-free adaptive control (MFAC) method and a sliding mode control (SMC) method. The main advantage of the MFAC method is that it relies only on the real-time measurement data of an ARV instead of any mathematical modeling information, and the SMC method guarantees the MFAC method's fast convergence and low overshooting. The proposed MFASMC control method can maneuver Haidou-1 ARV cruising at the desired forward speed, heading, and depth, even when the dynamic parameters of the ARV vary widely and external disturbances exist. It also addresses the problem of under-actuated motion control for the Haidou-1 ARV. Finally, the simulation results, including comparisons with a PID method and the MFAC method, demonstrate the effectiveness of our proposed method.

YTHDF3 phase separation regulates HSPA13-dependent clear cell renal cell carcinoma development and immune evasion.

Insufficient understanding about the immune evasion mechanism leads to the inability in predicting current immunotherapy effects in clear cell renal cell carcinoma (ccRCC) and sensitizing ccRCC to immunotherapy. RNA binding proteins (RBPs) can promote tumor progression and immune evasion. However, research on RBPs, particularly m6A reader YTHDF3, in ccRCC development and immune evasion is limited. In this study, we found that YTHDF3 level was downregulated in ccRCC and was an independent prognostic biomarker for ccRCC. Decreased YTHDF3 expression was correlated with the malignancy, immune evasion, and poor response to anti-programmed death ligand 1 (PD-L1)/CTLA-4 in ccRCC. YTHDF3 overexpression restrained ccRCC cell malignancy, PD-L1 expression, CD8+ T cell infiltration and activities in vivo, indicating its inhibitory role in ccRCC development and immune evasion. Mechanistically, YTHDF3 WT was found to have phase separation characteristics and suppress ccRCC malignancy and immune evasion. Whereas YTHDF3 mutant, which disrupted phase separation, abolished its function. YTHDF3 enhanced the degradation of its target mRNA HSPA13 by phase separation and recruiting DDX6, resulting in the downregulation of the downstream immune checkpoint PD-L1. HSPA13 overexpression restored ccRCC malignancy and immune evasion suppressed by YTHDF3 overexpression. In all, our results identify a new model of YTHDF3 in regulating ccRCC progression and immune evasion through phase separation.

Grid-less wideband direction of arrival estimation based on variational Bayesian inference.

Many recent works have addressed the problem of wideband direction of arrival (DOA) estimation using grid-less sparse techniques, and these methods have been shown to outperform the traditional wideband DOA estimation methods. However, these methods often suffer from the problem of requiring manual parameter tuning or high computational complexity, which reduces their practicality. To alleviate this problem, a grid-less wideband DOA estimation method based on variational Bayesian inference is proposed in this paper. The method approximates the posterior probability density function of DOA with the help of variational Bayesian inference, which does not require manual adjustment of parameters and can obtain accurate DOA estimation results with low computational complexity. Numerical simulations and real measurement data processing show that the proposed method has a higher DOA estimation accuracy than other grid-less wideband methods while providing higher computational speed.

Autonomous underwater vehicle fault diagnosis dataset.

The dataset contains 1225 data samples for 5 fault types (labels). We divided the dataset into the training set and the test set through random stratified sampling. The test set accounted for 20 % of the total dataset. Our experimental subject is 'Haizhe', which is a small quadrotor AUV developed in the laboratory. For each fault type, 'Haizhe' was tested several times. For each time, 'Haizhe' ran the same program and sailed underwater for 10-20 s to ensure that state data was long enough. The state data recorded in each test were then used as a data sample, and the corresponding fault type was the true label of the data sample. The dataset was used to validate a model-free fault diagnosis method proposed in our paper [1] and the complete dynamic model of 'Haizhe' AUV was reported in [2].