Explore the vast range of titles available.

This paper investigates the problem of practical prescribed time control design for strong interconnected nonlinear systems by utilizing the high-order fully actuated (HOFA) system approach. Firstly, to specify the convergence time and accuracy of the system outputs in advance regardless of initial conditions, the thought of practical prescribed time control is introduced. Then, through some recursive coordinate transformation, the considered strong interconnected nonlinear systems are transformed into the HOFA system model, and then, the controller is designed to eliminate the effect caused by nonlinearities in the system. Furthermore, with the aid of an algebraic graph theory result, all signals in the system can be ensured to be globally bounded by appropriately designing the linear part of the obtained closed-loop system. Finally, the simulation results show the effectiveness of the proposed method.

This paper investigates the distributed adaptive PI consensus tracking control for output-constrained nonlinear multiagent systems (MASs) with unknown control directions and unknown time-varying actuator faults under the directed graph. Firstly, an auxiliary filter network is constructed to estimate the output of the leader. Then, by introducing a nonlinear state transformation function, the asymmetric constraints on agents’ outputs can be achieved with unconstrained initial conditions. Furthermore, based on the Nussbaum-type function, a distributed adaptive PI controller is designed by utilizing the constructed generalized error for nonlinear MASs with unknown time-varying control directions caused by actuator faults. The proposed control scheme can guarantee that all signals in the closed-loop system are bounded and the distributed asymptotic consensus tracking is achieved. Finally, a simulation example illustrates the effectiveness of the proposed control strategy.

Seismic data quality is vital to geophysical applications, so that methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double-sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data, while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the data set without introducing pseudo-Gibbs artifacts when compared to other directional multi-scale transform methods such as curvelets.

Seismic surveys have become the primary measurement tool of exploration geophysics, both onshore and offshore, with significant signal processing needed to estimate the properties of earth subsurface via seismic wave propagation. The typical workflow for seismic includes three phases: acquisition, imaging, and interpretation. A high-quality imaging result for interpretation necessitates accurate data acquisition and efficient imaging algorithms. However, seismic data gathers may suffer from noisy and missing traces during acquisition which could possibly limit their use in the following imaging phase. As a convincing quantitative imaging technique, full waveform inversion (FWI) searches for the correct velocity model that can match the acquired seismic dataset. However, due to the high dimensionality of the model space, FWI is inherently a challenging problem, so that regularization techniques are typically applied to yield better posed models. Moreover, FWI also suffers from its prohibitive computational costs that mainly arise from forward modeling of the seismic wavefield for multiple sources at each iteration of a nonlinear minimization process. The dimensionality of the problem and the heterogeneity of the medium both stress the need for faster algorithms and sparse regularization techniques to accelerate and improve imaging results.This thesis presents a new reconstruction method to mitigate noise and interpolate missing traces in the acquired seismic dataset, as well as a new FWI framework to estimate subsurface models more accurately and efficiently. Both contributions involve sparse approximation of various types of data with respect to adaptive dictionaries that are learned by different strategies. The new seismic data reconstruction method involves a sparse representation over a parametric dictionary, which bridges a gap between model-based and data-driven sparse approximations. The new FWI framework adapts velocity model perturbations to orthonormal dictionaries that are trained in an online manner, and then exploits compressive sensing to significantly reduce the computational cost by requiring many fewer calculations of the forward model. Numerical experiments on synthetic seismic data and velocity models indicate that the new methods can achieve better performance compared to other state-of-theart methods.

This paper presents a methodology and workflow that overcome the limitations of the conventional Generative Adversarial Networks (GANs) for geological facies modeling. It attempts to improve the training stability and guarantee the diversity of the generated geology through interpretable latent vectors. The resulting samples are ensured to have the equal probability (or an unbiased distribution) as from the training dataset. This is critical when applying GANs to generate unbiased and representative geological models that can be further used to facilitate objective uncertainty evaluation and optimal decision-making in oil field exploration and development. We proposed and implemented a new variant of GANs called Info-WGAN for the geological facies modeling that combines Information Maximizing Generative Adversarial Network (InfoGAN) with Wasserstein distance and Gradient Penalty (GP) for learning interpretable latent codes as well as generating stable and unbiased distribution from the training data. Different from the original GAN design, InfoGAN can use the training images with full, partial, or no labels to perform disentanglement of the complex sedimentary types exhibited in the training dataset to achieve the variety and diversity of the generated samples. This is accomplished by adding additional categorical variables that provide disentangled semantic representations besides the mere randomized latent vector used in the original GANs. By such means, a regularization term is used to maximize the mutual information between such latent categorical codes and the generated geological facies in the loss function. Furthermore, the resulting unbiased sampling by Info-WGAN makes the data conditioning much easier than the conventional GANs in geological modeling because of the variety and diversity as well as the equal probability of the unconditional sampling by the generator.

Seismic data quality is vital to geophysical applications, so methods of data recovery, including denoising and interpolation, are common initial steps in the seismic data processing flow. We present a method to perform simultaneous interpolation and denoising, which is based on double-sparsity dictionary learning. This extends previous work that was for denoising only. The original double sparsity dictionary learning algorithm is modified to track the traces with missing data by defining a masking operator that is integrated into the sparse representation of the dictionary. A weighted low-rank approximation algorithm is adopted to handle the dictionary updating as a sparse recovery optimization problem constrained by the masking operator. Compared to traditional sparse transforms with fixed dictionaries that lack the ability to adapt to complex data structures, the double-sparsity dictionary learning method learns the signal adaptively from selected patches of the corrupted seismic data while preserving compact forward and inverse transform operators. Numerical experiments on synthetic seismic data indicate that this new method preserves more subtle features in the dataset without introducing pseudo-Gibbs artifacts when compared to other directional multiscale transform methods such as curvelets.

Full waveform inversion (FWI) delivers high-resolution images of the subsurface by minimizing iteratively the misfit between the recorded and calculated seismic data. It has been attacked successfully with the Gauss-Newton method and sparsity promoting regularization based on fixed multiscale transforms that permit significant subsampling of the seismic data when the model perturbation at each FWI data-fitting iteration can be represented with sparse coefficients. Rather than using analytical transforms with predefined dictionaries to achieve sparse representation, we introduce an adaptive transform called the Sparse Orthonormal Transform (SOT) whose dictionary is learned from many small training patches taken from the model perturbations in previous iterations. The patch-based dictionary is constrained to be orthonormal and trained with an online approach to provide the best sparse representation of the complex features and variations of the entire model perturbation. The complexity of the training method is proportional to the cube of the number of samples in one small patch. By incorporating both compressive subsampling and the adaptive SOT-based representation into the Gauss-Newton least-squares problem for each FWI iteration, the model perturbation can be recovered after an l1-norm sparsity constraint is applied on the SOT coefficients. Numerical experiments on synthetic models demonstrate that the SOT-based sparsity promoting regularization can provide robust FWI results with reduced computation.

Melittin has previously been found to have a positive effect on colorectal cancer (CRC) treatment, one of the most difficult-to-treat malignancies, but the mechanism by which this effect occurs remains unclear. We evaluated melittin’s pro-apoptotic and anti-metastatic effects on CRC in vitro and in vivo. The results showed that melittin-induced mitochondrial ROS bursts decreased ΔΨm, inhibited Bcl-2 expression, and increased Bax expression in both cells and tumor tissues. This led to increased mitochondrial membrane permeability and the release of pro-apoptotic factors, particularly the high expression of Cytochrome C, initiating the apoptosis program. Additionally, through wound-healing and transwell assays, melittin inhibited the migration and invasion of CRC cells. In vivo, the anti-metastatic effect of melittin was also verified in a lung metastasis mouse model. Western blotting and immunohistochemistry analysis indicated that melittin suppressed the expression of MMPs and regulated the expression of crucial EMT markers and related transcription factors, thereby inhibiting EMT. Furthermore, the melittin disrupts neovascularization, ultimately inhibiting the metastasis of CRC. In conclusion, melittin exerts anti-CRC effects by promoting apoptosis and inhibiting metastasis, providing a theoretical basis for further research on melittin as a targeted therapeutic agent for CRC.

Background and objectives Immune checkpoint inhibitors (ICIs) bring cancer patients tumor control and survival benefits, yet they also trigger immune-related adverse effects (irAEs), notably checkpoint inhibitor-related pneumonitis (CIP), affecting about 5% of patients among whom 1–2% experiencing severe grade 3 or higher pneumonitis. Current research points to potential links with T cell subset dysfunction and autoantibody increase, but the specific mechanisms underlying different grades of CIP are understudied. Methods Herein, we employed single-cell RNA sequencing (scRNA-seq) on bronchoalveolar lavage fluid (BALF) from CIP patients across varying severity levels, aiming to elucidate underlying immune environment and mechanisms of CIP progression at cellular and molecular levels. Findings Totally, 121,409 high qualified cells from BALF of 11 patients were annotated and categorized into five major cell types. Severe CIP (CIP-S) cases have a significant increase in the percentage of unreported epithelial cells in their bronchoalveolar lavage fluid compared with mild CIP (CIP-M) cases. These cells were defined as aberrant basaloid cells. They upregulated SOX9, increased the expression of CXCL3/5, recruited neutrophils, and activated the immune system. Additionally, macrophages in the CIP-S group had stronger antigen-presenting abilities and resulted in more CD8 + effective T cells infiltrated. Conclusions Utilizing single-cell sequencing of BALF, we discovered an enriched population of aberrant basaloid cells in CIP-S patients, which had not been previously reported. Aberrant basaloid cells may upregulate SOX9 via CXCL3/5-CXCR2 to recruit and activate neutrophils, and further activate the immune system, resulting in CIP-S. This finding could identify new targets for stratified treatment of CIP patients, holding promise of a novel approach for clinical guidance.