Alternating minimization algorithms for graph regularized tensor completion

We consider a low-rank tensor completion (LRTC) problem which aims to recover a tensor from incomplete observations. LRTC plays an important role in many applications such as signal processing, computer vision, machine learning, and neuroscience. A widely used approach is to combine the tensor completion data fitting term with a regularizer based on a convex relaxation of the multilinear ranks of the tensor. For the data fitting function, we model the tensor variable by using the Canonical Polyadic (CP) decomposition and for the low-rank promoting regularization function, we consider a graph Laplacian-based function which exploits correlations between the rows of the matrix unfoldings. For solving our LRTC model, we propose an efficient alternating minimization algorithm. Furthermore, based on the Kurdyka-{\L}ojasiewicz property, we show that the sequence generated by the proposed algorithm globally converges to a critical point of the objective function. Besides, an alternating direction method of multipliers algorithm is also developed for the LRTC model. Extensive numerical experiments on synthetic and real data indicate that the proposed algorithms are effective and efficient

Enhancing Rock Image Segmentation in Digital Rock Physics: A Fusion of Generative AI and State-of-the-Art Neural Networks

In digital rock physics, analysing microstructures from CT and SEM scans is crucial for estimating properties like porosity and pore connectivity. Traditional segmentation methods like thresholding and CNNs often fall short in accurately detailing rock microstructures and are prone to noise. U-Net improved segmentation accuracy but required many expert-annotated samples, a laborious and error-prone process due to complex pore shapes. Our study employed an advanced generative AI model, the diffusion model, to overcome these limitations. This model generated a vast dataset of CT/SEM and binary segmentation pairs from a small initial dataset. We assessed the efficacy of three neural networks: U-Net, Attention-U-net, and TransUNet, for segmenting these enhanced images. The diffusion model proved to be an effective data augmentation technique, improving the generalization and robustness of deep learning models. TransU-Net, incorporating Transformer structures, demonstrated superior segmentation accuracy and IoU metrics, outperforming both U-Net and Attention-U-net. Our research advances rock image segmentation by combining the diffusion model with cutting-edge neural networks, reducing dependency on extensive expert data and boosting segmentation accuracy and robustness. TransU-Net sets a new standard in digital rock physics, paving the way for future geoscience and engineering breakthroughs

Zero-Shot Digital Rock Image Segmentation with a Fine-Tuned Segment Anything Model

Accurate image segmentation is crucial in reservoir modelling and material characterization, enhancing oil and gas extraction efficiency through detailed reservoir models. This precision offers insights into rock properties, advancing digital rock physics understanding. However, creating pixel-level annotations for complex CT and SEM rock images is challenging due to their size and low contrast, lengthening analysis time. This has spurred interest in advanced semi-supervised and unsupervised segmentation techniques in digital rock image analysis, promising more efficient, accurate, and less labour-intensive methods. Meta AI's Segment Anything Model (SAM) revolutionized image segmentation in 2023, offering interactive and automated segmentation with zero-shot capabilities, essential for digital rock physics with limited training data and complex image features. Despite its advanced features, SAM struggles with rock CT/SEM images due to their absence in its training set and the low-contrast nature of grayscale images. Our research fine-tunes SAM for rock CT/SEM image segmentation, optimizing parameters and handling large-scale images to improve accuracy. Experiments on rock CT and SEM images show that fine-tuning significantly enhances SAM's performance, enabling high-quality mask generation in digital rock image analysis. Our results demonstrate the feasibility and effectiveness of the fine-tuned SAM model (RockSAM) for rock images, offering segmentation without extensive training or complex labelling

Glucose-fueled Micromotors with Highly Efficient Visible Light Photocatalytic Propulsion

Synthetic micro/nanomotors fueled by glucose are highly desired for numerous practical applications because of the biocompatibility of their required fuel. However, currently all of the glucose-fueled micro/nanomotors are based on enzyme-catalytic-driven mechanisms, which usually suffer from strict operation conditions and weak propulsion characteristics that greatly limit their applications. Here, we report a highly efficient glucose-fueled cuprous oxide@N-doped carbon nanotube (Cu_2O@N-CNT) micromotor, which can be activated by environment-friendly visible-light photocatalysis. The speeds of such Cu_2O@N-CNT micromotors can reach up to 18.71 μm/s, which is comparable to conventional Pt-based catalytic Janus micromotors usually fueled by toxic H_2O_2 fuel. In addition, the velocities of such motors can be efficiently regulated by multiple approaches, such as adjusting the N-CNT content within the micromotors, glucose concentrations, or light intensities. Furthermore, the Cu_2O@N-CNT micromotors exhibit a highly controllable negative phototaxis behavior (moving away from light sources). Such motors with outstanding propulsion in biological environments and wireless, repeatable, and light-modulated three-dimensional motion control are extremely attractive for future practical applications

Velocity Dependence of Moiré Friction

Friction force microscopy experiments on moiré superstructures of graphene-coated platinum surfaces demonstrate that in addition to atomic stick–slip dynamics, a new dominant energy dissipation route emerges. The underlying mechanism, revealed by atomistic molecular dynamics simulations, is related to moiré ridge elastic deformations and subsequent relaxation due to the action of the pushing tip. The measured frictional velocity dependence displays two distinct regimes: (i) at low velocities, the friction force is small and nearly constant; and (ii) above some threshold, friction increases logarithmically with velocity. The threshold velocity, separating the two frictional regimes, decreases with increasing normal load and moiré superstructure period. Based on the measurements and simulation results, a phenomenological model is derived, allowing us to calculate friction under a wide range of room temperature experimental conditions (sliding velocities of 1–104 nm/s and a broad range of normal loads) and providing excellent agreement with experimental observations

Experimental study on mechanical properties of single fracture-hole red sandstone

Various fractures and holes in the natural rock mass affected the mechanical properties of the rock mass and the safety construction of engineering. In this study, we investigated the mechanical properties of a single fracture-hole rock specimen using particle flow code 2D (PFC2D) numerical simulation software and through laboratory tests. We analysed the failure behaviours and mechanical properties of the rock specimen with a single fracture-hole specimen under different fracture angles. The failure modes of single fractured rock samples with different fracture angles were revealed. The fracture propagation and stress evolution of the rock specimen with a single fracture-hole under different fracture angles were investigated. The experimental results shown that the peak strength, peak strain, elastic modulus, initial fracture stress, and damage stress of the single fracture-hole rock specimen with different fracture angles were significantly less than those of the intact rock specimen. Moreover, fracture hole defects accelerated the generation of fractures and promote the failure of the rock specimen. The failure modes were divided into Y, inverted Y, and V types. Before the rock specimen fractures, the stress concentration area was mainly distributed at both ends of the fracture. The stress concentration area at both ends of the fracture gradually decreased, and the stress concentration area near the hole gradually increased as the fracture angle increased. By experiments, the acoustic emission of the model had gone through three stages: initial, steady growth, and rapid decline. The size of the inclination angle affected the number of acoustic emission hits and the generation of acoustic emission signals. Failure behaviours of the rock specimen with a single fracture-hole were systematically investigated, which could promoted the development of fracture rock mechanics and improved the understanding of instability failure mechanism in rock engineering, such as nuclear wasted treatment engineering and deep underground engineering

Lateralization difference in functional activity during Stroop tasks: a functional near-infrared spectroscopy and EEG simultaneous study

IntroductionConflict monitoring and processing is an important part of the human cognitive system, it plays a key role in many studies of cognitive disorders.MethodsBased on a Chinese word-color match Stroop task, which included incongruent and neutral stimuli, the Electroencephalogram (EEG) and functional Near-infrared Spectroscopy (fNIRS) signals were recorded simultaneously. The Pearson correlation coefficient matrix was calculated to analyze brain connectivity based on EEG signals. Granger Causality (GC) method was employed to analyze the effective connectivity of bilateral frontal lobes. Wavelet Transform Coherence (WTC) was used to analyze the functional connectivity of the bilateral hemisphere and ipsilateral hemisphere.ResultsResults indicated that brain connectivity analysis on EEG signals did not show any significant lateralization, while fNIRS analysis results showed the frontal lobes especially the left frontal lobe play the leading role in dealing with conflict tasks. The human brain shows leftward lateralization while processing the more complicated incongruent stimuli. This is demonstrated by the higher functional connectivity in the left frontal lobe and the information flow from the left frontal lobe to the right frontal lobe.DiscussionOur findings in brain connectivity during cognitive conflict processing demonstrated that the dual modality method combining EEG and fNIRS is a valuable tool to excavate more information through cognitive and physiological studies

The absence of one’s intimate partner promotes dyadic competition through enhanced interbrain synchronization between opponents

Competition is a common occurrence in life, but the influence of intimate relationships on people’s competitiveness remains unknown. Grounded in Darwin’s theory of sexual selection, this study utilized EEG hyperscanning technology to investigate the influence of the presence of romantic partners and the gender of competitors on the interbrain synchronization of female individuals during competitive contexts. The research results showed that in competitive interactions, there was a significant increase in Theta and Alpha frequency band activity between females and their competitors. Interbrain synchronization was strongest when their partners were not nearby and females competed with same gender competitors. The research results indicate that intimate companionship has an impact on the early alertness and late cognitive execution mechanisms of female individuals in competition, and due to intimate relationships, females pay more attention to same-gender competitors. This study demonstrates that the presence of intimate partners can affect a female’s competitive state and brain synchronization with opponents of different genders, improving the theoretical explanation of intimate relationships and competitive interactions