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Artificial muscle materials promise incredible applications in actuators, robotics and medical apparatus, yet the ability to mimic the full characteristics of skeletal muscles into synthetic materials remains a huge challenge. Herein, inspired by the dynamic sacrificial bonds in biomaterials and the self-strengthening of skeletal muscles by physical exercise, high performance artificial muscle material is prepared by rearrangement of sacrificial coordination bonds in the polyolefin elastomer via a repetitive mechanical training process. Biomass lignin is incorporated as a green reinforcer for the construction of interfacial coordination bonds. The prepared artificial muscle material exhibits high actuation strain (>40%), high actuation stress (1.5 MPa) which can lift more than 10,000 times its own weight with 30% strain, characteristics of excellent self-strengthening by mechanical training, strain-adaptive stiffening, and heat/electric programmable actuation performance. In this work, we show a facile strategy for the fabrication of intelligent materials using easily available raw materials. Artificial muscles have a wide range of applications yet truly mimetic designs remain a challenge. Here, the authors use dynamic sacrificial bonds which are rearranged via a mechanical training process to optimise the characteristics of self-strengthening, strain-adaptive stiffening and actuation.

Existing low-light image enhancement techniques face challenges in achieving high visual quality and computational efficiency, as well as in effectively removing noise and adjusting illumination in extremely dark scenes. To address these problems, in this paper, we propose an illumination enhancement network based on Retinex theory for fast and accurate brightening of images in low-illumination scenes. Two learning-based networks are carefully constructed: decomposition network and enhancement network. The decomposition network is responsible for decomposing the low-light input image into the initial reflectance and illumination map. The enhanced network includes two sub-modules: the illumination enhancement module and the reflection denoising module, which are used for efficient brightness enhancement and accurate reflectance. Specially, we have established a cascaded iterative lighting learning process and utilized weight sharing to conduct accurate illumination estimation. Additionally, unsupervised training losses are defined to improve the generalization ability of the model. The proposed illumination enhancement framework enables noise suppression and detail preservation of the final decomposition results. To establish the efficacy and superiority of the model, on the widely applicable LOL dataset, our approach achieves a significant 9.16% increase in PSNR compared to the classical Retinex-Net, and a remarkable enhancement of 19.26% compared to the latest SCI method.

Hangzhou Bay is facing the environmental pressure brought by the economic development of the surrounding areas, but the comprehensive and systematic study of heavy metals in the sediments is still insufficient. In this study, heavy metals (Cu, Pb, Zn, Cr, Ni and V) in 231 surface sediments in Hangzhou Bay were analyzed to evaluate their spatial distribution, contamination status and controlling factors. The contents of metals in sediments had a low level and were not facing serious ecological risk. However, most metals exhibited higher contents in the central and northeastern parts of Hangzhou Bay, while Cr, V and Zn showed elevated contents in the adjacent area of the Qiantang River mouth, where the environmental quality assessment of metals showed low-to-moderate contamination. The heavy metals in the sediments were primarily from natural sources and their distribution was mainly dominated by the grain size of the sediments. However, most metals in the central and northern coastal areas, as well as the Qiantang River mouth were found to have high contents and indicated relatively serious contamination. This study could provide detailed background information for the future study of heavy metal geochemistry in the region, and serve as a basis for the study of modeling the heavy metals environmental behavior in sediments in coastal zones.

To identify various oil well working conditions more accurately and practically from massive image data collected by multiple measured information sources of sucker-rod pumping wells, this paper proposes a working condition recognition method with three key aspects: curvelet pooling optimization technology, multi-source attention mechanism fusion feature extraction technology, and multi-source semi-supervised classification deep learning. Specifically: (a) Curvelet pooling optimization technology. We introduce the second-generation curvelet transform into the ResNet-50 pooling layer and adopt a collaborative learning pooling strategy of low-frequency and high-frequency information from the raw data decomposed via curvelet transform instead of max-pooling. This enhances the neural network’s capability to capture detailed features of complex image data. (b) Multi-source attention mechanism fusion feature extraction technology. We selected two information sources: measured ground dynamometer cards and measured electrical power cards. The multi-head self-attention mechanism enables interactive complementarity between curvelet-decomposed image data from each information source, while achieving dynamic weighted fusion of the interactive complementary data via the adaptive attention mechanism. This process yields optimal global feature representations of multi-source fused data. (c) Multi-source semi-supervised classification deep learning. By integrating multi-source fused feature data with a semi-supervised classification algorithm based on the dual strategy of dynamic adjustment of pseudo-label confidence and self-adaptive class fairness regularization, the method leverages abundant multi-source unlabeled samples to improve model classification performance and generalization ability under limited labeled training samples. This further enhances the accuracy and practicality of condition recognition. Experimental data were collected from a high-pressure, low-permeability, thin oil reservoir block in an oilfield in China. Extensive experiments demonstrate that the proposed method efficiently processes measured information source data in the sucker-rod pumping production system, improves the performance of traditional deep learning frameworks, explores the intrinsic correlations among multiple measured information source data of oil wells, and utilizes massive unlabeled working condition data to enhance the working condition recognition effect and engineering practicability with a minimal number of labeled samples. Code is available at https://github.com/Yoick/AMMFFECP .

Graph neural networks (GNN) have achieved remarkable success in various domains, yet incomplete node attribute data can significantly impair their performance. Graph completion learning (GCL) methods have been developed to address this issue, aiming to reconstruct missing node attributes based on existing structural relationships. However, the accuracy of these reconstructions is highly dependent on the quality of the initial graph structure, which often contains errors and inaccuracies. This paper proposes the evolving graph structure (EGS) framework for semi-supervised node classification with missing attributes. EGS dynamically reconstructs the attributes of the nodes and updates the graph structure through an alternating optimization approach. Specifically, we introduce a Dirichlet Energy function with dual constraints to formulate the objective function, which jointly optimizes node structure relationships and attribute reconstruction. Extensive experiments on five benchmark datasets, with different missing rates, and with seven GNN variants demonstrate the effectiveness of EGS, achieving state-of-the-art performance compared to existing GCL methods.

The complex and variable operating conditions of sucker-rod pumping wells pose a significant challenge for the timely and accurate identification of oil well operating conditions. Effective deep learning based on measured multi-source data obtained from the sucker-rod pumping well production site offers a promising solution to the challenge. However, existing deep learning-based operating condition recognition methods are constrained by several factors: the limitations of traditional operating condition recognition methods based on single-source and multi-source data, the need for large amounts of labeled data for training, and the high robustness requirement for recognizing complex and variable data. Therefore, we propose a semi-supervised class-incremental sucker-rod pumping well operating condition recognition method based on measured multi-source data distillation. Firstly, we select measured ground dynamometer cards and measured electrical power cards as information sources, and construct the graph neural network teacher models for data sources, and dynamically fuse the prediction probability of each teacher model through the Squeeze-and-Excitation attention mechanism. Then, we introduce a multi-source data distillation loss. It uses Kullback-Leibler (KL) divergence to measure the difference between the output logic of the teacher and student models. This helps reduce the forgetting of old operating condition category knowledge during class-incremental learning. Finally, we employ a multi-source semi-supervised graph classification method based on enhanced label propagation, which improves the label propagation method through a logistic regression classifier. This method can deeply explore the potential relationship between labeled and unlabeled samples, so as to further enhance the classification performance. Extensive experimental results show that the proposed method achieves superior recognition performance and enhanced engineering practicality in real-world class-incremental oil extraction production scenarios with complex and variable operating conditions.

In recent years, various algorithms using random finite sets (RFS) to solve the issue of simultaneous localization and mapping (SLAM) have been proposed. Compared with the traditional method, the advantage of the RFS method is that it can avoid data association, landmark appearance and disappearance, missed detections, and false alarms in Bayesian recursion. There are many problems in the existing robot SLAM methods, such as low estimation accuracy, poor back-end optimization, etc. On the basis of previous studies, this paper presents a labeled random finite set (L-RFS) SLAM method. We describe a scene where the sensor moves along a given path and avoids obstacles based on the L-RFS framework. Then, we use the labeled multi-Bernoulli filter (LMB) to estimate the state of the sensor and feature points. At the same time, the B-spline curve is used to smooth the obstacle avoidance path of the sensor. The effectiveness of the algorithm is verified in the final simulation.

Background Intestinal ischemia/reperfusion (I/R) injury has high morbidity and mortality rates. Gut microbiota is a potential key factor affecting intestinal I/R injury. Populations exhibit different sensitivities to intestinal I/R injury; however, whether this interpopulation difference is related to variation in gut microbiota is unclear. Here, to elucidate the interaction between the gut microbiome and intestinal I/R injury, we performed 16S DNA sequencing on the preoperative feces of C57BL/6 mice and fecal microbiota transplantation (FMT) experiments in germ-free mice. The transwell co-culture system of small intestinal organoids extracted from control mice and macrophages extracted from control mice or Toll-like receptor 2 (TLR2)-deficient mice or interleukin-10 (IL-10)-deficient mice were established separately to explore the potential mechanism of reducing intestinal I/R injury. Results Intestinal I/R-sensitive (Sen) and intestinal I/R-resistant (Res) mice were first defined according to different survival outcomes of mice suffering from intestinal I/R. Fecal microbiota composition and diversity prior to intestinal ischemia differed between Sen and Res mice. The relative abundance of Lactobacillus murinus ( L. murinus ) at the species level was drastically higher in Res than that in Sen mice. Clinically, the abundance of L. murinus in preoperative feces of patients undergoing cardiopulmonary bypass surgery was closely related to the degree of intestinal I/R injury after surgery. Treatment with L. murinus significantly prevented intestinal I/R-induced intestinal injury and improved mouse survival, which depended on macrophages involvement. Further, in vitro experiments indicated that promoting the release of IL-10 from macrophages through TLR2 may be a potential mechanism for L. murinus to reduce intestinal I/R injury. Conclusion The gut microbiome is involved in the postoperative outcome of intestinal I/R. Lactobacillus murinus alleviates mice intestinal I/R injury through macrophages, and promoting the release of IL-10 from macrophages through TLR2 may be a potential mechanism for L. murinus to reduce intestinal I/R injury. This study revealed a novel mechanism of intestinal I/R injury and a new therapeutic strategy for clinical practice. 7exUFWiAL2tK_L7CooxsWe Video Abstract.

Background Xanthophylls are a large class of carotenoids that are found in a variety of organisms and play particularly important roles in the light-harvesting and photoprotection processes of plants and algae. Violaxanthin is an important plant-derived xanthophyll with wide potential applications in medicines, foods, and cosmetics because of its antioxidant activity and bright yellow color. To date, however, violaxanthins have not been produced using metabolically engineered microbes on a commercial scale. Metabolic engineering for microbial production of violaxanthin is hindered by inefficient synthesis pathway in the heterologous host. We systematically optimized the carotenoid chassis and improved the functional expression of key enzymes of violaxanthin biosynthesis in Escherichia coli . Results Co-overexpression of crtY (encoding lycopene β-cyclase), crtZ (encoding β-carotene 3-hydroxylase), and ZEP (encoding zeaxanthin epoxidase) had a notable impact on their functions, resulting in the accumulation of intermediate products, specifically lycopene and β-carotene. A chassis strain that did not accumulate the intermediate was optimized by several approaches. A promoter library was used to optimize the expression of crtY and crtZ . The resulting strain DZ12 produced zeaxanthin without intermediates. The expression of ZEP was further systematically optimized by using DZ12 as the chassis host. By using a low copy number plasmid and a modified dithiol/disulfide system, and by co-expressing a full electron transport chain, we generated a strain producing violaxanthin at about 25.28 ± 3.94 mg/g dry cell weight with decreased byproduct accumulation. Conclusion We developed an efficient metabolically engineered Escherichia coli strain capable of producing a large amount of violaxanthin. This is the first report of a metabolically engineered microbial platform that could be used for the commercial production of violaxanthin.