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The manifestation of mechanical phenomena in quantum materials at the macroscopic level is intricately linked to pronounced electron‐electron interactions within their lattices, a relationship that becomes especially evident in low‐dimensional materials. Violet phosphorous (VP), a nascent 2D material distinguished by its unique vertically aligned tubular structures, has garnered considerable attention owing to its layer‐dependent electronic bandgap, exceptional carrier mobility, and robust air stability. Herein, a comprehensive exploration of the phonon modes exhibited by few‐layer VP through an integrated experimental‐theoretical approach, focusing on the modulation of their Raman response under the uniaxial strain along a‐axis, b‐axis, and tube direction, respectively, is undertaken. Density functional theory calculations highlight when strain is applied along the a‐ or b‐axis direction, the strain is predominantly mitigated through tube rotational adaptations instead of the change of bond length and bond angle, culminating in a pronounced anisotropic Raman response. Moreover, the strain engineering can effectively optimize the photoelectric response performance of VP, including increase the responsivity ≈2500% and elevate the anisotropic ratio from 2.26 to 3.38. This investigation not only confirms the superior stretchability and impact resistance properties of cross‐structured VP but also establishes the groundwork for exploring the strain‐induced anisotropic optoelectric properties to VP. [ABSTRACT FROM AUTHOR]

Maintenance and repair of expressways are becoming increasingly important due to the growing frequency of their use. Accurate pavement crack information extraction helps with routine maintenance and reduces the risk of traffic accidents. The traditional 2D crack image detection method has limitations and cannot effectively obtain depth information. Three-dimensional crack extraction from 3D point cloud has become a new solution that can capture pavement crack information more comprehensively and accurately. However, the existing algorithms are not effective in the feature extraction of cracks due to the different and irregular shapes and sizes of pavement cracks and interference from the external environment. To solve this, a new method for detecting pavement cracks in point clouds, namely point attention net (PAN), is herein proposed. It uses a two-branch attention fusion module to focus on space and feature information in the cloud and capture features of crack points at different scales. It also uses the Poly Loss function to solve the imbalance of foreground and background points in pavement point cloud data. Experiments on the LNTU-RDD-LiDAR dataset were carried out to verify the effectiveness of the proposed method. Compared with the traditional method and the latest point cloud segmentation technology, the performance indexes of mIoU, Acc, F1, and Rec achieved significant improvement, reaching 75.4%, 91.5%, 75.4%, and 67.1%, respectively. [ABSTRACT FROM AUTHOR]

Dynamic control of the motion in a catenane remains a big challenge as it requires precise design and sophisticated well‐organized structures. This paper reports the design and synthesis of a donor‐acceptor [2]catenane through mechanical interlocking, employing a crown ether featuring two dibenzylammonium salts on its side arms as the host and a cyclobis(paraquat‐p‐phenylene) (CBPQT ⋅ 4PF6) ring as the guest molecule. By addition of external acid or base, the catenane can form self‐complexed or decomplexed compounds to alter the cavity size of the crown ether ring, consequently affecting circumrotation rate of CBPQT ⋅ 4PF6 ring of the catenane. This study offers insights for the design and exploration of artificial molecular machines with intricate cascading responsive mechanisms. [ABSTRACT FROM AUTHOR]

Road pavement cracks automated detection is one of the key factors to evaluate the road distress quality, and it is a difficult issue for the construction of intelligent maintenance systems. However, pavement cracks automated detection has been a challenging task, including strong nonuniformity, complex topology, and strong noise-like problems in the crack images, and so on. To address these challenges, we propose the CrackSeg--an end-to-end trainable deep convolutional neural network for pavement crack detection, which is effective in achieving pixel-level, and automated detection via high-level features. In this work, we introduce a novel multiscale dilated convolutional module that can learn rich deep convolutional features, making the crack features acquired under a complex background more discriminant. Moreover, in the upsampling module process, the high spatial resolution features of the shallow network are fused to obtain more refined pixel-level pavement crack detection results. We train and evaluate the CrackSeg net on our CrackDataset, the experimental results prove that the CrackSeg achieves high performance with a precision of 98.00%, recall of 97.85%, F-score of 97.92%, and a mIoU of 73.53%. Compared with other state-of-the-art methods, the CrackSeg performs more efficiently, and robustly for automated pavement crack detection., 1. Introduction Pavement crack detection plays an important role in the field of road distress evaluation [1]. Traditional crack detection methods depend mainly on manual work and are limited by [...]

The black garlic is produced from the raw garlic by Milliard reaction at high temperature (~60–90°C) and humidity (~70–90%). In this process, the pungent odor and gastrointestinal irritation effects of the raw garlic are reduced. At the same time, unstable compounds such as allicin are converted into stable organosulfur compounds with antioxidant activity. Previous studies have confirmed that black garlic extract has anti-tumor effects and could inhibit the proliferation of various tumor cells, including breast cancer cells MCF-7. However, the mechanisms of the anti-tumor effects remain unclear. In this study, we found that the black garlic extract could inhibit the proliferation, invasion, and metastasis of estrogen receptor-positive breast cancer cells, promote their apoptosis, and inhibit their epithelial-mesenchymal transition. Mechanistically, the black garlic extract reduced the expression of the anti-apoptotic protein MCL-1, which was achieved by modulating the ROS-JNK signaling pathway. In addition, the black garlic extract also decreased the expression of BCL-2 and increased the expression of BAX and BIM. We also found that the black garlic extract, in combination with venetoclax, a BCL-2 inhibitor, synergistically kills the estrogen receptor-positive breast cancer cells. These results suggested that black garlic extract has great therapeutic value and prospects for estrogen receptor-positive breast cancer treatment. [ABSTRACT FROM AUTHOR]

The lack of large-scale, multi-scene, and multi-type pavement distress training data reduces the generalization ability of deep learning models in complex scenes, and limits the development of pavement distress extraction algorithms. Thus, we built the first large-scale dichotomous image segmentation (DIS) dataset for multi-type pavement distress segmentation, called ISTD-PDS7, aimed to segment highly accurate pavement distress types from natural charge-coupled device (CCD) images. The new dataset covers seven types of pavement distress in nine types of scenarios, along with negative samples with texture similarity noise. The final dataset contains 18,527 images, which is many more than the previously released benchmarks. All the images are annotated with fine-grained labels. In addition, we conducted a large benchmark test, evaluating seven state-of-the-art segmentation models, providing a detailed discussion of the factors that influence segmentation performance, and making cross-dataset evaluations for the best-performing model. Finally, we investigated the effectiveness of negative samples in reducing false positive prediction in complex scenes and developed two potential data augmentation methods for improving the segmentation accuracy. We hope that these efforts will create promising developments for both academics and the industry. [ABSTRACT FROM AUTHOR]

Agricultural plastic greenhouses (APGs) are crucial for sustainable agricultural planting, and accurate spatial distribution information acquisition is crucial. Deep learning network models can extract target features from remote sensing images more effectively than traditional interpretation methods, which face challenges like high workloads and poor repeatability. In this study, we aim to enhance the inventorying of Agricultural Plastic Greenhouses (APGs) by improving the extraction accuracy of their locations and numbers through remote sensing techniques. Utilizing GF-7 satellite imagery, we propose an enhanced U-Net convolutional neural network (CNN) model that incorporates edge information expansion and a joint loss function to optimize performance. The primary objective is to provide a rapid and accurate method for mapping APGs, which is crucial for effective agricultural management and environmental monitoring. The U-Net network’s accuracy was enhanced by 1.1% after expanding 3 × 3 sample edge information, and further by 1.9% by combining edge extension and loss function constraints. Our results demonstrate that the modified U-Net model significantly improves extraction accuracy compared to traditional methods, thereby facilitating better inventory management and planning for agricultural cash crops. This advancement not only supports farmers in optimizing resources but also contributes to sustainable agricultural practices by enabling precise monitoring of APG distribution.Implications: Compared to traditional interpretation methods, which suffer problems such as heavy workloads, small adaptation ranges and poor repeatability, deep learning network models can better extract target features from remote sensing images. In this study, we used GF-7 image data to improve the traditional U-Net convolutional neural network (CNN) model. The Canny operator and Gaussian kernel (GK) function were used for sample edge expansion, and the binary cross-entropy and GK functions were used to jointly constrain the loss. Finally, APGs were accurately extracted and compared to those obtained with the original model. The results indicated that the APG extraction accuracy of the U-Net network was improved through the expansion of sample edge information and adoption of joint loss function constraints. [ABSTRACT FROM AUTHOR]