Displacement and Deformation of the First Tunnel Lining During the Second Tunnel Construction

A three-dimensional twin tunnels scale model was established utilizing the discrete element method (DEM) with PFC3D. This model aims to investigate the displacement (in horizontal and vertical directions) and deformation of the first tunnel lining in four different cases which the clear distance of twin tunnels are 5, 10, 15 and 20 m during the second tunnel construction process. The numerical results indicate that the clear distance between twin tunnels and the distance between the measurement points of the first tunnel and the excavation area of the second tunnel are two most critical factors that influence the displacement and deformation of the first tunnel lining. Meanwhile, the soil arching effect, gravity, water pressure and lateral pressure also have an impact on the behavior of the first tunnel. The maximum disturbance of horizontal and vertical displacements occurred in the time points of finishing of the second tunnel. However, the horizontal displacement of the first tunnel is much more sensitive to the vertical displacement. The first tunnel turns to the right and down in direction while having an anticlockwise rotation (φ) during the process of construction of the second tunnel. In addition, the displacement and deformation of the lining of the first tunnel are critical to monitor, and the necessary precautions should be taken to decrease the risk of craze. In conclusion, the influence of the second tunnel excavation on the first tunnel lining could be neglected when their distance is more than 15 m

Lignins: Biosynthesis and Biological Functions in Plants

Lignin is one of the main components of plant cell wall and it is a natural phenolic polymer with high molecular weight, complex composition and structure. Lignin biosynthesis extensively contributes to plant growth, tissue/organ development, lodging resistance and the responses to a variety of biotic and abiotic stresses. In the present review, we systematically introduce the biosynthesis of lignin and its regulation by genetic modification and summarize the main biological functions of lignin in plants and their applications. We hope this review will give an in-depth understanding of the important roles of lignin biosynthesis in various plants’ biological processes and provide a theoretical basis for the genetic improvement of lignin content and composition in energy plants and crops

<i>OsYSL13</i> Is Involved in Iron Distribution in Rice

The uptake and transport of iron (Fe) in plants are both important for plant growth and human health. However, little is known about the mechanism of Fe transport in plants, especially for crops. In the present study, the function of yellow stripe-like 13 (YSL13) in rice was analyzed. OsYSL13 was highly expressed in leaves, especially in leaf blades, whereas its expression was induced by Fe deficiency both in roots and shoots. Furthermore, the expression level of OsYSL13 was higher in older leaves than that in younger leaves. OsYSL13 was located in the plasma membrane. Metal measurement revealed that Fe concentrations were lower in the youngest leaf and higher in the older leaves of the osysl13 mutant under both Fe sufficiency and deficiency conditions, compared with the wild type and two complementation lines. Moreover, the Fe concentrations in the brown rice and seeds of the osysl13 mutant were also reduced. Opposite results were found in OsYSL13 overexpression lines. These results suggest that OsYSL13 is involved in Fe distribution in rice

Comprehensive Analysis of Rice Laccase Gene (OsLAC) Family and Ectopic Expression of OsLAC10 Enhances Tolerance to Copper Stress in Arabidopsis

Laccases are encoded by a multigene family and widely distributed in plant genomes where they play roles oxidizing monolignols to produce higher-order lignin involved in plant development and stress responses. We identified 30 laccase genes (OsLACs) from rice, which can be divided into five subfamilies, mostly expressed during early development of the endosperm, growing roots, and stems. OsLACs can be induced by hormones, salt, drought, and heavy metals stresses. The expression level of OsLAC10 increased 1200-fold after treatment with 20 μM Cu for 12 h. The laccase activities of OsLAC10 were confirmed in an Escherichia coli expression system. Lignin accumulation increased in the roots of Arabidopsis over-expressing OsLAC10 (OsLAC10-OX) compared to wild-type controls. After growth on 1/2 Murashige and Skoog (MS) medium containing toxic levels of Cu for seven days, roots of the OsLAC10-OX lines were significantly longer than those of the wild type. Compared to control plants, the Cu concentration decreased significantly in roots of the OsLAC10-OX line under hydroponic conditions. These results provided insights into the evolutionary expansion and functional divergence of OsLAC family. In addition, OsLAC10 is likely involved in lignin biosynthesis, and reduces the uptake of Cu into roots required for Arabidopsis to develop tolerance to Cu

Robust visual recognition in poor visibility conditions: a prior knowledge-guided adversarial learning approach

Deep learning has achieved remarkable success in numerous computer vision tasks. However, recent research reveals that deep neural networks are vulnerable to natural perturbations from poor visibility conditions, limiting their practical applications. While several studies have focused on enhancing model robustness in poor visibility conditions through techniques such as image restoration, data augmentation, and unsupervised domain adaptation, these efforts are predominantly confined to specific scenarios and fail to address multiple poor visibility scenarios encountered in real-world settings. Furthermore, the valuable prior knowledge inherent in poor visibility images is seldom utilized to aid in resolving high-level computer vision tasks. In light of these challenges, we propose a novel deep learning paradigm designed to bolster the robustness of object recognition across diverse poor visibility scenes. By observing the prior information in diverse poor visibility scenes, we integrate a feature matching module based on this prior knowledge into our proposed learning paradigm, aiming to facilitate deep models in learning more robust generic features at shallow levels. Moreover, to further enhance the robustness of deep features, we employ an adversarial learning strategy based on mutual information. This strategy combines the feature matching module to extract task-specific representations from low visibility scenes in a more robust manner, thereby enhancing the robustness of object recognition. We evaluate our approach on self-constructed datasets containing diverse poor visibility scenes, including visual blur, fog, rain, snow, and low illuminance. Extensive experiments demonstrate that our proposed method yields significant improvements over existing solutions across various poor visibility conditions.Published versionThis research was funded by SunwayAI computing platform (SXHZ202103) and the National Key Research and Development Program (2021YFB2501403)

Regularized Denoising Masked Visual Pretraining for Robust Embodied PointGoal Navigation

Embodied PointGoal navigation is a fundamental task for embodied agents. Recent works have shown that the performance of the embodied navigation agent degrades significantly in the presence of visual corruption, including Spatter, Speckle Noise, and Defocus Blur, showing the weak robustness of the agent. To improve the robustness of embodied navigation agents to various visual corruptions, we propose a navigation framework called Regularized Denoising Masked AutoEncoders Navigation (RDMAE-Nav). In a nutshell, RDMAE-Nav mainly consists of two modules: a visual module and a policy module. In the visual module, a self-supervised pretraining method, dubbed Regularized Denoising Masked AutoEncoders (RDMAE), is designed to enable the Vision Transformers (ViT)-based visual encoder to learn robust representations. The bidirectional Kullback–Leibler divergence is introduced in RDMAE as the regularization term for a denoising masked modeling task. Specifically, RDMAE mitigates the gap between clean and noisy image representations by minimizing the bidirectional Kullback–Leibler divergence. Then, the visual encoder is pretrained by RDMAE. In contrast to existing works, RDMAE-Nav applies denoising masked visual pretraining for PointGoal navigation to improve robustness to various visual corruptions. Finally, the pretrained visual encoder with frozen weights is applied to extract robust visual representations for policy learning in the RDMAE-Nav. Extensive experiments show that RDMAE-Nav performs competitively compared with state of the arts (SOTAs) on various visual corruptions. In detail, RDMAE-Nav performs the absolute improvement: 28.2% in SR and 23.68% in SPL under Spatter; 2.28% in SR and 6.41% in SPL under Speckle Noise; and 9.46% in SR and 9.55% in SPL under Defocus Blur

Light-Promoted Arylsilylation of Alkenes with Hydrosilanes

Herein, we report light-promoted photo/hydrogen atom transfer dual catalysis for arylsilylation of alkenes via the radical–radical cross-coupling with diverse hydrosilanes, which provides a simple and efficient method to prepare various organosilicon compounds with a wide range of substrate scope and good functional group tolerance under transition-metal- and chemical-oxidant-free conditions. Furthermore, the arylsilylation of alkenes can also proceed via the possible electron donor–acceptor complex under exogenous photocatalyst-free conditions

Legislative Documents

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