Retrogressive thaw slumps along the Qinghai-Tibet Engineering Corridor: a comprehensive inventory and their distribution characteristics

The important Qinghai&ndash;Tibet Engineering Corridor (QTEC) covers the part of the Highway and Railway underlain by permafrost. The permafrost on the QTEC is sensitive to climate warming and human disturbance and suffers accelerating degradation. Retrogressive thaw slumps (RTSs) are slope failures due to the thawing of ice-rich permafrost. They typically retreat and expand at high rates, damaging infrastructure, and releasing carbon preserved in frozen ground. Along the critical and essential corridor, RTSs are commonly distributed but remain poorly investigated. To compile the first comprehensive inventory of RTSs, this study uses an iteratively semi-automatic method built on deep learning to delineate thaw slumps in the 2019 PlanetScope CubeSat images over a &sim;&thinsp;54&thinsp;000&thinsp;km2 corridor area. The method effectively assesses every image pixel using DeepLabv3+ with limited training samples and manually inspects the deep-learning-identified thaw slumps based on their geomorphic features and temporal changes. The inventory includes 875 RTSs, of which 474 are clustered in the Beiluhe region, and 38 are near roads or railway lines. The dataset is available at https://doi.org/10.5281/zenodo.6397029​​​​​​​ (Xia et al., 2021a), with the Chinese version at DOI: https://doi.org/10.11888/Cryos.tpdc.272672 (Xia et al.&nbsp;2021b). These RTSs tend to be located on north-facing slopes with gradients of 1.2&ndash;18.1∘ and distributed at medium elevations ranging from 4511 to 5212&thinsp;m&thinsp;a.s.l. They prefer to develop on land receiving relatively low annual solar radiation (from 2900 to 3200 &thinsp;kWh&thinsp;m&minus;2), alpine meadow covered, and loam underlay. Our results provide a significant and fundamental benchmark dataset for quantifying thaw slump changes in this vulnerable region undergoing strong climatic warming and extensive human activities. &nbsp;</p

The Impact Mechanism of Digitalization on Green Innovation of Chinese Manufacturing Enterprises: An Empirical Study

With the rapid development of the digital economy, promoting green innovation through digitalization has become an important means for manufacturing enterprises to improve their core competitiveness. However, the existing studies focus more on enterprise green technology innovation than green innovation, and the empirical tests mostly use regional-level data rather than enterprise-level data. This paper empirically examines the impact effect and mechanism of digitalization on green innovation in manufacturing enterprises using a sample of Chinese A-share listed manufacturing enterprises from 2013–2019. It is found that: digitalization significantly promotes the improvement of green innovation level in manufacturing enterprises; digitalization promotes green innovation more prominently in labor-intensive industries and manufacturing enterprises in central China than in capital- or technology-intensive industries and enterprises in eastern China; and digitalization can influence green innovation in manufacturing enterprises through three intermediary channels: promoting enterprise value chain upgrading, empowering industrial structure optimization, and enhancing technological innovation

Building Extraction from Airborne LiDAR Data Based on Multi-Constraints Graph Segmentation

Building extraction from airborne Light Detection and Ranging (LiDAR) point clouds is a significant step in the process of digital urban construction. Although the existing building extraction methods perform well in simple urban environments, when encountering complicated city environments with irregular building shapes or varying building sizes, these methods cannot achieve satisfactory building extraction results. To address these challenges, a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation was proposed in this paper. The proposed method mainly converted point-based building extraction into object-based building extraction through multi-constraints graph segmentation. The initial extracted building points were derived according to the spatial geometric features of different object primitives. Finally, a multi-scale progressive growth optimization method was proposed to recover some omitted building points and improve the completeness of building extraction. The proposed method was tested and validated using three datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS). Experimental results show that the proposed method can achieve the best building extraction results. It was also found that no matter the average quality or the average F1 score, the proposed method outperformed ten other investigated building extraction methods

Analysis of Droplet Transfer and Arc Swing in “TIG + AC” Twin-Wire Cross Arc Additive Manufacturing

Twin-wire and arc additive manufacturing (T-WAAM) has potential advantages in improving deposition efficiency and manufacturing functionally graded materials (FGMs), thus attracting much attention. However, there are few studies on the droplet transfer mode of T-WAAM. This paper analyzes the droplet transfer mode and arc swing in the “TIG + AC” twin-wire cross-arc additive manufacturing by in-situ observation with high-speed photography, revealing what factors influence the T-WAAM on deposition shaping the quality and what are the key mechanisms for process stability. Experiments show that with the main arc current provided by TIG 100 A and the twin-wire AC arc current 10 A, three different droplet transfer modes, namely the “free transfer + free transfer, bridge transfer + free transfer, bridge transfer + bridge transfer,” can be observed with the twin wires under different feeding speeds. The corresponding deposition and arc swing are quite different in quality. Through comparative analysis, it is found that the frequent extinguishment and ignition of the arc between electrode wires is the main factor for the instability in the additive manufacturing process. The “bridge transfer + free transfer” mode can obtain a large arc swing angle and a stable deposition, in which the cross arc has a significant stirring effect on the molten pool, and the deposition shape is well-made

YOLO-Weld: A Modified YOLOv5-Based Weld Feature Detection Network for Extreme Weld Noise

Weld feature point detection is a key technology for welding trajectory planning and tracking. Existing two-stage detection methods and conventional convolutional neural network (CNN)-based approaches encounter performance bottlenecks under extreme welding noise conditions. To better obtain accurate weld feature point locations in high-noise environments, we propose a feature point detection network, YOLO-Weld, based on an improved You Only Look Once version 5 (YOLOv5). By introducing the reparameterized convolutional neural network (RepVGG) module, the network structure is optimized, enhancing detection speed. The utilization of a normalization-based attention module (NAM) in the network enhances the network’s perception of feature points. A lightweight decoupled head, RD-Head, is designed to improve classification and regression accuracy. Furthermore, a welding noise generation method is proposed, increasing the model’s robustness in extreme noise environments. Finally, the model is tested on a custom dataset of five weld types, demonstrating better performance than two-stage detection methods and conventional CNN approaches. The proposed model can accurately detect feature points in high-noise environments while meeting real-time welding requirements. In terms of the model’s performance, the average error of detecting feature points in images is 2.100 pixels, while the average error in the world coordinate system is 0.114 mm, sufficiently meeting the accuracy needs of various practical welding tasks

Hedgehog signaling in tissue homeostasis, cancers, and targeted therapies

Abstract The past decade has seen significant advances in our understanding of Hedgehog (HH) signaling pathway in various biological events. HH signaling pathway exerts its biological effects through a complex signaling cascade involved with primary cilium. HH signaling pathway has important functions in embryonic development and tissue homeostasis. It plays a central role in the regulation of the proliferation and differentiation of adult stem cells. Importantly, it has become increasingly clear that HH signaling pathway is associated with increased cancer prevalence, malignant progression, poor prognosis and even increased mortality. Understanding the integrative nature of HH signaling pathway has opened up the potential for new therapeutic targets for cancer. A variety of drugs have been developed, including small molecule inhibitors, natural compounds, and long non-coding RNA (LncRNA), some of which are approved for clinical use. This review outlines recent discoveries of HH signaling in tissue homeostasis and cancer and discusses how these advances are paving the way for the development of new biologically based therapies for cancer. Furthermore, we address status quo and limitations of targeted therapies of HH signaling pathway. Insights from this review will help readers understand the function of HH signaling in homeostasis and cancer, as well as opportunities and challenges of therapeutic targets for cancer