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Airway remodeling is a remarkable pathological characteristic of chronic obstructive pulmonary disease (COPD), and long noncoding RNAs have been demonstrated to participate in COPD development and pathogenesis. Here, we investigate the role of long noncoding RNA GAS5 in cigarette smoke (CS)-induced airway remodeling. GAS5 expression is significantly lower in lung tissues of CS-exposed mice than in tissues of control mice without exposure to CS. Forced GAS5 overexpression suppresses CS-induced airway inflammation and remodeling. GAS5 overexpression also inhibits CS extract-induced inflammatory-cytokine expression and fibroblast activation in vitro. Regarding the mechanism, GAS5 acts as a sponge of miR-217-5p, thereby increasing PTEN expression. MiR-217-5p overexpression and PTEN knockdown separately reverse the inhibitory effects of GAS5 overexpression on the inflammatory-cytokine expression and fibroblast activation. Collectively, these results suggest that GAS5 can suppress airway inflammation and fibroblast activation by regulating miR-217-5p/PTEN axis, which may help develop novel therapeutic strategies against COPD.

This paper explores the development of elastic LiDAR technology, focusing specifically on key components relevant to solid target scanning applications. By analyzing its fundamentals and working mechanisms, the advantages of elastic LiDAR for precise measurement and environmental sensing are demonstrated. This paper emphasizes innovative advances in emitters and scanning systems, and examines the impact of optical design on performance and cost. Various ranging methods are discussed. Practical application cases of elastic LiDAR are presented, and future trends and challenges are explored. The purpose of this paper is to provide a comprehensive perspective on the technical details of elastic LiDAR, the current state of application, and future directions. All instances of "LiDAR" in this paper specifically refer to elastic LiDAR. [ABSTRACT FROM AUTHOR]

Due to adjustments to the operation plan of guided trains at high-speed railway stations, a large amount of information is inevitably displayed, sometimes with delays, omissions, and misalignments. The effective management of guidance information can provide important support for the personnel flow operation of high-speed railway stations. Aiming to meet the requirements of high real-time and high accuracy of guided job control, a closed-loop control method based on a guided job is proposed, which provides enhanced text detection and recognition in a target area. Firstly, using the introduction of the triplet attention mechanism in YOLOv5 and the addition of fusion modules, the feature pyramid network is used to enhance the effective feature and feature interactions between the modules to improve the detection speed of the display. Then, the text on the guide screen is recognized and extracted in combination with the PaddleOCR model, and then, the results are proofread against the original plan to adjust the screen information. Finally, the effectiveness and feasibility of the method are verified by experimental data, with the accuracy of the improved model reaching 90.6% and the speed reaching 1 ms, which meets the requirement of real-time closed-loop control of Screen-Based Guidance Operations. [ABSTRACT FROM AUTHOR]

In recent years, the safety issues of high-speed railways have remained severe. The intrusion of personnel or obstacles into the perimeter has often occurred in the past, causing derailment or parking, especially in the case of bad weather such as fog, haze, rain, etc. According to previous research, it is difficult for a single sensor to meet the application needs of all scenario, all weather, and all time domains. Due to the complementary advantages of multi-sensor data such as images and point clouds, multi-sensor fusion detection technology for high-speed railway perimeter intrusion is becoming a research hotspot. To the best of our knowledge, there has been no review of research on multi-sensor fusion detection technology for high-speed railway perimeter intrusion. To make up for this deficiency and stimulate future research, this article first analyzes the situation of high-speed railway technical defense measures and summarizes the research status of single sensor detection. Secondly, based on the analysis of typical intrusion scenarios in high-speed railways, we introduce the research status of multi-sensor data fusion detection algorithms and data. Then, we discuss risk assessment of railway safety. Finally, the trends and challenges of multi-sensor fusion detection algorithms in the railway field are discussed. This provides effective theoretical support and technical guidance for high-speed rail perimeter intrusion monitoring. [ABSTRACT FROM AUTHOR]

In view of the poor recognition results of long-distance intrusion targets in the research of high-speed railway perimeter intrusion detection, a high-speed railway perimeter intrusion detection method based on improved YOLOv5 is proposed. Firstly, the coordinate attention mechanism is integrated in the neck network of YOLOv5s model to improve the feature extraction capability of the algorithm. Secondly, a prediction layer is added to improve the detection performance of long-distance intruders. Further, the K-means clustering algorithm is used to obtain the appropriate anchors of the dataset. Finally, the bounding box regression loss function is improved to increase the positioning accuracy of the bounding box. In addition, a high-speed railway perimeter intrusion image sample library for typical scenes is constructed, and the improved model is trained and tested by using the sample library. The experimental results show that the improved model can effectively detect the intruders into the perimeter of high-speed railway. The improved algorithm improves the mean average precision (IOU =0.5) by 5.22% over the original YOLOv5s. The model has strong generalization ability in small target scenes. [ABSTRACT FROM AUTHOR]

Aiming at the complex engineering geological and environmental conditions of the tunnel, many unforeseen safety risk factors, the rigorousness of quality control and environment protection, and the heavy construction organization and coordination tasks, this research uses digitalization technology to achieve safe, high-quality and efficient tunnel construction. Firstly, BIM-based comprehensive early warning for tunnel construction organization progress and BIM-based 3D visualized progress-refined management are studied. Secondly, after studying the safety positioning management of personnel and mechanical equipment based on video analysis and identification and short-distance positioning, advanced prediction of poor geological conditions in tunnel construction, surrounding rock deformation monitoring and measurement, dynamic safety-step monitoring, and hazardous gas monitoring and early warning are achieved by utilizing BIM, Internet of Things, advanced forecasting, monitoring measurement and multiple safety risk identification and monitoring technologies. Thirdly, by studying the integration of BIM, 3D scanning, mobile internet technology and tunnel excavation, digital analysis and warning of over and under cut and flatness of tunnel engineering, automatic analysis and alarm of lining anti-cavitation, and analysis and judgment of engineering entity quality are accomplished. Lastly, combined with the actual engineering applications, the key technologies and their application effects are verified, which provides some reference for the digital information management of tunnel construction. [ABSTRACT FROM AUTHOR]