Search

Abstract Flame retardants play a crucial role in improving the flame retardant properties of polymer materials. In recent years, environmental problems caused by flame retardants have attracted widespread attention. It is urgent to use green and effective methods to prepare flame retardant polymers. Bioinspired nanocomposites with layered structures seem to provide effective ideas, but in general, their hydrophilic raw materials limit their applications in certain fields. Here, we prepared biomimetic composites with a layered “brick‐and‐mortar” structure by gravity‐induced deposition using polyimide as the polymer matrix and montmorillonite (MMT) as the filler. The well‐arranged structures of the composite material could isolate oxygen and prevent combustible gases from escaping. The gas barrier performance has been greatly improved, in which the water vapor transmission rate and the oxygen transmission rate decreased by 99.18% and three orders of magnitude, respectively. The flame retardant performance has also been improved, and its limiting oxygen index can reach 67.9%. The polyimide matrix can be converted to water‐insoluble by thermal imidization of water‐soluble poly (amic acid) salt precursors, which endows the composites with low hygroscopicity. The coating containing MMT can protect against polyurethane (PU) foam from fire. During the conical calorimetric test, the coated sample self‐extinguished, and the peak heat release rate, total heat release, and total smoke production are significantly decreased by 53.39%, 40.69%, and 53.03%, respectively. Taking advantage of these properties, this work utilizes a facile method to prepare biomimetic composites with low moisture absorption, excellent gas barrier properties, and flame retardancy, which have great application potential.

With the rapid development of the Internet,the security of network equipment has received extensive attention.Aiming at the problems of that the existing network equipment anomaly detection technology is destructive and difficult to detect,the paper uses the packets delay spent by the network equipment to transmit and process data packets as the detection basis,and proposes an anomaly detection scheme based on delay characteristics.The proposed scheme adopts side channel analysis,and it does not need to upgrade the equipment's software or hardware.It has the characteristics of non-intrusive and easy to implement.Firstly,the method uses the high-precision timing technology time stamp machine to collect the time delay information,and uses the genetic algorithm to extract the peak position feature of the delay distribution.Secondly,to solve the problem of the imbalance of data set,the method uses one-class support vector machine algorithm to construct anomaly detection algorithm.Finally,the validity of the method is verified by building an experimental platform,and the experimental results are evaluated.Experimental results show that the proposed method is feasible and effective.

The goat meat extract (GME) is novel nutritional supplement and may be used to enhance exercise performance or prevent physiological fatigue on elder or weak person. The present study evaluated the potential beneficial effects of GME on exercise performance, anti-fatigue potency, and changes in microbiota composition. Our results showed that 2X-to 3X-GME supplementation significantly improved forelimb grip strength and swimming time-to-exhaustion. Serum levels of lactate, AST and LDH were decreased as well as glycogen content of liver was higher in the 3X-GME group. The distinguished clusters of gut microbiota were dependent on different concentrations of the GME supplement. The gut microbiota including Eubacterium oxidoreducens, Neglecta timonensis, Raoultibacter timonensis, and Robinsoniella peoriensis were strongly correlated with GME supplementation. In summary, GME supplementation for could improve exercise endurance and did not cause any physiological or histopathological damage. These benefits potential might correlated with the gut microbiota composition alteration.

Fault diagnosis is crucial for repairing aircraft and ensuring their proper functioning. However, with the higher complexity of aircraft, some traditional diagnosis methods that rely on experience are becoming less effective. Therefore, this paper explores the construction and application of an aircraft fault knowledge graph to improve the efficiency of fault diagnosis for maintenance engineers. Firstly, this paper analyzes the knowledge elements required for aircraft fault diagnosis, and defines a schema layer of a fault knowledge graph. Secondly, with deep learning as the main method and heuristic rules as the auxiliary method, fault knowledge is extracted from structured and unstructured fault data, and a fault knowledge graph for a certain type of craft is constructed. Finally, a fault question-answering system based on a fault knowledge graph was developed, which can accurately answer questions from maintenance engineers. The practical implementation of our proposed methodology highlights how knowledge graphs provide an effective means of managing aircraft fault knowledge, ultimately assisting engineers in identifying fault roots accurately and quickly.

With the rapid development of intelligent driving vehicles, multi-task visual perception based on deep learning emerges as a key technological pathway toward safe vehicle navigation in real traffic scenarios. However, due to the high-precision and high-efficiency requirements of intelligent driving vehicles in practical driving environments, multi-task visual perception remains a challenging task. Existing methods typically adopt effective multi-task learning networks to concurrently handle multiple tasks. Despite the fact that they obtain remarkable achievements, better performance can be achieved through tackling existing problems like underutilized high-resolution features and underexploited non-local contextual dependencies. In this work, we propose YOLOPv3, an efficient anchor-based multi-task visual perception network capable of handling traffic object detection, drivable area segmentation, and lane detection simultaneously. Compared to prior works, we make essential improvements. On the one hand, we propose architecture enhancements that can utilize multi-scale high-resolution features and non-local contextual dependencies for improving network performance. On the other hand, we propose optimization improvements aiming at enhancing network training, enabling our YOLOPv3 to achieve optimal performance via straightforward end-to-end training. The experimental results on the BDD100K dataset demonstrate that YOLOPv3 sets a new state of the art (SOTA): 96.9% recall and 84.3% mAP50 in traffic object detection, 93.2% mIoU in drivable area segmentation, and 88.3% accuracy and 28.0% IoU in lane detection. In addition, YOLOPv3 maintains competitive inference speed against the lightweight YOLOP. Thus, YOLOPv3 stands as a robust solution for handling multi-task visual perception problems. The code and trained models have been released on GitHub. [ABSTRACT FROM AUTHOR]

Taking autonomous driving and driverless as the research object, we discuss and define intelligent high-precision map. Intelligent high-precision map is considered as a key link of future travel, a carrier of real-time perception of traffic resources in the entire space-time range, and the criterion for the operation and control of the whole process of the vehicle. As a new form of map, it has distinctive features in terms of cartography theory and application requirements compared with traditional navigation electronic maps. Thus, it is necessary to analyze and discuss its key features and problems to promote the development of research and application of intelligent high-precision map. Accordingly, we propose an information transmission model based on the cartography theory and combine the wheeled robot’s control flow in practical application. Next, we put forward the data logic structure of intelligent high-precision map, and analyze its application in autonomous driving. Then, we summarize the computing mode of “Crowdsourcing+Edge-Cloud Collaborative Computing”, and carry out key technical analysis on how to improve the quality of crowdsourced data. We also analyze the effective application scenarios of intelligent high-precision map in the future. Finally, we present some thoughts and suggestions for the future development of this field.

This letter proposes an augmented scheme of LT codes to improve the decoding success rate. The method involves substituting the 1‐s in full‐degree columns of the generator matrix of conventional LT codes with binary extension field elements and simultaneously increasing the ratio of full‐degree columns in the ideal soliton distribution. For non‐full‐degree columns, we retain the nonzero elements as 1‐s in the conventional LT codes generator matrix to preserve computational efficiency. Compared to conventional LT codes, the proposed method enhances the linear independence of the generator matrix, leading to a higher decoding success rate with minimal data packets. Experimental results demonstrate the effectiveness of the method at improving the performance of LT codes, with close to 100% decoding success rate achieved with around 5% data redundancy. [ABSTRACT FROM AUTHOR]

Physiological fatigue and toxic effects are commonly associated with chemotherapy. Beef extract (BE) is a nutritional supplement obtained by cooking beef. The present study examined the effects of several BE concentrations on 5-fluorouracil (5-FU)-induced fatigue and toxic effects in mice. Results showed BE supplementation ameliorated 5-FU-induced body and organ weight loss. All BE + 5-FU groups demonstrated increased grip strength (135.3–163.2 g), endurance time (567.3–911.3 s), and movement distance (23623–31225 cm) when compared with the 5-FU group (97.6 g, 265.2 s, and 13916 cm). Hepatic and muscular (0.76 and 1.06 mg/g) glycogen levels were maintained in the BE-3x + 5-FU group when compared with the 5-FU group (0.44 and 0.58 mg/g). Moreover, catalase (liver and muscle) and glutathione peroxidase (liver) activities were enhanced. The serum alanine aminotransferase, aspartate aminotransferase, and histopathological changes were ameliorated in the BE-2x + 5-FU and BE-3x + 5-FU groups. BE supplementation could provide a novel strategy for managing 5-FU-induced fatigue and toxic effects.

NaGlobal vigation Satellite System (GNSS) positioning technology is widely used for its high precision, global, and all-weather service. However, in complex environments such as urban canyons, GNSS performance is often degraded due to signal occlusion and even fails to achieve positioning due to the insufficient visible satellites. Because of the characteristics of large bandwidth, low latency, and high Base Station (BS) density, the fifth-Generation mobile communication (5G) technology has gradually become a trend for positioning in cities while offering traditional communication service. To supply the communication demands of the User Equipment (UE), only one BS is usually considered to establish a connection with the UE during the BS construction. However, the positioning accuracy with a single BS in urban canyons will be significantly reduced. To further improve the positioning accuracy in such extreme scenarios, this paper proposes a simplified 5G/GNSS fusion positioning system architecture using observations from only a 5G BS and a GNSS satellite. In this system, the GNSS receiver is mounted on the 5G BS, and the measurements provided by the receiver are used to form the differential code and complete the position estimation. The positioning mathematical models of the system based on the original code and differential code are derived. Then, the impacts of the measurements noise and the time synchronization error on the positioning accuracy are analyzed theoretically. Finally, the positioning performance is investigated by a set of simulation experiments. Numerical results show that under the existing 5G measurement noise and 2 m's code measurement noise, the improvement of the differential code based fusion positioning compared with the 5G-only positioning is more than 32%, which is also about 6% higher than the original code based fusion positioning. Besides, this improvement is not affected by the time synchronization error between the BS and the GNSS satellite. [ABSTRACT FROM AUTHOR]