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The Yolov4 detection algorithm does not sufficiently extract local semantic and location information. This study aims to solve this problem by proposing a Yolov4-based multiscale feature fusion detection system for high-speed train wheel tread defects. First, multiscale feature maps are obtained from a feature extraction backbone network. The proposed multiscale feature fusion network then fuses the underlying features of the original three scales. These fused features contain more defect semantic information and location details. Based on the fused features, a path aggregation network is used to fuse feature maps at different resolutions, with an improved loss function that speeds up the convergence of the network. Experimental results show that the proposed method is effective at detecting defects in the wheel treads of high-speed trains., Author(s): Changfan Zhang [1]; Xinliang Hu [2]; Jing He (corresponding author) [1]; Na Hou [1] 1. Introduction High-speed train fault diagnosis is crucial for the safe operation and maintenance of [...]

This article discusses the synthesis of ynamides, a class of N-alkynyl compounds, through various catalytic reactions. The authors highlight the importance of ynamides in the synthesis of nitrogen-containing scaffolds and summarize existing methods for their synthesis. They also present their own research on the direct catalytic addition of terminal alkynes to azodicarboxylates to produce ynehydrazides, which can then be converted into ynamides. The authors propose potential future directions for further research in this field. [Extracted from the article]

Countries with different democratic levels tend to show various degrees of emphasis on military expenditure and a consensus can hardly be reached on the economic effect of democracy and military expenditure. By applying a panel vector autoregressive (PVAR) approach, this article examines the potential dynamic and endogenous relationships among democracy, military expenditure and economic growth of 126 countries from 1990 to 2020. Furthermore, the k-means clustering algorithm is employed to account for the heterogeneous interaction between democracy and military expenditure in different countries. The empirical results reveal that the strong positive impact of democracy and the negative effect of military spending on economic growth exists in the full sample and the cluster of countries with low democratic levels and a high military burden. For democratic countries with low military expenditure, there is a more significant and negative impact both of military expenditure on democracy and vice versa. [ABSTRACT FROM AUTHOR]

Purposes To explore the teaching effect and value of virtual reality technology combined with PBL teaching method applied to sports anatomy.Procedures 119 undergraduate students majoring in physical education of Xianyang Normal University were selected as research objects.Randomly divided into experimental group (N=59) and control group (N=60).Methods The experimental group used virtual reality technology combined with PBL teaching method, and the control group used traditional teaching method to teach. At the end of the course, the test scores of the two groups and the evaluation results of self-learning effect were statistically analyzed. Results the test scores of the students in the experimental group were significantly higher than those in the control group (P

With the advent of smart cities, the significance of the Internet of Things (IoT) is gaining greater prominence. At the same time, the safety early warning system in the IoT has a significant impact on real-time monitoring and the response to potential risks. Despite the advancements made in edge-assisted IoT deployments, several challenges and constraints persist. Given the potential threat to life posed by safety-related messages, ensuring the authenticity of messages in the edge-assisted IoT safety warning system is crucial. However, considering the identity privacy of devices participating in the edge-assisted Internet of Things system, directly verifying the identity of the sending device is undesirable. To address this issue, in this work, we design a linkable group signature scheme that allows devices to anonymously send safety-related messages to edge nodes, defending against Sybil attacks while ensuring the traceability of malicious device identities. Then, we present a high-efficiency conditional privacy-preserving authentication (CPPA) scheme based on the designed group signatures for the safety warning system in edge-assisted IoT. This scheme effectively protects device identity privacy while providing a reliable authentication mechanism to ensure the credibility and traceability of alert messages. The proposed scheme contributes to the field of safety warning systems in the context of edge-assisted IoT, providing a robust solution for privacy preservation and authentication. [ABSTRACT FROM AUTHOR]

The challenging issues in infrared and visible image fusion (IVIF) are extracting and fusing as much useful information as possible contained in the source images, namely, the rich textures in visible images and the significant contrast in infrared images. Existing fusion methods cannot address this problem well due to the handcrafted fusion operations and the extraction of features only from a single scale. In this work, we solve the problems of insufficient information extraction and fusion from another perspective to overcome the difficulties in lacking textures and unhighlighted targets in fused images. We propose a multi-scale feature extraction (MFE) and joint attention fusion (JAF) based end-to-end method using a generative adversarial network (MJ-GAN) framework for the aim of IVIF. The MFE modules are embedded in the two-stream structure-based generator in a densely connected manner to comprehensively extract multi-grained deep features from the source image pairs and reuse them during reconstruction. Moreover, an improved self-attention structure is introduced into the MFEs to enhance the pertinence among multi-grained features. The merging procedure for salient and important features is conducted via the JAF network in a feature recalibration manner, which also produces the fused image in a reasonable manner. Eventually, we can reconstruct a primary fused image with the major infrared radiometric information and a small amount of visible texture information via a single decoder network. The dual discriminator with strong discriminative power can add more texture and contrast information to the final fused image. Extensive experiments on four publicly available datasets show that the proposed method ultimately achieves phenomenal performance in both visual quality and quantitative assessment compared with nine leading algorithms. [ABSTRACT FROM AUTHOR]

The electronic and second‐order nonlinear optical (NLO) properties of small Agn clusters (n = 1 to 3) doped graphyne (GY) and pyrazine‐modified graphyne (pyGY) have been investigated employing (time‐dependent) density functional theory calculations. A large triangular hole of GY provides an efficient site to accommodate the Agn cluster, and an Ag atom prefers to be located in the center of the pyGY, interacting with the two pyridine N atoms. The silver clusters have strong interactions with GY/pyGY, and the intramolecular charge transfer is significant for determining NLO properties. The first hyperpolarizability (β0) values can be modulated by the introduction of silver clusters. The results show that the second‐order NLO response of Ag2/Ag3 doped pyGY complex is stronger than that of corresponding Ag2/Ag3 doped GY complex, while the β0 for single Ag doped GY is significantly higher than that of Ag@pyGY. Hence, the NLO responses of these complexes depend on the size of the clusters and the type of nanosheets. All complexes exhibit excellent transparency in the deep ultraviolet region, especially Ag3@GY/pyGY. These results suggest that these Agn clusters doped GY/pyGY are excellent candidates for potential applications in optical devices. [ABSTRACT FROM AUTHOR]