Printed circuit boards (PCBs) are a critical component of modern electronic equipment, performing a crucial role in the electronic information industry chain. However, accurate detection of PCB defects can be challenging. To address this problem, this paper proposes an enhanced detection method based on an improved YOLOv7 network. First, the SwinV2_TDD module is proposed, which adds a convolutional layer to extract the local features of the PCB. Then, the Magnification Factor Shuffle Attention (MFSA) mechanism is introduced, which adds a convolutional layer to each branch of the Shuffle Attention (SA) to expand its depth and enhance the adaptability of the attention mechanism. The SwinV2_TDD module and MFSA mechanism are integrated into the YOLOv7 network, replacing some ELAN modules and changing the activation function to Mish. The evaluation indexes used are Precision (P), Recall (R), and mean Average Precision (mAP). Experimental results show that the enhanced method achieves an AP of 98.74%, indicating a significant improvement in PCB defect detection performance.

Blockchain technology is an emerging technology, and cryptocurrency is the most well-known and successful blockchain application. With the development of the concept of blockchain technology, scientists and practitioners have found the potential of blockchain technology in the supply chain, which has led to much research on blockchain applications. There have been many reviews on the subject, but most of them are specific to particular supply chains and lack specific research on published papers. This paper addresses this research gap by examining, through qualitative analysis, the study finds that blockchain applications are moving in the direction of efficient automation. Appropriate consensus algorithms in different supply chain contexts will improve efficiency while reducing costs, while smart contracts play a huge role in security. Therefore, this paper explored articles related to the topic and explored articles in recent years by keyword combination.

In pursuit of enhancing public safety and addressing challenges in driver behavior recognition, an intelligent recognition and detection method of driver b

Printed Circuit Boards (PCBs), as integral components of electronic products, play a crucial role in modern industrial production. However, due to the precision and complexity of PCBs, existing PCB defect detection methods exhibit some issues such as low detection accuracy and limited usability. In order to address these problems, a PCB defect detection method based on D-DenseNet (PCBDD-DDNet) has been proposed. This method capitalizes on the advantages of two deep learning networks, CDBN (Convolutional Deep Belief Networks) and DenseNet (Densely Connected Convolutional Networks), to construct the D-DenseNet (Combination of CDBN and DenseNet) network. Within this network, CDBN focuses on extracting low-level features, while DenseNet is responsible for high-level feature extraction. The outputs from both networks are integrated using a weighted averaging approach. Additionally, the D-DenseNet employs a multi-scale module to extract features from different levels. This is achieved by incorporating filters of sizes 3 × 3, 5 × 5, and 7 × 7 along the three paths of the CDBN network, multi-scale feature extraction network, and DenseNet network, effectively capturing information at various scales. To prevent overfitting and enhance network performance, the Adafactor optimization function and L2 regularization are introduced. Finally, online hard example mining mechanism (OHEM) is incorporated to improve the network’s handling of challenging samples and enhance the accuracy of the PCB defect detection network. The effectiveness of this PCBDD-DDNet method is demonstrated through experiments conducted on publicly available PCB datasets. And the method achieves a mAP (mean Average Precision) of 93.24%, with an accuracy higher than other classical networks. The results affirm the method’s efficacy in PCB defect detection.

In the context of escalating network adversarial challenges, effectively identifying a Webshell processed using evasion techniques such as encoding, obfuscation, and nesting remains a critical challenge in the field of cybersecurity. To address the poor detection performance of the existing Webshell detection methods for evasion samples, this study proposes a multimodal feature fusion-based evasion Webshell detection method (MMF-Detect). This method extracts RGB image features and textual vector features from two modalities: the visual and semantic modalities of Webshell file content. A multimodal feature fusion classification model was designed to classify features from both modalities to achieve Webshell detection. The multimodal feature fusion classification model consists of a text classifier based on a large language model (CodeBERT), an image classifier based on a convolutional neural network (CNN), and a decision-level feature fusion mechanism. The experimental results show that the MMF-Detect method not only demonstrated excellent performance in detecting a conventional Webshell but also achieved an accuracy of 99.47% in detecting an evasive Webshell, representing a significant improvement compared to traditional models.