The Internet of Energy (IoE) provides an effective networking technology for distributed green energy, which allows the connection of energy anywhere at any time. As an important part of the IoE, electric vehicles (EVs), and charging pile management are of great significance to the development of the IoE industry. Previous work has mainly focused on network performance optimization for its management, and few studies have considered the security of the management between EVs and charging piles. Therefore, this paper proposes a decentralized security model based on the lightning network and smart contract in the blockchain ecosystem; this proposed model is called the lightning network and smart contract (LNSC). The overall model involves registration, scheduling, authentication, and charging phases. The new proposed security model can be easily integrated with current scheduling mechanisms to enhance the security of trading between EVs and charging piles. Experimental results according to a realistic infrastructure are presented in this paper. These experimental results demonstrate that our scheme can effectively enhance vehicle security. Different performances of LNSC-based scheduling strategies are also presented.

Traffic sign detection is critical for autonomous driving technology. However, accurately detecting traffic signs in complex traffic environments remains challenge despite the widespread use of one‐stage detection algorithms known for their real‐time processing capabilities. In this paper, the authors propose a traffic sign detection method based on YOLO v8. Specifically, this study introduces the Space‐to‐Depth (SPD) module to address missed detections caused by multi‐scale variations of traffic signs in traffic scenes. The SPD module compresses spatial information into depth channels, expanding the receptive field and enhancing the detection capabilities for objects of varying sizes. Furthermore, to address missed detections caused by complex backgrounds such as trees, this paper employs the Select Kernel attention mechanism. This mechanism enables the model to dynamically adjust its focus and more effectively concentrate on key features. Additionally, considering the uneven distribution of training data, the authors adopted the WIoUv3 loss function, which optimizes loss calculation through a weighted approach, thereby improving the model's detection performance across various sizes and frequencies of instances. The proposed methods were validated on the CCTSDB and TT100K datasets. Experimental results demonstrate that the authors’ method achieves substantial improvements of 3.2% and 5.1% on the mAP50 metric compared to YOLOv8s, while maintaining high detection speed, significantly enhancing the overall performance of the detection system. The code for this paper is located at https://github.com/dusongjie/TSD‐YOLO‐Small‐Traffic‐Sign‐Detection‐Based‐on‐Improved‐YOLO‐v8

The Internet of Vehicles is the specific instantiation of the Internet of Things in the field of transportation. Vehicle and driving data are often used to mine information about people's lifestyles and preferences. Without advanced data encryption and strict authorization measures, personal privacy and security are at great risk. To solve the problem that the relatively low security of vehicles connected to the Internet may threaten the privacy of users, a blockchain-based sensitive data privacy-protection scheme for vehicles connected to the Internet is proposed. First, association rules are used to mine big data in the Internet of Vehicles. Then, a data security aggregation protocol is established. Finally, an end-to-end encryption mechanism is created. The privacy protection of big data in the Internet of Vehicles is achieved through static and dynamic strategies. The experimental results show that the scheme can improve the hiding rate of sensitive data, the key generation time and encryption time are shorter than those of typical technologies, and the encryption ability is better. Therefore, in protecting the privacy of big data in the Internet of Vehicles, the blockchain proposed in this paper has better privacy protection and encryption abilities and is applicable to the protection of sensitive data in the Internet of Vehicles.

We researched the diagnostic capabilities of deep learning on chest radiographs and an image classifier based on the COVID-Net was presented to classify chest X-Ray images. In the case of a small amount of COVID-19 data, data enhancement was proposed to expanded COVID-19 data 17 times. Our model aims at transfer learning, model integration and classify chest X-Ray images according to three labels: normal, COVID-19 and viral pneumonia. According to the accuracy and loss value, choose the models ResNet-101 and ResNet-152 with good effect for fusion, and dynamically improve their weight ratio during the training process. After training, the model can achieve 96.1% of the types of chest X-Ray images accuracy on the test set. This technology has higher sensitivity than radiologists in the screening and diagnosis of lung nodules. As an auxiliary diagnostic technology, it can help radiologists improve work efficiency and diagnostic accuracy.

The vehicle to everything (V2X) requires the real-time integration of all kinds of information on roads, pedestrians, the environment, and vehicles themselves. This information also needs to be shared with other vehicles. The effective integration of information and the strong privacy protection are the key restrictions on the development of the V2X. The previous privacy protection model has mainly focused on the centralized network, and there were problems with the centralized gateway and single-point decision, which were not suitable for the decentralized scenario. Therefore, this paper proposes a remote attestation security model based on a privacy-preserving blockchain. The overall model involves two core steps. First, the vehicle provides the network with an evidence of a credible identity and integrity. Secured, the vehicles in the network calculate the nodes to make their respective decisions, and the accounting nodes summarize the sub-conclusions, form the final results, and write them into data blocks. The analysis shows that it possesses the security features of decentralization, traceability, anonymity, irreplaceability, and high efficiency. The model framework, core block chain structure, and protocol process are described in detail. The experimental results based on a realistic infrastructure are presented. These experimental results demonstrate that our scheme can effectively enhance the security of the communications of intelligent vehicles in the V2X.

Deep learning approaches for tooth segmentation employ convolutional neural networks (CNNs) or Transformers to derive tooth feature maps from extensive training datasets. Tooth segmentation serves as a critical prerequisite for clinical dental analysis and surgical procedures, enabling dentists to comprehensively assess oral conditions and subsequently diagnose pathologies. Over the past decade, deep learning has experienced significant advancements, with researchers introducing efficient models such as U-Net, Mask R-CNN, and Segmentation Transformer (SETR). Building upon these frameworks, scholars have proposed numerous enhancement and optimization modules to attain superior tooth segmentation performance. This paper discusses the deep learning methods of tooth segmentation on dental panoramic radiographs (DPRs), cone-beam computed tomography (CBCT) images, intro oral scan (IOS) models, and others. Finally, we outline performance-enhancing techniques and suggest potential avenues for ongoing research. Numerous challenges remain, including data annotation and model generalization limitations. This paper offers insights for future tooth segmentation studies, potentially facilitating broader clinical adoption.