Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.

There has been a growing penetration of renewable energy sources (RES) into power grids in recent years. As extreme weather events and cyber-attacks frequently occur, grid resilience has been an important issue. Vehicle-to-Vehicle (V2V) energy trading has great potential to improve the stability and reliability of resilient power grids integrated with high-penetrated RES. V2V energy trading is a distributed peer-to-peer (P2P) application. As a decentralized distributed ledger technology, blockchain is an ideal platform for V2V energy trading. The consensus mechanism of blockchain determines whether the V2V energy trading blockchain (ETB) can improve grid resilience. However, most studies in the ETB currently utilize conventional consensus mechanisms. Due to their substantial computational requirements and communication overhead, these consensus algorithms are not well-suited for real-time service applications like energy trading. We propose a novel BAC-SDS consensus specifically for V2V ETB, thus enabling resilient grids to maximise the use of renewable energy. We propose an Electric Vehicle (EV) leader election based on cryptography and adopt the sharding technique to enhance the system's scalability. Furthermore, our approach ensures the secure transfer of energy and value, contingent upon the condition that all EVs exhibit reasonable behavior and retain the proofs they possess. We implement the V2V ETB on Hyperledger Fabric. The experiments demonstrate (1) that V2V ETB significantly enhances the resilience of the power grid compared to traditional centralized trading models and (2) the consensus mechanism proposed in this paper is better suited for V2V energy trading than existing mechanisms, exhibiting superior performance in terms of security, throughput, and scalability, thus further enhancing the resilience of the power grid.

Lung cancer remains a leading cause of cancer-related deaths worldwide, with accurate classification of lung nodules being critical for early diagnosis. Traditional radiological methods often struggle with high false-positive rates, underscoring the need for advanced diagnostic tools. In this work, we introduce DCSwinB, a novel deep learning-based lung nodule classifier designed to improve the accuracy and efficiency of benign and malignant nodule classification in CT images. Built on the Swin-Tiny Vision Transformer (ViT), DCSwinB incorporates several key innovations: a dual-branch architecture that combines CNNs for local feature extraction and Swin Transformer for global feature extraction, and a Conv-MLP module that enhances connections between adjacent windows to capture long-range dependencies in 3D images. Pretrained on the LUNA16 and LUNA16-K datasets, which consist of annotated CT scans from thousands of patients, DCSwinB was evaluated using ten-fold cross-validation. The model demonstrated superior performance, achieving 90.96% accuracy, 90.56% recall, 89.65% specificity, and an AUC of 0.94, outperforming existing models such as ResNet50 and Swin-T. These results highlight the effectiveness of DCSwinB in enhancing feature representation while optimizing computational efficiency. By improving the accuracy and reliability of lung nodule classification, DCSwinB has the potential to assist radiologists in reducing diagnostic errors, enabling earlier intervention and improved patient outcomes.

With the rapid development of the energy internet, the transaction of distributed renewable energy (DRE) is playing an increasingly important role in the energy market. However, in the transaction model of distributed renewable energy combined with public blockchain technology, nodes in the trading network can join or leave the network at any time without any permission, which hinders the regulation of electricity institutions. Corresponding to the transaction principle, a distributed renewable energy transaction authentication mechanism based on consortium blockchain is proposed in this paper. First, certificate authority nodes were set in the transaction network to provide nodes with access authority by controlling the public keys and private keys of trading participants so that they can complete their identity authentication. Next, essential chaincodes in the transaction authentication were designed and deployed on a Hyperledger Fabric blockchain site, and a simulation experiment of a simple DRE transaction was used to elaborate the details on the transaction process. Finally, the proposed model was evaluated according to its performance and proved to be practical and effective.