Efficient and effective path planning can significantly enhance the task execution capabilities of UAVs in complex environments. This paper proposes an improved sampling-based path planning algorithm, Bi-APF-RRT*, which integrates an Artificial Potential Field (APF) method with a newly introduced repulsive coefficient and incorporates dynamic step size adjustments. To further improve path planning performance, the algorithm introduces strategies such as dynamic goal biasing, target switching, and region-based adaptive sampling probability. The improved Bi-APF-RRT* algorithm effectively controls sampling direction and spatial distribution during the path search process, avoiding local optima and significantly improving the success rate and quality of path planning. To validate the performance of the algorithm, this paper conducts a comparative analysis of Bi-APF-RRT* against traditional RRT* in multiple simulation experiments. Quantitative results demonstrate that Bi-APF-RRT* achieves a 59.6% reduction in average computational time (from 5.97 s to 2.41 s), a 20.6% shorter path length (from 691.56 to 549.21), and a lower average path angle (reduced from 33.28° to 29.53°), while maintaining a 100% success rate compared to 95% for RRT*. Additionally, Bi-APF-RRT* reduces the average number of nodes in the search tree by 45.8% (from 381.17 to 206.5), showcasing stronger obstacle avoidance capabilities, faster convergence, and smoother path generation in complex 3D environments. The results highlight the algorithm’s robust adaptability and reliability in UAV path planning.

With the proliferation of blockchain technology in decentralized applications like decentralized finance and supply chain and identity management, smart contracts operating on a blockchain frequently encounter security issues such as reentrancy vulnerabilities, timestamp dependency vulnerabilities, tx.origin vulnerabilities, and integer overflow vulnerabilities. These security concerns pose a significant risk of causing substantial losses to user accounts. Consequently, the detection of vulnerabilities in smart contracts has become a prominent area of research. Existing research exhibits limitations, including low detection accuracy in traditional smart contract vulnerability detection approaches and the tendency of deep learning-based solutions to focus on a single type of vulnerability. To address these constraints, this paper introduces a smart contract vulnerability detection method founded on multimodal feature fusion. This method adopts a multimodal perspective to extract three modal features from the lifecycle of smart contracts, leveraging both static and dynamic features comprehensively. Through deep learning models like Graph Convolutional Networks (GCNs) and bidirectional Long Short-Term Memory networks (bi-LSTMs), effective detection of vulnerabilities in smart contracts is achieved. Experimental results demonstrate that the proposed method attains detection accuracies of 85.73% for reentrancy vulnerabilities, 85.41% for timestamp dependency vulnerabilities, 83.58% for tx.origin vulnerabilities, and 90.96% for integer Overflow vulnerabilities. Furthermore, ablation experiments confirm the efficacy of the newly introduced modal features, highlighting the significance of fusing dynamic and static features in enhancing detection accuracy.

In the realm of IoT sensor data security, particularly in areas like agricultural product traceability, the challenges of ensuring product origin and quality are paramount. This research presents a novel blockchain oracle solution integrating an enhanced MTAS signature algorithm derived from the Schnorr signature algorithm. The key improvement lies in the automatic adaptation of flexible threshold values based on the current scenario, catering to diverse security and efficiency requirements. Utilizing the continuously increasing block height of the blockchain as a pivotal blinding parameter, our approach strengthens signature verifiability and security. By combining the block height with signature parameters, we devise a distinctive signing scheme reliant on a globally immutable timestamp. Additionally, this study introduces a reliable oracle reputation mechanism for monitoring and assessing oracle node performance, maintaining both local and global reputations. This mechanism leverages smart contracts to evaluate each oracle’s historical service, penalizing or removing nodes engaged in inappropriate behaviors. Experimental results highlight the innovative contributions of our approach to enhancing on-chain efficiency and fortifying security during the on-chain process, offering promising advancements for secure and efficient IoT sensor data transmission.

With the rapid expansion of global trade, the complexity and diversification of supply chains have become increasingly significant. In particular, the supply chain for hazardous materials, involving chemicals and explosives, requires stringent regulation. Managing the flow of these high-risk goods necessitates a reliable access control system to ensure safety and compliance. Traditional supply chain management systems often rely on centralized databases and record-keeping systems, which are prone to tampering and single points of failure, making them inadequate for current high-security demands. This paper combines blockchain technology with a hazardous materials supply chain model. In the blockchain network, our innovation lies in the introduction of a transaction coordinator to create transaction sets for each supply chain entity along with smart contracts to implement access control for these transaction sets. We also propose a new hazardous materials supply chain model architecture and conduct experimental verification using simulated hazardous materials supply chain data. Our experimental results show that the proposed method performs excellently in throughput and latency tests, demonstrating the potential to enhance the efficiency and security of supply chain management.

In this paper, we propose MSA-ESRGAN, a novel super-resolution model designed to enhance the perceptual quality of images. The key innovation of our approach lies in the integration of a multi-scale attention U-Net discriminator, which allows for more accurate differentiation between subject and background areas in images. By leveraging this architecture, MSA-ESRGAN surpasses traditional methods and several state-of-the-art super-resolution models in terms of Natural Image Quality Evaluator (NIQE) scores as well as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) across various benchmark datasets, including BSD100, Set5, Set14, Urban100, and OST300. Additionally, subjective evaluations further confirm the enhanced visual quality delivered by MSA-ESRGAN, particularly in terms of preserving texture and overall image realism. To ensure a fair comparison with Real-ESRGAN, we initialized our generator with a pre-trained Real-ESRNET model and followed the same training setup. Our model was trained on the DIV2K dataset using high-resolution image patches and the Adam optimizer, incorporating exponential moving average (EMA) for stability and performance enhancement. Evaluations on multiple benchmark datasets demonstrate that MSA-ESRGAN consistently delivers superior perceptual quality, as evidenced by higher NIQE, PSNR, and SSIM scores compared to other methods. Specifically, our model shows significant improvements in both objective and subjective measures of image quality. Furthermore, an ablation study highlighted the critical role of our multi-scale attention U-Net discriminator in enhancing the model’s performance. The results underscore the effectiveness of MSA-ESRGAN in maintaining image naturalness and perceptual quality, providing a robust benchmark for blind super-resolution tasks.

Federated learning is a distributed learning framework designed to protect user privacy, widely applied across various domains. However, existing federated learning algorithms face challenges, including slow convergence, significant loss fluctuations during aggregation, and imbalanced client sampling. To address these issues, this paper introduces a high-performance federated learning aggregation algorithm. This algorithm combines a cyclic adaptive learning rate adjustment strategy with client-weighted random sampling, addressing the aforementioned problems. Weighted random sampling assigns client weights based on their sampling frequency, balancing client sampling rates and contributions to enhance model aggregation. Additionally, it adapts the learning rate based on client loss variations and communication rounds, accelerating model convergence and reducing communication costs. To evaluate this high-performance algorithm, experiments are conducted using well-known datasets MNIST and CIFAR-10. The results demonstrate significant improvements in convergence speed and loss stability. Compared to traditional federated learning algorithms, our approach achieves faster and more stable convergence while effectively reducing training costs.