It is crucial to ensure the privacy and authenticity of the owner’s information in car insurance claims. However, the current traditional car insurance claims scenario suffers from inefficiency, complex service, unreliable data, and data leakage. Therefore, considering the privacy and sensitivity of insurance information and car owner data, we can use blockchain, smart contracts, and zero-knowledge proof technology to improve the current problems. This paper proposes a novel car insurance claim scheme based on smart contracts, blockchain, and zero-knowledge proof. Our scheme focuses on preserving privacy in the car insurance authorization and claim process. We design a private smart contract for the creation and revocation of car insurance and public smart contract for the authorization and validation of car insurance. By using ZoKrates, generating zero-knowledge proofs off chain and verifying the proofs on chain reduces the amount of data storage and computation on chain and provides privacy protection for sensitive information. Experimental results confirm the efficacy of our scheme in terms of security and performance.

In order to improve the detection accuracy of printed circuit board (PCB), this paper proposes a PCB defect detection model SWS-Yolov8n based on the Yolov8 model. Firstly, the Shuffle Attention (SA) module is introduced to make the model have the ability to randomly fuse and exchange feature information at PCB defects; Secondly, it is proposed to improve the loss function to a dynamic non monotonic loss function, so that the model shifts a portion of the focus from high-quality anchor frames to normal mass; Finally, space to depth layer and non step convolutional layer are adopted to address the problem of fine-grained information on defect location is easy to be lost and the learning efficiency is low. After expanding the public dataset PKU-Market-PCB, experiments showed that the improved SWS-Yolov8n model increased the mean average accuracy (mAP) value by 4.47% compared to the original model. When applied to PCB defect detection tasks, it can effectively improve detection accuracy. In the future, this model can be further lightweight improved and deployed on embedded systems and mobile devices. Keywords: deep learning; PCB defect detection; Yolov8; attention mechanism; loss function

Title: FFD-SLAM: A Real-Time Visual SLAM Toward Dynamic Scenes with Semantic and Optical Flow Information | Keywords: dynamic object detection, optical flow filtering, image feature points elimination, SLAM algorithm | Author: Hao Zhang, Yu Wang, Tianjie Zhong, Fangyan Dong, and Kewei Chen

While portability is a key aspect of small mass spectrometers, they also need to be able to effectively screen the analytes from complex matrices. The accuracy and performance of mass spectrometers depend not only on the identification of parent ion signals in targets but also fragment ion signals. This paper reports on the construction of a device that uses a new time-domain excitation waveform called qualitative pre-scan waveform (QPSW) for its quadrupole ion trap mass spectrometry analyzer. This approach uses scanning to eliminate interfering ions, thus improving the sensitivity for the analyte and removing the cumbersome steps required to convert conventional stored waveform inverse Fourier transform (SWIFT) waveforms from the frequency domain to the time domain. Tests show that the target ions can be fragmented just by introducing air at the moment when the pinch valve is opened without any need for a carrier gas. This simplifies the equipment required, making the mass spectrometer more portable. Tests were undertaken to compare the performance with the use of conventional SWIFT waveforms and to optimize its experimental parameters so that the target ions were effectively accumulated in the ion trap. An approximate 5-fold improvement in sensitivity was achieved by QPSW compared to the SWIFT for 2 μg/mL ketamine. A 5-fold reduction in the limit of detection (LOD) to 20 ng/mL was also obtained for enrofloxacin. In addition, the stability was improved by QPSW with a relative standard deviation less than 13% compared to 16% for SWIFT.