Ultrasonic water meters have current widespread problems of poor site adaptability, low measurement accuracy, and poor stability. A high-precision intelligent ultrasonic water meter with self-diagnosis function and adaptive technology was proposed in this paper, which is focus on improving the measurement accuracy and repeatability of the small pipe diameters. The hardware circuit design of the ultrasonic water meter studied in this paper adopts the low-power STM32L053 microcontroller with Cortex-M0 core architecture and the high-precision 11 ps time resolution timing chip TDC-GP30 to complete the metering function. The software combines pulse width ratio and amplitude voltage measurement technology to make the water meter have self-diagnosis function, avoid measurement errors caused by accidental factors, improve adaptability and measurement accuracy, also the application of adaptive measurement period method is used to improve measurement repeatability. The software adopts the moving average filtering algorithm, which effectively reduces the fluctuation of the time-of-flight(ToF) difference and improve the measurement accuracy of the flow point in the low zone (between the minimum flow and the boundary flow). The experimental verification results show that the accuracy of the small-caliber ultrasonic water meter can reach within ± 1.5 %, and the repeatability is less than 0.5 %. In the face of fluid disturbances, adaptive technology is used in this water meter to adjust the measurement period and carry out self-diagnostic function research, and automatically respond to deal with abnormal faults, thereby realizing the demand for intelligence.

We numerically investigate the behaviors of a hydrodynamic squirmer interacting with a circular obstacle using the lattice Boltzmann method. It is interesting to find that in most cases, pullers (gaining thrust from the front) move forward while pushers (gaining thrust from the rear) move backward. We describe six swimming patterns of the squirmer: orbiting forward, ∞-loop, dancing forward, C-loop, orbiting backward, and scattering; notably, three of these have not been previously reported. The association between pressure and viscous stress can reasonably explain the movement of the squirmer near the obstacle. In certain modes, squirmers can swim faster near obstacles. The swimming Reynolds number Res and dipolarity β have significant effects on the averaged velocity and Strouhal number of the swimming patterns. A stronger polarity encourages the squirmers to swim faster and “bounce” more often.

Wheat scab is a severe fungal disease caused by Fusarium. The precise detection of scab is crucial for improving the wheat yield. The utilization of RGB images captured by unmanned aerial vehicle (UAV) can effectively enhance the wheat scab detection efficiency. However, UAV images are influenced by environmental conditions. Additionally, the scab in the image is extremely small and the background is complex. Therefore, to boost the wheat scab detection accuracy, this study proposes an end-to-end image adaptive enhancement and wheat scab detection network (IAE-SDNet). In the head of the network, an image adaptive enhancement (IAE) module aims to enhance the quality of UAV images and performs end-to-end learning with the transformer-based object detection module SDNet. A cascading attention inverse residual module (CIRMB) is designed to boost the feature extraction capacity for the disease area. A deformable multi-head attention encoder (DMHA-Encoder) is deployed to augment the semantic information of the advanced features of wheat while maintaining the representation ability of global attention. A multi-scale feature fusion block (MFFB) is designed to effectively fuse wheat disease features of different scales, reducing the impact caused by the inconsistency in size between the complex field background and the disease area. Finally, to optimize the detection of small lesion areas, an MNIoU bounding-box regression loss function is proposed. The experiment indicate that this method has improved Average Precision (AP) by 6% and the Recall by 5% compared with the baseline network. This study provides a feasible approach for the precise detection of wheat scab, with high application prospects.

Vinyl acetate is a restricted substance in food products. The quantification of the organic impurities in vinyl acetate is a major problem due to its activity, instability, and volatility. In this paper, while using the mass balance method to determine the purity of vinyl acetate, an improved method was established for the determination of the content of three impurities in vinyl acetate reference material, and the GC-FID peak area normalization for vinyl acetate was calibrated. The three trace organic impurities were identified by gas chromatography tandem high-resolution mass spectrometry to be methyl acetate, ethyl acetate, and vinyl propionate. The content and relative correction factors for the three organic impurities were measured. The purity of vinyl acetate determined by the mass balance method was 99.90% with an expanded uncertainty of 0.30%, and the total content of organic impurities was 0.08% with a relative correction factor of 1.23%. The vinyl acetate reference material has been approved as a national certified reference material in China as GBW (E) 062710.

Switch rail is a weak but essential component of high-speed rail (HSR) systems. Due to aging and the potential of fatigue damage accumulation, it has an urgent requirement for damage detection. An automatic classification method of switch rail damage based on feature integration and machine learning is proposed. According to the characteristics of switch rail and guided wave, several features extracted from different signal processing domains (such as time domain, power spectrum domain and time-frequency domain) are proposed and defined to characterize the complexity of switch rail damage. A damage index is defined to eliminate the effects of various environmental and operational conditions. A feature selection method based on binary particle swarm optimization (BPSO) is proposed. This method uses a new fitness function to select the most damage-sensitive features, eliminate the irrelevant and redundant features, and improve the classification performance. The least-squares support-vector machine (LS-SVM) is adopted to build an automatic classification model to reduce the probability of artificial error diagnosis and improve the generalization ability. Finally, experiment on the switch rail foot is conducted to verify the proposed method. The results show that the method has the ability of damage identification, which is better than traditional methods.