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The mechanical reliability of the gas-insulated metal-enclosed switchgear (GIS) is critical to the safe and stable operation of a power system. The internal stress induced in GIS insulators, which typically lead to cracking, can degrade the mechanical properties and compromise the reliability of the GIS. In this work, ultrasonic nondestructive measurements of the internal stress in a GIS 126 kV epoxy composite with the transverse waves are demonstrated for the first time. Our results show that the velocities of ultrasonic transverse waves increase linearly with the compressive stress and decrease linearly with the tensile stress. The acoustoelastic coefficients are −1.18 × 10−4/MPa and −1.96 × 10−4/MPa for measurements with transverse waves polarized vertically and parallelly to stress, respectively. Based on these acoustoelastic coefficients, the internal stresses are calculated, and the maximum absolute values of relative errors are 35.4% and 26.3% for measurements with the transverse wave polarized vertically and parallelly to stress, respectively.

With the rapid advancement of artificial neural network (ANN) algorithms, many researchers have applied these methods to mine gas prediction and achieved numerous research achievements. It is of great significance to study methods that can accurately predict the gas content for the prevention of gas disasters in mining areas. In order to enhance the accuracy, stability, and generalization capability of the gas content prediction model, the GASA-KELM prediction model was established using the GASA algorithm to improve the KELM initial parameter assignment method, and the prediction model based on BPNN and SVM was established under the same conditions. The experimental results show that the GASA-BPNN model failed to achieve the desired outcome within 800 iterations. On the other hand, the GASA-SVM and GASA-KELM models accomplished the goal in significantly fewer iterations, taking only 673 and 487 iterations, respectively. Moreover, the overall average relative errors of the cross-validated gas content predictions were 15.74%, 13.85%, and 9.87% for the three models, respectively. Furthermore, the total average variance of the test set was 3.99, 2.76, and 2.05 for the GASA-BPNN, GASA-SVM, and GASA-KELM models, respectively. As a result, compared with other ANN models, the GASA-KELM model demonstrates higher accuracy, stronger prediction stability, and generalization ability in the practical application. This novel model provides a basis for accurately predicting gas content and proposing effective regional gas management measures.

The residue of estrogenic-disrupting compounds (EDCs) that are secreted by cows, added as drugs, and present in the feed may exist in dairy products. A gold nanoparticles (AuNPs)-estrogen receptor (ER) cocktail colorimetric assay equipped with ER cocktail solid phase extraction (SPE) was established to screen EDCs. Nine EDCs with high, moderate, and low estrogenic activity were selected to be the representative targets. The recognition range of the colorimetric assay combined with the ER cocktail SPE was wider than that of a single ERα or ERβ. The lowest detection limit of the established assay was about 10−9 mg·mL−1. The detection limits of estrone, bisphenol A, and bisphenol B were about one order of magnitude lower than the method based on a single ER. The recoveries of the spiked nine EDCs were between 80.0% and 110.0%, and daidzein was identified in the dairy product. The developed method has potential application prospects in food safety and environmental monitoring.

Salmonella contamination in food production and processing is a serious threat to consumer health. More and more rapid detection methods have been proposed to compensate for the inefficiency of traditional bacterial cultures to suppress the high prevalence of Salmonella more efficiently. The contamination of Salmonella in foods can be identified by recognition elements and screened using rapid detection methods with different measurable signals (optical, electrical, etc.). Therefore, the different signal transduction mechanisms and Salmonella recognition elements are the key of the sensitivity, accuracy and specificity for the rapid detection methods. In this review, the bioreceptors for Salmonella were firstly summarized and described, then the current promising Salmonella rapid detection methods in foodstuffs with different signal transduction were objectively summarized and evaluated. Moreover, the challenges faced by these methods in practical monitoring and the development prospect were also emphasized to shed light on a new perspective for the Salmonella rapid detection methods applications.

Internal stress in basin insulators of gas-insulated metal-enclosed switchgear (GIS) can lead to cracks, which affects the safe operation of these apparatuses. In this research, we proposed a subsurface internal stress measurement method for GIS epoxy composites. This method is based on an ultrasonic longitudinal critically refracted (LCR) wave technique. In this study, some epoxy composite specimens were synthesized with similar materials and manufacturing processes to those of 252 kV GIS basin insulators. An ultrasonic stress measurement system that utilized the LCR wave technique was set-up to investigate the relationship between stress and LCR wave propagation time, as well as to measure the compressive stress of the epoxy specimen within 0–50 MPa. The results show that LCR wave propagation time linearly decreased when stress increased in the subsurface zone and the acoustoelastic coefficient was −4.95. We found the relative errors of stress measurements to be less than 13%.

Situations of internal stress in basin insulators inside gas-insulated metal-enclosed switchgear (GIS) can lead to cracks, which can influence the safety and stability of apparatus. However, there is currently no research on internal stress measurements for composites of GIS basin insulators, and only measurements for surface stress. In this paper, an internal stress measurement method for GIS epoxy composite is proposed using an ultrasonic longitudinal through-transmission technique based on the acoustoelastic effect. An internal stress measurement system is developed to investigate the relationship between the uniaxial compressive internal stress and the velocity of the ultrasonic wave vertical to the stress in epoxy composite within a range of 0−70 MPa, and to calculate the acoustoelastic coefficient of epoxy composite. The effects of system delay are eliminated in measuring the propagation time. Some epoxy composite cuboid specimens with similar materials and using a manufacturing process similar to those of 252 kV GIS basin insulators are synthesized, and the uniformity of the internal stress in cuboid specimens is verified by finite element simulation. The results reveal a linear increase of the ultrasonic longitudinal wave velocity with increasing stress. It has been shown that the average acoustoelastic coefficient of GIS epoxy composites, using the longitudinal waves vertical to the stress, is 4.556 × 10−5/MPa. Additionally, the absolute errors of the internal stress measurements are less than 12.397 MPa. This research shows that the ultrasonic method based on the acoustoelastic effect for measuring the internal stress in GIS epoxy composites is feasible.