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• A TDOA localisation process based on the SSA is proposed. • An error-correction method based on LSE is proposed. Partial discharge (PD) is an early warning of gas-insulated switchgear (GIS) insulation faults. Consequently, accurate localisation of PD is crucial for the safe operation of GIS. The time difference of arrival method based on acoustic emission technology is more favorable for the engineering application of PD source localisation owing to its noninvasive characteristics and the small amount of required data. However, because PD ultrasonic signal propagation in GIS will introduce a variety of errors, current localisation algorithms suffer from problems of low accuracy and poor stability. Therefore, this study simulate and analyse the ultrasonic propagation characteristics as well as the sources of errors in GIS, based on which a joint acoustic–electric localisation method based on the Salp Swarm Algorithm is proposed, and an error-correction method based on least squares estimation is introduced. The localisation results show that the proposed method can effectively reduce the localisation error caused by multiple sources of error, and the mean localisation error can be reduced to within 20 mm in different structural gas chambers of GIS, which is more accurate and more stable compared with other methods, and it has good value for engineering application. [ABSTRACT FROM AUTHOR]

Young-onset diabetes has been increasingly prevalent in China and most of the young patients with diabetes remain undiagnosed. Recently, the American Diabetes Association (ADA) updated their screening criteria and turned down the age threshold of diabetes screening from 45 years to 35 years, which highlighted the importance of identifying young individuals with diabetes. Herein, we aimed to evaluate the clinical relevance of updated ADA screening recommendations in Chinese adults and the metabolic features and risk factor profiles of these newly diagnosed individuals. Using a complex, multistage, probability sampling design, we analyzed data from a nationally representative sample of 98,658 Chinese adults in 2010. Participants without previously diagnosed diabetes were included into the present study. We calculated the proportion of individuals with diabetes eligible for screening and the number needed to screen (NNS) to identify one patient with diabetes by age groups. Setting an earlier age threshold of diabetes screening can identify additional 6.3 million patients with diabetes and 72.3 million individuals with prediabetes, and the proportion of identified individuals increased more in rural, underdeveloped, and central areas. The NNS in Chinese adults dropped significantly from 28 in 30–34 age group to 15 in 35–45 years of age and remained low afterwards. The undiagnosed patients with diabetes who met the new screening age threshold of ADA recommendation were characterized by younger age, lower blood pressure and blood lipids, but higher proportion of overweight and higher level of insulin resistance, and tended to have an unhealthy diet habit, including low intake of fruits and vegetables and high intake of sugar-sweetened beverages, compared to those aged over 45 years. The new age threshold of 35 years for diabetes screening would reduce the proportion of undiagnosed diabetes with high cost-effectiveness, given the NNS for a positive test result was much lower in 35–45 age group comparing to the lower age group in Chinese adults. • Starting diabetes screening at 35 years can identify 6.3 million undiagnosed patients. • It is cost-effective to start diabetes screening at 35 years in China. • High intake of sugar-sweetened drinks is more common in newly detected patients. [ABSTRACT FROM AUTHOR]

Purpose: We conducted the meta-analysis to respectively evaluate the risk of prenatal antibiotics use during specific trimesters (first, second, and third trimester) on childhood asthma or wheeze and to explore whether the association was biased by potential confounding.Methods: The quality of included articles was assessed according to Newcastle-Ottawa Quality Assessment Scale and the Strengthening the Reporting of Observational Studies in Epidemiology. A random effects model was used to calculate pooled risk ratios and corresponding 95% confidence interval (CI), and publication bias was tested by Egger statistical test.Results: Eight studies were included finally. We found a crude positive association of prenatal antibiotics use during each pregnancy trimester and risk of childhood asthma or wheeze with RRs of 1.28 (95% CI, 1.09-1.51) for the first trimester of pregnancy, 1.25 (95% CI, 1.02-1.52) for the second trimester, and 1.25 (95% CI, 1.05-1.49) for the third trimester. However, when considering potential factors of maternal infections and presence of siblings, the relationship for each trimester was insignificant.Conclusions: This systemic review and meta-analysis proposed a crude positive association between prenatal antibiotic use in every specific trimester and risk of childhood asthma or wheeze. However, adjustment for confounders decreased the relative risk estimates, supporting the concept that these associations are, at least in part, because of confounding by indication. [ABSTRACT FROM AUTHOR]

• A novel ZL is proposed for multi-source GIS PD diagnosis. • A novel PD semantic descriptor is proposed to extract PD semantic information. • A semantic embedded network is designed to enhance the discrimination. • A relationship network is adopted to learn the similarity between features. • The method proves high-accuracy diagnosis for multi-source PD. Partial discharge (PD) diagnosis in gas insulation switchgear (GIS) has been continuously studied as a hot issue. The diagnostic model developed at this stage requires the possible combination of all single PDs, and each combination has sufficient data. However, the demand for the number of multi-source PD samples increases in the form of an index due to the accidental and concurrency of different types of PD, where it is unrealistic to collect sufficient PD samples. To this end, we proposed zero-shot learning (ZL) to diagnose multi-source PD. The ZL diagnosis multi-source PD only required single PD for training. Specifically, an attention residual network was introduced for feature extraction. Then, a novel semantic descriptor was proposed to obtain the semantic attributes, and the semantic embedded features were mapped to the visual space with a full connection network. Finally, the relationship network adopted the similarity between the visual features and semantic embedded features, and the multi-source PD diagnosis was realized. In addition, the multi-source PD experiment in GIS was constructed to verify the proposed ZL. The experimental results demonstrated that the ZL could achieve 90.25 % accuracy when only single PD participated in the training. It provided novel solutions for the diagnosis of multi-source PD in GIS. [ABSTRACT FROM AUTHOR]

• A novel partial discharge (PD) pattern recognition method is proposed. • The optimal network for PD pattern recognition can be automatically constructed. • Mixed depthwise separable convolution is used to extract multiscale features of PD. • Self-attention mechanism is used to further enhance the feature extraction ability. • The performance of the proposed method has been verified by the PD dataset. Convolutional neural network (CNN) has been extensively used in pattern recognition of partial discharge (PD) in gas-insulated switchgear (GIS) because of its powerful feature extraction ability. However, at this stage, manual trial and error is needed to construct the CNN. Moreover, the model is designed for specific datasets, which will cause domain bias when applied to a new dataset. Therefore, a novel differentiable neural architecture search method is proposed to automatically construct a GIS PD pattern recognition model. First, a factorized hierarchical search space is used to design the CNN architecture. Then, a discrete search space is relaxed into a continuous search space through a search strategy based on Gumbel–softmax. Experiments show that the recognition accuracy of the proposed method can reach 97.625%. Furthermore, the proposed method has strong robustness and high precision against noise and strong tolerance for unbalanced datasets. [ABSTRACT FROM AUTHOR]