A New Validity Detection Method of Online Status Monitoring Data for Power Transformer

With the rapid development of digital operation and maintenance of transformers, the interference of abnormal data caused by the low reliability of online monitoring sensors has become increasingly evident, directly affecting the accuracy of transformer fault diagnosis and impacting the development of digital operation and maintenance of transformers. Therefore, detecting abnormal data and distinguishing whether abnormal data are caused by equipment faults is an important prerequisite for the accurate diagnosis of transformers. When the difference between normal and abnormal data from online monitoring is insignificant, the existing methods are not ideal for detecting abnormal transformer data and cannot determine invalid data caused by sensor failure. To accurately identify valid data and remove invalid data, this study proposes a combined association rule and improved density clustering method to detect abnormalities in the online monitoring data of transformers. First, association rules mimic the association relationships between transformer data sequences. Then, the association rules are combined with an improved density clustering method based on the number of categories–neighborhood radius threshold curve–to detect anomalies in the sequences. Finally, the accurate identification of abnormal data is achieved, and invalid data caused by sensor reasons are effectively screened, thereby screening valuable data generated by transformer faults. The test results indicate that the proposed method can effectively improve the accuracy and stability of abnormal data detection in online monitoring of transformers

Consanguineous‐derived homozygous WNT1 mutation results in osteogenesis imperfect with congenital ptosis and exotropia

Abstract Background Wnt signaling pathway plays an important role in promoting ostergenesis. WNT1 mutations have been considered as a major cause of ostergenesis imperfect (OI). We identified an OI patient with pathogenic consanguineous‐derived homozygous WNT1 missense mutation. Methods We designed and applied a panel of known 261 genes associated with hereditary bone diseases for targeted next‐generation sequencing to examine clinically diagnosed OI patients. Detected mutations were confirmed by Sanger sequencing. Results The female proband presented with severe OI with low bone density, multiple long bone fractures, short stature, and absence of dentinogenesis imperfect and brain malformation. She had congenital ptosis and exotropia with her left eye, and absence of blue sclera. The proband came from a consanguineous family and had a homozygous WNT1 missense mutation (c.677C>T, (p.S226L)). In addition, three other compound heterozygous mutations (c.1729C>T in FKBP10, c.1958A>C in FGFR3, c.760G>C in TRPV4) were also detected in her family members. Conclusion We report the first identified case of consanguineous derived homozygous WNT1 mutation leading to severe osteogenesis imperfecta with congenital ptosis and exotropia

Elucidating proximity magnetism through polarized neutron reflectometry and machine learning

Polarized neutron reflectometry is a powerful technique to interrogate the structures of multilayered magnetic materials with depth sensitivity and nanometer resolution. However, reflectometry profiles often inhabit a complicated objective function landscape using traditional fitting methods, posing a significant challenge for parameter retrieval. In this work, we develop a data-driven framework to recover the sample parameters from polarized neutron reflectometry data with minimal user intervention. We train a variational autoencoder to map reflectometry profiles with moderate experimental noise to an interpretable, low-dimensional space from which sample parameters can be extracted with high resolution. We apply our method to recover the scattering length density profiles of the topological insulator–ferromagnetic insulator heterostructure Bi2Se3/EuS exhibiting proximity magnetism in good agreement with the results of conventional fitting. We further analyze a more challenging reflectometry profile of the topological insulator–antiferromagnet heterostructure (Bi,Sb)2Te3/Cr2O3 and identify possible interfacial proximity magnetism in this material. We anticipate that the framework developed here can be applied to resolve hidden interfacial phenomena in a broad range of layered systems. </jats:p