Inverse correlation between urethral length and continence before and after native tissue pelvic floor reconstruction

Urethral length was evaluated retrospectively in patients with prolapse undergoing anterior native-tissue repair. Effects of age, prolapse stage, defect pattern, urodynamic and clinical stress test findings, and tension-free vaginal tape (TVT) surgery indication were analyzed using Mann–Whitney and Wilcoxon tests and linear and logistic regression. Of 394 patients, 61% had stage II/III and 39% had stage IV prolapse; 90% of defects were central (10% were lateral). Median pre- and postoperative urethral lengths were 14 and 22 mm (p < 0.01). Preoperative urethral length was greater with lateral defects [p < 0.01, B 6.38, 95% confidence interval (CI) 4.67–8.08] and increased stress incontinence risk (p < 0.01, odds ratio 1.07, 95% CI 1.03–1.12). Postoperative urethral length depended on prolapse stage (p < 0.01, B 1.61, 95% CI 0.85–2.38) and defect type (p = 0.02, B – 1.42, 95% CI – 2.65 to – 0.2). Postoperatively, TVT surgery was indicated in 5.1% of patients (median 9 months), who had longer urethras than those without this indication (p = 0.043). Native-tissue prolapse repair including Kelly plication increased urethral length, reflecting re-urethralization, particularly with central defects. The functional impact of urethral length in the context of connective tissue aging should be examined further.

Research on Wafer Surface Defect Pattern Detection Method Based on Incremental Learning

Wafer manufacturing is an important step in quality control and analysis in the semiconductor industry. The defect pattern classification algorithm of wafer maps has received extensive attention from academia and industry. At present, most methods for detecting wafer surface defect patterns focus on static data model classification and analysis. However, in the production process, static data models cannot satisfy the dynamic analysis of wafer defect patterns in the form of streaming data. In this regard, this paper proposes a wafer surface defect pattern detection method based on incremental learning. Our experiment uses Resnet as the backbone network, and the data set uses the WM811K wafer data set. Experiments have proved that our method can achieve better classification accuracy in the field of wafer defect detection, which provides the possibility for continuous learning of wafer defects in the future.

Structural Analysis of Low Defect Ammonothermally Grown GaN Wafers by Borrmann Effect X-ray Topography

X-ray topography defect analysis of entire 1.8-inch GaN substrates, using the Borrmann effect, is presented in this paper. The GaN wafers were grown by the ammonothermal method. Borrmann effect topography of anomalous transmission could be applied due to the low defect density of the substrates. It was possible to trace the process and growth history of the GaN crystals in detail from their defect pattern imaged. Microscopic defects such as threading dislocations, but also macroscopic defects, for example dislocation clusters due to preparation insufficiency, traces of facet formation, growth bands, dislocation walls and dislocation bundles, were detected. Influences of seed crystal preparation and process parameters of crystal growth on the formation of the defects are discussed.