Complex heterozygous mutations in hereditary spherocytosis: A case report.

Background: The aim of this study was to investigate the complex heterozygous mutations of and in the same individual and improve our understanding of hereditary spherocytosis (HS) in children. We also hope to promote the application of gene detection technology in children with HS, with the goals of identifying more related gene mutations, supporting the acquisition of improved molecular genetic information to further reveal the pathogenesis of HS in children, and providing important guidance for the diagnosis, treatment, and prevention of HS in children.

Case Summary: A 1-year and 5-month-old patient presented jaundice during the neonatal period, mild anemia 8 months later, splenic enlargement at 1 year and 5 months, and brittle red blood cell permeability.

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed.

The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering.

A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction.

The landing gear structure suffers from large loads during aircraft takeoff and landing, and an accurate prediction of landing gear performance is beneficial to ensure flight safety. Nevertheless, the landing gear performance prediction method based on machine learning has a strong reliance on the dataset, in which the feature dimension and data distribution will have a great impact on the prediction accuracy. To address these issues, a novel MCA-MLPSA is developed.

Research on Imbalanced Data Classification Based on Classroom-Like Generative Adversarial Networks.

Most of the research on machine learning classification methods is based on balanced data; the research on imbalanced data classification needs improvement. Generative adversarial networks (GANs) are able to learn high-dimensional complex data distribution without relying on a prior hypothesis, which has become a hot technology in artificial intelligence. In this letter, we propose a new structure, classroom-like generative adversarial networks (CLGANs), to construct a model with multiple generators.