Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning

Diagnosis of adverse neonatal outcomes is crucial for preterm survival since it enables doctors to provide timely treatment. Machine learning (ML) algorithms have been demonstrated to be effective in predicting adverse neonatal outcomes. However, most previous ML-based methods have only focused on predicting a single outcome, ignoring the potential correlations between different outcomes, and potentially leading to suboptimal results and overfitting issues. In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem. We then propose an MTL framework to jointly predict multiple adverse neonatal outcomes. In particular, the MTL framework contains shared hidden layers and multiple task-specific branches. Extensive experiments have been conducted using Electronic Health Records (EHRs) from 121 preterm neonates. Empirical results demonstrate the effectiveness of the MTL framework. Furthermore, the feature importance is analyzed for each neonatal outcome, providing insights into model interpretability

ITER Central Solenoid Insert Test Results

The ITER central solenoid (CS) is a highly stressed magnet that must provide 30 000 plasma cycles under the ITER prescribed maximum operating conditions. To verify the performance of the ITER CS conductor in conditions close to those for the ITER CS, the CS insert was built under a USA-Japan collaboration. The insert was tested in the aperture of the CSMC facility in Naka, Japan, during the first half of 2015. A magnetic field of up to 13 T and a transport current of up to 60 kA provided a wide range of parameters to characterize the conductor. The CS insert has been tested under direct and reverse charges, which allowed a wide range of strain variation and provided valuable data for characterization of the CS conductor performance at different strain levels. The CS insert test program had several important goals as follows. 1) Measure the temperature margin of the CS conductor at the relevant ITER CS operational conditions. 2) Study the effects of electromagnetic forces and strain in the cable on the CS conductor performance. 3) Study the effects of the warmup and cooldown cycles on the CS conductor performance. 4) Compare the conductor performance in the CS insert with the performance of the CS conductor in a straight hairpin configuration (hoop strain free) tested in the SULTAN facility. 5) Measure the maximum temperature rise of the cable as a result of quench. The main results of the CS insert testing are presented and discusse