First Indirect Drive Experiment Using a Six-Cylinder-Port Hohlraum.

The new hohlraum experimental platform and the quasi-3D simulation model are developed to enable the study of the indirect drive experiment using the six-cylinder-port hohlraum for the first time. It is also the first implosion experiment for the six laser-entrance-hole hohlraum to effectively use all the laser beams of the laser facility that is primarily designed for the cylindrical hohlraum. The experiments performed at the 100 kJ Laser Facility produce a peak hohlraum radiation temperature of ∼222 eV for ∼80 kJ and 2 ns square laser pulse. The inferred x-ray conversion efficiency η∼87% is similar to the cylindrical hohlraum and higher than the octahedral spherical hohlraum at the same laser facility, while the low laser backscatter is similar to the outer cone of the cylindrical hohlraum. The hohlraum radiation temperature and M-band (>1.6 keV) flux can be well reproduced by the quasi-3D simulation. The variations of the yield-over-clean and the hot spot shape can also be semiquantitatively explained by the calculated major radiation asymmetry of the quasi-3D simulation. Our work demonstrates the capability for the study of the indirect drive with the six-cylinder-port hohlraum at the cylindrically configured laser facility, which is essential for numerically assessing the laser energy required by the ignition-scale six-cylinder-port hohlraum.

First Inertial Confinement Fusion Implosion Experiment in Octahedral Spherical Hohlraum.

In inertial confinement approaches to fusion, the asymmetry of target implosion is a major obstacle to achieving high gain in the laboratory. A recently proposed octahedral spherical hohlraum makes it possible to naturally create spherical target irradiation without supplementary symmetry control. Before any decision is made to pursue an ignition-scale laser system based on the octahedral hohlraum, one needs to test the concept with the existing facilities. Here, we report a proof-of-concept experiment for the novel octahedral hohlraum geometry on the cylindrically configured SGIII laser facility without a symmetry control. All polar and equatorial self-emission images of the compressed target show a near round shape of convergence ratio 15 under both square and shaped laser pulses. The observed implosion performances agree well with the ideal spherical implosion simulation. It also shows limitations with using the existing facilities and adds further weight to the need to move to a spherical port geometry for future ignition laser facilities.

M 3: using mask-attention and multi-scale for multi-modal brain MRI classification.

Introduction: Brain diseases, particularly the classification of gliomas and brain metastases and the prediction of HT in strokes, pose significant challenges in healthcare. Existing methods, relying predominantly on clinical data or imaging-based techniques such as radiomics, often fall short in achieving satisfactory classification accuracy. These methods fail to adequately capture the nuanced features crucial for accurate diagnosis, often hindered by noise and the inability to integrate information across various scales. Methods: We propose a novel approach that mask attention mechanisms with multi-scale feature fusion for Multimodal brain disease classification tasks, termed M 3, which aims to extract features highly relevant to the disease. The extracted features are then dimensionally reduced using Principal Component Analysis (PCA), followed by classification with a Support Vector Machine (SVM) to obtain the predictive results. Results: Our methodology underwent rigorous testing on multi-parametric MRI datasets for both brain tumors and strokes. The results demonstrate a significant improvement in addressing critical clinical challenges, including the classification of gliomas, brain metastases, and the prediction of hemorrhagic stroke transformations. Ablation studies further validate the effectiveness of our attention mechanism and feature fusion modules. Discussion: These findings underscore the potential of our approach to meet and exceed current clinical diagnostic demands, offering promising prospects for enhancing healthcare outcomes in the diagnosis and treatment of brain diseases.

Application of machine learning to predict hospital visits for respiratory diseases using meteorological and air pollution factors in Linyi, China.

Urbanization and industrial development have resulted in increased air pollution, which is concerning for public health. This study evaluates the effect of meteorological factors and air pollution on hospital visits for respiratory diseases (pneumonia, acute upper respiratory infections, and chronic lower respiratory diseases). The test dataset comprises meteorological parameters, air pollutant concentrations, and outpatient hospital visits for respiratory diseases in Linyi, China, from January 1, 2016 to August 20, 2022. We use support vector regression (SVR) to build models that enable analysis of the effect of meteorological factors and air pollutants on the number of outpatient visits for respiratory diseases. Spearman correlation analysis and SVR model results indicate that NO2, PM2.5, and PM10 are correlated with the occurrence of respiratory diseases, with the strongest correlation relating to pneumonia. An increase in the daily average temperature and daily relative humidity decreases the number of patients with pneumonia and chronic lower respiratory diseases but increases the number of patients with acute upper respiratory infections. The SVR modeling has the potential to predict the number of respiratory-related hospital visits. This work demonstrates that machine learning can be combined with meteorological and air pollution data for disease prediction, providing a useful tool whereby policymakers can take preventive measures.