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ABSTRACT Using a quantum-mechanical close-coupling method, we calculate cross-sections for fine-structure excitation and relaxation of Si and S atoms in collisions with atomic hydrogen. Rate coefficients are calculated over a range of temperatures for astrophysical applications. We determine the temperature-dependent critical densities for the relaxation of Si and S in collisions with H and compare these to the critical densities for collisions with electrons. The present calculations should be useful in modelling environments exhibiting the [S i] 25 μm and [S i] 57 μm far-infrared emission lines or where cooling of S and Si by collisions with H is of interest. 

ABSTRACT Fine-structure transitions can be involved in various processes including photon absorption, charge transfer, and inelastic collisions between ions, electrons, and neutral atoms. We present fine-structure excitation and relaxation cross-sections for the collisions of the first few members of the carbon isoelectronic sequence (C, N+ and O2 +) with atomic hydrogen calculated using quantum-mechanical methods. For C, the scattering theory and computational approach is verified by comparison with previous calculations. The rate coefficients for the collisional processes are obtained. For N+ and O2 +, the transitions correspond to the lines [O iii] 52 μm, [O iii] 88 μm, [N ii] 122 μm, and [N ii] 205 μm, observed in the far-infrared in the local universe and more recently in high-redshift galaxies using radio interferometry. The influence of different potentials on the cross-sections and rate coefficients are demonstrated. 

Abstract The utility of the far-infrared lines of oxygen as diagnostics of gas outflows and for other applications depends on accurate descriptions of the rate coefficients for excitation (and relaxation) through collisions with electrons and with hydrogen atoms. For O and H collisions, earlier calculations of rate coefficients show discrepancies leading to ambiguity in astrophysical applications. In this note we introduce a methodology that yields consistent sets of rate coefficients for a number of cases. We then apply our method to the O–H system in order to investigate the discrepancies. The present rate coefficients will be of particular interest for modeling observations of astrophysical environments in the far-infrared. 

Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis.