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The stack effect in super high-rise buildings causes various negative problems in winter. The shuttle elevator shafts are the main vertical movement route and also the most vulnerable area suffered from the stack effect in super high-rise building. The elevator shaft cooling countermeasure is proved a suitable application under the operation condition. In this paper, the mitigation effect of the shuttle elevator shaft cooling countermeasure is investigated by field measurements and coupled numerical simulation in a typical super high-rise building in China. Field test was carried out under the stack dominated condition and hybrid dominated condition which mechanical ventilation system is applied. A coupled multi-zone and CFD model is built to simulate the shuttle elevator shaft cooling system in a super high-rise building based on the driven forces and air flow directions during stack phenomenon. The accuracy of this coupled model is verified by the comparison between the measurement and simulation results. The driving forces of stack effect through the shuttle elevator shaft, the optimization of the shuttle elevator shaft cooling configuration and the determination of mechanical air volume under different outdoor temperatures are investigated through the coupled simulation method. This study demonstrates that it is important to design the shaft cooling system in super high-rise buildings to ensure the safe and normal use of the shuttle elevators. • Coupled simulation method is proposed to analyze elevator shaft cooling system. • Driving forces of stack effect in shuttle elevator shaft are investigated. • Optimization of the shuttle elevator shaft cooling configuration is conducted. • Determination of mechanical air volume under different conditions is provided. [ABSTRACT FROM AUTHOR]

Intracranial aneurysm is a common life-threatening disease. Computed tomography angiography is recommended as the standard diagnosis tool; yet, interpretation can be time-consuming and challenging. We present a specific deep-learning-based model trained on 1,177 digital subtraction angiography verified bone-removal computed tomography angiography cases. The model has good tolerance to image quality and is tested with different manufacturers. Simulated real-world studies are conducted in consecutive internal and external cohorts, in which it achieves an improved patient-level sensitivity and lesion-level sensitivity compared to that of radiologists and expert neurosurgeons. A specific cohort of suspected acute ischemic stroke is employed and it is found that 99.0% predicted-negative cases can be trusted with high confidence, leading to a potential reduction in human workload. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to clinicians' assessment.