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Ash accumulation in wall-flow filters impacts key performance aspects such as pressure drop, filtration efficiency and catalytic reactivity. In this context, the capability to predict ash deposition and transport behavior is essential to ensure proper system performance under various operating conditions, throughout the useful lifetime of the particulate filter. Despite a large number of previous experimental studies, physically based ash migration models to predict ash transport and its impact on the performance of a wall-flow filter are still not available. The development, calibration and validation of a phenomenological semi-predictive model is presented in this work, based on extensive burner rig and transient engine tests. Combustion-derived ash was collected in several bare DPFs, while the filters’ pressure drop was monitored and the plug ash length was estimated via borescope and CT-scan tests. An example of how the developed model can be used to study design variations at an early development phase is included.

BACKGROUND China's secondary vocational medical education has played a crucial role in cultivating medical staff and improving public health response capabilities. However, the current system relies heavily on traditional methods and struggles to adapt to technological advancements such as artificial intelligence (AI). OBJECTIVE To explore the impact of AI on medical practice and suggest potential reforms for China's secondary vocational medical education system in response to these advancements. METHODS A literature review was conducted to assess the current state of AI in medicine and its implications for medical practice. Based on the findings, suggestions for reforming China's secondary vocational medical education were proposed. RESULTS AI shows great potential in improving diagnostic capabilities, treatment decisions, and patient management. However, it also raises concerns about job loss and the need for medical professionals to adapt to new technologies. To better prepare students for the future, China's secondary vocational medical education should focus on practical experience, skills development, medical ethics, humanities, and AI integration. Continuous assessment and long-term research are essential for evaluating the effectiveness of these reforms. CONCLUSIONS By addressing the shortcomings of the current system and embracing AI advancements, China's secondary vocational medical education can better prepare future medical professionals for the challenges and opportunities of an ever-evolving medical landscape.