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Abstract Background The association between smoking cessation and decreased mortality existed among former smokers has been well documented. However, evidence is limited for smokers with long-term exposure. This study aims to quantify the association between smoking cessation and mortality by years since quitting in older adults with long-term smoking history. Methods Data from Beijing Healthy Aging Cohort Study (BHACS), conducted among communities aged over 55 years old at recruitment, were collected via questionnaire between July 2009 and September 2015 and followed up for all-cause and cancer mortality until March 2021. Self-reported smoking status and years since quitting were collected at baseline. Cox proportional hazards models were used to examine the association between smoking cessation and all-cause and cancer mortality. Results A total of 11 235 participants (43.9% male) were included, with a mean age of 70.35 (SD 7.71) years. Former smokers comprised 31.7% of the cohort, with a median smoking duration of 43 (IQR: 34–50) years. During 71 573 person-years of follow-up, there were 1 617 deaths (14.4% of the total cohort), of which 872 (17.7%) occurred among male participants. Compared with never smokers, HR (95%CI) for participants who current smoked was 2.898 (2.092–4.013); quit smoking less than 10 years (medians [quartiles] 4 [1, 7] years) before recruitment was 2.738(1.972–3.802); 10 to 20 years (16 [13, 20] years), 1.807(1.286–2.540); and 20 years or more (30 [25, 37] years), 1.293(0.981–1.705). The risk of all-cause and cancer mortality decreased gradually over years since quitting. Quitting less than 10 years, 10 to 20 years and 20 years or more, former smokers avoided an estimated 8.4%, 57.5% and 84.6% of excess all-cause mortality associated with current smoking, respectively. The association between smoking cessation and decreased mortality was observed among former smokers regardless of smoking history. Conclusions In this study, current smoking was associated with nearly triple the mortality risk compared to never smoking. Smoking cessation, even after a long-term smoking history, was associated with significant decreases in the relative excess mortality linked to continuing smoking. The association were more pronounced in men.

Identifying the classes and locations of prohibited items is the target of security inspection. However, X-ray security inspection images with insufficient feature extraction, imbalance between easy and hard samples, and occlusion lead to poor detection accuracy. To address the above problems, an object-detection method based on YOLOv8 is proposed. Firstly, an ASFF (adaptive spatial feature fusion) and a weighted feature concatenation algorithm are introduced to fully extract the scale features from input images. In this way, the model can learn further details in training. Secondly, CoordAtt (coordinate attention module), which belongs to the hybrid attention mechanism, is embedded to enhance the learning of features of interest. Then, the slide loss function is introduced to balance the simple samples and the difficult samples. Finally, Soft-NMS (non-maximum suppression) is introduced to resist the conditions containing occlusion. The experimental result shows that mAP (mean average precision) achieves 90.2%, 90.5%, 79.1%, and 91.4% on the Easy, Hard, and Hidden sets of the PIDray and SIXray public test set, respectively. Contrasted with original model, the mAP of our proposed YOLOv8n model increased by 2.7%, 3.1%, 9.3%, and 2.4%, respectively. Furthermore, the parameter count of the modified YOLOv8n model is roughly only 3 million.