2篇
您的检索式：作者名="Ziyang Niu"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Healthy Lifestyles and Chronic Pain with New-Onset Metabolic-Related Multimorbidity among Older Adults—China,2011–2018显示文摘Summary What is already known about this topic?Chronic pain has been identified as a risk factor for cardiovascular diseases.Evidence shows that adopting a healthy lifestyle can help reduce the cardiometabolic risks associated with chronic pain.What is added by this report?Results from this cohort study suggest a positive association between chronic pain and the development of new-onset metabolic-related multimorbidity,specifically metabolic multimorbidity and cardiometabolic comorbidity,within middle-aged and older Chinese adults.Furthermore,adopting healthy lifestyles can potentially mitigate or even reverse these associations.What are the implications for public health practice?The results of our study emphasize the importance of promoting healthy lifestyles among older Chinese adults as a preventative measure against the medical burdens and cardiometabolic risks associated with chronic pain. | Ziyang Ren Yihao Zhao Guanyu Niu Xinyao Lian Xiaoying Zheng Shiyong Wu Jufen Liu | 2023 | China CDC weekly2023，5，16： | 0 |
| 2 | Bridge Crack Segmentation Method Based on Parallel Attention Mechanism and Multi-Scale Features Fusion显示文摘Regular inspection of bridge cracks is crucial to bridge maintenance and repair.The traditional manual crack detection methods are timeconsuming,dangerous and subjective.At the same time,for the existing mainstream vision-based automatic crack detection algorithms,it is challenging to detect fine cracks and balance the detection accuracy and speed.Therefore,this paper proposes a new bridge crack segmentationmethod based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+network framework.First,the improved lightweight MobileNetv2 network and dilated separable convolution are integrated into the original DeeplabV3+network to improve the original backbone network Xception and atrous spatial pyramid pooling(ASPP)module,respectively,dramatically reducing the number of parameters in the network and accelerates the training and prediction speed of the model.Moreover,we introduce the parallel attention mechanism into the encoding and decoding stages.The attention to the crack regions can be enhanced from the aspects of both channel and spatial parts and significantly suppress the interference of various noises.Finally,we further improve the detection performance of the model for fine cracks by introducing a multi-scale features fusion module.Our research results are validated on the self-made dataset.The experiments show that our method is more accurate than other methods.Its intersection of union(IoU)and F1-score(F1)are increased to 77.96%and 87.57%,respectively.In addition,the number of parameters is only 4.10M,which is much smaller than the original network;also,the frames per second(FPS)is increased to 15 frames/s.The results prove that the proposed method fits well the requirements of rapid and accurate detection of bridge cracks and is superior to other methods. | Jianwei Yuan Xinli Song Huaijian Pu Zhixiong Zheng Ziyang Niu | 2023 | Computers, Materials & Continua2023，，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员