Sarcopenia is associated with functional disability in older adults. However, no consistent conclusions have been reached considering the differences in the measurement and criteria of sarcopenia. We aimed to examine the association between sarcopenia status and functional disability based on China Health and Retirement Longitudinal Study (CHARLS). A nationally representative longitudinal study. Participants aged at least 60 years old from the CHARLS 2015y were included. Sarcopenia was assessed according to the Asian Working Group for Sarcopenia 2019 algorithm. The outcomes of this study were basic activities of daily living (ADLs) and instrumental activities of daily living (IADLs). The logistic regression model was conducted to analyze the cross-sectional and longitudinal associations between sarcopenia status and ADLs and IADLs disability. In the cross-sectional study, 37.2% of the 6893 participants were defined as having sarcopenia. Any form of sarcopenia was associated with ADLs and IADLs disability. During three years of follow-up, 786 (16.5%) participants developed new-onset ADLs disability, and 980 (22.5%) participants developed new-onset IADLs disability. Compared with the no-sarcopenia, participants with possible sarcopenia (OR: 1.62, 95%CI: 1.34-1.95), sarcopenia (OR: 1.58, 95%CI: 1.18-2.11), or total sarcopenia (OR: 1.58, 95%CI: 1.34-1.88) had a higher risk of ADLs disability. While, the risk of IADLs disability for participants with possible sarcopenia (OR: 1.68, 95%CI: 1.41-2.00), sarcopenia (OR: 1.87, 95%CI: 1.40-2.51), or total sarcopenia (OR: 1.71, 95%CI: 1.45-2.00) was still significantly increased. With statistical interaction between sarcopenia status and residence or sex in ADLs and IADLs disability, older adults in urban, with ORs ranging from 2.14 to 2.44, were at a higher risk of functional disability than those in rural. Possible sarcopenia was associated with a much higher risk of ADLs disability (OR: 1.68, 95%CI: 1.26-2.25) in males and a higher risk of IADLs disability (OR: 1.98, 95%CI: 1.56-2.52) in females. Sarcopenia was associated with an increased risk of ADLs and IADLs disability among older Chinese adults. Even possible sarcopenia still significantly impacted ADLs and IADLs disability, and this association varied by sex and residence.

The synthetic aperture radar (SAR) has a special ability to detect objects in any climate and weather conditions. Consequently, SAR images are widely used in maritime transportation safety and fishery law enforcement for maritime object detection. Currently, deep-learning models are being extensively used for the detection of objects from images. Among them, the feature pyramid network (FPN) uses pyramids for representing semantic information regardless of the scale and has an improved accuracy of object detection. It is also suitable for the detection of multiple small ship objects in SAR images. This study aims to resolve the problems associated with small-object and multi-object ship detection in complex scenarios e.g., when a ship nears the port, by proposing a detection method based on an optimized FPN model. The feature pyramid model is first embedded in a traditional region proposal network (RPN) and mapped into a new feature space for object identification. Subsequently, the k-means clustering algorithm based on the shape similar distance (SSD) measure is used to optimize the FPN. Initial anchor boxes and tests are created using the SAR ship dataset. Experimental results show that the proposed algorithm for object detection shows an accuracy of 98.62%. Compared with Yolo, the RPN based on VGG/ResNet, FPN based on VGG/ResNet, and other models in complex scenarios, the proposed model shows a higher accuracy rate and better overall performance.

A convolutional neural network fault diagnosis method based on frequency attention mechanism was designed for the problem that the traditional method cannot adaptively extract effective feature information in rolling bearing fault diagnosis and the diagnosis effect of rolling bearing is poor under strong environmental noise interference. Firs, the Mel-frequency cepstral coefficient (MFCC) of the bearing vibration signal was extracted. Second, to solve the problem of the channel attention mechanism adopting global average pooling (GAP) and neglecting channel internal characteristic information, the GAP was extended in the frequency domain, and a two-stage frequency component selection criterion was designed. The results show that the MFCC method can extract fault-sensitive features in industrial noise environments, improve the existing channel attention mechanism using frequency domain attention mechanism, and overcome the information loss caused by GAP of convolutional layer features in channel attention mechanism. Identification accuracy, recall rate, and F1-score are 100% on the rolling bearing simulation fault datasets of Case Western Reserve University and Central South University. Compared with the convolutional block attention module, the accuracy of the method combining spatial attention mechanism and channel attention mechanism is improved by 0.34 and 0.24%, respectively, and compared with other front-bearing fault diagnosis methods, it also offers significant improvement.

Synthetic aperture radar (SAR) can detect objects in various climate and weather conditions. Therefore, SAR images are widely used for maritime object detection in applications such as maritime transportation safety and fishery law enforcement. However, nearshore ship targets in SAR images are often affected by background clutter, resulting in a low detection rate, high false alarm rate, and high missed detection rate, especially for small-scale ship targets. To address this problem, in this paper, we propose a novel deep learning network with deformable convolution and attention mechanisms to improve the Feature Pyramid Network (FPN) model for nearshore ship target detection in SAR images with complex backgrounds. The proposed model uses a deformable convolutional neural network in the feature extraction network to adapt the convolution position to the target sampling point, enhancing the feature extraction ability of the target, and improving the detection rate of the ship target against the complex background. Moreover, this model uses a channel attention mechanism to capture the feature dependencies between different channel graphs in the feature extraction network and reduce the false detection rate. The designed experiments on a public SAR image ship dataset show that our model achieves 87.9% detection accuracy for complex scenes and 95.1% detection accuracy for small-scale ship targets. A quantitative comparison of the proposed model with several classical and recently developed deep learning models on the same SAR images dataset demonstrated the superior performance of the proposed method over other models.

Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multiple scenes and multiple sizes, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.