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Objectives: The static and dynamic correlations of postural stability to its three potential contributors, namely, proprioception, tactile sensation, and strength remain unclear among people with chronic ankle instability (CAI). This study aimed to compare static and dynamic postural stability, along with proprioception, tactile sensation, and strength between people with and without CAI and explore their correlations. Methods: Sixty-seven participants with CAI and 67 participants without CAI were enrolled in this study. Ankle proprioception, plantar tactile sensation, and lower limb strength were measured by a proprioception test device, a set of monofilaments, and a strength testing system, respectively. Static and dynamic postural stability were measured during standing and jump landing on a force plate and indicated by the root mean square of center of pressure and time to stability. Results: Compared to people without CAI, people with CAI had poorer postural stability, proprioception, tactile sensation, and strength. Both groups demonstrated correlation between proprioception and static postural stability, but only people without CAI showed correlation between proprioception and dynamic postural stability. Both groups demonstrated a correlation between tactile sensation and static postural stability, but not with dynamic stability. Both groups demonstrated a correlation between strength and both static and dynamic postural stability. Conclusions: People with CAI had deficits in static and dynamic postural stability, proprioception, tactile sensation, and strength. Among people with CAI, proprioception, tactile sensation, and strength can help maintain static postural stability; strength can help maintain dynamic postural stability, whereas proprioception may not provide sufficient information for dynamic postural stability. [ABSTRACT FROM AUTHOR]

Tin is one of the few rare metals with important strategic position in our country' but the technical limitations of the comprehensive utilization have led to the increasing scarcity of in ore resources' the large loss rate of useful components' and the increase in the amount of tailings over the years. Tailings not only occupy a lot of land' but also pollute the surrounding environment. Therefore, it has great significance of the green utilization of tin tailings resources to ensure the safety of tin mineral resources in China. In this paper, the current situation and characteristics of tin tailings resources at home and abroad was in reduced. It was summarized that it the key of secondary benefication of the in tailings is to solve the influence of sulfide minerals iron and manganese mineras and slime on comprehensive recovery of tine cassiterite and co-associated valuable components. Meanwhile, the technological characteristics and cases of tin tailings research status are also summarized. At present, the main methods to re-use tin tailings include recovery valuable metals by gravity separation, magnetic separation, flotation and combined beneficiation and metallurgy, used tailings as building materials or mining tailings as a whole, and treated waste stone containing tin as a special tailings to pre-concentrated. The common collectors and regulators of cassiterite flotation and their action mechanism were summarized. The researches which should be strengthen in the future were proposed, including enhancing the recovery of tine cassiterite, enhancing the new gravity separation equipment of cassiterite separation, enhancing the development of new green reagents, and enhancing development of new flotation equipment. The future development trend based on the research hot topic was proposed, with a view to providing reference for the green and efficient utilization of in tailings. [ABSTRACT FROM AUTHOR]

Timely and accurate extraction of pavement crack information is crucial to maintain service conditions and structural safety for infrastructures and reduce further road maintenance costs. Currently, deep learning techniques for automated pavement crack detection are far superior to traditional manual approaches in both speed and accuracy. However, existing deep learning models may easily lose crack details when processing images containing complex background textures or other noises. Although many studies have alleviated this challenge by introducing attention mechanisms, especially the non‐local (NL) block, which has the ability to efficiently capture long‐range dependencies to facilitate crack pixel capture, the huge computational cost of NL makes the inference time of the model too long, which is not conducive to practical implementation. In this study, a new module, namely, the pyramid region attention module (PRAM), was developed by combining the pyramid pooling module in the pyramid scene parsing network and optimized NL, which can achieve global multi‐scale context integration and long‐range dependencies capture at a relatively lower computational cost. By applying PRAM to deep skip connections in the modified U‐Net, an effective crack segmentation model called CrackResU‐Net was developed. The test results on the existing CrackForest dataset showed that CrackResU‐Net not only achieved an F1 score of 0.9580 but also took only 25.89 ms to process an image with a resolution of 480 × 320, which had advantages in accuracy and speed, compared with several other state‐of‐the‐art crack segmentation approaches. It was fully demonstrated that this approach could realize automatic fast and high‐precision recognition of pavement cracks for engineering purposes. [ABSTRACT FROM AUTHOR]

According to the latest statistics at the end of 2022, the total length of highways in China has reached 5.3548 million kilometers, with a maintenance mileage of 5.3503 million kilometers, accounting for 99.9% of the total maintenance coverage. Relying on inefficient manual pavement detection methods is difficult to meet the needs of large-scale detection. To tackle this issue, experiments were conducted to explore deep learning-based intelligent identification models, leveraging pavement distress data as the fundamental basis. The dataset encompasses pavement micro-cracks, which hold particular significance for the purpose of pavement preventive maintenance. The two-stage model Faster R-CNN achieved a mean average precision (mAP) of 0.938, which surpassed the one-stage object detection algorithms YOLOv5 (mAP: 0.91) and YOLOv7 (mAP: 0.932). To balance model weight and detection performance, this study proposes a YOLO-based optimization method on the basis of YOLOv5. This method achieves comparable detection performance (mAP: 0.93) to that of two-stage detectors, while exhibiting only a minimal increase in the number of parameters. Overall, the two-stage model demonstrated excellent detection performance when using a residual network (ResNet) as the backbone, whereas the YOLO algorithm of the one-stage detection model proved to be more suitable for practical engineering applications. [ABSTRACT FROM AUTHOR]

The decision-making process for pavement maintenance from a scientific perspective is based on accurate predictions of pavement performance. To improve the rationality of pavement performance indicators, comprehensive consideration of various influencing factors is necessary. To this end, four typical pavement performance indicators (i.e., Rutting Depth, International Roughness Index, Longitudinal Cracking, and Alligator Cracking) were predicted using the Long Term Pavement Performance (LTPP) database. Two types of data, i.e., local input variables and global input variables, were selected, and S-ANN and L-ANN models were constructed using a fully connected neural network. A comparative analysis of the predictive outcomes reveals the superior optimization of the L-ANN model. Subsequently, by incorporating structures such as self-attention mechanism, a novel predictive approach based on the Transformer architecture was proposed. The objective is to devise a more accurate predictive methodology for pavement performance indices, with the goal of guiding pavement maintenance and management efforts. Experimental results indicate that, through comparative analysis of three quantitative evaluation metrics (root mean square error, mean absolute error, coefficient of determination), along with visual scatter plots, the predictive model employing the fused Transformer architecture demonstrates higher robustness and accuracy within the domain of pavement performance prediction when compared to the L-ANN model. This outcome substantiates the efficacy and superiority of the model in terms of predictive performance, establishing it as a reliable tool for accurately reflecting the evolution of asphalt pavement performance. Furthermore, it furnishes a theoretical reference for determining optimal preventive maintenance timing for pavements. [ABSTRACT FROM AUTHOR]

The textural roughness of asphalt pavement surface is an important indicator to characterize pavement skid resistance. In this paper, multi-visual technology was applied to capture the surface image of asphalt pavement which was transformed into a visualized 3D point cloud model. Then, based on the principle of the digital elevation model (DEM), the disordered 3D point cloud is rasterized and projected into a 2D matrix which contains generalized point cloud elevation information. Meanwhile, the 2D matrix is transformed into grayscale to build the equivalent grayscale image. Furthermore, the fractal dimensions were calculated in terms of one-dimensional pavement section profile, two-dimensional grayscale, and equivalent grayscale to characterize the pavement roughness. The results showed that the fractal dimensions are positively correlated with the mean texture depth (MTD), and the fractal dimension of equivalent grayscale has the best correlation with MTD. It should be highlighted that the equivalent grayscale image is directly transformed by the reconstruction of the three-dimensional point cloud, and the grayscale value of each point can represent the elevation of different pavement surfaces. Therefore, the equivalent grayscale image can better reflect the real roughness of the pavement surface. Meanwhile, the proposed method in this paper can effectively reduce the influence of some factors (e.g., light and color, etc..) on the texture detection of the pavement surface. [ABSTRACT FROM AUTHOR]