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The wear condition of steel rails directly affects the safety of railway operations. Line-structured-light visual measurement technology is used for online measurement of rail wear due to its ability to achieve high-precision dynamic measurements. However, in dynamic measurements, the random deviation of the measurement plane caused by the vibration of the railcar results in changes in the actual measured rail profile relative to its cross-sectional profile, ultimately leading to measurement deviations. To address these issues, this paper proposes a method for three-dimensional measurement of steel rail cross-sectional profiles based on binocular line-structured light. Firstly, calibrated dual cameras are used to simultaneously capture the profiles of both sides of the steel rail in the same world coordinate system, forming the complete rail profile. Then, considering that the wear at the rail waist is zero in actual operation, the coordinate of the circle center on both sides of the rail waist are connected to form feature vectors. The measured steel rail profile is aligned with the corresponding feature vectors of the standard steel rail model to achieve initial registration; next, the rail profile that has completed the preliminary matching is accurately matched with the target model based on the iterative closest point (ICP) algorithm. Finally, by comparing the projected complete rail profile onto the rail cross-sectional plane with the standard 3D rail model, the amount of wear on the railhead can be obtained. The experimental results indicate that the proposed line-structured-light measurement method for the complete rail profile, when compared to the measurements obtained from the rail wear gauge, exhibits smaller mean absolute deviation (MAD) and root mean square error (RMSE) for both the vertical and lateral dimensions. The MAD values for the vertical and lateral measurements are 0.009 mm and 0.039 mm, respectively, while the RMSE values are 0.011 mm and 0.048 mm. The MAD and RMSE values for the vertical and lateral wear measurements are lower than those obtained using the standard two-dimensional rail profile measurement method. Furthermore, it effectively eliminates the impact of vibrations during the dynamic measurement process, showcasing its practical engineering application value. [ABSTRACT FROM AUTHOR]

Visual-based object detection systems are essential components of intelligent equipment for water surface environments. The diversity of water surface target types, uneven distribution of sizes, and difficulties in dataset construction pose significant challenges for water surface object detection. This article proposes an improved YOLOv5 target detection method to address the characteristics of diverse types, large quantities, and multiple scales of actual water surface targets. The improved YOLOv5 model optimizes the extraction of bounding boxes using K-means++ to obtain a broader distribution of predefined bounding boxes, thereby enhancing the detection accuracy for multi-scale targets. We introduce the GAMAttention mechanism into the backbone network of the model to alleviate the significant performance difference between large and small targets caused by their multi-scale nature. The spatial pyramid pooling module in the backbone network is replaced to enhance the perception ability of the model in segmenting targets of different scales. Finally, the Focal loss classification loss function is incorporated to address the issues of overfitting and poor accuracy caused by imbalanced class distribution in the training data. We conduct comparative tests on a self-constructed dataset comprising ten categories of water surface targets using four algorithms: Faster R-CNN, YOLOv4, YOLOv5, and the proposed improved YOLOv5. The experimental results demonstrate that the improved model achieves the best detection accuracy, with an 8% improvement in mAP@0.5 compared to the original YOLOv5 in multi-scale water surface object detection. [ABSTRACT FROM AUTHOR]

Considering the large contrast changing of surface targets and sea-sky background and the obvious difference of field depth, an improved image segmentation algorithm based on self-adaptive Mean-Shift is proposed. Spatial bandwidths are adaptively computed according to the estimation of gray distribution around the reference point; then the gray-level bandwidths are adaptively computed with a novel Bayesian theory in the corresponding windows; and finally adaptive segmentation is obtained. In the experiment, both the close and distant target frames, as well as target frames of different contrast, are extracted respectively from the surface vehicle video sequence. Compared with the traditional segmentation algorithm, experimental results prove that the proposed algorithm can effectively complete segmentation of surface target images. [ABSTRACT FROM AUTHOR]

An abstract of the article "Evolution of Russian Sport Policy after the Collapse of the Soviet Union," by Ma Zhongli is presented.

Although many epidemiologic studies have investigated the FXIII-A Val34Leu polymorphism and their associations with myocardial infarction (MI), definite conclusions can't be drawn. To clarify the effects of FXIII-A Val34Leu polymorphism on the risk of MI, a meta-analysis was performed. Related studies were identified from PubMed, Springer Link, Ovid, Chinese Wanfang Data Knowledge Service Platform, Chinese National Knowledge Infrastructure (CNKI), and Chinese Biology Medicine (CBM) till 10 August 2014. Pooled ORs and 95% CIs were used to assess the strength of the associations. A total of 12 studies including 3139 MI cases and 6343 healthy controls were involved in this meta-analysis. A significantly decreased MI risk was found (adjusted OR = 0.70, 95% CI 0.60-0.82, P < 0.00001). In the subgroup analysis by age, significantly decreased risks were found in the young population (OR = 0.70, 95% CI 0.54-0.91, P = 0.008) and old population (OR = 0.63, 95% CI 0.50-0.80, P = 0.0001). In the subgroup analysis by gender, significantly decreased risks were found in male (OR = 0.55, 95% CI 0.34-0.88, P = 0.01) and female (OR = 0.72, 95% CI 0.55-0.95, P = 0.02). When we limited the meta-analysis to studies that controlled for confounders such as age, sex, BMI, smoking, diabetes, hypertension, dyslipidemia, and fibrinogen, a significant association between FXIII-A Val34Leu polymorphism and MI risk remained. This meta-analysis provides the evidence that FXIII-A Val34Leu polymorphism may significant associated with the MI risk.