Search

The rational layout of tidal stream turbines (TSTs) is beneficial for making full use of tidal stream energy. It is essential to consider the wake radius and velocity distribution for determining the spacing between the TSTs. The wake is primarily affected by the turbulence intensity. The attenuation of turbulence results in the non-linear expansion of the wake. Additionally, the high turbulence in the near wake region inhibits the velocity deficit, which is more evident under high ambient turbulence intensity. Therefore, a radius and minimum velocity Jensen (RMV-Jensen) model is proposed to predict the wake radius and the wake velocity distribution downstream of a TST. The RMV-Jensen model consists of a radius block (R-Block) and a minimum velocity block (MV-Block). The R-Block is a piecewise exponential function based on the turbulence attenuation in the wake region, accurately describing the change of the wake expansion coefficient. The MV-Block is a wake minimum velocity model, and the inhibitory effect of turbulence on velocity deficit is considered for the first time. The RMV-Jensen model is applied to predict the wake distribution in the Zhoushan sea area. The prediction accuracy of the RMV-Jensen model is improved by 10%–20% compared to that of the classical Jensen model, according to the experimental results. [ABSTRACT FROM AUTHOR]

The synthesis of substituted 3‐oxo‐pyrimido[1,2‐b]indazoles from 3‐aminoindazoles and ketones has been established. It involves one molecule of 3‐aminoindazoles and two molecules of ketones via a [3+2+1] three‐component annulation process. 3‐Oxo‐pyrimido[1,2‐b]indazoles exhibit strong yellow fluorescence in various solvents. A wide range of products have anti‐oxidant activities. Cytotoxicity studies indicate that compounds 3 a, 3 aa and 3 ab have obvious effects on both HCT116 and A549 cancer cell lines. [ABSTRACT FROM AUTHOR]

Background and Aim: Glycated albumin (GA) is a biomarker monitoring glycemia 2–4 weeks before stroke onset. This study was designed to explore the association between GA levels with poststroke outcomes in patients with acute ischemic stroke or transient ischemic attack (TIA). Method: Participants with ischemic stroke or TIA who had a baseline GA measurement were included in the Third China National Stroke Registry study. The effect of GA on stroke recurrence, poor functional outcomes, and combined vascular events was examined during the 1‐year follow‐up period. Multivariate Cox and logistic regression models were performed to evaluate the association. Discrimination tests were used to examine the incremental predictive value of GA when incorporating it into the conventional model. Results: A total of 3861 participants were enrolled. At the 3‐month follow‐up, the elevated GA level was associated with an increased risk of poor functional outcomes (adjusted odds ratio [OR], 1.45; 95% confidence interval [CI], 1.01–2.09). A similar increase was observed for stroke recurrence (adjusted hazard ratio [HR], 1.56; 95% CI, 1.09–2.24), poor functional outcomes (adjusted OR, 1.62; 95% CI, 1.07–2.45), and combined vascular events (adjusted HR, 1.55; 95% CI, 1.09–2.20) at the 1‐year follow‐up. In nondiabetic patients, the association between GA and poor functional outcomes was more pronounced (adjusted OR, 1.62; 95% CI, 1.05–2.50). Adding GA into the conventional model resulted in slight improvements in predicting poor functional outcomes (net reclassification improvement [NRI]: 12.30% at 1 year). Conclusion: This study demonstrated that elevated GA levels in serum were associated with stroke adverse outcomes, including stroke recurrence, poor functional outcomes, and combined vascular events, in patients with ischemic stroke or TIA. [ABSTRACT FROM AUTHOR]

This study focused on correcting short-term wind speed forecasts extracted from GFS global forecast data sets at corresponding locations and heights. Data measurement mode and calibration method of the LiDAR data were discussed, and wind speed at three different heights (50m, 80m, and 100m) from floating LiDARs were employed. The results indicated that directly using the Kalman filter method with sliding adaptive weights to generate the average hysteresis deviation to correct the wind speed did not yield satisfactory results. Statistical analysis suggested that better standard deviation results can be obtained by lowering the ratio of the average hysteresis deviation in the forecast correction formula. When the average hysteresis deviation ratio was reduced to 0.1--0.5, the wind speed standard deviation after correction by the Kalman filtering method was generally lower than the wind speed standard deviation before correction, and the best ratio was 0.3. Using this hysteresis deviation ratio for wind speed correction at different forecast times and periods for these three LiDAR devices revealed that, except for individual data sets, more than 90% of the wind speed data sets exhibited reduced standard deviation after correction compared to the uncorrected data sets. After the introduction of this improved algorithm, the wind speed forecast improvement rate at each height ranged from 5% to 20%. [ABSTRACT FROM AUTHOR]

The current visual simultaneous localization and mapping (SLAM) systems require the use of matched feature point pairs to estimate camera pose and construct environmental maps. Therefore, they suffer from poor performance of the visual feature matchers. To address this problem, a visual SLAM using deep feature matcher is proposed, which is mainly composed of three parallel threads: Visual Odometry, Backend Optimizer and LoopClosing. In the Visual Odometry, the deep feature extractor with convolutional neural networks is utilized for extracting feature points in each image frame. Then, the deep feature matcher is used for obtaining the corresponding feature landmark pairs. Afterwards, a fusion method based on the last and the reference frame is proposed for camera pose estimation. The Backend Optimizer is designed to execute local bundle adjustment for a part of camera poses and landmarks (map points). While LoopClosing, consisting of a lightweight deep loop closure detector and the same matcher as the one used in Visual Odometry is utilized for loop correction based on pose graph. The proposed system has been tested extensively on most of benchmark KITTI odometry dataset. The experimental results show that our system yields better performance than the existing visual SLAM systems. It can not only run in real-time at a speed of 0.08 s per frame, but also reduce estimation error by at least 0.1 m. [ABSTRACT FROM AUTHOR]