No abstract

No abstract

Traffic-induced moving force identification (MFI) is a typical inverse problem in the field of bridge structural health monitoring. Lots of regularization-based methods have been proposed for MFI. However, the MFI accuracy obtained from the existing methods is low when the moving forces enter into and exit a bridge deck due to low sensitivity of structural responses to the forces at these zones. To overcome this shortcoming, a novel moving average Tikhonov regularization method is proposed for MFI by combining with the moving average concepts. Firstly, the bridge-vehicle interaction moving force is assumed as a discrete finite signal with stable average value (DFS-SAV). Secondly, the reasonable signal feature of DFS-SAV is quantified and introduced for improving the penalty function (||x|| 2 2 ) defined in the classical Tikhonov regularization. Then, a feasible two-step strategy is proposed for selecting regularization parameter and balance coefficient defined in the improved penalty function. Finally, both numerical simulations on a simply-supported beam and laboratory experiments on a hollow tube beam are performed for assessing the accuracy and the feasibility of the proposed method. The illustrated results show that the moving forces can be accurately identified with a strong robustness. Some related issues, such as selection of moving window length, effect of different penalty functions, and effect of different car speeds, are discussed as well.

With recent advances in resolution and field-of-view, spatially resolved sequencing has emerged as a cutting-edge technology that provides a technical foundation for interpreting large tissues at the spatial single-cell level. To handle the high-resolution spatial omics dataset with associated images and generate spatial single-cell level gene expression, a powerful one-stop toolbox is required. Here, we propose StereoCell, an image-facilitated cell segmentation framework for high-resolution and large field-of-view spatial omics. StereoCell offers a comprehensive and systematic solution to generating high-confidence spatial single-cell data, including image stitching, registration, nuclei segmentation, and molecule labeling. In image stitching and molecule labeling, StereoCell delivers the best-performing algorithms to reduce stitching error and improve the signal-to-noise ratio of single-cell gene expression compared to existing methods. Meanwhile, as demonstrated using mouse brain, StereoCell has been shown to obtain high-accuracy spatial single-cell data, which facilitates clustering and annotation.

The visible light communication (VLC) system using light-emitting diodes (LEDs) is able to achieve a higher transmission rate compared with the traditional radio frequency network. However, the throughput and average spectral efficiency (ASE) of the system may decrease due to the inter-cell interference and the inter-user interference, particularly for the indoor environment, where the LEDs are densely deployed in the case of multiple users. To suppress such interference, we introduce innovatively the improved tabu search (TS) algorithm into the frequency reuse in the multiuser VLC networks. We propose a bidirectional double tabu list strategy to avoid getting trapped in the situation of the local optimum. In addition, we design the evaluation function and aspiration criterion of the algorithm. We employ the improved TS algorithm to find the optical dynamic frequency reuse scheme by combining the interference graph. The interference graph is used to determine the interference relationships and to construct the solutions to the algorithm. The simulation results show that our proposed scheme effectively improves the throughput and the ASE of the system without lowering the fairness performance, especially in the scenario of numerous users.