Influence of Partially-Covered Riparian Vegetation on Flow in a Compound Channel

Vegetation is of great importance in hydraulic engineering as it can affect the flow structures of compound channels in many ways, including the velocity profiles, momentum exchange, and shear stress distributions. This complex flow structure in vegetated compound channels has attracted more and more research interests. However, most of the previous studies have been focusing on fully-covered vegetated compound channels, there are little studies on compound channels with partially vegetated floodplain. This research carried out novel experiments to investigate the flow structure of compound channels with partially-covered vegetation on the floodplain. The results showed that the discharge of the main channel decreases as the depth ratio increases. The retardation effect of vegetation on the flow of non-vegetated floodplain region decreases with the increasing water depth. In addition, the vertical velocity profile in the vegetated zone performs differently in various depth ratios, with its velocity taking a maximum around the middle-water depth zone under emergent cases, while being the maximum near the free surface under submerged cases.</jats:p

Impact of Different Vegetation Zones on the Velocity and Discharge of Open-Channel Flow

Different types of vegetation widely exist in rivers and wetlands. The vegetation will affect the ecological environment and flow process, thus becoming increasingly significant in river engineering and aquatic environmental management. Previous research on vegetated flow is mainly to understand the flow structure of open channels with fully covered one-layer vegetation. However, vegetation often grows along a river bank and co-exists in different heights. The present paper presents experimental results about the flow characteristics of an open-channel with two sides covered by differently layered vegetation, focusing on the effect of vegetation on the velocity distribution and discharge. Two heights of dowels in 10 cm and 20 cm were used to simulate rigid vegetation and arranged in a linear form on both sides of a channel bed under emergent and fully submerged flow conditions. The velocity at different positions was obtained using ADV (Acoustic Doppler Velocimetry). Measured results demonstrate that there exists a shear layer between free-flow and vegetated zones, indicating that the flow transition occurs between fast-moving flow in the free zone and slowly obstructed flow in the vegetated zone and induces a high shear layer and transverse coherent vortices near the interface. Furthermore, compared with the emergent condition, the discharge through the free-flow region slightly decreases under full submerged conditions while the discharge in the vegetated region increases, indicating that the vegetation does not significantly change the discharge percentage in the free region. These findings on differently-layered vegetation would help riparian management practices to maintain healthy ecological and habitat zones.</jats:p

New Dynamic Data-Driven Model for Predicting the Apparent Shear Force and Discharge of Compound Channels

In this paper, based on the concept of apparent shear, a new dynamic model for apparent shear force is obtained using a genetic algorithm program, a well-documented machine-learning software, which can examine the existing relationship among the variables and explore the influencing factors. It was found that a unified relationship exists between apparent shear force and the variables of ratios of area, height, width, and roughness. The obtained formula of interfacial apparent shear force can predict the flow discharge of compound channels, either zonal or total discharge. This study shows that the predicted flow using the new model agrees well with the data from the literature. This newly derived model for apparent shear force has a single expression for both smooth and roughened compound channels, which provides a simple and easy-use formula for engineers to apply for wide applications

The Impact of Double-layered Rigid Vegetation on Flow Structure

Flow structure in vegetated channels is highly three-dimensional. This study focuses on the characteristics of flow through the mixing layered vegetation partially covered in an open channel. A series of experiments were conducted to investigate the velocity distributions in the vegetated zone under different flow conditions. Reynolds shear stress was calculated based on 3D measurements using ADV (Acoustic Doppler velocimeter). Observed results show that the flow has distinct features in three layers: the lower, mediate, and upper layer. A prominent shear stress layer is found at the upper edge of tall vegetation, indicating strong momentum exchange in this transition region

Attention Mechanisms in Medical Image Segmentation: A Survey

Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed over 300 articles related to medical image segmentation, and divided them into two groups based on their attention mechanisms, non-Transformer attention and Transformer attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, i.e., the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, etc. We hope that this review can showcase the overall research context of traditional and Transformer attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios.Comment: Submitted to Medical Image Analysis, survey paper, 34 pages, over 300 reference

UniG-Encoder: A Universal Feature Encoder for Graph and Hypergraph Node Classification

Graph and hypergraph representation learning has attracted increasing attention from various research fields. Despite the decent performance and fruitful applications of Graph Neural Networks (GNNs), Hypergraph Neural Networks (HGNNs), and their well-designed variants, on some commonly used benchmark graphs and hypergraphs, they are outperformed by even a simple Multi-Layer Perceptron. This observation motivates a reexamination of the design paradigm of the current GNNs and HGNNs and poses challenges of extracting graph features effectively. In this work, a universal feature encoder for both graph and hypergraph representation learning is designed, called UniG-Encoder. The architecture starts with a forward transformation of the topological relationships of connected nodes into edge or hyperedge features via a normalized projection matrix. The resulting edge/hyperedge features, together with the original node features, are fed into a neural network. The encoded node embeddings are then derived from the reversed transformation, described by the transpose of the projection matrix, of the network's output, which can be further used for tasks such as node classification. The proposed architecture, in contrast to the traditional spectral-based and/or message passing approaches, simultaneously and comprehensively exploits the node features and graph/hypergraph topologies in an efficient and unified manner, covering both heterophilic and homophilic graphs. The designed projection matrix, encoding the graph features, is intuitive and interpretable. Extensive experiments are conducted and demonstrate the superior performance of the proposed framework on twelve representative hypergraph datasets and six real-world graph datasets, compared to the state-of-the-art methods. Our implementation is available online at https://github.com/MinhZou/UniG-Encoder

Identification and validation of prognostically relevant gene signature in melanoma

Background. Currently, effective genetic markers are limited to predict the clinical outcome of melanoma. High-throughput multiomics sequencing data have provided a valuable approach for the identification of genes associated with cancer prognosis. Method. The multidimensional data of melanoma patients, including clinical, genomic, and transcriptomic data, were obtained from The Cancer Genome Atlas (TCGA). These samples were then randomly divided into two groups, one for training dataset and the other for validation dataset. In order to select reliable biomarkers, we screened prognosis-related genes, copy number variation genes, and SNP variation genes and integrated these genes to further select features using random forests in the training dataset. We screened for robust biomarkers and established a gene-related prognostic model. Finally, we verified the selected biomarkers in the test sets (GSE19234 and GSE65904) and on clinical samples extracted from melanoma patients using qRT-PCR and immunohistochemistry analysis. Results. We obtained 1569 prognostic-related genes and 1101 copy-amplification, 1093 copy-deletions, and 92 significant mutations in genomic variants. These genomic variant genes were closely related to the development of tumors and genes that integrate genomic variation. A total of 141 candidate genes were obtained from prognosis-related genes. Six characteristic genes (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) were selected by random forest feature selection, many of which have been reported to be associated with tumor progression. Cox regression analysis was used to establish a 6-gene signature. Experimental verification with qRT-PCR and immunohistochemical staining proved that these selected genes were indeed expressed at a significantly higher level compared with the normal tissues. This signature comprised an independent prognostic factor for melanoma patients. Conclusions. We constructed a 6-gene signature (IQCE, RFX6, GPAA1, BAHCC1, CLEC2B, and AGAP2) as a novel prognostic marker for predicting the survival of melanoma patients