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Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed. In this paper, two novel unsupervised graph embedding methods, unsupervised graph embedding via adaptive graph learning (BAGE) and unsupervised graph embedding via variational adaptive graph learning (VBAGE) are proposed. The proposed methods expand the application range of GAEs on graph embedding, i.e, on the general datasets without graph structure. Meanwhile, the adaptive learning mechanism can initialize the adjacency matrix without being affected by the parameter. Besides that, the latent representations are embedded with the Laplacian graph structure to preserve the topology structure of the graph in the vector space. Moreover, the adjacency matrix can be self-learned for better embedding performance when the original graph structure is incomplete. With adaptive learning, the proposed method is much more robust to the graph structure. Experimental studies on several datasets validate our design and demonstrate that our methods outperform baselines by a wide margin in node clustering, node classification, link prediction, and graph visualization tasks.

As a hot topic in unsupervised learning, clustering methods have been greatly developed. However, the model becomes more and more complex, and the number of parameters becomes more and more with the continuous development of clustering methods. And parameter-tuning in most methods is a laborious work due to its complexity and unpredictability. How to propose a concise and beautiful model in which the parameters can be learned adaptively becomes a very meaningful problem. Aim at tackling this problem, we develop a novel self-weighted unsupervised linear discriminative analysis method, namely SWULDA. The proposed method not only avoids adjusting parameters but also explains the link between k-means and linear discriminant analysis (LDA). To obtain superior structural performance, the idea of minimizing the within-class scatter matrix and maximizing the between-class scatter matrix is embedded in the unsupervised model. Moreover, equipped with the proposed quadratic weighted optimization framework, the parameter can be adaptively learned. The extensive experiments on several datasets are conducted to validate the effectiveness of our method.

Ridge regression is frequently utilized by both supervised learning and semisupervised learning. However, the results cannot obtain the closed-form solution and perform manifold structure when ridge regression is directly applied to semisupervised learning. To address this issue, we propose a novel semisupervised feature selection method under generalized uncorrelated constraint, namely SFS. The generalized uncorrelated constraint equips the framework with the elegant closed-form solution and is introduced to the ridge regression with embedding the manifold structure. The manifold structure and closed-form solution can better save data's topology information compared to the deep network with gradient descent. Furthermore, the full rank constraint of the projection matrix also avoids the occurrence of excessive row sparsity. The scale factor of the constraint that can be adaptively obtained also provides the subspace constraint more flexibility. Experimental results on data sets validate the superiority of our method to the state-of-the-art semisupervised feature selection methods.

The objective of this study was to test the effect of four different staking sequences on the mechanical properties and the low viscosity impact behavior of bamboo bundle-based composites. Two different laminate staking structures were used to make the bamboo bundle veneers. The samples were labeled A through D. The density and mechanical properties of the samples, including the modulus of elasticity (MOE), the modulus of rupture (MOR), and the impact bending strength (IBS), were studied under parallel and perpendicular loading to the glue line. Three different impact energies of 50, 95, and 145 J were used to examine the impact response and failure mechanisms of the samples under low-velocity impact. The MOE and MOR values of the type A and type D laminates under both perpendicular and parallel loading to the glue line were found to be insignificant. The IBS values of the type A laminate under perpendicular and parallel loading to the glue line were higher than the values of the other composites. Among the configurations that were investigated in this study, the type D composite exhibited the best impact response and its total energy absorbed was much higher than the other samples.

Poisson equation lies in the heart of the numerical solutions of incompressible Navier–Stokes equations (NSEs) based on the popular projection methods, which decouple the pressure and velocities fields. This paper presents enhanced numerical solutions of the 2D incompressible NSEs inspired by a newly-developed Generalized Harmonic Polynomial Cell (GHPC) method for the Poisson equation, which has a fourth-order spatial accuracy. To achieve that, the original GHPC method is adapted to accommodate immersed boundaries, necessary when a staggered grid for velocities and pressure is applied. The finite difference method (FDM) is used in the spatial discretization of the diffusion and advection terms. Numerical analyses of lid-driven cavity flow, a Taylor–Green vortex, and flow around a smooth circular cylinder all show encouraging results, confirming the accuracy and efficiency of the new solver. Through comparison with an NSEs solver which applies second-order FDM for the Poisson equation, we demonstrate that the accuracy of the pressure solution can be greatly improved by applying the more accurate GHPC method for the Poisson equation, even though the accuracy of the velocities solutions are limited by the numerical schemes for advection–diffusion equations. The accuracy in the pressure solution can also be translated into CPU time saving to achieve a predefined accuracy. The present immersed-boundary GHPC Poisson equation solver can easily be ‘plugged’ into other existing NSEs solvers utilizing staggered grids.

Key villages of rural tourism have become an important carrier for the high-quality development of rural tourism. The precise identification of the spatial distribution characteristics and influencing factors of rural tourism key villages is of great value in promoting the quality upgrading of rural tourism in China and realizing the goal of rural revitalization strategy. The aim is to realize the complementary coordination and integration of urban and rural areas, promote rural transformation and increase farmers’ incomes. Taking three batches of 1199 key rural tourism villages in China as research samples, the nearest neighbor index, disequilibrium index and kernel density methods were used to reveal the spatial differentiation characteristics of key rural tourism villages and the influencing factors were further analyzed using the geographical connection rate and buffer analysis. The results showed that (1) the key villages of rural tourism demonstrated a typical agglomeration in the spatial distribution, with the Hu Huanyong line as the obvious boundary. (2) The very high-density agglomeration centers were mainly located in the Beijing–Tianjin–Hebei region and the Yangtze River Delta region, while the high-density agglomeration zones were mainly located in the North China Plain on the east side of the Taihang Mountains and the middle and lower reaches of the Yangtze River Basin. (3) The spatial distribution characteristics of hot regions were in the central and eastern regions; however, the cold regions were in the northwest and northeast regions. (4) The spatial distribution characteristics of key rural tourism villages were the result of the interaction and coupling of multiple factors. The key villages of rural tourism were mainly distributed in plain and hilly areas with dense river networks, dense populations, high levels of economic development, developed transportation and suburban areas.

Medium-sized cities are increasingly committed to the planning and construction of urban public spaces to meet people’s demand for high-quality urban life. Parks and squares are important parts of urban public spaces, and their vitality represents the quality of public spaces to a certain extent and reflects the happiness index of urban residents. At present, the functional areas and transportation networks of medium-sized cities are still developing. Due to the influence of urban construction, the planning of parks and squares in medium-sized cities has not yet caught up to that in larger cities. This study analyzed a medium-sized city, Jiaozuo, as an example, with the help of point of interest (POI) data, OpenStreetMap road network density data and WorldPop population data. The vitality of parks and squares in different functional spaces in the main urban area in Jiaozuo was quantitatively evaluated in terms of the four following aspects: urban space functional area characteristics, travel vitality index of urban residents, park and square attractiveness and the regional service levels of parks and squares. The effects of functional mixing, traffic network density, population density and spatial distribution on the vitality of parks and squares in medium-sized cities were also studied. The results showed that (1) the functional mixing in the main urban area in Jiaozuo was characterized by a spatial distribution of high in the center and low in the surrounding areas, with the highest functional mixing in the central part of the Jiefang District; (2) the travel dynamics of urban residents were characterized by a clear development of concentric circles radiating in a circular pattern; (3) the levels of service in parks and squares were particularly high in Jiefang District, with a spatial distribution of Jiefang District > Shanyang District > Macun District > Zhongzhan District; (4) under the condition that the service levels of each district were the same, the vitality values of the existing parks and squares in each district were compared and, from high to low, were Jiefang District (1.0–3.5), Shanyang District (0.2–2.0), Macun District (0–1.4) and Zhongzhan District (0–1.2). Functional mixing, road networks and population density had significant impacts on the vitality of parks and squares. Based on our study on the division of urban functional areas, we expanded the study to include urban microspaces. By evaluating the vitality of existing parks and squares and analyzing the influencing factors of spatial vitality, we found that it would be helpful to adopt targeted strategies to improve spatial vitality. Considering the spatial layouts of parks and squares, planning and constructing high-vitality parks and squares would be conducive to the future development of medium-sized cities. The existence of high-vitality spaces could also help to realize the sustainable development of cities.

Numerical modeling of ship bow waves is still hard work, partly due to their multiscale features. Direct three-dimensional (3D) computational fluid dynamics simulation could be an appropriate choice to investigate the problem. However, limited by computational resources, small scale phenomena such as spraying and wave breaking that could be observed during the ship wave generation process are usually simplified or not fully distinguished in a 3D simulation. In order to accurately capture the small scales flow field information with the available computational resource, a new Cartesian grid-based two-dimensional plus time (2D+t) method is developed in this paper, which is suitable for 3D slender ships. With this method, a 3D steady ship wave-making problem is transformed into a 2D unsteady wave-making problem of a deformable body. The boundary velocity of the deformable body is obtained with a novel interpolation algorithm, which is then enforced on the background Cartesian grid by a newly proposed immersed boundary method. The pressure boundary condition on the surface of the deformable body is explicitly considered in the solution of the pressure Poisson equation. Moreover, an extra open boundary condition is applied to the upper boundary of the computational domain to achieve a better conservation. The proposed model is validated with selected cases, showing that the model is capable of simulating both non-wave-breaking and wave-breaking problems.

A novel two-dimensional numerical wave tank based on the two-phase Navier–Stokes equations (NSEs) is presented. The popular projection method is applied to decouple the pressure and velocity fields, while the solutions are uniquely enhanced by a newly-developed immersed-boundary generalized harmonic polynomial cell (IB-GHPC) method for the pressure Poisson equation, which lies at the heart of the projection method. The GHPC method, originally proposed for the constant-coefficient Poisson equation, has been employed in solving the single-phase NSEs with success, though it cannot be directly applied for two-phase flows. In this paper, we show that the GHPC method can still be used in solving two-phase flow problems by introducing a pressure-correction method. By considering wave generation and propagation, the accuracy and convergence rate of the present numerical model is demonstrated. The solver is further validated against model tests for wave propagation over a submerged breakwater, and a perforated plate in oscillatory flows and incident waves. Excellent agreement with benchmark results confirms the accuracy and the validity of the new numerical wave tank towards more general wave–structure-interaction problems. The free-surface effect on the wave loads of a perforated plate is further investigated through applications of the present numerical model.

A regio- and stereoselective nickel-catalyzed three-component coupling reaction of aldehydes, 1,3-dienes, and alkenylzirconium reagents was realized. The ligand- and additive-free protocol afforded a convenient approach to the synthesis of skipped diene compounds bearing various functionals (e.g., hydroxyl, carbonyl, halide) and heterocyclic groups. The products were readily transformed into structurally diverse polyenes. The utility of this reaction was also demonstrated by the one-pot operation and scale-up preparation.

The diversity of recommendation has attracted a lot of attention in recommender systems due to its ability to improve user experience. Most of the diversified recommendation tasks usually exploit user-item interaction records for mining user explicit preferences, while rarely explore the user-item non-interaction records. For diversified recommendations, however, the neglected non-interaction records are especially important for capturing users’ potential interests to improve the diversity of recommendation. Moreover, the majority of diversified recommendation methods run in two stages: first optimizing the users and items embeddings by relevance, then generating the diversified items list by post-processing methods. These methods are not end-to-end thus can hardly reach global optimum. To solve above limitations, we propose an end-to-end Dynamic Diversified Graph framework (DDGraph) which constructs the user-item graph dynamically based on the users and items embeddings. Technically, we initialize a user-item interaction graph and dynamically update the graph by selecting a set of diverse items for each user and building links between the items and user. The selection of diverse items can be achieved by different candidate selection operators. Specifically, we design a Quantile Progressive Candidate Selection (QPCS) operator based on the latent space division. To the best of our knowledge, our method is the first to diversify recommendation results by dynamic end-to-end graph construction and the QPCS has a higher computational efficiency than other operators. Extensive experiments on the benchmark dataset illustrate the effectiveness and superiority of the DDGraph framework.

A modular synthesis of 2,8-dioxabicyclo[3.2.1]octane (2,8-DOBCO) ketals is realized by sequential catalysis. The key intermediates, e-hydroxyl-β,γ-unsaturated ketones, are readily available by Ni-catalyzed three-component coupling of 1,3-dienes, aldehydes, and acylzirconocene reagents. These polyfunctionalized ketones can undergo a V-catalyzed cascade of epoxidation and ring rearrangement to provide access to 6-hydroxyl-2,8-DOBCOs. Mechanistic studies indicate that the V(V) species, generated under epoxidation conditions, promotes the ring rearrangement in a stereospecific manner. Meanwhile, the e-hydroxyl-β,γ-unsaturated ketones are transformed into 6-amino-2,8-DOBCOs by a Cu-catalyzed cascade of aziridination and ring rearrangement. In the end, the one-pot approach toward 6-hydroxyl- or 6-amino-2,8-DOBCOs from 1,3-diene, aldehydes, and acylzirconocene reagents is more favorable.

The performance of graph-based feature selection methods relies heavily on the quality of the construction of the similarity matrix. However, most of the graphs on these methods are initially fixed, where few of them are constrained. Once the graph is determined, it will remain constant in the whole optimization process. In other words, in case that the graph constructed on the raw data is not appropriate, it will drag down the entire algorithm. Aiming to tackle this defect, a novel unsupervised feature selection via adaptive graph learning and constraint (EGCFS) is proposed to select the uncorrelated yet discriminative features by exploiting the embedded graph learning and constraint. The adaptive graph learning method incorporates the structure of the similarity matrix into the optimization process, which not only learns the graph structure adaptively but also obtains the closed-form solution of the graph coefficient. Special graph constraint is embedded with the feature selection process to connect nearer data points with larger probability. The idea of maximizing between-class scatter matrix and the adaptive graph structure is integrated into a uniform framework to obtain excellent structural performance. Moreover, the proposed embedded graph constraint not only performs with manifold structure but also validates the link between graph-based approach and k -means from a unique perspective. Experiments on several benchmark data sets verify the effectiveness and superiority of the proposed method.

Service pricing is a bottleneck in the development of innovation services, as it is the issue of most concern between the suppliers and demanders. In this paper, a negotiation pricing model that is based on the multiobjective genetic algorithm is developed for innovation service pricing. Regarding the service pricing process as a multiobjective problem, the objective functions, which include the service price, service efficiency, and service quality, for suppliers and demanders are constructed. As the solution of a multiobjective problem is typically a series of alternatives, another negotiation process is necessary for determining the final decision. A learning strategy is adopted during the negotiation process to simulate reality. Finally, the model is implemented for an innovation service transaction, the objective of which is to identify the optimal price plan. The results demonstrate that the model can provide quantitative decision support for the pricing of an innovation service and ultimately yield a win-win result for both the supplier and demander of the innovation service. Furthermore, the influence of the parameters during the negotiation process is analyzed in detail. The effects of the learning strategy on accelerating the negotiation process, as well as the chosen of reasonable parameters are given.

In this study, a two-dimensional Geometric Multigrid (GMG) model for Poisson equation with interface on Structured Adaptive Mesh Refinement (SAMR) grid is developed. The model is designed to be combined with Navier-Stokes solvers with staggered arrangements of variables, although solvers with collocated arrangement are also applicable. Based on a general GMG method, special attention for flux conservation is taken on the coarse-fine interface. As large density ratios commonly exist in interface flows such as free surface flows, Galerkin Coarse grid Approximation (GCA) method is adopted to generate coefficients on coarse grids to enhance the robustness and efficiency of the model. Benchmark case is carried out for the validation of the model, and it is demonstrated to obtain 2nd-order accuracy and acceptable efficiency for even the density ratio reaches 104. Point iteration and line relaxation iteration of Gauss-Seidel methods are compared to obtain a better performance. Furthermore, the model is implemented in a developed Navier-Stokes solver to validate its performance in simulating interface problems. The numerical results are compared with theoretical solution or experimental data from reliable sources.

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, especially in East Asia and China. Long noncoding RNAs (lncRNAs) are emerging as critical regulators that may be involved in the development and progression of cancers in humans. However, the contributions of lncRNAs to HCC development, metastasis, and recurrence remain largely unknown. In this study, we comprehensively investigated lncRNA expression profile in HCC and normal tissues using TCGA RNA sequencing data, one RNA sequencing dataset, and two microarray datasets from GEO. By analyzing these four datasets, we identified hundreds of expression‐dysregulated lncRNAs in HCC tissues compared with normal tissues. Genomic copy number variation analysis showed that many of those lncRNAs disorder are related to the copy number amplification or deletion. Moreover, several lncRNAs expression levels are associated with HCC patients' overall and recurrence‐free survival, such as RP1‐228H13.5, TMCC1‐AS1, LINC00205, and RP11‐307C12.11. Furthermore, we identified two lncRNAs termed PVT1 and SNHG7 that may be involved in HCC cells metastasis by comparing lncRNAs expression profiles between early recurrence HCC tissues with metastasis and late recurrence HCC tissues without metastasis. Finally, loss‐of‐function assays confirmed that knockdown of SNHG7 and PVT1 impaired HCC cells invasion. Taken together, these findings may provide a valuable resource for further identification of novel biomarkers and therapeutic targets for HCC patients.

Pathogenesis-related (PR) proteins are involved in plant defense and have multiple functional adaptations that assist in resisting various pathogens and tolerating environmental stress. Salix matsudana Koidz, a deciduous, rapidly growing willow species, can tolerate a range of adverse conditions. Comparative proteomic analysis previously revealed that the S. matsudana PR protein SmPR10 was abundant and up-regulated with 100 mM NaCl treatment. In this study, the SmPR10 gene from S. matsudana was cloned and characterized to determine its role in salt tolerance. The amino acid sequence of SmPR10 showed 98% and 93% sequence homology with PR proteins from S. purpurea and Populus trichocarpa , respectively. SmPR10 was localized in the cytoplasm of Arabidopsis protoplasts. SmPR10 transcript and protein levels were high in roots, and its expression was up-regulated in roots treated with 100 mM NaCl. SmPR10 was detected specifically in phloem fiber cells and root xylem by immunolocalization analysis. Moreover, heterogeneous overexpression of SmPR10 enhanced the salt tolerance of transgenic Arabidopsis plants as shown by analysis of root length, root number, and Na + flux, as well as physiological parameters such as chlorophyll content, MDA content, electrical conductivity, and SOD and POD enzyme activity levels. Our results reveal that SmPR10 plays an important role in salt tolerance and could serve as an important candidate gene to improve salt tolerance in woody species through genetic engineering.

Salix matsudana Koidz is a low cadmium (Cd)-accumulating willow, whereas its cultivated variety, Salix matsudana var. matsudana f. umbraculifera Rehd., is a high Cd-accumulating and tolerant willow (HCW). The physiological and molecular mechanisms underlying differential Cd accumulation and tolerance in the two Salix species are poorly understood. Here, we confirmed that the differential Cd translocation capacity from roots to the shoots leads to the differential Cd accumulation in their aboveground parts between these two willow genotypes. Cadmium accumulation happens preferentially in the transport pathway, and Cd is mainly located in the vacuolar, cell wall and intercellular space in HCW bark by cadmium location analysis at tissue and subcellular levels. Comparative transcriptome analysis revealed that higher expressions of several metal transporter genes (ATP-binding cassette transporters, K+ transporters/channels, yellow stripe-like proteins, zinc-regulated transporter/iron-regulated transporter-like proteins, etc.) are involved in root uptake and translocation capacity in HCW; meanwhile, ascorbate–glutathione metabolic pathways play essential roles in Cd detoxification and higher tolerance of the Cd-accumulator HCW. These results lay the foundation for further understanding the molecular mechanisms of Cd accumulation in woody plants and provide new insights into molecular-assisted-screening woody plant varieties for phytoremediation.

The numerical simulation of wave breaking problem is still a tough challenge, partly due to the large grid number and CPU time requirement for capturing the multi-scale structures embedded in it. In this paper, a two-dimensional two-phase flow model with Adaptive Mesh Refinement (AMR) is proposed for simulating solitary wave breaking problems. Fractional step method is employed for the velocity-pressure decoupling. The free surface flow is captured with the Volume-of-Fluid (VOF) method combined with Piecewise Linear Interface Calculation (PLIC) for the reconstruction of the interface. Immersed boundary (IB) method is utilized to account for the existence of solid bodies. A geometric multigrid method is adopted for the solution of Pressure Poisson Equation (PPE). Benchmark case of advection test is considered first to test the VOF method. Then the solitary wave propagation problem is utilized to validate the model on AMR grid as well as analyze the efficiency of AMR. Furthermore, the solitary wave past a submerged stationary stage problem is simulated to validate the combined IB-VOF-AMR model. All the numerical results are compared with analytic solutions, experimental data or other published numerical results, and good agreements are obtained. Finally, the influence of stage height on the occurrence of wave breaking is analyzed. The locations of wave breaking are summarized for different stage heights.

Cigarette smoking is the established risk factor for the development of urothelial carcinoma of bladder (UCB). However, the impact of smoking on the outcomes of urothelial carcinoma patients remains poorly investigated. Current evidence proves that cigarette smoking increases the risk of disease recurrence and potentially disease progression in patients with urothelial carcinoma of bladder. Current and heavy long-term smokers seem to be at the greatest risk. The majority of retrospective studies found an association between smoking status as well as cumulative exposure and disease recurrence in patients treated with TURB. The evidence for association with disease progression was less abundant. There are insufficient data investigating the impact on cancer-specific and overall survival in patients with bladder cancer. The impact of smoking with outcomes of T1 UCB or high risk disease, patients receiving intravesical therapies is limited because of a lack of data and controversial findings. Long-term smoking cessation seems to mitigate the detrimental effects of smoking in non-muscle-invasive and muscle-invasive bladder cancer.

The Residue Theorem is one of the fundamental theorems in complex analysis. It can be used to integrate some types of real functions that are difficult, or even unable, to be expressed in terms of fundamental functions. This paper reevaluates the application of the Residue Theorem in the real integration of one type of function that decay fast.

Summary The hyperaccumulating ecotype of Sedum alfredii Hance is a cadmium (Cd)/zinc/lead co‐hyperaccumulating species of Crassulaceae. It is a promising phytoremediation candidate accumulating substantial heavy metal ions without obvious signs of poisoning. However, few studies have focused on the regulatory roles of miRNAs and their targets in the hyperaccumulating ecotype of S. alfredii. Here, we combined analyses of the transcriptomics, sRNAs and the degradome to generate a comprehensive resource focused on identifying key regulatory miRNA‐target circuits under Cd stress. A total of 87 721 unigenes and 356 miRNAs were identified by deep sequencing, and 79 miRNAs were differentially expressed under Cd stress. Furthermore, 754 target genes of 194 miRNAs were validated by degradome sequencing. A gene ontology (GO) enrichment analysis of differential miRNA targets revealed that auxin, redox‐related secondary metabolism and metal transport pathways responded to Cd stress. An integrated analysis uncovered 39 pairs of miRNA targets that displayed negatively correlated expression profiles. Ten miRNA‐target pairs also exhibited negative correlations according to a real‐time quantitative PCR analysis. Moreover, a coexpression regulatory network was constructed based on profiles of differentially expressed genes. Two hub genes, ARF4 (auxin response factor 4) and AAP3 (amino acid permease 3), which might play central roles in the regulation of Cd‐responsive genes, were uncovered. These results suggest that comprehensive analyses of the transcriptomics, sRNAs and the degradome provided a useful platform for investigating Cd hyperaccumulation in S. alfredii, and may provide new insights into the genetic engineering of phytoremediation.

Light intensity is a major environmental factor affecting the growth and survival of trees in a forest. The effect of light reduction on photosynthesis and photorespiration of an evergreen broad-leaved tree, Phoebe bournei (Hemsley) Yang was examined with three levels of full light, 50.5% light, and 21.8% light. The results showed that shading led to significant increase in plant height and crown diameter. Light-saturated leaf photosynthetic rate (Amax), maximal carboxylation activity (Vcmax), maximum electron transfer rate (Jmax), stomatal conductance (gs), mesophyll conductance (gm) and chloroplast CO2 concentration (Cc) significantly increased in response to shade. Photorespiratory CO2 release rate (PR) was higher in plants grown under shade conditions than under full light. The relative limitations of gm (lm) was higher than the relative limitations of gs (ls) and the relative limitations of biochemical factors (lb) in leaves of P. bournei grown under full light, whereas lm was lower than ls and lb under shade. Our results suggest that increase of photosynthesis in P. bournei leaves grown under shade is associated with enhanced CO2 diffusion and biochemistry. And we propose that enhancement of the photorespiratory is essential for shade leaves to improve photosynthesis.

The collision of solitary waves is closely related to many physical problems. In this paper, the effects of a submerged bottom-mounted barrier in the head-on collision of two solitary waves are analyzed using a self-developed two-dimensional two-phase flow model. Advanced Adaptive Mesh Refinement (AMR) grid is adopted to reduce the computational cost. The free surface is captured with the Volume of Fraction (VOF) method combined with Piecewise Linear Interface Calculation (PLIC). The Immersed Boundary (IB) method is used to account for the existence of the barrier. Several benchmark problems including solitary wave propagation, head-on collision of two solitary waves, and solitary wave past a submerged barrier are considered to validate the accuracy of the model. Then the model is utilized to simulate the head-on collision of two solitary waves with the existence of a submerged bottom-mounted barrier. Both the symmetric (two waves with equal amplitudes) and asymmetric (two waves with unequal amplitudes) head-on collisions are investigated. The results with and without the barrier are compared and analyzed in detail. Special attention is paid to the variations of the wave amplitudes and the vortex sheddings.

LncRNA CBR3-AS1 has been suggested to promote malignancy in several types of human cancers, but the clinical significance and biological function of lncRNA CBR3-AS1 in osteosarcoma is still unknown. The purpose of our study is to explore the clinical significance of lncRNA CBR3-AS1 in osteosarcoma patients and the biological function in osteosarcoma cells. In our results, we found lncRNA CBR3-AS1 was highly-expressed in osteosarcoma tissues and cell lines, and associated with Enneking stage, distant metastasis and histological grade. Survival analysis indicated that the high-expression of lncRNA CBR3-AS1 was an independent poor prognostic factor for osteosarcoma patients. Loss-of-function studies showed knockdown of lncRNA CBR3-AS1 suppressed osteosarcoma cells proliferation, migration and invasion, and promotes cells apoptosis, but had no effect on cell-cycle distribution. There was no association between lncRNA CBR3-AS1 and CBR3 expression in osteosarcoma tissues, and knockdown of lncRNA CBR3-AS1 had no effect on CBR3 mRNA and protein expression osteosarcoma cells. In conclusion, lncRNA CBR3-AS1 serves an oncogenic role to regulate osteosarcoma cells proliferation, migration, invasion and apoptosis, and is an independent poor prognostic factor for osteosarcoma patients.