Tourism Flows Prediction based on an Improved Grey GM(1,1) Model

AbstractThis study analyzes the factors affecting the tourist flow. These factors include tourism resources, traffic conditions and so on. In recent years, the grey forecasting model has achieved good prediction accuracy with limited data and has been widely used in various research fields. However, the grey forecasting model still have some potential problems that need to be improved, such as applicate range and prediction accuracy. It is found that original data and background value are main factors affecting the accuracy of the proposed model's application. To solve these problems, this study develops a optimization model for the GM(1,1) model problem which includes optimization of initial and background values. In order to reduce errors caused by back-ground values, the “new information prior using” principle is followed, and a liner function is dopted in the construe of background. Numerical examples verified that the simulation and prediction accuracy of the short-term forcasts is significantly increased. As a result, the newly improved model yields a high prediction capability

Deep Generative Models on 3D Representations: A Survey

Generative models, as an important family of statistical modeling, target learning the observed data distribution via generating new instances. Along with the rise of neural networks, deep generative models, such as variational autoencoders (VAEs) and generative adversarial network (GANs), have made tremendous progress in 2D image synthesis. Recently, researchers switch their attentions from the 2D space to the 3D space considering that 3D data better aligns with our physical world and hence enjoys great potential in practice. However, unlike a 2D image, which owns an efficient representation (i.e., pixel grid) by nature, representing 3D data could face far more challenges. Concretely, we would expect an ideal 3D representation to be capable enough to model shapes and appearances in details, and to be highly efficient so as to model high-resolution data with fast speed and low memory cost. However, existing 3D representations, such as point clouds, meshes, and recent neural fields, usually fail to meet the above requirements simultaneously. In this survey, we make a thorough review of the development of 3D generation, including 3D shape generation and 3D-aware image synthesis, from the perspectives of both algorithms and more importantly representations. We hope that our discussion could help the community track the evolution of this field and further spark some innovative ideas to advance this challenging task

Modeling of Aqueous Urea Solution injection with characterization of spray-wall cooling effect and risk of onset of wall wetting

AbstractThe definition of a sufficiently resolved heat transfer model with spray cooling effect as a function of each droplet kinetic and thermal parameters is a key factor in the numerical simulation of aqueous urea (AUS) based Selective Catalytic Reduction (SCR) exhaust after-treatment systems.A consolidated spray-wall interaction model [1] has been implemented on the open source 3D finite volume software OpenFOAM and a critical investigation of its behaviour in engine representative conditions is reported.A simplified test case is used to highlight the influence of the chosen model on the numerical simulation of the system, reducing the importance of the other spray sub-models in the Lagrangian-Eulerian computational framework. The coupling between the droplet evaporation heat flux and the gas-solid interface thermal boundary condition has been studied, pointing out the significance of each contribution.The main focus of this work is to present reference conditions to simulate the spray-dry wall spray impingement behavior to determine the ‘onset of wall wetting’ thermal conditions

Global analysis of the relationship between reconstructed solar induced chlorophyll fluorescence (SIF) and gross primary production (GPP)

Solar-induced chlorophyll fluorescence (SIF) is increasingly known as an effective proxy for plant photosynthesis, and therefore, has great potential in monitoring gross primary production (GPP). However, the relationship between SIF and GPP remains highly uncertain across space and time. Here, we analyzed the SIF (reconstructed, SIFc)–GPP relationships and their spatiotemporal variability, using GPP estimates from FLUXNET2015 and two spatiotemporally contiguous SIFc datasets (CSIF and GOSIF). The results showed that SIFc had significant positive correlations with GPP at the spatiotemporal scales investigated (p p p > 0.05). Therefore, we propose a two-slope scheme to differentiate ENF from non-ENF biome and synopsize spatiotemporal variability of the GPP/SIFc slope. The relative biases were 7.14% and 11.06% in the estimated cumulative GPP across all EC towers, respectively, for GOSIF and CSIF using a two-slope scheme. The significantly higher GPP/SIFc slopes of the ENF biome in the two-slope scheme are intriguing and deserve further study. In addition, there was still considerable dispersion in the comparisons of CSIF/GOSIF and GPP at both site and biome levels, calling for discriminatory analysis backed by higher spatial resolution to systematically address issues related to landscape heterogeneity and mismatch between SIFc pixel and the footprints of flux towers and their impacts on the SIF–GPP relationship

Knocking-Down Cyclin A2 by siRNA Suppresses Apoptosis and Switches Differentiation Pathways in K562 Cells upon Administration with Doxorubicin

Cyclin A2 is critical for the initiation of DNA replication, transcription and cell cycle regulation. Cumulative evidences indicate that the deregulation of cyclin A2 is tightly linked to the chromosomal instability, neoplastic transformation and tumor proliferation. Here we report that treatment of chronic myelogenous leukaemia K562 cells with doxorubicin results in an accumulation of cyclin A2 and follows by induction of apoptotic cell death. To investigate the potential preclinical relevance, K562 cells were transiently transfected with the siRNA targeting cyclin A2 by functionalized single wall carbon nanotubes. Knocking down the expression of cyclin A2 in K562 cells suppressed doxorubicin-induced growth arrest and cell apoptosis. Upon administration with doxorubicin, K562 cells with reduced cyclin A2 showed a significant decrease in erythroid differentiation, and a small fraction of cells were differentiated along megakaryocytic and monocyte-macrophage pathways. The results demonstrate the pro-apoptotic role of cyclin A2 and suggest that cyclin A2 is a key regulator of cell differentiation. To the best of our knowledge, this is the first report that knocking down expression of one gene switches differentiation pathways of human myeloid leukemia K562 cells