Construction of a Responsive Web Service for Smooth Rendering of Large SSC Dataset: and the Corresponding Preprocessor for Source Data

This research focuses on a smooth rendering of continuous 2D map based on a smooth 3D vario-scale geographical data structure. A Space Scale Cube (SSC) offers non-redundant geometric data for the different level of details. SSC model represents geographic data as a closed polyhedron, to generate a 2D map; SSC is intersected with the projection plane; resulting in a set of 2D polygons. However, problems emerge when creating maps with a large sized SSC dataset under web environment due to limited bandwidth and decoding speed. Repetitively transmitting data from the server to the client can be time and bandwidth consuming. A preprocess should be applied to a source that allows the follow-up development of an online traffic and time-efficient prototype. After preprocessing, large sized data will be subdivided based on octree algorithm to minimize transmission time from server to the client; moreover, accessible to WebGL. A prototype has been developed which enables smooth and timely vario-scale map rendering against heavy user actions such as fast zooming and panning in a short period. Modified prototype program allows query of only relevant data chunks by current viewport position; it prevents repeated loading of same chunks; what is more, repeated transmission of data from outside to GPU is eliminated. A tree structure is embedded at the client side that facilitates retrieve time. Rendering happens every frame; hence the prototype responses to heavy user actions timely. Also, it can obtain coordinates in RD coordinate system by double clicking. After testing the modified program with a 9km by 9km dataset online, exceptional performance is indicated by a high average frame rate (57 fps) and low main memory occupation (with a network speed at 9MB/s). The prototype performance is significant affected by the client network condition; low network speed can decrease the frame rate. For instance, the web service achieved a frame rate of 47 fps at a network speed at 6MB/s.Geomatic

Formaldehyde Emission Pattern of Melamine Impregnated Paper Decorated Medium Density Fiberboard and its Furniture Products

Melamine-impregnated paper decorated medium-density fiberboard (MIP-MDF) is the main board used for wooden furniture materials in China, and the formaldehyde released from the board and furniture products is harmful to human beings and is a wide concern. This paper aimed to pay attention to the formaldehyde emission of MIP-MDF and its furniture products. This study utilized a 1 m³climatic chamber to measure the formaldehyde emission of MIP-MDF and nightstand made of MIP-MDF in relation to the time and load factors. According to the results, in the 2 to 24 h stage, the emission of formaldehyde in the nightstand made of MIP-MDF materials changed significantly, and the overall trend showed a changing trend of first increasing and then declining. The formaldehyde emission of MIP-MDF stabilized after 40 h, while the formaldehyde emission of the nightstand stabilized after 60 h. As time passed, the formaldehyde emission changes of MIP-MDF and nightstand were almost the same. The formaldehyde emission of MIP-MDF and nightstand has a specific positive correlation with the carrying load. With increased carrying loads, the formaldehyde emission tends to increase, but the degree of influence gradually decreases with the addition of the carrying loads

Study On Coexistence Of Brittle And Ductile Fractures In Nano Reinforcement Composites Under Different Loading Conditions

Experimental study on high volume fraction of metallic matrix nano composites (MMNCs) was conducted, including uniaxial tension, uniaxial compression, and three-point bending. The example materials were two magnesium matrix composites reinforced with 10 and 15% vol. SiC particles (50 nm size). Brittle fracture mode was exhibited under uniaxial tension and three-point bending, while shear dominated ductile fracture mode (up to 12% fracture strain) was observed under uniaxial compression. The original Modified Mohr–Coulomb (MMC) fracture model (Bai and Wierzbicki in Int J Fract 161:1–20, 2010; in a mixed space of stress invariants and equivalent strain) was transferred into a stress based MMC (sMMC) model. This model was demonstrated to be capable of predicting the coexistence of brittle and ductile fracture modes under different loading conditions for MMNCs. A material post-failure softening model was postulated along the damage accumulation to capture the above two different failure modes. This model was implemented to the Abaqus/Explicit as a material subroutine. Numerical simulations using finite element method well duplicated the material strength, fracture initiation sites and crack propagation modes of the Mg/SiC nano composites with a good accuracy. The proposed model has a good potential to predict fracture for a wide range of material with strength asymmetry and coexistence of brittle and ductile fractures modes

Study on Bulk-Surface Transport Separation and Dielectric Polarization of Topological Insulator Bi_1.2Sb_0.8Te_0.4Se_2.6

This study successfully fabricated the quaternary topological insulator thin films of Bi1.2Sb0.8Te0.4Se2.6 (BSTS) with a thickness of 25 nm, improving the intrinsic defects in binary topological materials through doping methods and achieving the separation of transport characteristics between the bulk and surface of topological insulator materials by utilizing a comprehensive Physical Properties Measurement System (PPMS) and Terahertz Time-Domain Spectroscopy (THz-TDS) to extract electronic transport information for both bulk and surface states. Additionally, the dielectric polarization behavior of BSTS in the low-frequency (10–107 Hz) and high-frequency (0.5–2.0 THz) ranges was investigated. These research findings provide crucial experimental groundwork and theoretical guidance for the development of novel low-energy electronic devices, spintronic devices, and quantum computing technology based on topological insulators

Construction and validation of a risk assessment model for acute kidney injury in patients with acute pancreatitis in the intensive care unit

Abstract Background To construct and validate a risk assessment model for acute kidney injury (AKI) in patients with acute pancreatitis (AP) in the intensive care unit (ICU). Methods A total of 963 patients diagnosed with acute pancreatitis (AP) from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database was included. These patients were randomly divided into training set (N = 674) and validation set (N = 289) at a ratio of 7:3. Clinical characteristics were utilized to establish a nomogram for the prediction of AKI during ICU stay. These variables were selected by the least absolute shrinkage and selection operation (LASSO) regression and included in multivariate logistic regression analysis. Variables with P-values less than 0.05 were included in the final model. A nomogram was constructed based on the final model. The predicted accuracy of the nomogram was assessed by calculating the receiver operating characteristic curve (ROC) and the area under the curve (AUC). Moreover, calibration curves and Hosmer-Lemeshow goodness-of-fit test (HL test) were performed to evaluate model performance. Decision curve analysis (DCA) evaluated the clinical net benefit of the model. Results A multivariable model that included 6 variables: weight, SOFA score, white blood cell count, albumin, chronic heart failure, and sepsis. The C-index of the nomogram was 0.82, and the area under the receiver operating characteristic curve (AUC) of the training set and validation set were 0.82 (95% confidence interval:0.79–0.86) and 0.76 (95% confidence interval: 0.70–0.82), respectively. Calibration plots showed good consistency between predicted and observed outcomes in both the training and validation sets. DCA confirmed the clinical value of the model and its good impact on actual decision-making. Conclusion We identified risk factors associated with the development of AKI in patients with AP. A risk prediction model for AKI in ICU patients with AP was constructed, and improving the treatment strategy of relevant factors in the model can reduce the risk of AKI in AP patients