scholarly journals Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach

2022 ◽  
Vol 13 ◽  
Author(s):  
Takeshi Osawa
Keyword(s):  
Predicting Model

scholarly journals Reasoning-predicting Model based on Strong Relevant Logic in Road Traffic Forecasting

2012 ◽  
Vol 33 ◽  
pp. 1105-1110 ◽  
Author(s):  
Dancheng Li ◽  
Zhiliang Liu ◽  
Cheng Liu ◽  
Binsheng Liu ◽  
Wei Zhang
Keyword(s):  
Road Traffic ◽  
Relevant Logic ◽  
Predicting Model ◽  
Traffic Forecasting ◽  
Model Based

scholarly journals GW29-e0300 Establishment of a predicting model for inpatient sudden cardiac death in a Chinese cardiac department population: a retrospective study

2018 ◽  
Vol 72 (16) ◽  
pp. C135
Author(s):  
Luxiang Shang ◽  
Xianhui Zhou ◽  
Jianghua Zhang ◽  
Wenhui Zhang ◽  
ZuKela TuErHong ◽  
...  
Keyword(s):  
Retrospective Study ◽  
Sudden Cardiac Death ◽  
Cardiac Death ◽  
Predicting Model

Modeling and experimental investigation of out-of-plane deformation on mechanical performance of composites manufactured by ATL

2016 ◽  
Vol 36 (5) ◽  
pp. 347-359 ◽  
Author(s):  
Qiyi Chu ◽  
Yong Li ◽  
Jun Xiao ◽  
Dajun Huan ◽  
Xiaodong Chen
Keyword(s):  
Tensile Strength ◽  
Deformation Rate ◽  
Mechanical Performance ◽  
Plane Deformation ◽  
Experimental Results ◽  
Predicting Model ◽  
Fiber Waviness ◽  
Manufacturing Method ◽  
Four Point Bending ◽  
Out Of Plane

The change of mold normal curvature along the trajectory may result in out-of-plane waviness during the automated laying process, on which the layup speed and temperature would have an effect. A new parameter, deformation rate, was defined by combining the effect of mold curvature change rate and layup speed. A predicting model was proposed based on the fiber waviness and interlaminar sliding model to calculate the relationship between stiffness retention and the layup process parameters, including deformation rate and temperature. An experimental study on the effect of different deformation parameters on the tensile performance of composites was carried out based on a new manufacturing method of plated specimens with different levels of waviness by means of a four-point bending fixture. The experimental results showed that when the deformation temperature increases from 20℃ to 80℃, the tensile strength increases first and then decreases while the tensile module keeps increasing. While the deformation rate decreases from 0.40 to 0.04 mm−1/s, both tensile strength and module showed an increasing trend. The predicting model being validated by experimental results can be utilized to optimize the layup process parameter to satisfy the quality and efficiency requirements.

Identification and Validation of Immune-Related Genes and Immune Cell Infiltration in Preeclampsia

2021 ◽  
Author(s):  
Rongxin Chen ◽  
Qing Han ◽  
Huale Zhang ◽  
Jianying Yan
Keyword(s):  
Immune Cell ◽  
Risk Model ◽  
Differentially Expressed ◽  
Cell Infiltration ◽  
Receptor Interaction ◽  
Immune Cell Infiltration ◽  
Multisystem Disease ◽  
Predicting Model ◽  
Immune Related Genes ◽  
Maternal Fetal Interface

Abstract Background Preeclampsia (PE) is a complex multisystem disease and its etiology remains unclear. The aim of this study was to identify potential immune-related diagnostic genes for PE, analyze the role of immune cell infiltration in PE, and explore the mechanism underlying PE-induced disruption of immune tolerance at the maternal-fetal interface. Methods We used the PE dataset GES25906 from Gene Expression Omnibus and immune-related genes from ImmPort database. The differentially expressed genes (DEGs) were identified using the “limma” package, and the differentially expressed immune-related genes (DEIGs) were extracted from the DEGs and immune-related genes using Venn diagrams. The potential functions of DEIGs were determined by Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Furthermore, the protein–protein interaction network was obtained from the STRING database, and it was visualized using Cytoscape software. Least absolute shrinkage and selection operator logistic regression was used to verify the diagnostic markers of PE and build a predicting model. The model was validated using datasets GSE66273 and GSE75010. Finally, CIBERSORT was used to evaluate the infiltration of immune cells in PE tissues. Results Six genes (ACTG1, ENG, IFNGR1, ITGB2, NOD1, and SPP1) enriched in Th17 cell differentiation, cytokine-cytokine receptor interaction, innate immune response, and positive regulation of MAPK cascade pathways were identified, and a predicting model was built. Datasets GSE66273 and GSE75010 were used to validate the model, and the area under the curve was 0.8333 and 0.8107, respectively. Immune cell infiltration analysis revealed an increase in plasma cells and gamma delta T cells and a decrease in resting natural killer cells in the high score group according to the predictive model risk values. Conclusions We developed a risk model to predict PE and proved that immune imbalance at the maternal-fetal interface plays a key role in the pathogenesis of PE.

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