Laminar flamelet modeling of pilot jet methane/air flames

Nonpremixed turbulent combustion is a prevalent phenomenon in many practical applications. Theoretical research like simulating a well quantified piloted methane/air jet flame can serve as a means to make this technology effective, economical and clean. In this work, a Masri-Bilger piloted methane/air jet flame has been modeled by invoking the laminar flamelet assumption in FLUENT. The purpose was to investigate the effects of chemical reaction mechanisms and scalar dissipation rates on the accuracy of the model. Smooke\u27s skeletal mechanism with 17 species and 25 reactions has been compared with GRI-Mech 3.0, a detailed mechanism consisting of 53 chemical species and 325 elementary reactions. The scalar dissipation rate was varied from 0.001 to 20 s -1. The results confirm the ability of the steady laminar flamelet model in qualitatively predicting the non-premixed, turbulent jet flame in terms of temperature and species profiles. The inclusion of detailed chemistry only led to marginally improved prediction. Scalar dissipation rate has a more pronounced influence on the predicted results, indicating that an appropriate nonzero dissipation rate is needed to better capture the underlying physics

Iron Contamination Mechanism and Reaction Performance Research on FCC Catalyst

FCC (Fluid Catalytic Cracking) catalyst iron poisoning would not only influence units’ product slate; when the poisoning is serious, it could also jeopardize FCC catalysts’ fluidization in reaction-regeneration system and further cause bad influences on units’ stable operation. Under catalytic cracking reaction conditions, large amount of iron nanonodules is formed on the seriously iron contaminated catalyst due to exothermic reaction. These nodules intensify the attrition between catalyst particles and generate plenty of fines which severely influence units’ smooth running. A dense layer could be formed on the catalysts’ surface after iron contamination and the dense layer stops reactants to diffuse to inner structures of catalyst. This causes extremely negative effects on catalyst’s heavy oil conversion ability and could greatly cut down gasoline yield while increasing yields of dry gas, coke, and slurry largely. Research shows that catalyst’s reaction performance would be severely deteriorated when iron content in E-cat (equilibrium catalyst) exceeds 8000 μg/g

RNA-Binding Protein MEX3A Interacting with DVL3 Stabilizes Wnt/β-Catenin Signaling in Endometrial Carcinoma

Disease recurrence and metastasis lead to poor prognosis in patients with advanced endometrial carcinoma (EC). RNA-binding proteins (RBPs) are closely associated with tumor initiation and metastasis, but the function and molecular mechanisms of RBPs in EC are unclear. RBPs were screened and identified using the TCGA, GEO, and RBPTD databases. The effect of MEX3A on EC was verified by in vitro and in vivo experiments. Gene set enrichment analysis (GSEA), immunofluorescence (IF), and co-immunoprecipitation (Co-IP) were used to identify potential molecular mechanisms of action. We identified 148 differentially expressed RBPs in EC. MEX3A was upregulated and related to poor prognosis in patients with EC. In vitro and vivo experiments demonstrated that MEX3A promoted the growth, migration, and invasion capacities of EC cells. Mechanistically, DVL3, a positive regulator of the Wnt/β-catenin pathway, also increased the proliferation and metastasis of EC cells. MEX3A enhanced EMT and played a pro-carcinogenic role by interacting with DVL3 to stabilize β-catenin and upregulated the expression of its downstream target genes. MEX3A is upregulated in EC and promotes tumor progression by activating EMT and regulating the Wnt/β-catenin pathway via DVL3. MEX3A may therefore be a novel therapeutic target for EC.</b

On the Relationships between Clustering and Spatial Co-location Pattern Mining

The goal of spatial co-location pattern mining is to find subsets of spatial features frequently located together in spatial proximity. Example co-location patterns include services requested frequently and located together from mobile devices (e.g., PDAs and cellular phones) and symbiotic species in ecology (e.g., Nile crocodile and Egyptian plover). Spatial clustering groups similar spatial objects together. Reusing research results in clustering, e.g. algorithms and visualization techniques, by mapping colocation mining problem into a clustering problem would be very useful. However, directly clustering spatial objects from various spatial features may not yield well-defined colocation patterns. Clustering spatial objects in each layer followed by overlaying the layers of clusters may not applicable to many application domains where the spatial objects in some layers are not clustered. In this paper, we propose a new approach to the problem of mining co-location patterns using clustering techniques. First, we propose a novel framework for co-location mining using clustering techniques. We show that the proximity of two spatial features can be captured by summarizing their spatial objects embedded in a continuous space via various techniques. We define the desired properties of proximity functions compared to similarity functions in clustering. Furthermore, we summarize the properties of a list of popular spatial statistical measures as the proximity functions. Finally, we show that clustering techniques can be applied to reveal the rich structure formed by co-located spatial features. A case study on real datasets shows that our method is effective for mining co-locations from large spatial datasets. 1This work was done when the second author was with the University of Minnesota.