No abstract

On the completion of a large-scale hydropower station, the change of the water area can cause a corresponding change of local weather. To examine such changes, this paper analyzed the effect of the reservoir in the head area of the Xiluodu hydropower station based on the temperature data of MODIS MYD11A2. The temperature differences (TD) between various locations in the study area and the reservoir were calculated to explore the TD in different seasons. The reservoir effect change intensity (RECI) was established to explore the impact of the reservoir on local weather changes in different flood seasons. The combination of the TD and RECI was applied to explore the role of the hydropower station in regulating the temperature of the surrounding reservoir. The results showed the following: (1) after hydropower station construction (HSC), the TD in the valleys decreased and the TD in the dry season was lower than that in the wet season; (2) the RECI had different distribution characteristics in different flood seasons of the reservoir, and the RECI was stronger in the wet season than that in the dry season; and (3) unlike in the plains, cooling and warming effects existed simultaneously in different parts of the mountains.

Background: Ovarian cancer (OC) has a high mortality rate and poses a severe threat to women’s health. However, abnormal gene expression underlying the tumorigenesis of OC has not been fully understood. This study aims to identify diagnostic characteristic genes involved in OC by bioinformatics and machine learning.Methods: We utilized five datasets retrieved from the Gene Expression Omnibus (GEO) database, The Cancer Genome Atlas (TCGA) database, and the Genotype-Tissue Expression (GTEx) Project database. GSE12470 and GSE18520 were combined as the training set, and GSE27651 was used as the validation set A. Also, we combined the TCGA database and GTEx database as validation set B. First, in the training set, differentially expressed genes (DEGs) between OC and non-ovarian cancer tissues (nOC) were identified. Next, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Disease Ontology (DO) enrichment analysis, and Gene Set Enrichment Analysis (GSEA) were performed for functional enrichment analysis of these DEGs. Then, two machine learning algorithms, Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE), were used to get the diagnostic genes. Subsequently, the obtained diagnostic-related DEGs were validated in the validation sets. Then, we used the computational approach (CIBERSORT) to analyze the association between immune cell infiltration and DEGs. Finally, we analyzed the prognostic role of several genes on the KM-plotter website and used the human protein atlas (HPA) online database to analyze the expression of these genes at the protein level.Results: 590 DEGs were identified, including 276 upregulated and 314 downregulated DEGs.The Enrichment analysis results indicated the DEGs were mainly involved in the nuclear division, cell cycle, and IL−17 signaling pathway. Besides, DEGs were also closely related to immune cell infiltration. Finally, we found that BUB1, FOLR1, and PSAT1 have prognostic roles and the protein-level expression of these six genes SFPR1, PSAT1, PDE8B, INAVA and TMEM139 in OC tissue and nOC tissue was consistent with our analysis.Conclusions: We screened nine diagnostic characteristic genes of OC, including SFRP1, PSAT1, BUB1B, FOLR1, ABCB1, PDE8B, INAVA, BUB1, TMEM139. Combining these genes may be useful for OC diagnosis and evaluating immune cell infiltration.

Regular-fractal topography on RF-switch MEMS surface is reported over different scale ranges. Surface topography is crucial in understanding underling physics associated with the surface contacts, switch working performance, and reliability. The complexity of these structures requires new techniques to characterize topography and then replicate the multi-scale regular-fractal structure for analysis. Topography on RF-switch contacting surfaces are scanned by atomic force microscopy (AFM) at different length scales (e.g. 1×1, 10×10 and 60×60 μm2). A sample allocation plan is designed to maximize the spatial representative of the AFM scanning patches with different resolutions and uniformly distributed sample patches. The scanning data are used for characterizing and model estimation. Hexagonal patterns are found on at coarser scales (e.g. 10×10 and 60×60 μm2). They were formed by the remnant (polymer) of etching process. Random irregularity is observed and the fractal structure at finer scales (e.g. 1×1 μm2) is identified. A regular-fractal model is proposed to decompose and characterize the regular and fractal structures with two model components: one for the regular geometric pattern and the other for fractal irregularity. The former uses a 2D cosine functions to characterize dominant modes in the regular (larger scale) patterns. The later summarizes random irregularity in finer scales with a statistical fractal model estimated from the data on the scattered sample patches. The model validation is made through the comparisons of topography and conventional roughness parameters between the results of simulation from the proposed model and that derived from AFM scanned data.

Geometric dimensioning & tolerancing (GD&T) and process capability indices (PCIs) play critical roles in quality assurance. Conventional PCIs, when used together with GD&T, strongly rely on certain assumptions (e.g. normality and regularity of specification region). GD&T requirements often involve interrelated tolerances, creating irregular tolerance regions. Violation of these assumptions misleads the results (18) and interpretation in applications. A non-conformity (NC) index is developed based on nonparametric distribution model and numerical assessment techniques. Kernel is used for probability density (pdf) estimation and Monte Carlo integration algorithm is adopted for NC analysis, i.e. integration of a pdf over a specification region. The method is validated by case study.