Research on the Improved Combinatorial Prediction Model of Steel Price Based on Time Series

Accurately predicting the price change of steel (main building materials) is an effective means to control and manage the cost of construction projects. It is one of the ways for construction enterprises to reasonably allocate building materials, save resources, reduce carbon emissions and reduce environmental pollution. Based on the monthly historical price data of 100 steel rebar (16 mm) from November 2010 to February 2019, the separation and retrieval process of the four components in the time series are improved. The improved multiplicative and additive models were used to make separate predictions, and the reasonable weight is given to combine the multiplication and addition model by the reciprocal of variance method. Finally, an improved prediction model of steel bar price combination with higher prediction accuracy is obtained. The prediction results show that the improved multiplication model and addition model have higher prediction accuracy, their MAPE are 2.62% and 2.36% respectively. Moreover, the prediction accuracy of the combined model is even higher, its MAPE is 2.29%. The prediction accuracy of the improved composite model is higher than that of the individual models. The improved combined prediction model of reinforcement price based on time series method can provide some reference and help for cost control and management in construction engineering, further reduce resource waste and construction non-point source pollution

The effect of calcium phosphate nanoparticles on hormone production and apoptosis in human granulosa cells

<p>Abstract</p> <p>Objectives</p> <p>Although many nanomaterials are being used in academia, industry and daily life, there is little understanding about the effects of nanoparticles on the reproductive health of vertebral animals, including human beings. An experimental study was therefore performed here to explore the effect of calcium phosphate nanoparticles on both steroid hormone production and apoptosis in human ovarian granulosa cells.</p> <p>Methods</p> <p>Calcium phosphate nanoparticles uptaking was evaluated by transmission electron microscopy (TEM). The cell cycle was assessed with propidium iodide-stained cells (distribution of cells in G0/G1, S, and G2/M phases) by flow cytometry. The pattern of cell death (necrosis and apoptosis) was analyzed by flow cytometry with annexin V-FITC/PI staining. The expression of mRNAs encoding P450scc, P450arom and StAR were determined by RT-PCR. Progesterone and estradiol levels were measured by radioimmunoassay.</p> <p>Results</p> <p>TEM results confirmed that calcium phosphate nanoparticles could enter into granulosa cells, and distributed in the membranate compartments, including lysosome and mitochondria and intracellular vesicles. The increased percentage of cells in S phase when cultured with nanoparticles indicated that there was an arrest at the checkpoint from phase S-to-G2/M (from 6.28 +/- 1.55% to 11.18 +/- 1.73%, p < 0.05). The increased ratio of S/(G2/M) implied the inhibition of DNA synthesis and/or impairment in the transition of the S progression stage. The apoptosis rate of normal granulosa cells was 7.83 +/- 2.67%, the apoptotic rate increased to 16.53 +/- 5.56% (P < 0.05) after the cells were treated with 100 microM calcium phosphate nanoparticles for 48 hours. Treatment with calcium phosphate nanoparticles at concentrations of 10-100 microM didn't significantly change either the progesterone or estradiol levels in culture fluid, and the expression levels of mRNAs encoding P450scc, P450arom and StAR after 48 h and 72 h period of treatment.</p> <p>Conclusion</p> <p>Calcium phosphate nanoparticles interfered with cell cycle of cultured human ovarian granulosa cells thus increasing cell apoptosis. This pilot study suggested that effects of nanoparticles on ovarian function should be extensively investigated.</p

Clinical and microbiological characterization of <it>Staphylococcus lugdunensis</it> isolates obtained from clinical specimens in a hospital in China

<p>Abstract</p> <p>Background</p> <p>Several reports have associated <it>Staphylococcus lugdunensis</it> with the incidence of severe infection in humans; however, the frequency and prevalence of this microorganism and thus the propensity of its antimicrobial drug resistance is unknown in China. The objective of the current study was to determine the prevalence of <it>Staphylococcus lugdunensis</it> among six hundred and seventy non-replicate coagulase negative <it>Staphylococcus</it> (CoNS) isolates collected in a 12-month period from clinical specimens in the General Hospital of the People’s Liberation Army in Beijing, China.</p> <p>Results</p> <p>Five (0.7%) of the 670 isolates of CoNS were identified as <it>S. lugdunensis</it>. Whereas three isolates were resistant to erythromycin, clindamycin, and penicillin and carried the <it>ermC</it> gene and a fourth one was resistant to cefoxitin and penicillin and carried the <it>mecA</it> gene, one isolate was not resistant to any of the tested antimicrobials. Pulse field gel electrophoretic analysis did not reveal widespread epidemiological diversity of the different isolates.</p> <p>Conclusion</p> <p>Hence, even though <it>S. lugdunensis</it> may be yet unrecognized and undefined in China, it still might be the infrequent cause of infection and profound multi-drug resistance in the same population.</p