30Cr13不锈钢冶炼过程夹杂物控制技术

  针对30Cr13生产过程中存在的轧材非金属夹杂物合格率偏低仅85%左右的问题，通过对30Cr13不锈钢精炼过程钢中非金属夹杂物存在形态进行分析，并结合现有精炼工艺制度，在实验室研究的基础上制定了不锈钢精炼过程非金属夹杂物控制方案。工业应用后，精炼结束钢中大于5 μm的非金属夹杂物总数、形状以及尺寸等参数均得到了有效改善，钢水洁净度大大提高。     

热处理工艺对30MnCrNiMo高强钢组织与性能的影响

借助全自动淬火膨胀仪测定钢的Ac₁、Ac₃相变点,通过对试验钢进行淬火+亚温淬火+回火热处理和淬火+回火热处理,研究了两种热处理工艺下30MnCrNiMo高强钢的组织与性能。结果表明:30MnCrNiMo高强钢的Ac₁、Ac₃相变点分别为653、807 ℃。采用淬火+亚温淬火+回火的热处理工艺所获得的马氏体和铁素体复相组织比直接淬火+回火得到的全马氏体组织更为细小、均匀,试验钢的屈服强度为1499 MPa,伸长率为14.0%,室温、-40 ℃冲击吸收能量分别为35.5和29.5 J,钢的塑性和冲击性能显著提升,有效改善了30MnCrNiMo高强钢的强韧性能。     

MiR-30b Is Involved in the Homocysteine-Induced Apoptosis in Human Coronary Artery Endothelial Cells by Regulating the Expression of Caspase 3

Homocysteine (Hcy) is an independent risk factor for a variety of cardiovascular diseases, such as coronary heart disease, hypertension, stroke, etc. There is a close relationship between the vascular endothelial cell apoptosis and these diseases. Recent studies have shown homocysteine can induce apoptosis in endothelial cells, which may be an important mechanism for the development of theses cardiovascular diseases. Although there are several reports about how the Hcy induces apoptosis in endothelial cells, the exact mechanism is not fully understood. MicroRNAs are small, non-coding RNA. Previous studies have shown that there is a close relationship between several microRNAs and cell apoptosis. However, there are no studies about the role of microRNAs in Hcy-induced apoptosis in endothelial cells so far. In this study, we constructed the model of homocysteine-induced apoptosis in human coronary artery endothelial cells (HCAECs) and found miR-30b was significantly down-regulated by 1 mmol/L Hcy. In addition, overexpression of miR-30b can improve the Hcy-induced apoptosis in HCAECs by downregulating caspase-3 expression. Therefore, miR-30b may play an important role in Hcy-induced apoptosis in endothelial cells.     

Accounting for the uncertainties in the estimation of average shear wave velocity using V S– N correlations

Site-specific seismic hazard analysis is crucial for designing earthquake resistance structures, particularly in seismically active regions. Shear wave velocity ( V _S) is a key parameter in such analysis, although the economy and other factors restrict its direct field measurement in many cases. Various V _S–SPT– N correlations are routinely incorporated in seismic hazard analysis to estimate the value of V _S. However, many uncertainties question the reliability of these estimated V _S values. This paper comes up with a statistical approach to take care of such uncertainties involved in V _S calculations. The measured SPT– N values from all the critical boreholes were converted into statistical parameters and passed through various correlations to estimate V _S at different depths. The effect of different soil layers in the boreholes on the Vs estimation was also taken into account. Further, the average shear wave velocity of the top 30 m soil cover ( V _S30) is estimated after accounting for various epistemic and aleatoric uncertainties. The scattering nature of the V _S values estimated using different V _S– N correlations was reduced significantly with the application of the methodology. Study results further clearly demonstrated the potential of the approach to eliminate various uncertainties involved in the estimation of V _S30 using general and soil-specific correlations.     

On the diffusion of mobile phone innovations for financial inclusion

This article investigates nexuses between innovations in mobile money and financial inclusion. Demand and supply factors that affect the diffusion of mobile services as well as macro-level institutional and economic factors are taken in account. The focus is on 148 countries with data mostly consisting of 2010–2014 averages. The empirical evidence is based on Tobit regressions. The study finds that when the empirical analysis is robust to multicollinearity, two main tendencies are apparent: the significant findings of Lashitew et al. (2019) [1] are confirmed and many new significant estimated coefficients emerge. While this study confirms the findings of the underlying research, it also goes further to improve the harmony in narratives between the predictors and the outcome variables. Accordingly, by accounting for multicollinearity, the earlier findings are now more consistent across the set of predictors (i.e. demand and supply factors) and the attendant financial inclusion outcomes (i.e. mobile money accounts, mobile used to send money and mobile used to receive money).     

The G-Protein-Coupled Estrogen Receptor (GPER/GPR30) in Ovarian Granulosa Cell Tumors

Ovarian granulosa cell tumors (GCTs) are thought to arise from cells of the ovarian follicle and comprise a rare entity of ovarian masses. We recently identified the G-protein-coupled estrogen receptor (GPER/GPR30) to be present in granulosa cells, to be regulated by gonadotropins in epithelial ovarian cancer and to be differentially expressed throughout folliculogenesis. Thus, supposing a possible role of GPER in GCTs, this study aimed to analyze GPER in GCTs. GPER immunoreactivity in GCTs (n = 26; n (primary diagnosis) = 15, n (recurrence) = 11) was studied and correlated with the main clinicopathological variables. Positive GPER staining was identified in 53.8% (14/26) of GCTs and there was no significant relation of GPER with tumor size or lymph node status. Those cases presenting with strong GPER intensity at primary diagnosis showed a significant reduced overall survival (p = 0.002). Due to the fact that GPER is regulated by estrogens, as well as gonadotropins, GPER may also be affected by endocrine therapies applied to GCT patients. Moreover, with our data supposing GPER to be associated with GCT prognosis, GPER might be considered as a possible confounder when assessing the efficacy of hormone-based therapeutic approaches in GCTs.     

Numerical evaluation of wind effects on a tall steel building by CFD

A comprehensive numerical study of wind effects on the Commonwealth Advisory Aeronautical Council (CAARC) standard tall building is presented in this paper. The techniques of Computational Fluid Dynamics (CFD), such as Large Eddy Simulation (LES), Reynolds Averaged Navier-Stokes Equations (RANS) Model etc., were adopted in this study to predict wind loads on and wind flows around the building. The main objective of this study is to explore an effective and reliable approach for evaluation of wind effects on tall buildings by CFD techniques. The computed results were compared with extensive experimental data which were obtained at seven wind tunnels. The reasons to cause the discrepancies of the numerical predictions and experimental results were identified and discussed. It was found through the comparison that the LES with a dynamic subgrid-scale (SGS) model can give satisfactory predictions for mean and dynamic wind loads on the tall building, while the RANS model with modifications can yield encouraging results in most cases and has the advantage of providing rapid solutions. Furthermore, it was observed that typical features of the flow fields around such a surface-mounted bluff body standing in atmospheric boundary layers can be captured numerically. It was found that the velocity profile of the approaching wind flow mainly influences the mean pressure coefficients on the building and the incident turbulence intensity profile has a significant effect on the fluctuating wind forces. Therefore, it is necessary to correctly simulate both the incident wind velocity profile and turbulence intensity profile in CFD computations to accurately predict wind effects on tall buildings. The recommended CFD techniques and associated numerical treatments provide an effective way for designers to assess wind effects on a tall building and the need for a detailed wind tunnel test.     

An equation by any other name is still the same: on spatial econometrics and spatial statistics

Statistics is a branch of mathematics concerned with the collection, quantification, analysis, interpretation, and presentation of real-world data, and the use of probability theory to estimate population parameters with these data. Spatial statistics is a subset of statistics that is concerned with handling the special problems associated with geographically distributed data, which include spatial point patterns, regional and lattice measurement aggregations, irregularly spaced site-specific measurements on a surface, and image analysis. Meanwhile, econometrics is concerned with the application of statistical methods to the study of economic data and problems. When coining the term spatial econometrics in 1979, Paelinck and Klaassen characterized it as a subset of econometrics that is concerned with the role of spatial dependence in regional economic model response and explanatory variables, asymmetries in spatial relationships, the specification of geographic structure governing spatial interactions, and the explicit modeling of space. We outline and discuss principal similarities (e.g., testing for the presence of spatial autocorrelation) and differences (e.g., map generalization) between spatial statistics and spatial econometrics. In doing so, our goal is to help clarify past, present, and future relationships between these two subfields.