This paper explores whether educational attainment has a cognitive reserve capacity in elder life. Using pilot data from the China Health and Retirement Longitudinal Study (CHARLS), we examined the impact of education on cognitive abilities at old ages. OLS results showed that respondents who completed primary school obtained 18.2 percent higher scores on cognitive tests than those who did not. We then constructed an instrumental variable (IV) by leveraging China's Great Famine of 1959–1961 as a natural experiment to estimate the causal effect of education on cognition. Two-stage least squares (2SLS) results provided sound evidence that completing primary school significantly increases cognition scores, especially in episode memory, by almost 20 percent on average. Moreover, Regression Discontinuity (RD) analysis provides further evidence for the causal interpretation, and shows that the effects are different for the different measures of cognition we explored. Our results also show that the Great Famine can result in long-term health consequences through the pathway of losing educational opportunities other than through the pathway of nutrition deprivation.
Efficient all-optical molecule-plasmon modulation is experimentally demonstrated by employing a compact T-shape single slit on a metal film coated with an azopolymer film, in which the azobenzene molecules can be reoriented by a pump beam. In the T-shape single slit, the transmission spectra exhibit periodic behaviors and are quite sensitive to variations of the refractive index of the azopolymer in the groove. Under a pump beam, the azobenzene molecules are reoriented, so the SPPs in the groove feel a refractive index quite different from that of the originally isotropic azopolymer with randomly orientations. This leads to a high modulation depth of about 53 % (3.3 dB) and a phase variation of >pi experimentally.
Methods for choosing a fixed set of knot locations in additive spline models are fairly well established in the statistical literature. The curse of dimensionality makes it nontrivial to extend these methods to nonadditive surface models, especially when there are more than a couple of covariates. We propose a multivariate Gaussian surface regression model that combines both additive splines and interactive splines, and a highly efficient Markov chain Monte Carlo algorithm that updates all the knot locations jointly. We use shrinkage prior to avoid overfitting with different estimated shrinkage factors for the additive and surface part of the model, and also different shrinkage parameters for the different response variables. Simulated data and an application to firm leverage data show that the approach is computationally efficient, and that allowing for freely estimated knot locations can offer a substantial improvement in out-of-sample predictive performance.
We present for the first time an efficient iterative method to directly solve the four-component Dirac-Kohn-Sham (DKS) density functional theory. Due to the existence of the negative energy continuum in the DKS operator, the existing iterative techniques for solving the Kohn-Sham systems cannot be efficiently applied to solve the DKS systems. The key component of our method is a novel filtering step (F) which acts as a preconditioner in the framework of the locally optimal block preconditioned conjugate gradient (LOBPCG) method. The resulting method, dubbed the LOBPCG-F method, is able to compute the desired eigenvalues and eigenvectors in the positive energy band without computing any state in the negative energy band. The LOBPCG-F method introduces mild extra cost compared to the standard LOBPCG method and can be easily implemented. We demonstrate our method in the pseudopotential framework with a planewave basis set which naturally satisfies the kinetic balance prescription. Numerical results for Pt$_{2}$, Au$_{2}$, TlF, and Bi$_{2}$Se$_{3}$ indicate that the LOBPCG-F method is a robust and efficient method for investigating the relativistic effect in systems containing heavy elements.
A bacterium capable of phosphorus removal was isolated. Through morphology observation and 16S rRNA gene sequence analysis, the isolate was identified as Salinivibrio sp. (named HG-1). Salinity tolerance and phosphorus removal efficiency under different salinity conditions of the strain were further investigated. The results showed that HG-1 grew well with the salt content varying from 1% to 13% and achieved the highest phosphorus removal efficiency under salt content of 3%. Furthermore, the single-factor and orthogonal experiment results indicated that the optimal phosphorus removal performance was obtained under the conditions with an initial pH of 6.5-7.0, C/N ratio of 9, temperature of 30°C and inoculation ratio of 10%. Under such a condition, the phosphorus removal efficiency could reach 100% in 24 hours. The strain HG-1 can independently complete phosphorus removal process, and thus could provide a novel and promising alternative for biological phosphorus removal under high salinity conditions.筛选出一株能够高效除磷的耐盐菌株HG-1。通过个体形态、菌落特征的观察和16S rRNA基因的序列分析, 初步鉴定为盐弧菌属(Salinivibrio sp.)。对菌株HG-1的耐盐性能及其在不同盐度下对磷酸盐的去除效果进行考察, 结果表明菌株对盐度的耐受范围为1%~13%, 最适盐度为3%。进一步的单因素和正交实验表明, 4个环境因素对菌株HG-1磷酸盐去除率影响的强弱为: pH>碳氮比>温度>接种量, 最优的除磷条件为pH 6.5~7.0, 温度30℃, 接种量10%, 碳氮比9, 在该条件下菌株在24小时内对磷酸盐的去除率可达100%。 将该菌株应用于高盐废水的处理, 可实现磷酸盐的有效去除, 具有良好的实际应用价值, 为高盐条件下生物除磷难题的解决提供了一条新的途径。
Magnetic nanoparticles (MNPs) modified simultaneously with amorphous Fe and Mn oxides (Mag-Fe-Mn) were synthesized to remove arsenite [As(III)] from water. Mag-Fe-Mn particles were fabricated through heterogeneous nucleation technique by employing the maghemite as the magnetic core and Fe Mn binary oxide (FMBO) as the coating materials. Powder X-ray diffraction, X-ray photoelectron spectroscopy (XPS), Raman spectroscopy, and transmission electron microscopy were utilized to characterize the hybrid material. With a saturation magnetization of 23.2 emu/g, Mag-Fe-Mn particles with size of 20 -50 nm could be easily separated from solutions with a simple magnetic process in short time (within 5 min). At pH 7.0, 200 mu g/L of As(III) could be easily decreased to below 10 mu g/L by Mag-Fe-Mn particles (0.1 g/L) within 20 min. As(III) could be effectively removed by Mag-Fe-Mn particles at initial pH range from 4 to 8 and the residual As was completely oxidized to less toxic arsenate [As(V)]. The co-occurring redox reactions between Mn oxide and As(III) was confirmed by XPS analysis. Chloride, sulfate, bicarbonate, and nitrate at common concentration range had negligible influence on As(III) removal, whereas, silicate and phosphate reduced the As(III) removal by competing with arsenic species for adsorption sites. As(M) removal was not obviously affected by natural organic matter (up to 8 mg/L as TOC). Mag-Fe-Mn could be regenerated with ternary solution of NaOH, NaCl, and NaClO. Throughout five consecutive cycles, the adsorption and desorption efficiencies maintained above 98% and 87%, respectively. Mag-Fe-Mn had a larger adsorption capacity for As(III) (47.76 mg/g) and could remove trace As(III) more thoroughly than MNPs modified solely with either Fe or Mn oxide due to the synergistic effect of the coating Fe and Mn oxides. This research extended the potential applicability of FMBO to a great extent and provided a convenient approach to efficiently remove trace As(III) from water. (C) 2013 Elsevier Ltd. All rights reserved.