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A ternary compound Al3CoNd2 was synthesized and its crystal structure parameters were determined by the Rietveld refinement method based on powder X-ray diffraction data. Results show that the compound crystallizes in the MgCu2-type structure (cubic Laves C15 phase, space group $Fd\bar{3}m$), with the lattice parameter of a = 7.8424(2) Ǻ, unit-cell volume of V = 482.33 Å3, and calculated density of Dcalc = 5.90 g.cm−3. The residual factors converge to Rp = 0.1024 and Rwp = 0.1287. The reference intensity ratio value obtained experimentally is 3.03. Magnetic susceptibility measurements indicate an agreement with the Curie–Weiss law in the temperature range of 385–450 K, and paramagnetic Curie temperature of θp = 379.9 K. Both rare-earth elements and cobalt ions contribute to the paramagnetic moment. The saturation magnetic moment and magnetic hysteresis loop were measured for the Al3CoNd2 compound at various temperatures. Results show that the saturation magnetic moment value decreases with an increase in temperature and the compound becomes a ferromagnet below the Curie temperature Tc. [ABSTRACT FROM AUTHOR]

The Al3FeNd2 single-phase alloy was prepared by melting stoichiometric Al, Fe, and Nd in a nonconsumable arc furnace filled with argon and then homogenizing annealing at a high temperature. The crystal structure of Al3FeNd2 was analyzed by an X-ray powder diffraction technique, Rietveld refinement analysis, and the first-principles simulation calculation method. The results show that the alloy crystallizes in the cubic cell, space group of Fd-3m (no. 227), and the lattice parameters of a = 7.8892 (1) Å, Z = 4, and Dcalc = 5.75 g/cm3. Al3FeNd2 exhibits ferrimagnetic properties at low temperatures and has a Curie temperature (Tc) of 353 K, and its saturation magnetic moment (Ms) is 3.33 μB/f.u. at 2 K. The simulation results also show that Al3FeNd2 is a ferrimagnet with a spontaneous magnetic moment of 4.11 μB/f.u., and it is believed that the spontaneous magnetization of this alloy originates from the electron spin in the Fe(16c)-3d band and Nd(8b)-4f. [ABSTRACT FROM AUTHOR]

In this article, we investigate the problem of nonparametrically estimating a conditional quantile function with mixed discrete and continuous covariates. A local linear smoothing technique combining both continuous and discrete kernel functions is introduced to estimate the conditional quantile function. We propose using a fully data-driven cross-validation approach to choose the bandwidths, and further derive the asymptotic optimality theory. In addition, we also establish the asymptotic distribution and uniform consistency (with convergence rates) for the local linear conditional quantile estimators with the data-dependent optimal bandwidths. Simulations show that the proposed approach compares well with some existing methods. Finally, an empirical application with the data taken from the IMDb website is presented to analyze the relationship between box office revenues and online rating scores. for this article are available online. [ABSTRACT FROM AUTHOR]

We introduce methods and theory for functional or curve time series with long-range dependence. The temporal sum of the curve process is shown to be asymptotically normally distributed, the conditions for this covering a functional version of fractionally integrated autoregressive moving averages. We also construct an estimate of the long-run covariance function, which we use, via functional principal component analysis, in estimating the orthonormal functions spanning the dominant subspace of the curves. In a semiparametric context, we propose an estimate of the memory parameter and establish its consistency. A Monte Carlo study of finite-sample performance is included, along with two empirical applications. The first of these finds a degree of stability and persistence in intraday stock returns. The second finds similarity in the extent of long memory in incremental age-specific fertility rates across some developed nations. for this article are available online. [ABSTRACT FROM AUTHOR]

A new ternary compound Al5NdNi2 was prepared by melting a stoichiometric mixture of aluminum, neodymium, and nickel in an arc furnace and annealing in vacuum. The crystal structure of Al5NdNi2 was studied by X-ray powder diffraction technique and Rietveld analysis. All diffraction lines of Al5NdNi2 were indexed, and the lattice parameters were refined with an orthorhombic structure type of space group Immm (No.71) using Rietveld analysis program DBWS-9807. The lattice parameters are presented, a = 7.0508(1) Å, b = 9.5690(1) Å, c = 3.9792(1) Å, V = 268.47 Å3, Z = 2, ρ = 4.91 g cm−3, and RIR = 1.23. [ABSTRACT FROM AUTHOR]

We study a semivarying coefficient model where the regressors are generated by the multivariate unit root I(1) processes. The influence of the explanatory vectors on the response variable satisfies the semiparametric partially linear structure with the nonlinear component being functional coefficients. A semiparametric estimation methodology with the first-stage local polynomial smoothing is applied to estimate both the constant coefficients in the linear component and the functional coefficients in the nonlinear component. The asymptotic distribution theory for the proposed semiparametric estimators is established under some mild conditions, from which both the parametric and nonparametric estimators are shown to enjoy the well-known super-consistency property. Furthermore, a simulation study is conducted to investigate the finite sample performance of the developed methodology and results. [ABSTRACT FROM AUTHOR]

In this article, we consider estimation of common structural breaks in panel data models with unobservable interactive fixed effects. We introduce a penalized principal component (PPC) estimation procedure with an adaptive group fused LASSO to detect the multiple structural breaks in the models. Under some mild conditions, we show that with probability approaching one the proposed method can correctly determine the unknown number of breaks and consistently estimate the common break dates. Furthermore, we estimate the regression coefficients through the post-LASSO method and establish the asymptotic distribution theory for the resulting estimators. The developed methodology and theory are applicable to the case of dynamic panel data models. Simulation results demonstrate that the proposed method works well in finite samples with low false detection probability when there is no structural break and high probability of correctly estimating the break numbers when the structural breaks exist. We finally apply our method to study the environmental Kuznets curve for 74 countries over 40 years and detect two breaks in the data. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]