Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

Nickel incorporation in Fe(II, III) hydroxysulfate Green Rust: effect on crystal lattice spacing and oxidation products

Ni(II)-Fe(II)-Fe(III) layered double hydroxides (LDH) or Ni-containing sulfate green rust (GR2) samples were prepared from Ni(II), Fe(II) and Fe(III) sulfate salts and analyzed with X ray diffraction. Nickel is readily incorporated in the GR2 structure and forms a solid solution between GR2 and a Ni(II)-Fe(III) LDH. There is a correlation between the unit cell a-value and the fraction of Ni(II) incorporated into the Ni(II)-GR2 structure. Since there is strong evidence that the divalent/trivalent cation ratio in GR2 is fixed at 2, it is possible in principle to determine the extent of divalent cation substitution for Fe(II) in GR2 from the unit cell a-value. Oxidation forms a mixture of minerals but the LDH structure is retained if at least 20 % of the divalent cations in the initial solution are Ni(II). It appears that Ni(II) is incorporated in a stable LDH structure. This may be important for two reasons, first for understanding the formation of LDHs, which are anion exchangers, in the natural environment. Secondly, this is important for understanding the fate of transition metals in the environment, particularly in the presence of reduced Fe compounds.

Microtubule-associated protein 1B, a growth-associated and phosphorylated scaffold protein.

Microtubule-associated protein 1B, MAP1B, is one of the major growth associated and cytoskeletal proteins in neuronal and glial cells. It is present as a full length protein or may be fragmented into a heavy chain and a light chain. It is essential to stabilize microtubules during the elongation of dendrites and neurites and is involved in the dynamics of morphological structures such as microtubules, microfilaments and growth cones. MAP1B function is modulated by phosphorylation and influences microtubule stability, microfilaments and growth cone motility. Considering its large size, several interactions with a variety of other proteins have been reported and there is increasing evidence that MAP1B plays a crucial role in the stability of the cytoskeleton and may have other cellular functions. Here we review molecular and functional aspects of this protein, evoke its role as a scaffold protein and have a look at several pathologies where the protein may be involved.

Growth, convergence and public investment : a bayesian model averaging approach

The aim of this paper is twofold. First, we study the determinants of economic growth among a wide set of potential variables for the Spanish provinces (NUTS3). Among others, we include various types of private, public and human capital in the group of growth factors. Also,we analyse whether Spanish provinces have converged in economic terms in recent decades. Thesecond objective is to obtain cross-section and panel data parameter estimates that are robustto model speci¯cation. For this purpose, we use a Bayesian Model Averaging (BMA) approach.Bayesian methodology constructs parameter estimates as a weighted average of linear regression estimates for every possible combination of included variables. The weight of each regression estimate is given by the posterior probability of each model.

Quasilocal density functional theory and its application within the extended Thomas-Fermi approximation

In this paper we propose a generalization of the density functional theory. The theory leads to single-particle equations of motion with a quasilocal mean-field operator, which contains a quasiparticle position-dependent effective mass and a spin-orbit potential. The energy density functional is constructed using the extended Thomas-Fermi approximation and the ground-state properties of doubly magic nuclei are considered within the framework of this approach. Calculations were performed using the finite-range Gogny D1S forces and the results are compared with the exact Hartree-Fock calculations

Relaxation times in a bistable system with parametric, white noise: Theory and experiment

We consider the effects of external, multiplicative white noise on the relaxation time of a general representation of a bistable system from the points of view provided by two, quite different, theoretical approaches: the classical Stratonovich decoupling of correlations and the new method due to Jung and Risken. Experimental results, obtained from a bistable electronic circuit, are compared to the theoretical predictions. We show that the phenomenon of critical slowing down appears as a function of the noise parameters, thereby providing a correct characterization of a noise-induced transition.

Growth, nodulation and nitrogen fixation of cowpea in soils amended with composted tannery sludge

Tannery wastes generation is increasing every year and a suitable method for tannery sludge management is necessary in order to decrease this environmental problem. The composting is recognized as a suitable method for sludge recycling.. The effect of tannery sludge compost (TSC) rates on growth, nodulation and N fixation of cowpea was investigated. Sandy and clayey soils were amended with TSC at rates of 0, 7.5, 15, 30, and 60 t ha-1. The shoot dry weight of cowpea plants 45 days after emergence (DAE) was greater in the TSC-amended than in the unamended soil. In the sandy soil, nodule dry weight increased with TSC application 45 DAE. In the clayey soil, 45 DAE, nodule dry weight decreased with TSC amendment levels greater than 7.5 t ha-1 compared to the unamended control. The application of TSC increased N accumulation in the cowpea plants. The results suggest that cowpea responds differently to TSC depending on the amendment rate and initial soil type.

A conspectus on estimating function theory and its applicability to recurrent modeling issues in forest biometry

Summary

Semiclassical coupled wave theory and its application to TE waves in one dimensional crystals

A semiclassical coupled-wave theory is developed for TE waves in one-dimensional periodic structures. The theory is used to calculate the bandwidths and reflection/transmission characteristics of such structures, as functions of the incident wave frequency. The results are in good agreement with exact numerical simulations for an arbitrary angle of incidence and for any achievable refractive index contrast on a period of the structure.

The effects of prolonged exposure to elevated temperatures and elevated CO2 levels on the growth, yield and dry matter partitioning of field-sown meadow fescue

Selostus: Kohotetun lämpötilan ja kohotetun CO2-pitoisuuden vaikutukset peltoon kylvetyn nurminadan kasvuun, satoon ja kuiva-aineen jakautumiseen

Perturbation theory and locality in the Field-Antifield formalism

The BatalinVilkovisky formalism is studied in the framework of perturbation theory by analyzing the antibracket BecchiRouetStoraTyutin (BRST) cohomology of the proper solution S0. It is concluded that the recursive equations for the complete proper solution S can be solved at any order of perturbation theory. If certain conditions on the classical action and on the gauge generators are imposed the solution can be taken local.

On the Schoenberg transformations in data analysis: theory and illustrations

The class of Schoenberg transformations, embedding Euclidean distances into higher dimensional Euclidean spaces, is presented, and derived from theorems on positive definite and conditionally negative definite matrices. Original results on the arc lengths, angles and curvature of the transformations are proposed, and visualized on artificial data sets by classical multidimensional scaling. A distance-based discriminant algorithm and a robust multidimensional centroid estimate illustrate the theory, closely connected to the Gaussian kernels of Machine Learning.