Revisiting the theory of interferometric wide-field synthesis

After several generations of interferometers in radioastronomy, wide-field imaging at high angular resolution is today a major goal for trying to match optical wide-field performances. All the radio-interferometric, wide-field imaging methods currently belong to the mosaicking family. Based on a 30 years old, original idea from Ekers & Rots, we aim at proposing an alternate formalism. Starting from their ideal case, we successively evaluate the impact of the standard ingredients of interferometric imaging. A comparison with standard nonlinear mosaicking shows that both processing schemes are not mathematically equivalent, though they both recover the sky brightness. In particular, the weighting scheme is very different in both methods. Moreover, the proposed scheme naturally processes the short spacings from both single-dish antennas and heterogeneous arrays. Finally, the sky gridding of the measured visibilities, required by the proposed scheme, may potentially save large amounts of hard-disk space and cpu processing power over mosaicking when handling data sets acquired with the on-the-fly observing mode. We propose to call this promising family of imaging methods wide-field synthesis because it explicitly synthesizes visibilities at a much finer spatial frequency resolution than the one set by the diameter of the interferometer antennas.Comment: 22 pages, 6 PostScript figures. Accepted for publication in Astronomy & Astrophysics. Uses aa LaTeX macros

Spatial Structures and Spatial Spillovers: A GME Approach

Spatial econometrics is a subdiscipline that have gained a huge popularity in the last twenty years, not only in theoretical econometrics but in empirical studies as well. Basically, spatial econometric methods measure spatial interaction and incorporate spatial structure into regression analysis. The specification of a matrix of spatial weights W plays a crucial role in the estimation of spatial models. The elements of this matrix measure the spatial relationships between two geographical locations i and j, and they are specified exogenously to the model. Several alternatives for W have been proposed in the literature, although binary matrices based on contiguity among locations or distance matrices are the most commons choices. One shortcoming of using this type of matrices for the spatial models is the impossibility of estimating “heterogeneous†spatial spillovers: the typical objective is the estimation of a parameter that measures the average spatial effect of the set of locations analysed. Roughly speaking, this is given by “ill-posed†econometric models where the number of (spatial) parameters to estimate is too large. In this paper, we explore the use of generalized maximum entropy econometrics (GME) to estimate spatial structures. This technique is very attractive in situations where one has to deal with estimation of “ill-posed†or “ill-conditioned†models. We compare by means of Monte Carlo simulations “classical†ML estimators with GME estimators in several situations with different availability of information.

The Contemporary Human Resources Function

Abstract Current companies encounter important challenges day to day. Many factors such as the economic globalization, the change in the way of working, the Internet boom, and the importance that the service sector is experiencing nowadays influence the new society in which persons and businesses actually cope. Each of these contingencies obligates the Human Resource function to adapt to new organizational goals moulding their strategies and tactics to the in fashion topics. In this work we present the classical functions integrated in the Human Resources department constituting its base of performance. We also deal equally with the high performance practices and the importance that is acquiring the binomial human resources management strategies – business performance such as new tendencies applicable in this area.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

OPTIMAL PRICING AND GRANT POLICIES FOR MUSEUMS

Considering two potential sources of income (public grants and ticket revenues),we have defined a theoretical model where the public agency is the principal and the manager of the museum is the agent. This model allows us to design the optimal contract between both sides and thus to establish the optimal values of grants, ticket prices, budget and effort applied by the manager. Furthermore, we have found a theoretical reason to explain the inelastic pricing strategy that has been found in some of the empirical research on cultural and sports economics. The main conclusion is that the optimal contract allows a Pareto optimum solution in prices that does not change if we introduce moral hazard into this relationship. This solution allows us to conclude that the public agency should regulate ticket prices in accordance with the social valuation. However, public grants and museum budgets would be affected by the existence of this problem, moving the equilibrium away from the Pareto optimum situation. In this case, even with a risk averse manager and a risk neutral public agency, grants and budgets will depend on results because higher budgets related to good results provide the main incentives to increase the manager’s level of effort. Although the focus of this paper is on museum administration, the model that we have developed can be easily generalized and applied to other institutions, such as schools, sport facilities or NGOs, which are able to raise funds directly from private (e. g. ticket revenues or membership fees) or public sources (e.g. public grants).cultural economics, grants, public prices, museums, principal- agent model