Three-dimensional inverse modelling of magnetic anomaly sources based on a genetic algorithm.

We present a modelling method to estimate the 3-D geometry and location of homogeneously magnetized sources from magnetic anomaly data. As input information, the procedure needs the parameters defining the magnetization vector (intensity, inclination and declination) and the Earth's magnetic field direction. When these two vectors are expected to be different in direction, we propose to estimate the magnetization direction from the magnetic map. Then, using this information, we apply an inversion approach based on a genetic algorithm which finds the geometry of the sources by seeking the optimum solution from an initial population of models in successive iterations through an evolutionary process. The evolution consists of three genetic operators (selection, crossover and mutation), which act on each generation, and a smoothing operator, which looks for the best fit to the observed data and a solution consisting of plausible compact sources. The method allows the use of non-gridded, non-planar and inaccurate anomaly data and non-regular subsurface partitions. In addition, neither constraints for the depth to the top of the sources nor an initial model are necessary, although previous models can be incorporated into the process. We show the results of a test using two complex synthetic anomalies to demonstrate the efficiency of our inversion method. The application to real data is illustrated with aeromagnetic data of the volcanic island of Gran Canaria (Canary Islands).

The Theoretical Framework and Methodology to Estimate the Farm Labour and Other Factor-Derived Demand and Output Supply Systems. Factor Markets Working Document No. 44, May 2013

This paper provides a conceptual framework for the estimation of the farm labour and other factor-derived demand and output supply systems. In order to analyse the drivers of labour demand in agriculture and account for the impact of policies on those decisions, it is necessary to acknowledge the interaction between the different factor markets. For this purpose, we present a review of the theoretical background to primal and dual representations of production and some empirical literature that has made use of derived demand systems. The main focus of the empirical work is to study the effect of market distortions in one market, through inefficient pricing, on the demand for other inputs. Therefore, own-price and cross-price elasticities of demand become key variables in the analysis. The dual cost function is selected as the most appropriate approach, where input prices are assumed to be exogenous. A commonly employed specification – and one that is particularly convenient due to its flexible form – is the translog cost function. The analysis consists of estimating the system of cost-share equations, in order to obtain the derived demand functions for inputs. Thus, the elasticities of factor substitution can be used to examine the complementarity/substitutability between inputs.

Homogeneous Spatial Units (HSU) - a Pan-European geographical basis for environmental and socio-economic modelling

The spatial data set delineates areas with similar environmental properties regarding soil, terrain morphology, climate and affiliation to the same administrative unit (NUTS3 or comparable units in size) at a minimum pixel size of 1km2. The scope of developing this data set is to provide a link between spatial environmental information (e.g. soil properties) and statistical data (e.g. crop distribution) available at administrative level. Impact assessment of agricultural management on emissions of pollutants or radiative active gases, or analysis regarding the influence of agricultural management on the supply of ecosystem services, require the proper spatial coincidence of the driving factors. The HSU data set provides e.g. the link between the agro-economic model CAPRI and biophysical assessment of environmental impacts (updating previously spatial units, Leip et al. 2008), for the analysis of policy scenarios. Recently, a statistical model to disaggregate crop information available from regional statistics to the HSU has been developed (Lamboni et al. 2016). The HSU data set consists of the spatial layers provided in vector and raster format as well as attribute tables with information on the properties of the HSU. All input data for the delineation the HSU is publicly available. For some parameters the attribute tables provide the link between the HSU data set and e.g. the soil map(s) rather than the data itself. The HSU data set is closely linked the USCIE data set.

Modelling, nitrogen dynamics in sugarcane systems: Recent advances and applications

Substantial amounts of nitrogen (N) fertiliser are necessary for commercial sugarcane production because of the large biomass produced by sugarcane crops. Since this fertiliser is a substantial input cost and has implications if N is lost to the environment, there are pressing needs to optimise the supply of N to the crops' requirements. The complexity of the N cycle and the strong influence of climate, through its moderation of N transformation processes in the soil and its impact on N uptake by crops, make simulation-based approaches to this N management problem attractive. In this paper we describe the processes to be captured in modelling soil and plant N dynamics in sugarcane systems, and review the capability for modelling these processes. We then illustrate insights gained into improved management of N through simulation-based studies for the issues of crop residue management, irrigation management and greenhouse gas emissions. We conclude by identifying processes not currently represented in the models used for simulating N cycling in sugarcane production systems, and illustrate ways in which these can be partially overcome in the short term. (c) 2005 Elsevier B.V. All rights reserved.

On the equivalence of weighted Country-Product-Dummy (CPD) Method and the Rao-System for multilateral price comparisons

The country-product-dummy (CPD) method, originally proposed in Summers (1973), has recently been revisited in its weighted formulation to handle a variety of data related situations (Rao and Timmer, 2000, 2003; Heravi et al., 2001; Rao, 2001; Aten and Menezes, 2002; Heston and Aten, 2002; Deaton et al., 2004). The CPD method is also increasingly being used in the context of hedonic modelling instead of its original purpose of filling holes in Summers (1973). However, the CPD method is seen, among practitioners, as a black box due to its regression formulation. The main objective of the paper is to establish equivalence of purchasing power parities and international prices derived from the application of the weighted-CPD method with those arising out of the Rao-system for multilateral comparisons. A major implication of this result is that the weighted-CPD method would then be a natural method of aggregation at all levels of aggregation within the context of international comparisons.

Output price subsidies in a stochastic world

This article studies the comparative statics of output subsidies for firms, with monotonic preferences over costs and returns, that face price and production uncertainty. The modeling of deficiency payments, support-price schemes, and stochastic supply shifts in a state-space framework is discussed. It is shown how these notions can be used, via a simple application of Shephard's lemma, to analyze input-demand shifts once comparative-static results for supply are available. A range of comparative-static results for supply are then developed and discussed.

Investigating the performance of MIMO systems from an electromagnetic perspective

Multiple input multiple output (MIMO) wireless systems use multiple element antennas (MEAs) tit the transmitter (TX) and the receiver (RX) in order to offer improved information rates (capacity) over conventional single antenna systems in rich scattering environments. In this paper, an example of a simple MIMO system is considered in which both antennas and scattering objects is are formed by wire dipoles. Such it system can be analyzed in the strict electromagnetic (EM) sense and its capacity can be determined for varying array size, interelement spacing, and distributions of scatterers. The EM model of this MIMO system can be used to assess the validity of single- or double-bounce scattering models for mixed line of sight (LOS) and non-line of sight (NLOS) signal-propagation conditions. (c) 2006 Wiley Periodicals, Inc.

Modelling context information with ORM

Context-aware applications rely on implicit forms of input, such as sensor-derived data, in order to reduce the need for explicit input from users. They are especially relevant for mobile and pervasive computing environments, in which user attention is at a premium. To support the development of context-aware applications, techniques for modelling context information are required. These must address a unique combination of requirements, including the ability to model information supplied by both sensors and people, to represent imperfect information, and to capture context histories. As the field of context-aware computing is relatively new, mature solutions for context modelling do not exist, and researchers rely on information modelling solutions developed for other purposes. In our research, we have been using a variant of Object-Role Modeling (ORM) to model context. In this paper, we reflect on our experiences and outline some research challenges in this area.

On the relationship between Bayesian error bars and the input data density

We investigate the dependence of Bayesian error bars on the distribution of data in input space. For generalized linear regression models we derive an upper bound on the error bars which shows that, in the neighbourhood of the data points, the error bars are substantially reduced from their prior values. For regions of high data density we also show that the contribution to the output variance due to the uncertainty in the weights can exhibit an approximate inverse proportionality to the probability density. Empirical results support these conclusions.

Guiding local regression using visualisation

Solving many scientific problems requires effective regression and/or classification models for large high-dimensional datasets. Experts from these problem domains (e.g. biologists, chemists, financial analysts) have insights into the domain which can be helpful in developing powerful models but they need a modelling framework that helps them to use these insights. Data visualisation is an effective technique for presenting data and requiring feedback from the experts. A single global regression model can rarely capture the full behavioural variability of a huge multi-dimensional dataset. Instead, local regression models, each focused on a separate area of input space, often work better since the behaviour of different areas may vary. Classical local models such as Mixture of Experts segment the input space automatically, which is not always effective and it also lacks involvement of the domain experts to guide a meaningful segmentation of the input space. In this paper we addresses this issue by allowing domain experts to interactively segment the input space using data visualisation. The segmentation output obtained is then further used to develop effective local regression models.

Regression with input-dependent noise: A Gaussian process treatment

Gaussian processes provide natural non-parametric prior distributions over regression functions. In this paper we consider regression problems where there is noise on the output, and the variance of the noise depends on the inputs. If we assume that the noise is a smooth function of the inputs, then it is natural to model the noise variance using a second Gaussian process, in addition to the Gaussian process governing the noise-free output value. We show that prior uncertainty about the parameters controlling both processes can be handled and that the posterior distribution of the noise rate can be sampled from using Markov chain Monte Carlo methods. Our results on a synthetic data set give a posterior noise variance that well-approximates the true variance.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

Structured neural network modelling of multi-valued functions for wind retrieval from scatterometer measurements

A conventional neural network approach to regression problems approximates the conditional mean of the output vector. For mappings which are multi-valued this approach breaks down, since the average of two solutions is not necessarily a valid solution. In this article mixture density networks, a principled method to model conditional probability density functions, are applied to retrieving Cartesian wind vector components from satellite scatterometer data. A hybrid mixture density network is implemented to incorporate prior knowledge of the predominantly bimodal function branches. An advantage of a fully probabilistic model is that more sophisticated and principled methods can be used to resolve ambiguities.

DEA models for ratio data:convexity consideration

Data envelopment analysis (DEA) is defined based on observed units and by finding the distance of each unit to the border of estimated production possibility set (PPS). The convexity is one of the underlying assumptions of the PPS. This paper shows some difficulties of using standard DEA models in the presence of input-ratios and/or output-ratios. The paper defines a new convexity assumption when data includes a ratio variable. Then it proposes a series of modified DEA models which are capable to rectify this problem.

Computer simulation of radio-frequency methane/hydrogen plasmas and their interaction with GaAs Surfaces

The following thesis describes the computer modelling of radio frequency capacitively coupled methane/hydrogen plasmas and the consequences for the reactive ion etching of (100) GaAs surfaces. In addition a range of etching experiments was undertaken over a matrix of pressure, power and methane concentration. The resulting surfaces were investigated using X-ray photoelectron spectroscopy and the results were discussed in terms of physical and chemical models of particle/surface interactions in addition to the predictions for energies, angles and relative fluxes to the substrate of the various plasma species. The model consisted of a Monte Carlo code which followed electrons and ions through the plasma and sheath potentials whilst taking account of collisions with background neutral gas molecules. The ionisation profile output from the electron module was used as input for the ionic module. Momentum scattering interactions of ions with gas molecules were investigated via different models and compared against results given by quantum mechanical code. The interactions were treated as central potential scattering events and the resulting neutral cascades were followed. The resulting predictions for ion energies at the cathode compared well to experimental ion energy distributions and this verified the particular form of the electrical potentials used and their applicability in the particular geometry plasma cell used in the etching experiments. The final code was used to investigate the effect of external plasma parameters on the mass distribution, energy and angles of all species impingent on the electrodes. Comparisons of electron energies in the plasma also agreed favourably with measurements made using a Langmuir electric probe. The surface analysis showed the surfaces all to be depleted in arsenic due to its preferential removal and the resultant Ga:As ratio in the surface was found to be directly linked to the etch rate. The etch rate was determined by the methane flux which was predicted by the code.