A guide to studying the socio-ecological transition in european agriculture

This paper shows the potential of the Social Metabolism approach to study the industrialization of the agriculture. It provides information about the physical functioning of agrarian systems over time and their spatial differences. It also sheds light on how the industrialisation of agriculture occurred; in other words, how the Socio-Ecological Transition (SET) took place in agriculture. The paper begins defining the characteristic features of the Organic Agrarian Metabolism (OAM), the starting point of Sociecological Transition. The next section examines the main changes there been in agrarian metabolism until its complete industrialization. This analysis is enriched by the concept of the SET since, by showing the paths followed by industrialisation from a physical perspective, it establishes the research agenda or points out a series of issues that should be prioritised in research; it facilitates identification of the driving forces for change that interact between social and environmental factors; and it establishes special scales in which transition occurs and the relationship between them. The paper ends with the application of this conceptual fremework to teh First Wave of industrialization in European Agriculture during 19th century.Social Metabolism; Socio-Ecological Transition; Preindustrial Agriculture; Industrialised Agriculture; Agricultural Change

Light- and strange-quark mass dependence of the ρ(770)\rho(770) meson revisited

Recent lattice data on $\pi\pi$-scattering phase shifts in the vector-isovector channel, pseudoscalar meson masses and decay constants for strange-quark masses smaller or equal to the physical value allow us to study the strangeness dependence of these observables for the first time. We perform a global analysis on two kind of lattice trajectories depending on whether the sum of quark masses or the strange-quark mass is kept fixed to the physical point. The quark mass dependence of these observables is extracted from unitarized coupled-channel one-loop Chiral Perturbation Theory. This analysis guides new predictions on the $\rho(770)$ meson properties over trajectories where the strange-quark mass is lighter than the physical mass, as well as on the SU(3) symmetric line. As a result, the light- and strange-quark mass dependence of the $\rho(770)$ meson parameters are discussed and precise values of the Low Energy Constants present in unitarized one-loop Chiral Perturbation Theory are given. Finally, the current discrepancy between two- and three-flavor lattice results for the $\rho(770)$ meson is studied.Comment: 44 pages, 41 figures, 11 table

The Importance of Creative Industry Agglomerations in Explaining the Wealth of European Regions

This paper examines the existence of regional agglomerations of manufacturing, service and creative industries, the relationship between these industries and the wealth of regions and their industrial structure. Through an analysis of 250 European regions, three important conclusions can be inferred from the results obtained in this paper. The first is that creative industries play an important role in the wealth of a region. The second is that the most creative regions are characterized by having more high-tech manufacturing industries than the rest of the regions although the number of low-tech manufacturing firms is similar. Lastly, the industrial structure of each region has a greater influence on regional wealth than the existence of industrial agglomerations. The importance of this paper resides in the fact that up until now no analysis has demonstrated that creative industries are the most important industries in regional wealth.

High-ISO long-exposure image denoising based on quantitative blob characterization

Blob detection and image denoising are fundamental, sometimes related tasks in computer vision. In this paper, we present a computational method to quantitatively measure blob characteristics using normalized unilateral second-order Gaussian kernels. This method suppresses non-blob structures while yielding a quantitative measurement of the position, prominence and scale of blobs, which can facilitate the tasks of blob reconstruction and blob reduction. Subsequently, we propose a denoising scheme to address high-ISO long-exposure noise, which sometimes spatially shows a blob appearance, employing a blob reduction procedure as a cheap preprocessing for conventional denoising methods. We apply the proposed denoising methods to real-world noisy images as well as standard images that are corrupted by real noise. The experimental results demonstrate the superiority of the proposed methods over state-of-the-art denoising methods

An enhanced classifier system for autonomous robot navigation in dynamic environments

In many cases, a real robot application requires the navigation in dynamic environments. The navigation problem involves two main tasks: to avoid obstacles and to reach a goal. Generally, this problem could be faced considering reactions and sequences of actions. For solving the navigation problem a complete controller, including actions and reactions, is needed. Machine learning techniques has been applied to learn these controllers. Classifier Systems (CS) have proven their ability of continuos learning in these domains. However, CS have some problems in reactive systems. In this paper, a modified CS is proposed to overcome these problems. Two special mechanisms are included in the developed CS to allow the learning of both reactions and sequences of actions. The learning process has been divided in two main tasks: first, the discrimination between a predefined set of rules and second, the discovery of new rules to obtain a successful operation in dynamic environments. Different experiments have been carried out using a mini-robot Khepera to find a generalised solution. The results show the ability of the system to continuous learning and adaptation to new situations.Publicad

Hierarchical genetic algorithms for composite laminate panels stress optimisation

IEEE International Conference on Systems, Man, and Cybernetics. Tokyo, 12-15 October 1999.Genetic algorithms (GAs) have demonstrated to be a powerful technique for solving optimisation problems. In this article, the problem of optimising the number of plies and their stacking sequence in the design of laminated composite panels is considered. This problem has special features that makes it different from traditional problems in which GAs have been applied, which make the problem a multiobjective optimisation one. Symmetry and equilibrium constraints have also been included in the solution. A modification of the canonical GA is needed and a new perspective for solving this problem by using GA techniques is introduced

Hydroelectric power plant management relying on neural networks and expert system integration

The use of Neural Networks (NN) is a novel approach that can help in taking decisions when integrated in a more general system, in particular with expert systems. In this paper, an architecture for the management of hydroelectric power plants is introduced. This relies on monitoring a large number of signals, representing the technical parameters of the real plant. The general architecture is composed of an Expert System and two NN modules: Acoustic Prediction (NNAP) and Predictive Maintenance (NNPM). The NNAP is based on Kohonen Learning Vector Quantization (LVQ) Networks in order to distinguish the sounds emitted by electricity-generating machine groups. The NNPM uses an ART-MAP to identify different situations from the plant state variables, in order to prevent future malfunctions. In addition, a special process to generate a complete training set has been designed for the ART-MAP module. This process has been developed to deal with the absence of data about abnormal plant situations, and is based on neural nets trained with the backpropagation algorithm.Publicad

Uniform coevolution for solving the density classification problem in cellular automata

Genetic and Evolutionary Computation Conference (GECCO 2000). Las Vegas, Nevada (USA), July 8-12 2000.Uniform Coevolution is based on competitive evolution ideas where the solution and example sets are evolving by means of a competition to generate difficult test beds for the solutions in a gradual way. The method has been tested with the density parity problem in cellular automata, where the selected examples can biased the solutions founded. The results show a high value of generality using Uniform coevolution, compared with no Co-evolutive approaches.Publicad