EMPIRICS OF THE METROPOLITAN PRODUCTIVITY PATTERNS IN EUROPE

This paper focuses on the main European metropolitan areas and builds empirics on their evolution over the process of economic integration these last twenty years. These metropolitan areas are acknowledged to be the main engines of economic development in Europe, and to concentrate larger and larger shares of population, activities, R&D resources… Different theoretical frameworks have grounded these cumulative dynamics. Recently, regional and development policies have also based their action on these areas, through the concept of polycentricity for instance. The paper rests thus on a database of the forty main European cities over the period 1975-2000, disaggregated in twenty sectors of activity. First of all, the paper analyses the processes of convergence in terms of productivity or sectoral similarities at work between the different metropolitan areas as well as the evolution of their specialization in terms of value added or employment. An analytical framework is outlined thereafter, based on the rates of growth of productivity and employment, which allows us to define a dynamic view of this convergence process, and to map the dynamic comparative advantages of sectors in our metropolitan areas. In addition to the in-depth analysis of the cities, the results of these different steps show that the metropolitan areas are the main vectors of the process of European integration; a standard model of the metropolitan area seems to emerge as a result of this process.METROPOLITAN AREAS, EUROPEAN INTEGRATION, URBAN GROWTH DYNAMICS, CONVERGENCE, SPECIALIZATION

On the methodology of feeding ecology in fish

Feeding ecology explains predator’s preference to some preys over others in their habitat and their competitions thereof. The subject, as a functional and applied biology, is highly neglected, and in case of fish, a uniform and consistent methodology is absent. The currently practiced methods are largely centred on mathematical indices and highly erroneous because of non-uniform outcomes. Therefore, it requires a relook into the subject to elucidate functional contributions and to make it more comparable and comprehensive science. In this article, approachable methodological strategies have been forwarded in three hierarchical steps, namely, food occurrence, feeding biology and interpretative ecology. All these steps involve wide ranges of techniques, within the scope of ecology but not limited to, and traverse from narrative to functional evolutionary ecology. The first step is an assumption-observation practice to assess food of fish, followed by feeding biology that links morphological, histological, cytological, bacteriological or enzymological correlations to preferred food in the environment. Interpretative ecology is the higher level of analysis in which the outcomes are tested and discussed against evolutionary theories. A description of possible pedagogics on the methods of feeding ecological studies has also been forwarded

User-centered visual analysis using a hybrid reasoning architecture for intensive care units

One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care

An architecturally constrained model of random number generation and its application to modeling the effect of generation rate

Random number generation (RNG) is a complex cognitive task for human subjects, requiring deliberative control to avoid production of habitual, stereotyped sequences. Under various manipulations (e.g., speeded responding, transcranial magnetic stimulation, or neurological damage) the performance of human subjects deteriorates, as reflected in a number of qualitatively distinct, dissociable biases. For example, the intrusion of stereotyped behavior (e.g., counting) increases at faster rates of generation. Theoretical accounts of the task postulate that it requires the integrated operation of multiple, computationally heterogeneous cognitive control (“executive”) processes. We present a computational model of RNG, within the framework of a novel, neuropsychologically-inspired cognitive architecture, ESPro. Manipulating the rate of sequence generation in the model reproduced a number of key effects observed in empirical studies, including increasing sequence stereotypy at faster rates. Within the model, this was due to time limitations on the interaction of supervisory control processes, namely, task setting, proposal of responses, monitoring, and response inhibition. The model thus supports the fractionation of executive function into multiple, computationally heterogeneous processes

Estimating Discrete Markov Models From Various Incomplete Data Schemes

The parameters of a discrete stationary Markov model are transition probabilities between states. Traditionally, data consist in sequences of observed states for a given number of individuals over the whole observation period. In such a case, the estimation of transition probabilities is straightforwardly made by counting one-step moves from a given state to another. In many real-life problems, however, the inference is much more difficult as state sequences are not fully observed, namely the state of each individual is known only for some given values of the time variable. A review of the problem is given, focusing on Monte Carlo Markov Chain (MCMC) algorithms to perform Bayesian inference and evaluate posterior distributions of the transition probabilities in this missing-data framework. Leaning on the dependence between the rows of the transition matrix, an adaptive MCMC mechanism accelerating the classical Metropolis-Hastings algorithm is then proposed and empirically studied.Comment: 26 pages - preprint accepted in 20th February 2012 for publication in Computational Statistics and Data Analysis (please cite the journal's paper

Uncertainty in the determination of soil hydraulic parameters and its influence on the performance of two hydrological models of different complexity

Data of soil hydraulic properties forms often a limiting factor in unsaturated zone modelling, especially at the larger scales. Investigations for the hydraulic characterization of soils are time-consuming and costly, and the accuracy of the results obtained by the different methodologies is still debated. However, we may wonder how the uncertainty in soil hydraulic parameters relates to the uncertainty of the selected modelling approach. We performed an intensive monitoring study during the cropping season of a 10 ha maize field in Northern Italy. The data were used to: i) compare different methods for determining soil hydraulic parameters and ii) evaluate the effect of the uncertainty in these parameters on different variables (i.e. evapotranspiration, average water content in the root zone, flux at the bottom boundary of the root zone) simulated by two hydrological models of different complexity: SWAP, a widely used model of soil moisture dynamics in unsaturated soils based on Richards equation, and ALHyMUS, a conceptual model of the same dynamics based on a reservoir cascade scheme. We employed five direct and indirect methods to determine soil hydraulic parameters for each horizon of the experimental profile. Two methods were based on a parameter optimization of: a) laboratory measured retention and hydraulic conductivity data and b) field measured retention and hydraulic conductivity data. The remaining three methods were based on the application of widely used Pedo-Transfer Functions: c) Rawls and Brakensiek, d) HYPRES, and e) ROSETTA. Simulations were performed using meteorological, irrigation and crop data measured at the experimental site during the period June – October 2006. Results showed a wide range of soil hydraulic parameter values generated with the different methods, especially for the saturated hydraulic conductivity Ksat and the shape parameter a of the van Genuchten curve. This is reflected in a variability of the modeling results which is, as expected, different for each model and each variable analysed. The variability of the simulated water content in the root zone and of the bottom flux for different soil hydraulic parameter sets is found to be often larger than the difference between modeling results of the two models using the same soil hydraulic parameter set. Also we found that a good agreement in simulated soil moisture patterns may occur even if evapotranspiration and percolation fluxes are significantly different. Therefore multiple output variables should be considered to test the performances of methods and model