Clustering of Gaussian Random Vector Fields in Multiple Trajectory Modelling

This paper concerns the estimation of multiple dynamical models from a set of observed trajectories. It proposes vector valued gaussian random fields, representing dynamical models and their vector fields, combined with a modified k- means clustering algorithm to assign observed trajectories to models. The assignment is done according to a likelihood function obtained from applying the random field associated to a cluster, to the data. The algorithm is shown to have several advantages when compared with others: 1) it does not depend on a grid, region of interest, grid resolution or interpolation method; 2) the estimated vector fields has an associated uncertainty which is given by the algorithm and taken into account. The paper presents results obtained on synthetic trajectories that illustrate the performance of the proposed algorithm

Accessibility and Development in Peripheral Regions. The Case for Beira Interior

Beira Interior is a Portuguese region located at the centre of Portugal, close to the Spanish border, and traditionally seen as a strongly peripheral region. In the last years the decrease in population and weaknesses of the industrial park have been justified on basis of shortness and/or lack of quality in transport infrastructure. In order to evaluate whether there is in fact a case in favour of infrastructure shortage we have developed a methodology that would allow us to identify the accessibility gains in the recent past, the ones foreseeable in the usually adopted planning periods and the ones possible in a asymptotic scenario of strong generalised accessibility, enabling this way to make explicit identification of the gains already achieved and the ones still possible. The evolution of values of the studied region was compared with the corresponding values in the region Litoral Centro – the region that was also used as benchmark in a previous consultation process to industrial key informants operating in Beira Interior. This thematic is extremely important for the region and for the country since the conclusions obtained will enable a better supported discussion on additional investment in transport infrastructure.

Analysis of competitiveness in Colombian family businesses

Purpose: Building on the resource-based view and the configuration theory, the purpose of this study uses a systemic and multidimensional competitiveness index (CI) i.e. that incorporates system constraints among the 10 competitive pillars that form the index to assess the competitiveness level and the connection between competitiveness and economic performance [return on assets (ROA)] in family businesses (FBs). Design/methodology/approach: For the empirical application, the use a unique primary data set drawn from the global competitiveness project (www.gcp.org) that includes information for 77 Colombian FBs for 2017. Cluster analysis is used to evaluate the potential relationship between competitiveness, the configuration of competitive pillars and economic performance (ROA). Findings: The results for the CI show that the main competitive strengths of the analysed firms are related to the introduction of product innovations and networks (suppliers and customers), while the limited use of technologies in their operations and the low online presence are the main competitive weaknesses of these firms. Additionally, the findings of the cluster analysis reveal that different configurations of competitiveness pillars are associated with different performance levels. Therefore, the results contribute to identifying how specific strategies aimed at improving different resources or capabilities contribute to enhance business competitiveness, and ultimately, performance. Originality/value: By using an index number that takes into account the multiple interactions between resources and capabilities, the proposed analysis not only sheds light on the drivers of competitiveness i.e. resources and capabilities, and its connection to performance but also contributes to understanding the boundaries of the businesses’ competitiveness system, as well as the strategies that can potentially enhance competitiveness, and subsequently, business performance.Peer ReviewedPreprin

Recursive bayesian identification of nonlinear autonomous systems

This paper concerns the recursive identification of nonlinear discrete-time systems for which the original equations of motion are not known. Since the true model structure is not available, we replace it with a generic nonlinear model. This generic model discretizes the state space into a finite grid and associates a set of velocity vectors to the nodes of the grid. The velocity vectors are then interpolated to define a vector field on the complete state space. The proposed method follows a Bayesian framework where the identified velocity vectors are selected by the maximum a posteriori (MAP) criterion. The resulting algorithms allow a recursive update of the velocity vectors as new data is obtained. Simulation examples using the recursive algorithm are presented