Seasonal dynamic factor analysis and bootstrap inference : application to electricity market forecasting

Year-ahead forecasting of electricity prices is an important issue in the current context of electricity markets. Nevertheless, only one-day-ahead forecasting is commonly tackled up in previous published works. Moreover, methodology developed for the short-term does not work properly for long-term forecasting. In this paper we provide a seasonal extension of the Non-Stationary Dynamic Factor Analysis, to deal with the interesting problem (both from the economic and engineering point of view) of long term forecasting of electricity prices. Seasonal Dynamic Factor Analysis (SeaDFA) allows to deal with dimensionality reduction in vectors of time series, in such a way that extracts common and specific components. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal one, by means of common factors following a multiplicative seasonal VARIMA(p,d,q)×(P,D,Q)s model. Besides, a bootstrap procedure is proposed to be able to make inference on all the parameters involved in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing to enhance the coverage of forecast confidence intervals. Concerning the innovative and challenging application provided, bootstrap procedure developed allows to calculate not only point forecasts but also forecasting intervals for electricity prices

Slide-Down Prevention for Wheeled Mobile Robots on Slopes

Wheeled mobile robots on inclined terrain can slide down due to loss of traction and gravity. This type of instability, which is different from tip-over, can provoke uncontrolled motion or get the vehicle stuck. This paper proposes slide-down prevention by real-time computation of a straightforward stability margin for a given ground-wheel friction coefficient. This margin is applied to the case study of Lazaro, a hybrid skid-steer mobile robot with caster-leg mechanism that allows tests with four or five wheel contact points. Experimental results for both ADAMS simulations and the actual vehicle demonstrate the effectiveness of the proposed approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The economic geography of football success: empirical evidence from european cities

Introduction. – 1. The geography of successful football teams: an analytical framework – 2. Empirical analysis – 2.1. Data, model estimation and results – 2.2. Cities and teams: some remarks about market size and teams’ performance – 3. Conclusions – 4. Annex

Inhibition of pneumococcal choline-binding proteins and cell growth by esters of bicyclic amines

Streptococcus pneumoniae is one of the major pathogens worldwide. The use of currently available antibiotics to treat pneumococcal diseases is hampered by increasing resistance levels; also, capsular polysaccharide-based vaccination is of limited efficacy. Therefore, it is desirable to find targets for the development of new antimicrobial drugs specifically designed to fight pneumococcal infections. Choline-binding proteins are a family of polypeptides, found in all S. pneumoniae strains, that take part in important physiologic processes of this bacterium. Among them are several murein hydrolases whose enzymatic activity is usually inhibited by an excess of choline. Using a simple chromatographic procedure, we have identified several choline analogs able to strongly interact with the choline-binding module (C-LytA) of the major autolysin of S. pneumoniae. Two of these compounds (atropine and ipratropium) display a higher binding affinity to C-LytA than choline, and also increase the stability of the protein. CD and fluorescence spectroscopy analyses revealed that the conformational changes of C-LytA upon binding of these alkaloids are different to those induced by choline, suggesting a different mode of binding. In vitro inhibition assays of three pneumococcal, choline-dependent cell wall lytic enzymes also demonstrated a greater inhibitory efficiency of those molecules. Moreover, atropine and ipratropium strongly inhibited in vitro pneumococcal growth, altering cell morphology and reducing cell viability, a very different response than that observed upon addition of an excess of choline. These results may open up the possibility of the development of bicyclic amines as new antimicrobials for use against pneumococcal pathologies

Multi-objective optimization with an adaptive resonance theory-based estimation of distribution algorithm

The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have an intrinsic shortcoming in their model-building algorithms that hamper their performance. In this work, we put forward the argument that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and a hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.This work was supported by projects Projects CICYT TIN2011-28620-C02- 01, CICYT TEC2011-28626-C02-02, CAM CONTEXTS (S2009/TIC-1485) and DPS2008-07029-C02-02.Publicad