Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Forecasting tourism demand to Catalonia: neural networks vs. time series models

The increasing interest aroused by more advanced forecasting techniques, together with the requirement for more accurate forecasts of tourismdemand at the destination level due to the constant growth of world tourism, has lead us to evaluate the forecasting performance of neural modelling relative to that of time seriesmethods at a regional level. Seasonality and volatility are important features of tourism data, which makes it a particularly favourable context in which to compare the forecasting performance of linear models to that of nonlinear alternative approaches. Pre-processed official statistical data of overnight stays and tourist arrivals fromall the different countries of origin to Catalonia from 2001 to 2009 is used in the study. When comparing the forecasting accuracy of the different techniques for different time horizons, autoregressive integrated moving average models outperform self-exciting threshold autoregressions and artificial neural network models, especially for shorter horizons. These results suggest that the there is a trade-off between the degree of pre-processing and the accuracy of the forecasts obtained with neural networks, which are more suitable in the presence of nonlinearity in the data. In spite of the significant differences between countries, which can be explained by different patterns of consumer behaviour,we also find that forecasts of tourist arrivals aremore accurate than forecasts of overnight stays.

Integrating defeasible argumentation with fuzzy ART neural networks for pattern classification

Many classification systems rely on clustering techniques in which a collection of training examples is provided as an input, and a number of clusters c1,...cm modelling some concept C results as an output, such that every cluster ci is labelled as positive or negative. Given a new, unlabelled instance enew, the above classification is used to determine to which particular cluster ci this new instance belongs. In such a setting clusters can overlap, and a new unlabelled instance can be assigned to more than one cluster with conflicting labels. In the literature, such a case is usually solved non-deterministically by making a random choice. This paper presents a novel, hybrid approach to solve this situation by combining a neural network for classification along with a defeasible argumentation framework which models preference criteria for performing clustering.

Modelling bird species richness with neural networks for forest landscape management in NE Spain

Para preservar la biodiversidad de los ecosistemas forestales de la Europa mediterránea en escenarios actuales y futuros de cambio global mediante una gestión forestal sostenible es necesario determinar cómo influye el medio ambiente y las propias características de los bosques sobre la biodiversidad que éstos albergan. Con este propósito, se analizó la influencia de diferentes factores ambientales y de estructura y composición del bosque sobre la riqueza de aves forestales a escala 1 × 1 km en Cataluña (NE de España). Se construyeron modelos univariantes y multivariantes de redes neuronales para respectivamente explorar la respuesta individual a las variables y obtener un modelo parsimonioso (ecológicamente interpretable) y preciso. La superficie de bosque (con una fracción de cabida cubierta superior a 5%), la fracción de cabida cubierta media, la temperatura anual y la precipitación estival medias fueron los mejores predictores de la riqueza de aves forestales. La red neuronal multivariante obtenida tuvo una buena capacidad de generalización salvo en las localidades con una mayor riqueza. Además, los bosques con diferentes grados de apertura del dosel arbóreo, más maduros y más diversos en cuanto a su composición de especies arbóreas se asociaron de forma positiva con una mayor riqueza de aves forestales. Finalmente, se proporcionan directrices de gestión para la planificación forestal que permitan promover la diversidad ornítica en esta región de la Europa mediterránea.

Time patterns and perceptions of online learning success factors

Online learning provides the opportunity to work on academic tasks at any time at the same time as doing other activities, such as using in web 2.0 tools. This study identifies factors that contribute to success in online learning from the students¿ perspective and their relationship with time patterns. A survey of learning outputs was used to find relationships between students¿ satisfaction, knowledge acquisition and knowledge transfer with time for working on academic tasks. In this study, 199 students from a university in Mexico completed the survey. Findings suggest that knowledge transfer has a significant association with the number of hours online per day, hours spent on social networks and the use made of e-learning during working hours. Learner satisfaction has a strong relationship with the time in years a learner has been using the Internet and the number of hours devoted to the course per week. The findings of this research will be helpful for faculty and instructional designers for implementing learning strategies.

QoS support in satellite and wireless networks: study under the network simulator (NS-2)

Aquest projecte es basa en l'estudi de l'oferiment de qualitat de servei en xarxes wireless i satel·litals. Per això l'estudi de les tècniques de cross-layer i del IEEE 802.11e ha sigut el punt clau per al desenvolupament teòric d’aquest estudi. Usant el simulador de xarxes network simulator, a la part de simulacions es plantegen tres situacions: l'estudi de la xarxa satel·lital, l'estudi del mètode d'accés HCCA i la interconnexió de la xarxa satel·lital amb la wireless. Encara que aquest últim punt, incomplet en aquest projecte, ha de ser la continuació per a futures investigacions.

Intonational patterns and comprehension in Broca's aphasia

The goal of the study is to determine whether the deficit is syntactic in nature and that aphasic individuals preserve their phonological abilities intact. This has important implications, since it contributes to achieve a more complete characterization of agrammatic comprehension and to report new data from the Romance languages. Here, an experimental task is designed and carried out with six catalan Broca's aphasics, in order to find out whether they preserve their phonological skills. The results establish that Catalan aphasics' comprehension does not result from a phonological impairment

Spatio-temporal variations in reproductive patterns and population structure of Pasiphaea multidentata (Decapoda: Caridea) in the Blanes canyon and adjacent margin, North-western Mediterranean Sea

The bathyal faunal communities of the NW Mediterranean slopes have been studied consistently in the last two decades, with a special focus on population structure, trophic dynamics and benthopelagic coupling of commercial deep-sea decapod crustaceans and fishes (reviewed in Sardà et al. 2004) and associated species (Cartes and Sardà, 1993; Company and Sardà, 1997, 2000; Cartes et al., 2001; Company et al., 2001, 2003, 2004). One of the major topographic features in the North-western Mediterranean slope is the presence of submarine canyons. Canyons play a major role in funnelling energy and organic matter from the shelf to bathyal and abyssal depths (Puig et al., 2000), but the implications of this enhanced organic supply in the deep-sea benthic communities is still mostly unknown. Trophic supply can follow two major pathways – vertical deposition in the water column (Billett et al., 1983; Baldwin et al., 1998; Lampitt et al., 2001) or down-slope advection on the margins (Puig et al., 2001; Bethoux et al., 2002; Canals et al., 2006) – and can be a limiting factor in the deep-sea, being especially important in the oligotrophic Mediterranean Sea (Sardà et al., 2004). Differences in the quantity, quality and timing of organic matter input to the deep seafloor have been used to explain patterns of biomass and abundance in benthic communities (Levin et al., 1994; Gooday & Turley, 1990; Billett et al., 2001; Galéron et al., 2001; Puig et al., 2001; Gage, 2003) as well as other biological process and in particular the existence of seasonal reproduction (Tyler et al., 1994; Company et al., 2004 (MEPS). Reproduction is a highly energetic process tightly linked to food availability and quality.

Cannabinoid addiction: behavioral models and neural correlates

The use of cannabis sativa preparations as recreational drugs can be traced back to the earliest civilizations. However, animal models of cannabinoid addiction allowing the exploration of neural correlates of cannabinoid abuse have been developed only recently. We review these models and the role of the CB1 cannabinoid receptor, the main target of natural cannabinoids, and its interaction with opioid and dopamine transmission in reward circuits. Extensive reviews on the molecular basis of cannabinoid action are available elsewhere (Piomelli et al., 2000;Schlicker and Kathmann, 2001).

Biana: a software framework for compiling biological interactions and analyzing networks

Background: The analysis and usage of biological data is hindered by the spread of information across multiple repositories and the difficulties posed by different nomenclature systems and storage formats. In particular, there is an important need for data unification in the study and use of protein-protein interactions. Without good integration strategies, it is difficult to analyze the whole set of available data and its properties.Results: We introduce BIANA (Biologic Interactions and Network Analysis), a tool for biological information integration and network management. BIANA is a Python framework designed to achieve two major goals: i) the integration of multiple sources of biological information, including biological entities and their relationships, and ii) the management of biological information as a network where entities are nodes and relationships are edges. Moreover, BIANA uses properties of proteins and genes to infer latent biomolecular relationships by transferring edges to entities sharing similar properties. BIANA is also provided as a plugin for Cytoscape, which allows users to visualize and interactively manage the data. A web interface to BIANA providing basic functionalities is also available. The software can be downloaded under GNU GPL license from http://sbi.imim.es/web/BIANA.php.Conclusions: BIANA's approach to data unification solves many of the nomenclature issues common to systems dealing with biological data. BIANA can easily be extended to handle new specific data repositories and new specific data types. The unification protocol allows BIANA to be a flexible tool suitable for different user requirements: non-expert users can use a suggested unification protocol while expert users can define their own specific unification rules.

Treatment patterns and health care resource utilization in a 1-year observational cohort study of outpatients with schizophrenia at risk of nonadherence treated with long-acting injectable antipsychotics

Purpose: To describe (1) the clinical profiles and the patterns of use of long-acting injectable (LAI) antipsychotics in patients with schizophrenia at risk of nonadherence with oral antipsychotics, and in those who started treatment with LAI antipsychotics, (2) health care resource utilization and associated costs. Patients and methods: A total of 597 outpatients with schizophrenia at risk of nonadherence, according to the psychiatrist's clinical judgment, were recruited at 59 centers in a noninterventional prospective observational study of 1-year follow-up when their treatment was modified. In a post hoc analysis, the profiles of patients starting LAI or continuing with oral antipsychotics were described, and descriptive analyses of treatments, health resource utilization, and direct costs were performed in those who started an LAI antipsychotic. Results: Therapy modifications involved the antipsychotic medications in 84.8% of patients, mostly because of insufficient efficacy of prior regimen. Ninety-two (15.4%) patients started an LAI antipsychotic at recruitment. Of these, only 13 (14.1%) were prescribed with first-generation antipsychotics. During 1 year, 16.3% of patients who started and 14.9% of patients who did not start an LAI antipsychotic at recruitment relapsed, contrasting with the 20.9% who had been hospitalized only within the prior 6 months. After 1 year, 74.3% of patients who started an LAI antipsychotic continued concomitant treatment with oral antipsychotics. The mean (median) total direct health care cost per patient per month during the study year among the patients starting any LAI antipsychotic at baseline was 1,407 ( 897.7). Medication costs (including oral and LAI antipsychotics and concomitant medication) represented almost 44%, whereas nonmedication costs accounted for more than 55% of the mean total direct health care costs. Conclusion: LAI antipsychotics were infrequently prescribed in spite of a psychiatrist-perceived risk of nonadherence to oral antipsychotics. Mean medication costs were lower than nonmedication costs.

Nonequilibrium patterns and shape fluctuations in reactive membranes

A simple kinetic model of a two-component deformable and reactive bilayer is presented. The two differently shaped components are interconverted by a nonequilibrium reaction, and a phenomenological coupling between local composition and curvature is proposed. When the two components are not miscible, linear stability analysis predicts, and numerical simulations show, the formation of stationary nonequilibrium composition/curvature patterns whose typical size is determined by the reactive process. For miscible components, a linearization of the dynamic equations is performed in order to evaluate the correlation function for shape fluctuations from which the behavior of these systems in micropipet aspiration experiments can be predicted.

Regional income inequality in Italy in the long run (1871–2001). Patterns and determinants

The chapter presents up-to-date estimates of Italy’s regional GDP, with the present borders, in ten-year benchmarks from 1871 to 2001, and proposes a new interpretative hypothesis based on long-lasting socio-institutional differences. The inverted U-shape of income inequality is confirmed: rising divergence until the midtwentieth century, then convergence. However, the latter was limited to the centrenorth: Italy was divided into three parts by the time regional inequality peaked, in 1951, and appears to have been split into two halves by 2001. As a consequence of the falling back of the south, from 1871 to 2001 we record σ-divergence across Italy’s regions, i.e. an increase in dispersion, and sluggish β-convergence. Geographical factors and the market size played a minor role: against them are both the evidence that most of the differences in GDP are due to employment rather than to productivity and the observed GDP patterns of many regions. The gradual converging of regional GDPs towards two equilibria instead follows social and institutional differences − in the political and economic institutions and in the levels of human and social capital – which originated in pre-unification states and did not die (but in part even increased) in postunification Italy.