Long-term modifications of coastal defences enhance marine biodiversity

Realization that hard coastal infrastructures support lower biodiversity than natural habitats has prompted a wealth of research seeking to identify design enhancements offering ecological benefits. Some studies showed that artificial structures could be modified to increase levels of diversity. Most studies, however, only considered the short-term ecological effects of such modifications, even though reliance on results from short-term studies may lead to serious misjudgements in conservation. In this study, a seven-year experiment examined how the addition of small pits to otherwise featureless seawalls may enhance the stocks of a highly-exploited limpet. Modified areas of the seawall supported enhanced stocks of limpets seven years after the addition of pits. Modified areas of the seawall also supported a community that differed in the abundance of littorinids, barnacles and macroalgae compared to the controls. Responses to different treatments (numbers and size of pits) were species-specific and, while some species responded directly to differences among treatments, others might have responded indirectly via changes in the distribution of competing species. This type of habitat enhancement can have positive long-lasting effects on the ecology of urban seascapes. Understanding of species interactions could be used to develop a rule-based approach to enhance biodiversity.

Long-term modifications of coastal defences enhance marine biodiversity

Realization that hard coastal infrastructures support lower biodiversity than natural habitats has prompted a wealth of research seeking to identify design enhancements offering ecological benefits. Some studies showed that artificial structures could be modified to increase levels of diversity. Most studies, however, only considered the short-term ecological effects of such modifications, even though reliance on results from short-term studies may lead to serious misjudgements in conservation. In this study, a seven-year experiment examined how the addition of small pits to otherwise featureless seawalls may enhance the stocks of a highly-exploited limpet. Modified areas of the seawall supported enhanced stocks of limpets seven years after the addition of pits. Modified areas of the seawall also supported a community that differed in the abundance of littorinids, barnacles and macroalgae compared to the controls. Responses to different treatments (numbers and size of pits) were species-specific and, while some species responded directly to differences among treatments, others might have responded indirectly via changes in the distribution of competing species. This type of habitat enhancement can have positive long-lasting effects on the ecology of urban seascapes. Understanding of species interactions could be used to develop a rule-based approach to enhance biodiversity.

A calibration approach based on Takagi-Sugeno fuzzy inference system for digital electronic compasses

Recently, the development of industrial processes brought on the outbreak of technologically complex systems. This development generated the necessity of research relative to the mathematical techniques that have the capacity to deal with project complexities and validation. Fuzzy models have been receiving particular attention in the area of nonlinear systems identification and analysis due to it is capacity to approximate nonlinear behavior and deal with uncertainty. A fuzzy rule-based model suitable for the approximation of many systems and functions is the Takagi-Sugeno (TS) fuzzy model. IS fuzzy models are nonlinear systems described by a set of if then rules which gives local linear representations of an underlying system. Such models can approximate a wide class of nonlinear systems. In this paper a performance analysis of a system based on IS fuzzy inference system for the calibration of electronic compass devices is considered. The contribution of the evaluated IS fuzzy inference system is to reduce the error obtained in data acquisition from a digital electronic compass. For the reliable operation of the TS fuzzy inference system, adequate error measurements must be taken. The error noise must be filtered before the application of the IS fuzzy inference system. The proposed method demonstrated an effectiveness of 57% at reducing the total error based on considered tests. (C) 2011 Elsevier Ltd. All rights reserved.

A Belief System's Organization Based on a Computational Model of the Dynamic Context: First Approximation

In this article we present a model of organization of a belief system based on a set of binary recursive functions that characterize the dynamic context that modifies the beliefs. The initial beliefs are modeled by a set of two-bit words that grow, update, and generate other beliefs as the different experiences of the dynamic context appear. Reason is presented as an emergent effect of the experience on the beliefs. The system presents a layered structure that allows a functional organization of the belief system. Our approach seems suitable to model different ways of thinking and to apply to different realistic scenarios such as ideologies.

Predicting and diagnosing faults in wastewater treatment process by Bayesian Belief Networks

Diagnosing faults in wastewater treatment, like diagnosis of most problems, requires bi-directional plausible reasoning. This means that both predictive (from causes to symptoms) and diagnostic (from symptoms to causes) inferences have to be made, depending on the evidence available, in reasoning for the final diagnosis. The use of computer technology for the purpose of diagnosing faults in the wastewater process has been explored, and a rule-based expert system was initiated. It was found that such an approach has serious limitations in its ability to reason bi-directionally, which makes it unsuitable for diagnosing tasks under the conditions of uncertainty. The probabilistic approach known as Bayesian Belief Networks (BBNS) was then critically reviewed, and was found to be well-suited for diagnosis under uncertainty. The theory and application of BBNs are outlined. A full-scale BBN for the diagnosis of faults in a wastewater treatment plant based on the activated sludge system has been developed in this research. Results from the BBN show good agreement with the predictions of wastewater experts. It can be concluded that the BBNs are far superior to rule-based systems based on certainty factors in their ability to diagnose faults and predict systems in complex operating systems having inherently uncertain behaviour.

An improved conflicting evidence combination approach based on a new supporting probability distance

To avoid counter-intuitive result of classical Dempster's combination rule when dealing with highly conflict information, many improved combination methods have been developed through modifying the basic probability assignments (BPAs) of bodies of evidence (BOEs) by using a certain measure of the degree of conflict or uncertain information, such as Jousselme's distance, the pignistic probability distance and the ambiguity measure. However, if BOEs contain some non-singleton elements and the differences among their BPAs are larger than 0.5, the current conflict measure methods have limitations in describing the interrelationship among the conflict BOEs and may even lead to wrong combination results. In order to solve this problem, a new distance function, which is called supporting probability distance, is proposed to characterize the differences among BOEs. With the new distance, the information of how much a focal element is supported by the other focal elements in BOEs can be given. Also, a new combination rule based on the supporting probability distance is proposed for the combination of the conflicting evidences. The credibility and the discounting factor of each BOE are generated by the supporting probability distance and the weighted BOEs are combined directly using Dempster's rules. Analytical results of numerical examples show that the new distance has a better capability of describing the interrelationships among BOEs, especially for the highly conflicting BOEs containing non-singleton elements and the proposed new combination method has better applicability and effectiveness compared with the existing methods.

A Bayesian approach to fuzzy hypotheses testing for the estimation of optimal age for vaccination against measles

Fuzzy Bayesian tests were performed to evaluate whether the mother`s seroprevalence and children`s seroconversion to measles vaccine could be considered as ""high"" or ""low"". The results of the tests were aggregated into a fuzzy rule-based model structure, which would allow an expert to influence the model results. The linguistic model was developed considering four input variables. As the model output, we obtain the recommended age-specific vaccine coverage. The inputs of the fuzzy rules are fuzzy sets and the outputs are constant functions, performing the simplest Takagi-Sugeno-Kang model. This fuzzy approach is compared to a classical one, where the classical Bayes test was performed. Although the fuzzy and classical performances were similar, the fuzzy approach was more detailed and revealed important differences. In addition to taking into account subjective information in the form of fuzzy hypotheses it can be intuitively grasped by the decision maker. Finally, we show that the Bayesian test of fuzzy hypotheses is an interesting approach from the theoretical point of view, in the sense that it combines two complementary areas of investigation, normally seen as competitive. (C) 2007 IMACS. Published by Elsevier B.V. All rights reserved.

Evaluation of an online, case-based interactive approach to teaching pathophysiology

AIM: The aim of this study was to evaluate a new pedagogical approach in teaching fluid, electrolyte and acid-base pathophysiology in undergraduate students. METHODS: This approach comprises traditional lectures, the study of clinical cases on the web and a final interactive discussion of these cases in the classroom. When on the web, the students are asked to select laboratory tests that seem most appropriate to understand the pathophysiological condition underlying the clinical case. The percentage of students having chosen a given test is made available to the teacher who uses it in an interactive session to stimulate discussion with the whole class of students. The same teacher used the same case studies during 2 consecutive years during the third year of the curriculum. RESULTS: The majority of students answered the questions on the web as requested and evaluated positively their experience with this form of teaching and learning. CONCLUSIONS: Complementing traditional lectures with online case-based studies and interactive group discussions represents, therefore, a simple means to promote the learning and the understanding of complex pathophysiological mechanisms. This simple problem-based approach to teaching and learning may be implemented to cover all fields of medicine.

A probabilistic approach to niche-based community models for spatial forecasts of assemblage properties and their uncertainties

Aim Conservation strategies are in need of predictions that capture spatial community composition and structure. Currently, the methods used to generate these predictions generally focus on deterministic processes and omit important stochastic processes and other unexplained variation in model outputs. Here we test a novel approach of community models that accounts for this variation and determine how well it reproduces observed properties of alpine butterfly communities. Location The western Swiss Alps. Methods We propose a new approach to process probabilistic predictions derived from stacked species distribution models (S-SDMs) in order to predict and assess the uncertainty in the predictions of community properties. We test the utility of our novel approach against a traditional threshold-based approach. We used mountain butterfly communities spanning a large elevation gradient as a case study and evaluated the ability of our approach to model species richness and phylogenetic diversity of communities. Results S-SDMs reproduced the observed decrease in phylogenetic diversity and species richness with elevation, syndromes of environmental filtering. The prediction accuracy of community properties vary along environmental gradient: variability in predictions of species richness was higher at low elevation, while it was lower for phylogenetic diversity. Our approach allowed mapping the variability in species richness and phylogenetic diversity projections. Main conclusion Using our probabilistic approach to process species distribution models outputs to reconstruct communities furnishes an improved picture of the range of possible assemblage realisations under similar environmental conditions given stochastic processes and help inform manager of the uncertainty in the modelling results

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Modelling software quality : a multidimensional approach

Les sociétés modernes dépendent de plus en plus sur les systèmes informatiques et ainsi, il y a de plus en plus de pression sur les équipes de développement pour produire des logiciels de bonne qualité. Plusieurs compagnies utilisent des modèles de qualité, des suites de programmes qui analysent et évaluent la qualité d'autres programmes, mais la construction de modèles de qualité est difficile parce qu'il existe plusieurs questions qui n'ont pas été répondues dans la littérature. Nous avons étudié les pratiques de modélisation de la qualité auprès d'une grande entreprise et avons identifié les trois dimensions où une recherche additionnelle est désirable : Le support de la subjectivité de la qualité, les techniques pour faire le suivi de la qualité lors de l'évolution des logiciels, et la composition de la qualité entre différents niveaux d'abstraction. Concernant la subjectivité, nous avons proposé l'utilisation de modèles bayésiens parce qu'ils sont capables de traiter des données ambiguës. Nous avons appliqué nos modèles au problème de la détection des défauts de conception. Dans une étude de deux logiciels libres, nous avons trouvé que notre approche est supérieure aux techniques décrites dans l'état de l'art, qui sont basées sur des règles. Pour supporter l'évolution des logiciels, nous avons considéré que les scores produits par un modèle de qualité sont des signaux qui peuvent être analysés en utilisant des techniques d'exploration de données pour identifier des patrons d'évolution de la qualité. Nous avons étudié comment les défauts de conception apparaissent et disparaissent des logiciels. Un logiciel est typiquement conçu comme une hiérarchie de composants, mais les modèles de qualité ne tiennent pas compte de cette organisation. Dans la dernière partie de la dissertation, nous présentons un modèle de qualité à deux niveaux. Ces modèles ont trois parties: un modèle au niveau du composant, un modèle qui évalue l'importance de chacun des composants, et un autre qui évalue la qualité d'un composé en combinant la qualité de ses composants. L'approche a été testée sur la prédiction de classes à fort changement à partir de la qualité des méthodes. Nous avons trouvé que nos modèles à deux niveaux permettent une meilleure identification des classes à fort changement. Pour terminer, nous avons appliqué nos modèles à deux niveaux pour l'évaluation de la navigabilité des sites web à partir de la qualité des pages. Nos modèles étaient capables de distinguer entre des sites de très bonne qualité et des sites choisis aléatoirement. Au cours de la dissertation, nous présentons non seulement des problèmes théoriques et leurs solutions, mais nous avons également mené des expériences pour démontrer les avantages et les limitations de nos solutions. Nos résultats indiquent qu'on peut espérer améliorer l'état de l'art dans les trois dimensions présentées. En particulier, notre travail sur la composition de la qualité et la modélisation de l'importance est le premier à cibler ce problème. Nous croyons que nos modèles à deux niveaux sont un point de départ intéressant pour des travaux de recherche plus approfondis.

Model-based objects recognition in industrial environments for autonomous vehicles control

Behavior-based navigation of autonomous vehicles requires the recognition of the navigable areas and the potential obstacles. In this paper we describe a model-based objects recognition system which is part of an image interpretation system intended to assist the navigation of autonomous vehicles that operate in industrial environments. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using a rule-based cooperative expert system

Model-based objects recognition in man-made environments

We describe a model-based objects recognition system which is part of an image interpretation system intended to assist autonomous vehicles navigation. The system is intended to operate in man-made environments. Behavior-based navigation of autonomous vehicles involves the recognition of navigable areas and the potential obstacles. The recognition system integrates color, shape and texture information together with the location of the vanishing point. The recognition process starts from some prior scene knowledge, that is, a generic model of the expected scene and the potential objects. The recognition system constitutes an approach where different low-level vision techniques extract a multitude of image descriptors which are then analyzed using a rule-based reasoning system to interpret the image content. This system has been implemented using CEES, the C++ embedded expert system shell developed in the Systems Engineering and Automatic Control Laboratory (University of Girona) as a specific rule-based problem solving tool. It has been especially conceived for supporting cooperative expert systems, and uses the object oriented programming paradigm

Integration of knowledge-based, qualitative and numeric tools for real time dynamic systems supervision

The proposal presented in this thesis is to provide designers of knowledge based supervisory systems of dynamic systems with a framework to facilitate their tasks avoiding interface problems among tools, data flow and management. The approach is thought to be useful to both control and process engineers in assisting their tasks. The use of AI technologies to diagnose and perform control loops and, of course, assist process supervisory tasks such as fault detection and diagnose, are in the scope of this work. Special effort has been put in integration of tools for assisting expert supervisory systems design. With this aim the experience of Computer Aided Control Systems Design (CACSD) frameworks have been analysed and used to design a Computer Aided Supervisory Systems (CASSD) framework. In this sense, some basic facilities are required to be available in this proposed framework: ·