Syndromic algorithms for detection of gambiense human african trypanosomiasis in South Sudan.

BACKGROUND: Active screening by mobile teams is considered the best method for detecting human African trypanosomiasis (HAT) caused by Trypanosoma brucei gambiense but the current funding context in many post-conflict countries limits this approach. As an alternative, non-specialist health care workers (HCWs) in peripheral health facilities could be trained to identify potential cases who need testing based on their symptoms. We explored the predictive value of syndromic referral algorithms to identify symptomatic cases of HAT among a treatment-seeking population in Nimule, South Sudan. METHODOLOGY/PRINCIPAL FINDINGS: Symptom data from 462 patients (27 cases) presenting for a HAT test via passive screening over a 7 month period were collected to construct and evaluate over 14,000 four item syndromic algorithms considered simple enough to be used by peripheral HCWs. For comparison, algorithms developed in other settings were also tested on our data, and a panel of expert HAT clinicians were asked to make referral decisions based on the symptom dataset. The best performing algorithms consisted of three core symptoms (sleep problems, neurological problems and weight loss), with or without a history of oedema, cervical adenopathy or proximity to livestock. They had a sensitivity of 88.9-92.6%, a negative predictive value of up to 98.8% and a positive predictive value in this context of 8.4-8.7%. In terms of sensitivity, these out-performed more complex algorithms identified in other studies, as well as the expert panel. The best-performing algorithm is predicted to identify about 9/10 treatment-seeking HAT cases, though only 1/10 patients referred would test positive. CONCLUSIONS/SIGNIFICANCE: In the absence of regular active screening, improving referrals of HAT patients through other means is essential. Systematic use of syndromic algorithms by peripheral HCWs has the potential to increase case detection and would increase their participation in HAT programmes. The algorithms proposed here, though promising, should be validated elsewhere.

quantiNemo: an individual-based program to simulate quantitative traits with explicit genetic architecture in a dynamic metapopulation.

quantiNemo is an individual-based, genetically explicit stochastic simulation program. It was developed to investigate the effects of selection, mutation, recombination and drift on quantitative traits with varying architectures in structured populations connected by migration and located in a heterogeneous habitat. quantiNemo is highly flexible at various levels: population, selection, trait(s) architecture, genetic map for QTL and/or markers, environment, demography, mating system, etc. quantiNemo is coded in C++ using an object-oriented approach and runs on any computer platform. Availability: Executables for several platforms, user's manual, and source code are freely available under the GNU General Public License at http://www2.unil.ch/popgen/softwares/quantinemo.

Population based screening - the difficulty of how to do more good than harm and how to achieve it.

Screening people without symptoms of disease is an attractive idea. Screening allows early detection of disease or elevated risk of disease, and has the potential for improved treatment and reduction of mortality. The list of future screening opportunities is set to grow because of the refinement of screening techniques, the increasing frequency of degenerative and chronic diseases, and the steadily growing body of evidence on genetic predispositions for various diseases. But how should we decide on the diseases for which screening should be done and on recommendations for how it should be implemented? We use the examples of prostate cancer and genetic screening to show the importance of considering screening as an ongoing population-based intervention with beneficial and harmful effects, and not simply the use of a test. Assessing whether screening should be recommended and implemented for any named disease is therefore a multi-dimensional task in health technology assessment. There are several countries that already use established processes and criteria to assess the appropriateness of screening. We argue that the Swiss healthcare system needs a nationwide screening commission mandated to conduct appropriate evidence-based evaluation of the impact of proposed screening interventions, to issue evidence-based recommendations, and to monitor the performance of screening programmes introduced. Without explicit processes there is a danger that beneficial screening programmes could be neglected and that ineffective, and potentially harmful, screening procedures could be introduced.

Parenteral anticoagulants in heart disease: Current status and perspectives (Section II). Position Paper of the ESC Working Group on Thrombosis - Task Force on Anticoagulants in Heart Disease.

Anticoagulants are a mainstay of cardiovascular therapy, and parenteral anticoagulants have widespread use in cardiology, especially in acute situations. Parenteral anticoagulants include unfractionated heparin, low-molecular-weight heparins, the synthetic pentasaccharides fondaparinux, idraparinux and idrabiotaparinux, and parenteral direct thrombin inhibitors. The several shortcomings of unfractionated heparin and of low-molecular-weight heparins have prompted the development of the other newer agents. Here we review the mechanisms of action, pharmacological properties and side effects of parenteral anticoagulants used in the management of coronary heart disease treated with or without percutaneous coronary interventions, cardioversion for atrial fibrillation, and prosthetic heart valves and valve repair. Using an evidence-based approach, we describe the results of completed clinical trials, highlight ongoing research with currently available agents, and recommend therapeutic options for specific heart diseases.

Machine learning for geospatial data : algorithms, software tools and case studies

Résumé Cette thèse est consacrée à l'analyse, la modélisation et la visualisation de données environnementales à référence spatiale à l'aide d'algorithmes d'apprentissage automatique (Machine Learning). L'apprentissage automatique peut être considéré au sens large comme une sous-catégorie de l'intelligence artificielle qui concerne particulièrement le développement de techniques et d'algorithmes permettant à une machine d'apprendre à partir de données. Dans cette thèse, les algorithmes d'apprentissage automatique sont adaptés pour être appliqués à des données environnementales et à la prédiction spatiale. Pourquoi l'apprentissage automatique ? Parce que la majorité des algorithmes d'apprentissage automatiques sont universels, adaptatifs, non-linéaires, robustes et efficaces pour la modélisation. Ils peuvent résoudre des problèmes de classification, de régression et de modélisation de densité de probabilités dans des espaces à haute dimension, composés de variables informatives spatialisées (« géo-features ») en plus des coordonnées géographiques. De plus, ils sont idéaux pour être implémentés en tant qu'outils d'aide à la décision pour des questions environnementales allant de la reconnaissance de pattern à la modélisation et la prédiction en passant par la cartographie automatique. Leur efficacité est comparable au modèles géostatistiques dans l'espace des coordonnées géographiques, mais ils sont indispensables pour des données à hautes dimensions incluant des géo-features. Les algorithmes d'apprentissage automatique les plus importants et les plus populaires sont présentés théoriquement et implémentés sous forme de logiciels pour les sciences environnementales. Les principaux algorithmes décrits sont le Perceptron multicouches (MultiLayer Perceptron, MLP) - l'algorithme le plus connu dans l'intelligence artificielle, le réseau de neurones de régression généralisée (General Regression Neural Networks, GRNN), le réseau de neurones probabiliste (Probabilistic Neural Networks, PNN), les cartes auto-organisées (SelfOrganized Maps, SOM), les modèles à mixture Gaussiennes (Gaussian Mixture Models, GMM), les réseaux à fonctions de base radiales (Radial Basis Functions Networks, RBF) et les réseaux à mixture de densité (Mixture Density Networks, MDN). Cette gamme d'algorithmes permet de couvrir des tâches variées telle que la classification, la régression ou l'estimation de densité de probabilité. L'analyse exploratoire des données (Exploratory Data Analysis, EDA) est le premier pas de toute analyse de données. Dans cette thèse les concepts d'analyse exploratoire de données spatiales (Exploratory Spatial Data Analysis, ESDA) sont traités selon l'approche traditionnelle de la géostatistique avec la variographie expérimentale et selon les principes de l'apprentissage automatique. La variographie expérimentale, qui étudie les relations entre pairs de points, est un outil de base pour l'analyse géostatistique de corrélations spatiales anisotropiques qui permet de détecter la présence de patterns spatiaux descriptible par une statistique. L'approche de l'apprentissage automatique pour l'ESDA est présentée à travers l'application de la méthode des k plus proches voisins qui est très simple et possède d'excellentes qualités d'interprétation et de visualisation. Une part importante de la thèse traite de sujets d'actualité comme la cartographie automatique de données spatiales. Le réseau de neurones de régression généralisée est proposé pour résoudre cette tâche efficacement. Les performances du GRNN sont démontrées par des données de Comparaison d'Interpolation Spatiale (SIC) de 2004 pour lesquelles le GRNN bat significativement toutes les autres méthodes, particulièrement lors de situations d'urgence. La thèse est composée de quatre chapitres : théorie, applications, outils logiciels et des exemples guidés. Une partie importante du travail consiste en une collection de logiciels : Machine Learning Office. Cette collection de logiciels a été développée durant les 15 dernières années et a été utilisée pour l'enseignement de nombreux cours, dont des workshops internationaux en Chine, France, Italie, Irlande et Suisse ainsi que dans des projets de recherche fondamentaux et appliqués. Les cas d'études considérés couvrent un vaste spectre de problèmes géoenvironnementaux réels à basse et haute dimensionnalité, tels que la pollution de l'air, du sol et de l'eau par des produits radioactifs et des métaux lourds, la classification de types de sols et d'unités hydrogéologiques, la cartographie des incertitudes pour l'aide à la décision et l'estimation de risques naturels (glissements de terrain, avalanches). Des outils complémentaires pour l'analyse exploratoire des données et la visualisation ont également été développés en prenant soin de créer une interface conviviale et facile à l'utilisation. Machine Learning for geospatial data: algorithms, software tools and case studies Abstract The thesis is devoted to the analysis, modeling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense machine learning can be considered as a subfield of artificial intelligence. It mainly concerns with the development of techniques and algorithms that allow computers to learn from data. In this thesis machine learning algorithms are adapted to learn from spatial environmental data and to make spatial predictions. Why machine learning? In few words most of machine learning algorithms are universal, adaptive, nonlinear, robust and efficient modeling tools. They can find solutions for the classification, regression, and probability density modeling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well-suited to be implemented as predictive engines in decision support systems, for the purposes of environmental data mining including pattern recognition, modeling and predictions as well as automatic data mapping. They have competitive efficiency to the geostatistical models in low dimensional geographical spaces but are indispensable in high-dimensional geo-feature spaces. The most important and popular machine learning algorithms and models interesting for geo- and environmental sciences are presented in details: from theoretical description of the concepts to the software implementation. The main algorithms and models considered are the following: multi-layer perceptron (a workhorse of machine learning), general regression neural networks, probabilistic neural networks, self-organising (Kohonen) maps, Gaussian mixture models, radial basis functions networks, mixture density networks. This set of models covers machine learning tasks such as classification, regression, and density estimation. Exploratory data analysis (EDA) is initial and very important part of data analysis. In this thesis the concepts of exploratory spatial data analysis (ESDA) is considered using both traditional geostatistical approach such as_experimental variography and machine learning. Experimental variography is a basic tool for geostatistical analysis of anisotropic spatial correlations which helps to understand the presence of spatial patterns, at least described by two-point statistics. A machine learning approach for ESDA is presented by applying the k-nearest neighbors (k-NN) method which is simple and has very good interpretation and visualization properties. Important part of the thesis deals with a hot topic of nowadays, namely, an automatic mapping of geospatial data. General regression neural networks (GRNN) is proposed as efficient model to solve this task. Performance of the GRNN model is demonstrated on Spatial Interpolation Comparison (SIC) 2004 data where GRNN model significantly outperformed all other approaches, especially in case of emergency conditions. The thesis consists of four chapters and has the following structure: theory, applications, software tools, and how-to-do-it examples. An important part of the work is a collection of software tools - Machine Learning Office. Machine Learning Office tools were developed during last 15 years and was used both for many teaching courses, including international workshops in China, France, Italy, Ireland, Switzerland and for realizing fundamental and applied research projects. Case studies considered cover wide spectrum of the real-life low and high-dimensional geo- and environmental problems, such as air, soil and water pollution by radionuclides and heavy metals, soil types and hydro-geological units classification, decision-oriented mapping with uncertainties, natural hazards (landslides, avalanches) assessments and susceptibility mapping. Complementary tools useful for the exploratory data analysis and visualisation were developed as well. The software is user friendly and easy to use.

Study of OSEM with different subsets in grating-based X-ray differential phase-contrast imaging.

Impressive developments in X-ray imaging are associated with X-ray phase contrast computed tomography based on grating interferometry, a technique that provides increased contrast compared with conventional absorption-based imaging. A new "single-step" method capable of separating phase information from other contributions has been recently proposed. This approach not only simplifies data-acquisition procedures, but, compared with the existing phase step approach, significantly reduces the dose delivered to a sample. However, the image reconstruction procedure is more demanding than for traditional methods and new algorithms have to be developed to take advantage of the "single-step" method. In the work discussed in this paper, a fast iterative image reconstruction method named OSEM (ordered subsets expectation maximization) was applied to experimental data to evaluate its performance and range of applicability. The OSEM algorithm with different subsets was also characterized by comparison of reconstruction image quality and convergence speed. Computer simulations and experimental results confirm the reliability of this new algorithm for phase-contrast computed tomography applications. Compared with the traditional filtered back projection algorithm, in particular in the presence of a noisy acquisition, it furnishes better images at a higher spatial resolution and with lower noise. We emphasize that the method is highly compatible with future X-ray phase contrast imaging clinical applications.

An assurance-based model to holistically assess the information security posture

EXECUTIVE SUMMARY : Evaluating Information Security Posture within an organization is becoming a very complex task. Currently, the evaluation and assessment of Information Security are commonly performed using frameworks, methodologies and standards which often consider the various aspects of security independently. Unfortunately this is ineffective because it does not take into consideration the necessity of having a global and systemic multidimensional approach to Information Security evaluation. At the same time the overall security level is globally considered to be only as strong as its weakest link. This thesis proposes a model aiming to holistically assess all dimensions of security in order to minimize the likelihood that a given threat will exploit the weakest link. A formalized structure taking into account all security elements is presented; this is based on a methodological evaluation framework in which Information Security is evaluated from a global perspective. This dissertation is divided into three parts. Part One: Information Security Evaluation issues consists of four chapters. Chapter 1 is an introduction to the purpose of this research purpose and the Model that will be proposed. In this chapter we raise some questions with respect to "traditional evaluation methods" as well as identifying the principal elements to be addressed in this direction. Then we introduce the baseline attributes of our model and set out the expected result of evaluations according to our model. Chapter 2 is focused on the definition of Information Security to be used as a reference point for our evaluation model. The inherent concepts of the contents of a holistic and baseline Information Security Program are defined. Based on this, the most common roots-of-trust in Information Security are identified. Chapter 3 focuses on an analysis of the difference and the relationship between the concepts of Information Risk and Security Management. Comparing these two concepts allows us to identify the most relevant elements to be included within our evaluation model, while clearing situating these two notions within a defined framework is of the utmost importance for the results that will be obtained from the evaluation process. Chapter 4 sets out our evaluation model and the way it addresses issues relating to the evaluation of Information Security. Within this Chapter the underlying concepts of assurance and trust are discussed. Based on these two concepts, the structure of the model is developed in order to provide an assurance related platform as well as three evaluation attributes: "assurance structure", "quality issues", and "requirements achievement". Issues relating to each of these evaluation attributes are analysed with reference to sources such as methodologies, standards and published research papers. Then the operation of the model is discussed. Assurance levels, quality levels and maturity levels are defined in order to perform the evaluation according to the model. Part Two: Implementation of the Information Security Assurance Assessment Model (ISAAM) according to the Information Security Domains consists of four chapters. This is the section where our evaluation model is put into a welldefined context with respect to the four pre-defined Information Security dimensions: the Organizational dimension, Functional dimension, Human dimension, and Legal dimension. Each Information Security dimension is discussed in a separate chapter. For each dimension, the following two-phase evaluation path is followed. The first phase concerns the identification of the elements which will constitute the basis of the evaluation: ? Identification of the key elements within the dimension; ? Identification of the Focus Areas for each dimension, consisting of the security issues identified for each dimension; ? Identification of the Specific Factors for each dimension, consisting of the security measures or control addressing the security issues identified for each dimension. The second phase concerns the evaluation of each Information Security dimension by: ? The implementation of the evaluation model, based on the elements identified for each dimension within the first phase, by identifying the security tasks, processes, procedures, and actions that should have been performed by the organization to reach the desired level of protection; ? The maturity model for each dimension as a basis for reliance on security. For each dimension we propose a generic maturity model that could be used by every organization in order to define its own security requirements. Part three of this dissertation contains the Final Remarks, Supporting Resources and Annexes. With reference to the objectives of our thesis, the Final Remarks briefly analyse whether these objectives were achieved and suggest directions for future related research. Supporting resources comprise the bibliographic resources that were used to elaborate and justify our approach. Annexes include all the relevant topics identified within the literature to illustrate certain aspects of our approach. Our Information Security evaluation model is based on and integrates different Information Security best practices, standards, methodologies and research expertise which can be combined in order to define an reliable categorization of Information Security. After the definition of terms and requirements, an evaluation process should be performed in order to obtain evidence that the Information Security within the organization in question is adequately managed. We have specifically integrated into our model the most useful elements of these sources of information in order to provide a generic model able to be implemented in all kinds of organizations. The value added by our evaluation model is that it is easy to implement and operate and answers concrete needs in terms of reliance upon an efficient and dynamic evaluation tool through a coherent evaluation system. On that basis, our model could be implemented internally within organizations, allowing them to govern better their Information Security. RÉSUMÉ : Contexte général de la thèse L'évaluation de la sécurité en général, et plus particulièrement, celle de la sécurité de l'information, est devenue pour les organisations non seulement une mission cruciale à réaliser, mais aussi de plus en plus complexe. A l'heure actuelle, cette évaluation se base principalement sur des méthodologies, des bonnes pratiques, des normes ou des standards qui appréhendent séparément les différents aspects qui composent la sécurité de l'information. Nous pensons que cette manière d'évaluer la sécurité est inefficiente, car elle ne tient pas compte de l'interaction des différentes dimensions et composantes de la sécurité entre elles, bien qu'il soit admis depuis longtemps que le niveau de sécurité globale d'une organisation est toujours celui du maillon le plus faible de la chaîne sécuritaire. Nous avons identifié le besoin d'une approche globale, intégrée, systémique et multidimensionnelle de l'évaluation de la sécurité de l'information. En effet, et c'est le point de départ de notre thèse, nous démontrons que seule une prise en compte globale de la sécurité permettra de répondre aux exigences de sécurité optimale ainsi qu'aux besoins de protection spécifiques d'une organisation. Ainsi, notre thèse propose un nouveau paradigme d'évaluation de la sécurité afin de satisfaire aux besoins d'efficacité et d'efficience d'une organisation donnée. Nous proposons alors un modèle qui vise à évaluer d'une manière holistique toutes les dimensions de la sécurité, afin de minimiser la probabilité qu'une menace potentielle puisse exploiter des vulnérabilités et engendrer des dommages directs ou indirects. Ce modèle se base sur une structure formalisée qui prend en compte tous les éléments d'un système ou programme de sécurité. Ainsi, nous proposons un cadre méthodologique d'évaluation qui considère la sécurité de l'information à partir d'une perspective globale. Structure de la thèse et thèmes abordés Notre document est structuré en trois parties. La première intitulée : « La problématique de l'évaluation de la sécurité de l'information » est composée de quatre chapitres. Le chapitre 1 introduit l'objet de la recherche ainsi que les concepts de base du modèle d'évaluation proposé. La maniéré traditionnelle de l'évaluation de la sécurité fait l'objet d'une analyse critique pour identifier les éléments principaux et invariants à prendre en compte dans notre approche holistique. Les éléments de base de notre modèle d'évaluation ainsi que son fonctionnement attendu sont ensuite présentés pour pouvoir tracer les résultats attendus de ce modèle. Le chapitre 2 se focalise sur la définition de la notion de Sécurité de l'Information. Il ne s'agit pas d'une redéfinition de la notion de la sécurité, mais d'une mise en perspectives des dimensions, critères, indicateurs à utiliser comme base de référence, afin de déterminer l'objet de l'évaluation qui sera utilisé tout au long de notre travail. Les concepts inhérents de ce qui constitue le caractère holistique de la sécurité ainsi que les éléments constitutifs d'un niveau de référence de sécurité sont définis en conséquence. Ceci permet d'identifier ceux que nous avons dénommés « les racines de confiance ». Le chapitre 3 présente et analyse la différence et les relations qui existent entre les processus de la Gestion des Risques et de la Gestion de la Sécurité, afin d'identifier les éléments constitutifs du cadre de protection à inclure dans notre modèle d'évaluation. Le chapitre 4 est consacré à la présentation de notre modèle d'évaluation Information Security Assurance Assessment Model (ISAAM) et la manière dont il répond aux exigences de l'évaluation telle que nous les avons préalablement présentées. Dans ce chapitre les concepts sous-jacents relatifs aux notions d'assurance et de confiance sont analysés. En se basant sur ces deux concepts, la structure du modèle d'évaluation est développée pour obtenir une plateforme qui offre un certain niveau de garantie en s'appuyant sur trois attributs d'évaluation, à savoir : « la structure de confiance », « la qualité du processus », et « la réalisation des exigences et des objectifs ». Les problématiques liées à chacun de ces attributs d'évaluation sont analysées en se basant sur l'état de l'art de la recherche et de la littérature, sur les différentes méthodes existantes ainsi que sur les normes et les standards les plus courants dans le domaine de la sécurité. Sur cette base, trois différents niveaux d'évaluation sont construits, à savoir : le niveau d'assurance, le niveau de qualité et le niveau de maturité qui constituent la base de l'évaluation de l'état global de la sécurité d'une organisation. La deuxième partie: « L'application du Modèle d'évaluation de l'assurance de la sécurité de l'information par domaine de sécurité » est elle aussi composée de quatre chapitres. Le modèle d'évaluation déjà construit et analysé est, dans cette partie, mis dans un contexte spécifique selon les quatre dimensions prédéfinies de sécurité qui sont: la dimension Organisationnelle, la dimension Fonctionnelle, la dimension Humaine, et la dimension Légale. Chacune de ces dimensions et son évaluation spécifique fait l'objet d'un chapitre distinct. Pour chacune des dimensions, une évaluation en deux phases est construite comme suit. La première phase concerne l'identification des éléments qui constituent la base de l'évaluation: ? Identification des éléments clés de l'évaluation ; ? Identification des « Focus Area » pour chaque dimension qui représentent les problématiques se trouvant dans la dimension ; ? Identification des « Specific Factors » pour chaque Focus Area qui représentent les mesures de sécurité et de contrôle qui contribuent à résoudre ou à diminuer les impacts des risques. La deuxième phase concerne l'évaluation de chaque dimension précédemment présentées. Elle est constituée d'une part, de l'implémentation du modèle général d'évaluation à la dimension concernée en : ? Se basant sur les éléments spécifiés lors de la première phase ; ? Identifiant les taches sécuritaires spécifiques, les processus, les procédures qui auraient dû être effectués pour atteindre le niveau de protection souhaité. D'autre part, l'évaluation de chaque dimension est complétée par la proposition d'un modèle de maturité spécifique à chaque dimension, qui est à considérer comme une base de référence pour le niveau global de sécurité. Pour chaque dimension nous proposons un modèle de maturité générique qui peut être utilisé par chaque organisation, afin de spécifier ses propres exigences en matière de sécurité. Cela constitue une innovation dans le domaine de l'évaluation, que nous justifions pour chaque dimension et dont nous mettons systématiquement en avant la plus value apportée. La troisième partie de notre document est relative à la validation globale de notre proposition et contient en guise de conclusion, une mise en perspective critique de notre travail et des remarques finales. Cette dernière partie est complétée par une bibliographie et des annexes. Notre modèle d'évaluation de la sécurité intègre et se base sur de nombreuses sources d'expertise, telles que les bonnes pratiques, les normes, les standards, les méthodes et l'expertise de la recherche scientifique du domaine. Notre proposition constructive répond à un véritable problème non encore résolu, auquel doivent faire face toutes les organisations, indépendamment de la taille et du profil. Cela permettrait à ces dernières de spécifier leurs exigences particulières en matière du niveau de sécurité à satisfaire, d'instancier un processus d'évaluation spécifique à leurs besoins afin qu'elles puissent s'assurer que leur sécurité de l'information soit gérée d'une manière appropriée, offrant ainsi un certain niveau de confiance dans le degré de protection fourni. Nous avons intégré dans notre modèle le meilleur du savoir faire, de l'expérience et de l'expertise disponible actuellement au niveau international, dans le but de fournir un modèle d'évaluation simple, générique et applicable à un grand nombre d'organisations publiques ou privées. La valeur ajoutée de notre modèle d'évaluation réside précisément dans le fait qu'il est suffisamment générique et facile à implémenter tout en apportant des réponses sur les besoins concrets des organisations. Ainsi notre proposition constitue un outil d'évaluation fiable, efficient et dynamique découlant d'une approche d'évaluation cohérente. De ce fait, notre système d'évaluation peut être implémenté à l'interne par l'entreprise elle-même, sans recourir à des ressources supplémentaires et lui donne également ainsi la possibilité de mieux gouverner sa sécurité de l'information.

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

Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Minimization of Nyquist ghosting for echo-planar imaging at ultra-high fields based on a "negative readout gradient" strategy.

PURPOSE: To improve the traditional Nyquist ghost correction approach in echo planar imaging (EPI) at high fields, via schemes based on the reversal of the EPI readout gradient polarity for every other volume throughout a functional magnetic resonance imaging (fMRI) acquisition train. MATERIALS AND METHODS: An EPI sequence in which the readout gradient was inverted every other volume was implemented on two ultrahigh-field systems. Phantom images and fMRI data were acquired to evaluate ghost intensities and the presence of false-positive blood oxygenation level-dependent (BOLD) signal with and without ghost correction. Three different algorithms for ghost correction of alternating readout EPI were compared. RESULTS: Irrespective of the chosen processing approach, ghosting was significantly reduced (up to 70% lower intensity) in both rat brain images acquired on a 9.4T animal scanner and human brain images acquired at 7T, resulting in a reduction of sources of false-positive activation in fMRI data. CONCLUSION: It is concluded that at high B(0) fields, substantial gains in Nyquist ghost correction of echo planar time series are possible by alternating the readout gradient every other volume.

Subjectively adapted high capacity lossless image data hiding based on prediction errors

This article reports on a lossless data hiding scheme for digital images where the data hiding capacity is either determined by minimum acceptable subjective quality or by the demanded capacity. In the proposed method data is hidden within the image prediction errors, where the most well-known prediction algorithms such as the median edge detector (MED), gradient adjacent prediction (GAP) and Jiang prediction are tested for this purpose. In this method, first the histogram of the prediction errors of images are computed and then based on the required capacity or desired image quality, the prediction error values of frequencies larger than this capacity are shifted. The empty space created by such a shift is used for embedding the data. Experimental results show distinct superiority of the image prediction error histogram over the conventional image histogram itself, due to much narrower spectrum of the former over the latter. We have also devised an adaptive method for hiding data, where subjective quality is traded for data hiding capacity. Here the positive and negative error values are chosen such that the sum of their frequencies on the histogram is just above the given capacity or above a certain quality.

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimizationcriteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed,and their relationship with existing algorithms is indicated. As withother previously proposed solutions, the minimum bit error rate algorithmconverges to the open-loop transmission scheme for very poor CSI estimates.

Probabilistic algorithms for blind adaptive multiuser detection

In this paper, two probabilistic adaptive algorithmsfor jointly detecting active users in a DS-CDMA system arereported. The first one, which is based on the theory of hiddenMarkov models (HMM’s) and the Baum–Wech (BW) algorithm,is proposed within the CDMA scenario and compared withthe second one, which is a previously developed Viterbi-basedalgorithm. Both techniques are completely blind in the sense thatno knowledge of the signatures, channel state information, ortraining sequences is required for any user. Once convergencehas been achieved, an estimate of the signature of each userconvolved with its physical channel response (CR) and estimateddata sequences are provided. This CR estimate can be used toswitch to any decision-directed (DD) adaptation scheme. Performanceof the algorithms is verified via simulations as well as onexperimental data obtained in an underwater acoustics (UWA)environment. In both cases, performance is found to be highlysatisfactory, showing the near–far resistance of the analyzed algorithms.

Task Support in Scenario Process: Case LUT 2016

This thesis attempts to find whether scenario planning supports the organizational strategy as a method for addressing uncertainty. The main issues are why, what and how scenario planning fits in organizational strategy and how the process could be supported to make it more effective. The study follows the constructive approach. It starts with examination of competitive advantage and the way that an organization develops strategy and how it addresses the uncertainty in its operational environment. Based on the conducted literature review, scenario methods would seem to provide versatile platform for addressing future uncertainties. The construction is formed by examining the scenario methods and presenting suitable support methods, which results in forming of the theoretical proposition for supporter scenario process. The theoretical framework is tested in laboratory conditions, and the results from the test sessions are used a basis for scenario stories. The process of forming the scenarios and the results are illustrated and presented for scrutiny

Methods for Region Based Paper Surface Roughness Measurement

Paperin pinnan karheus on yksi paperin laatukriteereistä. Sitä mitataan fyysisestipaperin pintaa mittaavien laitteiden ja optisten laitteiden avulla. Mittaukset vaativat laboratorioolosuhteita, mutta nopeammille, suoraan linjalla tapahtuville mittauksilla olisi tarvetta paperiteollisuudessa. Paperin pinnan karheus voidaan ilmaista yhtenä näytteelle kohdistuvana karheusarvona. Tässä työssä näyte on jaettu merkitseviin alueisiin, ja jokaiselle alueelle on laskettu erillinen karheusarvo. Karheuden mittaukseen on käytetty useita menetelmiä. Yleisesti hyväksyttyä tilastollista menetelmää on käytetty tässä työssä etäisyysmuunnoksen lisäksi. Paperin pinnan karheudenmittauksessa on ollut tarvetta jakaa analysoitava näyte karheuden perusteella alueisiin. Aluejaon avulla voidaan rajata näytteestä selvästi karheampana esiintyvät alueet. Etäisyysmuunnos tuottaa alueita, joita on analysoitu. Näistä alueista on muodostettu yhtenäisiä alueita erilaisilla segmentointimenetelmillä. PNN -menetelmään (Pairwise Nearest Neighbor) ja naapurialueiden yhdistämiseen perustuvia algoritmeja on käytetty.Alueiden jakamiseen ja yhdistämiseen perustuvaa lähestymistapaa on myös tarkasteltu. Segmentoitujen kuvien validointi on yleensä tapahtunut ihmisen tarkastelemana. Tämän työn lähestymistapa on verrata yleisesti hyväksyttyä tilastollista menetelmää segmentoinnin tuloksiin. Korkea korrelaatio näiden tulosten välillä osoittaa onnistunutta segmentointia. Eri kokeiden tuloksia on verrattu keskenään hypoteesin testauksella. Työssä on analysoitu kahta näytesarjaa, joidenmittaukset on suoritettu OptiTopolla ja profilometrillä. Etäisyysmuunnoksen aloitusparametrit, joita muutettiin kokeiden aikana, olivat aloituspisteiden määrä ja sijainti. Samat parametrimuutokset tehtiin kaikille algoritmeille, joita käytettiin alueiden yhdistämiseen. Etäisyysmuunnoksen jälkeen korrelaatio oli voimakkaampaa profilometrillä mitatuille näytteille kuin OptiTopolla mitatuille näytteille. Segmentoiduilla OptiTopo -näytteillä korrelaatio parantui voimakkaammin kuin profilometrinäytteillä. PNN -menetelmän tuottamilla tuloksilla korrelaatio oli paras.