A Transportable Reflectarray Antenna for Satellite Ku-band Emergency Communications

The design of a Ku-band reconfigurable reflectarray antenna for emergency satellite communications is presented. Bidirectional high data rate satellite links are needed in emergency conditions where other telecommunication infrastructures are not available. In order to operate in this type of scenario, an antenna should be deployable, transportable, and easily repointable. The need of an automatic and fast satellite location and pointing system leads to a completely electronic reconfigurable antenna. The operative bandwidth is from 10.7 to 12.5 GHz for reception and from 14 up to 14.5 GHz for transmission (30% of relative bandwidth). The selected antenna architecture is based on a dual reflectarray system comprising a passive subreflectarray and an active main reflectarray made of reconfigurable 1-bit elementary cells based on PIN diodes.

Assessment of the rapid test based on an immunochromatography technique for detecting anti-Treponema pallidum antibodies

A rapid test based on an immunochromatography assay - Determine™ Syphilis TP (Abbott Lab.) for detecting specific antibodies to Treponema pallidum was evaluated against serum samples from patients with clinical, epidemiological and serological diagnosis of syphilis, patients with sexually transmitted disease other than syphilis, and individuals with negative serology for syphilis. The Determine™ test presented the sensitivity of 93.6%, specificity of 92.5%, and positive predictive value and negative predictive value of 95.2% and 93.7%, respectively. One serum sample from patient with recent latent syphilis showed a prozone reaction. Determine™ is a rapid assay, highly specific and easy to perform. This technique obviates the need of equipment and its diagnostic features demonstrate that it may be applicable as an alternative assay for syphilis screening under some emergency conditions or for patients living in remote localities.

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.

Relay protection in active distribution networks

Increasingly growing share of distributed generation in the whole electrical power system’s generating system is currently a worldwide tendency, driven by several factors, encircling mainly difficulties in refinement of megalopolises’ distribution networks and its maintenance; widening environmental concerns adding to both energy efficiency approaches and installation of renewable sources based generation, inherently distributed; increased power quality and reliability needs; progress in IT field, making implementable harmonization of needs and interests of different-energy-type generators and consumers. At this stage, the volume, formed by system-interconnected distributed generation facilities, have reached the level of causing broad impact toward system operation under emergency and post-emergency conditions in several EU countries, thus previously implementable approach of their preliminary tripping in case of a fault, preventing generating equipment damage and disoperation of relay protection and automation, is not applicable any more. Adding to the preceding, withstand capability and transient electromechanical stability of generating technologies, interconnecting in proximity of load nodes, enhanced significantly since the moment Low Voltage Ride-Through regulations, followed by techniques, were introduced in Grid Codes. Both aspects leads to relay protection and auto-reclosing operation in presence of distributed generation generally connected after grid planning and construction phases. This paper proposes solutions to the emerging need to ensure correct operation of the equipment in question with least possible grid refinements, distinctively for every type of distributed generation technology achieved its technical maturity to date and network’s protection. New generating technologies are equivalented from the perspective of representation in calculation of initial steady-state short-circuit current used to dimension current-sensing relay protection, and widely adopted short-circuit calculation practices, as IEC 60909 and VDE 0102. The phenomenon of unintentional islanding, influencing auto-reclosing, is addressed, and protection schemes used to eliminate an sustained island are listed and characterized by reliability and implementation related factors, whereas also forming a crucial aspect of realization of the proposed protection operation relieving measures.

L'effritement du consentement au profit d'une meilleure justice distributive de la recherche biomédicale avec des sujets humains : une étude comparative en situation d'urgence

"Mémoire présenté à la Faculté des études supérieures en vue de l'obtention du grade de maîtrise en droit option Droit et Biotechnologies". Ce mémoire a été accepté à l'unanimité et classé parmi les 15% des mémoires de la discipline.

Lactato sanguíneo como indicador precoce de prognóstico em emergência

Foram avaliados no Hospital Veterinário da Arrábida (HVA) em Azeitão, entre o período de 1 de Outubro de 2010 a 28 de Fevereiro de 2011, 64 animais em emergência, dos quais 21 pertenciam à espécie felina e 43 à espécie canina. Procedeu-se à medição dos níveis de lactato sérico tipo A durante a triagem nos 64 animais, com o objectivo de se estabelecer uma correlação com o prognóstico. Todos os animais entraram com o Síndrome de Resposta Inflamatória Sistémica (SRIS) e alguns apresentaram Sepsis. Da análise da população total, observou-se que para níveis de lactato sanguíneo entre os 2,5 e os 5 mmol/L, a taxa de mortalidade foi de 4%, já para níveis de lactato entre os 5 e os 7 mmol/L esta subiu para os 25 % e finalmente para níveis de lactato maiores que 7 mmol/L a taxa de mortalidade atingiu os 72%. A morbilidade foi definida com base na média do número de dias de internamento e complicações associadas de cada animal, sendo que para níveis de lactato entre os 0 e 2,5 mmol/L a espécie canina apresentou 2 dias e a felina apresentou 0, uma vez que não houve casos. Para níveis de lactato sanguíneo entre os 2,5 e 5 mmol/L os cães exibiram 2,3 e os gatos 2,8 dias. Entre 5 e 7 mmol/L os cães exibiram 3dias e os gatos 3,6. Para níveis séricos maiores que 7 mmol/L os cães apresentaram 6,1 dias enquanto que os gatos exibiram 4,3.

A emergência do conceito da interculturalidade no ensino e aprendizagem de língua estrangeira

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Validation of the CFD code NEPTUNE for a full scale simulator for decay heat removal systems with in-pool heat exchangers

In the present work, a multi physics simulation of an innovative safety system for light water nuclear reactor is performed, with the aim to increase the reliability of its main decay heat removal system. The system studied, denoted by the acronym PERSEO (in Pool Energy Removal System for Emergency Operation) is able to remove the decay power from the primary side of the light water nuclear reactor through a heat suppression pool. The experimental facility, located at SIET laboratories (PIACENZA), is an evolution of the Thermal Valve concept where the triggering valve is installed liquid side, on a line connecting two pools at the bottom. During the normal operation, the valve is closed, while in emergency conditions it opens, the heat exchanger is flooded with consequent heat transfer from the primary side to the pool side. In order to verify the correct system behavior during long term accidental transient, two main experimental PERSEO tests are analyzed. For this purpose, a coupling between the mono dimensional system code CATHARE, which reproduces the system scale behavior, with a three-dimensional CFD code NEPTUNE CFD, allowing a full investigation of the pools and the injector, is implemented. The coupling between the two codes is realized through the boundary conditions. In a first analysis, the facility is simulated by the system code CATHARE V2.5 to validate the results with the experimental data. The comparison of the numerical results obtained shows a different void distribution during the boiling conditions inside the heat suppression pool for the two cases of single nodalization and three volume nodalization scheme of the pool. Finaly, to improve the investigation capability of the void distribution inside the pool and the temperature stratification phenomena below the injector, a two and three dimensional CFD models with a simplified geometry of the system are adopted.

Online distribution management framework by incorporating wind generation

In distribution system operations, dispatchers at control center closely monitor system operating limits to ensure system reliability and adequacy. This reliability is partly due to the provision of remote controllable tie and sectionalizing switches. While the stochastic nature of wind generation can impact the level of wind energy penetration in the network, an estimate of the output from wind on hourly basis can be extremely useful. Under any operating conditions, the switching actions require human intervention and can be an extremely stressful task. Currently, handling a set of switching combinations with the uncertainty of distributed wind generation as part of the decision variables has been nonexistent. This thesis proposes a three-fold online management framework: (1) prediction of wind speed, (2) estimation of wind generation capacity, and (3) enumeration of feasible switching combinations. The proposed methodology is evaluated on 29-node test system with 8 remote controllable switches and two wind farms of 18MW and 9MW nameplate capacities respectively for generating the sequence of system reconfiguration states during normal and emergency conditions.

Cortical regional hyperperfusion in nonconvulsive status epilepticus measured by dynamic brain perfusion CT

BACKGROUND AND PURPOSE: Nonconvulsive status epilepticus (NCSE) is associated with a mortality rate of up to 18%, therefore requiring prompt diagnosis and treatment. Our aim was to evaluate the diagnostic value of perfusion CT (PCT) in the differential diagnosis of NCSE versus postictal states in patients presenting with persistent altered mental states after a preceding epileptic seizure. We hypothesized that regional cortical hyperperfusion can be measured by PCT in patients with NCSE, whereas it is not present in postictal states. MATERIALS AND METHODS: Nineteen patients with persistent altered mental status after a preceding epileptic seizure underwent PCT and electroencephalography (EEG). Patients were stratified as presenting with NCSE (n = 9) or a postictal state (n = 10) on the basis of clinical history and EEG data. Quantitative and visual analysis of the perfusion maps was performed. RESULTS: Patients during NCSE had significantly increased regional cerebral blood flow (P > .0001), increased regional cerebral blood volume (P > .001), and decreased (P > .001) mean transit time compared with the postictal state. Regional cortical hyperperfusion was depicted in 7/9 of patients with NCSE by ad hoc analysis of parametric perfusion maps during emergency conditions but was not a feature of postictal states. The areas of hyperperfusion were concordant with transient clinical symptoms and EEG topography in all cases. CONCLUSIONS: Visual analysis of perfusion maps detected regional hyperperfusion in NCSE with a sensitivity of 78%. The broad availability and short processing time of PCT in an emergency situation is a benefit compared with EEG. Consequently, the use of PCT in epilepsy may accelerate the diagnosis of NCSE. PCT may qualify as a complementary diagnostic tool to EEG in patients with persistent altered mental state after a preceding seizure.

Fast algorithms for automatic mapping with space-limited covariance functions

In this paper we discuss a fast Bayesian extension to kriging algorithms which has been used successfully for fast, automatic mapping in emergency conditions in the Spatial Interpolation Comparison 2004 (SIC2004) exercise. The application of kriging to automatic mapping raises several issues such as robustness, scalability, speed and parameter estimation. Various ad-hoc solutions have been proposed and used extensively but they lack a sound theoretical basis. In this paper we show how observations can be projected onto a representative subset of the data, without losing significant information. This allows the complexity of the algorithm to grow as O(n m 2), where n is the total number of observations and m is the size of the subset of the observations retained for prediction. The main contribution of this paper is to further extend this projective method through the application of space-limited covariance functions, which can be used as an alternative to the commonly used covariance models. In many real world applications the correlation between observations essentially vanishes beyond a certain separation distance. Thus it makes sense to use a covariance model that encompasses this belief since this leads to sparse covariance matrices for which optimised sparse matrix techniques can be used. In the presence of extreme values we show that space-limited covariance functions offer an additional benefit, they maintain the smoothness locally but at the same time lead to a more robust, and compact, global model. We show the performance of this technique coupled with the sparse extension to the kriging algorithm on synthetic data and outline a number of computational benefits such an approach brings. To test the relevance to automatic mapping we apply the method to the data used in a recent comparison of interpolation techniques (SIC2004) to map the levels of background ambient gamma radiation. © Springer-Verlag 2007.

Effect of a national primary care pay for performance scheme on emergency hospital admissions for ambulatory care sensitive conditions: controlled longitudinal study.

OBJECTIVE: To estimate the impact of a national primary care pay for performance scheme, the Quality and Outcomes Framework in England, on emergency hospital admissions for ambulatory care sensitive conditions (ACSCs). DESIGN: Controlled longitudinal study. SETTING: English National Health Service between 1998/99 and 2010/11. PARTICIPANTS: Populations registered with each of 6975 family practices in England. MAIN OUTCOME MEASURES: Year specific differences between trend adjusted emergency hospital admission rates for incentivised ACSCs before and after the introduction of the Quality and Outcomes Framework scheme and two comparators: non-incentivised ACSCs and non-ACSCs. RESULTS: Incentivised ACSC admissions showed a relative reduction of 2.7% (95% confidence interval 1.6% to 3.8%) in the first year of the Quality and Outcomes Framework compared with ACSCs that were not incentivised. This increased to a relative reduction of 8.0% (6.9% to 9.1%) in 2010/11. Compared with conditions that are not regarded as being influenced by the quality of ambulatory care (non-ACSCs), incentivised ACSCs also showed a relative reduction in rates of emergency admissions of 2.8% (2.0% to 3.6%) in the first year increasing to 10.9% (10.1% to 11.7%) by 2010/11. CONCLUSIONS: The introduction of a major national pay for performance scheme for primary care in England was associated with a decrease in emergency admissions for incentivised conditions compared with conditions that were not incentivised. Contemporaneous health service changes seem unlikely to have caused the sharp change in the trajectory of incentivised ACSC admissions immediately after the introduction of the Quality and Outcomes Framework. The decrease seems larger than would be expected from the changes in the process measures that were incentivised, suggesting that the pay for performance scheme may have had impacts on quality of care beyond the directly incentivised activities.

Emergency department visits for non-life-threatening conditions : evolution over 13 years in a Swiss urban teaching hospital

INTRODUCTION: A large proportion of visits to our Emergency Department (ED) are for non-life-threatening conditions. We investigated whether patients' characteristics and reasons for consultation had changed over 13 years. METHODS: Consecutive adult patients with non-life-threatening conditions at triage were included in the spring of 2000 and in the summer of 2013. In both years patients completed a similar questionnaire, which addressed their reasons for consultation and any previous consultation with a general practitioner (GP). RESULTS: We included 581 patients in 2013 vs 516 in 2000, with a mean age of 44.5 years vs 46.4 years (p=0.128). Of these patients, 54.0% vs 57.0% were male (p=0.329), 55.5% vs 58.7% were Swiss (p=0.282), 76.4% were registered with a GP in both periods, but self-referral increased from 52.0% to 68.8% (p<0.001); 57.7% vs., 58.3% consulted during out-of- hours (p=0.821). Trauma-related visits decreased from 34.2% to 23.7% (p<0.001). Consultations within 12 hours of onset of symptoms dropped from 54.5% to 30.9%, and delays of ≥1 week increased from 14.3% to 26.9% (p<0.001). The primary motive for self-referral remained unawareness of an alternative, followed in 2013 by dissatisfaction with the GP's treatment or appointment. Patients who believed that their health problem would not require hospitalisation increased from 52.8% to 74.2% and those who were actually hospitalised decreased from 24.9% to 13.9% (all p<0.001). CONCLUSION: The number of visits for non-life-threatening consultations continue to increase. Our ED is used by a large proportion of patients as a convenient alternative source of primary care.

Rapid assessment of potential ground-water contamination under emergency response conditions /

Mode of access: Internet.