Equilibrium points and central configurations for the Lennard-Jones 2-and 3-body problems

Abstract. In this paper we study the relative equilibria and their stability for a system of three point particles moving under the action of a Lennard{Jones potential. A central con guration is a special position of the particles where the position and acceleration vectors of each particle are proportional, and the constant of proportionality is the same for all particles. Since the Lennard{Jones potential depends only on the mutual distances among the particles, it is invariant under rotations. In a rotating frame the orbits coming from central con gurations become equilibrium points, the relative equilibria. Due to the form of the potential, the relative equilibria depend on the size of the system, that is, depend strongly of the momentum of inertia I. In this work we characterize the relative equilibria, we nd the bifurcation values of I for which the number of relative equilibria is changing, we also analyze the stability of the relative equilibria.

Collection de copies de pièces sur l'histoire de France, formée par François DE CAMPS, abbé de Signy (1723). II PROVINCES. 57 Lyon et Lyonnais.

« De la souveraineté des rois de France sur la ville et le comté de Lion » (fol. 2, 93, 120) : pièces justificatives, 942-1320 (132, 183). — Lettre critique sur l'Histoire civile ou consulaire de la ville de Lyon du P. Menestrier (90). — Primatie de Lyon (186). Extraits « ex cartulario episcopatus Diensis, quod est conventus fratrum Minimorum Parisiensium, » 1158-1229 (193). Diplômes de l'empereur Frédéric II, copies extraites des archives du Domaine royal en Languedoc, arm. B (196). Inventaire des chartes du Trésor relatives au Lyonnais (218).

Collection de copies de pièces sur l'histoire de France, connue sous le nom de Portefeuilles de Fontanieu, formée par Gaspard-Moyse DE FONTANIEU (1767). PREMIÈRE PARTIE — Histoire par règnes. 149-150 Charles VIII (1494-1498).

Contient : Lettre de Louis, duc d'Orléans [Louis XII] sur la mort de Jean Galéas, duc de Milan (1497) ; copie du temps

A new heuristic method for solving spatially constrained forest planning problems based on mitigation of infeasibilities radiating outward from a forced choice

[Abstract]

Highway Runoff Study, HR-1037, Progress Report, 1984

The focus of highway runoff monitoring programs is on the identification of highway contributions to nonpoint source degradation of surface and groundwater quality. The results of such studies will assist the Iowa Department of Transportation (DOT) in the development of maintenance practices that will minimize the impact of highway transportation networks on water quality while at the same time maintaining public safety. Highway runoff monitoring research will be useful in developing a basis to address issues in environmental impact statements for future highway network expansions. Further, it will lead to optimization of cost effectiveness/environmental factors related to de-icing, weed and dust control, highway drainage, construction methods, etc. In this report, the authors present the data accumulated to date with a preliminary interpretation of the significance of the data. The report will discuss the site setup, operational aspects of data collection, and problems encountered. In addition, recommendations are included to optimize information gained from the study.

Forensic imaging: New challenges for radiographers : B-588

Purpose: Cross-sectional imaging techniques have pioneered forensic medicine. The involvement of a radiographer and formation of "forensic radiographers" allows an improvement of the quality of radiological examinations and facilitates the implementation of techniques, such as sample collections, and the performance of post-mortem angiography. Methods and Materials: During a period of three months, five radiographers with clinical experience have undergone a special training in order to learn procedures dedicated to forensic imaging. These procedures involved: I). acquisition of MDCT data, II). sample collection for toxicological or histological analyses by performing CT-guided biopsies and liquid sampling, III). post-mortem angiography and IV). post-processing of all data acquired. To perform the post-mortem angiography, radiographers were in charge of the preparation of the perfusion device and the investigated body. Therefore, cannulas were inserted into the femoral vessels and connected to the machine. For angiography, the radiographers had to synchronize the perfusion with the CT-acquisitions. Results: All five radiographers have acquired new skills to become "forensic radiographers". They were able to perform post-mortem MDCT, sample collection, post-mortem angiography and post-processing of the acquired data all by themselves. Most problems have been observed concerning the preparation of the body for post-mortem angiography. Conclusion: Our experience shows that radiographers are able to perform high quality examinations after a short period of training. Their collaboration is well accepted by the forensic team and regarding the increase of radiological exams in forensic department, it would be nonsense to exclude radiographers from the forensic-radiological team.

Pediatric drug-related problems: a multicenter study in four French-speaking countries.

BACKGROUND: Pediatric intensive care patients represent a population at high risk for drug-related problems. There are few studies that compare the activity of clinical pharmacists between countries. OBJECTIVE: To describe the drug-related problems identified and interventions by four pharmacists in a pediatric cardiac and intensive care unit. SETTING: Four pediatric centers in France, Quebec, Switzerland and Belgium. METHOD: This was a six-month multicenter, descriptive and prospective study conducted from August 1, 2009 to January 31, 2010. Drug-related problems and clinical interventions were compiled from four pediatric centers in France, Quebec, Switzerland and Belgium. Data on patients, drugs, intervention, documentation, approval and estimated impact were compiled. MAIN OUTCOME MEASURE: Number and type of drug-related problems encountered in a large pediatric inpatient population. RESULTS: A total of 996 interventions were recorded: 238 (24 %) in France, 278 (28 %) in Quebec, 351 (35 %) in Switzerland and 129 (13 %) in Belgium. These interventions targeted 270 patients (median 21 months old, 53 % male): 88 (33 %) in France, 56 (21 %) in Quebec, 57 (21 %) in Switzerland and 69 (26 %) in Belgium. The main drug-related problems were inappropriate administration technique (29 %), untreated indication (25 %) and supra-therapeutic dose (11 %). The pharmacists' interventions were mostly optimizing the mode of administration (22 %), dose adjustment (20 %) and therapeutic monitoring (16 %). The two major drug classes that led to interventions were anti-infectives for systemic use (23 %) and digestive system and metabolism drugs (22 %). Interventions mainly involved residents and all clinical staff (21 %). Among the 878 (88 %) proposed interventions requiring physician approval, 860 (98 %) were accepted. CONCLUSION: This descriptive study illustrates drug-related problems and the ability of clinical pharmacists to identify and resolve them in pediatric intensive care units in four French-speaking countries.

Management of faecal incontinence and constipation in adults with central neurological diseases.

BACKGROUND: People with neurological disease have a much higher risk of both faecal incontinence and constipation than the general population. There is often a fine line between the two conditions, with any management intended to ameliorate one risking precipitating the other. Bowel problems are observed to be the cause of much anxiety and may reduce quality of life in these people. Current bowel management is largely empirical with a limited research base. OBJECTIVES: To determine the effects of management strategies for faecal incontinence and constipation in people with neurological diseases affecting the central nervous system. SEARCH STRATEGY: We searched the Cochrane Incontinence Group Specialised Trials Register (searched 26 January 2005), the Cochrane Central Register of Controlled Trials (Issue 2, 2005), MEDLINE (January 1966 to May 2005), EMBASE (January 1998 to May 2005) and all reference lists of relevant articles. SELECTION CRITERIA: All randomised or quasi-randomised trials evaluating any types of conservative or surgical measure for the management of faecal incontinence and constipation in people with neurological diseases were selected. Specific therapies for the treatment of neurological diseases that indirectly affect bowel dysfunction were also considered. DATA COLLECTION AND ANALYSIS: Two reviewers assessed the methodological quality of eligible trials and two reviewers independently extracted data from included trials using a range of pre-specified outcome measures. MAIN RESULTS: Ten trials were identified by the search strategy, most were small and of poor quality. Oral medications for constipation were the subject of four trials. Cisapride does not seem to have clinically useful effects in people with spinal cord injuries (three trials). Psyllium was associated with increased stool frequency in people with Parkinson's disease but did not alter colonic transit time (one trial). Prucalopride, an enterokinetic did not demonstrate obvious benefits in this patient group (one study). Some rectal preparations to initiate defaecation produced faster results than others (one trial). Different time schedules for administration of rectal medication may produce different bowel responses (one trial). Mechanical evacuation may be more effective than oral or rectal medication (one trial). There appears to be a benefit to patients in one-off educational interventions from nurses. The clinical significance of any of these results is difficult to interpret. AUTHORS' CONCLUSIONS: There is still remarkably little research on this common and, to patients, very significant condition. It is not possible to draw any recommendation for bowel care in people with neurological diseases from the trials included in this review. Bowel management for these people must remain empirical until well-designed controlled trials with adequate numbers and clinically relevant outcome measures become available.

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.

First-passage and escape problems in the Feller process

The Feller process is an one-dimensional diffusion process with linear drift and state-dependent diffusion coefficient vanishing at the origin. The process is positive definite and it is this property along with its linear character that have made Feller process a convenient candidate for the modeling of a number of phenomena ranging from single-neuron firing to volatility of financial assets. While general properties of the process have long been well known, less known are properties related to level crossing such as the first-passage and the escape problems. In this work we thoroughly address these questions.