Automation of DEM Cutting for Hydrologic/Hydraulic Modeling, TR-631, 2015

Hydrologic analysis is a critical part of transportation design because it helps ensure that hydraulic structures are able to accommodate the flow regimes they are likely to see. This analysis is currently conducted using computer simulations of water flow patterns, and continuing developments in elevation survey techniques result in higher and higher resolution surveys. Current survey techniques now resolve many natural and anthropogenic features that were not practical to map and, thus, require new methods for dealing with depressions and flow discontinuities. A method for depressional analysis is proposed that uses the fact that most anthropogenically constructed embankments are roughly more symmetrical with greater slopes than natural depressions. An enforcement method for draining depressions is then analyzed on those depressions that should be drained. This procedure has been evaluated on a small watershed in central Iowa, Walnut Creek of the South Skunk River, HUC12 # 070801050901, and was found to accurately identify 88 of 92 drained depressions and place enforcements within two pixels, although the method often tries to drain prairie pothole depressions that are bisected by anthropogenic features.

Machine learning for spatial environmental data. Theory, applications and software

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Two-step postmortem angiography with a modified heart-lung machine: preliminary results.

OBJECTIVE: The purpose of this study was to adapt and improve a minimally invasive two-step postmortem angiographic technique for use on human cadavers. Detailed mapping of the entire vascular system is almost impossible with conventional autopsy tools. The technique described should be valuable in the diagnosis of vascular abnormalities. MATERIALS AND METHODS: Postmortem perfusion with an oily liquid is established with a circulation machine. An oily contrast agent is introduced as a bolus injection, and radiographic imaging is performed. In this pilot study, the upper or lower extremities of four human cadavers were perfused. In two cases, the vascular system of a lower extremity was visualized with anterograde perfusion of the arteries. In the other two cases, in which the suspected cause of death was drug intoxication, the veins of an upper extremity were visualized with retrograde perfusion of the venous system. RESULTS: In each case, the vascular system was visualized up to the level of the small supplying and draining vessels. In three of the four cases, vascular abnormalities were found. In one instance, a venous injection mark engendered by the self-administration of drugs was rendered visible by exudation of the contrast agent. In the other two cases, occlusion of the arteries and veins was apparent. CONCLUSION: The method described is readily applicable to human cadavers. After establishment of postmortem perfusion with paraffin oil and injection of the oily contrast agent, the vascular system can be investigated in detail and vascular abnormalities rendered visible.

Small access (30F) clinical central venous smart cannulation: is it adequate?

OBJECTIVES: To assess the performance of 45F vs. 36F smartcanula in CPB with gravity drainage alone. METHODS: Twenty patients were randomly assigned to two groups receiving for venous drainage a smartcanula which is collapsed over a mandrel for trans-atrial insertion into the inferior vena cava and expanded in situ to either 45F or 36F. RESULTS: Valve replacement/repair was realized in 7/10 and/or CABG in 6/10 for 36F (69+/-13 years) vs. 5/10 and 5/10, respectively, for 45F (63+/-11 years: NS). Body weight and surface area (BSA) were 83+/-9 kg (1.9+/-0.2 m2, max 2.2 m2) for 36F vs. 79+/-6 kg: NS (1.9+/-0.1 m2 (NS), max 2.1 m2) for 45F. Insertion and access orifice diameter (area) was 6 mm and 10 mm (78.5 mm2) for the 36F vs. 6 mm and 13 mm (132 mm2) for the 45F (+69%). Calculated target pump flow (2.4 l/min/m2) was 4.7+/-0.4 l/min for 36F vs. 4.5+/-0.3 l/min for 45F. Achieved pump flow accounted for 5.0+/-0.3 l/min for 36F (8% above target) vs. 4.8+/-0.3 l/min for 45F (8% above target): NS. The water balance during the pump run (clear volume added minus hemofilter and urine output) was 2.2+/-0.3 l for 36F vs. 2.0 l for 45F: NS. CONCLUSION: Due to its 'open' wall (the vena cava provides the seal), its reduced wall thickness (range: 0.0-0.4 mm), and its self-expanding design, the 36F smartcanula requiring a 30F access orifice has sufficient drainage capacity by gravity alone for full CPB in adults with a BSA up to 2.2 mm2.

HIV-1 elite controllers: beware of super-infections.

Super- and co-infection with HIV-1 are generally associated with accelerated disease progression. We report on the outcome of super-infection in two HIV-1 infected individuals previously known as elite controllers. Both presented an acute retroviral syndrome following super-infection and showed an immuno-virological progression thereafter. Host genotyping failed to reveal any of the currently recognized protective factors associated with slow disease progression. This report indicates that elite controllers should be informed of the risk of super-infection, and illustrates the complexity of mounting broad anti-HIV immunity.

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.

Soil compaction by machine traffic and least limiting water range related to soybean yield

The research aimed to evaluate machine traffic effect on soil compaction and the least limiting water range related to soybean cultivar yields, during two years, in a Haplustox soil. The six treatments were related to tractor (11 Mg weight) passes by the same place: T0, no compaction; and T1*, 1; T1, 1; T2, 2; T4, 4 and T6, 6. In the treatment T1*, the compaction occurred when soil was dried, in 2003/2004, and with a 4 Mg tractor in 2004/2005. Soybean yield was evaluated in relation to soil compaction during two agricultural years in completely randomized design (compaction levels); however, in the second year, there was a factorial scheme (compaction levels, with and without irrigation), with four replicates represented by 9 m² plots. In the first year, soybean [Glycine max (L.) Merr.] cultivar IAC Foscarim 31 was cultivated without irrigation; and in the second year, IAC Foscarim 31 and MG/BR 46 (Conquista) cultivars were cultivated with and without irrigation. Machine traffic causes compaction and reduces soybean yield for soil penetration resistance between 1.64 to 2.35 MPa, and bulk density between 1.50 to 1.53 Mg m-3. Soil bulk density from which soybean cultivar yields decrease is lower than the critical one reached at least limiting water range (LLWR =/ 0).

A Classification of Tools for Learning Logic

A table showing a comparison and classification of tools (intelligent tutoring systems) for e-learning of Logic at a college level.

Improvement of rehabilitation possibilities with the MotionMaker (TM)

"MotionMaker (TM)" is a stationary programmable test and training system for the lower limbs developed at the 'Ecole Polytechnique Federale de Lausanne' with the 'Fondation Suisse pour les Cybertheses'.. The system is composed of two robotic orthoses comprising motors and sensors, and a control unit managing the trans-cutaneous electrical muscle stimulation with real-time regulation. The control of the Functional Electrical Stimulation (FES) induced muscle force necessary to mimic natural exercise is ensured by the control unit which receives a continuous input from the position and force sensors mounted on the robot. First results with control subjects showed the feasibility of creating movements by such closed-loop controlled FES induced muscle contractions. To make exercising with the MotionMaker (TM) safe for clinical trials with Spinal Cord Injured (SCI) volunteers, several original safety features have been introduced. The MotionMaker (TM) is able to identify and manage the occurrence of spasms. Fatigue can also be detected and overfatigue during exercise prevented.