Pressure Relief and Other Joint Rehabilitation Techniques, HR-1042, 1987

A study of four major concrete pavement joint rehabilitation techniques has been conducted, including: pressure relief joints, full-depth repairs, partial-depth repairs and joint resealing. The products of this research include the following for each technique: a summary of published research, detailed documentation of the design and performance of the 36 projects, conclusions and recommendations of the state highway engineers panel, "Design and Construction Guidelines" and "Guide Specifications." The latter two products are prepared for use by state highway agencies. The results of this study are based upon a review of literature, extensive field surveys and analysis of 36 rehabilitation projects, and the experience of an expert panel of state highway engineers.

Reduction of Concrete Deterioration by Ettringite Using Crystal Growth Inhibition Techniques: Part II Field Evaluation of Inhibitor Effectiveness, TR-469, 2004

The effects of diethylenetriaminpenta(methylenephosphonic acid) (DTPMP), a phosphonate inhibitor, on the growth of delayed ettringite have been evaluated using concrete in highway US 20 near Williams, Iowa, and the cores of six highways subject to moderate (built in 1992) or minor (built in 1997) deterioration. Application of 0.01 and 0.1 vol. % DTPMP to cores was made on a weekly or monthly basis for one year under controlled laboratory-based freeze-thaw and wet-dry conditions over a temperature range of -15 degrees to 58 degrees C to mimic extremes in Iowa roadway conditions. The same concentrations of phosphonate were also applied to cores left outside (roof of Science I at Iowa State University) over the same period of time. Nineteen applications of 0.1 vol. % DTPMP with added deicing salt solution (about 23 weight % NACL) were made to US 20 during the winters of 2003 and 2004. In untreated samples, air voids, pores, and occasional cracks are lined with acicular ettringite crystals (up to 50 micrometers in length) whereas air voids, pores, and cracks in concrete from the westbound lane of US 20 are devoid of ettringite up to a depth of about 0.5 mm from the surface of the concrete. Ettringite is also absent in zones up to 6 mm from the surface of concrete slabs placed on the roof of Science I and cores subject to laboratory-based freeze-thaw experiments. In these zones, the relatively high concentration of DTPMP caused it to behave as a chelator. Stunted ettringite crystals 5 to 25 micrometers in length, occasionally coated with porlandite, form on the margins of these zones indicating that in these areas DTPMP behaved as an inhibitor due to a reduction in the concentration of phosphonate. Analyses of mixes of ettringite and DTPMP using electrospray mass spectrometry suggests that the stunting of ettringite growth is caused by the adsorption of a Ca2+ ion and a water molecule to deprotonated DTPMP on the surface of the {0001} face of ettringite. It is anticipated that by using a DTPMP concentration of between 0.001 and 0.01 vol. % for the extended life of a highway (i.e. >20 years), deterioration caused by the expansive growth of ettringite will be markedly reduced.

Investigation of Improved Utility Cut Repair Techniques to Reduce Settlement in Repaired Areas, Phase II, TR-566, 2010

Trench maintenance problems are caused by improper backfill placement and construction procedures. This report is part of a multiphase research project that aims to improve long-term performance of utility cut restoration trenches. The goal of this research is to improve pavement patch life and reduce maintenance of the repaired areas. The objectives were to use field-testing data, laboratory-testing data, and long-term monitoring (elevation survey and falling weight deflectometer testing) to suggest and modify recommendations from Phase I and to identify the principles of trench subsurface settlement and load distribution in utility cut restoration areas by using instrumented trenches. The objectives were accomplished by monitoring local agency utility construction from Phase I, constructing and monitoring the recommended trenches from Phase I, and instrumenting trenches to monitor changes in temperature, pressure, moisture content, and settlement as a function of time to determine the influences of seasonal changes on the utility cut performance.

Identification of Laboratory Techniques to Optimize Superpave HMA Surface Friction Characteristics, TR-450, 2010

Wet pavement friction is known to be one of the most important roadway safety parameters. In this research, frictional properties of flexible (asphalt) pavements were investigated. As a part of this study, a laboratory device to polish asphalt specimens was refined and a procedure to evaluate mixture frictional properties was proposed. Following this procedure, 46 different Superpave mixtures, one stone matrix asphalt (SMA) mixture and one porous friction course (PFC) mixture were tested. In addition, 23 different asphalt and two concrete field sections were also tested for friction and noise. The results of both field and laboratory measurements were used to develop an International Friction Index (IFI)-based protocol for measurement of the frictional characteristics of asphalt pavements for laboratory friction measurements. Based on the results of the study, it appears the content of high friction aggregate should be 20% or more of the total aggregate blend when used with other, polish susceptible coarse aggregates; the frictional properties increased substantially as the friction aggregate content increased above 20%. Both steel slag and quartzite were found to improve the frictional properties of the blend, though steel slag had a lower polishing rate. In general, mixes containing soft limestone demonstrated lower friction values than comparable mixes with hard limestone or dolomite. Larger nominal maximum aggregate size mixes had better overall frictional performance than smaller sized mixes. In addition, mixes with higher fineness moduli generally had higher macrotexture and friction.

Similarity in food cleaning techniques within matrilines in wild vervet monkeys.

Social learning and the formation of traditions rely on the ability and willingness to copy one another. A central question is under which conditions individuals adapt behaviour to social influences. Here, we demonstrate that similarities in food processing techniques emerge on the level of matrilines (mother-offspring) but not on the group level in an experiment on six groups of wild vervet monkeys that involved grapes covered with sand. Monkeys regularly ate unclean grapes but also used four cleaning techniques more similarly within matrilines: rubbing in hands, rubbing on substrate, open with mouth, and open with hands. Individual cleaning techniques evolved over time as they converged within matrilines, stabilised at the end and remained stable in a follow-up session more than one year later. The similarity within matrilines persisted when we analyzed only foraging events of individuals in the absence of other matriline members and matriline members used more similar methods than adult full sisters. Thus, momentary conversion or purely genetic causation are unlikely explanations, favouring social learning as mechanism for within matriline similarities. The restriction of traditions to matriline membership rather than to the group level may restrict the development of culture in monkeys relative to apes or humans.

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.

Laser treatment of 13 benign oral vascular lesions by three different surgical techniques

Objectives: Benign Oral Vascular Lesions (BOVLs) are a group of vascular diseases characterized by congenital, inflammatory or neoplastic vascular dilations clinically evidenced as more or less wide masses of commonly dark bluish color. If traumatized BOVLs are characterized by a great risk of hemorrhage and their treatment usually requires great caution to prevent massive bleeding. In the last decades lasers have dramatically changed the way of treatment of BOVLs permitting the application of even peculiar techniques that gave interesting advantages in their management reducing hemorrhage risks. The aim of this study was to evaluate the capabilities and disadvantages of three laser assisted techniques in the management of BOVLs. Study design: In this study 13 BOVLs were treated by three different laser techniques: the traditional excisional biopsy (EB), and two less invasive techniques, the transmucosal thermocoagulation (TMT) and the intralesional photocoagulation (ILP). Two different laser devices were adopted in the study: a KTP laser (DEKA, Florence, Italy, 532nm) and a GaAlAs laser (Laser Innovation, Castelgandolfo, Italy, 808nm) selected since their great effectiveness on hemoglobin. Results: In each case, lasers permitted safe treatments of BOVLs without hemorrhages, both during the intervention and in the post-operative period. The minimally invasive techniques (TMT and ILP) permitted even the safe resolution of big lesions without tissue loss. Conclusions: Laser devices confirm to be the gold standard in BOVLs treatment, permitting even the introduction of minimal invasive surgery principles and reducing the risks of hemorrhage typical of these neoplasms. As usual in laser surgery, it is necessary a clear knowledge of the devices and of the laser-tissue interaction to optimize the results reducing risks and disadvantages

An international university network for online teaching of pathology in Frenchspeaking countries

Introduction: Building online courses is a highly time consuming task for teachers of a single university. Universities working alone create high-quality courses but often cannot cover all pathological fields. Moreover this often leads to duplication of contents among universities, representing a big waste of teacher time and energy. We initiated in 2011 a French university network for building mutualized online teaching pathology cases, and this network has been extended in 2012 to Quebec and Switzerland. Method: Twenty French universities (see & for details), University Laval in Quebec and University of Lausanne in Switzerland are associated to this project. One e-learning Moodle platform (http://moodle.sorbonne-paris-cite.fr/) contains texts with URL pointing toward virtual slides that are decentralized in several universities. Each university has the responsibility of its own slide scanning, slide storage and online display with virtual slide viewers. The Moodle website is hosted by PRES Sorbonne Paris Cité, and financial supports for hardware have been obtained from UNF3S (http://www.unf3s.org/) and from PRES Sorbonne Paris Cité. Financial support for international fellowships has been obtained from CFQCU (http://www.cfqcu.org/). Results: The Moodle interface has been explained to pathology teachers using web-based conferences with screen sharing. The teachers added then contents such as clinical cases, selfevaluations and other media organized in several sections by student levels and pathological fields. Contents can be used as online learning or online preparation of subsequent courses in classrooms. In autumn 2013, one resident from Quebec spent 6 weeks in France and Switzerland and created original contents in inflammatory skin pathology. These contents are currently being validated by senior teachers and will be opened to pathology residents in spring 2014. All contents of the website can be accessed for free. Most contents just require anonymous connection but some specific fields, especially those containing pictures obtained from patients who agreed for a teaching use only, require personal identification of the students. Also, students have to register to access Moodle tests. All contents are written in French but one case has been translated into English to illustrate this communication (http://moodle.sorbonne-pariscite.fr/mod/page/view.php?id=261) (use "login as a guest"). The Moodle test module allows many types of shared questions, making it easy to create personalized tests. Contents that are opened to students have been validated by an editorial committee composed of colleagues from the participating institutions. Conclusions: Future developments include other international fellowships, the next one being scheduled for one French resident from May to October 2014 in Quebec, with a study program centered on lung and breast pathology. It must be kept in mind that these e-learning programs highly depend on teachers' time, not only at these early steps but also later to update the contents. We believe that funding resident fellowships for developing online pathological teaching contents is a win-win situation, highly beneficial for the resident who will improve his knowledge and way of thinking, highly beneficial for the teachers who will less worry about access rights or image formats, and finally highly beneficial for the students who will get courses fully adapted to their practice.

Estudi i aplicació de les tècniques de simulació en una xarxa distribuïda

L'objectiu d'aquest treball és veure com s'apliquen aquestes tècniques en l'estudi d'un sistema informàtic distribuït

Synthesis of Traffic Calming Techniques in Work Zones, 2009

Nationwide, over 1,000 fatalities and 40,000 injuries occur annually in work zones, which include both construction zones and areas where maintenance is performed. The majority (85%) of work zone accidents result from unsafe driver behavior, and vehicle speed is often a factor in work zone crashes. In order to address speed and driver behavior near work zones, roadway agencies have developed different traffic calming measures. The objective of this research is to summarize the effectiveness of different traffic calming treatments for reducing speeds in work zones. This project 1. identified work zone traffic calming treatments for which information has not been well summarized, 2. identified state of the art and new technologies for work zone traffic calming, and 3. synthesized research related to items 1 and 2

The promotion and assessment of generic skills from interdisciplinary teaching teams

The paper explains a teaching project financed by the University of Barcelona (UB). It focuses on ageneric skill of the University of Barcelona, which is defined as "the learning capability andresponsibility”, and in which analytical and synthesis skills are included. It follows a multidisciplinaryapproach including teachers of Mathematics, World Economics and Economic History. All of us sharethe same students during the first and the second course of the grade in Economics at the Faculty ofEconomics and Business. The project has been developed in three stages. The first one has beendone during the first semester of the course 2012/13, being applied to first year students on thesubjects of Mathematics and Economic History. The second phase is being to be done during thesecond semester only on the Economic History subject. A third stage is going to be done next course2013/14 to second year students on the subject of World Economics. Each different teaching teamhas developed specific materials and assessment tools for each one of the subjects included in theproject. The project emphasizes two teaching dimensions: the elaboration of teaching materials topromote the acquisition of generic skills from an interdisciplinary point of view, and the design ofspecific tools to assess such skills. The first results of the first phase of the project shows cleardeficiencies in the analytical skill regarding to first year students.

The promotion and assessment of generic skills from interdisciplinary teaching teams

The paper explains a teaching project financed by the University of Barcelona (UB). It focuses on ageneric skill of the University of Barcelona, which is defined as "the learning capability andresponsibility”, and in which analytical and synthesis skills are included. It follows a multidisciplinaryapproach including teachers of Mathematics, World Economics and Economic History. All of us sharethe same students during the first and the second course of the grade in Economics at the Faculty ofEconomics and Business. The project has been developed in three stages. The first one has beendone during the first semester of the course 2012/13, being applied to first year students on thesubjects of Mathematics and Economic History. The second phase is being to be done during thesecond semester only on the Economic History subject. A third stage is going to be done next course2013/14 to second year students on the subject of World Economics. Each different teaching teamhas developed specific materials and assessment tools for each one of the subjects included in theproject. The project emphasizes two teaching dimensions: the elaboration of teaching materials topromote the acquisition of generic skills from an interdisciplinary point of view, and the design ofspecific tools to assess such skills. The first results of the first phase of the project shows cleardeficiencies in the analytical skill regarding to first year students.