Use of active personal dosemeters in interventional radiology and cardiology: Tests in laboratory conditions and recommendations - ORAMED project

Active personal dosemeters (APD) have been found to be very efficient tools to reduce occupational doses in many applications of ionizing radiation. In order to be used in interventional radiology and cardiology (IR/IC), APDs should be able to measure low energy photons and pulsed radiation with relatively high instantaneous personal dose equivalent rates. A study concerning the optimization of the use of APDs in IR/IC was performed in the framework of the ORAMED project, a Collaborative Project (2008-2011) supported by the European Commission within its 7th Framework Program. In particular, eight commercial APDs were tested in continuous and pulsed X-ray fields delivered by calibration laboratories in order to evaluate their performances. Most of APDs provide a response in pulsed mode more or less affected by the personal dose equivalent rate, which means they could be used in routine monitoring provided that correction factors are introduced. These results emphasize the importance of adding tests in pulsed mode in type-test procedures for APDs. Some general recommendations are proposed in the end of this paper for the selection and use of APDs at IR/IC workplaces.

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.

The ager tarraconensis project (right side of river Francoli) (PAT): the application of geophysical survey to identify rural Roman settlement typologies

The methodology of the ager Tarraconensis project also included geophysical surveys aiming to distinguish different categories of rural settlements. Two geophysical techniques (resistivity and magnetometry) were combined to reveal traces of unearth structures from a selection of sites identified from the field survey. Results of geophysical surveys of these seven sites as well as conclusions obtained from this approach are discussed here.

Demonstration Project 85, Video Imagery Systems for Highway Applications, HR-1047, 1990

The report describes the state of the art video equipment used and experiences gained from the 6,800 mile field test. The first objective of this project was to determine if laser disc equipment could capture and store usable roadway images while operating in a mobile environment. The second objective was to evaluate methods of using optical disc storage and retrieval features to enhance highway planning and design function. Several highway departments have attempted to use video technology to replace the traditional 16 and 35 mm film format used in photologging. These attempts have met with limited success because of the distortion caused by video technology not being capable of dealing with highway speeds. The distortion has caused many highway signs to be unreadable and, therefore, clients have labeled the technology unusable. Two methods of using optical laser disc storage and retrieval have been successfully demonstrated by Wisconsin and Connecticut Departments of Transportation. Each method provides instantaneous retrieval and linking of images with other information. However, both methods gather the images using 35 mm film techniques. The 35 mm film image is then transferred to laser disc. Eliminating the film conversion to laser disc has potential for saving $4 to $5 per logging mile. In addition to a cost savings, the image would be available immediately as opposed to delays caused by film developing and transferring to laser disc. In June and November of 1986 Iowa DOT staff and cooperating equipment suppliers demonstrated the concept of direct image capture. The results from these tests were promising and an FHWA Demonstration program established. Since 1986 technology advancements have been incorporated into the design that further improve the image quality originally demonstrated.

Construction of the Iowa Highway 60 Precast Prestressed Concrete Pavement Bridge Approach Slab Demonstration Project, HR-1085, 2007

Reconstruction of bridge approach slabs which have failed due to a loss of support from embankment fill consolidation or erosion can be particularly challenging in urban areas where lane closures must be minimized. Precast prestressed concrete pavement is a potential solution for rapid bridge approach slab reconstruction which uses prefabricated pavement panels that can be installed and opened to traffic quickly. To evaluate this solution, the Iowa Department of Transportation constructed a precast prestressed approach slab demonstration project on Highway 60 near Sheldon, Iowa in August/September 2006. Two approach slabs at either end of a new bridge were constructed using precast prestressed concrete panels. This report documents the successful development, design, and construction of the precast prestressed concrete bridge approach slabs on Highway 60. The report discusses the challenges and issues that were faced during the project and presents recommendations for future implementation of this innovative construction technique.

Modelling ecological niches with support vector machines

1. The ecological niche is a fundamental biological concept. Modelling species' niches is central to numerous ecological applications, including predicting species invasions, identifying reservoirs for disease, nature reserve design and forecasting the effects of anthropogenic and natural climate change on species' ranges. 2. A computational analogue of Hutchinson's ecological niche concept (the multidimensional hyperspace of species' environmental requirements) is the support of the distribution of environments in which the species persist. Recently developed machine-learning algorithms can estimate the support of such high-dimensional distributions. We show how support vector machines can be used to map ecological niches using only observations of species presence to train distribution models for 106 species of woody plants and trees in a montane environment using up to nine environmental covariates. 3. We compared the accuracy of three methods that differ in their approaches to reducing model complexity. We tested models with independent observations of both species presence and species absence. We found that the simplest procedure, which uses all available variables and no pre-processing to reduce correlation, was best overall. Ecological niche models based on support vector machines are theoretically superior to models that rely on simulating pseudo-absence data and are comparable in empirical tests. 4. Synthesis and applications. Accurate species distribution models are crucial for effective environmental planning, management and conservation, and for unravelling the role of the environment in human health and welfare. Models based on distribution estimation rather than classification overcome theoretical and practical obstacles that pervade species distribution modelling. In particular, ecological niche models based on machine-learning algorithms for estimating the support of a statistical distribution provide a promising new approach to identifying species' potential distributions and to project changes in these distributions as a result of climate change, land use and landscape alteration.

Demonstration Project using Railroad Flatcars for Low-Volume Road Bridges, TR-444, 2003

The use of Railroad Flatcars (RRFCs) as the superstructure on low-volume county bridges has been investigated in a research project conducted by the Bridge Engineering Center at Iowa State University. These bridges enable county engineers to replace old, inadequate county bridge superstructures for less than half the cost and in a shorter construction time than required for a conventional bridge. To illustrate their constructability, adequacy, and economy, two RRFC demonstration bridges were designed, constructed, and tested: one in Buchanan County and the other in Winnebago County. The Buchanan County Bridge was constructed as a single span with 56-ft-long flatcars supported at their ends by new, concrete abutments. The use of concrete in the substructure allowed for an integral abutment at one end of the bridge with an expansion joint at the other end. Reinforced concrete beams (serving as longitudinal connections between the three adjacent flatcars) were installed to distribute live loads among the RRFCs. Guardrails and an asphalt milling driving surface completed the bridge. The Winnebago County Bridge was constructed using 89-ft-long flatcars. Preliminary calculations determined that they were not adequate to span 89 ft as a simple span. Therefore, the flatcars were supported by new, steel-capped piers and abutments at the RRFCs' bolsters and ends, resulting in a 66-ft main span and two 10-ft end spans. Due to the RRFC geometry, the longitudinal connections between adjacent RRFCs were inadequate to support significant loads; therefore, transverse, recycled timber planks were utilized to effectively distribute live loads to all three RRFCs. A gravel driving surface was placed on top of the timber planks, and a guardrail system was installed to complete the bridge. Bridge behavior predicted by grillage models for each bridge was validated by strain and deflection data from field tests; it was found that the engineered RRFC bridges have live load stresses significantly below the AASHTO Bridge Design Specification limits. To assist in future RRFC bridge projects, RRFC selection criteria were established for visual inspection and selection of structurally adequate RRFCs. In addition, design recommendations have been developed to simplify live load distribution calculations for the design of the bridges. Based on the results of this research, it has been determined that through proper RRFC selection, construction, and engineering, RRFC bridges are a viable, economic replacement system for low-volume road bridges.

Low-Cost Rural Surface Alternatives: Demonstration Project, TR-664, 2015

The goals of this project were to implement several stabilization methods for preventing or mitigating freeze-thaw damage to granular surfaced roads and identify the most effective and economical methods for the soil and climate conditions of Iowa. Several methods and technologies identified as potentially suitable for Iowa were selected from an extensive analysis of existing literature provided with Iowa Highway Research Board (IHRB) Project TR-632. Using the selected methods, demonstration sections were constructed in Hamilton County on a heavily traveled two-mile section of granular surfaced road that required frequent maintenance during previous thawing periods. Construction procedures and costs of the demonstration sections were documented, and subsequent maintenance requirements were tabulated through two seasonal freeze-thaw periods. Extensive laboratory and field tests were performed prior to construction, as well as before and after the two seasonal freeze-thaw periods, to monitor the performance of the demonstration sections. A weather station was installed at the project site and temperature sensors were embedded in the subgrade to monitor ground temperatures up to a depth of 5 ft and determine the duration and depths of ground freezing and thawing. An economic analysis was performed using the documented construction and maintenance costs, and the estimated cumulative costs per square yard were projected over a 20-year timeframe to determine break-even periods relative to the cost of continuing current maintenance practices. Overall, the sections with biaxial geogrid or macadam base courses had the best observed freeze-thaw performance in this study. These two stabilization methods have larger initial costs and longer break-even periods than aggregate columns, but counties should also weigh the benefits of improved ride quality and savings that these solutions can provide as excellent foundations for future paving or surface upgrades.

Mississippi River investigations. Project no. F-109-R-1, 1986.

Two investigations are included in this document: 1) An Evaluation of Largemouth Bass Populations in the Upper Mississippi River and 2) An Evaluation of the Effects of a Change in Commercial Harvest Regulations on the Channel Catfish Population Inhabiting the Upper Mississippi River

Final Report (SPR Project 90-00-RB10-012) on the Maintenance Asset Management Project Phase I, 2012

This project resulted in the development of a proof of concept for a features inventory process to be used by field staff. The resulting concept is adaptable for different asset classes (e.g. culverts, guardrail) and able to leverage existing DOT resources such as the videolog and LRS and our current technology platforms including Oracle and our GIS web infrastructure. The concept examined the feasibility of newly available technologies, such as mobile devices, while balancing ease of use in the field. Implementation and deployment costs were also important considerations in evaluating the success of the project. These project funds allowed the pilot to address the needs of two DOT districts. A report of findings was prepared, including recommendations for or against full deployment of the pilot solution.

Final Report (SPR Project 90-00-RB10-012) on the Maintenance Asset Management Project Phase II, 2013

This project resulted in the development of a proof of concept for a features inventory process to be used by field staff. The resulting concept is adaptable for different asset classes (e.g. culverts, guardrail) and able to leverage existing DOT resources such as the videolog and LRS and our current technology platforms including Oracle and our GIS web infrastructure. The concept examined the feasibility of newly available technologies, such as mobile devices, while balancing ease of use in the field. Implementation and deployment costs were also important considerations in evaluating the success of the project. These project funds allowed the pilot to address the needs of two DOT districts. A report of findings was prepared, including recommendations for a full deployment of a field data collection.

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).