Scientific data dissemination a data catalogue to assist research organizations

The constant scientific production in the universities and in the research centers makes these organizations produce and acquire a great amount of data in a short period of time. Due to the big quantity of data, the research organizations become potentially vulnerable to the impacts on information booms that may cause a chaos as far as information management is concerned. In this context, the development of data catalogues comes up as one possible solution to the problems such as (I) the organization and (II) the data management. In the scientific scope, the data catalogues are implemented with the standard for digital and geospatial metadata and are broadly utilized in the process of producing a catalogue of scientific information. The aim of this work is to present the characteristics of access and storage of metadata in databank systems in order to improve the description and dissemination of scientific data. Relevant aspects will be considered and they should be analyzed during the stage of planning, once they can determine the success of implementation. The use of data catalogues by research organizations may be a way to promote and facilitate the dissemination of scientific data, avoid the repetition of efforts while being executed, as well as incentivate the use of collected, processed an also stored.

Effectiveness of Work Zone Intelligent Transportation Systems, TPF-5(081), 2013

In the last decade, Intelligent Transportation Systems (ITS) have increasingly been deployed in work zones by state departments of transportation. Also known as smart work zone systems they improve traffic operations and safety by providing real-time information to travelers, monitoring traffic conditions, and managing incidents. Although there have been numerous ITS deployments in work zones, a framework for evaluating the effectiveness of these deployments does not exist. To justify the continued development and implementation of smart work zone systems, this study developed a framework to determine ITS effectiveness for specific work zone projects. The framework recommends using one or more of five performance measures: diversion rate, delay time, queue length, crash frequency, and speed. The monetary benefits and costs of ITS deployment in a work zone can then be computed using the performance measure values. Such ITS computations include additional considerations that are typically not present in standard benefit-cost computations. The proposed framework will allow for consistency in performance measures across different ITS studies thus allowing for comparisons across studies or for meta analysis. In addition, guidance on the circumstances under which ITS deployment is recommended for a work zone is provided. The framework was illustrated using two case studies: one urban work zone on I-70 and one rural work zone on I-44, in Missouri. The goals of the two ITS deployments were different – the I-70 ITS deployment was targeted at improving mobility whereas the I-44 deployment was targeted at improving safety. For the I-70 site, only permanent ITS equipment that was already in place was used for the project and no temporary ITS equipment was deployed. The permanent DMS equipment serves multiple purposes, and it is arguable whether that cost should be attributed to the work zone project. The data collection effort for the I-70 site was very significant as portable surveillance captured the actual diversion flows to alternative routes. The benefit-cost ratio for the I-70 site was 2.1 to 1 if adjusted equipment costs were included and 6.9 to 1 without equipment costs. The safety-focused I-44 ITS deployment had an estimated benefit-cost ratio of 3.2 to 1.

Correlation between microtensile bond strength data and clinical outcome of Class V restorations.

OBJECTIVE: To determine if the results of resin-dentin microtensile bond strength (µTBS) is correlated with the outcome parameters of clinical studies on non-retentive Class V restorations. METHODS: Resin-dentin µTBS data were obtained from one test center; the in vitro tests were all performed by the same operator. The µTBS testing was performed 8h after bonding and after 6 months of storing the specimens in water. Pre-test failures (PTFs) of specimens were included in the analysis, attributing them a value of 1MPa. Prospective clinical studies on cervical restorations (Class V) with an observation period of at least 18 months were searched in the literature. The clinical outcome variables were retention loss, marginal discoloration and marginal integrity. Furthermore, an index was formulated to be better able to compare the laboratory and clinical results. Estimates of adhesive effects in a linear mixed model were used to summarize the clinical performance of each adhesive between 12 and 36 months. Spearman correlations between these clinical performances and the µTBS values were calculated subsequently. RESULTS: Thirty-six clinical studies with 15 adhesive/restorative systems for which µTBS data were also available were included in the statistical analysis. In general 3-step and 2-step etch-and-rinse systems showed higher bond strength values than the 2-step/3-step self-etching systems, which, however, produced higher values than the 1-step self-etching and the resin modified glass ionomer systems. Prolonged water storage of specimens resulted in a significant decrease of the mean bond strength values in 5 adhesive systems (Wilcoxon, p<0.05). There was a significant correlation between µTBS values both after 8h and 6 months of storage and marginal discoloration (r=0.54 and r=0.67, respectively). However, the same correlation was not found between µTBS values and the retention rate, clinical index or marginal integrity. SIGNIFICANCE: As µTBS data of adhesive systems, especially after water storage for 6 months, showed a good correlation with marginal discoloration in short-term clinical Class V restorations, longitudinal clinical trials should explore whether early marginal staining is predictive for future retention loss in non-carious cervical restorations.

Field Data Collection and Reporting Study, HR-377, 1997

A review of the Iowa Department of Transportation's field data collection and reporting system has been performed. Included were several systems used by the Office of Construction and Local Jurisdictions. The entire field data collection and reporting systems for asphalt cement concrete (ACC) paving, portland cement concrete (PCC) paving, and PCC structures were streamlined and computerized. The field procedures for materials acceptance were also reviewed. Best practices were identified and a method was developed to prioritize materials so transportation agencies could focus their efforts on high priority materials. Iowa State University researchers facilitated a discussion about Equal Employment Opportunity (EEO) and Affirmative Action (AA) procedures between the Office of Construction field staff and the Office of Contracts. A set of alternative procedures was developed. Later the Office of Contracts considered these alternatives as they developed new procedures that are currently being implemented. The job close-out package was reviewed and two unnecessary procedures were eliminated. Numerous other procedures were reviewed and flowcharted. Several changes have been recommended that will increase efficiency and allow staff time to be devoted to higher priority activities. It is estimated the improvements in ACC paving, PCC paving and structural concrete will by similar to three full time equivalent (FTE) positions to field construction, field materials and Office of Materials. Elimination of EEO interviews will be equivalent to one FTE position. It is estimated that other miscellaneous changes will be equivalent to at least one other FTE person. This is a total five FTEs. These are conservative estimates based on savings that are easily quantified. It is likely that total positive effect is greater when items that are difficult to quantify are considered.

Maximizing the Use of Roadway Weather Information Systems: Final Report, TR-404, 1998

This project was proposed as Phase I of a 2-phase program to evaluate the present use of weather information by Iowa Department of Transportation (IaDOT) personnel, recommend revised procedures, and then implement the resulting recommendations. Midway through Phase I (evaluation phase) the FORETELL project was funded. This project is a multi-state venture that engages the National Weather Service (NWS) and the Forecast Systems Laboratory of the National Oceanic and Atmospheric Administration and proposes to supplant the current weather information-generation and distribution system with an advanced system based on state-of-the-art technologies. The focus of the present project was therefore refined to consider use of weather data by IaDOT personnel, and the training programs needed to more effectively use these data. Results of the survey revealed that two major areas - training of personnel on use of data from whatever source and more precise information of frost formation - are not addressed in the FORETELL project. These aspects have been the focus of the present project.

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.

Multi-Purpose ESS/ITS Data Collection Sites, SPR72-00-0003-042, 2014

This document presents the results of a state-of-practice survey of transportation agencies that are installing intelligent transportation sensors (ITS) and other devices along with their environmental sensing stations (ESS) also referred to as roadway weather information system (RWIS) assets.

A novel approach to reducing number of sensing units for wearable gait analysis systems.

Gait analysis methods to estimate spatiotemporal measures, based on two, three or four gyroscopes attached on lower limbs have been discussed in the literature. The most common approach to reduce the number of sensing units is to simplify the underlying biomechanical gait model. In this study, we propose a novel method based on prediction of movements of thighs from movements of shanks. Datasets from three previous studies were used. Data from the first study (ten healthy subjects and ten with Parkinson's disease) were used to develop and calibrate a system with only two gyroscopes attached on shanks. Data from two other studies (36 subjects with hip replacement, seven subjects with coxarthrosis, and eight control subjects) were used for comparison with the other methods and for assessment of error compared to a motion capture system. Results show that the error of estimation of stride length compared to motion capture with the system with four gyroscopes and our new method based on two gyroscopes was close ( -0.8 ±6.6 versus 3.8 ±6.6 cm). An alternative with three sensing units did not show better results (error: -0.2 ±8.4 cm). Finally, a fourth that also used two units but with a simpler gait model had the highest bias compared to the reference (error: -25.6 ±7.6 cm). We concluded that it is feasible to estimate movements of thighs from movements of shanks to reduce number of needed sensing units from 4 to 2 in context of ambulatory gait analysis.

Maintenance of polymorphism : a theoretical approach using multilocus systems

Résumé La thématique de cette thèse peut être résumée par le célèbre paradoxe de biologie évolutive sur le maintien du polymorphisme face à la sélection et par l'équation du changement de fréquence gamétique au cours du temps dû, à la sélection. La fréquence d'un gamète xi à la génération (t + 1) est: !!!Equation tronquée!!! Cette équation est utilisée pour générer des données utlisée tout au long de ce travail pour 2, 3 et 4 locus dialléliques. Le potentiel de l'avantage de l'hétérozygote pour le maintien du polymorphisme est le sujet de la première partie. La définition commune de l'avantage de l'hétérozygote n'etant applicable qu'a un locus ayant 2 allèles, cet avantage est redéfini pour un système multilocus sur les bases de précédentes études. En utilisant 5 définitions différentes de l'avantage de l'hétérozygote, je montre que cet avantage ne peut être un mécanisme général dans le maintien du polymorphisme sous sélection. L'étude de l'influence de locus non-détectés sur les processus évolutifs, seconde partie de cette thèse, est motivée par les travaux moléculaires ayant pour but de découvrir le nombre de locus codant pour un trait. La plupart de ces études sous-estiment le nombre de locus. Je montre que des locus non-détectés augmentent la probabilité d'observer du polymorphisme sous sélection. De plus, les conclusions sur les facteurs de maintien du polymorphisme peuvent être trompeuses si tous les locus ne sont pas détectés. Dans la troisième partie, je m'intéresse à la valeur attendue de variance additive après un goulot d'étranglement pour des traits sélectionés. Une études précédente montre que le niveau de variance additive après goulot d'étranglement augmente avec le nombre de loci. Je montre que le niveau de variance additive après un goulot d'étranglement augmente (comparé à des traits neutres), mais indépendamment du nombre de loci. Par contre, le taux de recombinaison a une forte influence, entre autre en regénérant les gamètes disparus suite au goulot d'étranglement. La dernière partie de ce travail de thèse décrit un programme pour le logiciel de statistique R. Ce programme permet d'itérer l'équation ci-dessus en variant les paramètres de sélection, recombinaison et de taille de populations pour 2, 3 et 4 locus dialléliques. Cette thèse montre qu'utiliser un système multilocus permet d'obtenir des résultats non-conformes à ceux issus de systèmes rnonolocus (la référence en génétique des populations). Ce programme ouvre donc d'intéressantes perspectives en génétique des populations. Abstract The subject of this PhD thesis can be summarized by one famous paradox of evolu-tionary biology: the maintenance of polymorphism in the face of selection, and one classical equation of theoretical population genetics: the changes in gametic frequencies due to selection and recombination. The frequency of gamete xi at generation (t + 1) is given by: !!! Truncated equation!!! This equation is used to generate data on selection at two, three, and four diallelic loci for the different parts of this work. The first part focuses on the potential of heterozygote advantage to maintain genetic polymorphism. Results of previous studies are used to (re)define heterozygote advantage for multilocus systems, since the classical definition is for one diallelic locus. I use 5 different definitions of heterozygote advantage. And for these five definitions, I show that heterozygote advantage is not a general mechanism for the maintenance of polymorphism. The study of the influence of undetected loci on evolutionary processes (second part of this work) is motivated by molecular works which aim at discovering the loci coding for a trait. For most of these works, some coding loci remains undetected. I show that undetected loci increases the probability of maintaining polymorphism under selection. In addition, conclusions about the factor that maintain polymorphism can be misleading if not all loci are considered. This is, therefore, only when all loci are detected that exact conclusions on the level of maintained polymorphism or on the factor(s) that maintain(s) polymorphism could be drawn. In the third part, the focus is on the expected release of additive genetic variance after bottleneck for selected traits. A previous study shows that the expected release of additive variance increases with an increase in the number of loci. I show that the expected release of additive variance after bottleneck increases for selected traits (compared with neutral), but this increase is not a function of the number of loci, but function of the recombination rate. Finally, the last part of this PhD thesis is a description of a package for the statistical software R that implements the Equation given above. It allows to generate data for different scenario regarding selection, recombination, and population size. This package opens perspectives for the theoretical population genetics that mainly focuses on one locus, while this work shows that increasing the number of loci leads not necessarily to straightforward results.

Dynamic Modelling of Material Flows and Sustainable Resource Use: Case Studies in Regional Metabolism and Space Life Support Systems

Sustainable resource use is one of the most important environmental issues of our times. It is closely related to discussions on the 'peaking' of various natural resources serving as energy sources, agricultural nutrients, or metals indispensable in high-technology applications. Although the peaking theory remains controversial, it is commonly recognized that a more sustainable use of resources would alleviate negative environmental impacts related to resource use. In this thesis, sustainable resource use is analysed from a practical standpoint, through several different case studies. Four of these case studies relate to resource metabolism in the Canton of Geneva in Switzerland: the aim was to model the evolution of chosen resource stocks and flows in the coming decades. The studied resources were copper (a bulk metal), phosphorus (a vital agricultural nutrient), and wood (a renewable resource). In addition, the case of lithium (a critical metal) was analysed briefly in a qualitative manner and in an electric mobility perspective. In addition to the Geneva case studies, this thesis includes a case study on the sustainability of space life support systems. Space life support systems are systems whose aim is to provide the crew of a spacecraft with the necessary metabolic consumables over the course of a mission. Sustainability was again analysed from a resource use perspective. In this case study, the functioning of two different types of life support systems, ARES and BIORAT, were evaluated and compared; these systems represent, respectively, physico-chemical and biological life support systems. Space life support systems could in fact be used as a kind of 'laboratory of sustainability' given that they represent closed and relatively simple systems compared to complex and open terrestrial systems such as the Canton of Geneva. The chosen analysis method used in the Geneva case studies was dynamic material flow analysis: dynamic material flow models were constructed for the resources copper, phosphorus, and wood. Besides a baseline scenario, various alternative scenarios (notably involving increased recycling) were also examined. In the case of space life support systems, the methodology of material flow analysis was also employed, but as the data available on the dynamic behaviour of the systems was insufficient, only static simulations could be performed. The results of the case studies in the Canton of Geneva show the following: were resource use to follow population growth, resource consumption would be multiplied by nearly 1.2 by 2030 and by 1.5 by 2080. A complete transition to electric mobility would be expected to only slightly (+5%) increase the copper consumption per capita while the lithium demand in cars would increase 350 fold. For example, phosphorus imports could be decreased by recycling sewage sludge or human urine; however, the health and environmental impacts of these options have yet to be studied. Increasing the wood production in the Canton would not significantly decrease the dependence on wood imports as the Canton's production represents only 5% of total consumption. In the comparison of space life support systems ARES and BIORAT, BIORAT outperforms ARES in resource use but not in energy use. However, as the systems are dimensioned very differently, it remains questionable whether they can be compared outright. In conclusion, the use of dynamic material flow analysis can provide useful information for policy makers and strategic decision-making; however, uncertainty in reference data greatly influences the precision of the results. Space life support systems constitute an extreme case of resource-using systems; nevertheless, it is not clear how their example could be of immediate use to terrestrial systems.

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.

Performance Measures for Iowa Transportation Systems, 2006

The purpose of performance measures in planning operations is to identify and track meaningful, quantifiable measures that reflect progress toward the goals of the plan. The Iowa Department of Transportation (DOT) has already adopted performance measures in a number of operational areas, including highway maintenance, highway safety, public transportation, and aeronautics. This report is an initial effort to utilize performance measures for transportation system planning. The selected measures provide a cross-section of system performance indicators across three selected transportation planning goals (safety, efficiency, and quality of life) and five transportation modes (highways/bridges, public transit, railroads, aviation, and pedestrian/bicycle). These performance measures are exploratory in nature, and constitute a first attempt to apply performance measures in the context of a statewide, multimodal transportation plan from the Iowa DOT. As such, the set of performance measures that the Iowa DOT uses for planning will change over time as more is learned about the application of such measures. The performance measures explained in this document were developed through consultation with Iowa DOT modal staff (aviation, railroads, highways, public transportation, and pedestrian/bicycle) and the Office of Traffic and Safety. In addition, faculty and staff at the Iowa State University Center for Transportation Research and Education were consulted about performance measurement and data within their areas of expertise.

Soil macrofauna under integrated crop-livestock systems in a Brazilian Cerrado Ferralsol

The objective of this work was to assess the effects of integrated crop-livestock systems, associated with two tillage and two fertilization regimes, on the abundance and diversity of the soil macrofauna. Four different management systems were studied: continuous pasture (mixed grass); continuous crop; two crop-livestock rotations (crop/pasture and pasture/crop); and native Cerrado as a control. Macrofauna was sampled using a modified Tropical Soil Biology and Fertility method, and all individuals were counted and identified at the morphospecies level for each plot. A total of 194 morphospecies were found, distributed among 30 groups, and the most representative in decreasing order of density were: Isoptera, Coleoptera larvae, Formicidae, Oligochaeta, Coleoptera adult, Diplopoda, Hemiptera, Diptera larvae, Arachnida, Chilopoda, Lepidoptera, Gasteropoda, Blattodea and Orthoptera. Soil management systems and tillage regimes affected the structure of soil macrofauna, and integrated crop-livestock systems, associated with no-tillage, especially with grass/legume species associations, had more favorable conditions for the development of "soil engineers" compared with continuous pasture or arable crops. Soil macrofauna density and diversity, assessed at morphospecies level, are effective data to measure the impact of land use in Cerrado soils.

Neural networks complemented with genetic algorithms and fuzzy systems for predicting nitrogenous effluent variables in wastewater treatment plants

This work focuses on the prediction of the two main nitrogenous variables that describe the water quality at the effluent of a Wastewater Treatment Plant. We have developed two kind of Neural Networks architectures based on considering only one output or, in the other hand, the usual five effluent variables that define the water quality: suspended solids, biochemical organic matter, chemical organic matter, total nitrogen and total Kjedhal nitrogen. Two learning techniques based on a classical adaptative gradient and a Kalman filter have been implemented. In order to try to improve generalization and performance we have selected variables by means genetic algorithms and fuzzy systems. The training, testing and validation sets show that the final networks are able to learn enough well the simulated available data specially for the total nitrogen

Reliable selfing rate estimates from imperfect population genetic data.

Genotypic frequencies at codominant marker loci in population samples convey information on mating systems. A classical way to extract this information is to measure heterozygote deficiencies (FIS) and obtain the selfing rate s from FIS = s/(2 - s), assuming inbreeding equilibrium. A major drawback is that heterozygote deficiencies are often present without selfing, owing largely to technical artefacts such as null alleles or partial dominance. We show here that, in the absence of gametic disequilibrium, the multilocus structure can be used to derive estimates of s independent of FIS and free of technical biases. Their statistical power and precision are comparable to those of FIS, although they are sensitive to certain types of gametic disequilibria, a bias shared with progeny-array methods but not FIS. We analyse four real data sets spanning a range of mating systems. In two examples, we obtain s = 0 despite positive FIS, strongly suggesting that the latter are artefactual. In the remaining examples, all estimates are consistent. All the computations have been implemented in a open-access and user-friendly software called rmes (robust multilocus estimate of selfing) available at http://ftp.cefe.cnrs.fr, and can be used on any multilocus data. Being able to extract the reliable information from imperfect data, our method opens the way to make use of the ever-growing number of published population genetic studies, in addition to the more demanding progeny-array approaches, to investigate selfing rates.