Toxic substances in blood: an analysis of current recommendations under a Bayesian (decision) approach

This article extends existing discussion in literature on probabilistic inference and decision making with respect to continuous hypotheses that are prevalent in forensic toxicology. As a main aim, this research investigates the properties of a widely followed approach for quantifying the level of toxic substances in blood samples, and to compare this procedure with a Bayesian probabilistic approach. As an example, attention is confined to the presence of toxic substances, such as THC, in blood from car drivers. In this context, the interpretation of results from laboratory analyses needs to take into account legal requirements for establishing the 'presence' of target substances in blood. In a first part, the performance of the proposed Bayesian model for the estimation of an unknown parameter (here, the amount of a toxic substance) is illustrated and compared with the currently used method. The model is then used in a second part to approach-in a rational way-the decision component of the problem, that is judicial questions of the kind 'Is the quantity of THC measured in the blood over the legal threshold of 1.5 μg/l?'. This is pointed out through a practical example.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Elicitation of subjective crop yield PDF

En este trabajo se investiga la coherencia y confiabilidad de estimaciones de funciones de densidad de probabilidad (FDP) subjetivas de rendimientos de cultivos realizadas por un amplio grupo de agricultores. Se utilizaron tres técnicas de elicitación diferentes: el método de estimación de FDP en dos pasos, la distribución Triangular y la distribución Beta. Los sujetos entrevistados ofrecieron estimaciones para los valores puntuales de rendimientos de cultivos (medio, máximo posible, más frecuente y mínimo posible) y para las FDP basadas en la estimación de intervalos. Para evaluar la persistencia, se utilizaron los conceptos de persistencia temporal y persistencia metodológica. Los resultados son interesantes para juzgar la adecuación de las técnicas de estimación de probabilidades subjetivas a los sistemas de ayuda en la toma de decisiones en agricultura.

Cost Estimation of Engineering Environment Services

Työn tavoitteena oli kehittää tutkittavan insinööriyksikön projektien kustannusestimointiprosessia, siten että yksikön johdolla olisi tulevaisuudessa käytettävänään tarkempaa kustannustietoa. Jotta tämä olisi mahdollista, ensin täytyi selvittää yksikön toimintatavat, projektien kustannusrakenteet sekä kustannusatribuutit. Tämän teki mahdolliseksi projektien kustannushistoriatiedon tutkiminen sekä asiantuntijoiden haastattelu. Työn tuloksena syntyi kohdeyksikön muiden prosessien kanssa yhteensopiva kustannusestimointiprosessi sekä –malli.Kustannusestimointimenetelmän ja –mallin perustana on kustannusatribuutit, jotka määritellään erikseen tutkittavassa ympäristössä. Kustannusatribuutit löydetään historiatietoa tutkimalla, eli analysoimalla jo päättyneitä projekteja, projektien kustannusrakenteita sekä tekijöitä, jotka ovat vaikuttaneet kustannusten syntyyn. Tämän jälkeen kustannusatribuuteille täytyy määritellä painoarvot sekä painoarvojen vaihteluvälit. Estimointimallin tarkuutta voidaan parantaa mallin kalibroinnilla. Olen käyttänyt Goal – Question – Metric (GQM) –menetelmää tutkimuksen kehyksenä.

Pumppukäyttöjen energiankulutuksen määrittäminen prosessimittauksia käyttäen

Pumppukäytöt vastaavat noin neljännestä Euroopan alueen sähkömoottoreissa kuluvasta energiasta. Energian hinnan nousun vuoksi energian säästäminen ja energiatehokkuus ovat nousseet tärkeään asemaan paljon energiaa kuluttavassa teollisuudessa. Pumppukäyttöjen hyötysuhteen parantaminen on noussut olennaiseen osaan paperi- ja kartonkiteollisuuden energiatehokkuustarkasteluissa. Tässä työssä tarkastellaan kartonkikoneen pumppukäyttöjen toiminnan energiatehokkuutta moottorin virtamittausten perusteella. Analyysi perustuu moottorin akselitehon määrittämiseen ja sen perusteella tehtävään pumpun toimintapisteen laskentaan. Työssä esitellään käytetyt estimointimenetelmät ja niillä saadut tulokset kartonkikoneen pumppukäytöille. Lisäksi työssä arvioidaan kolmen yksittäisen pumppukäytön energiankulutuksen säästöpotentiaalia. Työssä käytettyä menetelmää voidaan käyttää sekä vakio- että vaihtonopeuspumppukäyttöjen toiminnan ja hyötysuhteen analysointiin.

Energy-efficient control strategies for variable speed driven parallel pumping systems based on pump operation point monitoring with frequency converters

The pumping processes requiring wide range of flow are often equipped with parallelconnected centrifugal pumps. In parallel pumping systems, the use of variable speed control allows that the required output for the process can be delivered with a varying number of operated pump units and selected rotational speed references. However, the optimization of the parallel-connected rotational speed controlled pump units often requires adaptive modelling of both parallel pump characteristics and the surrounding system in varying operation conditions. The available information required for the system modelling in typical parallel pumping applications such as waste water treatment and various cooling and water delivery pumping tasks can be limited, and the lack of real-time operation point monitoring often sets limits for accurate energy efficiency optimization. Hence, alternatives for easily implementable control strategies which can be adopted with minimum system data are necessary. This doctoral thesis concentrates on the methods that allow the energy efficient use of variable speed controlled parallel pumps in system scenarios in which the parallel pump units consist of a centrifugal pump, an electric motor, and a frequency converter. Firstly, the suitable operation conditions for variable speed controlled parallel pumps are studied. Secondly, methods for determining the output of each parallel pump unit using characteristic curve-based operation point estimation with frequency converter are discussed. Thirdly, the implementation of the control strategy based on real-time pump operation point estimation and sub-optimization of each parallel pump unit is studied. The findings of the thesis support the idea that the energy efficiency of the pumping can be increased without the installation of new, more efficient components in the systems by simply adopting suitable control strategies. An easily implementable and adaptive control strategy for variable speed controlled parallel pumping systems can be created by utilizing the pump operation point estimation available in modern frequency converters. Hence, additional real-time flow metering, start-up measurements, and detailed system model are unnecessary, and the pumping task can be fulfilled by determining a speed reference for each parallel-pump unit which suggests the energy efficient operation of the pumping system.

Analyse bayésienne et classification pour modèles continus modifiés à zéro

Les modèles à sur-représentation de zéros discrets et continus ont une large gamme d'applications et leurs propriétés sont bien connues. Bien qu'il existe des travaux portant sur les modèles discrets à sous-représentation de zéro et modifiés à zéro, la formulation usuelle des modèles continus à sur-représentation -- un mélange entre une densité continue et une masse de Dirac -- empêche de les généraliser afin de couvrir le cas de la sous-représentation de zéros. Une formulation alternative des modèles continus à sur-représentation de zéros, pouvant aisément être généralisée au cas de la sous-représentation, est présentée ici. L'estimation est d'abord abordée sous le paradigme classique, et plusieurs méthodes d'obtention des estimateurs du maximum de vraisemblance sont proposées. Le problème de l'estimation ponctuelle est également considéré du point de vue bayésien. Des tests d'hypothèses classiques et bayésiens visant à déterminer si des données sont à sur- ou sous-représentation de zéros sont présentées. Les méthodes d'estimation et de tests sont aussi évaluées au moyen d'études de simulation et appliquées à des données de précipitation agrégées. Les diverses méthodes s'accordent sur la sous-représentation de zéros des données, démontrant la pertinence du modèle proposé. Nous considérons ensuite la classification d'échantillons de données à sous-représentation de zéros. De telles données étant fortement non normales, il est possible de croire que les méthodes courantes de détermination du nombre de grappes s'avèrent peu performantes. Nous affirmons que la classification bayésienne, basée sur la distribution marginale des observations, tiendrait compte des particularités du modèle, ce qui se traduirait par une meilleure performance. Plusieurs méthodes de classification sont comparées au moyen d'une étude de simulation, et la méthode proposée est appliquée à des données de précipitation agrégées provenant de 28 stations de mesure en Colombie-Britannique.

Distribución hiperbólica generalizada: una aplicación en la selección de portafolios y en la cuantificación de medidas de riesgo de mercado

En este trabajo se implementa una metodología para incluir momentos de orden superior en la selección de portafolios, haciendo uso de la Distribución Hiperbólica Generalizada, para posteriormente hacer un análisis comparativo frente al modelo de Markowitz.

Estimation of the stoichiometry of volatile fatty acid production in the rumen of lactating cows

The purpose of this study was to improve the prediction of the quantity and type of Volatile Fatty Acids (VFA) produced from fermented substrate in the rumen of lactating cows. A model was formulated that describes the conversion of substrate (soluble carbohydrates, starch, hemi-cellulose, cellulose, and protein) into VFA (acetate, propionate, butyrate, and other VFA). Inputs to the model were observed rates of true rumen digestion of substrates, whereas outputs were observed molar proportions of VFA in rumen fluid. A literature survey generated data of 182 diets (96 roughage and 86 concentrate diets). Coefficient values that define the conversion of a specific substrate into VFA were estimated meta-analytically by regression of the model against observed VFA molar proportions using non-linear regression techniques. Coefficient estimates significantly differed for acetate and propionate production in particular, between different types of substrate and between roughage and concentrate diets. Deviations of fitted from observed VFA molar proportions could be attributed to random error for 100%. In addition to regression against observed data, simulation studies were performed to investigate the potential of the estimation method. Fitted coefficient estimates from simulated data sets appeared accurate, as well as fitted rates of VFA production, although the model accounted for only a small fraction (maximally 45%) of the variation in VFA molar proportions. The simulation results showed that the latter result was merely a consequence of the statistical analysis chosen and should not be interpreted as an indication of inaccuracy of coefficient estimates. Deviations between fitted and observed values corresponded to those obtained in simulations. (c) 2005 Elsevier Ltd. All rights reserved.

Estimating the benefits of farm animal welfare legislation using the contingent valuation method

This paper presents the method and findings of a contingent valuation (CV) study that aimed to elicit United Kingdom citizens' willingness to pay to support legislation to phase out the use of battery cages for egg production in the European Union (EU). The method takes account of various biases associated with the CV technique, including 'warm glow', 'part-whole' and sample response biases. Estimated mean willingness to pay to support the legislation is used to estimate the annual benefit of the legislation to UK citizens. This is compared with the estimated annual costs of the legislation over a 12-year period, which allows for readjustment by the UK egg industry. The analysis shows that the estimated benefits of the legislation outweigh the costs. The study demonstrates that CV is a potentially useful technique for assessing the likely benefits associated with proposed legislation. However, estimates of CV studies must be treated with caution. It is important that they are derived from carefully designed surveys and that the willingness to pay estimation method allows for various biases. (C) 2003 Elsevier Science B.V. All rights reserved.

A time-domain probe method for three-dimensional rough surface reconstructions

The task of this paper is to develop a Time-Domain Probe Method for the reconstruction of impenetrable scatterers. The basic idea of the method is to use pulses in the time domain and the time-dependent response of the scatterer to reconstruct its location and shape. The method is based on the basic causality principle of timedependent scattering. The method is independent of the boundary condition and is applicable for limited aperture scattering data. In particular, we discuss the reconstruction of the shape of a rough surface in three dimensions from time-domain measurements of the scattered field. In practise, measurement data is collected where the incident field is given by a pulse. We formulate the time-domain fieeld reconstruction problem equivalently via frequency-domain integral equations or via a retarded boundary integral equation based on results of Bamberger, Ha-Duong, Lubich. In contrast to pure frequency domain methods here we use a time-domain characterization of the unknown shape for its reconstruction. Our paper will describe the Time-Domain Probe Method and relate it to previous frequency-domain approaches on sampling and probe methods by Colton, Kirsch, Ikehata, Potthast, Luke, Sylvester et al. The approach significantly extends recent work of Chandler-Wilde and Lines (2005) and Luke and Potthast (2006) on the timedomain point source method. We provide a complete convergence analysis for the method for the rough surface scattering case and provide numerical simulations and examples.

Conditionally unbiased estimation in phase II/III clinical trials with early stopping for futility

Seamless phase II/III clinical trials combine traditional phases II and III into a single trial that is conducted in two stages, with stage 1 used to answer phase II objectives such as treatment selection and stage 2 used for the confirmatory analysis, which is a phase III objective. Although seamless phase II/III clinical trials are efficient because the confirmatory analysis includes phase II data from stage 1, inference can pose statistical challenges. In this paper, we consider point estimation following seamless phase II/III clinical trials in which stage 1 is used to select the most effective experimental treatment and to decide if, compared with a control, the trial should stop at stage 1 for futility. If the trial is not stopped, then the phase III confirmatory part of the trial involves evaluation of the selected most effective experimental treatment and the control. We have developed two new estimators for the treatment difference between these two treatments with the aim of reducing bias conditional on the treatment selection made and on the fact that the trial continues to stage 2. We have demonstrated the properties of these estimators using simulations

Estimation after subpopulation selection in adaptive seamless trials

During the development of new therapies, it is not uncommon to test whether a new treatment works better than the existing treatment for all patients who suffer from a condition (full population) or for a subset of the full population (subpopulation). One approach that may be used for this objective is to have two separate trials, where in the first trial, data are collected to determine if the new treatment benefits the full population or the subpopulation. The second trial is a confirmatory trial to test the new treatment in the population selected in the first trial. In this paper, we consider the more efficient two-stage adaptive seamless designs (ASDs), where in stage 1, data are collected to select the population to test in stage 2. In stage 2, additional data are collected to perform confirmatory analysis for the selected population. Unlike the approach that uses two separate trials, for ASDs, stage 1 data are also used in the confirmatory analysis. Although ASDs are efficient, using stage 1 data both for selection and confirmatory analysis introduces selection bias and consequently statistical challenges in making inference. We will focus on point estimation for such trials. In this paper, we describe the extent of bias for estimators that ignore multiple hypotheses and selecting the population that is most likely to give positive trial results based on observed stage 1 data. We then derive conditionally unbiased estimators and examine their mean squared errors for different scenarios.

Comparison of hexamethyidisilazane and critical point drying treatments for SEM analysis of anaerobic biofilms and granular sludge

We present a fast procedure for scanning electron microscopy (SEM) analysis in which hexamethyldisilazane (HMDS) solvent, instead of the critical point drying, is used to remove liquids from a microbiological specimen. The results indicate that the HMDS solvent is suitable for drying samples of anaerobic cells for examination by SEM and does not cause cell structure disruption.

Análise de séries temporais de coordenadas estimadas com gps: Uma proposta metodológica para detecção, remoção e recuperação de efeitos sazonais

GPS active networks are more and more used in geodetic surveying and scientific experiments, as water vapor monitoring in the atmosphere and lithosphere plate movement. Among the methods of GPS positioning, Precise Point Positioning (PPP) has provided very good results. A characteristic of PPP is related to the modeling and / or estimation of the errors involved in this method. The accuracy obtained for the coordinates can reach few millimeters. Seasonal effects can affect such accuracy if they are not consistent treated during the data processing. Coordinates time series analyses have been realized using Fourier or Harmonics spectral analyses, wavelets, least squares estimation among others. An approach is presented in this paper aiming to investigate the seasonal effects included in the stations coordinates time series. Experiments were carried out using data from stations Manaus (NAUS) and Fortaleza (BRFT) which belong to the Brazilian Continuous GPS Network (RBMC). The coordinates of these stations were estimated daily using PPP and were analyzed through wavelets for identification of the periods of the seasonal effects (annual and semi-annual) in each time series. These effects were removed by means of a filtering process applied in the series via the least squares adjustment (LSQ) of a periodic function. The results showed that the combination of these two mathematical tools, wavelets and LSQ, is an interesting and efficient technique for removal of seasonal effects in time series.