Mass detection on mammograms: influence of signal shape uncertainty on human and model observers.

We studied the influence of signal variability on human and model observers for detection tasks with realistic simulated masses superimposed on real patient mammographic backgrounds and synthesized mammographic backgrounds (clustered lumpy backgrounds, CLB). Results under the signal-known-exactly (SKE) paradigm were compared with signal-known-statistically (SKS) tasks for which the observers did not have prior knowledge of the shape or size of the signal. Human observers' performance did not vary significantly when benign masses were superimposed on real images or on CLB. Uncertainty and variability in signal shape did not degrade human performance significantly compared with the SKE task, while variability in signal size did. Implementation of appropriate internal noise components allowed the fit of model observers to human performance.

The efficiency of direct payments versus tax reductions under uncertainty

This paper analyzes the optimal behavior of farmers in the presence of direct payments and uncertainty. In an empirical analysis for Switzerland, it confirms previously obtained theoretical results and determines the magnitude of the theoretical predicted effects. The results show that direct payments increase agricultural production between 3.7% to 4.8%. Alternatively to direct payments, the production effect of tax reductions is evaluated in order to determine its magnitude. The empirical analysis corroborates the theoretical results of the literature and demonstrates that tax reductions are also distorting, but to a substantially lesser degree if losses are not offset. However, tax reductions, independently whether losses are offset or not, lead to higher government spending than pure direct payments

Quality uncertainty and allocation of decision rights in the European protected designation of origin

The paper considers some issue in the governance of the European Protected Designation of Origin (PDO). The PDO systems are the outcomes of both farmers and consumers expectations and connect the valorisation of the agricultural and rural resources of given territories to the quality of typical products. A critical point in the governance of the PDO systems is represented by the connection between the quality strategies and the uncertainty. The paper argues that the PDO systems can be thought of as strictly coordinated subsystems in which the ex post governance play a critical role in coping with quality uncertainty. The study suggests that the society's inducements given raise to complex organizational systems in which the allocation of decision rights to PDO collective organizations play a major role. The empirical analysis is carried out by examining ten Italian PDO systems in order to identify the decision rights allocated.

Entrepreneurial decision-making using the knightian uncertainty approach

The article discusses the behavioral aspects that affect the entrepreneurs' decision making under the Knightian uncertainty approach. Since the profit arising from entrepreneurial activity represents the reward of an immeasurable and subjective risk, it has been hypothesized that innovative entrepreneurs have excessive optimism and confidence, which leads them to invest in high-risk activities. A behavioral model of decision making under uncertainty is used to test the hypothesis of overconfidence. This model is based on Bayesian inference, which allows us to model the assumption that these entrepreneurs are overconfident. We conclude that, under the hypothesis of overconfidence, these entrepreneurs decide to invest, despite the fact that the expected utility model indicates the contrary. This theoretical finding could explain why there are a large number of business failures in the first years of activity.

A Tool for optimum design of Waste Water Treatment Plants under uncertainty

L’objectiu principal és presentar un nou prototipus d’eina per al disseny de les plantes de tractament d’aigües residuals utilitzant models mecànics dinàmics quantificant la incertesa

Traiter l'hypertension artérielle: l'incertitude des définitions et des cibles [Managing hypertension: the uncertainty of definitions and targets].

In clinical practice, physicians are confronted with a multitude of definitions and treatment goals for arterial hypertension, depending of the diagnostic method used (e.g. office, home and ambulatory blood pressure measurement) and the underlying disease. The historical background and evidence of these different blood pressure thresholds are discussed in this article, as well as some recent treatment guidelines. Besides, the debate of the "J curve", namely the possible risks associated with an excessive blood pressure reduction, is discussed.

Fault diagnosis by refining the parameter uncertainty space of nonlinear dynamic systems

This paper deals with fault detection and isolation problems for nonlinear dynamic systems. Both problems are stated as constraint satisfaction problems (CSP) and solved using consistency techniques. The main contribution is the isolation method based on consistency techniques and uncertainty space refining of interval parameters. The major advantage of this method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements, and model errors. Interval calculations bring independence from the assumption of monotony considered by several approaches for fault isolation which are based on observers. An application to a well known alcoholic fermentation process model is presented

Robust fault detection using consistency techniques for uncertainty handling

Often practical performance of analytical redundancy for fault detection and diagnosis is decreased by uncertainties prevailing not only in the system model, but also in the measurements. In this paper, the problem of fault detection is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using modal interval analysis and consistency techniques. Consistency techniques are then shown to be particularly efficient to check the consistency of the analytical redundancy relations (ARRs), dealing with uncertain measurements and parameters. Through the work presented in this paper, it can be observed that consistency techniques can be used to increase the performance of a robust fault detection tool, which is based on interval arithmetic. The proposed method is illustrated using a nonlinear dynamic model of a hydraulic system

Selective neurodegeneration induced in rotation-mediated aggregate cell cultures by a transient switch to stationary culture conditions: a potential model to study ischemia-related pathogenic mechanisms.

Aggregating brain cell cultures at an advanced maturational stage (20-21 days in vitro) were subjected for 1-3 h to anaerobic (hypoxic) and/or stationary (ischemic) conditions. After restoration of the normal culture conditions, cell loss was estimated by measuring the release of lactate dehydrogenase as well as the irreversible decrease of cell type-specific enzyme activities, total protein and DNA content. Ischemia for 2 h induced significant neuronal cell death. Hypoxia combined with ischemia affected both neuronal and glial cells to different degrees (GABAergic neurons>cholinergic neurons>astrocytes). Hypoxic and ischemic conditions greatly stimulated the uptake of 2-deoxy-D-glucose, indicating increased glucose consumption. Furthermore, glucose restriction (5.5 mM instead of 25 mM) dramatically increased the susceptibility of neuronal and glial cells to hypoxic and ischemic conditions. Glucose media concentrations below 2 mM caused selective neuronal cell death in otherwise normal culture conditions. GABAergic neurons showed a particularly high sensitivity to glucose restriction, hypoxia, and ischemia. The pattern of ischemia-induced changes in vitro showed many similarities to in vivo findings, suggesting that aggregating brain cell cultures provide a useful in vitro model to study pathogenic mechanisms related to brain ischemia.

Pre-Treatment for Reduction of Asphalt Absorption in Porous Aggregate, April 2005

The objective of this research was to evaluate the performance of the product Ultracote® (a polymer based additive produced by Ultrapave, a division of Goodyear) as an aggregate pre-treatment for the reduction of asphalt binder absorption in hot mix asphalt (HMA). The product was tested with a paving project in Louisa county, Iowa with aggregate that had historically shown very high asphalt binder absorption. Results of the testing did not provide any evidence of reduction in binder absorption.

BME-based uncertainty assessment of the Chernobyl fallout

The vast territories that have been radioactively contaminated during the 1986 Chernobyl accident provide a substantial data set of radioactive monitoring data, which can be used for the verification and testing of the different spatial estimation (prediction) methods involved in risk assessment studies. Using the Chernobyl data set for such a purpose is motivated by its heterogeneous spatial structure (the data are characterized by large-scale correlations, short-scale variability, spotty features, etc.). The present work is concerned with the application of the Bayesian Maximum Entropy (BME) method to estimate the extent and the magnitude of the radioactive soil contamination by 137Cs due to the Chernobyl fallout. The powerful BME method allows rigorous incorporation of a wide variety of knowledge bases into the spatial estimation procedure leading to informative contamination maps. Exact measurements (?hard? data) are combined with secondary information on local uncertainties (treated as ?soft? data) to generate science-based uncertainty assessment of soil contamination estimates at unsampled locations. BME describes uncertainty in terms of the posterior probability distributions generated across space, whereas no assumption about the underlying distribution is made and non-linear estimators are automatically incorporated. Traditional estimation variances based on the assumption of an underlying Gaussian distribution (analogous, e.g., to the kriging variance) can be derived as a special case of the BME uncertainty analysis. The BME estimates obtained using hard and soft data are compared with the BME estimates obtained using only hard data. The comparison involves both the accuracy of the estimation maps using the exact data and the assessment of the associated uncertainty using repeated measurements. Furthermore, a comparison of the spatial estimation accuracy obtained by the two methods was carried out using a validation data set of hard data. Finally, a separate uncertainty analysis was conducted that evaluated the ability of the posterior probabilities to reproduce the distribution of the raw repeated measurements available in certain populated sites. The analysis provides an illustration of the improvement in mapping accuracy obtained by adding soft data to the existing hard data and, in general, demonstrates that the BME method performs well both in terms of estimation accuracy as well as in terms estimation error assessment, which are both useful features for the Chernobyl fallout study.