Improving the reliability of cohesion policy databases.

In this contribution, we present an innovative data-driven model to reconstruct a reliable temporal pattern for time-lagged statistical monetary figures. Our research cuts across several domains regarding the production of robust economic inferences and the bridging of top-down aggregated information from central databases with disaggregated information obtained from local sources or national statistical offices. Our test bed case study is the European Regional Development Fund (ERDF).

Global Sensitivity Analysis with Mixtures: A Generalized Functional ANOVA Approach.

This work investigates aspects of the global sensitivity analysis of computer codes when alternative plausible distributions for the model inputs are available to the analyst. Analysts may decide to explore results under each distribution or to aggregate the distributions, assigning, for instance, a mixture. In the first case, we lose uniqueness of the sensitivity measures, and in the second case, we lose independence even if the model inputs are independent under each of the assigned distributions.

Global sensitivity analysis in epidemiological modeling.

Operations researchers worldwide rely extensively on quantitative simulations to model alternative aspects of the COVID-19 pandemic. Proper uncertainty quantification and sensitivity analysis are fundamental to enrich the modeling process and communicate correctly informed insights to decision-makers. We develop a methodology to obtain insights on key uncertainty drivers, trend analysis and interaction quantification through an innovative combination of probabilistic sensitivity techniques and machine learning tools.

Irrigated areas drive irrigation water withdrawals.

A sustainable management of global freshwater resources requires reliable estimates of the water demanded by irrigated agriculture. This has been attempted by the Food and Agriculture Organization (FAO) through country surveys and censuses, or through Global Models, which compute irrigation water withdrawals with sub-models on crop types and calendars, evapotranspiration, irrigation efficiencies, weather data and irrigated areas, among others. Here we demonstrate that these strategies err on the side of excess complexity, as the values reported by FAO and outputted by Global Models are largely conditioned by irrigated areas and their uncertainty.

Rapamycin Plus Vildagliptin to Recover β-Cell Function in Long-Standing Type 1 Diabetes: A Double-Blind, Randomized Trial.

Aim: The aim of this study was to investigate whether treatment with rapamycin plus vildagliptin restores β-cell function in patients with long-standing type 1 diabetes.

Methods: A phase 2, single-center, randomized, double-blind, placebo-controlled study was conducted in long-standing type 1 diabetes patients randomly assigned (1:1:1) to 4 weeks of rapamycin (group 2), 4 weeks of rapamycin plus 12 weeks of vildagliptin (group 3), or double placebo (group 1). The primary outcome was the proportion of participants with a positive response to the Mixed-Meal Tolerance Test (C-peptide at 90 minutes > 0.

Fighting the Curse of Sparsity: Probabilistic Sensitivity Measures From Cumulative Distribution Functions.

Quantitative models support investigators in several risk analysis applications. The calculation of sensitivity measures is an integral part of this analysis. However, it becomes a computationally challenging task, especially when the number of model inputs is large and the model output is spread over orders of magnitude.

Which Parameters Are Important? Differential Importance Under Uncertainty.

In probabilistic risk assessment, attention is often focused on the expected value of a risk metric. The sensitivity of this expectation to changes in the parameters of the distribution characterizing uncertainty in the inputs becomes of interest. Approaches based on differentiation encounter limitations when (i) distributional parameters are expressed in different units or (ii) the analyst wishes to transfer sensitivity insights from individual parameters to parameter groups, when alternating between different levels of a probabilistic safety assessment model.

On the Relationship between Safety and Decision Significance.

Risk analysts are often concerned with identifying key safety drivers, that is, the systems, structures, and components (SSCs) that matter the most to safety. SSCs importance is assessed both in the design phase (i.e.

Deciding with Thresholds: Importance Measures and Value of Information.

Risk-informed decision making is often accompanied by the specification of an acceptable level of risk. Such target level is compared against the value of a risk metric, usually computed through a probabilistic safety assessment model, to decide about the acceptability of a given design, the launch of a space mission, etc. Importance measures complement the decision process with information about the risk/safety significance of events.

A Common Rationale for Global Sensitivity Measures and Their Estimation.

Measures of sensitivity and uncertainty have become an integral part of risk analysis. Many such measures have a conditional probabilistic structure, for which a straightforward Monte Carlo estimation procedure has a double-loop form. Recently, a more efficient single-loop procedure has been introduced, and consistency of this procedure has been demonstrated separately for particular measures, such as those based on variance, density, and information value.

A Protocol for the Global Sensitivity Analysis of Impact Assessment Models in Life Cycle Assessment.

The life cycle assessment (LCA) framework has established itself as the leading tool for the assessment of the environmental impact of products. Several works have established the need of integrating the LCA and risk analysis methodologies, due to the several common aspects. One of the ways to reach such integration is through guaranteeing that uncertainties in LCA modeling are carefully treated.

Uncertainty in climate change modeling: can global sensitivity analysis be of help?

Integrated assessment models offer a crucial support to decisionmakers in climate policy making. For a full understanding and corroboration of model results, analysts ought to identify the exogenous variables that influence the model results the most (key drivers), appraise the relevance of interactions, and the direction of change associated with the simultaneous variation of uncertain variables. We show that such information can be directly extracted from the data set produced by Monte Carlo simulations.

Moment independent importance measures: new results and analytical test cases.

Moment independent methods for the sensitivity analysis of model output are attracting growing attention among both academics and practitioners. However, the lack of benchmarks against which to compare numerical strategies forces one to rely on ad hoc experiments in estimating the sensitivity measures. This article introduces a methodology that allows one to obtain moment independent sensitivity measures analytically.

Cancer cell reprogramming: stem cell differentiation stage factors and an agent based model to optimize cancer treatment.

The recent tumor research has lead scientists to recognize the central role played by cancer stem cells in sustaining malignancy and chemo-resistance. A model of cancer presented by one of us describes the mechanisms that give rise to the different kinds of cancer stem-like cells and the role of these cells in cancer diseases. The model implies a shift in the conceptualization of the disease from reductionism to complexity theory.

A methodology for determining interactions in probabilistic safety assessment models by varying one parameter at a time.

In risk analysis problems, the decision-making process is supported by the utilization of quantitative models. Assessing the relevance of interactions is an essential information in the interpretation of model results. By such knowledge, analysts and decisionmakers are able to understand whether risk is apportioned by individual factor contributions or by their joint action.

Sensitivity analysis of model output with input constraints: a generalized rationale for local methods.

In this work, we introduce a generalized rationale for local sensitivity analysis (SA) methods that allows to solve the problems connected with input constraints. Several models in use in the risk analysis field are characterized by the presence of deterministic relationships among the input parameters. However, SA issues related to the presence of constraints have been mainly dealt with in a heuristic fashion.

Epistemic uncertainty in the ranking and categorization of probabilistic safety assessment model elements: issues and findings.

In this work, we study the effect of epistemic uncertainty in the ranking and categorization of elements of probabilistic safety assessment (PSA) models. We show that, while in a deterministic setting a PSA element belongs to a given category univocally, in the presence of epistemic uncertainty, a PSA element belongs to a given category only with a certain probability. We propose an approach to estimate these probabilities, showing that their knowledge allows to appreciate "the sensitivity of component categorizations to uncertainties in the parameter values" (U.

Decision making during nuclear power plant incidents: a new approach to the evaluation of precursor events.

Renewed interest in precursor analysis has shown that the evaluation of near misses is an interdisciplinary effort, fundamental within the life of an organization for reducing operational risks and enabling accident prevention. The practice of precursor analysis has been a part of nuclear power plant regulation in the United States for over 25 years. During this time, the models used in the analysis have evolved from simple risk equations to quite complex probabilistic risk assessments.

Measuring uncertainty importance: investigation and comparison of alternative approaches.

Uncertainty importance measures are quantitative tools aiming at identifying the contribution of uncertain inputs to output uncertainty. Their application ranges from food safety (Frey & Patil (2002)) to hurricane losses (Iman et al. (2005a, 2005b)).

Pharmacokinetics of a sustained release formulation of pyridoxal phosphate of buflomedil after single or repeated oral doses in healthy volunteers.

The pharmacokinetics of a sustained release (SR) formulation of pyridoxal phosphate of buflomedil (Pirxane retard) has been studied after oral administration to healthy volunteers using among else a gaschromatographic dosage method. After oral administration of 400 mg of the SR formulation, pyridoxal phosphate of buflomedil has a much slower kinetics compared to the normal formulation (tmax:approx. 1.