Search results for "Uncertainty"
showing 10 items of 1010 documents
On the empirical spectral distribution for certain models related to sample covariance matrices with different correlations
2021
Given [Formula: see text], we study two classes of large random matrices of the form [Formula: see text] where for every [Formula: see text], [Formula: see text] are iid copies of a random variable [Formula: see text], [Formula: see text], [Formula: see text] are two (not necessarily independent) sets of independent random vectors having different covariance matrices and generating well concentrated bilinear forms. We consider two main asymptotic regimes as [Formula: see text]: a standard one, where [Formula: see text], and a slightly modified one, where [Formula: see text] and [Formula: see text] while [Formula: see text] for some [Formula: see text]. Assuming that vectors [Formula: see t…
Uncertainty quantification on a spatial Markov-chain model for the progression of skin cancer
2019
AbstractA spatial Markov-chain model is formulated for the progression of skin cancer. The model is based on the division of the computational domain into nodal points, that can be in a binary state: either in ‘cancer state’ or in ‘non-cancer state’. The model assigns probabilities for the non-reversible transition from ‘non-cancer’ state to the ‘cancer state’ that depend on the states of the neighbouring nodes. The likelihood of transition further depends on the life burden intensity of the UV-rays that the skin is exposed to. The probabilistic nature of the process and the uncertainty in the input data is assessed by the use of Monte Carlo simulations. A good fit between experiments on mi…
Disentangling Derivatives, Uncertainty and Error in Gaussian Process Models
2020
Gaussian Processes (GPs) are a class of kernel methods that have shown to be very useful in geoscience applications. They are widely used because they are simple, flexible and provide very accurate estimates for nonlinear problems, especially in parameter retrieval. An addition to a predictive mean function, GPs come equipped with a useful property: the predictive variance function which provides confidence intervals for the predictions. The GP formulation usually assumes that there is no input noise in the training and testing points, only in the observations. However, this is often not the case in Earth observation problems where an accurate assessment of the instrument error is usually a…
Implementation of dosimetry equipment and phantoms at the MedAustron light ion beam therapy facility
2017
Purpose: To describe the implementation of dosimetry equipment and phantoms into clinical practice of light ion beam therapy facilities. This work covers standard dosimetry equipment such as computerized water scanners, films, 2D-array, thimble and plane parallel ionization chambers, but also dosimetry equipment specifically devoted to the pencil beam scanning delivery technique such as water columns, scintillating screens or multi-layer ionization chambers. Method: Advanced acceptance testing procedures developed at MedAustron and complementary to the standard acceptance procedures proposed by the manufacturer are presented. Detailed commissioning plans have been implemented for each piece…
Uncertainty management in the measurements of low frequency magnetic fields
2014
The paper deals with low-frequency magnetic field measurements carried out by using a broadband and isotropic instrument. These measurements are characterized by very high uncertainty values, which imply a high risk of wrong decisions when there is the need to establish if a site complies or does not comply with specified emission limits. To reduce this risk, we decided to perform the so called “uncertainty management” that is the discipline of optimizing the cost of a measurement versus the uncertainty target. The task is achieved by using the PUMA method that is an iterative technique originally conceived for geometrical and mechanical measurements. The approach is completely based on the…
Integrated modelling of the influence of urbanization and climate change on river water quality
2010
Climate change is one of the most important drivers modifying the hydrologic and environmental characteristics of natural catchments. When dealing with the quality of natural waters, these factors should be weighed up against anthropogenic factors that may increase or decrease the effect of climatic modifications. However, a detailed and more generalised analysis of such environmental impacts at a relatively small scale is currently lack. This paper aims to fill this gap. The use of a holistic approach is also required by the EU Water Framework Directive, which prescribes integrated analysis for river basin management in order to meet environmental and ecological objectives. In order to qua…
Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds.
2022
The data currently described was generated within the EU/FP7 HeCaToS project (Hepatic and Cardiac Toxicity Systems modeling). The project aimed to develop an in silico prediction system to contribute to drug safety assessment for humans. For this purpose, multi-omics data of repeated dose toxicity were obtained for 10 hepatotoxic and 10 cardiotoxic compounds. Most data were gained from in vitro experiments in which 3D microtissues (either hepatic or cardiac) were exposed to a therapeutic (physiologically relevant concentrations calculated through PBPK-modeling) or a toxic dosing profile (IC20 after 7 days). Exposures lasted for 14 days and samples were obtained at 7 time points (therapeutic…
Uncertainty assessment of a membrane bioreactor model using the GLUE methodology
2010
A mathematical model for the simulation of physical-biological organic removal by means of a membrane bioreactor (MBR) has been previously developed and tested. This paper presents an analysis of the uncertainty of the MBR model. Particularly, the research explores the applicability of the Generalised Likelihood Uncertainty Estimation (GLUE) methodology that is one of the most widely used methods for investigating the uncertainties in the hydrology and that now on is spreading in other research field. For the application of the GLUE methodology, several Monte Carlo simulations have been run varying the all model influential parameters simultaneously. The model was applied to an MBR pilot pl…
Probabilistic Flood Hazard Mapping Using Bivariate Analysis Based on Copulas
2017
This study presents a methodology to extract probabilistic flood hazard maps in an area subject to flood risk, taking into account uncertainties in the definition of design hydrographs. Particularly, the authors present a new method to produce probabilistic inundation and flood hazard maps in which the hydrological input (i.e., synthetic flood design event) to a 2D hydraulic model has been obtained by using a bivariate statistical analysis (copulas) to generate flood peak discharges and volumes. This study also aims to quantify the contribution of boundary conditions’ uncertainty in order to evaluate the effect of this uncertainty source on probabilistic flood hazard mapping. Different comb…
Statistical upscaling of ecosystem CO2 fluxes across the terrestrial tundra and boreal domain: Regional patterns and uncertainties
2021
The regional variability in tundra and boreal carbon dioxide (CO2) fluxes can be high, complicating efforts to quantify sink-source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high-latitude (i.e., tundra and boreal) sites to a…