102,195 research outputs found
Le gare e i contratti di servizio nel trasporto pubblico locale
No full textL’articolo analizza la relazione intercorrente tra il disegno dei contratti di servizio (e della gara) e l’esito della gara nel trasporto pubblico locale. Sono stati raccolti dati con un’indagine riguardante 50 stazioni appaltanti. Dall’analisi emerge che il disegno ottimale del contratto (e della gara) dipendono in modo cruciale dall’obiettivo del regolatore. La partecipazione è incoraggiata nel caso di un modello semi-rigido, contratti gross cost, incentivi deboli e possibilità di sub-appalto. Vice versa, i ribassi sono favoriti da contratti di lunga durata, forti incentivi e di nuovo dalla possibilità di sub-tendering. Infine, l’avere un nuovo vincitore è favorita da incentivi forti, disponibilità dei beni patrimoniali.This paper analyses the relationship between the service contract (and tender) design and the outcome of the tender in the local public tran sport. Data are collected through a survey involving about 50 territorial entities. It emerges that optimal contract (and tender) design crucially depends on the objective function of the regulator. Participation is encouraged by a semi-rigid model, by gross cost contracts, by weak incentives and by opportunity of sub-contracting. Vice versa, rebates are favoured by long duration of the contract, by strong incentives and (again) by opportunity for sub-contracting. Finally, having a new winner is favoured by strong incentives, by asset availability
Parameter Estimation
No full textIn general, a model describes-through suitable equations-the relationship between some inputs and some (measurable) outputs. This relationship is constituted from two parts: the model structure, that is, the mathematical law that describes a family of possible behaviors and the model parameters, that is, those quantities that can vary from one situation to another and that modulate and completely define the relationship. Assuming that model structure is known, the present chapter presents the basic concepts and techniques for parameter estimation (also called model identification), that is, the capability of deriving numerical values for model parameters from a set of noisy measurements. In particular, by using suitable case studies taken from the literature, Fisherian (e.g., least squares, maximum likelihood) and Bayesian estimators (e.g., maximum a posteriori) are presented, the latter also with probability distributions handled through Markov chain Monte Carlo simulation techniques. Analysis of the residuals for model checking and computation of the parameter estimate precisions are also discussed. © 2014 Elsevier Inc. All rights reserved
Le gare e i contratti di servizio nel trasporto pubblico locale
No full textL’articolo analizza la relazione intercorrente tra il disegno dei contratti di servizio (e della gara) e l’esito della gara nel trasporto pubblico locale. Sono stati raccolti dati con un’indagine riguardante 50 stazioni appaltanti. Dall’analisi emerge che il disegno ottimale del contratto (e della gara) dipendono in modo cruciale dall’obiettivo del regolatore. La partecipazione è incoraggiata nel caso di un modello semi-rigido, contratti gross cost, incentivi deboli e possibilità di sub-appalto. Vice versa, i ribassi sono favoriti da contratti di lunga durata, forti incentivi e di nuovo dalla possibilità di sub-tendering. Infine, l’avere un nuovo vincitore è favorita da incentivi forti, disponibilità dei beni patrimoniali
Fast spline smoothing via spectral factorization concepts
No full textWhen tuning the smoothness parameter of nonparametric regression splines, the evaluation of the so-called degrees of freedom is one of the most computer-intensive tasks. In the paper, a closed-form expression of the degrees of freedom is obtained for the case of cubic splines and equally spaced data when the number of data tends to infinity. State-space methods, Kalman filtering and spectral factorization techniques are used to prove that the asymptotic degrees of freedom are equal to the variance of a suitably defined stationary process. The closed-form expression opens the way to fast spline smoothing algorithms whose computational complexity is about one-half of standard methods (or even one-fourth under further approximations)
Non-Invasive Continuous-Time Blood Pressure Estimation from a Single Channel PPG Signal using Regularized ARX Models
No full textContinuous blood pressure (BP) monitoring can help in preventing hypertension and other cardiovascular diseases. In principle, an indirect non-invasive continuous-time measurement of BP is possible by exploiting the photoplethysmography (PPG) signal, which can be obtained through wearable optical sensor devices. However, a model of the PPG-to-BP dynamical system is needed. In this study, we investigate if autoregressive with exogenous input (ARX) models with kernel-based regularization are suited for the scope. We analyzed 10 PPG time-series acquired on different individuals by a wearable optical sensor and correspondent BP reference values to evaluate feasibility of continuous BP estimation from a single PPG source. This first proof-of-concept study shows promising results in continuous BP estimation during resting states
A new dynamic index of insulin sensitivity
No full textInsulin sensitivity is a crucial parameter of glucose metabolism. The standard measures of insulin sensitivity obtained by an euglycaemic hyperinsulinaemic clamp, SI(clamp), or by the minimal model (MM), SI, do not account for the dynamics of insulin action, i.e., how fast or slow insulin action reaches its plateau value. This is an important physiological information. In this paper we formally define a new insulin sensitivity index which also incorporates information on the dynamics of insulin action, SID, show its properties, and exemplify how it can be measured both with the clamp and the MM method. Then, by resorting to real and synthetic data, we show both in IVGTT MM and clamp studies why this new index SID offers, in comparison with SI, a more comprehensive picture of the control of insulin on glucose
Nonparametric input estimation in physiological systems: problems, methods, case studies
No full textInput estimation from output data is an important problem in the analysis of physiological systems, because many signals of interest are not directly accessible to measurement. When the system is time-invariant, this problem is often referred to as deconvolution. Three representative physiological problems, regarding hormone secretion, insulin dynamics, and hepatic glucose production, are used to illustrate the major challenges: ill-conditioning, confidence intervals assessment, infrequent and nonuniform sampling, nonnegativity constraints, and computational efficiency. The paper provides a critical overview of the existing techniques, focusing on regularization theory and Bayesian estimation. In order to overcome some inadequacies of the existing methods, some new results are derived. In particular, the connection between the maximum-likelihood estimate of the regularization parameter and the notion of equivalent degree of freedom is studied. Moreover, a fast SVD-based numerical algorithm is developed that includes the optimization of the regularization parameter, and the computation of confidence intervals. The proposed techniques are validated on a benchmark problem and are shown to provide effective solutions to the three physiological case studies
A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information.
No full textIn the last decade, improvements in diabetes daily management have become possible thanks to the development of minimally-invasive portable sensors which allow continuous glucose monitoring (CGM) for several days. In particular, hypo and hyperglycemia can be promptly detected when glucose exceeds the normal range thresholds, and even avoided through the use of on-line glucose prediction algorithms. Several algorithms with prediction horizon (PH) of 15-30-45 min have been proposed in the literature, e. g. including AR/ARMA time-series modeling and neural networks. Most of them are fed by CGM signals only. The purpose of this work is to develop a new short-term glucose prediction algorithm based on a neural network that, in addition to past CGM readings, also exploits information on carbohydrates intakes quantitatively described through a physiological model. Results on simulated data quantitatively show that the new method outperforms other published algorithms. Qualitative preliminary results on a real diabetic subject confirm the potentialities of the new approach
On the existence of universal wall functions in in-cylinder simulations using a low-Reynolds RANS turbulence model
No full textHeat Transfer plays a fundamental role in internal combustion engines, as able to affect several aspects, such as efficiency, emissions and reliability. As for this last, a proper heat transfer prediction is mandatory for the estimation of the engine temperatures at peak power condition, it being the most critical one from a thermal point of view. At part-load/low revving speed operations, heat transfer is detrimental for the engine efficiency, deeply reducing indicated work of the burnt gases on the piston. Focusing on the in-cylinder domain, 3D-CFD simulations represent an irreplaceable tool for the estimation of gas-to-wall heat fluxes. Several models have been developed in the past, aiming at providing a reliable estimation of the heat transfer at any condition in terms of load and revving speed. To save computational cost and time, the most diffused wall approach for the numerical simulation of confined reacting flows is the high-Reynolds one, which means that heat transfer model is based on a thermal wall function. Unfortunately, wall functions (logarithmic profiles of the inertial layer) can be claimed only at restricted conditions, such as isothermal steady-state flow, velocity parallel to the wall and negligible pressure gradient. In practice, none of these assumptions is valid for industrial applications such as an in-cylinder simulation. Therefore in these cases, as demonstrated by different works in the past, wall functions do not exist and their adoption leads to a non-negligible error in the estimation of the heat transfer. The main goal of this work is to build up a methodology able to investigate the presence of wall functions in actual industrial applications, in particular in 3D-CFD in-cylinder analyses. Compared to previous works available in literature, where DNS or LES are carried out on simplified geometries and/or at low revving speed conditions because of the computational cost, in the present paper a RANS approach to turbulence and a low-Reynolds wall treatment are adopted. Moreover, a new strategy to obtain dimensionless profiles of velocity and temperature from computed fields is introduced. At first, the proposed methodology is validated on a 2D plane channel. Then, a preliminary application on a research engine, namely the GM Pancake engine, is proposed, showing that dimensionless profiles of velocity and temperature calculated on the combustion chamber walls are remarkably different from standard analytical wall functions
- …
