Bayesian inference analysis of the uncertainty linked to the evaluation of potential flood damage in urban areas.

Flood damage in urbanized watersheds may be assessed by combining the flood depth–damage curves and the outputs of urban flood models. The complexity of the physical processes that must be simulated and the limited amount of data available for model calibration may lead to high uncertainty in the model results and consequently in damage estimation. Moreover depth–damage functions are usually affected by significant uncertainty related to the collected data and to the simplified structure of the regression law that is used. The present paper carries out the analysis of the uncertainty connected to the flood damage estimate obtained combining the use of hydraulic models and depth–damage curves. A Bayesian inference analysis was proposed along with a probabilistic approach for the parameters estimating. The analysis demonstrated that the Bayesian approach is very effective considering that the available databases are usually short

Effects of Losartan and Irbesartan administration on brain angiotensinogen mRNA levels

Losartan, 2-n-butyl-4-chloro-5-hydroxymethyl-1-[(2'(1H-tetrazol-5-yl)-biphenil-4-yl)methyl] imidazole, and Irbesartan, 2-n-butyl-3-[(2'-(1H-tetrazol-5-yl)-biphenyl-4-yl)methyl]-1,3-diaza-spiro[4,4]non -1-en-4-one, are two angiotensin AT1 receptor antagonists largely used in human health care as antihypertensive agents. Their ability to cross the blood-brain barrier and to influence the central renin-angiotensin system are widely investigated, but how this brain system responds to the subchronic and chronic block of the angiotensin AT1 receptor is still unknown. Normotensive rats were intragastrically implanted for 7- and 30-day administration, with a dose of 3 and 30 mg/kg body weight. Treatments were shown to influence, in a dose-, time- and brain-area-dependent manner, angiotensinogen mRNA levels in scanned areas. This study showed a general up-regulation of angiotensinogen mRNA expression after 7 days and a widespread down-regulation or basal level of expression after a 30-day administration of two angiotensin AT1 receptor antagonists

Aerogels for energy and environmental applications

Aerogels are emerging as one of the most intriguing and promising groups of microporous materials, characterized by impressive properties such as low density, high surface area, high porosity and tunable surface chemistry. Fostering unique thermal and acoustic insulation features, for several decades they mainly received attention from the aerospace and building sectors. More recently, new great opportunities arose due to significant advances in the drying technologies that currently, represent the enabling step for aerogel synthesis and fabrication. This process-ability dramatically increased the interest toward aerogels from new disciplines. This explains why in the last decade the Environmental Science and Energy fields significantly contributed to the expansion of the aerogel technology, suggesting novel uses and applications and contributing to extend the group of materials that can be synthetized by aerogel processing. New, unforeseen properties emerged for aerogel materials, such as adsorption of contaminants and fluids purification, catalysis of different reactions, electrical conductivity. The present short-review aims at providing a critical overview of the key advances in the development of aerogels for energy and environmental applications, especially emphasizing the common strategies and properties that are turning aerogels into one of the new key emerging technologies of these areas of science

Italian and Argentine olive oils: a NMR and gas chromatographic study

High-field Nuclear Magnetic Resonance (NMR) spectroscopy and Gas Chromatography (GC) were used to analyze 16 monovarietal olive oils obtained from few matched Mediterranean cultivars grown in experimental fields located in Italy and in the Catamarca region of Argentina. The Catamarca region is characterized by extreme pedoclimatic conditions and by a wild spontaneous vegetation. The proposed sampling allows to study the effect of different pedoclimatic conditions on olive oil composition. GC gives the fatty acid profile of olive oil samples. 1H and 13C NMR techniques provide different information: the 1H NMR spectrum allows the measurement of minor components of olive oils such as b-sytosterol, hexanal, trans-2-hexenal, formaldehyde, squalene, cycloartenol and linolenic acid; the 1C NMR spectrum allows to obtain information about glycerol tri-esters of olive oils, i.e., about their acyl composition and positional distribution on glycerol moiety. All the NMR and GC results have been submitted to Linear Discriminant Analysis (LDA) and Tree Cluster Analysis (TCA). A careful analysis of the statistical results allows to select the Mediterranean cultivars less affected by the climatic conditions present in the Catamarca region. The selected cultivars produce olive oils which keep their Mediterranean characteristics and which can be proposed as colonizing plants in this wild Argentine region.La espectroscopía de Resonancia Magnética Nuclear de alta resolución (RMN) y Cromatografía Gaseosa (CG) fueron utilizadas para analizar 16 monovariedades de aceites de oliva, obtenidas de algunos olivares Mediterráneos cultivados contemporáneamente en campos experimentales localizados en Italia y en la región de Catamarca en Argentina. Estas muestras permiten estudiar diferentes condiciones pedoclimáticas en la composición de los aceite de oliva. La CG proporciona el perfil en ácidos grasos de los aceites de oliva y las técnicas RMN 1H y RMN 13C suministran diferentes informaciones: el espectro RMN 1H permite medir los componentes menores del aceite de oliva tales como b-sitoesterol, hexanol, trans - 2 hexanol, formaldehido, escualeno, cicloartenol y ácido linolénico y el espectro RMN 13C da información referente a los triésteres de glicerol de los aceites de oliva, por ejemplo, la composición y distribución de la posición acílica en el glicerol. Los datos de CG y RMN han sido sometidos a un análisis discriminante lineal (LDA) y a un análisis cluster en árbol (TCA). Un minucioso análisis de estos resultados ha permitido seleccionar olivares que han sido menos afectados por las condiciones climáticas presentes en la región de Catamarca. Los olivares seleccionados producen aceites de oliva que pueden mantener sus características Mediterráneas y pueden ser propuestos como plantas colonizantes en esta región silvestre de Argentina

Bayesian Model Averaging Approach For Reducing Urban Flooding Damage Estimation Uncertainty

Uncertainty analysis is useful to define the level of reliability of a modelling application, but operational methods are needed to identify the best modelling structure for a specific problem based on uncertainty reduction criteria. One interesting example is given by flood damage estimation problem where different possible modelling solution and flood damage estimation can be used depending on the case study. Past literature showed that several modelling structures may be equally reliable in terms of calibration ability but they may produce different uncertainty levels. The use of the Bayesian approach for uncertainty analysis has been stimulated by its rigorous theoretical framework and by the possibility of evaluating the impact of new knowledge on the modelling predictions. Model structure uncertainty is associated with the assumptions reflected in model conceptualization and mathematical structure. An unfortunate truth in model development is that no matter how many resources are invested in developing a particular model, there remain conditions and situations in which the model is unsuitable to give an accurate forecast. Reliance on a single model typically overestimates the confidence and increases the statistical bias of the forecast. Bayesian model-averaging (BMA) techniques look to overcome the limitations of a single model by linearly combining a number of competing models into a single new model forecast. This method showed that a pooled forecast of competing models outperformed any single model forecast. The early applications of model-averaging for hydrological systems resulted in a point forecast. An extension of these approaches uses a multiple linear regression model to compute model weights while assuming a model Gaussian distribution, which allows for a probabilistic performance evaluation. In the present paper, BMA was applied to several flood damage estimation models in order to identify the best combination of models to analyse urban flooding distribution in Palermo city centre (Italy)

A Decision Support System For Identifying Real Losses In Water Distribution Networks

The investigation of real losses is of paramount importance for water utility willing to control operational costs and environmental and social impacts of water supply. Real losses reduction could be achieved by continuous maintenance actions and occasional pipe substitutions. Maintenance activities in the distribution network represent one of the largest items in a water utility economic balance and, therefore, any approach aimed at maintenance optimization catches the interest of water managers. These strategies are constrained by the amount of funds, which are usually available not in a single instalment but yearly and spread over a time period of several years. Several models have been developed for determining network components’ optimal replacement time and rehabilitation planning by means of a Decision Support System - DSS. DSS actually provides highly valuable indications for infrastructure state and rehabilitation actions to undertake. DSS can also be used to address real losses investigation campaigns in order to focus monitoring efforts in those areas where real losses are probability higher. In the present paper, a procedure for leak detention in a water distribution network is proposed. The procedure is based on network flow and pressure monitoring, joined together with numerical dynamic modelling. The comparison between monitoring and modelling response is used to highlight the network pipes where real losses are most probably present. The decisional outputs are the selection of the pipes which have to be better investigated by means of active search methods. Considering the errors in monitoring data and the simplification in numerical modelling, the approach is coupled with uncertainty analysis in order to provide a probable leakage distribution with a specified level of reliability. The procedure is applied to a laboratory case study and the results obtained show that the proposed procedure has the potential to be a useful tool for rehabilitation scheduling

Pumps as turbines (PATs) in water distribution networks affected by intermittent service

A hydraulic model was developed in order to evaluate the potential energy recovery from the use of centrifugal pumps as turbines (PATs) in a water distribution network characterized by the presence of private tanks. The model integrates the Global Gradient Algorithm (GGA), with a pressure-driven model that permits a more realistic representation of the influence on the network behaviour of the private tanks filling and emptying. The model was applied to a real case study: a District Metered Area in Palermo (Italy). Three different scenarios were analysed and compared with a baseline scenario (Scenario 0 - no PAT installed) to identify the system configuration with added PATs that permits the maximal energy recovery without penalizing the hydraulic network performance. In scenarios involving PAT on service connections, the specification of PAT operational parameters was also evaluated by means of Monte Carlo Analysis. The centralized solution with a PAT installed downstream of the inlet node of the analysed district, combined with local PATs on the larger service connections, proves to be the most energy-efficient scenario