C. Guarnaccia

Multiplicity studies and effective energy in ALICE at the LHC

In this work we explore the possibility to perform “effective energy” studies in very high energy collisions at the CERN large hadron collider (LHC). In particular, we focus on the possibility to measure in pp collisions the average charged multiplicity as a function of the effective energy with the ALICE experiment, using its capability to measure the energy of the leading baryons with the zero degree calorimeters. Analyses of this kind have been done at lower centre-of-mass energies and have shown that, once the appropriate kinematic variables are chosen, particle production is characterized by universal properties: no matter the nature of the interacting particles, the final states have identical features. Assuming that this universality picture can be extended to ion–ion collisions, as suggested by recent results from RHIC experiments, a novel approach based on the scaling hypothesis for limiting fragmentation has been used to derive the expected charged event multiplicity in AA interactions at LHC. This leads to scenarios where the multiplicity is significantly lower compared to most of the predictions from the models currently used to describe high energy AA collisions. A mean charged multiplicity of about 1000–2000 per rapidity unit (at η∼0) is expected for the most central Pb–Pb collisions at

Energy retrofit assessment through automated valuation models: An Italian case study

sqrt{s_{{text{NN}}}} = 5.5,text{TeV}

Honking noise contribution to road traffic noise prediction

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Validation of Seasonal ARIMA Models on Road Traffic Noise Measurements

With reference to the current issue of energy efficiency of residential buildings, the aim of this research is to analyze the possible influence of the energy performance component on the property prices formation. The study sample consists of two hundred residential units recently sold and located in the city of Bari (Italy). The implemented methodology is represented by a data-driven technique that employs a genetic algorithm to identify the functional expressions. The elaborations carried out have allowed the identification of a statistically reliable and easily interpretable model, which denotes an appreciable contribution of the energy component on housing prices.With reference to the current issue of energy efficiency of residential buildings, the aim of this research is to analyze the possible influence of the energy performance component on the property prices formation. The study sample consists of two hundred residential units recently sold and located in the city of Bari (Italy). The implemented methodology is represented by a data-driven technique that employs a genetic algorithm to identify the functional expressions. The elaborations carried out have allowed the identification of a statistically reliable and easily interpretable model, which denotes an appreciable contribution of the energy component on housing prices.

Statistical and Semi-Dynamical Road Traffic Noise Models Comparison with Field Measurements

The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.

Study of QGP signatures with the ϕ → K + K – signal in Pb-Pb ALICE events

The Time Series Analysis (TSA) technique is largely used in economics and related field, to understand the slope of a given univariate dataset and to predict its future behaviour. The Seasonal AutoRegressive Integrated Moving Average (SARIMA) models are a class of TSA models that, based on the periodicity observed in the series, build a predictive function that can extend the forecast to a given number of future periods. In this paper, these techniques are applied to a dataset of equivalent sound levels, measured in an urban environment. The periodic pattern will evidence a strong influence of human activities (in particular road traffic) on the noise observed. All the three models will exploit the seasonality of the series and will be calibrated on a partial dataset of 800 data. Once the parameters of the models will be evaluated, all the forecasting functions will be tested and validated on a dataset not used before. The performances of all the models will be evaluated in terms of errors values and distributions, such as introducing some error indexes that explain the peculiar features of the models results.

A Review of Traffic Noise Predictive Models

The need for road traffic noise monitoring is growing in urban areas due to the growth of vehicles number and to the consequent increase of risk for human health. Noise measurements cannot be performed everywhere, or even in a large number of sites, because of high costs and time consumption. For this reasons, Road Traffic Noise predictive Models (RTNMs) can be implemented to estimate the noise levels at any distance, knowing certain parameters needed as input of the RTNM. In this paper, the main statistical RTNMs are presented, together with the implementation of two innovative and advanced models: the EU suggested model (CNOSSOS-EU) and a research model presented by Quartieri et al. (2010). These models will be compared with noise measurements performed in different sites and with different traffic conditions, in order to avoid bias from geometry or other features of the area under study. The main conclusion is that the application of innovative models and the inclusion of dynamical information about traffic flow, will lead to better results with respect to statistical models.

Final test of the MRPC production for the ALICE TOF detector.

The φ → KK decay channel in Pb-Pb collisions at LHC is studied through a full simulation of the ALICE detector. The study focuses on possible signatures in this channel of quark-gluon plasma (QGP) formation. On a basis of 10 collisions at high centrality some proposed QGP signatures are clearly visible both in KK invariant mass and transverse mass distributions. The high significance of this observation appears to reside heavily on the use of the TOF (Time Of Flight) system of ALICE in addition to its central tracking detectors. PACS. PACS-key discribing text of that key – PACS-key discribing text of that key