1,721,091 research outputs found
Road Traffic Noise Predictions by means of L10 Modelling with a Multilinear Regression Calibrated on Simulated Data
No full textEstimation of road traffic noise is fundamental for the health of people living in urban areas, and it is usually assessed based on field-measured data. Real data may not always be available, anyway, and for this reason, predictive models play an important role in the evaluation and controlling of the noise impact. In this contribution, the authors present a multilinear regressive model calibrated on simulated noise levels instead that on real measured ones, correlating percentile noise levels to independent traffic variables. The model efficiency is then evaluated on two field measurement datasets by analyzing data statistics and error metrics. Results show that the model provides good results in terms of mean error (less than 1 dBA on average) even if slight underestimations and overestimations are present. The presented model, then, can be used to assess the impact of road traffic noise anytime field measurements are not available, or even predict it when designing new road infrastructures
Problematiche anestesiologiche.
No full textil paziente anziano con frattura di femore costituisce per l’anestesista una occasione importante per mettere a punto molte tematiche di competenza , dalla valutazione del rischio e delle coomorbilità preoperatorie all’impiego di tecniche di anestesia , di monitoraggio e di trattamento intensivo evolute e successivamente di valutazione di qualità della cura che rispettino le regole fondamentali della medicina dell’evidenza
Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment
No full textFeatured Application: The road traffic noise predictive model presented in this paper can be calibrated without field measurements and can be applied in several traffic conditions, with an average error lower than 2 dBA. The assessment of road traffic noise is very important for the health of people living in urban areas. Noise is usually assessed by field measurements, and predictive models play an important role when experimental data are not available. Nevertheless, when they are based on regression techniques, predictive models suffer from the drawback of strong dependence on the calibration data. In this paper, the authors present a regressive model calibrated on computed noise levels without the need for field measurements. The independence from field measurements makes the model flexible and adjustable for any road traffic condition possible. A multilinear regression technique is applied to establish the correlation between the computed equivalent noise levels and several independent variables, including, among others, traffic flow and distance. The model is then validated on a large field measurement database to check its efficiency in terms of prediction accuracy. The validation is performed both via error distribution analysis and using different error metrics. The results are encouraging, showing that the model provides good results in terms of the average error (less than 2 dBA) and is not susceptible to the presence of outliers in the input data that correspond to unconventional conditions of the traffic flow
Optimization of Dataset Generation for a Multilinear Regressive Road Traffic Noise Model
No full textAccording to the European Environmental Agency, road traffic noise is one of the worst and most prevalent kinds of environmental pollutants, which causes health problems to a constantly increasing number of people in urban areas throughout Europe. It has been proved that prolonged exposure to sound levels exceeding 55 dBA is harmful and causes severe problems like sleep disturbances, tiredness, lack of concentration, high blood pressure and, in the worst case, sudden death. A precise and constant evaluation of sound level in inhabited areas is therefore desired (and in some cases compelled by laws), but collection of actual noise data is not easy and sometimes not possible. For this reason, Road Traffic Noise (RTN) models are very handy: one can (more or less precisely) estimate the noise emitted in a certain area having certain road traffic characteristics. The application of RTN models, anyway, also has problems. First of all, an RTN model has to be built and calibrated by using real collected noise data. Moreover, when trying to apply an RTN model on road traffic situations that are far away from the site of collection, the models generally fail. To overcome such problems, in this contribution, a road traffic dataset has been computed by randomly generating values of traffic variables like the number of vehicles per unit of time, their velocities, and their distance from the receiver. Then, by applying a multiregressive function on the dataset, the obtained coefficients have been used to calibrate and validate the presented model. The three steps (generation of the dataset, calibration of the model, and validation on a real dataset) are detailly investigated
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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