In order to manage noise pollution and reduce its environmental impact and health outcomes, several regulations have been issued in the last few decades, defining acoustic indicators and their thresholds. However, the acoustic environment can be considered a resource, focusing on people’s subjective perception of sounds in accordance with the soundscape approach. The integration of the tools, already applied by the legislation, and the soundscape technique produces a more thorough and comprehensive evaluation of the environmental noise that is necessary for its management. Starting from the best practice of the soundscape in urban planning, this paper presents an application of this approach at the Fisciano campus of the University of Salerno (Italy). The overarching goal is the comparison between the physical parameters, obtained by measuring the sound pressure level, and the psychoacoustic ones, derived by questionnaires given to a group of local experts during a soundwalk. The results will show, for example, some areas characterized by high sound pressure levels and a good perception of the soundscape. As a consequence, the application would seem to have discrepancies between the results of the two methods, but a deeper analysis can reveal further information to the traditional measurements that allow a more accurate knowledge of the acoustic environment.

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. L_eq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

In this paper a statistical study on the time series of water levels measured, during 2014, in the water tank of Cesine, Avellino (Italy), is presented. In particular, the autoregressive integrated moving average (ARIMA) forecasting methodology is applied to model and forecast the daily water levels. This technique combines the autoregression and the moving average approaches, with the possibility to differentiate the data, to make the series stationary. In order to better describe the trend, over time, of the water levels in the reservoir, three ARIMA models are calibrated, validated and compared: ARIMA (2,0,2), ARIMA (3,1,3), ARIMA (6,1,6). After a preliminary statistical characterization of the series, the models' parameters are calibrated on the data related to the first 11 months of 2014, in order to keep the last month of data for validating the results. For each model, a graphical comparison with the observed data is presented, together with the calculation of the summary statistics of the residuals and of some error metrics. The results are discussed and some further possible applications are highlighted in the conclusions.

To reduce environmental noise pollution and to safeguard people’s well-being, it is urgently necessary to move towards sustainable urban development and reconcile demographic and economic growth with the protection and restoration of the environment and the improvement of the quality of human lives. This challenge should be a concern to policymakers, who must issue regulations and define the appropriate actions for noise monitoring and management, and citizens, who must be sensitive to the problem and act accordingly. Starting from an analysis of several crowdsourcing noise data collection tools, this paper focuses on the definition of a methodology for data analysis and mapping. The sound sensing system, indeed, enables mobile devices, such as smartphones and tablets, to become a low-cost data collection for monitoring environmental noise. For this study, the “NoiseCapture” application developed in France by CNRS and IFSTTAR has been utilized. The measurements acquired in 2018 and 2019 at the Fisciano Campus at the University of Salerno were integrated with the kernel density estimation. This is a spatial analysis technique that allows for the elaboration of sound level density maps, defined spatially and temporally. These maps, overlaid on a campus facilities map, can become tools to support the appropriate mitigation actions.

As cardiac diseases, which mostly result in heart failure, are increasing rapidly worldwide, heart transplantation seems the only solution for saving lives. However, this practice is not always possible due to several reasons, such as scarcity of donors, rejection of organs from recipient bodies, or costly medical procedures. In the framework of nanotechnology, nanomaterials greatly contribute to the development of these cardiovascular scaffolds as they provide an easy regeneration of the tissues. Currently, functional nanofibers can be used in the production of stem cells and in the regeneration of cells and tissues. The small size of nanomaterials, however, leads to changes in their chemical and physical characteristics that could alter their interaction and exposure to stem cells with cells and tissues. This article aims to review the naturally occurring biodegradable nanomaterials that are used in cardiovascular tissue engineering for the development of cardiac patches, vessels, and tissues. Moreover, this article also provides an overview of cell sources used for cardiac tissue engineering, explains the anatomy and physiology of the human heart, and explores the regeneration of cardiac cells and the nanofabrication approaches used in cardiac tissue engineering as well as scaffolds.

A model has been developed for the calculation of the Radon activity concentration in indoor air, taking into account all the different important parameters responsible of the global Radon dynamics, from its generation into the source (soil, water or building material) to its accumulation in confined spaces. The Radon accumulation into buildings is mainly due to the transport from the soil dominated by diffusion, pressure driven flow or a mixture of both depending on the real conditions. Experimental observations and measurements were carried out on different sites in the Campania Region (Italy) to gain valuable information about the various parameters influencing the indoor Radon level. The acquired results indicate that, generally, in the regional area, indoor Radon accumulation is not only due to the transport from the ground to the building, driven by pressure differences, through the basement, but it is also due to the direct exhalation from building materials. So, aim of this work was to refine a physical mathematical model of the Radon dynamics, developed in a previous work by the authors, by gathering and incorporating realistic experimental values. A graphical plot has been elaborated for an easy visualization and interpretation of the data.