Modelling sound absorption properties of broom fibers using artificial neural networks

The use of broom to produce fibers has ancient roots. The Greeks appreciated its resistance to water and for this reason they used it to manufacture sailing ropes. But broom fiber was also appreciated for its sound absorption qualities. In this study, a new methodology was developed for the numerical modeling of the acoustic behavior of broom fibers. First, the characteristics of the different varieties of broom were examined and the procedures for processing the samples to be analyzed were described. Subsequently, the results of the measurements of the following acoustic properties of the material were analyzed: air flow resistance, porosity, and sound absorption coefficient. Finally, the results of the numerical modeling of the acoustic coefficient were reported using an algorithm based on artificial neural networks. The results obtained are compared with a model based on linear regression. The model based on neural networks showed high values of the Pearson correlation coefficient (0.989), indicating a high number of correct predictions

Wind turbine noise prediction using random forest regression

Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been analyzed to characterize the phenomenon. An appropriate number of observations were then extracted, and these data were pre-processed. Subsequently two models of prediction of sound pressure levels were built at the receiver: a model based on multiple linear regression, and a model based on Random Forest algorithm. As predictors wind speeds measured near the wind turbines and the active power of the turbines were selected. Both data were measured by the SCADA system of wind turbines. The model based on the Random Forest algorithm showed high values of the Pearson correlation coeffcient (0.981), indicating a high number of correct predictions. This model can be extremely useful, both for the receiver and for the wind farm manager. Through the results of the model it will be possible to establish for which wind speed values the noise produced by wind turbines become dominant. Furthermore, the predictive model can give an overview of the noise produced by the receiver from the system in different operating conditions. Finally, the prediction model does not require the shutdown of the plant, a very expensive procedure due to the consequent loss of production.

Research for the presence of unmanned aerial vehicle inside closed environments with acoustic measurements

Small UAVs (unmanned aerial vehicle) can be used in many sectors such as the acquisition of images or the transport of objects. Small UAVs have also been used for terrorist activities or to disturb the flight of airplanes. Due to the small size and the presence of only rotating parts, drones escape traditional controls and therefore represent a danger. This paper reports a methodology for identifying the presence of small UAVs inside a closed environment by measuring the noise emitted during the flight. Acoustic measurements of the noise emitted by a drone inside a large environment (12.0 x 30.0 x 12.0 m) were performed. The noise was measured with a sound level meter placed at different distances (5, 10, and 15 m), to characterize the noise in the absence of anthropic noise. In this configuration, a typical tonal component of drone noise is highlighted at the frequency of one-third of an octave at 5000 Hz due to the rotation of the blades. This component is also present 15 m away from the source point. Subsequent measurements were performed by introducing into the environment, through a loudspeaker, the anthropogenic noise produced by the buzz of people and background music. It is possible to distinguish the typical tonal component of UAV noise at the frequency of 5000 Hz even when the level of recording of anthropogenic noise emitted by the loudspeaker is at the maximum power tested. It is therefore possible to search for the presence of small UAVs inside a specific closed environment with only acoustic measurements, paying attention to the typical frequency of noise emission equal to 5000 Hz

Sound attenuation of an acoustic barrier made with metamaterials

Although the first studies of them date back to a half century ago to Viselago, metamaterials represent a new solution in applied acoustics and noise control fields. In this paper, after a brief introduction to the state of art of metamaterials for acoustic applications, the sound attenuation of an acoustic barrier made following metamaterial rules is investigated. A 1:10 scale model was built using cylindrical bars, 30 cm high and 1.5 cm in diameter. The length of the barrier was 100 cm. The barrier was investigated for four combinations of the rows of the bars, spacing bars to create different regular geometries. The insertion losses of each configuration are reported

New Acoustic Design for the Piscina Mirabilis Located nearby the Port of Misenum

Many heritage buildings from ancient Rome are being refurbished based on their original plan’s structure. One of them is the piscina mirabilis located nearby in Naples, which was a cistern used by the Romans to collect drinkable water for the navy waiting in the port of Misenum. The piscina mirabilis has similar architectural characteristics to a “cathedral”; however, its current precarious architectural state is the result of high levels of humidity that have caused the proliferation of mould on its vertical and horizontal surfaces over the centuries. Acoustic measurements were conducted inside the piscina mirabilis, highlighting an existing condition of the room being very reverberant, not suitable for occasional speech and conversations. The design proposed by the authors involves some mitigation solutions for the acoustics, mainly focused on controlling the low–medium frequencies and the realization of a restoration project consisting of a raised timber-floored walkway that runs along the perimeter walls, with the addition of water covering the existing floor as a natural element dominating the room volume, which represents the primary function of the building in antiquity. A waterfall was designed to be on the northern side wall. Acoustic studies were an important part of the refurbishment strategy, and a mitigation solution was devised to control medium–low frequencies by using inflated balloons of different sizes that were suspended from the ceiling vaults instead of widely used acoustic panels. The proposed strategy lowered the reverberation time by 3–4 s to accommodate a minimal level of conversational understanding. Such a solution is appropriate for this heritage building as well as other future conservation projects

An artificial neural network approach to modelling absorbent asphalts acoustic properties

Sound-absorbing asphalts are particularly useful for reducing noise emissions from vehicular traffic. This solution is perfectly suited for urban areas, in fact the use of sound-absorbing asphalt represents a noise control measure with a negligible environmental impact. In the present work, the results of an experimental investigation on sound-absorbing asphalts were reported. First, the characteristics of the sound-absorbing asphalts used were experimentally found. Then, the measurements of the sound absorption coefficient of the asphalt specimens were investigated. In the final part, numerical simulation model with artificial neural networks of the acoustic coefficient were compared with the data obtained from the measurements. The neural network model showed good Pearson correlation coefficient values (0.894) which can be used with good accuracy to predict the sound absorption coefficient

Machine learning-based tools for wind turbine acoustic monitoring

The identification and separation of sound sources has always been a difficult problem for acoustic technicians to tackle. This is due to the considerable complexity of a sound that is made up of many contributions at different frequencies. Each sound has a specific frequency spectrum, but when many sounds overlap it becomes difficult to discriminate between the different contributions. In this case, it can be extremely useful to have a tool that is capable of identifying the operating conditions of an acoustic source. In this study, measurements were made of the noise emitted by a wind turbine in the vicinity of a sensitive receptor. To identify the operating conditions of the wind turbine, average spectral levels in one-third octave bands were used. A model based on a support vector machine (SVM) was developed for the detection of the operating conditions of the wind turbine; then a model based on an artificial neural network was used to compare the performance of both models. The high precision returned by the simulation models supports the adoption of these tools as a support for the acoustic characterization of noise in environments close to wind turbines

A comparison between numerical simulation models for the prediction of acoustic behavior of giant reeds shredded

Giant reeds represent a natural fiber widely available in some areas of the world. Its use can be particularly useful as the uncontrolled growth of giant reeds can be a problem because large areas are invaded by them and the crops are damaged. In this study, two models of numerical simulation of the acoustic behavior of giant reeds were put in comparison: the Hamet model and a model based on artificial neural networks. First, the characteristics of the reeds were examined and the procedures for the preparation of the samples to be analyzed were described. Then air flow resistance, porosity and sound absorption coefficient were measured and analyzed in detail. Finally, the results of the numerical modeling of the acoustic coefficient were compared. The neural network-based model showed high Pearson correlation coefficient value, indicating a large number of correct predictions

Ketamine's antidepressant effect is mediated by energy metabolism and antioxidant defense system.

Fewer than 50% of all patients with major depressive disorder (MDD) treated with currently available antidepressants (ADs) show full remission. Moreover, about one third of the patients suffering from MDD does not respond to conventional ADs and develop treatment-resistant depression (TRD). Ketamine, a non-competitive, voltage-dependent N-Methyl-D-aspartate receptor (NMDAR) antagonist, has been shown to have a rapid antidepressant effect, especially in patients suffering from TRD. Hippocampi of ketamine-treated mice were analysed by metabolome and proteome profiling to delineate ketamine treatment-affected molecular pathways and biosignatures. Our data implicate mitochondrial energy metabolism and the antioxidant defense system as downstream effectors of the ketamine response. Specifically, ketamine tended to downregulate the adenosine triphosphate (ATP)/adenosine diphosphate (ADP) metabolite ratio which strongly correlated with forced swim test (FST) floating time. Furthermore, we found increased levels of enzymes that are part of the 'oxidative phosphorylation' (OXPHOS) pathway. Our study also suggests that ketamine causes less protein damage by rapidly decreasing reactive oxygen species (ROS) production and lend further support to the hypothesis that mitochondria have a critical role for mediating antidepressant action including the rapid ketamine response

A Geological Itinerary Through the Southern Apennine Thrust-Belt (Basilicata—Southern Italy)

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