Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering.

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.

How the COVID-19 Pandemic Muted and Remixed the World's Acoustics for a While.

Purpose of Review: This review aims to analyze the effects of the pandemic on the world's sound environment. Recent Findings: The confinements associated with the pandemic led to a reduction in sound levels worldwide and a change in the perception of soundscapes in the absence of traffic noise and human-generated noise. Summary: In response to the COVID-19 pandemic, many countries and regions around the world adopted a series of interventions in 2020 that have been referred to as "lockdown" or "confinement." These sets of restrictions had a clear and obvious consequence derived from the absence of people in the streets and the reduction of daily activity and commuting, which caused an unprecedented silencing on a large scale. Along with the silence that ensued, the pandemic and the confinements affected acoustics and our relationship with sounds on different scales. In the cities, this phenomenon had a strong reduction in acoustic intensity due to the absence of vehicles on the streets. Perhaps this was more perceptible in our neighborhoods, with notable changes in their soundscapes, first due to the absence of people in the streets and later due to more outdoor activity derived from the fear of the spread of the virus in indoor spaces. The longer periods of time spent in our homes during the lockdowns also highlighted the importance of sound insulation in buildings and the acoustic conditioning of our schools or homes.

Does exposure to noise pollution influence the incidence and severity of COVID-19?

Research that analyzes the effect of different environmental factors on the impact of COVID-19 focus primarily on meteorological variables such as humidity and temperature or on air pollution variables. However, noise pollution is also a relevant environmental factor that contributes to the worsening of chronic cardiovascular diseases and even diabetes. This study analyzes the role of short-term noise pollution levels on the incidence and severity of cases of COVID-19 in Madrid from February 1 to May 31, 2020. The following variables were used in the study: daily noise levels averaged over 14 days; daily incidence rates, average cumulative incidence over 14 days; hospital admissions, Intensive Care Unit (ICU) admissions and mortality due to COVID-19. We controlled for the effect of the pollutants PM10 and NO2 as well as for variables related to seasonality and autoregressive nature. GLM models with Poisson regressions were carried out using significant variable selection (p < 0.05) to calculate attributable RR. The results of the modeling using a single variable show that the levels of noise (leq24 h) were related to the incidence rate, the rate of hospital admissions, the ICU admissions and the rate of average cumulative incidence over 14 days. These associations presented lags, and the first association was with incidence (lag 7 and lag 10), then with hospital admissions (lag 17) and finally ICU admissions (lag 22). There was no association with deaths due to COVID-19. In the results of the models that included PM10, NO2, Leq24 h and the control variables simultaneously, we observed that only Leq24 h went on to become a part of the models using COVID-19 variables, including the 14-day average cumulative incidence. These results show that noise pollution is an important environmental variable that is relevant in relation to the incidence and severity of COVID-19 in the Province of Madrid.

Machine Learning Algorithm to Predict Acidemia Using Electronic Fetal Monitoring Recording Parameters.

BACKGROUND: Electronic fetal monitoring (EFM) is the universal method for the surveillance of fetal well-being in intrapartum. Our objective was to predict acidemia from fetal heart signal features using machine learning algorithms. METHODS: A case-control 1:2 study was carried out compromising 378 infants, born in the Miguel Servet University Hospital, Spain. Neonatal acidemia was defined as pH < 7.10. Using EFM recording logistic regression, random forest and neural networks models were built to predict acidemia. Validation of models was performed by means of discrimination, calibration, and clinical utility. RESULTS: Best performance was attained using a random forest model built with 100 trees. The discrimination ability was good, with an area under the Receiver Operating Characteristic curve (AUC) of 0.865. The calibration showed a slight overestimation of acidemia occurrence for probabilities above 0.4. The clinical utility showed that for 33% cutoff point, missing 5% of acidotic cases, 46% of unnecessary cesarean sections could be prevented. Logistic regression and neural networks showed similar discrimination ability but with worse calibration and clinical utility. CONCLUSIONS: The combination of the variables extracted from EFM recording provided a predictive model of acidemia that showed good accuracy and provides a practical tool to prevent unnecessary cesarean sections.

Changes in noise levels in the city of Madrid during COVID-19 lockdown in 2020.

The lockdown that Madrid has suffered during the months of March to June 2020 to try to control and minimize the spread of COVID-19 has significantly altered the acoustic environment of the city. The absence of vehicles and people on the streets has led to a noise reduction captured by the monitoring network of the City of Madrid. In this article, an analysis has been carried out to describe the reduction in noise pollution that has occurred and to analyze the changes in the temporal patterns of noise, which are strongly correlated with the adaptation of the population's activity and behavior to the new circumstances. The reduction in the sound level ranged from 4 to 6 dBA for the indicators Ld, Le, and Ln, and this is connected to a significant variation in the daily time patterns, especially during weekends, when the activity started earlier in the morning and lasted longer at midday, decreasing significantly in the afternoon.

A Taxonomy Proposal for the Assessment of the Changes in Soundscape Resulting from the COVID-19 Lockdown.

Many countries around the world have chosen lockdown and restrictions on people's mobility as the main strategies to combat the COVID-19 pandemic. These actions have significantly affected environmental noise and modified urban soundscapes, opening up an unprecedented opportunity for research in the field. In order to enable these investigations to be carried out in a more harmonized and consistent manner, this paper makes a proposal for a set of indicators that will enable to address the challenge from a number of different approaches. It proposes a minimum set of basic energetic indicators, and the taxonomy that will allow their communication and reporting. In addition, an extended set of descriptors is outlined which better enables the application of more novel approaches to the evaluation of the effect of this new soundscape on people's subjective perception.

A Digital Signal Processor Based Acoustic Sensor for Outdoor Noise Monitoring in Smart Cities.

Presently, large cities have significant problems with noise pollution due to human activity. Transportation, economic activities, and leisure activities have an important impact on noise pollution. Acoustic noise monitoring must be done with equipment of high quality. Thus, long-term noise monitoring is a high-cost activity for administrations. For this reason, new alternative technological solutions are being used to reduce the costs of measurement instruments. This article presents a design for a versatile electronic device to measure outdoor noise. This device has been designed according to the technical standards for this type of instrument, which impose strict requirements on both the design and the quality of the device's measurements. This instrument has been designed under the original equipment manufacturer (OEM) concept, so the microphone-electronics set can be used as a sensor that can be connected to any microprocessor-based device, and therefore can be easily attached to a monitoring network. To validate the instrument's design, the device has been tested following the regulations of the calibration laboratories for sound level meters (SLM). These tests allowed us to evaluate the behavior of the electronics and the microphone, obtaining different results for these two elements. The results show that the electronics and algorithms implemented fully fit within the requirements of type 1 noise measurement instruments. However, the use of an electret microphone reduces the technical features of the designed instrument, which can only fully fit the requirements of type 2 noise measurement instruments. This situation shows that the microphone is a key element in this kind of instrument and an important element in the overall price. To test the instrument's quality and show how it can be used for monitoring noise in smart wireless acoustic sensor networks, the designed equipment was connected to a commercial microprocessor board and inserted into the infrastructure of an existing outdoor monitoring network. This allowed us to deploy a low-cost sub-network in the city of Málaga (Spain) to analyze the noise of conflict areas due to high levels of leisure noise. The results obtained with this equipment are also shown. It has been verified that this equipment meets the similar requirements to those obtained for type 2 instruments for measuring outdoor noise. The designed equipment is a two-channel instrument, that simultaneously measures, in real time, 86 sound noise parameters for each channel, such as the equivalent continuous sound level (Leq) (with Z, C, and A frequency weighting), the peak level (with Z, C, and A frequency weighting), the maximum and minimum levels (with Z, C, and A frequency weighting), and the impulse, fast, and slow time weighting; seven percentiles (1%, 5%, 10%, 50%, 90%, 95%, and 99%); as well as continuous equivalent sound pressure levels in the one-third octave and octave frequency bands.

Beyond sound level monitoring: Exploitation of social media to gather citizens subjective response to noise.

Subjective response to noise is probably the most important goal in environmental acoustics. Traditional surveys have the drawback of high cost deriving from its development and execution, the limited number of participants, and the duration of the surveying campaign. The main drawbacks of online surveys are the low participation, or the self-produced bias that concerns about the topic can raise. In both cases, the process of designing questionnaires, implementing the survey, and analysing the results can be long, expensive and ineffective to monitor changes in the short-term. With the creation of Online Social Networks (OSN), people have changed the manner they communicate and use technology. Nowadays, people can provide information regarding their likes, opinion and discomfort about any topic, including noise, in just a few minutes with their smartphone. These Internet opinions can be analysed automatically using machine learning and Natural Language Processing techniques to get insights that can help in the early detection of noise problems, or in the prior assessment of action plans. This information can be significant helpful in addressing noise management by local authorities and stakeholders. The purpose of this paper is to present a novel methodology, based on machine learning, allowing for the gathering and processing of OSN text data, enabling the generation of a system able to exploit the data to detect noise complaints and to classify them by source. This methodology has been piloted in a case study using Twitter, and the main results are presented.

Communicating airport noise emission data to the general public.

Despite the efforts that the aviation industry has undertaken during the last few decades, noise annoyance remains high, partly because of the continuous transport demands of modern societies and partly because of changes in citizen expectations and their growing environmental concerns. Although modern aircraft are considerably quieter than their predecessors, the number of complaints has not decreased as much as expected. Therefore, the aeronautical sector has tried more sociological and/or psychological strategies to gain acceptance through awareness and community engagement. In this regard, noise communication to the public is crucial for managers and policy makers. Noise information is a difficult technical topic for non-experts, which is an issue that must first be addressed to take advantage of the new possibilities that have recently been opened by the internet and information and communication technologies. In this review paper, we have compiled the literature that shows the increasing importance of communicating noise information from aircraft and the variety of indicators used to communicate with the public. We also examined the methods of representing noise data, using visualization strategies, and new tools airports are currently using to address this communication problem.

Consequences of moduli stabilization in the Einstein-Maxwell landscape.

A toy landscape sector is introduced as a compactification of the Einstein-Maxwell model on a product of two spheres. Features of the model include moduli stabilization, a distribution of the effective cosmological constant of the dimensionally reduced 1 + 1 spacetime, which is different from the analogous distribution of the Bousso-Polchinski landscape, and the absence of the so-called α* problem. This problem arises when the Kachru-Kallosh-Linde-Trivedi stabilization mechanism is naively applied to the states of the Bousso-Polchinski landscape. The model also contains anthropic states, which can be readily constructed without needing any fine-tuning.