Increase In Noise Level Research Articles

Assessment and mapping of noise pollution in recreation spaces using geostatistic method after COVID-19 lockdown in Turkey

Increased use of recreational areas after the lifting of COVID-19 pandemic restrictions has led to increased noise levels. This study aims to determine the level of noise pollution experienced in recreational areas with the increasing domestic and international tourism activities after the lifting of pandemic lockdowns, to produce spatial distribution maps of noise pollution, and to develop strategic planning suggestions for reducing noise pollution in line with the results obtained. Antalya-Konyaaltı Beach Recreation Area, the most important international tourism destination of Turkey, is determined as the study area. To determine the existing noise pollution, 31 measurement points were marked at 100 m intervals within the study area. Noise measurements were taken during the daytime (07:00–19:00), evening (19:00–23:00), and nighttime (23:00–07:00) on weekdays (Monday, Wednesday, Friday) and weekends (Sunday) over 2 months in the summer when the lockdown was lifted. In addition, the sound level at each measurement point was recorded for 15 min, while the number of vehicles passing through the area during the same period was determined. The database created as a result of measurements and observations was analyzed using statistical and geostatistical methods. After the analysis of the data, it was found that the co-kriging-stable model showed superior performance in noise mapping. Additionally, it was revealed that there is a high correlation between traffic density and noise intensity, with the highest equivalent noise level (Leq) on weekdays and weekend evenings due to traffic and user density. In conclusion, regions exposed to intense noise pollution were identified and strategic planning recommendations were developed to prevent/reduce noise sources in these identified regions.

Image denoising method of HV insulator damage based on U-Net neural network

The noise of the image will lead to large errors in the image processing of high-voltage insulator damage. In order to reduce the impact of noise, a denoising method for high-voltage insulator damage image is designed based on U-Net neural network. The specific method is to define the perception features of the real domain of the HV insulator damage image, to obtain the input and output values in the convolution layer, to construct the excitation function of the convolution layer, to calculate the average value in the selected region, and to obtain the network image feature mapping results. Based on this, displacement changes were acquired by calculating the mean and variance of image data, and multi-scale image features were extracted combined with U-Net neural network. Thus, the image denoising model of high voltage insulator damage is established. Through the continuous optimization algorithm, the value of loss function reaches the predetermined standard, and the denoising of high voltage insulator damage image is finally realized. The experimental results show that as the noise level increases, the peak signal-to-noise(PSNR) ratio obtained by this denoising method shows a decreasing trend. When the noise level is 5 dB and 75 dB, the PSNR values of this denoising method on the color dataset are 27.6 dB and 26.8 dB, respectively. In the grayscale dataset, when the noise level is 75 dB, the PSNR value is 26.9 dB. From the experimental data, it can be seen that the proposed U-Net neural network denoising method is better.

Uncertain damage identification methods based on residual force vector under the influence of measurement noise

This paper proposes probabilistic and interval methods, grounded in the residual force vector technique, to quantify measurement uncertainties and effectively integrate them into the identification process. In the probabilistic method, the measurement noise is assumed to be a zero-mean normal distribution, while the stiffness parameters of structural elements are assumed to follow a normal distribution, represented by mean values and standard deviations. In the interval method, the measurement noise is modelled as interval numbers, and the interval perturbation technique is employed to derive interval bounds for elemental stiffness parameters. A two-step model updating strategy is implemented, separately addressing pristine and damaged structures, to mitigate modeling errors. By using the results of probabilistic damage identification, a novel damage index termed damage expectation capable of capturing both the extent and probability of damage is introduced. Furthermore, an interval damage index is introduced to quantify the damage based on interval damage identification results. The impact of noise levels on the statistical properties of uncertain damage indices is also investigated. Numerical examples reveal that deterministic indexes exhibit a monotonic increase in average values with increasing noise levels. Particularly, as noise levels rise, the standard deviation of the combination index initially surpasses the deterministic damage index but eventually diminishes. Through the proposed methodology, structural damage can be identified even in the presence of uncertainties. The efficacy of the proposed damage identification technique is also validated by experimental data.

Effects of Climate Change on Oceanic Sonar Use in the Upper European Continental Shelf

As the global effects of climate change become more known year by year, it becomes ever-more pertinent to examine the effects this may bring for every aspect of modern life we rely on. One topic of focus is that of multi-frequency sonar communication and navigational systems, which rely on well-established relationships relating to wave speed, signal intensity, and attenuation. We compiled data on oceanic temperature, acidity, and salinity in the Upper European Shelf, which includes the North Sea and Mediterranean Sea, from 2006 to 2072 using the CMIP5 future climate model in the RCP8.5 scenario. We calculate that the speed of sound in the northern European oceanic area will decrease by almost 18 m/s by 2072, with an average yearly decrease in sound speed by 0.37 m/s. The attenuation of sound through water will change year by year, calculated based on a higher-order polynomial regression dependent on the frequency of sonar used. The maximum operating ranges of active low-frequency, mid-frequency, and high-frequency sonar systems would theoretically change by +0.06%, -0.19%, and +0.71%, respectively per year, if no other factors are affected. Due to increased sound propagation, the ambient noise level of the ocean would also increase and have some counter-effect to the increased detection range however that increase in noise level was not quantitatively analyzed in this study.

Night Time Image Enhancement

Night time image enhancement plays a crucial role in various applications such as surveillance, autonomous driving, and photography. However, capturing high-quality images in low-light conditions remains challenging due to limited visibility and increased noise levels. In this project, we propose a novel approach for enhancing nighttime images using MIRNet, a state-of-the-art deep learning architecture specifically designed for low-light image enhancement tasks. We collect a dataset of low-light images paired with their corresponding well-exposed counterparts and train the MIRNet model to learn the mapping between the two modalities. The architecture of MIRNet incorporates convolutional layers with residual connections to effectively capture low-light image features and generate visually pleasing enhancements. We evaluate the performance of our approach on a diverse range of nighttime scenes and compare the results against existing methods. Our experiments demonstrate that MIRNet produces superior results in enhancing nighttime images, significantly improving visibility, reducing noise, and preserving image details. The proposed approach holds promise for real-world applications where high-quality nighttime imagery is essential for decision-making and visual analysis. Keywords: Night time image enhancement, MIRNet, Deep learning, Low-light imaging, Image Processing,Convolutional neural networks (CNNs),Residual connections, Supervised learning, Dataset preparation,Imagequalityimprovement,Noisereduction,Visibilityenhancement,Surveillance,Autonomousdriving,Photography.

A Convergence Path to Deep Learning on Noisy Labels.

In many real-world machine learning classification applications, the model performance based on deep neural networks (DNNs) oftentimes suffers from label noise. Various methods have been proposed in the literature to address this issue, primarily by focusing on designing noise-tolerant loss functions, cleaning label noise, and correcting the objective loss. However, the noise-tolerant loss functions face challenges when the noise level increases. This article aims to reveal a convergence path of a trained model in the presence of label noise, and here, the convergence path depicts the evolution of a trained model over epochs. We first propose a theorem to demonstrate that any surrogate loss function can be used to learn DNNs from noisy labels. Next, theories on the general convergence path for the deep models under label noise are presented and verified through a series of experiments. In addition, we design an algorithm based on the proposed theorems that make efficient corrections on the noisy labels and achieve strong robustness in the DNN models. We designed several experiments using benchmark datasets to assess noise tolerance and verify the theorems presented in this article. The comprehensive experimental results firmly confirm our theoretical results and also clearly validate the effectiveness of our method under various levels of label noise.

Crowdsourced cycling data applications to estimate noise pollution exposure during urban cycling

This research demonstrates a methodology to integrate freely available datasets to understand the relationship between road traffic noise and cycling experiences in a medium sized city. An illustrative example of the methodology was drawn from data for Dublin, Ireland. We aggregate local environmental data with 81,403 Strava cycle trips, contextualised by feedback from 335 cyclists to estimate exposure levels and infer impacts on experiences and behaviours. Results demonstrate that cyclists recognise that they are subjected to increased noise levels and experience negative psychophysical consequences as a result, but they tend to downplay the impact of noise as merely a minor annoyance. Noise also impacts behaviour, most noticeably through temporal and spatial detours. Geospatial mapping was used to visualise the relationship between noise pollution and cycling activity. Estimating traffic noise levels across two cycle routes, direct vs popular detour, revealed a +10 dB(A) increase in exposure for a saving of approximately 4 min on the direct route compared to the detour. Spatial inequities in exposure levels may have serious health consequences for cyclists in a city such as Dublin. The methodology is demonstrated as suitable for policy level interventions and planning purposes.

Analysis The Effect Of Magnet Structure On The Unbalance Magnetic Pull Of Fractional Slot Number Type In Permanent Magnet Machine

Within this study, the effect of magnet structure on the unbalanced magnetic draw (UMP) of fractional slot number permanent magnet machines (PMMs) was methodically investigated. The literature revealed that fractional slot number PMMs use a variety of magnet structure configurations, including radial, parallel, and hybrid arrangements. Different magnet configurations were discovered to affect the distribution of magnetic flux density, thereby impacting UMP concentrations. The effect of various magnet geometries, including arc, rectangular, and trapezoidal, on UMP was examined. Moreover, the selection of magnet materials, such as ferrite, neodymium-iron-boron, and samarium-cobalt, influenced the magnetic field distribution and UMP characteristics. Due to their particular slot and pole combinations, fractional slot number PMMs were found to be more susceptible to UMP than other PMM varieties. Unbalanced forces caused by UMP resulted in unwanted vibrations, increased noise levels, and possible machine durability issues. Numerous factors influence UMP in fractional slot number PMMs, including the number of slots and poles, the length of the stator-rotor air gap, the magnet pole breadth, and the magnet thickness. The level of UMP was affected by the interaction of these factors with magnet structure configurations.

Bayesian cell therapy process optimization.

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.

Iterative Reconstruction Algorithms in Magneto-Acousto-Electrical Computed Tomography (MAE-CT) for Image Quality Improvement.

Magneto-acousto-electrical computed tomography (MAE-CT) is a recently developed rotational magneto-acousto-electrical tomography (MAET) method, which can map the conductivity parameter of tissues with high spatial resolution. Since the imaging mode of MAE-CT is similar to that of CT, the reconstruction algorithms for CT are possible to be adopted for MAE-CT. Previous studies have demonstrated that the filtered back-projection (FBP) algorithm, which is one of the most common CT reconstruction algorithms, can be used for MAE-CT reconstruction. However, FBP has some inherent shortcomings of being sensitive to noise and non-uniform distribution of views. In this study, we introduced iterative reconstruction (IR) method in MAE-CT reconstruction and compared its performance with that of the FBP. The numerical simulation, the phantom, and in vitro experiments were performed, and several IR algorithms (ART, SART, SIRT) were used for reconstruction. The results show that the images reconstructed by the FBP and IR are similar when the data is noise-free in the simulation. As the noise level increases, the images reconstructed by SART and SIRT are more robust to the noise than FBP. In the phantom experiment, noise and some stripe artifacts caused by the FBP are removed by SART and SIRT algorithms. In conclusion, the IR method used in CT is applicable in MAE-CT, and it performs better than FBP, which indicates that the state-of-the-art achievements in the CT algorithm can also be adopted for the MAE-CT reconstruction in the future.

Experimental Light Rail Traffic Noise Assessment in a Metropolitan Area

The growth in the utilization and development of rail transport within urban networks is crucial for transitioning towards a more sustainable form of mobility. However, challenges related to discomfort and noise pollution arising from rail traffic must be addressed and mitigated to foster a harmonious coexistence between residents and trains. This study focuses on analyzing an experimental campaign conducted on the surface metropolitan network of Porto to study and identify the frequency content and pressure levels associated with light rail traffic. The presented experimental campaign holds significant relevance as it comprises various and distinct circulation conditions within the railway network, enabling a comprehensive characterization of railway noise. The collected data indicates a noticeable increase in sound pressure levels as the speed of circulation rises, particularly emphasizing the 1/3 octave band centered around 1000 Hz. The choice of tracks with components having a limited capacity for absorbing acoustic energy leads to a significant rise in noise levels compared to track solutions with elements exhibiting excellent acoustic energy absorption. Furthermore, the study highlights a substantial increase in noise levels (10 dBA) associated with small radius curves, even at low speeds. These findings underscore the importance of considering the track characteristics and geometric features in noise assessment within rail networks. Therefore, the insights gained from this experimental campaign contribute significantly to the understanding and comprehensive characterization of railway noise under diverse circulation conditions within the railway network.

Level-adaptive sound masking in the open-plan office: How does it influence noise distraction, coping, and mental health?

In the open-plan office, intelligible speech is a major distractor, reducing cognitive performance. Sound masking emits an electronic broadband sound to increase the background sound level in a controlled manner. To date, most studies on sound masking are short-term laboratory studies that do not consider aspects of mental health. The current study aims to evaluate, using a longitudinal field study with intervention (N = 42) and control (N = 41) floors at two organizations, whether level-adaptive sound masking could reduce intelligible speech and increase mental health, while being exposed to level-adaptive sound masking for two to three months. The study consists of two subjective measurements, prior- and post-intervention, using survey questions on coping strategies, noise distraction, and ten mental health indicators (short-and long-term consequences). The increase in background noise level (at company 1 from 28.7 dB(A) to 41.9 dB(A) and at company 2 from 32.4 dB(A) to 42.6 dB(A)) at both organizations significantly reduced intelligible speech distraction. Short-term mental health aspects were rated more positively, and level-adaptive sound masking also reduced the frequency with which people put on radio or headphones to cope with noise.

Assessing the robustness of a machine-learning model for early detection of pancreatic adenocarcinoma (PDA): evaluating resilience to variations in image acquisition and radiomics workflow using image perturbation methods.

To evaluate robustness of a radiomics-based support vector machine (SVM) model for detection of visually occult PDA on pre-diagnostic CTs by simulating common variations in image acquisition and radiomics workflow using image perturbation methods. Eighteen algorithmically generated-perturbations, which simulated variations in image noise levels (σ, 2σ, 3σ, 5σ), image rotation [both CT image and the corresponding pancreas segmentation mask by 45° and 90° in axial plane], voxel resampling (isotropic and anisotropic),gray-level discretization [bin width (BW) 32 and 64)], and pancreas segmentation (sequential erosions by 3, 4, 6, and 8 pixels and dilations by 3, 4, and 6 pixels from the boundary), were introduced to the original (unperturbed) test subset (n = 128; 45 pre-diagnostic CTs, 83 control CTs with normal pancreas). Radiomic features were extracted from pancreas masks of these additional test subsets, and the model's performance was compared vis-a-vis the unperturbed test subset. The model correctly classified 43 out of 45 pre-diagnostic CTs and 75 out of 83 control CTs in the unperturbed test subset, achieving 92.2% accuracy and 0.98 AUC. Model's performance was unaffected by a three-fold increase in noise level except for sensitivity declining to 80% at 3σ (p = 0.02). Performance remained comparable vis-a-vis the unperturbed test subset despite variations in image rotation (p = 0.99), voxel resampling (p = 0.25-0.31), change in gray-level BW to 32 (p = 0.31-0.99), and erosions/dilations up to 4 pixels from the pancreas boundary (p = 0.12-0.34). The model's high performance for detection of visually occult PDA was robust within a broad range of clinically relevant variations in image acquisition and radiomics workflow.

Interactional aerodynamics and acoustics of a rotor with an airframe in hover

The demand for the development of urban air mobility (UAM) powered by electric systems has been steadily rising across private and public sectors. Most UAM flights incorporate a distributed electric propulsion system to enhance aircraft safety and reliability, which entails an increase in the number of rotors or propellers. Consequently, aerodynamics and aeroacoustics are significantly influenced by strong interactions between the rotor and the airframe. In this study, we conducted a computational investigation to examine the impact of rotor–airframe interaction on aerodynamic and aeroacoustic characteristics. This examination considered variations in airframe shape and the distance between the rotor and airframe. The aerodynamic analysis was executed using the lattice-Boltzmann method simulation, in which acoustic predictions were made using the Ffowcs Williams–Hawkings(FW–H) acoustic analogy with a permeable surface. The airframe consists of two geometries: a cylinder and a cone. Tip vortex breakdown and the transition into the turbulent wake state were captured in both airframes, and a fountain flow was affected by the downwash circulation generated under certain proximity of airframe cases. The acoustic prediction results showed that high-intensity noise radiated over the broad surface of the airframe in the conical airframe case. Significant thrust force fluctuations and an increase in noise level were observed at the smallest rotor tip clearance, S/R=−0.1, compared to the isolated rotor. Furthermore, the noise contribution of the rotor and airframe was compared, revealing that the airframe noise level was even higher than the rotor noise at S/R=−0.1.

Effects of quantum noise on Shor’s algorithm

Shor’s quantum factoring algorithm (Shor’s algorithm) can solve factorization problem of large integers by using a fully-operational quantum computer with the complexity of polynomial-time level, thereby cracking a series of encryption algorithms (such as Rivest-Shamir-Adleman encryption algorithm, and Diffie-Hellman key exchange protocol) whose security is guaranteed by factorizing large integers, which is a difficult problem. We are currently in a noisy intermediate-scale quantum era, which means that we can only operate on quantum computers with a limited number of qubits and we have to take care of the effects of quantum noise. Quantum states on a quantum computer are prone to quantum noise caused by low-fidelity gates or interactions between qubits and the environment, which results in inaccurate measurements. We study the influence of quantum noise on Shor’s algorithm through 3 typical quantum noise channels: the depolarizing channel, the state preparation and measurement channel, and the thermal relaxation channel. We successfully simulate the factorization of the numbers 15, 21, and 35 into their corresponding prime factors by using the quantum circuit we have constructed on a classical computer. Then we simulate a running quantum circuit of Shor’s algorithm in a noisy environment with different level of noise for a certain type of noise channel and present numerical results. We can obtain precise measurements by calculating the state vector prior to measurement, instead of simulating and measuring expending much time, which contributes to higher efficiency. Each experiment is repeated 1000 times to reduce discrepancy. Our research indicates that Shor’s algorithm is easily affected by quantum noise. Successful rate of Shor’s algorithm decreases exponentially with the increase of noise level in the depolarizing channel, where the successful rate is an indicator we propose in this research to quantify the influence of noise on Shor’s algorithm, meanwhile the noise in the state preparation and measurement channel and the thermal relaxation channel can linearly affect the successful rate of Shor’s algorithm. There are <inline-formula><tex-math id="M1">\begin{document}$O(n^4) $\end{document}</tex-math><alternatives><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20231414_M1.jpg"/><graphic xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="5-20231414_M1.png"/></alternatives></inline-formula> quantum gates in the circuit, each of which is disrupted by noise in depolarizing channel during running the circuit, meanwhile there are only <i>O</i>(<i>n</i>) interruptions caused by noise in state preparation and measurement channel since we repeat the measurements only <i>O</i>(<i>n</i>) times in the circuit where <i>n</i> is the number of bits of the integer about to be factored. Linear relationship in thermal relaxation channel is mainly due to the large gap between quantum gate time and relaxation time even if each gate in the circuit is disrupted by noise in thermal relaxation channel such as depolarizing channel. The present research results can be used for correcting the subsequent errors, improving Shor’s algorithm, and providing guidance for the fidelity required in engineering implementation of Shor’s algorithm.

Analysis of the concentration of traffic flows in cities and towns when assessing the potential noise level in residential areas

The paper is aimed at analyzing the concentration of traffic flows that influence the noise pollution nature in the urban environment (using the city of Murom as an example). It has been established that the historically established structure of the city traffic flows, interregional freight traffic through the city, and, especially, the territorial division of the city into two large districts with natural restrictions on transport connections between them, lead to the formation of potentially increased noise level zones. Areas of the city territory where noise level measurements should be carried out were identified. Under these conditions, a conclusion was made about the relevance of creating a noise passport for large and small cities.

Investigating the impact of sample desiccation on Itrax XRF core scanner signal reproducibility

AbstractSediment samples tend to dry out during storage and are, therefore, stored refrigerated at about 4°C after wrapping in plastic foil. During XRF core scanning however, the samples must be taken out of their cover, increasing the risk of drying and formation of desiccation cracks on the surface. Because scan times can often amount to several hours and at highest resolution may take over a day to complete, the core will progressively dry out during scanning. With this study we aim to increase our understanding of how this slow drying of the samples during scanning and storage influences the XRF signal because of changes in water content, sediment surface topography, and the development of small, but slowly expanding cracks in the sediment core. Results show that the desiccation of samples during scanning and storage influence the XRF measurements in several ways. Most importantly, slow desiccation of the cores results in both a general lowering of the sample surface, and a shortening of the core due to shrinkage. Larger distance between sediment surface and detector leads to increased noise levels and poor reproducibility for many elements, while the shrinking of cores may shift individual data points between runs, resulting in poor reproducibility and offsets between datasets obtained at different times. Moreover, the loss of light elements, such as hydrogen and oxygen, can influence the matrix effect, especially for organic‐rich sediment. Because the XRF signals of individual elements are affected to different degrees, these changes may induce artificial shifts and biases in many elemental ratios commonly used for paleoenvironmental reconstruction.

Assessment of working conditions and features of respiratory disorders in modern refractory manufacturing

Introduction. Broad prospects for development of refractory manufacturing in Russia necessitate studies of the work environment, especially of the indoor air quality, and respiratory health effects of adverse occupational factors. 
 The purpose of our study was to conduct a general hygienic assessment of working conditions of the core personnel engaged in contemporary refractory manufacturing for more than 10 years and establish their respiratory functional status.
 Materials and methods. We assessed working conditions of press operators of a Ural refractory manufacturing plant in compliance with Russian Guidelines 
 R 2.2.2006–05. The respiratory functional status was established by the results of clinical instrumental testing of refractory production workers who were examined and/or treated at the occupational health clinic of the Yekaterinburg Medical Research Center for Prophylaxis and Health Protection in Industrial Workers 
 in 2015–2021.
 Results. We gave a general assessment of working conditions of press operators engaged in the production of spinel periclase-carbon and fireclay-dinas molded refractories in the first refractory workshop, aluminosilicate refractories at the molding site, and corundum-graphite products in the second refractory workshop. 
 We analyzed the results of instrumental testing and established characteristics of clinical disorders of the respiratory system.
 Limitations. The study has regional (Sverdlovsk Region) and occupational (workers of a refractory manufacturing plant) limitations.
 Conclusion. Occupational risk factors for refractory production workers include silica-containing aerosols, which levels exceed maximum allowable concentrations (Class 3.1), aerosols of disintegration of aluminum oxides (in refractory workshop No. 2), iron in concentrations below MACs (Class 2), higher concentrations of volatile products of phenol formaldehyde resins (Class 3.1 in terms of phenol and up to 3.4 in terms of formaldehyde), increased noise levels for all jobs (Class 3.2), and heavy physical labour (Classes 3.1–3.3). Features of clinical respiratory disorders in the core personnel (press operators) were scarcity of complaints and physical data, absence of pronounced coniotic (respirable dust-related) X-ray findings and inflammatory (septic and allergic) blood changes. Moderate obstructive airway disorders were observed in 30 % of the examined cases.

Numerical model for vibro-acoustics analysis of tyre-road noise generation caused by speed bumps

Electric vehicles, particularly when driving at low speeds, present a solution to mitigate the impact of road traffic on noise pollution. However, specific urban traffic scenarios still give rise to increased noise levels due to road irregularities such as speed bumps. To address this issue, a model that combines a dynamic analysis using FEM with an acoustic analysis using BEM is developed. The dynamic analysis simulates a quarter vehicle, employing a 3D tyre model equipped with a suspension system, to replicate impacts against obstacles. The resulting tyre vibration serves as a sound source for the study of acoustic propagation. To validate this model, low-speed track tests were conducted using an electric vehicle driving over a speed bump. The instrumentation used was an acoustic camera, which allowed the evaluation of the sound pressure level reached during the tests.

The use of a piece sound absorber to reduce the impact of noise on workers in the dairy industry on the example of industrial enterprises of the Republic of Mordovia

The paper analyzes the working conditions of workers in the dairy industry, which showed that the prevailing harmful production factor is the increased noise level. It has been proven that noise has a negative impact not only on the hearing organs, but also on the whole organism as a whole as a general biological irritant, therefore, reducing noise expansion due to engineering solutions is an urgent task of our time. The article assesses the noise impact on operators of the dairy industry as part of a special assessment of working conditions. The analysis showed that the excess noise level is observed at all workplaces. To protect workers from an increased noise level, a design of a sound-suppressing piece sound absorber is proposed, which is distinguished by high sanitary and hygienic properties. The use of sound-suppressing piece sound absorbers will improve working conditions from a harmful class to an acceptable one.