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Purpose: The aim of this study was to measure individual exposure to noise and its effect on the levels of lipid peroxidation and enzymatic antioxidant defense. Methods: In this cross-sectional study, 94 male workers working in a food factory in Shahroud, Iran, were selected as the case group and 112 people were also included as the control group. The level of exposure to noise was measured using a dosimeter and the equivalent level was calculated. The levels of malondialdehyde (MDA), catalase (Cat), superoxide dismutase (SOD), and Total Antioxidant Capacity (TAC) were measured in the serum samples of all participants. Independent t test, one-way ANOVA, and multivariate linear regression were used to analyze the data. Results: The levels of MDA, Cat, and TAC were significantly higher in staff exposed to noise than in the controls. The starch and warehouse units' staff were exposed to the highest (99.3 ± 3.23 dB) and lowest (77.1 ± 9.68 dB) mean levels of sound pressure. Based on the linear regression model, noise exposure level was the most important predictor variable for levels of MDA (β = 0.48, CI 95% = 0.04–0.93), Cat (β = 0.43, CI 95% = 0.05–0.83), and TAC (β = 0.11, CI 95% = 0.06–0.16). Conclusions: Noise exposure increases the production of free radicals. As exposure rate increases, the levels of MDA, Cat, and TAC enzymes increase as well. [ABSTRACT FROM AUTHOR]

The low-frequency noise generated by wind turbines is known as one of the risk factors for health. The aim of this study was to study the noise effect of wind turbine on the general health of staff at Manjil wind farm. For this purpose, workers were divided into three groups: maintenance, security, and office staff. Equivalent sound levels were measured for each group. Individual's health data were assessed using the 28-item General Health Questionnaire. Pearson correlation, analysis of variance, and multiple regression tests were used for data analysis in the R software. Statistical analysis results showed that the noise exposure is significantly correlated to all sub-scales of general health, except for depression. The low-frequency noise from wind turbines can cause harmful effects on the health of workers that are too close to the wind turbine and receive very intense noise. [ABSTRACT FROM AUTHOR]

This paper introduces two new techniques to enhance both efficiency and signal bandwidth in delta–sigma-based transmitters. At first step, a technique called quantization noise reduction (QNR), is introduced to enhance the coding efficiency. By filtering out part of the quantization noise in the whole band of the signal, while the signal envelope is maintained almost constant, the coding efficiency is improved without imposing any additional nonlinearity or distortion to the system. By utilizing this technique for an orthogonal frequency division multiplexing (OFDM) signal with 1.25-MHz bandwidth and 80 times oversampling, with 8.1-dB peak-to-average power ratio (PAPR), the coding efficiency is improved from 8.8% to 14.5% while the signal-to-noise distortion ratio (SNDR) of the system remains 43 dB. In the next step by using a controlled filtering on in-band quantization noise along with QNR technique, the bandwidth of the signal and efficiency are increased simultaneously without losing as much linearity. The second technique is called quantization noise reduction with in-band filtering or (QNRIF). QNRIF is applied on an OFDM signal with 1.25-MHz bandwidth, with the same PAPR and only 16 times oversampling. The result for the coding efficiency is improved from 7.7% to 18.7% with 41-dB SNDR. [ABSTRACT FROM AUTHOR]

The affordable grid resolutions in conventional large-eddy simulations (LESs) of high Reynolds jet flows are unable to capture the sound generated by fluid motions near and beyond the grid cut-off scale. As a result, the frequency spectrum of the extrapolated sound field is artificially truncated at high frequencies. In this paper, a new method is proposed to account for the high frequency noise sources beyond the resolution of a compressible flow simulation. The large-scale turbulent structures as dominant radiators of sound are captured in LES, satisfying filtered Navier-Stokes equations, while for small-scale turbulence, a Kolmogorov's turbulence spectrum is imposed. The latter is performed via a wavelet-based extrapolation to add randomly generated small-scale noise sources to the LES near-field data. Further, the vorticity and instability waves are filtered out via a passive wavelet-based masking and the whole spectrum of filtered data are captured on a Ffowcs-Williams/Hawkings (FW-H) surface surrounding the near-field region and are projected to acoustic far-field. The algorithm can be implemented as a separate postprocessing stage and it is observed that the computational time is considerably reduced utilizing a hybrid of many-core and multi-core framework, i.e. MPI-CUDA programming. The comparison of the results obtained from this procedure and those from experiments for high subsonic and transonic jets, shows that the far-field noise spectrum agree well up to 2 times of the grid cut-off frequency. [ABSTRACT FROM AUTHOR]