Noise Reduction Rating Research Articles

Developing and validating a risk assessment method for noise-induced hearing loss in workers

Various risk factors can affect noise-induced hearing loss (NIHL) among employees. This research sought to establish and validate a risk assessment method for NIHL using these risk factors. This cross-sectional research was carried out with 220 workers from a steel manufacturing facility. At first, their demographic characteristics and information related to the studies’ items were gathered by the researcher-made questionnaire. In the next step, the noise exposure values of the participants were measured by the sound pressure level meter based on the ISO 9612 standard. Moreover, a pure tone audiometric test of bone and air conduction was used to measure hearing loss in people. Ultimately, a new index for risk assessment was created. Indirect effect coefficients of individual factors such as work experience, age, smoking, and effective diseases were 0.266, 0.227, 0.056, and 0.064, respectively. The coefficients of noise exposure items including occupational noise and leisure noise were obtained as 0.687 and 0.660. The personal protective equipment (PPE) items including use of PPE, noise reduction rating of PPE, and awareness of PPE were 0.194, 0.147, and 0.127, respectively. These coefficients were utilized to create a new index. The overall score of the index was divided into four categories using optimal cut-off values of 4.85, 6.84, and 7.59. According to the findings, the OHLRA methods were able to account for 74 percent of the hearing loss values. The results showed that the novel index for risk assessment had proper validity in the prediction of NIHL.

Measured outdoor-indoor noise reduction through various open window sizes

Quantifying the effect of window openings on composite noise reduction of building façades is useful for evaluation with respect to noise regulations that prescribe indoor sound level limits with open window conditions. There are historical documents that indicate overall sound level reductions of open and closed windows, but there is little data available in octave band or one-third octave band resolution, or on the effect of the opening size. A series of Outdoor-Indoor Noise Reduction measurements were performed on a residential façade with closed-window and various open-window configurations. The test room was normally furnished with an approximate room volume of 64 m3. The open window areas ranged from approximately 1 to 13% of the total façade area. In addition to presenting one-third octave band results, the overall noise reduction ratings were computed using the reference sound spectrum from ASTM E1332. The overall noise reduction results show a trend of higher reduction with smaller open window area as expected. The results of the measurements also show that the slope of the trend varies with frequency.

Improved GRACE‐FO Gravity Field Solution by Combining Different Accelerometer Transplant Products

AbstractGravity field solutions determined from the Gravity Recovery and Climate Experiment Follow‐on (GRACE‐FO) are affected by the unexpected performance degradation of one accelerometer onboard, which is circumvented by the accelerometer data transplant (ACT) technique. Three operational ACT products are presently available: JPL‐ACT and JPL‐ACH from NASA Jet Propulsion Laboratory (JPL) and TUG‐ACT from Graz University of Technology (TUG). Considering the heterogeneous qualities of individual ACT products, we propose to combine them to improve the gravity field models after a comprehensive assessment of individual products using a consistent Level‐1B data processing. The combination is carried out on the normal equation of gravity field solutions. Spectral, temporal, and spatial evaluations over the period October 2018 to December 2022 demonstrate that the combined solution significantly reduces the noise relative to solutions using individual ACT products, and the average noise reduction rates over JPL‐ACT range from 5% to 12% with varying post‐processing filters and C20 and C30 coefficient treatments, which are double those of the second‐best JPL‐ACH. Moreover, the C20 estimate exhibits significant improvement in the combined solution with its deviation from the satellite laser ranging (SLR) solution showing a root mean square (RMS) value of 7.6 × 10−11, compared to similar RMS values of 15.9 × 10−11, 12.1 × 10−11, and 14.9 × 10−11 for JPL‐ACT, JPL‐ACH, and TUG‐ACT, respectively. This improvement helps greatly reduce the amplitude of the 161‐day spurious signal over polar regions. For C30, the JPL‐ACT gives degraded estimates with an RMS of differences to SLR of 6.2 × 10−11, while the differences are greatly reduced to 2.6 × 10−11, 2.7 × 10−11 and 3.6 × 10−11 for the combined solution, JPL‐ACH and TUG‐ACT, respectively, approaching the formal error of SLR estimates.

Semi-active vibro-acoustic control of vehicle transmission systems using a metal rubber-based isolator

This work is concerned about employing a semi-active metal rubber (MR)-based isolator to suppress noise and vibration for vehicle transmission systems. The adaptive stiffness and damping properties of MR material play a key role in the semi-active control effect. In this regard, a particular automotive transmission component—precisely, a plate dedicated to accommodating bearings—has been selected as the focus of research to control its noise and vibration. A microcomputer-driven control system is applied, featuring characteristics such as high performance, compact size, lightweight design, and energy efficiency, which make it ideal for testing in the automotive sector. The efficacy of the devised control scheme is assessed through the deployment of accelerometers and sound intensity probes to quantify the vibrations and the radiated acoustic power, respectively. Interesting noise and vibration reduction rates are achieved using both open-loop and closed-loop strategies. This research opens the horizons to employ MR material in a semi-active configuration for industrial requirements surpassing its classical passive implementation, notably in terms of broadband attenuation performance.

The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke.

Computed tomography perfusion (CTP) and computed tomography angiography (CTA) are valuable tools for diagnosing acute ischemic stroke (AIS). It is essential to obtain high-quality CTP and CTA images in short time. This study aimed to evaluate the image quality and diagnostic performance of brain CTP and CTA images generated from CTP reconstructed by a deep learning image reconstruction (DLIR) algorithm on patients with AIS. The study prospectively enrolled 54 patients with suspected AIS undergoing non-contrast CT and CTP within 24 hours. CTP datasets were reconstructed with three levels of adaptive statistical iterative reconstruction-Veo algorithm [ASIR-V 0% with filtered back projection (FBP), ASIR-V 40%, and ASIR-V 80%] and three levels of DLIR, including low (DLIR-L), medium (DLIR-M), and high (DLIR-H). CTA images were generated using the CTP datasets at the peak arterial phase. Objective parameters including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and noise reduction rate. Subjective evaluation was assessed according to Abels scoring system. Perfusion parameters and detection accuracy for infarction core lesions were evaluated. The objective and subjective image quality of CTA images were also evaluated. All reconstructions produced similar CT values (P>0.05). With the increase of ASIR-V and DLIR reconstruction strength, image noise decreased, while SNR and CNR increased for CTP images, especially in white matter. DLIR-H, DLIR-M, and ASIR-V80% yielded higher subjective scores than did ASIR-V40% and FBP. DLIR-H provided the highest noise reduction rate and detection accuracy. No significant difference was found in conventional parameters, the volume of infarct core, or ischemic penumbra among the 6 groups (P>0.05). The objective evaluation of reconstructed CTA images was comparable in DLIR-H, DLIR-M, and ASIR-V80% (P>0.05). The subjective scores of the DLIR-H and DLIR-M images were higher than those of the other groups, especially ASIR-V40% and FBP (P<0.05). Compared with FBP and ASIR-V40%, DLIR-H, DLIR-M, and ASIR-V80% improved the overall image quality of CTP and CTA images to varying degrees. Furthermore, DLIR-H and DLIR-M showed the best performance. DLIR-H is the best choice in diagnosing AIS with improved detection accuracy for cerebral infarction. Reconstructing CTA images using CTP datasets could reduce contrast agent and radiation dose.

Optimization Research on the Space-V-Type Biomimetic Surface Grooves of a Marine Centrifugal Pump

The biomimetic surface with Space-V grooves can effectively reduce flow resistance and noise. Our investigation was in order to further enhance the drag reduction and noise reduction performance of a marine centrifugal pump with Space-V-groove-shaped biomimetic surfaces. A regression equation was established with response surface methodology between the total sound pressure level and the height (h), width (s), and spacing (b) of the biomimetic groove structure. The interaction effects of various parameters on the total sound pressure level were analyzed, and the parameter range was determined at the lowest total sound pressure level. The hydraulic performance and interior noise of the model before and after optimization were compared. The results showed that the total sound pressure level initially decreased and then increased with increasing groove height. Similarly, with an increase in groove width, the total sound pressure level decreased at first, then increased. When the height of the bionic groove is 0.5–0.7 mm, the groove width is 0.4–0.7 mm, the groove spacing is 0.7–1.3 mm, and the total sound pressure level of the centrifugal pump is the smallest, which is 180–182 dB. On the other hand, the total sound pressure level increased as groove spacing increased. Through the use of an optimized Space-V groove model, under rated working conditions, the model head is increased by 0.27 m and the efficiency is increased by 1.21%. In addition, the optimized model has excellent drag and noise reduction performance, with the drag reduction rate of 3.73% and noise reduction rate of 1.81%, which are, respectively, increased by 0.87% and 0.45% compared with before optimization. The performance of centrifugal pumps for ships can be greatly improved.

Protecting beyond the bone-conduction limit: Lessons learned developing and fielding a passive Hearing Protection Helmet

Traditional hearing protection devices focus on attenuating air-conducted sound and are limited by the bone conduction threshold (Berger, 1983). In extreme noise fields, such as aircraft maintainers operating near jet engines on aircraft carriers, the bone conducted pathway carries sufficient energy to damage the cochlea. Here, we describe the development, qualification, and field-testing of a passive Hearing Protection Helmet (HPH) designed to surpass the bone conduction limit. The HPH features a noise-isolating shell sealed to the head via a compliant edge seal. When worn with foam earplugs and tested to ANSI S12.6-2016 (Experimenter Fit), the Noise Reduction Rating (NRR) of the HPH is 38 dB and the Noise Reduction Statistic (NRSA) (ANSI S12.68) (80% to 20% of users) is 45 to 52 dB. We also describe challenges associated with user acceptance of the HPH. User feedback led to the integration of an electronic hear-through system to restore auditory situational awareness. Users also rejected wired communication-enabled ear plugs, resulting in the integration of ear cup speakers and adding challenges associated with setting the hear-through sound pressure level limits. We conclude with anecdotes from trial deployments aboard U.S. Navy Aircraft Carriers in 2020, 2022, and 2023.

Occupational noise level exposures outside and inside agricultural tractor cabs

The farm tractor fleet in the Brazilian market has increased. They are machines known to emit much noise. Noise can cause severe damage and disturbance in people who work directly with and around these machines. Therefore, the work aimed to evaluate the noise levels emitted by two cab farm tractors of different engine power, analyzing the effect of engine speed and distance radius from the noise source on occupational health. The noise level outside and inside of the tractor cab, the attenuation index, and the noise level reduction provided by the cab sound insulation were determined. Attenuation indexes and noise reduction ratings were satisfactory in studying the noise levels inside and outside the tractor cab. The tractor with the most power in the engine has better sound insulation compared to those lowest power. The noise levels that operators are exposed to in the cab were lower than the limits established by Brazilian government laws. On the other hand, outside the cab, the noise levels emitted by the tractor of greater power are not safe for the workers' health if they are at a distance radius from the noise source of less than 7 m, requiring the use of an ear protector.

Deep learning image reconstruction for quality assessment of iodine concentration in computed tomography: A phantom study.

Recently, deep learning reconstruction (DLR) technology aiming to improve image quality with minimal radiation dose has been applied not only to pediatric scans, but also to computed tomography angiography (CTA). To evaluate image quality characteristics of filtered back projection (FBP), hybrid iterative reconstruction [Adaptive Iterative Dose Reduction 3D (AIDR 3D)], and DLR (AiCE) using different iodine concentrations and scan parameters. Phantoms with eight iodine concentrations (ranging from 1.2 to 25.9 mg/mL) located at the edge of a cylindrical water phantom with a diameter of 19 cm were scanned. Data were reconstructed with FBP, AIDR 3D, and AiCE using various scan parameters of tube current and voltage using a 320 row-detector CT scanner. Data obtained using different reconstruction techniques were quantitatively compared by analyzing Hounsfield units (HU), noise, and contrast-to-noise ratios (CNRs). HU values of FBP and AIDR 3D were constant even when the iodine concentration was changed, whereas AiCE showed the highest HU value when the iodine concentration was low, but the HU value reversed when the iodine concentration exceeded a certain value. In the AIDR 3D and AiCE, the noise decreased as the tube current increased, and the change in noise when the iodine concentration was inconsistent. AIDR 3D and AiCE yielded better noise reduction rates than with FBP at a low tube current. The noise reduction rate of AIDR 3D and AiCE compared to that of FBP showed characteristics ranging from 7% to 35%, and the noise reduction rate of AiCE compared to that of AIDR 3D ranged from 2.0% to 13.3%. The evaluated reconstruction techniques showed different image quality characteristics (HU value, noise, and CNR) according to dose and scan parameters, and users must consider these results and characteristics before performing patient scans.

Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.

To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.

Quadratic-Kalman-Filter-Based Sensor Fault Detection Approach for Unmanned Aerial Vehicles

Sensors are crucial for the control of unmanned aerial vehicles (UAVs). However, sensor faults will inevitably appear over time. Therefore, it is important to develop a sensor fault detection approach for the reliability of UAV. This article presents a novel model-based UAV fault detection approach based on quadratic Kalman filter (QKF). First, an accurate kinematic and dynamic model of UAVs is established, where the model is linearized and discretized for Kalman filter (KF). Second, the first KF is used for denoising, the secondKF is used to detrend, and residuals are calculated for detection. It is worth mentioning that the second KF is a modified Sage–Husa adaptive KF, which can automatically estimate the measurement noise variance. Compared with traditional approaches, this approach has the advantages of noise reduction, self-adaptation, divergence avoidance, and high detection rate. Simulation and experimental results show the effectiveness of the proposed approach, which can accurately detect the abrupt and incipient fault of an inertial measurement unit (IMU) sensor. At the same time, it can get the higher fault detection rates (FDRs) compared with conventional KF. Furthermore, this approach also provides the leading information and foundation for UAV fault-tolerant control.

Sound absorption property of hierarchical three-dimensional flat-knitted spacer fabric with microfiber

The harm of sound pollution to human health is nonnegligible. To construct a good sound absorbent fabric, we proposed a combination of flat-knitting and microfibers, which is a simple and effective way to prepare a hierarchical three-dimensional spacer fabric with macroscopic and microscopic pore structure. The effects of spacer layer structure, surface yarn thickness and sea islands composite filaments split durations on sound absorption property were investigated. The sound absorption property was evaluated by the sound absorption coefficient and noise reduction rate. The results show that the sound absorption property of the spacer fabric is improved by splitting, especially under the middle frequency range (1000–3000 Hz). What is more, the noise reduction rate of our layered hierarchical spacer fabric increased by 4.1% with the thickness decreased by 21.1% compared with a recently reported hierarchical porous sound absorbent fabric.

Personal attenuation ratings versus derated noise reduction ratings for hearing protection devices.

National and international regulatory and consensus standards setting bodies have previously proposed derating hearing protector ratings to provide a better match between ratings determined in a laboratory and the real-world measurements of attenuation for workers. The National Institute for Occupational Safety and Health has proposed a derating scheme that depends upon the type of protector. This paper examines four real-world studies where personal attenuation ratings (PARs) were measured at least twice, before and after an intervention in earplug fitting techniques. Results from these studies indicate that individualized earplug fitting training dramatically improves a worker's achieved PAR value. Additionally, derating schemes fail to accurately predict the majority of achieved PARs. Because hearing protector fit testing systems are now readily available for use in the workplace, personal attenuation ratings provide a better estimate of worker noise exposures and are able to identify those persons who need additional instruction in fitting hearing protection devices.

Improvement of natural fiber's properties and evaluation of its applicability as eco-friendly materials in noise pollution control.

The use of green and eco-natural fibers due to availability, biodegradability, reasonable cost, and non-toxic effects are known as potential acoustic materials in research. Nevertheless, in this area, the use of chemical treatment in natural fibers as an effective strategy to improve the overall properties and acoustic performance is faced with limitations. So that the current paper aims to investigate the effect of alkali treatment on the mechanical and acoustical properties of jute fibers and evaluation of its applicability in noise pollution control. To determine the effect of alkali treatment on the sound absorption behavior of jute fibers, a sufficient amount of both types of fibers (raw and treated) were used to the fabrication of acoustic samples with thicknesses of 30, 40 and 50mm at a density of 200kg/m3. The acoustic evaluation was measured by the Impedance tube system according to transfer function method using by the standard ISO 10534-2. The morphological and tensile properties of fibers were evaluated by the Scanning Electron Microscopy (SEM) and tensile test (ASTM C1557-14 standard). Moreover, Infrared Spectroscopy (FTIR) and X-ray diffraction (XRD) were used to study and compare the chemical properties of raw and treated fibers. The result showed that the mean tensile strength and crystallinity index (CI) of treated fibers comparing with raw fibers increased by 61.66% and 3.26% respectively. The use of Alkali treatment helped to improve sound absorption performance of jute fibers with different thicknesses. Furthermore, noise reduction rate (NRC) in treated acoustic samples compared to untreated with a thickness of 50mm increased from 0.66 to 0.69. Finally, the alkali treatment has enhanced the properties of jute fibers and confirmed the applicability of these fibers in acoustic absorption. The online version contains supplementary material available at 10.1007/s40201-022-00799-x.

Impact of deep learning-based image reconstruction on image quality compared with adaptive statistical iterative reconstruction-Veo in renal and adrenal computed tomography.

OBJECTIVE: To evaluate image quality of deep learning-based image reconstruction (DLIR) in contrast-enhanced renal and adrenal computed tomography (CT) compared with adaptive statistical iterative reconstruction-Veo (ASiR-V).METHODS: We prospectively recruited 52 patients. All images were reconstructed with ASiR-V 30%, ASiR-V 70%, and DLIR at low, medium, and high reconstruction strengths. CT number, noise, noise reduction rate, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated within the region of interest (ROI) on subcutaneous fat, bilateral renal cortices, renal medulla, renal arteries, and adrenal glands. For qualitative analyses, the differentiation of the renal cortex and medulla, conspicuity of the adrenal gland boundary, sharpness, artifacts, and subjective noise were assessed. The overall image quality was calculated on a scale from 0 (worst) to 15 (best) based on the five values above and the score≥9 was acceptable.RESULTS: CT number does not significantly differ between the reconstruction datasets. Noise does not significantly differ between ASiR-V 30% and DLIR-L, but it is significantly lower using ASiR-V 70%, DLIR-M, and DLIR-H. The noise reduction rate relative to ASiR-V 30% is significantly different between the DLIR groups and ASiR-V 70%, and DLIR-H yields the highest noise reduction rate (61.6%). SNR and CNR are higher for DLIR-M, DLIR-H, and ASiR-V 70% than for ASiR-V 30% and DLIR-L. DLIR-H shows the best SNR and CNR. The overall image quality yields the same pattern for DLIR-H, with the highest score. Percentages of cases with overall image quality score≥9 are 100% (DLIR-H), 94.23% (DLIR-M), 90.38% (ASiR-V70%), 67.31% (DLIR-L), and 63.46% (ASiR-V30%), respectively.CONCLUSIONS: DLIR significantly improved the objective and subjective image quality of renal and adrenal CTs, yielding superior noise reduction compared with ASiR-V.

Methods for validation of electromechanical evaluation of hearing protection

Preventing hearing injury while maintaining situational awareness is of critical importance while using hearing protection devices (HPDs). Determining the optimal HPD for such varied conditions as operating machinery, riding in aircraft, and using firearms is a non-trivial task. Currently, the Noise Reduction Rating (NRR) is the only required HPD specification; other important characteristics of advanced HPDs are not reported in a standardized manner despite the availability of relevant test standards. Barriers to standardized performance reporting are the use of human subjects and the range of performance capabilities of commercially available HPDs. To complement existing test standards and support standardization of performance reporting across a broader range of performance capabilities, a suite of quantitative, sensor-based tests is being developed to quickly, inexpensively, and comprehensively evaluate candidate HPDs. The five-test battery evaluates signal quality, sound localization, level-dependent frequency response, electronic self-noise, and impulse noise response. In parallel, a set of human subject tests is being developed to validate the electromechanical metrics. Here, we will present (1) preliminary results comparing human performance with complementary electromechanical data and (2) a software tool under development incorporating the electromechanical tests to optimize HPD selection for application-specific hearing protection.

A modified filtered‐x least mean square/fourth algorithm using weighted reference signal for active noise control

AbstractFiltered‐x least mean square (FxLMS) algorithm and its variants have been prevalently applied in the field of active noise control. However, these algorithms still suffer from relatively low convergence rates and steady‐state noise reduction. In this article, we propose a new idea to improve the performance of the conventional filtered‐x least mean square/fourth (FxLMS/F) algorithm by weighting reference signal samples, thus a weighted reference signal FxLMS/F (WRS‐FxLMS/F) algorithm is presented. Besides, to solve the contradiction between convergence speed and steady‐state noise reduction, the convex combination method is combined with the WRS‐FxLMS/F algorithm, and a convex combination WRS‐FxLMS/F (CWRS‐FxLMS/F) algorithm is developed. The computational complexity of the proposed algorithms is analyzed, and the simulation results demonstrate that the proposed algorithms have better performance both in the steady‐state noise reduction and convergence rate than the traditional FxLMS, NFxLMS, and FxLMS/F algorithm.

Неразрушающий контроль стальных тосов с использованием оптимизированного метода опорных векторов

In this paper, to address the problems of poor signal noise reduction and low recognition rate in wire rope leakage magnetic detection, we propose the algorithm MSVDW, which uses a combination of median filtering, singular value decomposition (SVD) and wavelet transform, to denoise the collected three-dimensional MFL signals. Then, false color is used to enhance the image. The image is then localized and segmented using the modulus maximum method. The color moments are extracted from the images and used as the input of the particle swarm algorithm optimized support vector machine (PSO-SVM) for training and recognition. The experimental results show that the noise reduction algorithm proposed in this paper effectively reduces the noise of the leakage signal, the false color image enhances the defect image information, and the algorithm of PSO-SVM greatly improves the recognition rate of defects.

Technical Note: Scanner dependence of adaptive statistical iterative reconstruction with 3D noise power spectrum central frequency and noise magnitude ratios.

In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed. The homogeneous module of the ACR CT phantom was scanned on the 64 and 256slices CT scanners from the same vendor in the range of 15-40mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFRxy ), CFR in the z-direction (CFRz ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFRxy and CFRz should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction. When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0mGy, but there was no observable dose-dependence on the 64-slice. Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.

Verifying earplug attenuation and evaluating the effectiveness of one-on-one training along with earplug fit testing at nine facilities in China.

The purpose of this study was twofold: (1) to measure the personal attenuation ratings (PARs) in Chinese workers wearing hearing protection devices (HPDs), to evaluate the effectiveness of the single number rating (SNR), the noise reduction rating (NRR), and the associated derated values of earplugs; and (2) to evaluate the effectiveness of one-on-one training along with earplug fit testing on PAR improvement. Noise exposure measurements, one-on-one training, and fit tests to measure earplug attenuation were conducted at nine manufacturing facilities located in eastern China from 2016 to 2017. 503 workers participated in the study. Ninety-three percent were male. 199 workers were provided one-on-one training. Before training, 14% and 15% of the workers achieved the attenuation predicted by the manufacturer's SNR and NRR, 56% and 65% exceeded the derated SNR and NRR, respectively. Following one-on-one training, mean PAR improved significantly by 15 dB (p < 0.01); 26% of the workers achieved SNR and NRR, 79% and 91% met the associated derated values, respectively. Labeled noise attenuation ratings and their derated values overestimated the actual level of attenuation among workers wearing HPDs. One-on-one training along with earplug fit testing contributed to improved PARs.