Noise Threshold Research Articles

ANN-based averaging scheme for damage detection of high-rise buildings under noisy conditions

Structural health monitoring (SHM) is crucial for assessing the condition of deteriorated high-rise buildings subjected to sudden and hazardous loads. This study proposes a novel averaging scheme to enhance the performance of an existing hybrid damage detection technique based on Artificial Neural Networks (ANNs). The proposed technique is validated numerically on a 30-storey building. The objective is to address the discrepancy between noise levels used during training and those present in generated modal data, thus mitigating the impact of measurement noise on damage predictions. By employing a series of ANNs trained with varying noise levels, a diverse range of predictions is obtained. These predictions are averaged to yield decisive conclusions, even when indecisive predictions outnumber decisive ones. This averaging scheme effectively reduces the influence of random noise, particularly when there is a notable disparity between the actual noise levels in measured data and statistical networks. Moreover, this study investigates the impact of the number of measurements on noise reduction, recommending approximately 100 measurements, in line with other experimental studies. Through the integration of the averaging scheme and increased measurement numbers, the ANN-based damage detection technique achieves remarkable accuracy in damage detection. Storey-level detection can be achieved when the noise levels in mode shapes reach up to 3.5 %. Additionally, the approach exhibits promising results in detecting damaged walls, with a noise threshold of up to 3 %. For damaged columns, a more modest threshold of 0.75 % suffices for light and complex damage scenarios.

Limits of three‐dimensional target detectability of logging while drilling deep‐sensing electromagnetic measurements from numerical modelling

AbstractSubsurface energy resources are often found in three‐dimensional and non‐spatially continuous rock formations that exhibit electrical anisotropy. Deep‐sensing tri‐axial borehole electromagnetic measurements are currently being used to detect three‐dimensional fluid‐bearing subsurface formations, but borehole environmental, geometrical and instrument‐design factors, together with measurement noise, constrain their practical range of detection and spatial resolution. By understanding the interplay of the above factors on the detectability and sensitivity of borehole deep electromagnetic measurements, one can potentially quantify the uncertainty of both target spatial location (relative to the well trajectory) and electrical resistivity contrast, thereby improving the certainty of three‐dimensional well navigation in real time. We implement a finite‐volume method to numerically solve Maxwell's equations for three‐dimensional electrically anisotropic heterogeneous rock formations in the calculation of magnetic fields measured with deep‐sensing tri‐axial borehole electromagnetic instruments. The calculated magnetic fields at measurement locations are described as the per cent difference between measurements acquired in rock formations with and without conductive or resistive three‐dimensional targets. We quantify the maximum radial distance of detection from the well trajectory and the spatial sensitivity of a commercially available deep‐sensing electromagnetic instrument with respect to environmental factors and measurement acquisition parameters by assuming that the borehole electromagnetic instrument can reliably detect offset three‐dimensional targets if the corresponding per cent measurement difference exceeds the threshold for measurement noise. Results indicate that commercially available tri‐axial deep‐sensing borehole electromagnetic instruments can achieve maximum detection distances between a quarter of and a full transmitter–receiver spacing. In addition, we show that radial detection distances vary from 0.3 to 2 skin depths depending on the geological environment, that is, target conductivity contrast with respect to the embedding background, electrical anisotropy of the background formation and targets and measurement acquisition parameters, that is, frequency and transmitter–receiver spacings. The above findings are not only important for instrument design and measurement‐acquisition planning but also for the effective implementation of real‐time inversion‐based measurement interpretation procedures.

Stereo Matching Algorithm of Multi-Feature Fusion Based on Improved Census Transform

This article proposes an improved stereo matching algorithm in order to address the issue that the conventional Census transform is overly dependent on the center pixel of the window, which makes the algorithm susceptible to noise interference and results in low matching accuracy in regions with weak texture and complex texture. In the cost calculation stage, the noise threshold is set utilizing the absolute difference detection approach, and pixels that exceed the threshold are replaced with the mean gray values of the neighboring pixels in the 3 × 3 window. This stage also includes the introduction of the gradient cost, which is coupled with the edge and feature point information to provide the final matching cost. The cross approach is employed to build the adaptive support domain and aggregate the costs during the cost aggregation stage. The disparity is finally calculated using the WTA technique, and a multi-step refinement process is employed to produce the final disparity map. The experiments demonstrate that the proposed algorithm has good anti-noise performance. Compared with other improved algorithms or composite algorithms, the average matching rate of the four standard images on the Middlebury test platform is 5.53%, which is higher than the remaining algorithms, indicating that the matching accuracy is high. The proposed algorithm provides ideas for subsequent improved algorithms.

Abstract 18317: Implications of Noise on Deep Learning Models for 12-Lead ECG Construction

Introduction: ECG construction with deep learning can help standardize ECGs and remove noise and artifacts, transforming potentially unusable ECGs into clinically useful ones. However, noise levels, often unpredictable in clinical settings, may impact model performance. Hypothesis: This study explores the relationship between ECG noise levels and deep learning model performance in constructing noise-free ECGs. We hypothesize that beyond a certain noise threshold, the model's output ceases to be clinically usable. Aims: Our study aims to determine noise effects on the performance of deep learning models in constructing 12-Lead ECGs, providing insights into the model's robustness and the optimal conditions for its application. Methods: Using 250 digital 12-Lead ECGs from the PTB database, we injected Gaussian noise (mean 0.0, standard deviations between 0.0 and 1.0) into these ECGs, all processed and scaled to unitless values (-1 to +1) and resampled at 100Hz. A model was asked to construct a noise-free ECG from a full 12-lead ECG with noise injection, which was compared to the original noise-free ECG. Results: The findings reveal two distinct linear phases in the relationship between noise and model performance (measured by mean absolute error, MAE). Between standard deviations of 0.0 and 0.03, the MAE increased marginally, while a sharp increase occurred between 0.03 and 1.0. Importantly, critical features of the original ECG were retained for noise levels up to 0.08, implying a noise threshold for clinical usability. Conclusions: Deep learning models, though not entirely resistant to noise, demonstrate efficacy in constructing ECGs up to certain noise levels (standard deviation 0.03). Beyond this, performance declines markedly. These findings outline the limits and potential of deep learning models like ECGio in clinical practice. Future research should focus on resilience to higher noise levels or noise-reduction preprocessing strategies.

A smart sewer detection approach based on wavelet denoising of in-sewer temperature sensing measurement

Urban sewer detection is important for the proper conveyance of sanitary water to wastewater treatment plant prior to environmental discharge. An effective approach to address this important process still needs to be developed. This study introduced a novel data-driven approach to sewer detection utilizing in-sewer distributed temperature sensing (DTS) measurement combined with wavelet-based denoising of DTS data. It underlines that the effective denoising of DTS data, and consequently the accurate determination of DTS noise threshold, is pivotal to reliable sewer detection. DTS background noise is chiefly influenced by the threshold rescaling. A reliable DTS background noise threshold was found to be ±0.25 °C in a field study, established with the threshold rescaling of a level-dependent estimation of level noise, and the associated threshold selection rule of heuristics threshold or minimum maximum variance. Deviation from this threshold could hamper the identification of true inflow or infiltration points. Applying the established threshold to the study site, our study identified two sewer problematic points including a groundwater infiltration point, and a clean water inflow point based on generated three-value image. Further interpretation of the three-value image revealed that both groundwater infiltration and clean water inflow into the sewer exhibited intermittent instead of constant behavior, which was due to time-variable water head difference associated with sewage discharge variation over the daily period and rainfall events. Thus, the methodology offers considerable potential for urban sewer detection, especially for its performance to capture intermittent sewer infiltrations and inflows without draining sewers.

Application of EMD combined with wavelet algorithm for filtering slag noise in steel cord conveyor belt

This study proposes a noise reduction technique that combines empirical mode decomposition (EMD) with wavelet thresholding to remove noise interference generated by metal slag in the damage signal of steel cord conveyor belts. The initial step involves decomposing the original signal into high-frequency and low-frequency intrinsic mode function (IMF) components using empirical mode decomposition (EMD), guided by the metal slag signal characteristics analysis. The high-frequency noise is filtered away while the low-frequency IMF components are kept intact through the wavelet threshold de-noising technique. The slag noise-containing IMF components are deleted, and the filtered signal and the low-frequency IMF components are utilized for signal reconstruction to provide the optimum noise reduction result. The study demonstrates that this method is more effective in suppressing metal slag noise, environmental noise, and other noise interferences than traditional EMD decomposition noise reduction and wavelet threshold noise reduction methods. This research provides a valuable reference for noise reduction in steel cord conveyor belt detection data.

Impact of Prematurity on Auditory Processing in Children.

Prematurity is one of the most crucial risk factors negatively affecting the maturation of the auditory system. Children born preterm demonstrate high rates of hearing impairments. Auditory processing difficulties in preterm children might be a result of disturbances in the central auditory system development and/or sensory deprivation due to peripheral hearing loss. To investigate auditory processing in preterm children, we utilized a set of psychoacoustic tests to assess temporal processing and speech intelligibility. A total of 241 children aged 6-11 years old (136 born preterm and 105 healthy full-term children forming the control group) were assessed. The preterm children were divided into three groups based on their peripheral hearing status: 74 normal hearing (NH group); 30 children with bilateral permanent sensorineural hearing loss (SNHL group) and 32 children with bilateral auditory neuropathy spectrum disorder (ANSD group). The results showed significantly worse performance in all tests in premature children compared with full-term children. NH and SNHL groups showed significant age-related improvement in speech recognition thresholds in noise that might signify a "bottom-up" auditory processing maturation effect. Overall, all premature children had signs of auditory processing disorders of varying degrees. Analyzing and understanding the auditory processing specificity in preterm children can positively contribute to the more effective implementation of rehabilitation programs.

A multi-physics approach for modelling noise mitigation using an air-bubble curtain in impact pile driving

Underwater noise from offshore pile driving has raised significant concerns over its ecological impact on marine life. To protect the marine environment and maintain the sustainable development of wind energy, strict governmental regulations are imposed. Assessment and mitigation of underwater noise are usually required to ensure that sound levels stay within the noise thresholds. The air-bubble curtain system is one of the most widely applied noise mitigation techniques. This paper presents a multi-physics approach for modeling an air-bubble curtain system in application to offshore pile driving. The complete model consists of four modules: (i) a compressible flow model to account for the transport of compressed air from the offshore vessel to the perforated hose located in the seabed; (ii) a hydrodynamic model for capturing the characteristics of bubble clouds in varying development phases through depth; (iii) an acoustic model for predicting the sound insertion loss of the air-bubble curtain; and (iv) a vibroacoustic model for the prediction of underwater noise from pile driving which is coupled to the acoustic model in (iii) through a boundary integral formulation. The waterborne and soilborne noise transmission paths are examined separately, allowing us to explore the amount of energy channeled through the seabed and through the bubble curtain in the water column. A parametric study is performed to examine the optimal configuration of the double bubble curtain system for various soil conditions and pile configurations. Model predictions are compared with measured data. The model allows for a large number of simulations to examine different configurations of a single bubble curtain and a double big bubble curtain.

Experimental study on ultrasonic wave propagation characteristics of gas-liquid two-phase flow in riser annulus

Deepwater drilling projects face challenges such as delays in gas kick monitoring and a high risk of well control failure. How to monitor gas kicks as early and accurately as possible is one of the keys to preventing significant accidents such as well blowouts. Monitoring gas kicks at risers is a promising method for early monitoring of gas kicks in deepwater drilling. In this paper, by constructing an experimental device for ultrasonic gas kicks monitoring of the riser, the response law of the ultrasonic signal to the gas-liquid two-phase flow was tested under different air intake, drill pipe rotation speeds, and liquid flow rates. This was done by constructing an experimental riser ultrasonic gas kick monitoring device. The signal-to-noise ratio and the root-mean-square error are used as measurement standards, and the wavelet threshold noise reduction method is used to reduce noise-containing signals. The ultrasonic sensitivity characteristics of the gas-liquid two-phase flow are demonstrated by analyzing the signal in the time domain and frequency domain: peak amplitude, attenuation coefficient, wave velocity, and fundamental frequency. A non-linear relationship exists between peak amplitude, fundamental frequency, and air intake, whereas an approximately linear relationship is evident between the attenuation coefficient and wave velocity with air intake. A quantitative characterization relationship between ultrasonic characteristic parameters and air intake was obtained by conducting a multiple regression analysis using the least-squares method on the attenuation coefficient, wave velocity, fundamental frequency, and air intake. Experiments demonstrate that the relative error between the actual air intake of 86.4 % of the test data group and the theoretical air intake determined based on the quantitative characterization relationship is less than 10 %.

A Multi-Scale Anti-Multipath Algorithm for GNSS-RTK Monitoring Application.

During short baseline measurements in the Real-Time Kinematic Global Navigation Satellite System (GNSS-RTK), multipath error has a significant impact on the quality of observed data. Aiming at the characteristics of multipath error in GNSS-RTK measurements, a novel method that combines improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and adaptive wavelet packet threshold denoising (AWPTD) is proposed to reduce the effects of multipath error in GNSS-RTK measurements through modal function decomposition, effective coefficient sieving, and adaptive thresholding denoising. It first utilizes the ICEEMDAN algorithm to decompose the observed data into a series of intrinsic mode functions (IMFs). Then, a novel IMF selection method is designed based on information entropy to accurately locate the IMFs containing multipath error information. Finally, an optimized adaptive denoising method is applied to the selected IMFs to preserve the original signal characteristics to the maximum possible extent and improve the accuracy of the multipath error correction model. This study shows that the ICEEMDAN-AWPTD algorithm provides a multipath error correction model with higher accuracy compared to singular filtering algorithms based on the results of simulation data and GNSS-RTK data. After the multipath correction, the accuracy of the E, N, and U coordinates increased by 49.2%, 65.1%, and 56.6%, respectively.

Occupational Hearing Loss Associated with the Combined Exposure of Solvents and Noise: A Systematic Review and Meta-Analysis

To analyze the predominant frequencies of hearing threshold shift and the prevalence of hearing loss related to the co-exposure to noise and solvents. A systematic review and meta-analysis were performed by retrieving published articles from Web of Science, PubMed, Scopus, Embase, and ProQuest until July 2023. Data were extracted in line with the Cochrane Collaboration Handbook, and the Newcastle-Ottawa Scale and Agency for Healthcare Research and Quality were used to assess the studies’ quality. The meta-analysis was used to estimate the odds ratios (ORs) with 95% confidence interval (CI). I2 and Q statistics were used to prove the heterogeneity. A total of 22 selected studies (9948 workers), six cohort studies and 16 cross-sectional studies were included. The results revealed that 43.7%, 41.3%, and 53.6% of the participants presented with hearing loss due to noise exposure, solvent exposure, and combined exposure to noise and solvent, respectively. The workers exposed to both noise and solvents had a higher risk of hearing loss than those exposed to noise (overall weighted odds ratio [OR]: 1.76) or solvents (overall-weighted OR: 2.02) alone. The poorer hearing threshold in the combined noise and solvents exposure group was mainly at high frequencies (3, 4, 6, and 8 kHz), with a peak of 29.47 dB HL at 6 kHz. The noise-exposed group’s peak hearing threshold was 28.87 dB HL at 4 kHz. The peak hearing threshold of the solvent-exposed group was 28.65 dB HL at 6 kHz. The workers exposed to noise and solvent simultaneously had a higher prevalence of hearing loss than those exposed to solvents. Co-exposure to noise and solvents increases the odds of hearing loss. The dominant hearing threshold changes occurred at 3, 4, 6, and 8 kHz, and the peak value appeared at 6 kHz in workers co-exposed to noise and solvents.

Estimation of ambient marine noise levels in New South Wales

Marine and offshore infrastructure is increasingly becoming more prominent in NSW resulting in a requirement to investigate the potential behavioral and physiological impacts to marine fauna during construction and operational phases. Typically, detailed modeling is undertaken to compare the predicted underwater noise levels against noise thresholds with limited consideration to the existing ambient noise levels specific to the site. Vessel movements are known to be the primary contributor to ambient underwater noise levels from anthropogenic sources whereas prevailing noise is generally dominated by natural sources such as wind, waves, precipitation, and seismic activity. Underwater noise monitoring of baseline conditions is not always feasible and can also be cost-prohibitive to undertake. Additionally, there is currently limited publicly available underwater noise measurement data along the eastern coastline of NSW, specifically in shallow waters at depths less than 50 m. This study includes simple procedures to allow for preliminary estimates of the key contributors to the marine noise environment in the context of the Eastern coastline of NSW, with the aim of producing maps estimating existing marine noise levels for common descriptors such as SPL(rms) and SEL(24hour).

A Tremor Suppression and Noise Removal Algorithm for Microscopic Robot-Assisted Cataract Surgery

Ophthalmic surgery consists of a series of precise manipulations performed using a surgical microscope. A surgical robot with a master–slave paradigm is commonly adopted to improve the precision and guidance intelligence for ophthalmic surgery; however, the surgeon's hand tremor, misoperation, inherent characteristics of the robot, and environmental interference may cause tremors of the end-effector of the surgical manipulator, thus increasing the risk of soft tissue damage and poor prognosis for the patient. A symmetrical threshold noise reduction combiner (STIC) is proposed to suppress hand tremors and reduce environmental noise interference. Additionally, the inverse kinematic model of the eight-degrees-of-freedom surgical robot for cataract surgery is developed, with the constraints of continuous circular capsulorhexis manipulation. Simulation results show that the signal-to-noise ratio of the STIC is increased from 35.3907 to 57.0158 dB, whereas the maximum, average, standard deviation, and root mean square of the output of the STIC are decreased by 64.79%, 77.24%, 74.50%, and 75.86%, respectively. Three sets of in vitro experimental results show that these four indicators of the STIC performance are reduced by 59.73%, 76.57%, 57.23%, and 65.02%, respectively. These results validate the feasibility and effectiveness of the proposed method on hand tremor suppression and inherent high-frequency disturbance elimination, thereby enhancing the accuracy of the microsurgical operation.

An Algorithmic Approach towards Remote Sensing Imagery Data Restoration Using Guided Filters in Real-Time Applications

The images captured from SAR sensors are inherently weakened by speckle noise. The SAR image processing community targeted this problem with many feature-based filters. Since SAR images are low-contrast images, edge retention is the most crucial aspect to consider. This helps in the efficient retrieval of information. This paper provides a two-step edge-preserving homomorphic SAR image despeckling technique that implements a guided filter as the first step, and a modified method of noise thresholding using the bivariate shrinkage rule and canny edge operator in the Discrete Orthonormal Stockwell Transform (DOST) domain as the second step. The use of a canny edge operator improves overall edge preservation after despeckling. The use of noise thresholding delivers the highest level of speckle reduction in the DOST domain. The detected edges are added to the residual part obtained after removing the noise to produce more informative content. According to several qualitative and quantitative criteria, the suggested approach is compared to some of the newest despeckling methods. The execution time of the proposed method is around 7.2679 seconds. Upon conducting qualitative and quantitative analysis, it has been determined that the proposed method surpasses all other despeckling methods that were compared.

Noise Reduction of Cable Partial Discharge Vibration Signal based on EEMD-CIIT method

Abstract The cable partial discharge vibration signal is often polluted by noise. It is necessary to denoise the signal, improve the signal-to-noise ratio, and then realize the feature extraction of the vibration signal. Therefore, this paper builds a cable partial discharge test platform, collects the time-domain waveform of vibration signals under different working conditions, analyzes the noise reduction effect of ensemble empirical mode decomposition clearing repetition interval threshold noise reduction method under different threshold functions, and analyzes the difference and approximation degree between the simulated signal and the real signal after noise reduction by using signal-to-noise ratio and correlation, By comparing the difference of statistical characteristics in different defects before and after noise reduction, the wavelet threshold denoising method is established synchronously and compared with the proposed method. The results show that the high-frequency part of the vibration signal has obvious random noise components, and the effective frequency is mainly concentrated in the low-frequency band; The smoothing shear absolute deviation penalty threshold function has the smoothest curve after noise reduction and the highest approximation to the real signal. The high-frequency noise component is eliminated, and the low-frequency band frequency component is effectively retained. The method proposed in this paper can be extended to the noise reduction of vibration signals of other industrial equipment and provide technical support for the signal noise reduction of various industrial equipment.

Association Between Speech Reception Threshold in Noise and Multimorbidity: The UK Biobank Study.

To investigate the association between hearing function, as approached with the functional auditory capacity, and multimorbidity. Cross-sectional study. The UK Biobank was established from 2006 to 2010 in the United Kingdom. This cross-sectional analysis included 165,524 participants who provided baseline information on hearing function. Functional auditory capacity was measured with a digit triplet test. Three categories were defined according to the speech reception threshold in noise (SRTn): normal (SRTn < -5.5 dB signal-to-noise ratio [SNR]), insufficient (SRTn ≥ -5.5 to ≤ -3.5 dB SNR) and poor hearing function (SRTn > -3.5 dB SNR). To define multimorbidity, 9 chronic diseases were considered, including chronic obstructive pulmonary disease, dementia, Parkinson's disease, stroke, cancer, depression, osteoarthritis, coronary heart disease, and diabetes; multimorbidity was defined as the coexistence of 2 or more in the same individual. Analyses were conducted using logistic models adjusted for relevant confounders. Among the study participants, 54.5% were women, and the mean (range) age was 56.7 (39-72) years. The prevalence of insufficient and poor hearing function and multimorbidity was 13% and 13.2%, respectively. In comparison with having a normal SRTn, the odds ratio (95% confidence interval) of multimorbidity associated with insufficient SRTn was 1.13 (1.08-1.18), and with poor SRTn was 1.25 (1.14-1.37). Insufficient and poor hearing function was associated with multimorbidity. This association suggests common biological pathways for many of the considered morbidities.

BO-MADRSN: Bayesian optimized multi-attention residual shrinkage networks for industrial soft sensor modeling

Convolutional neural networks have been widely utilized in industrial soft sensor modeling because of its excellent feature extraction capabilities in recent years. However, industrial data are often multimodal and contain noise, which affects the feature extraction capability of soft sensor models and leads to potentially large errors in the final key variable results. The solution to the above problems is based on denoising techniques and higher-level feature extraction techniques. Therefore, this paper proposes a Bayesian optimized Multi-Attention deep residual shrinkage network for industrial soft sensor model, referred to as BO-MADRSN. Firstly, in order to filter the noise in the original data during feature extraction, we used a soft thresholding method, where the thresholds are determined by back-propagation algorithm. Secondly, in order to retain the information of the data as much as possible and reduce the cases of zero after soft threshold filtering, we construct a residual network-like structure to complement the results obtained after soft threshold noise reduction by performing attentional feature extraction on the original features through the shortcut connections of this structure. Thirdly, we perform hierarchical denoising by stacking multiple residual blocks, and this structure facilitates network training. In addition, a Bayesian optimization strategy is introduced to select the optimal hyperparameters. In this paper, the results on several real industrial datasets verify the effectiveness and superiority of the proposed soft sensor model.

Subthreshold electrical noise alters walking balance control in individuals with cerebral palsy

BackgroundSensory deficits in individuals with cerebral palsy (CP) play a critical role in balance control. However, there is a lack of effective interventions that address sensory facilitation to improve walking balance. Stochastic Resonance (SR) stimulation involves delivering sub threshold noise to improve balance in patients with sensory deficits by enhancing the detection of sensory input. Research questionTo investigate the immediate effects of SR on walking balance in individuals with and without CP. MethodsThirty-four participants (17 CP, 17 age-and sex-matched typically developing controls or TD) between 8 and 24 years of age were recruited. SR stimulation was applied to the muscles and ligaments of ankle and hip joint. An optimal SR intensity during walking was determined for each subject. Participants walked on a self-paced treadmill for three trials of two minutes each using a random order of SR stimulation (SR) and no stimulation (noSR) control conditions. Our primary outcome measure was minimum lateral margin of stability (MOS). Secondary outcome measures include anterior MOS before heelstrike and spatiotemporal gait parameters. We performed two-way mixed ANOVAs with group (CP, TD) as between-subject and condition (noSR, SR) as within subject factors. ResultsCompared to walking without SR, there was a small but significant increase in the lateral and anterior MOS with SR stimulation, implying that a larger impulse was needed to become unstable, in turn implying higher stability. Step width and step ength decreased with SR for the CP group with SR stimulation. There were no significant effects for other spatiotemporal variables. SignificanceSub threshold electrical noise can slightly improve walking balance control in individuals with CP. SR stimulation, through enhanced proprioception, may have improved the CP group’s awareness of body motion during walking, thus leading them to adopt a more conservative stability strategy to prevent a potential fall.

The Effect of Hemodialysis on Spectral and Temporal Processing Abilities and Speech Perception in Noise among Individuals with Chronic Kidney Disease

Introduction: The effect of chronic kidney disease (CKD) on hearing is well documented in the literature. Several studies have investigated the effect of hemodialysis on the peripheral auditory system among individuals with CKD. However, studies investigating the effect of hemodialysis on speech perception and auditory processing abilities are limited. The present study investigated the effect of hemodialysis on few auditory processing abilities and speech perception in noise among adults with CKD. Methods: A total of 25 adults with CKD undergoing hemodialysis regularly participated in the study. Spectral ripple discrimination threshold (SRDT), gap detection threshold (GDT), amplitude-modulation detection threshold (AMDT), and speech recognition threshold in noise (SRTn) were measured before and after hemodialysis. Paired samples “t” test was carried out to investigate the effect of hemodialysis on thresholds. Results: Results showed a significant improvement for SRDT, GDT, AMDT, and SRTn after hemodialysis among individuals with CKD. Discussion: Hemodialysis showed a positive effect on speech perception in noise and auditory processing abilities among individuals with CKD.

A Converse for Fault-tolerant Quantum Computation

As techniques for fault-tolerant quantum computation keep improving, it is natural to ask: what is the fundamental lower bound on space overhead? In this paper, we obtain a lower bound on the space overhead required for &amp;#x03F5;-accurate implementation of a large class of operations that includes unitary operators. For the practically relevant case of sub-exponential depth and sub-linear gate size, our bound on space overhead is tighter than the known lower bounds. We obtain this bound by connecting fault-tolerant computation with a set of finite blocklength quantum communication problems whose accuracy requirements satisfy a joint constraint. The lower bound on space overhead obtained here leads to a strictly smaller upper bound on the noise threshold for noise that are not degradable. Our bound directly extends to the case where noise at the outputs of a gate are non-i.i.d. but noise across gates are i.i.d.