Noise Level Research Articles

Discharge communication during transitions from emergency care to home.

The healthcare system in Canada is overwhelmed and requires reform. Good discharge communication is a cornerstone of patient safety and quality care. In the Emergency Department (ED), good discharge communications means that patients leave with a clear understanding of their health condition, and the steps they need to take to continue their recovery at home. The fragmented nature of communication in the ED coupled with long wait times and high noise levels pose significant risks to the continuity of information exchange. Additional communication barriers arise for many patients due to a lack of control, language differences, low health literacy, and feelings of fear and uncertainty. Multiple interventions have been evaluated to improve ED discharge communication, but further work is needed to engage all end users in a theory-based approach. Addressing challenges related to successful discharge communication requires a multifaceted approach that includes improving institutional policies, adopting innovative co-designed interventions, and leveraging technology.

Influence of Dolphin-Watching Tourism Vessels on the Whistle Emission Pattern of Common Dolphins and Bottlenose Dolphins

Recent years have seen a notable rise in dolphin-watching boat activities along the Algarve coast in Portugal, potentially affecting the common dolphin (Delphinus delphis) and bottlenose dolphin (Tursiops truncatus) local populations. This study examines the impact of increasing underwater noise levels from these boats on dolphin vocalizations. Field recordings were conducted from June to September 2022, analyzing dolphin whistles in various boat presence scenarios. The results indicate significant changes in whistle-frequency characteristics with boat presence, including increased start, low, and high frequencies, alongside a decrease in the number of inflection points in modulated whistles. The changes might negatively impact dolphin populations viability, underscoring the need for further research. Additionally, improved mitigation strategies may be necessary to reduce the potential negative effects of dolphin watching on cetacean communication and behavior in the Algarve region.

Multi-scale mechanical and acoustic characterization of low noise pavements

Using data on in situ performance of low noise pavements, three well performing mixtures were selected: SDA 4, SDA 6, and SDA 8. These mixtures and the corresponding mastic (filler + bitumen + additive) were tested for their mechanical and acoustic performance in non-aged and aged states using a multi-scale approach (mm to km). Thereafter, an optimization protocol was applied to these mastics (mm-scale) and promising combinations were implemented at the mixture scale (cm-scale). As a result, the best performing mixture parameters were implemented in the m-scale slabs where the mechanical and acoustic performance were determined. The proposed work could uniquely combine knowledge from in situ long term and laboratory performance of low noise pavements. Using a traffic simulator and measurements before and after trafficking, the acoustic modeling showed that the modified mixtures’ noise levels were maintained after loading. This indicates that the modification assured acoustical integrity of the mixture after loading.

The efficiency of portable air cleaners in reducing cross-exposure through respiratory aerosols: Effects of flowrate, location, and unit type

This study evaluates the efficacy of portable air cleaners (PACs) in a controlled climate chamber that simulates an office environment, assessing their impact on respiratory particle transmission between two thermal manikins (representing an infected and an exposed individual) as well as on the noise level in the chamber. The study explores three types of PAC, namely floor-type (PAC1), table (PAC2) and personalized (PAC3) in various locations and operation modes. The particles were generated using an aerosol generator and introduced into the infected manikin's exhalation; the particle concentration at the exposed manikin's breathing zone (BZ) was measured using an aerodynamic particle sizer. The results showed that the PAC2, operating at a flow rate of 97 m³/h, significantly reduced the intake fraction (IF) by over 90 % within the first hour, proving to be the most effective in minimizing cross-exposure risks while maintaining sound levels within the acceptable limits for office rooms. In contrast, PAC3, with a lower flow rate of 13 m³/h, reduced IF by only 21.6 % after 60 min. The result also showed that settings with higher flow rates (higher than 134 m3/h) resulted in noise levels above the maximum allowable for office spaces for all tested PACs. Additionally, prolonged operation did not further decrease IF significantly after reaching optimal reduction levels within 30–60 min, depending on the PAC type and settings. Further, the study showed that strategic placement away from direct alignment with occupants' BZ is recommended to optimize aerosol removal and noise management.

Water ice particles detected by SELENE's Spectral Profiler at lunar shadowed regions in various local times and latitudes

Processes of water (OH and H2O) migration on the Moon remain unclear, prompting active research. Understanding lunar water migration requires investigation of the trapping and diffusion properties of water at various latitudes and local times. This study analyzed visible to near-infrared spectral data obtained by the Spectral Profiler (SP) onboard SELENE for shadowed regions at various local times and latitudes, not limited to the polar permanently shadowed regions. We assessed SP data for shadowed regions in 60 areas, each spanning a 10° × 10° latitude–longitude grid. Of the 1,061,907 analyzed shadowed-region data, 41,385 at various latitudes exhibited significant absorption in the 1.25 and 1.5 µm bands, indicating water ice particles. Data with the two absorption features suggest the presence of a water ice frost layer covering the lunar surface or suspended water ice particles above the lunar surface, at various latitude shadowed regions. Our spectral simulations have quantified the ice particles as being 0.1–1 µm in diameter, with a column density of 10–4–10–3 kg/m2. The spectral parameters for band absorption at the 1.5 µm band show symmetry between morning and evening sides, which is potentially attributed to the absence of variations in ice grain size and quantity. The 1.5 µm band absorption shows an increasing trend toward terminator regions, indicating variation in the water ice distribution and likely reflecting temperature conditions for water retention. The latitudinal trend of ice grain size and quantity remains uncertain because of the observed noise levels. Observations of water ice particles in shadowed regions at various latitudes and local times can provide new constraints on trapping and diffusion processes of lunar water migration.

Study on the aerodynamic performance and noise of windshield area in high-speed trains at a frequency of 38 hz

The noise level of a high-speed train is a crucial indicator of its operational quality. A significant low-frequency interior noise, referred to as ‘38 Hz’ in this context, has been detected during the operation of high-speed trains. This notable noise primarily occurs in the train attendant’s cabin near the windshield area. In this study, we conducted a comprehensive full-scale test to investigate the preliminary cause behind this significant ‘38 Hz’ noise. Our investigation reveals that it is associated with aerodynamic noise originating from the windshield area. To identify the root cause of this ‘38 Hz’ noise in the windshield area, we established a computational fluid dynamics (CFD) based model for aerodynamic noise analysis. The causes were found to be related to both the size and opening of the windshield cavity. Simulation results demonstrate that closing the upper opening significantly reduces the volume of ‘38 Hz’ noise compared to the original outer windshield structure design. We verified and validated these findings through an extensive full-scale test, which showed that by implementing our proposed modified windshield structure, there was approximately 7 dB reduction in significant ‘38 Hz’ noise observed within the train attendant’s cabin near the windshield area.

The Research of Intra-Pulse Modulated Signal Recognition of Radar Emitter under Few-Shot Learning Condition Based on Multimodal Fusion

Radar radiation source recognition is critical for the reliable operation of radar communication systems. However, in increasingly complex electromagnetic environments, traditional identification methods face significant limitations. These methods often struggle with high noise levels and diverse modulation types, making it difficult to maintain accuracy, especially when the Signal-to-Noise Ratio (SNR) is low or the available training data are limited. These difficulties are further intensified by the necessity to generalize in environments characterized by a substantial quantity of noisy, low-quality signal samples while being constrained by a limited number of desirable high-quality training samples. To more effectively address these issues, this paper proposes a novel approach utilizing Model-Agnostic Meta-Learning (MAML) to enhance model adaptability in few-shot learning scenarios, allowing the model to quickly learn with limited data and optimize parameters effectively. Furthermore, a multimodal fusion neural network, DCFANet, is designed, incorporating residual blocks, squeeze and excitation blocks, and a multi-scale CNN, to fuse I/Q waveform data and time–frequency image data for more comprehensive feature extraction. Our model enables more robust signal recognition, even when the signal quality is severely degraded by noise or when only a few examples of a signal type are available. Testing on 13 intra-pulse modulated signals in an Additive White Gaussian Noise (AWGN) environment across SNRs ranging from −20 to 10 dB demonstrated the approach’s effectiveness. Particularly, under a 5−way5−shot setting, the model achieves high classification accuracy even at −10dB SNR. Our research underscores the model’s ability to address the key challenges of radar emitter signal recognition in low-SNR and data-scarce conditions, demonstrating its strong adaptability and effectiveness in complex, real-world electromagnetic environments.

Classroom indoor air quality and noise level assessment of different educational institutions in a university area in Bangladesh

Introduction: The classroom environment is crucial for fostering effective learning and safeguarding teacher-student health. This study assessed the Indoor Air Quality (IAQ) and noise levels in classrooms across three institutions: a university, a secondary and higher secondary school (called a school and college), and a primary school. Materials and methods: Various IAQ parameters such as Particulate Matters (PM2.5 and PM10), Carbon monoxide (CO), Carbon dioxide (CO2), Total Volatile Organic Compound (TVOC), and temperature, Relative Humidity (RH), light, and noise levels were measured using calibrated instruments from February to March 2024. Results: The air pollutants and noise levels varied among the institutions. The mean values of PM 2.5 (76.6 µg/m3) and PM (116.7 µg/m3) were highest The mean values of PM 2.5 (76.6 µg/m3) and PM (116.7 µg/m3) were highest in the primary school, while CO (0.82 ppm), light (92.9 lux), temperature (27.6 °C), and noise levels (77.6 dB) peaked in the school and college. University classrooms showed the maximum concentrations of CO2 (804.9 ppm), TVOC (32.9 ppb), and RH (58.6%). In all institutions, PM2.5, PM10, and noise levels exceeded WHO-recommended limits, whereas CO, CO2, RH, and temperature remained within their respective standards. Light levels were below Occupational Safety and Health Administration (OSHA) guidelines. Correlation analysis showed significant positive correlations between PM2.5 PM10, and CO. Hazard Quotient (HQ) values for PM 2.5 and PM10 exceeded 1.0, indicating potential health risks. Variations in pollutants and noise levels among institutions may be due to classroom facilities, student density, ventilation systems, and student activities. Conclusion: Long-term exposure to IAQ pollutants and noise levels could impair cognitive function, respiratory health, and overall well-being of the students and educators. Implementing proper ventilation (e.g., HEPA filters) systems, soundproofing acoustic panels, and continuous IAQ monitoring is recommended for a safer classroom environment.

The Effect of Root-Mean-Square and Loudness-Based Calibration Approach on the Acceptable Noise Level

environment contains many background noises in everyday conversations, it is necessary to evaluate the noise tolerance. The Acceptable Noise Level (ANL) provides an approach to quantifying the maximum amount of background noise a listener is willing to put up with while listening to a target story without becoming tense. Exploring noise tolerance in speech perception, the study investigates how different calibration methods impact normal hearing participants’ monotic and dichotic ANL results. Methods: This investigation utilizes a pared-sample t-test for statistical analysis, adopting a comparative observational approach. This study applied the Persian version of the typical ANL test. Two approaches have equalized the target and background stimuli: Root Mean Squared (RMS) and loudness match calibration via Adobe Audition. Using these modified materials the Most Comfortable Level (MCL), the Background Noise Level (BNL), and ANL were compared in terms of RMS and loudness match calibration. Fifty normal persons aged (18–39), under the conditions of monotic and dichotic listening, participated in this study. Results: The statistical analysis using a paired-sample t-test revealed no significant differences in the outcomes of the ANL test between the calibrations of RMS and loudness matching under both monotic and dichotic listening conditions (p=0.31 and p=0.67, respectively). Conclusion: The study suggests that calibration procedures, namely RMS and Loudness matching, do not affect ANL in either monotic or dichotic conditions. Keywords: Loudness; calibration; acceptable noise level; root mean square

Impact of easy-care and softener treatments on fabric frictional noise properties: a study on two protective fabrics

This research aimed to reduce fabric frictional noise to enhance wearing comfort, as such noise can be bothersome. This goal was achieved through chemical surface treatments, specifically cross-linker treatment and silicone softener treatment. Frictional noise from the treated fabrics was generated, recorded, and analyzed using a frictional noise generator, fast Fourier transform, and sixth-band analysis to determine the total noise level. A novel aspect of this work is the use of psychoacoustic characterization to evaluate the fabric frictional noise by calculating Zwicker’s parameters using the Brüel and Kjær application. In addition, the mechanical properties were tested using the Kawabata compression and surface testers, with data comparison achieved using Student’s t-test. The results showed that softeners are more effective than cross-linkers in reducing the total frictional noise level, loudness, and fluctuation strength. However, cross-linkers are better at reducing sharpness. Neither treatment affected the roughness of the frictional noise. In summary, both easy-care and softener treatments help reduce discomfort and produce less loud and sharp fabric frictional noise.

Monitoring and measuring noise pollution in urban environments (Case study: Khorramabad city)

Introduction: Environmental noise, caused by traffic, leads to increased risk of health problems. Nowadays, noise pollution is one of the most important problems in the communities which adversely affects the health. The aim of this study was to assess the noise pollution in Khorramabad city. Materials and methods: To measure the noise level in the city, 39 stations near to sensitive places such as hospitals, educational centers, and so on were selected. Measurements were performed, for 30 min, in the mornings (7-10 AM) and evenings (5-8 PM) using a standard sound level meter. The measurements were made on working days during the week and assuming the traffic load was the same. Results: The sound level in all 39 considered stations was higher than the standard of residential-commercial in more than 90% of the stations compared to the environmental standard. The mean of sound measured in the monitored stations was equal to 68.28±2.6 dB. The maximum measured sound level belonged to station S2. In station S2, the maximum measured sound was reported as 81.72±11.3 dB. The findings showed that sound level in the evenings were slightly higher than the mornings. Also, in the evening, the highest and lowest measured sound values belonged to stations S1 and S20. The amount of measured sound level at stations S1 and S20 was equal to 80.9 and 39.7 dB, respectively. Based on findings, the highest and lowest sound recorded belonged to stations S39 and S20 which area type of stations S20 and S39 was commercial and residential, respectively. Conclusion: This study showed that the average level of sound pressure was higher than the permissible limit, so the necessity of planning is suggested to reduce the level of noise pollution and consequently reduce the anxiety level of citizens and increase health.

Traffic noise prediction model using GIS and ensemble machine learning: a case study at Universiti Teknologi Malaysia (UTM) Campus.

This study represents a pioneering effort to integrate geographic information systems (GIS) and ensemble machine learning methods to predict noise levels on a university campus. Three ensemble models including random forest (RF), gradient boosting (GB), and extreme gradient boosting (XGB) were developed to predict traffic noise based on data collected over a 4-week period at the Universiti Teknologi Malaysia (UTM) campus. Noise measurements were obtained during peak morning hours (7:30 to 9:30 a.m.) on weekdays within the UTM campus in Johor. Additional predictor variables, including data from the digital elevation model (DEM) and land use, were incorporated to capture the complex nonlinear relationships influencing noise levels. The models were optimized through hyperparameter tuning, resulting in high precision, as evidenced by performance metrics such as the coefficient of determination (R2), mean absolute error (MAE), and mean squared error (MSE). The XGB model emerged as the most accurate, with R2 = 0.96, MAE = 0.9, and MSE = 0.3. Noise maps generated using the inverse distance weighting (IDW) interpolation technique highlighted the spatial distribution of noise levels, classified into five classes considering WHO standards. The findings identified distance from roads, the number of light vehicles, and proximity to green areas as the most significant predictors. However, challenges remain in accurately predicting noise levels associated with other predictors. The outcomes of the study indicate the superior performance of the XGB model compared to the GB and RF models. The study recommends several measures to manage and control noise pollution on the UTM campus, including raising awareness, regulating and enforcing vehicle speed limits, reevaluating land use, installing sound insulation systems, and planting trees and vegetation buffer zones around and within educational buildings.

Rapid monitoring of structural deformation based on unsupervised segmentation model

This paper presents a novel approach for rapid structural deformation monitoring using an enhanced segmentation model, SAM-DM (Segment Anything Model-Deformation Monitoring). The method enhances the SAM model by integrating an innovative point prompt generation technique, image fusion, and connected domain analysis, enabling structural deformation monitoring with just a single manual click on the target area in the initial video frame. Specifically, this manual click generates coordinate information for the target area, serving as a point prompt for the SAM-DM model. This process results in the creation of a high-quality binary mask, which is then fused with the original image to isolate only the target area. Subsequently, connected domain analysis is employed to automatically process the mask, extracting the pixel centroid coordinates of the target area, which are then used as prompts for segmenting subsequent frames. This approach allows continuous tracking of changes in the target area's coordinates, thereby facilitating rapid structural deformation monitoring. Experimental results demonstrate that this method can accurately monitor deformation across all target points and adapt to varying lighting conditions and noise levels. Applied to monitor the bending deformation of reinforced concrete beams and the in-plane deformation of concrete slabs, the method achieves results closely aligned with actual measurements, yielding relative errors of 0.95 % and 0.48 %, respectively. Additionally, in monitoring the vibrational deformation of steel frames, the primary frequency of the curve precisely matches measured values, confirming that the SAM-DM-based method delivers both high accuracy and strong robustness.

Adaptive nonlinear filtering algorithms for removal of non-stationary noise in electronystagmographic signals

Non-stationary physiological noise poses significant difficulties due to its time-varying and previously unknown characteristics. When processing electronystagmographic signals, linear filtering can distort diagnostically significant rapid changes caused by saccadic eye movements. In such cases, nonlinear filters based on robust estimators are more appropriate, whereas linear filtering effectively reduces white noise in parts of a signal with linear behaviour. Therefore, an adaptive signal processing approach that varies in nonlinearity from highly nonlinear robust estimation to linear averaging and combines their benefits appears promising. We have proposed a low-complexity adaptive method for switching filter sets and relevant filter parameter settings based on the estimated noise level and local signal behaviour. This method does not require time for filter parameter modification and does not need prior information on the signal model and noise variance. Based on this method, we have designed adaptive nonlinear filtering algorithms to exploit the advantages of both the nonlinear robust and linear averaging estimators. We have evaluated the filtering quality statistically using the minimum mean square error criteria and the maximum signal-to-noise ratio for a model signal with different levels of additive Gaussian noise. The proposed real-time adaptive filtering algorithms demonstrate a significant efficiency improvement compared to the commonly used filters. The proposed adaptive myriad algorithm achieves the highest efficiency by adjusting a sample myriad linearity parameter depending on the local estimate of a signal scale and changing the sliding window length and the coefficient affecting the linearity parameter.

Association between residential noise exposure and burnout among healthcare workers in Taiwan: a cross-sectional study

Few studies have explored the association between residential noise exposure and burnout. In this study, we investigated the association between residential noise exposure and burnout prevalence among 5416 health-care workers in Taiwan from 2012 to 2017. Burnout was evaluated using the Mandarin version of the Copenhagen Burnout Inventory by considering both continuous and binary measures. We applied ordinary Kriging models to calculate the annual average residential noise exposure at an individual level. Multivariable linear regression models and logistic regression models were employed. Restricted cubic splines were used to explore dose–response relationships. The median age of the health-care workers was 31.5 years. In the multivariable linear regression models, exposure to residential noise (per 1 dBA) was associated with increases in personal burnout and work-related burnout scores by 1.59 ± 0.25 and 1.38 ± 0.20, respectively. In the multivariable logistic regression models, the adjusted odds ratios were 1.24 (95% confidence interval [CI]: 1.16, 1.32) for personal burnout and 1.19 (95% CI: 1.13, 1.26) for work-related burnout per 1-dBA increase in residential noise exposure. Linear dose–response associations of burnout with residential noise level were detected. Our findings suggest that exposure to residential noise may increase the risk of burnout among health-care workers.

Bayesian inference of state feedback control parameters for fo perturbation responses in cerebellar ataxia.

Behavioral speech tasks have been widely used to understand the mechanisms of speech motor control in typical speakers as well as in various clinical populations. However, determining which neural functions differ between typical speakers and clinical populations based on behavioral data alone is difficult because multiple mechanisms may lead to the same behavioral differences. For example, individuals with cerebellar ataxia (CA) produce atypically large compensatory responses to pitch perturbations in their auditory feedback, compared to typical speakers, but this pattern could have many explanations. Here, computational modeling techniques were used to address this challenge. Bayesian inference was used to fit a state feedback control (SFC) model of voice fundamental frequency (fo) control to the behavioral pitch perturbation responses of speakers with CA and typical speakers. This fitting process resulted in estimates of posterior likelihood distributions for five model parameters (sensory feedback delays, absolute and relative levels of auditory and somatosensory feedback noise, and controller gain), which were compared between the two groups. Results suggest that the speakers with CA may proportionally weight auditory and somatosensory feedback differently from typical speakers. Specifically, the CA group showed a greater relative sensitivity to auditory feedback than the control group. There were also large group differences in the controller gain parameter, suggesting increased motor output responses to target errors in the CA group. These modeling results generate hypotheses about how CA may affect the speech motor system, which could help guide future empirical investigations in CA. This study also demonstrates the overall proof-of-principle of using this Bayesian inference approach to understand behavioral speech data in terms of interpretable parameters of speech motor control models.

Wideband microwave frequency response measurement based on optical phase-locked loop

Frequency response measurement, or the forward transmission coefficient (S21) measurement for a two-port network, is the key function of a vector network analyzer (VNA). In this paper, a broadband and high dynamic range (DR) microwave S21 parameter measurement scheme based on an optical phase-locked loop (OPLL) is proposed. By heterodyning two phase-locked hybrid integrated ultra-narrow linewidth lasers, a microwave signal with low phase noise and spurious level is generated as the incident signal and reference signal, and the signal frequency can be easily manipulated over a wide range by tuning the master laser wavelength. In the receiver, the radio frequency (RF) signals are down-converted to intermediate frequency (IF) signals with the phase-locked lasers. By sampling and processing the IF signals the S21 parameter of the DUT can be acquired. A proof-of-concept experiment is performed, and with available photodetectors, phase modulators and phase-locked loops, a measurable range of 2 to 18 GHz is achieved. The demonstrated minimum frequency resolution of the OPLL-based RF signal synthesizer is 10 Hz. The system DR exceeds 68 dB at an equivalent resolution bandwidth of 1 kHz. The S21 parameters of a power divider and a bandpass filter are measured, and the results are well consistent with those of a commercial VNA. The DR and measurable range limit factors and possible extension methods are discussed. The proposed approach offers a high potential way to develop a wideband, high DR, and fully integrated VNA.

A fast block sparse Kaczmarz algorithm for sparse signal recovery

The randomized sparse Kaczmarz (RSK) method is an iterative algorithm for computing sparse solutions of linear systems. Recently, Tondji and Lorenz analyzed the parallel version of the RSK method and established its linear expected convergence by implementing a randomized control scheme for subset selection at each iteration. Expanding upon this groundwork, we explore a natural extension of the randomized control scheme: greedy strategies such as the Motzkin criteria. Specifically, we propose a fast block sparse Kaczmarz algorithm based on the Motzkin criterion. It is proved that the proposed method converges linearly to the sparse solutions of the linear systems. Additionally, we offer error estimates for linear systems with noisy right-hand sides, and show that the proposed method converges within an error threshold of the noise level. Numerical results substantiate the feasibility of our proposed method and highlight its superior convergence rate compared to the parallel version of the RSK method.

Whale Optimization Algorithm for structural damage detection, localization, and quantification

This paper introduces an approach for vibration-based damage detection based on matrix updating aided by the Whale Optimization Algorithm (WOA). The methodology uses the Data-driven Stochastic Subspace Identification (SSI-DATA) technique to determine the modal parameters, which are compared with those obtained from both healthy and damaged conditions of the structure. The methodology’s efficacy is assessed through three distinct steps: numerical simulations, experimental data, and real-world data from a bridge. Initially, numerical analyses are conducted on a cantilever beam, a 10-bar truss, and a Warren truss subjected to environmental vibrations with varying damage cases and noise levels. Subsequently, experimental validations are performed on a test system and in the Z24 Bridge. Results from the computational simulations demonstrate the method’s promise to identify, locate, and quantify single and multiple damage cases, even amidst signal noise, variations in the first vibration mode as minimal as 0.015%, and complex structures with 54 elements. Moreover, the matrix updating method utilizing WOA showcased superior accuracy compared to existing techniques in the literature. In addition, the Z24 Bridge example validated the capability of the presented damage detection method to localize structural damage solely based on natural frequencies.

Development of auditory cognition in 5- to 10-year-old children: Focus on speech-in-babble-noise perception.

Speech-in-noise perception is consistently reported to be impaired in learning disorders, which stresses the importance of documenting its developmental course in young children. In this cross-sectional study, ninety children (41 females, 5.5-11.6 years old) and nineteen normal-hearing adults (15 females, 20-30 years old) were tested with a newly developed closed-set speech perception in babble-noise test, combining two levels of phonological difficulty and two noise levels. Results showed that speech-in-babble-noise perception takes a definite maturation step around 7 years of age (d = 1.17, grade effect) and is not mature at 10 years of age when compared to young adults (d = 0.94, group effect). Developmental trajectories of both accuracy and response times were evaluated, with influences of psycholinguistic factors, to foster the development of adequate screening tests.