Vocal Load of University Professors: Preliminary Results

Purpose: To describe the acoustic characteristics of a classroom, voice quality, fatigue, and vocal load of university professors. Methods: Exploratory, observational, longitudinal, and descriptive study with a single group of participants, including vocal monitoring data over two weeks. Acoustic characterization of the classroom, perceptual-auditory evaluation, and acoustic analysis of voice samples were conducted before and after classes. Vocal dosimetry was performed during classes, and the Vocal Fatigue Index (VFI) was assessed at the beginning of each week. Descriptive analysis of the findings was conducted, and randomization test was performed to verify the internal reliability of the judge. Results: All participants reported speaking loudly in the classroom, with the majority reporting vocal changes in the past six months, and only one participant reported a current vocal change. The classroom had acoustical measures and estimations that deviated from established standards. The professors used high vocal intensities during classes. After the classes, an increase in the absolute values of the aggregated data for CAPE-V, jitter, and fundamental frequency was found, varying within the range of normality. Furthermore, there was an observed increase in both post-lesson intensity and VFI when comparing the two-week period. Conclusions: Vocal intensities and VFI were possibly impacted by the acoustics of the classroom. The increase in average VFI between the weeks may be attributed to a cumulative fatigue sensation. Further research with a larger number of participants and in acoustically conditioned classrooms is suggested in order to evaluate collective intervention proposals aimed at reducing the vocal load on teachers.

Objetivo: Describir las características acústicas, calidad vocal, fatiga y carga vocal de profesores universitarios. Métodos: Estudio exploratorio, observacional, longitudinal, descriptivo con un solo grupo de participantes y datos de monitoreo vocal durante dos semanas. Se realizó caracterización acústica de la sala, evaluación auditiva-perceptiva y acústica de muestras de voz antes y después de las clases. Se realizó dosimetría vocal durante las clases y se verificó el Índice de Fatiga Vocal (IFV) en dos semanas. Se realizó un análisis descriptivo de los hallazgos y una prueba de aleatorización para verificar la confiabilidad interna del juez. Resultados: Todos los participantes informaron hablar en voz alta en clase, la mayoría informó cambios vocales en los últimos seis meses y solo uno informó cambios vocales actuales. La sala presentó mediciones y estimaciones acústicas fuera de las normas establecidas. Los profesores utilizaron intensidades vocales altas durante las clases. Hubo un aumento en los valores absolutos de los datos agrupados para CAPE-V, jitter y frecuencia fundamental, variando dentro de los límites normales, después de las clases. La intensidad después de las clases y el IFV, en la comparación entre las dos semanas, mostraron un aumento. Conclusiones: La dosis vocal y el IFV posiblemente se vieron afectados por la acústica del aula. El aumento del IFV medio entre semanas pudo deberse a la sensación de cansancio acumulada. Se sugieren nuevas investigaciones con un mayor número de participantes y que se realicen en la sala acondicionada acústicamente para evaluar propuestas de intervención colectiva, con el objetivo de reducir la carga vocal de los docentes.

Serum metabolome perturbation in relation to noise exposure: Exploring the potential role of serum metabolites in noise-induced arterial stiffness.

Noise pollution has grown to be a major public health issue worldwide. We sought to profile serum metabolite expression changes related to occupational noise exposure by untargeted metabolomics, as well as to evaluate the potential roles of serum metabolites in occupational noise-associated arterial stiffness (AS). Our study involved 30 noise-exposed industrial personnel (Lipo group) and 30 noise-free controls (Blank group). The untargeted metabolomic analysis was performed by employing a UPLC-HRMS. The associations of occupational noise and significant differential metabolites (between Blank/Lipo groups) with AS were evaluated using multivariable-adjusted generalized linear models. We performed the least absolute shrinkage and selection operator regression analysis to further screen for AS's risk metabolites. We explored 177 metabolites across 21 categories significantly differentially expressed between Blank/Lipo groups, and these metabolites were enriched in 20 metabolic pathways. Moreover, 15 metabolites in 4 classes (including food, glycerophosphocholine, sphingomyelin [SM] and triacylglycerols [TAG]) were adversely associated with AS (all P < 0.05). Meanwhile, five metabolites (homostachydrine, phosphatidylcholine (PC) (32:1e), PC (38:6p), SM (d41:2) and TAG (45:1) have been proven to be useful predictors of AS prevalence. However, none of these 15 metabolites were found to have a mediating influence on occupational noise-induced AS. Our study reveals specific metabolic changes caused by occupational noise exposure, and several metabolites may have protective effects on AS. However, the roles of serum metabolites in noise-AS association remain to be validated in future studies.

Evaluation of four computed tomography reconstruction algorithms using a coronary artery phantom.

Background: Despite advancements in coronary computed tomography angiography (CTA), challenges in positive predictive value and specificity remain due to limited spatial resolution. The purpose of this experimental study was to investigate the effect of 2nd generation deep learning-based reconstruction (DLR) on the quantitative and qualitative image quality in coronary CTA. Methods: A vessel model with stepwise non-calcified plaque was scanned using 320-detector CT. Image reconstruction was performed using four techniques: hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), DLR, and 2nd generation DLR. The luminal peak CT number, contrast-to-noise ratio (CNR), and edge rise slope (ERS) were quantitatively evaluated via profile curve analysis. Two observers qualitatively graded the graininess, lumen sharpness, and overall lumen visibility on the basis of the degree of confidence for the stenosis severity using a five-point scale. Results: The image noise with HIR, MBIR, DLR, and 2nd generation DLR was 23.0, 21.0, 16.9, and 9.5 HU, respectively. The corresponding CNR (25% stenosis) was 15.5, 15.9, 22.1, and 38.3, respectively. The corresponding ERS (25% stenosis) was 203.2, 198.6, 228.9, and 262.4 HU/mm, respectively. Among the four reconstruction methods, the 2nd generation DLR achieved the significantly highest CNR and ERS values. The score of 2nd generation DLR in all evaluation points (graininess, sharpness, and overall lumen visibility) was higher than those of the other methods (overall vessel visibility score, 2.6±0.5, 3.8±0.6, 3.7±0.5, and 4.6±0.5 with HIR, MBIR, DLR, and 2nd generation DLR, respectively). Conclusions: 2nd generation DLR provided better CNR and ERS in coronary CTA than HIR, MBIR, and previous-generation DLR, leading to the highest subjective image quality in the assessment of vessel stenosis.

Effects of White Noise Sound on the Severity of Muscle Vaccination Pain in Children Under Two Years.

Objectives: Pain and its control is a significant health problem worldwide. The present study aimed to determine the effects of white noise (bird sound) on the severity of muscle vaccination pain in children under two years old. Materials & Methods: This study was a case-control study conducted in 2021. The samples included seventy children under two years old referred to the health centers in Ramsar City, Iran. The samples were selected using the convenient sampling method and divided into experimental and control groups. The data were collected using the demographic characteristics questionnaire, facial expression, and pain assessment in pediatric patients (FLACC). They were then analyzed by SPSS16 using an independent t-test and analysis of covariance (P<0.05). Results: A significant difference was observed between the severity of muscle vaccination pain in children in the two groups (p=0.042); the pain intensity mean in the experimental group (6.45±2.01) was lower than the control group (8.94±1.28). Conclusion: This method can be a harmless and inexpensive intervention to reduce pain intensity and behavioral pain responses in infants during painful procedures, especially vaccination.

Air pollution, traffic noise, greenness, and temperature and the risk of incident type 2 diabetes: Results from the KORA cohort study.

Introduction: Type 2 diabetes (T2D) is a major public health concern, and various environmental factors have been associated with the development of this disease. This study aimed to investigate the longitudinal effects of multiple environmental exposures on the risk of incident T2D in a German population-based cohort. Methods: We used data from the KORA cohort study (Augsburg, Germany) and assessed exposure to air pollutants, traffic noise, greenness, and temperature at the participants' residencies. Cox proportional hazard models were used to analyze the associations with incident T2D, adjusting for potential confounders. Results: Of 7736 participants included in the analyses, 10.5% developed T2D during follow-up (mean: 15.0 years). We found weak or no association between environmental factors and the risk of T2D, with sex and education level significantly modifying the effects of air pollutants. Conclusion: Our study contributes to the growing body of literature investigating the impact of environmental factors on T2D risks and suggests that the impact of environmental factors may be small.

Segmentation method of magnetic resonance imaging brain tumor images based on improved UNet network.

Background: Glioma is a primary malignant craniocerebral tumor commonly found in the central nervous system. According to research, preoperative diagnosis of glioma and a full understanding of its imaging features are very significant. Still, the traditional segmentation methods of image dispensation and machine wisdom are not acceptable in glioma segmentation. This analysis explores the potential of magnetic resonance imaging (MRI) brain tumor images as an effective segmentation method of glioma. Methods: This study used 200 MRI images from the affiliated hospital and applied the 2-dimensional residual block UNet (2DResUNet). Features were extracted from input images using a 2×2 kernel size (64-kernel) 1-step 2D convolution (Conv) layer. The 2DDenseUNet model implemented in this study incorporates a ResBlock mechanism within the UNet architecture, as well as a Gaussian noise layer for data augmentation at the input stage, and a pooling layer for replacing the conventional 2D convolutional layers. Finally, the performance of the proposed protocol and its effective measures in glioma segmentation were verified. Results: The outcomes of the 5-fold cross-validation evaluation show that the proposed 2DResUNet and 2DDenseUNet structure has a high sensitivity despite the slightly lower evaluation result on the Dice score. At the same time, compared with other models used in the experiment, the DM-DA-UNet model proposed in this paper was significantly improved in various indicators, increasing the reliability of the model and providing a reference and basis for the accurate formulation of clinical treatment strategies. The method used in this study showed stronger feature extraction ability than the UNet model. In addition, our findings demonstrated that using generalized die harm and prejudiced cross entropy as loss functions in the training process effectively alleviated the class imbalance of glioma data and effectively segmented glioma. Conclusions: The method based on the improved UNet network has obvious advantages in the MRI brain tumor portrait segmentation procedure. The result showed that we developed a 2D residual block UNet, which can improve the incorporation of glioma segmentation into the clinical process.

U-Net enhanced real-time LED-based photoacoustic imaging.

Photoacoustic (PA) imaging is hybrid imaging modality with good optical contrast and spatial resolution. Portable, cost-effective, smaller footprint light emitting diodes (LEDs) are rapidly becoming important PA optical sources. However, the key challenge faced by the LED-based systems is the low light fluence that is generally compensated by high frame averaging, consequently reducing acquisition frame-rate. In this study, we present a simple deep learning U-Net framework that enhances the signal-to-noise ratio (SNR) and contrast of PA image obtained by averaging low number of frames. The SNR increased by approximately four-fold for both in-class in vitro phantoms (4.39 ± 2.55) and out-of-class in vivo models (4.27 ± 0.87). We also demonstrate the noise invariancy of the network and discuss the downsides (blurry outcome and failure to reduce the salt & pepper noise). Overall, the developed U-Net framework can provide a real-time image enhancement platform for clinically translatable low-cost and low-energy light source-based PA imaging systems.

Health effects of holistic housing renovation in a disadvantaged neighbourhood in the Netherlands: a qualitative exploration among residents and professionals.

BACKGROUND: Holistic housing renovations combine physical housing improvements with social and socioeconomic interventions (e.g. referral to social services, debt counselling, involvement in decision-making, promoting social cohesion). In a deprived neighbourhood in Utrecht, the Netherlands, this paper examined residents' and professionals' experiences, ideas, and perceptions regarding holistic housing renovation, its health effects, and underlying mechanisms explaining those effects. METHODS: Semi-structured in-depth interviews were conducted with 21 social housing residents exposed to holistic housing renovation, and 12 professionals involved in either the physical renovation or social interventions implemented. Residents were interviewed in various renovation stages (before, during, after renovation). Transcripts were deductively and inductively coded using qualitative software. RESULTS: Residents experienced and professionals acknowledged renovation stress caused by nuisance from construction work (noise, dust), having to move stuff around, and temporary moving; lack of information and control; and perceived violation of privacy. Involvement in design choices was appreciated, and mental health improvement was expected on the long term due to improved housing quality and visual amenity benefits. Social contact between residents increased as the renovation became topic for small talk. Few comments were made regarding physical health effects. The interviews revealed a certain amount of distrust in and dissatisfaction with the housing corporation, construction company, and other authorities. CONCLUSIONS: Renovation stress, aggravated by lack of information and poor accessibility of housing corporation and construction company, negatively affects mental health and sense of control. Potential stress relievers are practical help with packing and moving furniture, and increased predictability by good and targeted communication. Social interventions can best be offered after renovation, when residents live in their renovated apartment and the nuisance and stress from the renovation is behind them. Social partners can use the period leading up to the renovation to show their faces, offer practical help to reduce renovation stress, and increase residents' trust in their organization and authorities in general. This might also contribute to residents' willingness to accept help with problems in the social domain after renovation.

Toward diffusion tensor imaging as a biomarker in neurodegenerative diseases: technical considerations to optimize recordings and data processing.

Neuroimaging biomarkers have shown high potential to map the disease processes in the application to neurodegenerative diseases (NDD), e.g., diffusion tensor imaging (DTI). For DTI, the implementation of a standardized scanning and analysis cascade in clinical trials has potential to be further optimized. Over the last few years, various approaches to improve DTI applications to NDD have been developed. The core issue of this review was to address considerations and limitations of DTI in NDD: we discuss suggestions for improvements of DTI applications to NDD. Based on this technical approach, a set of recommendations was proposed for a standardized DTI scan protocol and an analysis cascade of DTI data pre-and postprocessing and statistical analysis. In summary, considering advantages and limitations of the DTI in NDD we suggest improvements for a standardized framework for a DTI-based protocol to be applied to future imaging studies in NDD, towards the goal to proceed to establish DTI as a biomarker in clinical trials in neurodegeneration.

Dual-role transcription factors stabilize intermediate expression levels.

Precise control of gene expression levels is essential for normal cell functions, yet how they are defined and tightly maintained, particularly at intermediate levels, remains elusive. Here, using a series of newly developed sequencing, imaging, and functional assays, we uncover a class of transcription factors with dual roles as activators and repressors, referred to as condensate-forming level-regulating dual-action transcription factors (TFs). They reduce high expression but increase low expression to achieve stable intermediate levels. Dual-action TFs directly exert activating and repressing functions via condensate-forming domains that compartmentalize core transcriptional unit selectively. Clinically relevant mutations in these domains, which are linked to a range of developmental disorders, impair condensate selectivity and dual-action TF activity. These results collectively address a fundamental question in expression regulation and demonstrate the potential of level-regulating dual-action TFs as powerful effectors for engineering controlled expression levels.

Variations in the Cadherin 23 Gene Associated With Noise-Induced Hearing Loss.

Background: The relationship between CDH23 gene variants and NIHL is unclear. This study investigates the association between cadherin 23 (CDH23) gene variants and noise-induced hearing loss (NIHL). Methods: This is a case-control study. Workers who were exposed to noise from a steel factory in North China were recruited and divided into two groups: the case group (both ears' high-frequency threshold average [BHFTA] ≥40dB) and the control group (BHFTA ≤25 dB). This study used the generalised multifactor dimensionality reduction method to analyse the association among 18 single-nucleotide polymorphisms (SNPs) in CDH23 and NIHL. Logistic regression was performed to investigate the main effects of SNPs and the interactions between cumulative noise exposure (CNE) and SNPs. Furthermore, CNE was adjusted for age, gender, smoking, drinking, physical exercise and hypertension. Results: This study recruited 1,117 participants. The results showed that for rs11592462, participants who carried the GG genotype showed an association with NIHL greater than that of those who carried the CC genotype. Accordingly, genetic variation in the CDH23 gene could play an essential role in determining individual susceptibility to NIHL. Conclusion: Genetic variations in the CDH23 gene may play an important role in determining individual susceptibility to NIHL. These results provide new insight into the pathogenesis and early prevention of NIHL.

A survey of label-noise deep learning for medical image analysis.

Several factors are associated with the success of deep learning. One of the most important reasons is the availability of large-scale datasets with clean annotations. However, obtaining datasets with accurate labels in the medical imaging domain is challenging. The reliability and consistency of medical labeling are some of these issues, and low-quality annotations with label noise usually exist. Because noisy labels reduce the generalization performance of deep neural networks, learning with noisy labels is becoming an essential task in medical image analysis. Literature on this topic has expanded in terms of volume and scope. However, no recent surveys have collected and organized this knowledge, impeding the ability of researchers and practitioners to utilize it. In this work, we presented an up-to-date survey of label-noise learning for medical image domain. We reviewed extensive literature, illustrated some typical methods, and showed unified taxonomies in terms of methodological differences. Subsequently, we conducted the methodological comparison and demonstrated the corresponding advantages and disadvantages. Finally, we discussed new research directions based on the characteristics of medical images. Our survey aims to provide researchers and practitioners with a solid understanding of existing medical label-noise learning, such as the main algorithms developed over the past few years, which could help them investigate new methods to combat with the negative effects of label noise.

Attention Mobilization as a Modulator of Listening Effort: Evidence From Pupillometry.

Listening to speech in noise can require substantial mental effort, even among younger normal-hearing adults. The task-evoked pupil response (TEPR) has been shown to track the increased effort exerted to recognize words or sentences in increasing noise. However, few studies have examined the trajectory of listening effort across longer, more natural, stretches of speech, or the extent to which expectations about upcoming listening difficulty modulate the TEPR. Seventeen younger normal-hearing adults listened to 60-s-long audiobook passages, repeated three times in a row, at two different signal-to-noise ratios (SNRs) while pupil size was recorded. There was a significant interaction between SNR, repetition, and baseline pupil size on sustained listening effort. At lower baseline pupil sizes, potentially reflecting lower attention mobilization, TEPRs were more sustained in the harder SNR condition, particularly when attention mobilization remained low by the third presentation. At intermediate baseline pupil sizes, differences between conditions were largely absent, suggesting these listeners had optimally mobilized their attention for both SNRs. Lastly, at higher baseline pupil sizes, potentially reflecting overmobilization of attention, the effect of SNR was initially reversed for the second and third presentations: participants initially appeared to disengage in the harder SNR condition, resulting in reduced TEPRs that recovered in the second half of the story. Together, these findings suggest that the unfolding of listening effort over time depends critically on the extent to which individuals have successfully mobilized their attention in anticipation of difficult listening conditions.

Sensitivity volume as figure-of-merit for maximizing data importance in electrical impedance tomography.

Objective.Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the periphery of a heterogeneous conductor are inverted to map its internal conductivity. The EIT method proposed here aims to improve computational speed and noise tolerance by introducing sensitivity volume as a figure-of-merit for comparing EIT measurement protocols.Approach.Each measurement is shown to correspond to a sensitivity vector in model space, such that the set of measurements, in turn, corresponds to a set of vectors that subtend a sensitivity volume in model space. A maximal sensitivity volume identifies the measurement protocol with the greatest sensitivity and greatest mutual orthogonality. A distinguishability criterion is generalized to quantify the increased noise tolerance of high sensitivity measurements.Main result.The sensitivity volume method allows the model space dimension to be minimized to match that of the data space, and the data importance to be increased within an expanded space of measurements defined by an increased number of contacts.Significance.The reduction in model space dimension is shown to increasecomputational efficiency, accelerating tomographic inversion by several orders of magnitude, while the enhanced sensitivitytolerates higher noiselevels up to several orders of magnitude larger than standard methods.

Effect of concurrent exposure to noise and hand-transmitted vibration on auditory and cognitive attention under simulated work with construction tools.

Objectives. This study aimed to investigate the effects of separate and concurrent exposure to occupational noise and hand-transmitted vibration (HTV) on auditory and cognitive attention. Methods. The experimental study was conducted with 40 construction workers who were exposed to noise (A-weighted equivalent sound pressure level of 90 dB) and to HTV (10 m/s2 at 31.5 Hz), and concurrent exposure to both for 30 min under simulated work with vibrating equipment used in construction. Cognitive performance aspects were then evaluated from each individual in two pre-exposure and post-exposure settings for each session. Results. The effect sizes of concurrent exposure (HTV + noise) and separate exposure to noise on auditory attention were very close (effect size = 0.648 and 0.626). The largest changes in the difference of response time in both types of attention (selective and divided attention) were related to the concurrent exposure scenario and then exposure to HTV, respectively. The highest effects for the correct response of selective and divided attention are related to concurrent exposure (HTV + noise) and then noise exposure, respectively. Conclusion. The HTV effect during concurrent exposure is hidden in auditory attention, and noise has the main effects. The divided attention was more affected than the selective attention in the different scenarios.

A profile on the WISE cortical strip for intraoperative neurophysiological monitoring.

INTRODUCTION: During intraoperative neurophysiological monitoring in neurosurgery, brain electrodes are placed to record electrocorticography or to inject current for direct cortical stimulation. A low impedance electrode may improve signal quality. AREAS COVERED: We review here a brain electrode (WISE Cortical Strip, WCS®), where a thin polymer strip embeds platinum nanoparticles to create conductive electrode contacts. The low impedance contacts enable a high signal-to-noise ratio, allowing for better detection of small signals such as high-frequency oscillations (HFO). The softness of the WCS may hinder sliding the electrode under the dura or advancing it to deeper structures as the hippocampus but assures conformability with the cortex even in the resection cavity. We provide an extensive review on WCS including a market overview, an introduction to the device (mechanistics, cost aspects, performance standards, safety and contraindications) and an overview of the available pre- and post-approval data. EXPERT OPINION: The WCS improves signal detection by lower impedance and better conformability to the cortex. The higher signal-to-noise ratio improves the detection of challenging signals. The softness of the electrode may be a disadvantage in some applications and an advantage in others.

Quantitative Prediction of Surface Hardness in Cr12MoV Steel and S136 Steel with Two Magnetic Barkhausen Noise Feature Extraction Methods.

The correlation between magnetic Barkhausen noise (MBN) features and the surface hardness of two types of die steels (Cr12MoV steel and S136 steel in Chinese standards) was investigated in this study. Back-propagation neural network (BP-NN) models were established with MBN magnetic features extracted by different methods as the input nodes to realize the quantitative prediction of surface hardness. The accuracy of the BP-NN model largely depended on the quality of the input features. In the extraction process of magnetic features, simplifying parameter settings and reducing manual intervention could significantly improve the stability of magnetic features. In this study, we proposed a method similar to the magnetic Barkhausen noise hysteresis loop (MBNHL) and extracted features. Compared with traditional MBN feature extraction methods, this method simplifies the steps of parameter setting in the feature extraction process and improves the stability of the features. Finally, a BP-NN model of surface hardness was established and compared with the traditional MBN feature extraction methods. The proposed MBNHL method achieved the advantages of simple parameter setting, less manual intervention, and stability of the extracted parameters at the cost of small accuracy reduction.

Absolute Ranging with Time Delay Interferometry for Space-Borne Gravitational Wave Detection.

In future space-borne gravitational wave (GW) detectors, time delay interferometry (TDI) will be utilized to reduce the overwhelming noise, including the laser frequency noise and the clock noise etc., by time shifting and recombining the data streams in post-processing. The successful operation of TDI relies on absolute inter-satellite ranging with meter-level precision. In this work, we numerically and experimentally demonstrate a strategy for inter-satellite distance measurement. The distances can be coarsely determined using the technique of arm-locking ranging with a large non-ambiguity range, and subsequently TDI can be used for precise distance measurement (TDI ranging) by finding the minimum value of the power of the residual noises. The measurement principle is introduced. We carry out the numerical simulations, and the results show millimeter-level precision. Further, we perform the experimental verifications based on the fiber link, and the distances can be measured with better than 0.05 m uncertainty, which can well satisfy the requirement of time delay interferometry.

Fast Coincidence Filter for Silicon Photomultiplier Dark Count Rate Rejection.

Silicon Photomultipliers find applications across various fields. One potential Silicon Photomultiplier application domain is neutrino telescopes, where they may enhance the angular resolution. However, the elevated dark count rate associated with Silicon Photomultipliers represents a significant challenge to their widespread utilization. To address this issue, it is proposed to use Silicon Photomultipliers and Photomultiplier Tubes together. The Photomultiplier Tube signals serve as a trigger to mitigate the dark count rate, thereby preventing undue saturation of the available bandwidth. This paper presents an investigation into a fast and resource-efficient method for filtering the Silicon Photomultiplier dark count rate. A low-resource and fast coincident filter has been developed, which removes the Silicon Photomultiplier dark count rate by using as a trigger the Photomultiplier Tube input signals. The architecture of the coincidence filter, together with the first results obtained, which validate the effectiveness of this method, is presented.

Fast Fabrication Nanopores on a PMMA Membrane by a Local High Electric Field Controlled Breakdown.

The sensitivity and accuracy of nanopore sensors are severely hindered by the high noise associated with solid-state nanopores. To mitigate this issue, the deposition of organic polymer materials onto silicon nitride (SiNx) membranes has been effective in obtaining low-noise measurements. Nonetheless, the fabrication of nanopores sub-10 nm on thin polymer membranes remains a significant challenge. This work proposes a method for fabricating nanopores on polymethyl methacrylate (PMMA) membrane by the local high electrical field controlled breakdown, exploring the impact of voltage and current on the breakdown of PMMA membranes and discussing the mechanism underlying the breakdown voltage and current during the formation of nanopores. By improving the electric field application method, transient high electric fields that are one-seven times higher than the breakdown electric field can be utilized to fabricate nanopores. A comparative analysis was performed on the current noise levels of nanopores in PMMA-SiNx composite membranes and SiNx nanopores with a 5 nm diameter. The results demonstrated that the fast fabrication of nanopores on PMMA-SiNx membranes exhibited reduced current noise compared to SiNx nanopores. This finding provides evidence supporting the feasibility of utilizing this technology for efficiently fabricating low-noise nanopores on polymer composite membranes.