It is well known that exposure to high noise levels can adversely affect human hearing. Legislation exists in Europe to control or restrict the level of noise to which employees may be exposed during the course of their work. While the noise levels to which a worker may be exposed is well defined in air, human sensitivity to noise is different in high-pressure and mixed-gas conditions. Relatively little research exists to define human hearing in these circumstances, and few measurements exist of the levels of noise to which divers working in these conditions are exposed. A study using specially designed equipment has been undertaken in Norwegian waters to sample the noise levels present during typical saturation dives undertaken by commercial divers working in the Norwegian oil and gas industry. The divers were working in heliox at depths of 30 msw and 120 msw. It found noise levels were generally dominated by self-noise: flow noise while breathing and communications. The noise levels, both when corrected for the difference in hearing sensitivity under pressure in mixed gas and uncorrected, would exceed legislated limits for noise exposure in a working day without the use of noisy tools.

Download full-text PDF

Source

Publication Analysis

Top Keywords

noise levels
20
divers working
12
noise
11
noise exposure
8
commercial divers
8
human hearing
8
levels
6
exposure commercial
4
divers
4
divers norwegian
4

Similar Publications

Bacterial spores are highly resilient and capable of surviving extreme conditions, making them a persistent threat in contexts such as disease transmission, food safety, and bioterrorism. Their ability to withstand conventional sterilization methods necessitates rapid and accurate detection techniques to effectively mitigate the risks they present. In this study, we introduce a surface-enhanced Raman spectroscopy (SERS) approach for detecting spores by targeting calcium dipicolinate acid (CaDPA), a biomarker uniquely associated with bacterial spores.

View Article and Find Full Text PDF

Aim: The aim of this study was to assess the subjective experiences of adults with different cochlear implant (CI) configurations-unilateral cochlear implant (UCI), bilateral cochlear implant (BCI), and bimodal stimulation (BM)-focusing on their perception of speech in quiet and noisy environments, music, environmental sounds, people's voices and tinnitus.

Methods: A cross-sectional survey of 130 adults who had undergone UCI, BCI, or BM was conducted. Participants completed a six-item online questionnaire, assessing difficulty levels and psychological impact across auditory domains, with responses measured on a 10-point scale.

View Article and Find Full Text PDF

Measuring the effects of motion corruption in fetal fMRI.

Hum Brain Mapp

February 2025

Computational Imaging Research Lab, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.

Irregular and unpredictable fetal movement is the most common cause of artifacts in in utero functional magnetic resonance imaging (fMRI), affecting analysis and limiting our understanding of early functional brain development. The accurate detection of corrupted functional connectivity (FC) resulting from motion artifacts or preprocessing, instead of neural activity, is a prerequisite for reliable and valid analysis of FC and early brain development. Approaches to address this problem in adult data are of limited utility in fetal fMRI.

View Article and Find Full Text PDF

Object pose estimation is essential for computer vision applications such as quality inspection, robotic bin picking, and warehouse logistics. However, this task often requires expensive equipment such as 3D cameras or Lidar sensors, as well as significant computational resources. Many state-of-the-art methods for 6D pose estimation depend on deep neural networks, which are computationally demanding and require GPUs for real-time performance.

View Article and Find Full Text PDF

Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).

Materials And Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!