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.
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ACS Sens
January 2025
Department of Physics, Umeå University, Umeå SE-901 87, Sweden.
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 PDFAudiol Res
January 2025
Otolaryngology Unit, Department of Traslational Medicine and Neuroscience-DiBrain, University of Bari, 70124 Bari, Italy.
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.
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 PDFFront Robot AI
January 2025
Department of Materials and Production, Aalborg University, Aalborg, Denmark.
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 PDFJAMIA Open
February 2025
Institute for Informatics, Data Science and Biostatistics, Washington University, Saint Louis, MO 63110, United States.
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.
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