Impact of signal-to-noise ratio and contrast definition on the sensitivity assessment and benchmarking of fluorescence molecular imaging systems.

Significance: Standardization of fluorescence molecular imaging (FMI) is critical for ensuring quality control in guiding surgical procedures. To accurately evaluate system performance, two metrics, the signal-to-noise ratio (SNR) and contrast, are widely employed. However, there is currently no consensus on how these metrics can be computed. Aim: We aim to examine the impact of SNR and contrast definitions on the performance assessment of FMI systems. Approach: We quantified the SNR and contrast of six near-infrared FMI systems by imaging a multi-parametric phantom. Based on approaches commonly used in the literature, we quantified seven SNRs and four contrast values considering different background regions and/or formulas. Then, we calculated benchmarking (BM) scores and respective rank values for each system. Results: We show that the performance assessment of an FMI system changes depending on the background locations and the applied quantification method. For a single system, the different metrics can vary up to ∼ 35 dB (SNR), ∼ 8.65 a . u . (contrast), and ∼ 0.67 a . u . (BM score). Conclusions: The definition of precise guidelines for FMI performance assessment is imperative to ensure successful clinical translation of the technology. Such guidelines can also enable quality control for the already clinically approved indocyanine green-based fluorescence image-guided surgery.

Enhancing visual seismocardiography in noisy environments with adaptive bidirectional filtering for Cardiac Health Monitoring.

BACKGROUND: Wearable sensors have revolutionized cardiac health monitoring, with Seismocardiography (SCG) at the forefront due to its non-invasive nature. However, the substantial motion artefacts have hindered the translation of SCG-based medical applications, primarily induced by walking. In contrast, our innovative technique, Adaptive Bidirectional Filtering (ABF), surpasses these challenges by refining SCG signals more effectively than any motion-induced noise. ABF leverages a noise-cancellation algorithm, operating on the benefits of the Redundant Multi-Scale Wavelet Decomposition (RMWD) and the bidirectional filtering framework, to achieve optimal signal quality. METHODOLOGY: The ABF technique is a two-stage process that diminishes the artefacts emanating from motion. The first step by RMWD is the identification of the heart-associated signals and the isolating samples with those related frequencies. Subsequently, the adaptive bidirectional filter operates in two dimensions: it uses Time-Frequency masking that eliminates temporal noise while engaging in non-negative matrix Decomposition to ensure spatial correlation and dorsoventral vibration reduction jointly. The main component that is altered from the other filters is the recursive structure that changes to the motion-adapted filter, which uses vertical axis accelerometer data to differentiate better between accurate SCG signals and motion artefacts. OUTCOME: Our empirical tests demonstrate exceptional signal improvement with the application of our ABF approach. The accuracy in heart rate estimation reached an impressive r-squared value of 0.95 at - 20 dB SNR, significantly outperforming the baseline value, which ranged from 0.1 to 0.85. The effectiveness of the motion-artifact-reduction methodology is also notable at an SNR of - 22 dB. Consequently, ECG inputs are not required. This method can be seamlessly integrated into noisy environments, enhancing ECG filtering, automatic beat detection, and rhythm interpretation processes, even in highly variable conditions. The ABF method effectively filters out up to 97% of motion-related noise components within the SCG signal from implantable devices. This advancement is poised to become an integral part of routine patient monitoring.

Experimentation on Sargassum wightii as a flash powder igniter for attaining eco-friendly combustion on pyrotechnics.

Firecrackers are a vital element of cultural festivities that happen worldwide. However, the hazardous by-products they emit have a significant impact on environmental pollution, leading to the greenhouse effect and climate change. Aluminium powder serves as the fuel in the traditional flash powder mixture, nitrate of potassium serves as an oxidizing agent, and sulphur acts as the igniter at exact concentrations. The presence of sulfur in the flash powder mixture is critical as it acts as an igniter, contributing to the formation of sulfur dioxide, which can cause environmental harm. We carried out an experiment employing Sargassum wightii brown seaweed powder as a replacement for sulphur at specific amounts to lessen the effects of sulphur in flash powder. We discovered that Sargassum wightii brown seaweed powder may replace up to 50% of the sulphur significance in the flash powder mixture without impairing the flash powder's traditional performance. Our experiments included impact and friction sensitivity, SEM, and FTIR analyses to evaluate the improved flash powder composition. The results revealed that the modified flash powder mixture SP5 and SP10 emits less emission by 12% and 21%, and produces similar noise performance of 108 and 107 dB(A) to the normal flash powder composition (SP), affirming that the SP10 flash powder is a viable alternative. Moreover, in our relentless pursuit to mitigate the detrimental effects on our environment, we have ingeniously introduced a novel product-the Chinese cracker made from vegetable waste paper. Not only does this innovative solution address concerns regarding land pollution, but it also presents a sustainable approach to consumer goods.

Noise-Aware Active Learning to Develop High-Temperature Shape Memory Alloys with Large Latent Heat.

Shape memory alloys (SMAs) with large latent heat absorbed/released during phase transformation at elevated temperatures benefit their potential application on thermal energy storage (TES) in high temperature environment like power plants, etc. The desired alloys can be designed quickly by searching the vast component space of doped NiTi-based SMAs via data-driven method, while be challenging with the noisy experimental data. A noise-aware active learning strategy is proposed to accelerate the design of SMAs with large latent heat at elevated phase transformation temperatures based on noisy data. The optimal noise level is estimated by minimizing the model error with incorporation of a range of noise levels as noise hyper-parameters into the noise-aware Kriging model. The employment of this strategy leads to the discovery of the alloy with latent heat of -36.08 J g-1, 9.2% larger than the best value (-33.04 J g-1) in the original training dataset within another four experiments. Additionally, the alloy represents high austenite finish temperature (481.71°C) and relatively small hysteresis. This promotes the latent heat TES application of SMAs in high temperature circumstance. It is expected that the noise-aware approach can be convenient for the accelerated materials design via the data-driven method with noisy data.

Enhancing autumn greenway walking experience: Exploring the combined effects of noise and thermal environment.

The evaluation of outdoor green spaces is influenced by diverse sensory perceptions. Traffic noise and thermal conditions significantly impact greenway-walking satisfaction; their optimization is vital for improving user experience and encouraging outdoor engagement. The study examines a typical Beijing greenway during autumn, focusing on strategies to enhance the walking experience under the combined effects of noise and thermal environments through mobile measurements and surveys. The results show that: 1) The interplay between noise and thermal factors varies depending on the walking state. Upon arrival, an increase in noise significantly worsens thermal comfort; higher sound levels intensify warm thermal sensations, though this effect is not consciously perceived. Upon departure, the effect of noise on thermal perception is not obvious. In both walking states, thermal sensation significantly affects subjective noise perception, yet the trends of influence differ. Subjective noise loudness increases as thermal comfort worsens, showing significant correlation only upon departure. 2) During autumn greenway walks, acoustic factors exert a greater impact on Overall Environmental Satisfaction (OES), with subjective noise loudness being more influential than noise level, followed by air temperature (Ta). Greater noise decreases OES, while OES increases initially with Ta and then decreases. The integrated effects of noise-thermal factors on OES show significant changes. 3) To enhance the autumn greenway-walking experience, the advised parameters are A-weighted Sound Level (ASL) ≤ 59.12 dBA and 15.17 °C ≤ Ta ≤ 18.75 °C. Finally, three design strategies are proposed: reducing subjective noise loudness, differentiating design based on walking status and balancing acoustic-thermal perceptual preferences.

The influence of occupational noise exposure on blood pressure and hearing loss among female workers of childbearing age.

OBJECTIVES: Women exposed to occupational noise experience adverse pregnancy outcomes. Therefore, we initiated a large, population-based, cross-sectional study to further investigate the effects of occupational noise on hearing and blood pressure among female workers of childbearing age. STUDY DESIGN AND SETTING: A total of 6981 childbearing-aged female workers were selected for this cross-sectional study. Basic characteristics of participants were analyzed by comparing the exposed and control groups. Logistic regression models were employed to calculate the odds ratios (ORs) and 95% confidences intervals (CIs) for the associations of occupational noise with levels of hearing loss and blood pressure. The associations were further explored through stratification by age and duration of noise exposure. RESULTS: Compared with participants not exposed to occupational noise, increasing years of occupational noise exposure were independently associated with an elevated risk of hypertension after adjustment of age, industry classification, enterprise size and economic type. Compared to participants not exposed to occupational noise, only the prevalence of bilateral hearing loss was significantly higher after adjustments for age, industry classification, enterprise size and economic type. Compared with those with normal hearing, the ORs and 95% CIs were 1.97 (0.95-4.07), 2.22 (1.05-4.68) and 1.29 (1.06-1.57) for bilateral, unilateral and any ear hearing loss, respectively. CONCLUSIONS: Occupational noise exposure is positively associated with both hypertension and bilateral hearing loss among female workers of childbearing age. Those exposed to occupational noise show an increased risk of hypertension after adjusting for potential confounders.

Quieting the neonatal intensive care unit: A quality improvement initiative.

BACKGROUND: The neonatal intensive care unit (NICU) is vital for preterm infants but is often plagued by harmful noise levels. Excessive noise, ranging from medical equipment to conversations, poses significant health risks, including hearing impairment and neurodevelopmental issues. The American Academy of Pediatrics recommends strict sound limits to safeguard neonatal well-being. Strategies such as education, environmental modifications, and quiet hours have shown to reduce noise levels. However, up to 60% of the noises remain avoidable. High noise exposure exacerbates physiological disturbances, impacting vital functions and long-term neurological outcomes. Effective noise reduction in the NICU is crucial for promoting optimal neonatal development. AIM: To measure the sound levels in a NICU and reduce ambient sound levels by at least 10% from baseline. METHODS: A quasi-experimental quality improvement project was conducted over 4 mo in a 20-bed level 3 NICU in a tertiary care medical college. Baseline noise levels were recorded continuously using a sound level meter. The interventions included targeted education, environmental modifications, and organizational changes, and were implemented through three rapid Plan-Do-Study-Act (PDSA) cycles. Weekly feedback and monitoring were conducted, and statistical process control charts were used for analysis. The mean noise values were compared using the paired t-test. RESULTS: The baseline mean ambient noise level in the NICU was 67.8 dB, which decreased to 50.5 dB after the first cycle, and further decreased to 47.4 dB and 51.2 dB after subsequent cycles. The reduction in noise levels was 21% during the day and 28% at night, with an overall decrease of 25% from baseline. The most significant reduction occurred after the first PDSA cycle (mean difference of -17.3 dB, P < 0.01). Peak noise levels decreased from 110 dB to 88.24 dB after the intervention. CONCLUSION: A multifaceted intervention strategy reduced noise in the NICU by 25% over 4 months. The success of this initiative emphasizes the significance of comprehensive interventions for noise reduction.

Anthropogenic noise and light pollution decrease the repeatability of activity patterns and dampen expression of chronotypes in a free-living songbird.

Anthropogenic environmental change is introducing a suite of novel disturbance factors, which can have wide-ranging effects on mean behavior and behavioral repeatability. For example, exposure to sensory pollutants, such as anthropogenic noise and artificial light at night (ALAN), may affect consistent and repeatable individual-level timing of daily activity, which is referred to as chronotypes. Although chronotypes have been increasingly documented in wild animal populations and may affect fitness, evidence for long-term stability across life-history stages and seasons is notably lacking. Furthermore, how multiple anthropogenic stressors may interact to erode or magnify the expression of chronotypes remains unclear. We tested for existence of chronotypes across life-history stages and seasons in suburban female great tits (Parus major), using emergence time from nest boxes in the morning as a proxy for activity onset. We then examined joint effects of noise pollution and ALAN on expression of chronotypes, and tested for effects of noise, ALAN, and weather conditions on mean emergence time. We found repeatability of daily activity patterns (emergence times) across life-history stages and seasons, providing evidence of chronotypes, as well as interactive effects of anthropogenic disturbance factors and weather conditions on population mean behavior. Furthermore, across-season repeatability of emergence times was approximately double in magnitude in low light and low noise conditions, relative to in conditions with higher light and/or noise pollution. Thus, joint exposure to these sensory pollutants tends to erode expression of chronotypes. This effect was driven by higher among-individual variance in the relatively undisturbed environment and collapse of this variance in the more disturbed environments. Decreased repeatability in environments with high disturbance levels may reduce potential for behavioral traits, such as chronotype, to be the target of selection and limit adaptability.

Impact of Audio-Visual Complexity on Symptomatology of Laryngeal Dystonia: A Virtual Reality Study.

BACKGROUND: Laryngeal dystonia (LD) is an isolated focal dystonia characterized by involuntary spasms in laryngeal muscles selectively impairing speech production. Anecdotal observations reported the worsening of LD symptoms in stressful or vocally demanding situations. OBJECTIVES: To examine the impact of surrounding audio-visual complexity on LD symptomatology for a better understanding of disorder phenomenology. METHODS: We developed well-controlled virtual reality (VR) environments of real-life interpersonal communications to investigate how different levels of audio-visual complexity may impact LD symptoms. The VR experiments were conducted over five consecutive days, during which each patient experienced 10 h of 4100 experimental trials in VR with gradually increasing audio-visual complexity. Daily reports were collected about patients' voice changes, as well as their comfort, engagement, concentration, and drowsiness from using VR technology. RESULTS: After a weekly VR exposure, 82% of patients reported changes in their voice symptoms related to changes in background audio-visual complexity. Significant differences in voice symptoms were found between the first two levels of the audio-visual challenge complexity independent of study sessions or VR environments. CONCLUSION: This study demonstrated that LD symptoms are impacted by audio-visual background across various virtual realistic settings. These findings should be taken into consideration when planning behavioral experiments or evaluating the outcomes of clinical trials in these patients. Moreover, these data show that VR presents a reliable and useful technology for providing real-life assessments of the impact of various experimental settings, such as during the testing of novel therapeutic interventions in these patients. LEVEL OF EVIDENCE: Level 3 Laryngoscope, 2024.

Exposure to aircraft noise exacerbates cardiovascular and oxidative damage in three mouse models of diabetes.

BACKGROUND: Epidemiology links noise to increased risk of metabolic diseases like diabetes and obesity. Translational studies in humans and experimental animals showed that noise causes reactive oxygen species (ROS)-mediated cardiovascular damage. The interaction between noise and diabetes, specifically potential additive adverse effects, remains to be determined. METHODS AND RESULTS: C57BL/6 mice were treated with streptozotocin (i.p. injections, 50 mg/kg/d for 5d) to induce type-1 diabetes, with S961 (subcutaneous osmotic minipumps, 0.57 mg/kg/d for 7d) or fed a high-fat diet (HFD, 20 weeks) to induce type-2 diabetes. Control and diabetic mice were exposed to aircraft noise to an average sound pressure level of 72 dB(A) for 4d. While body weight was unaffected, noise reduced insulin production in all diabetes models. The oral glucose tolerance test showed only an additive aggravation by noise in the HFD model. Noise increased blood pressure and aggravated diabetes-induced aortic, mesenteric, and cerebral arterioles endothelial dysfunction. ROS formation in cerebral arterioles, the aorta, the heart, and isolated mitochondria was consistently increased by noise in all models of diabetes. Mitochondrial respiration was impaired by diabetes and noise, however without additive effects. Noise increased ROS and caused inflammation in adipose tissue in the HFD model. RNA sequencing data and alteration of gene pathway clusters also supported additive damage by noise in the setting of diabetes. CONCLUSION: In all three models of diabetes, aircraft noise exacerbates oxidative stress, inflammation, and endothelial dysfunction in mice with pre-existing diabetes. Thus, noise may potentiate the already increased cardiovascular risk in diabetic patients.

Traffic noise significantly contributes to an increased risk of cardiometabolic diseases (including diabetes and obesity) in the general population via stress hormones, inflammation and oxidative stress, all of which contribute to impaired vascular function and high blood pressure. However, the extent to which noise affects pre-existing diabetes is not sufficiently explained, which prompted us to investigate the molecular mechanisms responsible for noise-mediated exacerbation of cardiometabolic complications in three different animal models with diabetes mellitus: Noise exposure in diabetic mice caused further impairment of insulin signalling, increased blood pressure, and damage of small and large blood vessels as well as oxidative stress in the aorta, brain, and heart.Our functional observations were supported by gene analyses indicating combined effects of noise and diabetes on gene groups related to inflammation and metabolism, suggesting a need for further studies in humans to investigate how noise impacts cardiovascular risk in vulnerable groups such as patients with diabetes.

Adaptive and self-learning Bayesian filtering algorithm to statistically characterize and improve signal-to-noise ratio of heart-rate data in wearable devices.

The use of wearable sensors to monitor vital signs is increasingly important in assessing individual health. However, their accuracy often falls short of that of dedicated medical devices, limiting their usefulness in a clinical setting. This study introduces a new Bayesian filtering (BF) algorithm that is designed to learn the statistical characteristics of signal and noise, allowing for optimal smoothing. The algorithm is able to adapt to changes in the signal-to-noise ratio (SNR) over time, improving performance through windowed analysis and Bayesian criterion-based smoothing. By evaluating the algorithm on heart-rate (HR) data collected from Garmin Vivoactive 4 smartwatches worn by individuals with amyotrophic lateral sclerosis and multiple sclerosis, it is demonstrated that BF provides superior SNR tracking and smoothing compared with non-adaptive methods. The results show that BF accurately captures SNR variability, reducing the root mean square error from 2.84 bpm to 1.21 bpm and the mean absolute relative error from 3.46% to 1.36%. These findings highlight the potential of BF as a preprocessing tool to enhance signal quality from wearable sensors, particularly in HR data, thereby expanding their applications in clinical and research settings.

Noise-Induced Hearing Loss Awareness and Prevention: A Cross-Sectional Study Among the Population of the Southern Region of Saudi Arabia.

BACKGROUND: Noise-induced hearing loss (NIHL) is a prevalent and preventable health issue globally. This study aims to evaluate the symptoms, knowledge, beliefs, and preventive practices regarding NIHL among the general population of the southern region of Saudi Arabia. MATERIALS & METHODS: A cross-sectional study was conducted from May to July 2024, using a self-administered, validated electronic questionnaire distributed in Arabic via social media platforms. The questionnaire assessed socio-demographic data, NIHL awareness, attitudes toward prevention, and personal practices regarding noise exposure. The sample included 400 participants analyzed using SPSS version 23 (IBM Corp., Armonk, NY), with associations measured through the chi-square and Fisher's exact tests. RESULTS: Most participants were Saudi nationals (97%) and females (81.3%). Symptoms of NIHL, such as tinnitus and the need to increase TV or radio volume, were prevalent among participants. Most participants (88.5%) were aware that high sound levels affect hearing, yet only 9.5% correctly identified the minimum duration of exposure that could harm hearing. Social media was the primary source of information (51.3%). Positive preventive practices were noted, with 66% lowering device volumes and 55.3% recommending sound restrictions on tablets. Significant associations were found between better preventive practices and higher income as well as marital status. CONCLUSION: The study highlights the high basic awareness of NIHL but identifies critical knowledge gaps regarding the minimum sound level and duration that affect hearing negatively. Enhanced public health education and technological interventions are needed to improve prevention practices. Future research should include longitudinal studies and diverse populations to better understand and address NIHL.

Long-term nighttime aircraft noise exposure and risk of hypertension in a prospective cohort of female nurses.

There is growing interest in cardiometabolic outcomes associated with nighttime noise, given that noise can disturb sleep and sleep disturbance can increase cardiometabolic risk such as hypertension. However, there is little empirical research evaluating the association between nighttime aircraft noise and hypertension risk. In this study, we expand on previous work to evaluate associations between nighttime aircraft noise exposure and self-reported hypertension incidence in the Nurses' Health Studies (NHS/NHSII), two US-wide cohorts of female nurses. Annual nighttime average aircraft sound levels (Lnight) surrounding 90 airports for 1995-2015 (in 5-year intervals) were modeled using the Aviation Environmental Design Tool and assigned to participants' geocoded addresses over time. Hypertension risk was estimated for each cohort using time-varying Cox proportional-hazards models for Lnight dichotomized at 45 dB (dB), adjusting for individual-level hypertension risk factors, area-level socioeconomic status, region, and air pollution. Random effects meta-analysis was used to combine cohort results. Among 63,229 NHS and 98,880 NHSII participants free of hypertension at study baseline (1994/1995), we observed 33,190 and 28,255 new hypertension cases by 2014/2013, respectively. Although ∼1% of participants were exposed to Lnight ≥45 dB, we observed an adjusted hazard ratio (HR) of 1.10 (95% CI: 0.96, 1.27) in NHS and adjusted HR of 1.12 (95% CI: 0.98, 1.28) in NHSII, comparing exposure to Lnight ≥45 versus <45 dB(A). In meta-analysis, we observed an adjusted HR of 1.11 (95% CI: 1.01, 1.23). These results were attenuated with adjustment for additional variables such as body mass index. Our findings support a modest positive association between nighttime aircraft noise and hypertension risk across NHS/NHSII, which may reinforce the concept that sleep disturbance contributes to noise-related disease burden.

Automatic motion artifact detection in electrodermal activity signals using 1D U-net architecture.

We developed a method for automated detection of motion and noise artifacts (MNA) in electrodermal activity (EDA) signals, based on a one-dimensional U-Net architecture. EDA has been widely employed in diverse applications to assess sympathetic functions. However, EDA signals can be easily corrupted by MNA, which frequently occur in wearable systems, particularly those used for ambulatory recording. MNA can lead to false decisions, resulting in inaccurate assessment and diagnosis. Several approaches have been proposed for MNA detection; however, questions remain regarding the generalizability and the feasibility of implementation of the algorithms in real-time especially those involving deep learning approaches. In this work, we propose a deep learning approach based on a one-dimensional U-Net architecture using spectrograms of EDA for MNA detection. We developed our method using four distinct datasets, including two independent testing datasets, with a total of 9602 128-s EDA segments from 104 subjects. Our proposed scheme, including data augmentation, spectrogram computation, and 1D U-Net, yielded balanced accuracies of 80.0 ± 13.7 % and 75.0 ± 14.0 % for the two independent test datasets; these results are better than or comparable to those of other five state-of-the-art methods. Additionally, the computation time of our feature computation and machine learning classification was significantly lower than that of other methods (p < .001). The model requires only 0.28 MB of memory, which is far smaller than the two deep learning approaches (4.93 and 54.59 MB) which were used as comparisons to our study. Our model can be implemented in real-time in embedded systems, even with limited memory and an inefficient microprocessor, without compromising the accuracy of MNA detection.

Road traffic noise prediction model based on artificial neural networks.

This paper proposes a model based on machine learning for the prediction of road traffic noise for the city of Bogota-Colombia. The input variables of the model were: vehicle capacity, speed, type of flow and number of lanes. The input data were obtained through measurement campaigns in which audio and video recordings were made. The audio recordings, made with a measuring microphone calibrated at a height of 4 meters, made it possible to calculate the noise levels through software processing. On the other hand, by processing the video data, the capacity, and speed of the vehicle were obtained. This process was carried out by means of a classifier trained with images of vehicles taken in the field and free databases. In order to determine the machine learning algorithm to be used, five models were compared, which were configured with their respective hyperparameters obtained through mesh search. The results showed that the Multilayer Perceptron (MLP) regression had the best fit with an MAE of 0.86 dBA for the test data. Finally, the proposed MLP regressor was compared with some classical statistical models used for road traffic noise prediction. The main conclusion is that the MLP regressor obtained the best error and fit indicators with respect to traditional statistical models.

Inversion method of NHV based on novel model parameter space acquisition strategy and its application in the Tonghai basin site in Yunnan, China.

The imaging of subsurface soil velocity structures from ambient noise inversion is a difficult problem. Few recording points and a simplified 1-D layered profile lead to important non-uniqueness. From our point of view, improving the reliability of processing methods of the observed data to obtain noise horizontal-to-vertical spectral ratio (NHV) curves and setting a complete model parameter space are important tasks to reduce the non-uniqueness of inversion. In this study, using a local site near the border of the Tonghai Basin, China, as a case study, we first demonstrate how to identify and mitigate the influence of industrial sources using surface observations to obtain more reliable NHV curves. Then, a new strategy to determine model parameter space is proposed, that is, stratifying soil layers based on the number of NHV peaks and determining the shear wave velocities, thicknesses, and their ranges based on the empirical relationship between sedimentary thickness and resonant frequency (h-f r). Subsequently, combining the model parameter space acquisition strategy with the NHV inversion, a novel NHV inversion approach is developed and applied to obtain the 2-D V S profile of the investigated Tonghai site. The inverted 2-D V S profile aligns favorably with the frequency-depth conversion results of the measured NHV curves (NHV-profiling) and the measured borehole profiles, affirming the reliability of the proposed NHV inversion method. Finally, by comparing the empirical transfer functions from the strong-motion recordings, we validated the applicability of the inverted models for characterizing site effects. The model parameter space acquisition strategy proposed in this paper and the analysis procedure of the observed data are also applicable to other study areas, which can provide a referable approach to quickly and effectively acquire the soil layer velocity structure of the site.

Assessment of noise pollution-prone areas using an explainable geospatial artificial intelligence approach.

This research aims to use the power of geospatial artificial intelligence (GeoAI), employing the categorical boosting (CatBoost) machine learning model in conjunction with two metaheuristic algorithms, the firefly algorithm (CatBoost-FA) and the fruit fly optimization algorithm (CatBoost-FOA), to spatially assess and map noise pollution prone areas in Tehran city, Iran. To spatially model areas susceptible to noise pollution, we established a comprehensive spatial database encompassing data for the annual average Leq (equivalent continuous sound level) from 2019 to 2022. This database was enriched with critical spatial criteria influencing noise pollution, including urban land use, traffic volume, population density, and normalized difference vegetation index (NDVI). Our study evaluated the predictive accuracy of these models using key performance metrics, including root mean square error (RMSE), mean absolute error (MAE), and receiver operating characteristic (ROC) indices. The results demonstrated the superior performance of the CatBoost-FA algorithm, with RMSE and MAE values of 0.159 and 0.114 for the training data and 0.437 and 0.371 for the test data, outperforming both the CatBoost-FOA and CatBoost models. ROC analysis further confirmed the efficacy of the models, achieving an accuracy of 0.897, CatBoost-FOA with an accuracy of 0.871, and CatBoost with an accuracy of 0.846, highlighting their robust modeling capabilities. Additionally, we employed an explainable artificial intelligence (XAI) approach, utilizing the SHAP (Shapley Additive Explanations) method to interpret the underlying mechanisms of our models. The SHAP results revealed the significant influence of various factors on noise-pollution-prone areas, with airport, commercial, and administrative zones emerging as pivotal contributors.

Behavioural stochastic resonance across the lifespan.

Stochastic resonance (SR) is the phenomenon wherein the introduction of a suitable level of noise enhances the detection of subthreshold signals in non linear systems. It manifests across various physical and biological systems, including the human brain. Psychophysical experiments have confirmed the behavioural impact of stochastic resonance on auditory, somatic, and visual perception. Aging renders the brain more susceptible to noise, possibly causing differences in the  SR phenomenon between young and elderly individuals. This study investigates the impact of noise on motion detection accuracy throughout the lifespan, with 214 participants ranging in age from 18 to 82. Our objective was to determine the optimal noise level to induce an SR-like response in both young and old populations. Consistent with existing literature, our findings reveal a diminishing advantage with age, indicating that the efficacy of noise addition progressively diminishes. Additionally, as individuals age, peak performance is achieved with lower levels of noise. This study provides the first insight into how SR changes across the lifespan of healthy adults and establishes a foundation for understanding the pathological alterations in perceptual processes associated with aging.

Functional hearing impairment common in Parkinson's disease: Insights from a pilot study.

INTRODUCTION: Hearing impairments in Parkinson's Disease (PD) have received limited attention in the past, possibly because PD patients often report no perceived hearing disability, yet negative consequences of hearing impairment might aggravate communication difficulties and social withdrawal. OBJECTIVE: Our aim was to investigate functional hearing (speech in noise recognition) in PD and evaluate its relationship to neuropsychiatric symptoms, cognition and quality of life. METHODS: Participants with PD were recruited in a tertiary movement disorder clinic. Demographic, audiological, neuropsychiatric and quality of life data were collected. Participants underwent pure tone audiometry (PTA) and Hearing in Noise test (HINT) as a part of their audiological evaluation. RESULTS: A total of 29 participants (mean age: 65.8±8.3 years, M:F= 1.6:1, mean disease duration 5.2 ± 4.0 years) completed the study. All assessments were done in the ON state. 19/29 (65.5 %) participants had normal tone audiometry for age; functional hearing loss, however, was present in 17/29 (58.6 %) according to the HINT. 65 % (11/17) of the affected participants had a disease duration of <4 years. The majority (72.4 %) with poor functional hearing did not perceive any hearing impairment. Hearing deficits did not correlate with non-motor symptoms (NMS), including cognition or other quality of life measures. CONCLUSIONS: Functional hearing loss is common in PD, often presents early in the disease and the majority of PD patients are unaware of their functional hearing loss. Its potential impact on cognition, communication and quality of life requires further investigation and tailored treatment.

Maritime traffic alters distribution of the harbour porpoise in the North Sea.

The North Sea is one of the most industrialised marine regions globally. We integrated cetacean-dedicated aerial surveys (2015-2022) with environmental covariates and ship positions from the Automatic Identification System (AIS) to investigate the disturbance radius and duration on harbour porpoise distribution. This study is based on 81,511 km of line-transect survey effort, during which 6511 harbour porpoise groups (8597 individuals) were sighted. Several proxies for ship disturbance were compared, identifying those best explaining the observed distribution. Better model performance was achieved by integrating maritime traffic, with frequent traffic representing the most significant disturbance to harbour porpoise distribution. Porpoises avoided areas frequented by numerous vessels up to distances of 9 km. The number of ships and average approach distance over time improved model performance, while reasons for the lower performance of predicted ship sound levels remain unclear. This study demonstrates the short-term effects of maritime traffic on harbour porpoise distribution.