Maryam Naghibolhosseini

We employ the Hamiltonian Monte Carlo (HMC) algorithm to estimate model parameters and quantify their uncertainties in a fractional-order lumped-element model of the human ear in a Bayesian inference framework. The model, originally developed by Naghibolhosseini and Long (2018), incorporates fractional-order elements to capture viscoelastic memory effects in ear tissues that otherwise cannot adequately be represented via conventional integer-order models. Using previously optimized model parameters to construct informative priors, we perform Bayesian parameter estimation via the No-U-Turn Sampler (NUTS) implementation. From the inferred posterior distributions, we compute the model’s outer-middle ear gain (OMEG) and validate predictions against experimental OMEG derived from DPOAE measurements. Additionally, we compare stapes velocity transfer functions and ear canal pressure gain with established experimental and computational literature. HMC sampling yields well-convergent posterior distributions for all parameters, centered near original optimized values with uncertainty quantified through credible intervals. The posterior predictive OMEG frequency response closely matches the experimental OMEG measurements. Interestingly, the Bayesian-derived parameter sets correctly exhibit stapes velocity resonances near 1 kHz and ear canal pressure gain peaks between 2.5 and 4 kHz, with amplification ranging from 4 to 12 dB, consistent with cadaveric experimental measurements and existing computational models. The model demonstrates a minimal intersubject variability while capturing realistic biological variations within experimentally reported ranges. The present Bayesian HMC simulation approach then provides a robust uncertainty quantification for fractional-order ear model parameter inference, maintaining physiologically consistent predictions across multiple validation datasets. Hence, the proposed framework enhances the model’s credibility by establishing a firm foundation for further developing probabilistic diagnostic tools for hearing assessment in the future.

Background: Evidence from high-income countries suggests that COVID-19 may adversely affect cognitive functioning, yet population-based data from African countries remain scarce. Understanding how COVID-19 symptom burden, chronic disease, and mental health intersect to shape cognitive outcomes is critical in low-resource settings disproportionately affected by structural and health system constraints. Methods: Cross-sectional data were collected from 3,058 adults (M_age = 27.2 years) in Botswana, Ghana, Nigeria, and Tanzania between April 2020 and November 2022 using the Sonde Health platform. Participants self-reported sociodemographic characteristics, COVID-19 symptoms, chronic disease diagnoses, mental health symptoms, physical activity, and sedentary behavior. Executive function was assessed using the Stroop Color–Word interference score. Multivariable linear regression models estimated adjusted mean differences in executive function associated with COVID-19 symptom burden and chronic disease, controlling for sociodemographic, health, mental health, and behavioral factors. Effect modification by country was evaluated using interaction terms (p < 0.10). Results: Executive function declined with increasing COVID-19 symptom burden, with Stroop scores decreasing from 0.14 among participants reporting no symptoms to 0.07 among those reporting three or more symptoms (p < 0.001). Being symptom-free was associated with better executive function in Ghana (adjusted mean difference = 0.06; 95% CI: 0.00, 0.11) and Nigeria (adjusted mean difference = 0.07; 95% CI: 0.02, 0.12), but not in Botswana or Tanzania. Lower chronic disease burden was associated with better executive function in Nigeria (adjusted mean difference = 0.16; 95% CI: 0.06, 0.26). Higher educational attainment was consistently associated with better executive function across countries. Conclusions: COVID-19 symptom burden and chronic disease were associated with poorer executive function across the four African countries studied, with substantial cross-country variation. Education emerged as a consistent protective factor. These findings highlight the importance of integrated, context-sensitive approaches that address both physical and mental health to support cognitive well-being during and beyond public health crises.

The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on analyzing the failure of transmission lines, specifically considering the effects of wind, ambient temperature, and current demands, incorporating minimal and significant pre-existing damage. We propose a multiphysics framework to analyze the transmission line failures across sensitive and affected states of the United States, integrating historical data on wind and ambient temperature. By combining numerical simulation with historical data analysis, our research assesses the impact of varying environmental conditions on the reliability of transmission lines. Our methodology begins with a deterministic approach to model temperature and damage evolution, using phase-field modeling for fatigue and damage coupled with electrical and thermal models. Later, we adopt the probability collocation method to investigate the stochastic behavior of the system, enhancing our understanding of uncertainties in model parameters, conducting sensitivity analysis to identify the most significant model parameters, and estimating the probability of failures over time. This approach allows for a comprehensive analysis of factors affecting transmission line reliability, contributing valuable insights into improving power line’s resilience against environmental conditions.

Laryngeal high-speed videoendoscopy (HSV) is a powerful tool to study the detailed vibratory characteristics of the vocal folds. The use of HSV in connected speech enables us to study the transitory behaviors of the vocal folds during different vocalizations. The onset and offset of voicing contain critical information about the health and function of the vocal folds. In this work, HSV is used to study how the voicing begins/ends at the laryngeal level at the onset/offset of phonation. HSV data were obtained during production of connected speech from participants with non-pathological voices and patients with different neurogenic voice disorders. The data collection was performed using a Photron FASTCAM mini AX200 high-speed camera coupled with a flexible nasolaryngoscope. The glottal attack time and glottal offset time were measured at the onset and offset of different vocalizations, respectively, from the HSV data. The findings show differences in the onset and offset measures between the two groups of participants. The vocal fold vibratory behaviors will be discussed qualitatively and quantitatively using these measures. The use of HSV in connected speech could help us develop accurate voice assessment procedures to assess the vocal folds function during connected speech.

Adductor spasmodic dysphonia (AdSD) disrupts laryngeal muscle control during speech and, therefore, affects the onset and offset of phonation. In this study, the goal is to use laryngeal high-speed videoendoscopy (HSV) to measure the glottal attack time (GAT) and glottal offset time (GOT) during connected speech for normophonic (vocally normal) and AdSD voices. A monochrome HSV system was used to record readings of six CAPE-V sentences and part of the “Rainbow Passage” from the participants. Three raters visually analyzed the HSV data using a playback software to measure the GAT and GOT. The results show that the GAT was greater in the AdSD group than in the normophonic group; however, the clinical significance of the amount of this difference needs to be studied further. More variability was observed in both GATs and GOTs of the disorder group. Additionally, the GAT and GOT time series were found to be nonstationary for the AdSD group while they were stationary for the normophonic voices. This study shows that the GAT and GOT measures can be potentially used as objective markers to characterize AdSD. The findings will potentially help in the development of standardized measures for voice evaluation and the accurate diagnosis of AdSD.

The goal of this study is to compare three of the most commonly used primary-level relation paradigms (i.e., Scissors, Boys Town ‘Optimal’, and Equal-Level) in generation of distortion product otoacoustic emissions (DPOAEs) in normal hearing adults. The generator and reflection components were extracted from DPOAEs in each paradigm. The generator and reflection component levels and input/output (I/O) functions were compared across paradigms and primary-tone levels. The results showed a different I/O function growth behavior across frequency and levels among paradigms. The Optimal paradigm showed a systematic change in the generator and reflection component levels and I/O slopes across primary levels among subjects. Moreover, the levels and slopes in the Optimal paradigm were more distinct across levels with less variations across frequency leading to a systematic change in the DPOAE fine structure across levels. The I/O functions were found to be more sensitive to the selected paradigm; especially the I/O function for the reflection component. The I/O functions of the reflection components showed large variability across frequencies due to different frequency shifts in their microstructure depending on the paradigm. The findings of this study suggested the Optimal paradigm as the proper primary-level relation to study cochlear amplification/compression. The findings of this study shows that care needs to be taken in comparing the findings of different studies that generated DPOAEs with a different level-relation paradigm.