Region Models Research Articles

Adulteration detection of edible oil by one‐class classification and outlier detection

AbstractEdible oil adulteration is a mostly practiced phenomenon. However, the traditional discriminant methods fail to detect oil adulteration involving more than one adulterant. Recently, one‐class classifiers were built for food or oil authentication. Unfortunately, as it is hard to determine the application domain of the one‐class classifier, high prediction error was obtained for real samples in market surveillance. In this study, a new method was developed based on one‐class classification and outlier detection for edible oil adulteration detection in market surveillance. The model population was constructed using Monte Carlo sampling of unidentified inspected samples to select the plateau region exhibiting the highest accumulated absolute centered residual (ACR) values. Subsequently, the number of models in the plateau region was validated by the theoretical ones calculated by the classical probability model. The models in the plateau region with the highest cumulative accumulated ACR values were used to identify adulterated oils. Furthermore, the cross‐validation was conducted by comparing identification results from two different Monte Carlo sampling ratios to ensure the accuracy of our method. Both single adulteration and multiple adulteration of peanut oils were prepared to validate our method. Moreover, this method was used to detect adulteration of sesame oils, which have already been identified by the markers in our previous study. The validation results of three datasets indicated that this method could effectively identify adulterated samples and therefore provide a novel solution for inspecting potential adulteration in practice.

Hourglass cascaded recurrent stereo matching network

Stereo matching acts a crucial role in computer vision and robotics applications. An accurate cost volume and robust disparity regression method are essential for stereo matching of high accuracy. Following GCNet and PSMNet, constructing 4D cost volume and then using the soft argmin method to regress has been dominated. However, it will encounter many difficulties due to the multi-modal distribution of cost volume. One of the reasons for this multi-modal distribution is the occlusion area which not be possible to find a matching region on the reference image and rarely discussed. In this paper, we propose to use global context information could improve the performance of model in occluded regions. Recently, novel recurrent neural network regression methods are proposed, but most of them regress disparity maps from 3D cost volume. In this paper, we propose the new combinatorial paradigm that combine stacked hourglass modules and recurrent neural networks to further aggregate 4D cost volume and regress disparity respectively. The proposed method can be seamlessly integrated into most stereo matching networks, we improved the accuracy by 45% for PSMNet and 38% for GwcNet in our experiment. Experimental results on Scene Flow, KITTI2012, KITTI2015, and ETH3D datasets show our method is competitive. The code is available at: https://github.com/truman1211/HCRnet.

Cutaneous squamous cell carcinoma characterized by MALDI mass spectrometry imaging in combination with machine learning

Cutaneous squamous cell carcinoma (SCC) is an increasingly prevalent global health concern. Current diagnostic and surgical methods are reliable, but they require considerable resources and do not provide metabolomic insight. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables detailed, spatially resolved metabolomic analysis of tissue samples. Integrated with machine learning, MALDI-MSI could yield detailed information pertaining to the metabolic alterations characteristic for SCC. These insights have the potential to enhance SCC diagnosis and therapy, improving patient outcomes while tackling the growing disease burden. This study employs MALDI-MSI data, labelled according to histology, to train a supervised machine learning model (logistic regression) for the recognition and delineation of SCC. The model, based on data acquired from discrete tumor sections (n = 25) from a mouse model of SCC, achieved a predictive accuracy of 92.3% during cross-validation on the labelled data. A pathologist unacquainted with the dataset and tasked with evaluating the predictive power of the model in the unlabelled regions, agreed with the model prediction for over 99% of the tissue areas. These findings highlight the potential value of integrating MALDI-MSI with machine learning to characterize and delineate SCC, suggesting a promising direction for the advancement of mass spectrometry techniques in the clinical diagnosis of SCC and related keratinocyte carcinomas.

Modelling the Charpy Impact Ductile-Brittle Transition of a Ship Plate Steel with CAFE Modelling

In the present work, a cellular automata finite element model (CAFE) was developed to model the ductile-brittle transition of a Grade A ship plate steel. Therefore, ductile and brittle cellular automata (CA) arrays of cells were created in the model to integrate material data at microstructural level, along with the ductile and brittle fracture processes. Microstructural data was analysed with Weibull distributions and incorporated in CAFE model using random number generators, along with ductile and brittle fracture parameters. Ductile fracture was modelled with Rousselier damage model; hence damage model parameters were calibrated with experimental data. Brittle fracture was modelled with Beremin model, and four different cleavage particles, found in a Grade A ship plate steel, were incorporated in CAFE model in order to model a competition of particles nucleating microcracks of critical size in the damage regions of Impact Charpy tests and four-point double-notch bend tests performed at low temperature. The mechanical properties the plate steel was measured in the transition region and incorporated in CAFE model, along with ductile-brittle transition rules. The present CAFE model was able to simulate distributions of microcracks in the notch region of four-point double-notch bend models (in the transition region), which correlated with experimental data. CAFE model was also able to simulate microvoids in the notch region of Charpy specimens along with the load-displacement Charpy curve for room test temperature, with very good agreement with experimental data. Once CAFE model was validated at micro and structural level, it was applied to model the typical scatter of impact Charpy energy values in the transition region of Grade A ship plate steel with good agreement with the measured ductile-brittle transition curved of the plate steel. Keywords: cellular automata, finite element modelling, ductile-brittle transition, damage modelling.

Decomposition in an extreme cold environment and associated microbiome-prediction model implications for the postmortem interval estimation.

The accurate estimation of postmortem interval (PMI), the time between death and discovery of the body, is crucial in forensic science investigations as it impacts legal outcomes. PMI estimation in extremely cold environments becomes susceptible to errors and misinterpretations, especially with prolonged PMIs. This study addresses the lack of data on decomposition in extreme cold by providing the first overview of decomposition in such settings. Moreover, it proposes the first postmortem microbiome prediction model for PMI estimation in cold environments, applicable even when the visual decomposition is halted. The experiment was conducted on animal models in the second-coldest region in the United States, Grand Forks, North Dakota, and covered 23 weeks, including the winter months with temperatures as low as -39°C. Random Forest analysis models were developed to estimate the PMI based either uniquely on 16s rRNA gene microbial data derived from nasal swabs or based on both microbial data and measurable environmental parameters such as snow depth and outdoor temperatures, on a total of 393 samples. Among the six developed models, the best performing one was the complex model based on both internal and external swabs. It achieved a Mean Absolute Error (MAE) of 1.36 weeks and an R2 value of 0.91. On the other hand, the worst performing model was the minimal one that relied solely on external swabs. It had an MAE of 2.89 weeks and an R2 of 0.73. Furthermore, among the six developed models, the commonly identified predictors across at least five out of six models included the following genera: Psychrobacter (ASV1925 and ASV1929), Carnobacterium (ASV2872) and Pseudomonas (ASV1863). The outcome of this research provides the first microbial model able to predict PMI with an accuracy of 9.52 days over a six-month period of extreme winter conditions.

Biomechanical Effect of Different Posterior Fixation Techniques on Stability and Adjacent Segment Degeneration in Treating Thoracolumbar Burst Fracture With Osteoporosis: A Finite Element Analysis.

Finite element analysis. To investigate the biomechanical effect of four posterior fixation techniques on stability and adjacent segment degeneration in treating thoracolumbar burst fractures with osteoporosis. In terms of stability and adjacent segment degeneration, there remains no consensus or guidelines on the optimal technique for the treatment of thoracolumbar burst fractures in patients with osteoporosis. Images of CT scans were imported into MIMICS and further processed by Geomagic to build three-dimensional models of the T10-L5 region. A v-shaped osteotomy was performed on the L1 vertebral body to simulate a burst fracture in the setting of osteoporosis. Subsequently, four fixation techniques were designed using SolidWorks software. Range of motion (ROM) of the global spine, ROM distribution, ROM of adjacent segment, Von Mises stress on adjacent intervertebral disks, and facet joints were analyzed. Among the four groups, the cortical bone screw fixation (CBT) showed the highest global ROM at 1.86°, while long-segmented pedicle screw fixation (LSPS) had the lowest global ROM at 1.25°. The LSPS had the smallest percentage of ROM of fractured vertebral body to fixed segment at 75.04%, suggesting the highest stability after fixation. The maximum ROM of the adjacent segment was observed in the CBT at 1.32°, while the LSPS exhibited the smallest at 0.89°. However, the LSPS group experienced larger maximum stress on the adjacent intervertebral disks (9.60MPa) and facet joints (3.36MPa), indicating an increasing risk of adjacent segment disease. LSPS provided the greatest stability, while CBT provided the smallest amount of stability. However, the elevated stress on adjacent intervertebral disks and facet joints after LSPS fixation increased the possibility of adjacent segment degeneration. Cement-augmented pedicle screw fixation (CAPS) and combined cortical bone screw and pedicle screw fixation (CBT-PS) demonstrated significant biomechanical advantages in providing moderate fixation strength while reducing stress on the intervertebral disks and facet joints.

Multi-aspect assessment of a novel geothermal-driven polygeneration arrangement using water electrolyzer, methanol synthesis unit, and Goswami cycle

Geothermal energy’s renewable nature with minimal emissions compared to fossil fuels has led to valuable polygeneration models in geothermal-rich regions, promoting energy sustainability. Key challenges include improving integrated structures due to these resources’ low/medium temperature nature and assessing the feasibility of producing essential products (e.g., hydrogen and methanol), typically produced in structures with substantial pollution. The proposed system’s innovations focus on reducing irreversibility through thermal integration, introducing a negative CO2 emission structure, and utilizing CO2 hydrogenation based on geothermal energy for methanol production. Hence, the system encompasses a geothermal power plant, a Goswami cycle, a direct hydrogenation methanol synthesis unit, and a proton exchange membrane electrolyzer. The findings show generating methanol at a rate of 0.85 kg/s, domestic hot water at a rate of 2.231 kg/s, chilled water at a rate of 3.931 kg/s, and electric power at a capacity of 21,530 kW. Hence, the system’s thermal, electrical, and exergy efficiencies are determined to be 7.54 %, 3.87 %, and 47 %, respectively. Furthermore, the sum unit cost of products and the levelized energy cost equal 0.0272 $/GJ and 0.17 $/kWh, correspondingly. Additionally, a net present value of 65.86 M$ and a total yearly cost of 37.52 M$ are the results of the economic assessment. The current research demonstrates a negative CO2 emission from an environmental standpoint. The attainment of negative CO2 emissions is due to using CO2 as input material in the system. Also, the system’s net present value surpasses the total annual cost by 1.75 times, mainly due to its ability to produce products like methanol, generating revenue to offset initial costs. The proposed system is ideal for geothermal energy sites near seawater, promoting environmental preservation by reducing pollutants. Finally, it is suggested that the system’s operation be optimized and an experiential setup be used for future research.

Concept of robust climate-friendly flight planning under multiple climate impact estimates

The spatiotemporal dependency of aviation-induced non-CO2 climate effects can be incorporated into flight planning tools to generate climate-friendly flight plans. However, estimating climate impact is challenging and associated with high uncertainty. To ensure the effectiveness of such an operational measure, sources that induce uncertainty need to be identified and considered when planning climate-aware trajectories. The mismatch between different assessments of climate impact is an important indicator of uncertainty. This study introduces a concept aimed at planning robust climate-optimized aircraft trajectories under multiple climate impact estimates. The objective is to generate climate-optimal trajectories that achieve mitigation potential consistent with all available assessments. Case studies show that, even when there is a significant discrepancy between input models in specific regions, the proposed approach can effectively generate trajectories to mitigate the climate impact with a high level of confidence.

Lifestyle Model in Regions with Extreme Environmental and Climate Conditions

The article introduces an analysis of human psychology in extreme conditions. On the one hand, scientific interest to extremology, i.e., the effect of extreme factors on people, is growing. On the other side, this analysis revealed a lack of empirical data on cognitive dynamics before, during, and after an extreme situation, as well as after spending a long time in extreme environmental and climate conditions. The research objective was to study the lifestyle of people (n = 66) who live in regions with extreme environmental and climate conditions. The theoretical analysis and the empirical data made it possible to construct a lifestyle model. Its structure consisted of core, peripheral, and background semantic layers. The core structure included balanced temporal continuum, practical activity, personal activity, and basic lifestyle. The temporal locus became disrupted if the core lifestyle structures were out of balance. Fatalism and fixation on the past led to negative reassessment of one’s life, which could be compensated by activating one’s inner resources and endowing semantic layers with a positive context.

Modeling realistic synaptic inputs of CA1 hippocampal pyramidal neurons and interneurons via Adaptive Generalized Leaky Integrate-and-Fire models

Computational models of brain regions are crucial for understanding neuronal network dynamics and the emergence of cognitive functions. However, current supercomputing limitations hinder the implementation of large networks with millions of morphological and biophysical accurate neurons. Consequently, research has focused on simplified spiking neuron models, ranging from the computationally fast Leaky Integrate and Fire (LIF) linear models to more sophisticated non-linear implementations like Adaptive Exponential (AdEX) and Izhikevic models, through Generalized Leaky Integrate and Fire (GLIF) approaches. However, in almost all cases, these models are tuned (and can be validated) only under constant current injections and they may not, in general, also reproduce experimental findings under variable currents. This study introduces an Adaptive GLIF (A-GLIF) approach that addresses this limitation by incorporating a new set of update rules. The extended A-GLIF model successfully reproduces both constant and variable current inputs, and it was validated against the results obtained using a biophysical accurate model neuron. This enhancement provides researchers with a tool to optimize spiking neuron models using classic experimental traces under constant current injections, reliably predicting responses to synaptic inputs, which can be confidently used for large-scale network implementations.

Interrelationships of CO2, CH4, and N2O fluxes in snow-covered temperate soils, Northern Japan

This study focuses on assessing the concentrations, fluxes, and production rates of greenhouse gases (CO2, CH4, and N2O) in a cold temperate grassland soil underlying snowpack during the winter of 1996/7 in northern Japan. Results included mean ± standard deviation (range) correlation coefficients (R2) for CO2–CH4 concentrations and CO2–N2O concentrations of 0.93 ± 0.07 (0.81–0.99) and 0.96 ± 0.06 (0.83–0.99) for winter, and 0.74 ± 0.17 (0.55–0.92) and 0.96 ± 0.05 (0.88–0.99) for summer, respectively. This suggests close relationships between the mechanisms of CO2 and N2O production and the oxidation of CH4, which are influenced by factors such as oxygen availability, temperature, and moisture in the soil. Furthermore, the study found that winter fluxes of CO2–CH4 and CO2–N2O through the snowpack showed positive linear correlations. Winter CO2 emissions accounted for 96 % of the variability in CH4 oxidation and 77 % of the variability in N2O emissions. This demonstrates that winter CO2 emissions were affected to the magnitude of CH4 oxidations and N2O emissions in the soil. These findings have implications for the modification of terrestrial ecosystem models in temperate regions, particularly in assessing contributions from winter greenhouse gas fluxes to overall annual emissions. Understanding the interrelationships and dynamics of greenhouse gases throughout the year is crucial for accurate modeling and predictions of ecosystem responses to climate change.

Solar Radiation Prediction in Adrar, Algeria: A Case Study of Hybrid Extreme Machine-Based Techniques

This study delves into the application of hybrid extreme machine-based techniques for solar radiation prediction in Adrar, Algeria. The models under evaluation include the Extreme Learning Machine (ELM), Weighted Extreme Learning Machine (WELM), and Self-Adaptive Extreme Learning Machine (SA-ELM), with a comparative analysis based on various performance metrics. The results show that SA-ELM achieves the highest accuracy with an R2 of 0.97, outperforming ELM and WELM by 4.6% and 15.4% respectively in terms of R2. SA-ELM also has the lowest MPE, RMSE and RRMSE values, indicating a higher accuracy in predicting global radiation. Furthermore, comparison with previously employed prediction techniques solidifies SA-ELM’s superiority, evident in its 0.275 RMSE.The study explores different input combinations for predicting global radiation in the study region, concluding that incorporating all relevant inputs yields optimal performance, although reduced input scenarios can still provide practical accuracy when data availability is limited. These results highlight the effectiveness of the SA-ELM model in accurately predicting global radiation, which is expected to have significant implications for renewable energy applications in the region. However, further testing and evaluation of the models in different regions and under different weather conditions is recommended to improve the generalizability and robustness of the results.

The EENSANE (Eastern European Seismic Ambient Noise) project: providing a new free database of ambient noise cross-correlations and crustal seismic models in the Carpathian-Pannonian Region and beyond

The EENSANE (Eastern European Seismic Ambient Noise) project: providing a new free database of ambient noise cross-correlations and crustal seismic models in the Carpathian-Pannonian Region and beyond

Tracking Vibrio: population dynamics and ecology of Vibrio parahaemolyticus and V. vulnificus in an Alabama estuary.

Vibrio spp. are bacteria found in estuaries worldwide; some species can cause illness and infections in humans. Relationships between Vibrio spp. abundance, salinity, and temperature are well documented, but correlations to other environmental parameters are less understood. This study identifies unique correlates (e.g., E-W wind scalar and harmful algal species) that could potentially inform the next iteration of predictive Vibrio models for the EMSS region. Additionally, these correlates may allow existing environmental monitoring efforts to be leveraged in providing data inputs for future Vibrio risk models. An observed correlation between salinity and V. vulnificus/particle-size associations suggests that predicted environmental changes may affect the abundance of Vibrio spp. in certain reservoirs, which may alter which vectors present the greatest vibrio risk.

Deep learning-based cervical lesion segmentation in colposcopic images

Artificial intelligence assisted cancer detection has changed the ream of diagnosis precision. This study aims to propose a segmentation network using artificial intelligence for accurately segmenting the cervix region and acetowhite lesions in cervigram images, addressing the shortage of skilled colposcopists and streamlining the training process. A computational approach is employed to develop and train a deep learning model specifically tailored for cervix region and acetowhite lesion segmentation in cervigram images. A dataset acquired in collaboration with KIDWAI memorial cancer research institute is used for building the model. Cervigram images are collected for training and validation, and a deep learning architecture is constructed and trained using annotated datasets. The segmentation network based on efficientnet architecture and atrous spatial pyramid pooling is designed to accurately identify and delineate the target regions, with performance evaluation conducted using precision, accuracy, recall, dice score, and specificity metrics. The proposed segmentation network achieves a precision of 0.7387±0.1541, accuracy of 0.9291, recall of 0.7912±0.1439, dice score of 0.7431±0.1506, and specificity of 0.9589±0.0131, indicating its reliability and robustness in segmenting cervix regions and acetowhite lesions in cervigram images. This research demonstrates the feasibility and effectiveness of using artificial intelligence-based computational models for cervix region and acetowhite lesion segmentation in cervigram images. It provides a foundation for further investigations into classifying cervix malignancy using AI techniques, potentially enhancing early detection and treatment of cervical cancer while addressing the shortage of skilled professionals in the field

Windstorm losses in Europe – What to gain from damage datasets

Windstorms are among the most impacting natural hazards affecting Western and Central Europe. Information on the associated impacts and losses are essential for risk assessment and the development of adaptation and mitigation strategies. In this study, we compare reported and estimated windstorm losses from five datasets belonging to three categories: Indices combining meteorological and insurance aspects, natural hazard databases, and loss reports from insurance companies. We analyse the similarities and differences between the datasets in terms of reported events, the number of storms per dataset and the ranking of specific storm events for the period October 1999 to March 2022 across 21 European countries. A total of 94 individual windstorms were documented. Only 11 of them were reported in all five datasets, while the large majority (roughly 60%) was solely recorded in single datasets. Results show that the total number of storms is different in the various datasets, although for the meteorological indices such number is fixed a priori. Additionally, the datasets often disagree on the storm frequency per winter season. Moreover, the ranking of storms based on reported/estimated losses varies in the datasets. However, these differences are reduced when the ranking is calculated relative to storm events that are common in the various datasets. The results generally hold for losses aggregated at European and at country level. Overall, the datasets provide different views on windstorm impacts. Thus, to avoid misleading conclusions, we use no dataset as “ground truth” but treat all of them as equal. We suggest that these different views can be used to test which features are relevant for calibrating windstorm models in specific regions. Furthermore, it could enable users to assign an uncertainty range to windstorm losses. We conclude that a combination of different datasets is crucial to obtain a representative picture of windstorm associated impacts.

Characterization of Surface Pressure Accuracy and Spatial and Temporal Variations of The GPT3 And HGPT2 Models in The Chinese Region

The accuracy of surface pressure is crucial for obtaining high-precision zenith hydrostatic delay(ZHD). In order to verify the accuracy of the Global Pressure and Temperature model (HGPT2 ) and the Global Pressure and Temperature (GPT3) in obtaining ground air pressure in China, as well as the spatiotemporal variation characteristics of the errors of the two models, this paper uses the ERA5 reanalysis dataset and combines it with actual meteorological data from weather stations to evaluate their performance. The results show that the results show that the HGPT2 model has better accuracy in simulating atmospheric pressure in China than the GPT3 model, with atmospheric pressure bias of -0.54 hPa and -2.09 hPa, and RMS of 4.42 hPa and 4.91 hPa, respectively. This article analyzes the spatial and temporal characteristics of model errors based on China's climate types. The performance of the GPT3 and HGPT2 models varies in different regions, and their accuracy in simulating atmospheric pressure changes is poorer in northern and northwestern China.

Simulating emission line galaxies for the next generation of large-scale structure surveys

ABSTRACT We investigate emission line galaxies across cosmic time by combining the modified L-Galaxies semi-analytical galaxy formation model with the JiuTian cosmological simulation. We improve the tidal disruption model of satellite galaxies in L-Galaxies to address the time dependence problem. We utilize the public code cloudy to compute emission line ratios for a grid of H ii region models. The emission line models assume the same initial mass function as that used to generate the spectral energy distribution of semi-analytical galaxies, ensuring a coherent treatment for modelling the full galaxy spectrum. By incorporating these emission line ratios with galaxy properties, we reproduce observed luminosity functions for H α, H β, [O ii], and [O iii] in the local Universe and at high redshifts. We also find good agreement between model predictions and observations for autocorrelation and cross-correlation functions of [O ii]-selected galaxies, as well as their luminosity dependence. The bias of emission line galaxies depends on both luminosity and redshift. At lower redshifts, it remains constant with increasing luminosity up to around $\sim 10^{42.5}\, {\rm erg\, s^{-1}}$ and then rises steeply for higher luminosities. The transition luminosity increases with redshift and becomes insignificant above z = 1.5. Generally, galaxy bias shows an increasing trend with redshift. However, for luminous galaxies, the bias is higher at low redshifts, as the strong luminosity dependence observed at low redshifts diminishes at higher redshifts. We provide a fitting formula for the bias of emission line galaxies as a function of luminosity and redshift, which can be utilized for large-scale structure studies with future galaxy surveys.

A Broadly Applicable Coarse Alignment Framework for the Point Cloud of Hull Blocks

_ The terrestrial laser scanning (TLS) technology, which has been widely used in recent years, provides a new approach to control the construction quality of hull blocks in shipyards. As the cloud-model registration problem plays the most significant role in the data processing stage of utilizing the TLS devices, this paper concentrates on developing a broadly applicable coarse alignment framework for the point cloud of hull blocks. This framework involves two steps: the recognition of the connection regions of the point clouds and the coarse registration method according to the classification results. To detect the connection regions automatically, a hand-crafted simple model of the connection regions of hull blocks is built and its supporting detection method is proposed, besides, considering the recent overwhelming success of deep learning methods, a deep learning network suitable for large hull block datasets is constructed according to the Point-Net, and convolutional neural networks paradigms. Then, the coarse alignment method based on the detected connection regions is proposed. Experimental results illustrate the good performance of the proposed framework. Keywords computers in construction; shipbuilding; automation

Exploring the association between social determinants and aphasia impairment: A retrospective data integration approach.

Traditionally, the study of aphasia focused on brain trauma, clinical biomarkers, and cognitive processes, rarely considering the social determinants of health. This study evaluates the relationship between aphasia impairment and demographic, socioeconomic, and contextual determinants among people with aphasia (PWA). PWA indexed within AphasiaBank-a database populated by multiple clinical aphasiology centers with standardized protocols characterizing language, neuropsychological functioning, and demographic information-were matched with respondents in the Medical Expenditure Panel Survey based on response year, age, sex, race, ethnicity, time post stroke, and mental health status. Generalized log-linear regression models with bootstrapped standard errors evaluated the association between scores on the Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) and demographic, economic, and contextual characteristics accounting for clustering of respondents and the stratification of data collection. Region, age, and income specific models tested the sensitivity of results. PWA over age 60 had 2.4% (SE = 0.020) lower WAB-R AQ scores compared with younger PWA. Compared to White PWA, Black and Hispanic PWA had 4.7% (SE = 0.03) and 0.81% (SE = 0.06) lower WAB-R AQ scores, respectively, as did those and living in the Southern US (-2.2%, SE = 0.03) even after controlling for age, family size, and aphasia type. Those living in larger families (β = 0.005, SE = 0.008), with income over $30,000 (β = 0.017, SE = 0.022), and a college degree (β = 0.030, SE = 0.035) had higher WAB-R AQ relative to their counterparts. Region-specific models showed that racial differences were only significant in the South and Midwest, while ethnic differences are only significant in the West. Sex differences only appeared in age-specific models. Racial and ethnic differences were not significant in the high-income group regression. These findings support evidence that circumstances in which individuals live, work, and age are significantly associated with their health outcomes including aphasia impairment.