Objective Quality Research Articles

Optimizing Semantic Segmentation of Street Views with SP-UNet for Comprehensive Street Quality Evaluation

Traditional street quality evaluations are often subjective and limited in scale, failing to capture the nuanced and dynamic aspects of urban environments. This paper presents a novel and data-driven approach for objective and comprehensive street quality evaluation using street view images and semantic segmentation. The proposed SP-UNet (Spatial Pyramid UNet) is a multi-scale segmentation model that leverages the power of VGG16, SimSPPF (Simultaneous Spatial and Channel Pyramid Pooling), and MLCA (Multi-Level Context Attention) attention mechanisms. This integration effectively enhances feature extraction, context aggregation, and detail preservation. The model’s average intersection over union, Mean Pixel Accuracy, and overall accuracy achieving improvements of 5.83%, 6.52%, and 2.37% in mIoU, Mean Pixel Accuracy (mPA), and overall accuracy, respectively. Further analysis using the CRITIC method highlights the model’s strengths in various street quality dimensions across different urban areas. The SP-UNet model not only improves the accuracy of street quality evaluation but also offers valuable insights for urban managers to enhance the livability and functionality of urban environments.

Subjective and Objective Quality Assessment of Colonoscopy Videos.

Captured colonoscopy videos usually suffer from multiple real-world distortions, such as motion blur, low brightness, abnormal exposure, and object occlusion, which impede visual interpretation. However, existing works mainly investigate the impacts of synthesized distortions, which differ from real-world distortions greatly. This research aims to carry out an in-depth study for colonoscopy Video Quality Assessment (VQA). In this study, we advance this topic by establishing both subjective and objective solutions. Firstly, we collect 1,000 colonoscopy videos with typical visual quality degradation conditions in practice and construct a multi-attribute VQA database. The quality of each video is annotated by subjective experiments from five distortion attributes (i.e., temporal-spatial visibility, brightness, specular reflection, stability, and utility), as well as an overall perspective. Secondly, we propose a Distortion Attribute Reasoning Network (DARNet) for automatic VQA. DARNet includes two streams to extract features related to spatial and temporal distortions, respectively. It adaptively aggregates the attribute-related features through a multi-attribute association module to predict the quality score of each distortion attribute. Motivated by the observation that the rating behaviors for all attributes are different, a behavior guided reasoning module is further used to fuse the attribute-aware features, resulting in the overall quality. Experimental results on the constructed database show that our DARNet correlates well with subjective ratings and is superior nine state-of-the-art methods.

Assessment of a New CT Detector and Filtration Technology: Part 2-Image Quality in Phantoms, Cadavers, and Patients.

The purpose of this work was to evaluate the image quality of a commercial CT scanner equipped with a novel detector and filtration technology called PureVision Optics (PVO). CT number, noise, contrast-to-noise ratio (CNR), modulation transfer function (MTF), and noise power spectrum (NPS) were assessed using the ACR CT Accreditation phantom scanned with various acquisitions at 80 kV, 100 kV, 120 kV, and 135 kV, each with multiple CTDIvol values of 20 mGy, 40 mGy, and 65 mGy. Artifacts were evaluated in an anthropomorphic head phantom, a cadaver head, and in patient studies. Two neuroradiologists assessed image quality features in various patients who were examined with unenhanced brain CT on both scanners. Compared with the conventional scanner, for the same CTDIvol, the PVO scanner produced 20.3% less image noise (P < 0.001), 18.9% higher CNR (P < 0.01), and 24.6% higher spatial resolution (P < 0.001). Streak artifacts were less severe with the PVO scanner for the phantom, cadaver, and patient scans (P < 0.05). Radiologists scored the PVO scanner as significantly better for visualization of the cerebrospinal fluid space over the cerebral sulci in high convexity, image noise in gray and white matter, and artifacts in the posterior fossa. They also significantly preferred the PVO scanner for visualization of the border between brain gray and white matter, cerebrospinal fluid space around the mesencephalon, and overall diagnostic acceptability. For matched CTDIvol values, the scanner equipped with PVO technology produced better objective and subjective image quality metrics in brain CT imaging compared with a conventional CT scanner without PVO. In clinical settings, PVO may allow for lower doses while enhancing imaging through dense areas, improving visualization of subtle details, and offering more effective options for examining obese patients.This research received financial support from Canon Medical Systems USA. The study design and data were fully controlled by the coauthors, of which none are employees or consultants of Canon Medical Systems USA.

Characterization and evaluation methods of fused deposition modeling and stereolithography additive manufacturing for clinical linear accelerator photon and electron radiotherapy applications.

To propose comprehensive characterization methods of additive manufacturing (AM) materials for MV photon and MeV electron radiotherapy. This study investigated 15 AM materials using CT machines. Geometrical accuracy, tissue-equivalence, uniformity, and fabrication parameters were considered. Selected soft tissue equivalent filaments were used to fabricate slab phantoms and compared with water equivalent RW3 phantom by delivering planar 6 & 10 MV photons and 6, 9, 12, 15, & 18MeV electrons. Finally, a 3D printed CT-Electron Density characterization phantom was fabricated. Materials used to print test objects can simulate tissues from adipose (relative electron density, ρe=0.72) up to near inner bone-equivalent (ρe=1.08). Lower densities such as breast and lung can be simulated using infills from 90% down to 30%, respectively. The gyroid infill pattern shows the lowest CT number variation and is recommended for low infill percentage printing. CT number uniformity can be observed from 40% up to 100% infill, while printing orientation does not significantly affect the CT number. The measured doses using the 3D printed phantoms show to have good agreement with TPS calculated dose for photon (< 1% difference) and electron (< 5% difference). Varying the printed slab thicknesses shows very similar response (< 3% difference) compared with RW3 slabs except for 6MeV electrons. Lastly, the fabricated CT-ED phantom generally matches the lung- up to the soft tissue- equivalence. The proposed methods give the outline for characterization of AM materials as tissue-equivalent substitute. Printing parameters affect the radiological quality of 3D-printed object.

A Review Study on the Construction of Medical Institution Service Quality Performance Evaluation System under the Background of Internet Plus

With the advent of the "Internet Plus" era, the medical service industry is undergoing a significant transformation. This paper systematically reviews and analyzes the construction process of the medical institution service quality performance evaluation system, aiming to provide theoretical support and practical guidance for the improvement of service quality in internet hospitals. The article first introduces the challenges and opportunities faced by the medical service industry under the "Internet Plus" background, then discusses the definition, dimensions, and theoretical framework of medical service quality evaluation. Through a comparative analysis of the service quality evaluation systems of Chinese and American medical institutions, the similarities and differences in the construction of medical service evaluation systems between the two countries are revealed. Finally, based on the "Internet Plus" background, the paper proposes principles, objectives, indicator system design, and implementation and feedback mechanisms for constructing a service quality performance evaluation system. The research results indicate that constructing a comprehensive and objective service quality evaluation system is of great significance for optimizing resource allocation, innovating service models, improving service efficiency, and reducing service costs, which helps to promote the implementation of the Healthy China strategy.

Analyzing Urban Air Quality Perceptions: Integrating Socio-Demographic Patterns with Sensor-Based Measurements Using Regression Model and Multidimensional Scaling

In this study, the urban air quality perceptions are explored in the metropolitan area of Târgu Mureș, Romania, emphasizing the interaction between socio-demographic factors, air quality measures, and industrial activity. The research addresses the need to understand how public perceptions align with objective air quality data and industrial influences, aiming to support sustainable urban planning. Data were gathered through a structured survey of 321 respondents and complemented by air quality measurements, including PM2.5 and PM10, and industrial production data. Statistical analyses, such as regression models and multidimensional scaling (PROXSCAL), were applied to identify patterns and relationships between socio-demographic characteristics, perceived air quality, and environmental factors. The results reveal significant links between demographic factors (e.g., age, awareness of local initiatives) and perceptions of air quality, alongside a nuanced interaction between air quality indicators and industrial activity. Respondents frequently identified traffic and industrial emissions as major contributors to air pollution, which was corroborated by sensor data trends. The findings underline the importance of integrating public perceptions with empirical data to design targeted policies and foster community engagement. This comprehensive approach provides actionable insights for improving urban air quality and advancing sustainable practices in mid-sized cities like Târgu Mureș.

Trauma history and persistent poor objective and subjective sleep quality among midlife women.

Whereas some work links trauma exposure to poor subjective sleep quality, studies largely rely upon limited trauma measures and self-reported sleep at one time point. It is unknown whether trauma is related to persistent poor sleep, whether associations differ based on childhood versus adulthood trauma, and whether trauma exposure is related to poorer objectively assessed sleep. We tested whether childhood or adult trauma associated with persistent poor objectively and subjectively measured sleep at two time points in midlife women. One hundred sixty-seven women aged 40-60 at baseline were assessed twice 5 years apart. At baseline, women reported childhood trauma (Child Trauma Questionnaire), adult trauma (Brief Trauma Questionnaire), demographics, depressive symptoms, apnea symptoms, and medical history, and provided physical measures. At both visits, women completed 3 days of actigraphy (total sleep time [TST], wake after sleep onset [WASO]) and reported sleep quality (Pittsburgh Sleep Quality Index). Relations of childhood and adult trauma exposure, respectively, with persistent poor sleep at both baseline and follow-up visits (TST [<6 hours], WASO [>30 minutes], Pittsburgh Sleep Quality Index [>5]) were assessed in logistic regression models, adjusted for age, race/ethnicity, education, body mass index, sleep medications, nightshift work, apnea, depressive symptoms, vasomotor symptoms, and alcohol use. Childhood trauma was related to persistent high WASO (odds ratio [95% confidence interval] = 2.16 [1.04-4.50], P = 0.039, multivariable). Adult trauma was related to persistent poor sleep quality (odds ratio [95% confidence interval] = 2.29 [1.07-4.93], P = 0.034, multivariable). Trauma was unrelated to persistent short TST. Childhood and adult trauma, respectively, were related to persistent poor objective sleep continuity and subjective sleep quality in midlife women, independent of risk factors.

Towards Quality Assessment for Arbitrary Translational 6DoF Video: Subjective Quality Database and Objective Assessment Metric.

Arbitrary translational Six Degrees of Freedom (6DoF) video represents a transitional stage towards immersive terminal videos, allowing users to freely switch viewpoints for a 3D scene experience. However, the increased freedom of movement introduces new distortions that significantly impact human visual perception quality. Therefore, it is crucial to explore quality assessment (QA) to validate its application feasibility. In this study, we conduct subjective and objective QAs of arbitrary translational 6DoF videos. Subjectively, we establish an arbitrary translational 6DoF synthesized video quality database, specifically exploring path navigation in 3D space, which has often been limited to planar navigation in previous studies. We simulate path navigation distortion, rendering distortion, and compression distortion to create a subjective QA database. Objectively, based on the spatio-temporal distribution characteristics of various distortions, we propose a no-reference video quality assessment (VQA) metric for arbitrary translational 6DoF videos. The experimental results on the established subjective dataset fully demonstrate the effectiveness and superiority of the proposed objective method.

Academically based regional quality improvement hubs: Advancing Medicaid's quality strategy in the state of Ohio through state‐academic partnerships

AbstractIntroductionIn 2022, the Ohio Department of Medicaid (ODM) launched a Managed Care Population Health and Quality Strategy to improve healthcare quality and equity for Medicaid Managed Care enrollees. Aligned with national quality objectives, the strategy focuses on personalized care, service coordination for complex needs, reducing health disparities, and includes performance incentives for Managed Care Organizations (MCOs) and innovative provider payment models. While Ohio has made progress in quality improvement, challenges remain in addressing statewide health indicators and disparities and helping healthcare providers adapt to performance‐based models. This report outlines a new approach that builds on Ohio's partnership with six colleges of medicine (CoMs) to support provider organizations and engage stakeholders in quality improvement (QI).MethodsODM established Regional QI Hubs within Ohio's CoMs to advance population health initiatives using the Model for Improvement developed by the Associate in Process Improvement. These academically based hubs collaborate with local healthcare clinics, community partners, and payers on QI projects to enhance care, reduce disparities, and strengthen health systems. By engaging stakeholders in designing and testing change ideas using Plan‐Do‐Study‐Act cycles and electronic health record data feedback, QI Hubs further the goals of the learning health system.ResultsKey lessons highlight the benefits of engaging academic institutions to build internal QI capacity and promote health equity. The model required substantial capacity building and commitment on behalf of academic institutions and strengthening of regional partnerships. Collaboration between MCOs and health clinics is focused on standardizing processes to access services and implement best practices. Patient, family, and community engagement efforts aim to improve patient experience and address drivers of health equity. Each partner leverages resources and benefits from the collaboration.ConclusionsOhio's academically based Regional QI Hub Model offers a promising approach to advancing population health. Policymakers are encouraged to consider integrating academic expertise into state quality strategies.

Theoretical Foundations of Open Air Architecture for the Phenomenon of Place

The architecture and spatial qualities of educational and scientific objects play an important role in the formation of an intellectual and socially integral personality. The paper considers the educational public space as a place of interaction between the people and environment.Purpose: The analysis of the architectural environment of scientific and educational centers as a space of interaction between the people and environment and scientific prerequisites for theoretical foundations of phenomenon of place.Methodology: The analysis of functional zoning and development of spaces, phenomenological analysis, geographical conceptualization of spaces.Research findings: The physical environment not only affects the place but also the human perception, feelings, emotions and interpretations. This provides further research into the formation of scientific foundations for the creation of the phenomenon of place through architecture.

A Multivariate Stopping Rule for Survey Data Collection: Empirical Evaluation from a Panel Study

Surveys are experiencing declining response rates. With more and more effort expended to combat these declining response rates, the cost of large-scale surveys has continued to rise. Stopping rules are one of the interventions used to improve the efficiency of data collection. However, most previously proposed stopping rules have focused only on the quality of a single estimate or assumed sufficient funds for nonresponse follow-up. In multipurpose surveys, there may be data quality objectives that must be met for multiple estimates with a constraint on costs. In this paper, we introduce a multivariate stopping rule that aims to maximize the tradeoff between the cost of data collection and the quality of multiple estimates. The multivariate stopping rule uses predicted costs and mean squared errors of different estimates to evaluate alternative sets of cases for stopping. The proposed stopping rule is illustrated using data from the Health and Retirement Study. In the multivariate stopping rule, each estimate receives a prespecified weight that determines the relative importance in the design. We found that assigning a relatively larger estimate-level weight to a survey variable did not necessarily result in better data quality for that variable. One plausible reason is that the prediction quality varies across variables.

Evaluation of a Deep Learning Denoising Algorithm for Dose Reduction in Whole-Body Photon-Counting CT Imaging: A Cadaveric Study.

Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at lower doses. This study aims to evaluate the effectiveness of a deep learning (DL)-based denoising algorithm in maintaining diagnostic image quality in whole-body PCCT imaging at reduced radiation levels, using real intraindividual cadaveric scans. Twenty-four cadaveric human bodies underwent whole-body CT scans on a PCCT scanner (NAEOTOM Alpha, Siemens Healthineers) at four different dose levels (100%, 50%, 25%, and 10% mAs). Each scan was reconstructed using both ADMIRE level 2 and a DL algorithm (ClariCT.AI, ClariPi Inc.), resulting in 192 datasets. Objective image quality was assessed by measuring CT value stability, image noise, and contrast-to-noise ratio (CNR) across consistent regions of interest (ROIs) in the liver parenchyma. Two radiologists independently evaluated subjective image quality based on overall image clarity, sharpness, and contrast. Inter-rater agreement was determined using Spearman's correlation coefficient, and statistical analysis included mixed-effects modeling to assess objective and subjective image quality. Objective analysis showed that the DL denoising algorithm did not significantly alter CT values (p ≥ 0.9975). Noise levels were consistently lower in denoised datasets compared to the Original (p < 0.0001). No significant differences were observed between the 25% mAs denoised and the 100% mAs original datasets in terms of noise and CNR (p ≥ 0.7870). Subjective analysis revealed strong inter-rater agreement (r ≥ 0.78), with the 50% mAs denoised datasets rated superior to the 100% mAs original datasets (p < 0.0001) and no significant differences detected between the 25% mAs denoised and 100% mAs original datasets (p ≥ 0.9436). The DL denoising algorithm maintains image quality in PCCT imaging while enabling up to a 75% reduction in radiation dose. This approach offers a promising method for reducing radiation exposure in clinical PCCT without compromising diagnostic quality.

Cannabis use, sleep and mood disturbances among persons with epilepsy - A clinical and polysomnography study from a Canadian tertiary care epilepsy center.

Interest in anti-seizure properties of cannabinoids is increasing, with the rise in prevalence of recreational and medical cannabis use, especially across Canada. In a recent study on people with epilepsy (PWE), cannabis use showed a strong association with poor psychosocial health. Sleep and mood comorbidities are highly prevalent in epilepsy, and are common motivations for cannabis use. The primary objective of this study was to assess demographic, subjective and objectively assessed sleep quality and mood related differences among PWE who regularly use cannabis compared to those who do not. Consecutive consenting patients with a confirmed epilepsy diagnosis, admitted to our Epilepsy Monitoring Unit, over a 3-year period (2019-2022) were enrolled. Detailed epilepsy-related data and self-reported sleep [Pittsburgh Sleep quality index (PSQI)], Epworth Sleepiness Scale (ESS)], mood [(Beck's Depression Inventory (BDI) and Beck's Anxiety inventory (BAI)] and cannabis use related data were collected. Overnight polysomnography (PSG) was conducted on the first night of admission, with simultaneous 18-channel video-EEG. Sleep (PSG) scoring followed American Academy of Sleep Medicine guidelines by a scorer blinded to clinical details. Among 51 patients with similar seizure control, 25 (13 F) reported cannabis use (mean age 36.3+14.8 years) and were significantly younger than 26 (18 F) non-users (mean age 48.3+15 years). Cannabis users had significantly better subjective sleep quality (mean PSQI scores 7.2+2.9 vs 10.2+5.2 respectively). Most patients endorsed sleepiness (Cannabis users with ESS scores greater than 10; 91.3 %, 77.3 % in non-users) and moderate to extreme depression (BDI) scores. No significant differences were observed in objective sleep parameters. BDI score significantly predicted PSQI and ESS scores on multiple logistic regression analysis. Despite a significant age difference, self-reported sleep quality is better among PWE who report regular cannabis use compared to non-users. However, there is no significant difference in objective sleep quantity and quality from PSG between the two groups. Additionally, severity of depressive symptoms is a significant predictor of sleep quality and of excessive daytime sleepiness among PWE.

Detection of Woody Breast Condition in Broiler Breast Fillets Using Light Scattering Imaging

HighlightsBroadband light scattering images were acquired from broiler breast fillets affected with woody breast (WB).Both deep-learning-based and hand-crafted features were extracted from scattering images for model development.The hand-crafted scattering image features performed better in differentiating normal from WB-affected samples.An overall accuracy of 92.3% was achieved by the model based on selected hand-crafted scattering features.Abstract. Muscular myopathies such as woody or wooden breast, which impair the eating quality and marketability of poultry products, are threatening the profitability of poultry industries worldwide, with an estimated annual loss exceeding $500 million for the United States (U.S.) poultry industry. WB-affected fillets are characterized by abnormal tissue hardness and muscle rigidity with varying degrees of severity. The assessment of WB conditions at processing facilities currently relies on tactile palpation combined with a visual examination by trained personnel. This approach is subjective, labor-intensive, costly, and may induce contamination due to physical contact. Optical imaging technology offers a promising alternative for objective and non-invasive quality assessment of broiler meat. This study presents a novel investigation of light scattering imaging (LSI) that captures light-scattering characteristics of meat tissues for the detection of WB conditions in broiler breast fillets. Broadband scattering images, induced under the illumination of a highly focused broadband light beam, were acquired from broiler meat samples. Two types of image features, i.e., (1) deep-learning-based and (2) hand-crafted scattering features, were extracted for building classification models using regularized linear discriminant analysis to differentiate samples into two categories, i.e., “Normal” (no WB) and “Defective” (WB-affected). Deep-learning-based features yielded an overall classification accuracy of 80.4%, while an accuracy of 91.7% was obtained by hand-crafted scattering features, representing a significant improvement of 11.3% (P &amp;lt; 0.01). Furthermore, feature reduction approaches, including MRMR (minimum redundancy maximum relevance) and PCA (principal component analysis), were conducted to reduce the model complexity, leading to a further accuracy improvement to 92.3% with the top-ranked 65 features identified by MRMR and 25 principal components (PCs), respectively. This study has demonstrated the LSI technique is promising for WB assessment of broiler breast meat. Keywords: Light scattering, Machine learning, Optical imaging, Poultry, Woody breast.

High-resolution Diffusion-weighted Imaging of the Prostate Using Multiplexed Sensitivity-encoding: Comparison with the Conventional and Reduced Field-of-view Techniques

To compare objective and subjective image quality, lesion conspicuity, and apparent diffusion coefficient (ADC) of high-resolution multiplexed sensitivity-encoding diffusion-weighted imaging (MUSE-DWI) with conventional DWI (c-DWI) and reduced FOV DWI (rFOV-DWI) in prostate MRI. Forty-seven patients who underwent prostate MRI, including c-DWI, rFOV-DWI, and MUSE-DWI, were retrospectively evaluated. SNR and ADC of normal prostate tissue and contrast-to-noise ratio (CNR) and ADC of prostate cancer (PCa) were measured and compared between the three sequences. Image quality and lesion conspicuity were independently graded by two radiologists using a 5-point scale and compared between the three sequences. The SNR of normal prostate tissue was significantly higher with rFOV-DWI than with the other two DWI techniques (P ≤ 0.01). The CNR of the PCa was significantly higher with rFOV-DWI than with MUSE-DWI (P < 0.05). The ADC of normal prostate tissue measured by rFOV-DWI was lower than that measured by MUSE-DWI and c-DWI (P < 0.01), while there was no difference in the ADC of cancers. In the qualitative analysis, MUSE-DWI showed significantly higher scores than rFOV-DWI and c-DWI for visibility of anatomy and overall image quality in both readers, and significantly higher scores for distortion in one of the two readers (P < 0.001). There was no difference in lesion conspicuity between the three sequences. High-resolution MUSE-DWI showed higher image quality and reduced distortion compared to c-DWI, while maintaining a wide FOV and similar ADC quantification, although no difference in lesion conspicuity was observed.

NeRF View Synthesis: Subjective Quality Assessment and Objective Metrics Evaluation

NeRF View Synthesis: Subjective Quality Assessment and Objective Metrics Evaluation

Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images

Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images

Non-Intrusive Speech Quality Assessment Based on Deep Neural Networks for Speech Communication.

Traditionally, speech quality evaluation relies on subjective assessments or intrusive methods that require reference signals or additional equipment. However, over recent years, non-intrusive speech quality assessment has emerged as a promising alternative, capturing much attention from researchers and industry professionals. This article presents a deep learning-based method that exploits large-scale intrusive simulated data to improve the accuracy and generalization of non-intrusive methods. The major contributions of this article are as follows. First, it presents a data simulation method, which generates degraded speech signals and labels their speech quality with the perceptual objective listening quality assessment (POLQA). The generated data is proven to be useful for pretraining the deep learning models. Second, it proposes to apply an adversarial speaker classifier to reduce the impact of speaker-dependent information on speech quality evaluation. Third, an autoencoder-based deep learning scheme is proposed following the principle of representation learning and adversarial training (AT) methods, which is able to transfer the knowledge learned from a large amount of simulated speech data labeled by POLQA. With the help of discriminative representations extracted from the autoencoder, the prediction model can be trained well on a relatively small amount of speech data labeled through subjective listening tests. Fourth, an end-to-end speech quality evaluation neural network is developed, which takes magnitude and phase spectral features as its inputs. This phase-aware model is more accurate than the model using only the magnitude spectral features. A large number of experiments are carried out with three datasets: one simulated with labels obtained using POLQA and two recorded with labels obtained using subjective listening tests. The results show that the presented phase-aware method improves the performance of the baseline model and the proposed model with latent representations extracted from the adversarial autoencoder (AAE) outperforms the state-of-the-art objective quality assessment methods, reducing the root mean square error (RMSE) by 10.5% and 12.2% on the Beijing Institute of Technology (BIT) dataset and Tencent Corpus, respectively. The code and supplementary materials are available at https://github.com/liushenme/AAE-SQA.

A Simple Polarization-Based Fringe Projection Profilometry Method for Three-Dimensional Reconstruction of High-Dynamic-Range Surfaces

Three-dimensional (3D) reconstruction of high-dynamic-range (HDR) surfaces plays an important role in the fields of computer vision and image processing. Traditional 3D measurement methods often face the risk of information loss when dealing with surfaces that have HDR characteristics. To address this issue, this paper proposes a simple 3D reconstruction method, which combines the features of non-overexposed regions in polarized and unpolarized images to improve the reconstruction quality of HDR surface objects. The optimum fringe regions are extracted from images with different polarization angles, and the non-overexposed regions in normally captured unpolarized images typically contain complete fringe information and are less affected by specular highlights. The optimal fringe information from different polarized image groups is gradually used to replace the incorrect fringe information in the unpolarized image, resulting in a complete set of fringe data. Experimental results show that the proposed method requires only 24~36 images and simple phase fusion to achieve successful 3D reconstruction. It can effectively mitigate the negative impact of overexposed regions on absolute phase calculation and 3D reconstruction when reconstructing objects with strongly reflective surfaces.

OBJECTIVE SLEEP QUALITY ASSOCIATES WITH PLASMA BIOMARKER FOR COGNITIVE IMPAIRMENT

Abstract Aim Emerging evidence suggests that poor sleep (i.e., lack of sleep and low sleep quality) significantly impacts the risk of severe cognitive impairment. However, research on the use of sleep quality metrics as early predictors of cognitive impairment risk remains scarce. In this study, we investigated the association between objective sleep quality measures and plasma levels of Eotaxin-1, an early biomarker for cognitive impairment, among nurses. Methods We used data from 35 nurses who underwent multiple home sleep studies with electroencephalograms on workdays and days off were included. Objective sleep quality measurements included percentage of stage-3 non-rapid eye movement sleep in total sleep time (%N3) and wake time after sleep onset (WASO). Blood samples were collected in all participants and plasma levels of multiple cytokines (27Plex Cytokine array), including Eotaxin-1 were measured by Luminex multiplexing. Results Sleep quality indicators showed that WASO increased by 8% on days off compared to workdays, while %N3 decreased by 1% (p&amp;lt;.001). After adjusting for age, sex, shift, and BMI, higher WASO (β=1.22, p&amp;lt;.05) and lower %N3 (β=-5.09, p&amp;lt;.05) on workdays were significantly associated with increasing levels of Eotaxin-1. Sleep parameters on days off did not significantly correlate with Eotaxin-1 levels. Conclusion Our findings indicate a correlation between objective sleep quality measurements on workdays and an early biomarker of cognitive impairment. Further research is necessary to elucidate the underlying mechanisms linking sleep quality to future cognitive impairment, as well as to explore the potential differential associations of these parameters between workdays and days off.