Disk Images Research Articles

Solar shape variations across cycles 24 and 25: Observations from 2010 to 2023

The longest continuous time-series of solar oblateness measurements, initiated in 2010 and still ongoing, has been obtained from data collected by the Helioseismic Magnetic Imager (HMI) aboard NASA's Solar Dynamics Observatory (SDO). Based on HMI data, we developed two methods for determining the solar oblateness at 617.33\,nm in the continuum . The first method involves determining solar oblateness using HMI solar disk images and limb observations from twenty-three SDO satellite roll calibration maneuvers between 2010 and 2023 . Through meticulous analysis of these observation sequences, we obtained a precise measurement of solar oblateness using this technique, yielding a value of 9.02 (pm 0.72)times 10$^ $ (6.28pm 0.50\,kilometers), unaffected by brightness contamination from sunspots and magnetically induced excess emission. We also verified the polarization independence of light, showing consistent HMI solar oblateness measurements across Stokes states. Interestingly, our solar oblateness time-series, based on HMI solar disk images and limb observations, seems to be in anti-phase with solar activity. The second method we used relies on determining solar oblateness from HMI helioseismic inference of internal rotation. With this approach, we obtained a solar oblateness of 8.40 (pm 0.02)times 10$^ $ (5.85pm 0.01 kilometers) with a variation in phase with solar activity (0.05times 10$^ $ (0.04\,kilometers at 1\,sigma ) over an 11--year sunspot cycle). This outcome is troubling as it conflicts with our results obtained from the HMI solar limb observations

Classification of Pseudopapilledema and Papilledema Using Decision Tree and Hu Moments

Pseudopapilledema, characterized by an anomalous elevation of the optic disc without retinal nerve fiber layer edema, often mimics the presentation of true papilledema caused by increased intracranial pressure. Accurate differentiation between these conditions is critical to avoid unnecessary invasive procedures. This study employs a Decision Tree classifier to classify optic disc images into three categories: normal, papilledema, and pseudopapilledema. The dataset, obtained from Kaggle, consists of imbalanced images segmented using the Canny edge detection method and features extracted using Hu Moments. The dataset was divided into 80% training and 20% testing sets. Performance was evaluated using 5-fold cross-validation, yielding an average accuracy of 53.61%, precision of 55.20%, recall of 54.12%, and F1-score of 55.17%. The study provides a comprehensive analysis of the classifier's performance, including visualizations such as segmentation results, scatter plots of Hu Moments, and confusion matrices. The results indicate that while the Decision Tree classifier demonstrates moderate effectiveness, there is significant room for improvement. The research highlights the potential of machine learning models in medical diagnostics but also underscores the need for more robust algorithms and diverse datasets. Future work should focus on incorporating more complex models and expanding the dataset to enhance diagnostic accuracy. These findings contribute to the field of medical image analysis and propose a non-invasive diagnostic tool that, when integrated with clinical expertise, could improve patient outcomes and reduce unnecessary procedures

Glaucoma Screening and Referral Risk Factors in a High-Risk Population: Follow-up Study of the Manhattan Vision Screening Study (NYC-SIGHT)

Précis: Community-based eye health screenings that incorporated fundus photography and optometric exams in a high-risk NYC population effectively identified a higher than average number of participants that required an in-office glaucoma evaluation. Purpose: To report glaucoma screening rates and risk factors associated with referral for in-office glaucoma evaluation in the Manhattan Vision Screening and Follow-up Study (NYC-SIGHT). Methods: In this 5-year, cluster-randomized clinical trial, eligible individuals aged 40 and older were recruited from affordable housing developments and senior centers. Visual acuity with correction, intraocular pressure (IOP) measurements, and non-mydriatic fundus photography were conducted. Images were graded by a glaucoma specialist; those with an abnormal image were referred; those who failed the screening or had an unreadable fundus image were examined by the study optometrist. Chi-squared tests and stepwise multivariate logistic regression analyses were conducted to determine factors associated with glaucoma referral. Results: 708 participants were screened; 189 (26.6%) were referred for an in-office glaucoma evaluation due to an abnormal optic disc image (n=138) or abnormal optometric exam (n=51). Those referred had a mean age 68.5 ±11.7 years and were 60% female, 57% Black, and 37% Hispanic. Stepwise multivariate logistic regression showed participants with self-reported glaucoma (OR 8.096, 95% CI 4.706–13.928, P=0.000), IOP>23 mmHg at the screening (OR 3.944, 95% CI 1.704–9.128, P=0.001), or wore prescription eyeglasses (OR 1.601, 95% CI 1.034–2.48, P=0.035) had higher odds of being referred for an in-office glaucoma evaluation. Of those referred, 106 (56%) attended, 36 participants (34%) were diagnosed with glaucoma and 38 participants (35.8%) as glaucoma suspects. Conclusion: Our findings support public health approaches that focus community-based eye health screenings on high-risk populations and prioritize underserved communities.

Performance of a Deep Learning System and Performance of Optometrists for the Detection of Glaucomatous Optic Neuropathy Using Colour Retinal Photographs.

Glaucoma is the leading cause of irreversible blindness, with a significant proportion of cases remaining undiagnosed globally. The interpretation of optic disc and retinal nerve fibre layer images poses challenges for optometrists and ophthalmologists, often leading to misdiagnosis. AI has the potential to improve diagnosis. This study aims to validate an AI system (a convolutional neural network based on the Inception-v3 architecture) for detecting glaucomatous optic neuropathy (GON) using colour fundus photographs from a UK population and to compare its performance against Australian optometrists. A retrospective external validation study was conducted, comparing AI's performance with that of 11 AHPRA-registered optometrists in Australia on colour retinal photographs, evaluated against a reference (gold) standard established by a panel of glaucoma specialists. Statistical analyses were performed using sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC). For referable GON, the sensitivity of the AI (33.3% [95%CI: 32.4-34.3) was significantly lower than that of optometrists (65.1% [95%CI: 64.1-66.0]), p < 0.0001, although with significantly higher specificity (AI: 97.4% [95%CI: 97.0-97.7]; optometrists: 85.5% [95%CI: 84.8-86.2], p < 0.0001). The optometrists demonstrated significantly higher AUROC (0.753 [95%CI: 0.744-0.762]) compared to AI (0.654 [95%CI: 0.645-0.662], p < 0.0001). The AI system exhibited lower performance than optometrists in detecting referable glaucoma. Our findings suggest that while AI can serve as a screening tool, both AI and optometrists have suboptimal performance for the nuanced diagnosis of glaucoma using fundus photographs alone. Enhanced training with diverse populations for AI is essential for improving GON detection and addressing the significant challenge of undiagnosed cases.

CTRing: An R package to extract wood density profiles from computed tomography images of discs and logs

Accurately determining the position of pith and accessing tree-ring density profiles, including intra-ring variations, is important for both the forest industry and dendroclimatology. Although several available methods exist for acquiring this information, such as X-ray computed tomography (CT), micro-CT, and X-ray films, the availability of open-source programs for extracting data remains limited. The CTRing package in the R environment integrates a series of functions to detect precisely the pith and tree-ring boundaries and generate tree-ring density profiles using CT images of tree cross sections. Before processing, grey values are transformed into density using a calibration function. Pith position is then detected by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree-ring profiles along the pith-to-bark path of interest are inspected visually, and tree-ring boundaries can be easily added or removed manually via a graphical user interface. After correcting for tree-ring boundaries, the inflection points of a 3rd-degree polynomial obtained from density profiles are used to delimit the earlywood–latewood transition. We tested this package using 60 CT-scanned images of white spruce (Picea glauca (Moench) Voss) discs collected at various tree heights (0 %, 25 %, 50 % and 75 % of the total tree height as well as at 1.3 m). The pith detection function had an average mean error of 0.72 mm with 95 % of the automatically detected pith locations that differed by less than 2 mm from their manually located positions. Error decreased toward the apex of the tree. The functions of the CTRing package are flexible and can be easily implemented or adapted. The package could also be used with simple images of discs to obtain ring-width time series; however, this use must be evaluated further. Future work with this package involves assessing the use of low-quality images and ring-porous species.

JWST Imaging of Edge-on Protoplanetary Disks. III. Drastic Morphological Transformation Across the Mid-infrared in Oph163131

We present JWST broadband images of the highly inclined protoplanetary disk SSTc2d J163131.2-242627 (Oph163131) from 2.0 to 21 μm. The images show a remarkable evolution in disk structure with wavelength, quite different from previous JWST observations of other edge-on disks. At 2.0 and 4.4 μm, Oph163131 shows two scattering surfaces separated by a dark lane, typical of highly inclined disks. Starting at 7.7 μm, however, (1) the two linear nebulosities flanking the dark lane disappear; (2) the brighter nebula tracing the disk upper surface transitions into a compact central source distinctly larger than the JWST point-spread function and whose intrinsic size increases with wavelength; and (3) patches of extended emission appear at low latitudes, and at surprisingly large radii nearly twice that of the scattered light seen with Hubble Space Telescope and NIRCam, and of the gas. We interpret the compact central source as thermal emission from the star and the inner disk that is not seen directly, but which instead is able to progressively propagate to greater distances at longer wavelengths. The lack of sharp-edged structures in the extended patchy emission argues against the presence of shocks and suggests photoexcitation or stochastic heating of material smoothly flowing away from the star along the disk surface. Finally, the dark lane thickness decreases significantly between 0.6 and 4.4 μm, which indicates that the surface layers of Oph163131 lack grains larger than 1 μm.

Advanced Analysis of OCT/OCTA Images for Accurately Differentiating Between Glaucoma and Healthy Eyes Using Deep Learning Techniques.

To evaluate the discriminative power of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) images, identifying the best image combination for differentiating glaucoma from healthy eyes using deep learning (DL) with a convolutional neural network (CNN). This cross-sectional study included 157 subjects contributing 1,106 eye scans. We used en-face images of the superficial and choroid layers for OCTA-based vessel density and OCT-based structural thickness of the macula (M) and optic disc (D). Images were preprocessed, resized, and normalized for CNN analysis. The CNN architecture had two components: one extracted features for each image type (OCT-D, OCT-M, OCTA-D, OCTA-M), while the second combined these features to classify eyes as healthy or glaucomatous. Performance was measured by accuracy, sensitivity, specificity, and area under the curve (AUC). For OCT images, the D+M combination outperformed disc (D) or macula (M) alone in three of the four metrics. For OCTA images, D+M also performed better than D or M alone, with D+M outperforming disc (D) in all criteria. Across all metrics for combined OCT+OCTA images, D+M performed better than D or M alone, and the macula (M) outperformed the disc (D). In disc (D) imaging, OCTA outperformed both OCT and OCT+OCTA in accuracy, sensitivity, and specificity, while OCT+OCTA had a higher AUC. OCTA consistently outperformed OCT and OCT+OCTA across all metrics for combined D+M images. The OCTA D+M combination performed best, followed by the OCT+OCTA D+M combination. When both en-face images are available, OCTA is preferred. Always include both disc and macula images for optimal diagnosis.

Hands-on parallel & distributed computing with Raspberry Pi devices and clusters

Parallel and distributed computing (PDC) concepts are now required topics for accredited undergraduate computer science programs. However, introducing PDC into the CS curriculum is challenging for several reasons, including an instructor's lack of PDC knowledge and difficulties in accessing PDC hardware. This paper addresses both of these challenges by presenting free, interactive, web-based PDC teaching modules using inexpensive Raspberry Pi single board computers (SBCs). Our materials include a free disk image that makes it possible for instructors to build Raspberry Pi clusters in minutes and use our software in a variety of curricular contexts. Our multi-year assessment of these materials on students and faculty members indicates that: (i) our materials increased students' confidence regarding important PDC concepts and motivated them to study PDC further; and (ii) our materials increased faculty members' confidence and preparedness in teaching key PDC concepts at their own institutions.

Decoding Quantum Gravity Information with Black Hole Accretion Disk

Integrating loop quantum gravity with classical gravitational collapse models offers an effective solution to the black hole singularity problem and predicts the formation of a white hole in the later stages of collapse. Furthermore, the quantum extension of Kruskal spacetime indicates that white holes may convey information about earlier companion black holes. Photons emitted from the accretion disks of these companion black holes enter the black hole, traverse the highly quantum region, and then re-emerge from white holes in our universe. This process enables us to observe images of the companion black holes’ accretion disks, providing insights into quantum gravity. In our study, we successfully obtained these accretion disk images. Our results indicate that these accretion disk images are confined within a circle with a radius equal to the critical impact parameter, while traditional accretion disk images are typically located outside this circle. As the observational angle increases, the accretion disk images transition from a ring shape to a shell-like shape. Furthermore, the positional and width characteristics of these accretion disk images are opposite to those of traditional accretion disk images. These findings provide valuable references for astronomical observations aimed at validating the investigated quantum gravity model.

REXPACO ASDI: joint unmixing and deconvolution of the circumstellar environment by angular and spectral differential imaging

ABSTRACT Angular and spectral differential imaging is an observational technique of choice to investigate the immediate vicinity of stars. By leveraging the relative angular motion and spectral scaling between on-axis and off-axis sources, post-processing techniques can separate residual star light from light emitted by surrounding objects such as circumstellar discs or point-like objects. This paper introduces a new algorithm that jointly unmixes these components and deconvolves disc images. The proposed algorithm is based on a statistical model of the residual star light, accounting for its spatial and spectral correlations. These correlations are crucial yet remain inadequately modelled by existing reconstruction algorithms. We employ dedicated shrinkage techniques to estimate the large number of parameters of our correlation model in a data-driven fashion. We show that the resulting separable model of the spatial and spectral covariances captures very accurately the star light, enabling its efficient suppression. We apply our method to data sets from the Very Large Telescope/Spectro-Polarimetry High-contrast Exoplanet REsearch instrument and compare its performance with standard algorithms (median subtraction, PCA, PACO). We demonstrate that considering the multiple correlations within the data significantly improves reconstruction quality, resulting in better preservation of both disc morphology and photometry. With its unique joint spectral modelling, the proposed algorithm can reconstruct discs with circular symmetry (e.g. rings, spirals) at intensities one million times fainter than the star, without needing additional reference data sets free from off-axis objects.

Unveiling the HD 95086 system at mid-infrared wavelengths with JWST/MIRI

Context. Mid-infrared imaging of exoplanets and disks is now possible with the coronographs of the Mid-InfraRed Instrument (MIRI) on the James Webb Space Telescope (JWST). This wavelength range unveils new features of young directly imaged systems and allows us to obtain new constraints for characterizing the atmosphere of young giant exoplanets and associated disks. Aims. These observations aim to characterize the atmosphere of the planet HD 95086 b by adding mid-infrared information so that the various hypotheses about its atmospheric parameters values can be unraveled. Improved images of cirsumstellar disks are provided. Methods. We present the MIRI coronagraphic imaging of the system HD 95086 obtained with the F1065C, F1140, and F2300C filters at central wavelengths of 10.575 µm, 11.3 µm, and 23 µm, respectively. We explored the method for subtracting the stellar diffraction pattern in the particular case when bright dust emitting at short separation is present. Furthermore, we compared different methods for extracting the photometry of the planet. Using the atmospheric models Exo-REM and ATMO, we measured the atmospheric parameters of HD 95086 b. Results. The planet HD 95086 b is detected at the two shortest MIRI wavelengths F1065C and F1140C. The contribution from the inner disk of the system is also detected. It is similar to that in the HR 8799 system. The outer colder belt is imaged at 23 µm. Background objects are observed in all filters. The mid-infrared photometry provides better constraints on the atmospheric parameters. We evaluate a temperature of 800–1050 K, consistent with one previous hypothesis that only used near-infrared data. The radius measurement of 1.0–1.14 RJup is better aligned with evolutionary models, but still smaller than predicted. These observations allow us to refute the hypothesis of a warm circumplanetary disk. Conclusions. HD 95086 is one of the first exoplanetary systems to be revealed at mid-infrared wavelengths. This highlights the interests and challenges of observations at these wavelengths.

Re-imagen: Generating coherent background activity in synthetic scenario-based forensic datasets using large language models

Due to legal and privacy-related restrictions, the generation of synthetic data is recommended for creating datasets for digital forensic education and training. One challenge when synthesizing scenario-based forensic data is the creation of coherent background activity besides evidential actions. This work leverages the creative writing abilities of large language models (LLMs) to generate personas and actions that describe the background usage of a device consistent with the created persona. These actions are subsequently converted into a machine-readable format and executed on a virtualized device using VM control automation. We introduce Re-imagen, a framework that combines state-of-the-art LLMs and a recent unintrusive GUI automation tool to produce synthetic disk images that contain arguably coherent “wear-and-tear” artifacts that current synthesis platforms lack. While, for now, the focus is on the coherence of the generated background activity, we believe that the proposed approach is a step toward more realistic synthetic disk image generation.

A Portable Linux-based Firmware for NVMe Computational Storage Devices

Abstract: Over the years, interest in computational storage devices has been growing steadily. This is largely due to the rise of data-intensive applications, such as machine learning, online video distribution, astrophysics, and genomics. Moving compute operations closer to the data provides benefits in terms of scaling possibilities and energy efficiency. The development of computational storage devices has been limited by the need for specialized and complex hardware. In this work we propose a portable Linux-based firmware framework for the development of NVMe computational storage devices. Our firmware runs on a variety of hardware platforms ranging from expensive FPGA solutions to inexpensive off-the-shelf single board computers. The firmware leverages the vast Linux software ecosystem to facilitate the development and prototyping of novel computational storage devices. We benchmark our firmware on multiple hardware platforms and demonstrate its versatility through several computational examples including a content-aware disk image search engine based on natural language processing and AI-driven image recognition.

On the Containment of the Unit Disc Image by Analytical Functions in the Lemniscate and Nephroid Domains

Suppose that A1 is a class of analytic functions f:D={z∈C:|z|&lt;1}→C with normalization f(0)=1. Consider two functions Pl(z)=1+z and ΦNe(z)=1+z−z3/3, which map the boundary of D to a cusp of lemniscate and to a twi-cusped kidney-shaped nephroid curve in the right half plane, respectively. In this article, we aim to construct functions f∈A0 for which (i) f(D)⊂Pl(D)∩ΦNe(D) (ii) f(D)⊂Pl(D), but f(D)⊄ΦNe(D) (iii) f(D)⊂ΦNe(D), but f(D)⊄Pl(D). We validate the results graphically and analytically. To prove the results analytically, we use the concept of subordination. In this process, we establish the connection lemniscate (and nephroid) domain and functions, including gα(z):=1+αz2, |α|≤1, the polynomial gα,β(z):=1+αz+βz3, α,β∈R, as well as Lerch’s transcendent function, Incomplete gamma function, Bessel and Modified Bessel functions, and confluent and generalized hypergeometric functions.

Observational features of reflection asymmetric black holes

The Kerr spacetime is symmetric with respect to a well-defined equatorial plane. When testing the equatorial reflection symmetry of an isolated black hole, one is at the same time testing the Kerr hypothesis in General Relativity. In this work, we investigate the possible observational features when a Keplerian disk is surrounding a rotating black hole without reflection symmetry. When such symmetry is broken, generically, the photon trajectories around the black hole and the Keplerian orbits on the accretion disk are distorted vertically away from the equatorial plane by an amount that depends on their distance to the black hole. In the reflection asymmetric spacetime we are considering, these two kinds of orbits are distorted in opposite directions. Interestingly, while the size and shape of black hole shadows closely resemble those of Kerr black holes, distinct observational characteristics can emerge in the disk image and emission line profiles. When observing the disk edge-on, a pronounced concave shape may appear along its innermost edge on the incoming side. Furthermore, distinctive horn-like features might be observed on the spectral line profile at the blue-shifted side. These special features can serve as compelling indicators of the reflection asymmetry present in rotating black holes.

A Study on the Improvement of YOLOv5 and the Quality Detection Method for Cork Discs

Combining machine vision and deep learning, optical detection technology can achieve intelligent inspection. To address the issues of low efficiency and poor consistency in the quality classification of cork discs used for making badminton heads, research on optimizing the YOLOv5 image-processing algorithm was conducted and applied to cork disc quality detection. Real-time images of cork discs were captured using industrial cameras, and a dataset was independently constructed. A GAN-based defect synthesis algorithm was employed to resolve the lack of defect samples. An attention mechanism was embedded in the YOLOv5 backbone network to enhance feature representation. The number of anchors in the YOLOv5 detection layer was reduced to address similar sample sizes, a center-matching strategy was designed to balance positive samples, and a shortest-distance label assignment algorithm was developed to eliminate ambiguities, improving accuracy and reducing postprocessing complexity. Detection results were integrated into quality classification. Experiments on the NVIDIA RTX3080 GPU demonstrated that the optimized algorithm improved the original YOLOv5 F1 score by 2.4% and mF1 score by 9.0%, achieving a quality classification F1 score of 95.1%, a processing speed of 178.5 FPS, and an mAP of 81.5%. Comparative experiments showed that the improved algorithm achieved the best detection accuracy on the cork disc dataset while maintaining high processing speed.

Thin accretion disk images of rotating hairy Horndeski black holes

Thin accretion disk images of rotating hairy Horndeski black holes

Automated Windows-Based Timelining Tool for Memory and Disk Image Analysis: Leveraging Timsort Algorithm with WinPmem, FTK Imager, Volatility 3 and The Sleuth Kit

As technology evolves, so does the threat of cyberattacks – making Digital Forensics crucial for damage control and prevention. This paper aims to address the inefficiencies faced by investigators in a forensic setting by automating the processes necessary for disk and memory image acquisition, forensic artifact parsing, and timeline generation. Leveraging publicly available tools such as: WinPmem, FTK Imager, Volatility 3 (VOL3), and The Sleuth Kit (TSK), the developed Python script is then able to provide for a clearer insight into the series of events that have transpired during a cyber incident through the generation of detailed and cohesively organized timelines, then using the Timsort algorithm for timeline analysis.

Atypical arachnoid cyst with progression: a case report

A 26-year-old female patient applied to the outpatient clinic in 2018 due to pain that had been present for a long time (16 years) and had recently spread from the top of the head to the nape of the neck. It was learned from the patient's anamnesis that her pain was relieved with analgesics. Although no pathological findings were found in the neurological examination, brain MRI was requested to reveal possible intracranial pathological conditions. In the MRI images taken, a cystic mass with a lobulated contour and thin septa of 24x31x40 mm and similar intensity to CSF was detected in the right parasagittal area posteriorly at the vertex level. The patient's control brain MRI images 3 months later showed no increase in cyst size. However, the patient's anamnesis, which came for a control in 2022, showed that his current complaints and headaches had increased. The MR images showed that the existing cyst had reached a size of approximately 60x35 mm. Medical treatment was initially planned for the patient (Acetazolamide, 750 mg/day). The MR taken one month later showed that there was no reduction in the cyst size and the optic discs were slightly swollen in the fundus examination. Considering the cyst growth, the patient's failure to benefit from medical treatment and the swelling noted in the optic discs, the patient was fitted with a cystoperitoneal shunt. The patient's early postoperative tomography image showed a significant reduction in the cyst (50.15x25.66) and a slightly swollen optic disc image with blurred borders was detected in the fundus examination. The patient's complaints regressed, and in the radiological images it was determined that the cyst continued to shrink [(45.8x23.19 mm) and (41.1x20.3 mm)], the shunt was in place, and she had no clinical complaints. As a result, it was argued that patients with atypically located arachnoid cysts, as in this case, should be taken under close clinical and radiological follow-up, and surgical treatment methods should definitely be considered in patients who are found to have an increase in cyst size and clinical findings during this follow-up and who do not respond despite medical treatment.

Association between quantity and quality of carbohydrate intake and glaucoma: a cross-sectional study from the NHANES database.

Glaucoma is a public health problem among the worldwide population. Dietary as a modifiable factor have been reported to be associated with glaucoma. This study aimed to explore the association between quantity and quality of carbohydrate (CH) intake and glaucoma among U.S. adults. In this cross-sectional study, data of participants aged ≥ 40years old were extracted from the National Health and Nutrition Examination Survey (NHANES) 2005-2008. CH intake information were obtained by 24-h dietary recall interview. Glaucoma was defined by regraded disc images. Covariates included demographic information, physical examination, laboratory values, complications and nutrients intake. The weighted univariable and multivariate logistic regression models were used to assess the association between the quantity and quality of CH intake and glaucoma. Subgroup analyses based on the history of hypertension were further assessed the association. The weighted population included a total of 4789 participants, of whom 119 (2.48%) had glaucoma. After adjusting for age, adrenal cortical steroids, hypertension, chronic kidney diseases, diabetes and energy intake, high quantity (OR = 1.83, 95%CI: 1.08-3.11) and low quality (OR = 0.44, 95CI%: 0.20-0.98) of CH intake were associated with the higher odds of glaucoma. High quantity of CH intake (OR = 2.06, 95%CI: 1.15-3.69) was associated with the high odds of glaucoma in hypertension, while high quality of CH intake (fiber-to-CH ratio: OR = 0.23, 95%CI: 0.06-0.82; CH-to-fiber and fiber-to-added sugars ratio: OR = 0.10, 95%CI: 0.02-0.53) were associated with the lower odds of glaucoma in participants without hypertension. In NAHNES 2005-2008, higher quantity and lower quality CH intake were associated with the high odds of glaucoma, especially among patients without hypertension. This study provides a theoretical basis for the health management of glaucoma patients from the perspective of dietary intake.