Spatial Filtering Research Articles

Tightly coupled stereo vision-inertial odometry based on point and line feature

Abstract To improve the accuracy and robustness of visual simultaneous localization and mapping (SLAM) in low-texture environments, this paper proposes a robust and fast stereo vision inertial SLAM pose estimation method that combines point and line features with an inertial measurement unit (IMU). The method tightly couples visual point and line features with IMU constraints, forming a least-squares problem through the error of each constraint term for nonlinear optimization. To address the issues of over-segmentation and time consumption in traditional line segment detection (LSD) algorithms, an improved LSD algorithm is adopted to accelerate line feature extraction. This approach merges nearby line segments based on spatial geometric relationships and filters out invalid segments, improving the time efficiency of the algorithm. Finally, experiments conducted in low-texture environments demonstrate that our algorithm achieves high localization accuracy and robustness.

Polarization-multiplexed zoom Moiré metalens for edge-enhanced imaging

Optical image processing with high operational efficiency has been applied as a pre-processing imaging system for image recognition. Edge-enhanced imaging as a high-efficiency optical image processing method is of great significance for feature extraction and target recognition. However, the edge-enhanced imaging system based on the 4F system and the spatial filter transforms mainly work under coherent light illumination conditions, without continuously zooming to track the spatial position of the target. Here, we demonstrate a polarization-multiplexed zoom Moiré metalens for edge-enhanced imaging under incoherent light illumination. Metalens is designed to generate polarization-dependent optical transfer functions that produce edge-enhanced images with a resolution of 1.2 µm by digital subtraction. Furthermore, continuous zoom at the range of 1-2× is realized by constructing a Moiré metalens composed of cascaded metasurfaces. The cascaded metasurfaces consist of two center-aligned dielectric metasurfaces, each with a Moiré phase sensitive to the rotation angle. By rotating the metasurface, the phase profile of the cascaded metasurfaces changes, and the effect of continuous zoom is realized. The focal length can be actively changed from 38 µm to 77 µm with the focusing efficiency of 50.3%. This metalens can be applied to machine vision, microscopic imaging, and promotes the development of multi-functional integrated optical systems.

Synchrotron CT dosimetry for wiggler operation at reduced magnetic field and spatial modulation with bow tie filters.

The Australian Synchrotron Imaging and Medical Beamline (IMBL) uses a superconducting multipole wiggler (SCMPW) source, dual crystal Laue monochromator and 135 m propagation distance to enable imaging and computed tomography (CT) studies of large samples with mono-energetic radiation. This study aimed to quantify two methods for CT dose reduction: wiggler source operation at reduced magnetic field strength, and beam modulation with spatial filters placed upstream from the sample. Transmission measurements with copper were used to indirectly quantify the influence of third harmonic radiation. Operation at lower wiggler magnetic field strength reduces dose rates by an order of magnitude, and suppresses the influence of harmonic radiation, which is of significance near 30 keV. Beam shaping filters modulate the incident beam profile for near constant transmitted signal, and offer protection to radio-sensitive surface organs: the eye lens, thyroid and female breast. Their effect is to reduce the peripheral dose and the dose to the scanned volume by about 10% for biological samples of 35-50 mm diameter and by 20-30% for samples of up to 160 mm diameter. CT dosimetry results are presented as in-air measurements that are specific to the IMBL, and as ratios to in-air measurements that may be applied to other beamlines. As CT dose calculators for small animals are yet to be developed, results presented here and in a previous study may be used to estimate absorbed dose to organs near the surface and the isocentre.

Adaptive content-aware spatial regularized correlation filter tracking algorithm

In order to solve the annoying boundary effects in correlation filter (CF) trackers induced by cyclic shift when sampling training patches, and improve the tracking performance, an adaptive content aware spatially regularized correlation filter (ACSRCF) is proposed. Firstly, real negative samples are generated from the background area around the target object, so as to alleviate the filter degradation by the fake negative samples induced from the circularly shifted object patches. Secondly, the locality sensitive histogram (LSH) based foreground feature is extracted and incorporated with the spatial regularization weight which is updated adaptively according to the varied object-oriented appearances. Thereafter, the CF model is optimized using the alternative direction method of multipliers (ADMM) in which the model is decomposed into two sub-problems and the LSH-based features are used in iteration for obtaining the optimal solutions. Finally, the proposed method is evaluated on 5 public benchmarks. The experimental results show that the accuracy and success rate scores of our method on OTB 50 dataset are 90.3%and 66.1%, respectively, exceeding the other CF trackers .The data on the OTB100 dataset is 92.2%and 69.2%, and the accuracy first ranks among all the trackers, while the success rate is ahead of other CF trackers.

Single- and dual-tube He-Ne lasers for high-power excitation of LG01-mode vortex beam

Abstract In this study, aiming to extend the power of LG01-mode vortex light at 632.8nm, the commercial He-Ne laser was upgraded to dual-tube gain geometry with the assistance of an intracavity spot defect for spatial filtering. We investigated the impact of using single and double He-Ne gas gain tubes on the powers of vortex laser output , and achieved a maximum 4.7-mW power of LG01-mode vortex light, correspionding to an optical-optical conversion efficiency of 39.2% and making it the highest power of vortex light obtained in He-Ne laser.

Broadband low radar cross section frequency selective surface radome based on phase cancellation and spatial filtering

AbstractThis letter introduces a novel design approach for broadband radar cross section (RCS) reduction of frequency selective surface (FSS) radomes. The approach integrates the principles of phase cancellation and spatial filtering together through a hybrid design method. The phase cancellation is obtained through the checkerboard arrangement of units, and the spatial filtering characteristics is achieved by the slot etched on the ground plane. Experimental and simulation results demonstrate that etching slots on the ground plane maintains broadband in‐phase reflection and bandpass characteristics, thereby extending the bandwidth of RCS reduction through a combination of two operation bands. Theoretical analysis of the working mechanism is also provided using equivalent circuit models. In comparison to the conventional radomes, the proposed FSS radome achieves significant bandwidth improvement for RCS reduction, indicating that it has promising prospects in future low‐RCS radome applications.

Observation of Standing Slow Magneto-acoustic Waves in a Flaring Active Region Corona Loop

Intensity fluctuations are frequently observed in different regions and structures of the solar corona. These fluctuations may be caused by magneto-hydrodynamic (MHD) waves in coronal plasma. MHD waves are prime candidates for the dynamics, energy transfer, and anomalous temperature of the solar corona. In this paper, analysis is conducted on intensity and temperature fluctuations along the active region coronal loop (NOAA AR 13599) near solar flares. The intensity and temperature as functions of time and distance along the loop are extracted using images captured by the Atmospheric Imaging Assembly (AIA) instrument onboard the Solar Dynamics Observatory (SDO) space telescope. To observe and comprehend the causes of intensity and temperature fluctuations, after conducting initial processing, and applying spatial and temporal frequency filters to data, enhanced distance-time maps of these variables are drawn. The space-time maps of intensities show standing oscillations at wavelengths of 171, 193, and 211 Å with greater precision and clarity than earlier findings. The amplitude of these standing oscillations (waves) decreases and increases over time. The average values of the oscillation period, damping time, damping quality, projected wavelength, and projected phase speed of standing intensity oscillations are in the range of 15–18 minutes, 24–31 minutes, 1.46″–2″, 132″–134″, and 81–100 km s−1, respectively. Also, the differential emission measure peak temperature values along the loop are found in the range of 0.51–3.98 MK, using six AIA passbands, including 94, 131, 171, 193, 211, and 335 Å. Based on the values of oscillation periods, phase speeds, damping time, and damping quality, it is inferred that the fluctuations in intensity are related to standing slow magneto-acoustic waves with weak damping.

A supervised data-driven spatial filter denoising method for speech artifacts in intracranial electrophysiological recordings

Abstract Neurosurgical procedures that enable direct brain recordings in awake patients offer unique opportunities to explore the neurophysiology of human speech. The scarcity of these opportunities and the altruism of participating patients compel us to apply the highest rigor to signal analysis. Intracranial electroencephalography (iEEG) signals recorded during overt speech can contain a speech artifact that tracks the fundamental frequency (F0) of the participant’s voice, involving the same high-gamma frequencies that are modulated during speech production and perception. To address this artifact, we developed a spatial-filtering approach to identify and remove acoustic-induced contaminations of the recorded signal. We found that traditional reference schemes jeopardized signal quality, whereas our data-driven method denoised the recordings while preserving underlying neural activity.

Eddy viscosity enhanced temporal direct deconvolution model for temporal large-eddy simulation of turbulent channel flow

In this work, a novel eddy viscosity enhanced temporal direct deconvolution model (TDDM) is proposed for temporal large-eddy simulation (TLES) of turbulent channel flow at large filter widths. To improve the accuracy of the constant eddy viscosity (CEV) model, particularly in the near-wall region, a damping function is incorporated to refine its performance. Moreover, a spatial filtering strategy is introduced to reduce the aliasing errors associated with the computation of subfilter-scale (SFS) stress, thereby enhancing numerical stability. In the a posteriori study, the accuracy of the CEV model is assessed comprehensively by comparing the TLES results with corresponding temporally filtered direct numerical simulation data. The results demonstrate that the CEV-enhanced TDDM provides accurate predictions across various statistical properties of velocity, instantaneous flow structures, kinetic energy spectra, and SFS energy fluxes. The coefficient sensitivity analysis of the CEV model reveals that the model coefficient significantly influences low Reynolds number flows, while its impact on high Reynolds number flows is relatively small. TLES on coarse grids demonstrate that the CEV-enhanced TDDM exhibits strong robustness and accuracy at different grid resolutions. Additionally, the CEV-enhanced TDDM in high Reynolds number flows is stable and accurate at remarkably large filter widths.

Inducing alternating magnetic fields for real-time non-contact fault localization within electric energy storage component arrays.

With the wide application of electric energy storage component arrays, such as battery cell arrays, capacitor arrays, and inductor arrays, their potential safety risks have gradually drawn the public attention. However, existing technologies cannot realize rapid, precise, and nondestructive localization of the faulty component within these large-scale arrays, especially for a component with an early stage short-circuit fault. To address this challenge, this paper proposes a magnetic field based method and realizes precise fault localization by inducing an alternating magnetic field from the target array, unlike previous research where a static magnetic field was induced. Through establishing a physical model of the short-circuit component as well as the whole array, a spatial filtering algorithm based on beamforming techniques is utilized to process the measured magnetic field data in real time. Both the simulation and experimental results demonstrate the capability of the proposed method in enhancing the security of electric energy storage component arrays. Within an imaging area of 80 × 80 mm2, the proposed method can accurately locate the faulty component out of a nine-component array, with an error of only 0.72mm for capacitors and 0.91mm for battery cells.

Navigating Stroke Care: Geospatial Assessment of Regional Stroke Center Accessibility

IntroductionReducing time between stroke onset and hospital intervention is crucial for positive outcomes in stroke patients. While EMS utilization decreases time to intervention, many US regions are not within timely proximity to an advanced-care-capable stroke center (ASC), defined as a comprehensive or thrombectomy-capable center. This study aims to utilize geographic methodology to identify regions in Wisconsin with both high stroke mortality and low physical accessibility to certified stroke centers (SCs), particularly ASCs. MethodsGeocoded mortality records for stroke death between 2015 and 2020 were accessed from the Wisconsin Department of Health Services. Indirectly age-standardized mortality ratios (SMRs) were estimated continuously across Wisconsin using adaptive spatial filtering and mortality records at the census block group level; the surface was then averaged by census tract for tract level SMRs. Addresses for SC locations within Wisconsin and bordering states were collected, and drive times from Wisconsin census tract centroids to the nearest SC subtypes were estimated. Drive times and mortality ratios were evaluated at the tract level alongside Rural-Urban Commuting Area (RUCA) codes. Spatial error regression modeling was used to determine RUCA classifications with the highest stroke risk independent of accessibility to stroke centers. ResultsApproximately 50%, 68%, and 78% of Wisconsin residents resided within 30, 45, and 60 minutes of an ASC, respectively. Median drive time from census tract centroids to the nearest ASC were highest for rural tracts (M=90 minutes, IQR=68-115) compared to small-town (M=82 minutes, IQR=49-113), micropolitan (M=53 minutes, IQR=43-77), and metropolitan tracts (M=19 minutes, IQR=11-35; p<0.001). Clusters of high stroke SMRs were found in urban centers as well as rural areas irrespective of county declinations. Spatial regression modeling suggested small-town census tracts had the highest SMR irrespective of physical accessibility to care and spatial correlation. In small-town census tracts >45 minutes from the nearest ASC, the median stroke SMR was 1.12 (IQR=0.94-1.40) with 226,000 residents and 150 stroke deaths per year. ConclusionSmall-town areas are associated with both long drive distance to ASC locations and high stroke mortality. Geographical analyses reveal apparent stroke care deserts and may inform strategic allocation of emergency medicine resources and coverage.

Evaluation of spatial resolution effects in rough wall-bounded turbulence

This study investigates the impact of insufficient spanwise spatial resolution on the measurement accuracy of streamwise velocity fluctuations over rough walls. We use a direct numerical simulation (DNS) database of turbulent open-channel flow over three-dimensional sinusoidal roughness with varied wavelengths and roughness heights. Employing a triple decomposition, we investigate both the attenuation of the turbulent fluctuations (about the local mean), u′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u^\\prime$$\\end{document} and the dispersive stresses (roughness-induced fluctuations of the time-averaged mean about the global mean), U~\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ilde{U}}$$\\end{document}. A boxcar filter on DNS data is applied to investigate the effects of spanwise spatial filtering on these quantities. Our analysis reveals the significance of two key length-scale ratios for velocity measurements over rough walls: the wire length relative to the spatially and temporally plane-averaged Kolmogorov scale at the roughness crest (l/⟨η⟩k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l/\\langle \\eta \\rangle _k$$\\end{document}), and the wire length relative to the roughness spanwise wavelength (l/Λy\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l/\\Lambda _y$$\\end{document}). We observe that maintaining l/⟨η⟩k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l/\\langle \\eta \\rangle _k$$\\end{document} constant while increasing l/Λy\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l/\\Lambda _y$$\\end{document} attenuates the variance of U~\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ilde{U}}$$\\end{document} and u′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$u^\\prime$$\\end{document} within the roughness sublayer. When fixing l/Λy\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l/\\Lambda _y$$\\end{document}, an increase in l/⟨η⟩k\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$l/\\langle \\eta \\rangle _k$$\\end{document} influences the turbulent fluctuations across all wall-normal locations. These findings highlight the necessity of considering both length scales when evaluating spanwise spatial resolution in turbulence measurements over rough walls.

Ensemble strategies exploration for the calibration data optimized spatial filters based SSVEP recognition algorithms

Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) exhibit high information transfer rate (ITR) characteristics. However, the electroencephalogram (EEG) data encoding SSVEP patterns has a low signal-to-noise ratio, leading to limitations in applications. To enhance the application of SSVEP-BCIs, various SSVEP recognition algorithms have been developed by utilizing prior knowledge based on the calibration data optimized spatial filters (CDOSF). Yet, the process of combining spatial filters from multiple stimuli remains unexplored, resulting in performance bottlenecks for SSVEP recognition and application scenarios. In this study, we provide an overview of four popular CDOSF-based SSVEP recognition algorithms and introduce three ensemble strategies to extend these recognition algorithms. For comparison purposes, we selected three benchmark SSVEP datasets and conducted comparative experiments on the three ensemble strategies across the four recognition algorithms. The experimental results demonstrate that the ensemble strategies significantly enhance SSVEP recognition accuracy and ITR. Ablation studies also confirm that ensemble strategies possess characteristics of requiring less calibration data, fewer EEG channels, and insensitivity to parameters. The ensemble strategies extended CDOSF-based recognition algorithms offer a novel choice for constructing SSVEP-BCIs.

Genetic-algorithm-assisted design of chiral honeycomb membrane acoustic metamaterials for broadband noise suppression

Low-frequency sound wave reduction is hardly feasible for traditional sound absorbers such as porous media. In contrast, locally resonant acoustic metamaterials act as spatial frequency filters, effectively reducing low-frequency noise. However, these materials can have narrow bandgaps, add extra mass to the main system, and function only within a specifically adjusted frequency range. Therefore, this paper proposes a methodology for designing three-dimensional (3D) chiral honeycomb membrane-type acoustic metamaterials (MAM) based on acoustic circular dichroism (ACD), negative effective mass, and local resonance mechanisms (LRM). The paper also uses the genetic algorithm (GA) to maximize sound transmission attenuation and widen bandgaps. The accuracy of the simulations is validated with available experimental data. The key idea of the present study is to automatically design a MAM structure using modern optimization tools. A developed GA aims to maximize the average sound transmission loss (STL) across the desired low-frequency range of 0–850 Hz by optimizing the value of structural parameters associated with the configurations of masses, including rotation and linear offset, as well as their geometrical dimensions, including length and width. COMSOL Livelink with MATLAB is employed as a practical co-simulation tool to find an optimum value for structural parameters. Results indicate a 35% improvement in average STL along with an ultra-wide bandgap with a 507% bandgap coverage factor (BGCF) in the frequency range of interest, verifying the reliability of the developed algorithm. In addition, sound mitigation incidence is justified by exploring the negative effective parameters of the unit cell and displacement vector fields. The proposed designs demonstrate superior performance as both initial and optimized models broke the mass law within the investigated frequency range. Finally, the effect of the selected geometrical parameters on the objective function is observed through sensitivity analysis. This study presents the practical use of optimization algorithms to develop MAMs.

Fast Bayesian Inference for Spatial Mean-Parameterized Conway–Maxwell–Poisson Models

Count data with complex features arise in many disciplines, including ecology, agriculture, criminology, medicine, and public health. Zero inflation, spatial dependence, and non-equidispersion are common features in count data. There are currently two classes of models that allow for these features—the mode-parameterized Conway–Maxwell–Poisson (COMP) distribution and the generalized Poisson model. However both require the use of either constraints on the parameter space or a parameterization that leads to challenges in interpretability. We propose spatial mean-parameterized COMP models that retain the flexibility of these models while resolving the above issues. We use a Bayesian spatial filtering approach in order to efficiently handle high-dimensional spatial data and we use reversible-jump MCMC to automatically choose the basis vectors for spatial filtering. The COMP distribution poses two additional computational challenges—an intractable normalizing function in the likelihood and no closed-form expression for the mean. We propose a fast computational approach that addresses these challenges by, respectively, introducing an efficient auxiliary variable algorithm and pre-computing key approximations for fast likelihood evaluation. We illustrate the application of our methodology to simulated and real datasets, including Texas HPV-cancer data and US vaccine refusal data. Supplementary materials for this article are available online.

Decoding motor imagery loaded on steady-state somatosensory evoked potential based on complex task-related component analysis

Background and objectiveMotor Imagery (MI) recognition is one of the most critical decoding problems in brain- computer interface field. Combined with the steady-state somatosensory evoked potential (MI-SSSEP), this new paradigm can achieve higher recognition accuracy than the traditional MI paradigm. Typical algorithms do not fully consider the characteristics of MI-SSSEP signals. Developing an algorithm that fully captures the paradigm's characteristics to reduce false triggering rate is the new step in improving performance. MethodsThe idea to use complex signal task-related component analysis (cTRCA) algorithm for spatial filtering processing has been proposed in this paper according to the features of SSSEP signal. In this research, it's proved from the analysis of simulation signals that task-related component analysis (TRCA) as typical method is affected when the response between stimuli has reduced correlation and the proposed algorithm can effectively overcome this problem. The experimental data under the MI-SSSEP paradigm have been used to identify right-handed target tasks and three unique interference tasks are used to test the false triggering rate. cTRCA demonstrates superior performance as confirmed by the Wilcoxon signed-rank test. ResultsThe recognition algorithm of cTRCA combined with mutual information-based best individual feature (MIBIF) and minimum distance to mean (MDM) can obtain AUC value up to 0.89, which is much higher than traditional algorithm common spatial pattern (CSP) combined with support vector machine (SVM) (the average AUC value is 0.77, p < 0.05). Compared to CSP+SVM, this algorithm model reduced the false triggering rate from 38.69 % to 20.74 % (p < 0.001). ConclusionsThe research prove that TRCA is influenced by MI-SSSEP signals. The results further prove that the motor imagery task in the new paradigm MI-SSSEP causes the phase change in evoked potential. and the cTRCA algorithm based on such phase change is more suitable for this hybrid paradigm and more conducive to decoding the motor imagery task and reducing false triggering rate.

Spatial filters in neural network models of visual cortex do not need to be learned

Spatial filters in neural network models of visual cortex do not need to be learned

Correlations between ultrafast power Doppler perfusion imaging variables and clinical disease activity in rheumatoid arthritis: potential applications for diagnosing and treating patients in deep clinical remission.

This study aimed to evaluate the ability of ultrafast power Doppler (PD) to assess disease activity in rheumatoid arthritis (RA) by examining the correlations between variables from ultrafast PD perfusion imaging and clinical measures of disease activity. Thirty-three RA patients underwent clinical assessments of disease activity and ultrasound scans of bilateral wrists using both ultrafast and conventional PD systems. A spatial singular value decomposition filter was applied to the ultrafast PD imaging. Singular vectors representing perfusion and fast flows were selected to produce perfusion images. All images were quantitatively analyzed with computer assistance and scored semiquantitatively (0-3) by a physician for synovial vascularity. The Pearson correlation coefficients between image variables and clinical indices were calculated. The correlation coefficients ranged from weakly to moderately positive between ultrafast PD variables and clinical indices (r=0.221-0.374, all P&lt;0.05). The strongest correlations were observed for synovial PD brightness with the 28-joint Disease Activity Score based on C-Reactive Protein (DAS28-CRP) and the Simplified Disease Activity Index (SDAI). In patients within the deep clinical remission (dCR) subgroup, synovial PD brightness showed stronger correlations with DAS28-CRP, the Clinical Disease Activity Index, and SDAI (r=0.578-0.641, all P&lt;0.001). The correlation coefficients between conventional PD variables and clinical indices were similar to those observed with ultrafast PD variables. Ultrafast PD imaging effectively extracts capillary blood signals and generates perfusion images. In the RA population, ultrafast PD variables exhibit weak-to-moderate correlations with clinical indices, with these correlations being notably stronger in dCR patients.

PM2.5 estimation and its relationship with NO2 and SO2 in China from 2016 to 2020

ABSTRACT The distribution of surface particulate matter (PM2.5) exhibits significant spatiotemporal patterns, especially with a spatial clustering effect. Therefore, resolving spatial characteristics is essential in the modeling process. This study proposes a space–time varying coefficients (STVC-LG) model by considering spatial effects from the perspective of eigenvector spatial filtering and incorporating spatial effects and temporal features into the LightGBM model to estimate ground PM2.5. A site-based cross-validation result of the estimated daily 1 km PM2.5 concentration in China from 2016 to 2020 shows that the proposed model offers high accuracy, an R2 of 0.88, and an RMSE of 12.21 μg/m3 (17% and 32% enhanced compared to the original LightGBM model). It uses a series of spatial eigenvectors to describe different spatial patterns. It then constructs spatial interaction terms by combining the eigenvectors and influencing variables, allowing the values of the variable to vary spatially with the eigenvectors. The obtained PM2.5, with a decreasing trend, had a clear distributional consistency with NO2 and SO2 according to the co-occurrence distribution maps, particularly in winter. In areas where high concentrations co-occur, both NO2 and SO2 concentrations are Granger-causing PM2.5 concentrations from a statistical perspective.

Large eddy simulation analysis of a model reactive tracer through spatial filtering

Large eddy simulations (LES) provide a methodology for both analyzing and simulating multi-scale flows when the smallest scales of motion cannot be resolved. Within environmental flows there exist numerous biogeochemical processes involving tracers undergoing reactions. In this study, we perform an a posteriori LES analysis on a direct numerical simulation of an idealized model reactive tracer subjected to three-dimensional turbulence induced by a Rayleigh–Taylor instability. The governing equations, including an advection–diffusion–reaction equation for the reactive tracer, are filtered, and the resulting sub-filter-scale terms are expressed in terms of interactions between scales. The procedure is demonstrated for a generalized degree N polynomial reaction function. Various spectral filters are applied to the data and compared. The preferential choice is to use the widest filter possible with a smoothed cutoff. The sub-filter-scale reaction term that results from filtering the reaction function is considered for each of the filter choices. When using a particularly harsh filter, local balances are found for the resolved scale and cross-scale components of the sub-filter-scale reaction term. The same result is shown for the vertical sub-filter-scale flux for both a reactive and a passive tracer. The components of the sub-filter-scale reaction and vertical flux terms involving interactions at the sub-filter-scale do not show any evidence of local balances and are distributed around the fine turbulent structures in the flow. This suggests that parameterizations for the sub-filter-scale terms would benefit from considering event specific dynamics.