Spatial Mean Research Articles

A larger reduction of spatial mean QRS-T angle during cardiac resynchronisation therapy is associated with a lower risk of mortality and heart failure hospitalisation

Abstract Background Predicting clinical outcome in heart failure patients undergoing cardiac resynchronization therapy (CRT) remains challenging, necessitating improved risk stratification for CRT recipients. Previous studies have shown an association between large spatial peak and mean QRS-T angles and cardiovascular disease. Little is known, however, about their relation to clinical outcome following CRT. Purpose To investigate the association between spatial peak and mean QRS-T angles and long-term clinical outcome following CRT initiation. Methods All patients with native LBBB receiving CRT at a large-volume tertiary care center between 2015 and 2020 were retrospectively evaluated. Spatial peak and mean QRS-T angles were derived from digital pre- and post-CRT 12-lead ECGs that were processed using Glasgow algorithm and Kors’ regression transformation. The QRS-T angles and their change after CRT were analyzed in relation to the primary composite endpoint of heart failure hospitalisation or all-cause mortality using Cox regression analysis adjusted for clinical covariates (age, gender, CRT-P or CRT-D, secondary ICD indication, ischemic etiology, NYHA class, LVEF, diabetes, atrial fibrillation, baseline QRS duration, NT-proBNP, and eGFR). Results The study group comprised 250 patients (a median age [Q1–Q3] of 72.2 years [64.5–76.3]; 22% female; 58% New York Heart Association (NYHA) class III-IV; LVEF 27% [22–30]) who were followed over a median follow-up time of 4.5 years [3.2–5.8] after CRT implantation. The median post-CRT spatial peak and mean QRS-angles were 142° [116–160] and 152° [127–166], and the median angle was decreased by 16° [-0.6–41] and 14° [-0.4–38], respectively. Both a larger post-CRT mean QRS-T angle (HR 1.20, 95%CI 1.08-1.33, p=<.001) and peak QRS-T angle (HR 1.08, 95%CI 1.01-1.17, p=0.046) were associated with the primary endpoint. A larger reduction of mean QRS-T angle, but not peak QRS-T angle, was associated with a risk reduction (HR 0.87, 95%CI 0.79-0.95, p<0.003). In Kaplan-Meier analysis, patients with a post-CRT mean QRS-T angle above median had a higher risk of reaching the endpoint (log-rank p=0.002, Figure 1). Conclusion Our results show that a larger magnitude of the post-CRT spatial peak and mean QRS-T angles, and a larger reduction of the latter, are associated with long-term heart failure hospitalisation and death. These findings suggest that spatial QRS-T angle may have a potential role in refined risk stratification of CRT patients.Figure 1.K-M curve spatial mean QRS-T

Hysteresis in seasonal land-atmospheric interactions over India and its characteristics across croplands and forests

Abstract Satellite-derived Vegetation Optical Depth and Soil Moisture (SM) data reveal the critical role of the soil-vegetation continuum in storing rainwater during the Indian Summer Monsoon and supporting evapotranspiration (ET) during the dry non-monsoon season. During the non-monsoon drier period, the climatologically estimated spatial mean of ET exceeds precipitation input, a phenomenon known as the Soil-Vegetation Capacitor (SVC) effect, which is pivotal in maintaining ecosystem productivity. Notably, our analysis reveals significant variations in the capacitor period between croplands and forests, with croplands exhibiting a ~77 days longer due to dual crop seasons influenced by regional precipitation. The well-recognized hysteresis curves, observed in magnetization and soil-water characteristic curves (SWCC), highlight phenomena where a system's state is influenced by its historical inputs or states and are integral to our findings. We report a previously undocumented seasonal hysteresis in the relationship between the evaporative fraction (EVF) and SM for Indian croplands and forests. We further found that the croplands SM-EVF relation exhibits a reversal in hysteresis in the case of root-zone SM. The surface SM-EVF hysteresis is not present in forests with large root depths and reduced soil evaporation due to high canopy shading, and yet it is present for the root-zone SM. With its reversal for croplands, the newly found hysteresis must be addressed in redefining the critical SM threshold to demarcate the energy and water-limiting regimes. It should be incorporated in the land surface modeling parameterization. Additionally, we observed hysteresis in the soil moisture-gross primary productivity (SM-GPP) relationship across both land covers and soil profiles (surface and root-zone), underscoring the need to investigate such processes to consider their dynamics in future ecological and hydrological models.

Comparison of indicators to evaluate the performance of climate models

AbstractThe evaluation of climate models is a crucial step in climate studies. It consists of quantifying the resemblance of model outputs to reference data to identify models with superior capacity to replicate specific climate variables. Clearly, the choice of the evaluation indicator significantly impacts the results, underscoring the importance of selecting an indicator that properly captures the characteristics of a “good model”. This study examines the behaviour of six indicators, considering spatial correlation, distribution mean, variance and shape. Monthly data for precipitation, temperature and teleconnection patterns in Central America were utilized in the analysis. A new multicomponent measure was selected based on these criteria to assess the performance of 32 CMIP6 models in reproducing the annual seasonal cycle of these variables. The top six models were determined using multicriteria methods. It was found that even the best model reproduces one derived climatic variable poorly in this region. The proposed measure and selection method can contribute to enhancing the accuracy of climatological research based on climate models.

Changes in the Spatial Patterns of Near‐Surface Soil Moisture and Environmental Controlling Factors Before and After a Landslide

ABSTRACTRain‐induced landslides are common natural disturbances in forested headwaters that can strongly alter the spatial distributions of hydrological and environmental features. Although many studies have reported the impacts of landslides on the spatial patterns of soil moisture or their environmental controls, studies that have used detailed in situ data sets collected at the same location before and after a landslide are lacking. This study investigated the spatial distribution pattern of the near‐surface soil moisture and environmental controls, including topographic, soil and vegetation features, in a headwater catchment using ground‐based measurements with high spatial resolution after a landslide in 2016. The data set was compared to measurements taken at the same location before the landslide to explore whether the landslide altered the amount, spatial distribution, or potential controlling factors of near‐surface soil moisture. The mean soil moisture decreased across the site after the landslide, even under conditions of greater rainfall input than before the landslide. Spatial variation in soil moisture decreased in the high‐disturbance area but increased in the low‐disturbance area, although autocorrelation distances of soil moisture changed little. The relationships between the spatial mean and standard deviation of soil moisture markedly changed from a convex‐upward shape to a convex‐downward shape in the highly disturbed area. This indicates that the spatial mean of soil moisture exhibited its greatest spatial variation under moderate conditions before the landslide, with the lowest spatial variation occurring after the landslide. Most of the same controlling factors (i.e., slope gradient, topographic wetness index, vegetation density, soil porosity and saturated hydraulic conductivity) were explored after the landslide, but their influence levels greatly decreased or even disappeared. Thus, the landslide weakened the spatial connectedness between soil moisture and environmental features, which has not yet been restored 6 years after the landslide. We suggest that the connectedness between hydrological responses and environmental features is crucial for restoration. Their connectedness can serve as an indicator to identify the stage of ecological succession from the disturbances of a landslide.

Modeling cheatgrass distribution, abundance, and response to climate change as a function of soil microclimate.

Exotic annual grass invasions in water-limited systems cause degradation of native plant and animal communities and increased fire risk. The life history of invasive annual grasses allows for high sensitivity to interannual variability in weather. Current distribution and abundance models derived from remote sensing, however, provide only a coarse understanding of how species respond to weather, making it difficult to anticipate how climate change will affect vulnerability to invasion. Here, we derived germination covariates (rate sums) from mechanistic germination and soil microclimate models to quantify the favorability of soil microclimate for cheatgrass (Bromus tectorum L.) establishment and growth across 30 years at 2662 sites across the sagebrush steppe system in the western United States. Our approach, using four bioclimatic covariates alone, predicted cheatgrass distribution with accuracy comparable to previous models fit using many years of remotely-sensed imagery. Accuracy metrics from our out-of-sample testing dataset indicate that our model predicted distribution well (72% overall accuracy) but explained patterns of abundance poorly (R2 = 0.22). Climatic suitability for cheatgrass presence depended on both spatial (mean) and temporal (annual anomaly) variation of fall and spring rate sums. Sites that on average have warm and wet fall soils and warm and wet spring soils (high rate sums during these periods) were predicted to have a high abundance of cheatgrass. Interannual variation in fall soil conditions had a greater impact on cheatgrass presence and abundance than spring conditions. Our model predicts that climate change has already affected cheatgrass distribution with suitable microclimatic conditions expanding 10%-17% from 1989 to 2019 across all aspects at low- to mid-elevation sites, while high- elevation sites (>2100 m) remain unfavorable for cheatgrass due to cold spring and fall soils.

Regional Spatial Mean of Ionospheric Irregularities Based on K-Means Clustering of ROTI Maps

In this paper, we investigate and propose the application of an unsupervised machine learning clustering method to characterize the spatial and temporal distribution of ionospheric plasma irregularities over the Western African equatorial region. The ordinary Kriging algorithm was used to interpolate the rate of change of the total electron content (TEC) index (ROTI) over gridded 0.5° by 0.5° latitude and longitude regional maps in order to simulate the level of ionospheric plasma irregularities in a quasi-real-time scenario. K-means was used to obtain a spatial mean index through an optimal stratification of regional post-processed ROTI maps. The results obtained could be adapted by appropriate K-means algorithms to a real-time scenario, as has been performed for other applications. This method could allow us to monitor plasma irregularities in real time over the African region and, therefore, lead to the possibility of mitigating their effects on satellite-based location systems in the said region.

Deep learning-based bias correction of ISMR simulated by GCM

Simulated data of atmospheric variables like precipitation is very important for climate science research, especially for understanding future scenarios. Such data is generated by global or regional climate models (GCM/RCM), for both past and future periods under different initial conditions. Unfortunately, these models frequently display biases in simulated precipitation data compared to ground observations, due to their inability to incorporate the entire physics of the process accurately. It is imperative to mitigate such biases using contemporary techniques to make the simulated data a valuable end product. Our aim in this research is to correct seasonal (from June to September) Indian Summer Monsoon Rainfall (ISMR) data as simulated by the Climate Forecast System (CFS) over the Indian subcontinent, with the Global Precipitation Climatology Project (GPCP) data serving as the ground truth reference. This period is important as more than one-third of India's annual rainfall occurs during these months. This study presents a novel Deep Learning (DL) based architecture known as the Convolutional Neural Network for Bias Correction (CNNBC) that aims to calibrate the model-simulated data with past observations, to address these biases while preserving the statistical properties and spatial correlations relationships among grid points and enhancing the spatial mean of precipitation estimates. To evaluate the effectiveness of our proposed method, we compare its performance to that of three other statistical and DL techniques: quantile mapping (QM), quantile delta mapping (QDM), and the SRDRN model. The comparative analysis demonstrates that the CNNBC model outperforms the other in terms of our task.

Spatiotemporal Evolution Disparities of Vegetation Trends over the Tibetan Plateau under Climate Change

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions.

An improved meteorological variables-based aerosol optical depth estimation method by combining a physical mechanism model with a two-stage model

A two-stage model integrating a spatiotemporal linear mixed effect (STLME) and a geographic weight regression (GWR) model is proposed to improve the meteorological variables-based aerosol optical depth (AOD) retrieval method (Elterman retrieval model-ERM). The proposed model is referred to as the STG-ERM model. The STG-ERM model is applied over the Beijing-Tianjin-Hebei (BTH) region in China for the years 2019 and 2020. The results show that data coverage increased by 39.0% in 2019 and 40.5% in 2020. Cross-validation of the retrieval results versus multi-angle implementation of atmospheric correction (MAIAC) AOD shows the substantial improvement of the STG-ERM model over earlier meteorological models for AOD estimation, with a determination coefficient (R2) of daily AOD of 0.86, root mean squared prediction error (RMSE) and the relative prediction error (RPE) of 0.10 and 36.14% in 2019 and R2 of 0.86, RMSE of 0.12 and RPE of 37.86% in 2020. The fused annual mean AOD indicates strong spatial variation with high value in south plain and low value in northwestern mountainous areas of the BTH region. The overall spatial seasonal mean AOD ranges from 0.441 to 0.586, demonstrating strongly seasonal variation. The coverage of STG-ERM retrieved AOD, as determined in this exercise by leaving out part of the meteorological data, affects the accuracy of fused AOD. The coverage of the meteorological data has smaller impact on the fused AOD in the districts with low annual mean AOD of less than 0.35 than that in the districts with high annual mean AOD of greater than 0.6. If available, continuous daily meteorological data with high spatiotemporal resolution can improve the model performance and the accuracy of fused AOD. The STG-ERM model may serve as a valuable approach to provide data to fill gaps in satellite-retrieved AOD products.

MR–CT image fusion method of intracranial tumors based on Res2Net

BackgroundInformation complementarity can be achieved by fusing MR and CT images, and fusion images have abundant soft tissue and bone information, facilitating accurate auxiliary diagnosis and tumor target delineation.PurposeThe purpose of this study was to construct high-quality fusion images based on the MR and CT images of intracranial tumors by using the Residual-Residual Network (Res2Net) method.MethodsThis paper proposes an MR and CT image fusion method based on Res2Net. The method comprises three components: feature extractor, fusion layer, and reconstructor. The feature extractor utilizes the Res2Net framework to extract multiscale features from source images. The fusion layer incorporates a fusion strategy based on spatial mean attention, adaptively adjusting fusion weights for feature maps at each position to preserve fine details from the source images. Finally, fused features are input into the feature reconstructor to reconstruct a fused image.ResultsQualitative results indicate that the proposed fusion method exhibits clear boundary contours and accurate localization of tumor regions. Quantitative results show that the method achieves average gradient, spatial frequency, entropy, and visual information fidelity for fusion metrics of 4.6771, 13.2055, 1.8663, and 0.5176, respectively. Comprehensive experimental results demonstrate that the proposed method preserves more texture details and structural information in fused images than advanced fusion algorithms, reducing spectral artifacts and information loss and performing better in terms of visual quality and objective metrics.ConclusionThe proposed method effectively combines MR and CT image information, allowing the precise localization of tumor region boundaries, assisting clinicians in clinical diagnosis.

An Assessment of Changes in the Thermal Environment during the COVID-19 Lockdown: Case Studies from the Greenland and Norwegian Seas

The COVID-19 lockdown had a significant impact on human activities, reducing anthropogenic heat and CO2 emissions. To effectively assess the impact of the lockdown on the thermal environment, we used the sliding paired t-test, which we improved from the traditional sliding t-test by introducing the paired t-test for sliding statistical tests, to test the abrupt change in the thermal environment. Furthermore, an additive decomposition model and wavelet analysis method were used to analyze the characteristics of trend and irregular change, coherence, and phase difference of the time series data with respect to the thermal environment. We chose the Greenland Sea and the Norwegian Sea, regions highly sensitive to changes in climate and ocean circulation, as case studies and used remote sensing data of the sea surface temperature (SST) and the atmospheric CO2 concentration data obtained from the Goddard Earth Sciences Data and Information Services Center from January 2015 to December 2021 for the analysis. The results show that although the annual spatial mean SST in 2020 is lower than the mean of all 7 years in most areas of the two seas, there is no evidence of a significant mutation in the decrease in the SST during the lockdown in 2020 compared with the temperatures before, according to the sliding paired t-test. The analysis of the irregular components of the monthly mean SST decomposed by an additive decomposition model also does not show the anomalously low SST during the lockdown in 2020. In addition, the lockdown had almost no impact on the increasing trend of CO2 concentration. The wavelet analysis also shows that there is no obvious anomaly in coherence or phase difference between the periodic variation of the SST and the CO2 concentrations in 2020 compared with other years. These results suggest that the direct effect of the COVID-19 lockdown on the thermal environment of the study area could be negligible.

Self-immunity study of quadrotor UAV based on Modelica system modeling and disturbance feed-forward compensation

For addressing the challenges of decreased attitude and trajectory tracking accuracy and a delayed response in the flight control operations of quadcopter unmanned aerial vehicles (UAVs) under the uncertainties of model parameters and external disturbances, this study leverages the advantages of the non-causal declarative modeling language Modelica in system modeling and simulation. In addition, it incorporates the nonlinear Active Disturbance Rejection Control (ADRC) framework for disturbance observation, estimation, and compensation. A state observer is designed to mitigate the impact of external disturbances and model uncertainties through feed-forward compensation, and stability analysis is conducted. Numerical simulations for hover resistance demonstrate that, compared to the cascade proportional integral differential (PID) control strategy, PID-NLADRC reduces the maximum deviation induced by wind disturbances by ∼50% and shortens the disturbance influence time by around 40%. Simulations for different trajectories, such as planar or spatial, smooth or abrupt changes, indicate that under the PID-NLADRC control strategy, the real-time spatial distance deviation mean is reduced by 69.5%, and the peak time is shortened by 75.7%. Validation through multi-objective applications and physical experiments highlights the advantages of PID-NLADRC in terms of positioning accuracy, rapid tracking, and disturbance suppression, aligning well with the fast, precise, and robust flight control requirements of quadcopter UAVs.

Improved soil moisture estimation and detection of irrigation signal by incorporating SMAP soil moisture into the Indian Land Data Assimilation System (ILDAS)

Land surface models have facilitated the estimation of soil moisture over a range of spatiotemporal scales. However, limitations in model parameterization and under-representation of anthropogenic processes restrict their ability to estimate local-scale soil moisture variability, especially over irrigated areas. Assimilation of satellite-based soil moisture retrievals into land surface models can be a viable approach to overcome these constraints, specially over highly irrigated countries such as India, where such applications are rare. Additionally, large-scale validation of modeled soil moisture has been limited over India till now due to lack of a representative station network. By assimilating Soil Moisture Active Passive (SMAP)-based estimates into the state-of-the-art Indian Land Data Assimilation System (ILDAS) and combining with a new soil moisture station network of more than 200 stations, this study demonstrates improved soil moisture estimations and capture of irrigation signals over the region. The Noah-MP land surface model is forced by multiple local and global meteorological datasets and Ensemble Kalman Filter (EnKF) is used for assimilation of soil moisture. Comparison of open-loop and data assimilated soil moisture against station soil moisture data shows relative spatial mean improvement of 0.0178 in correlation and 0.0029 m3/m3 in RMSE. Further statistical comparison with in-situ data has also shown better results over most of the stations, as evident from improved correlations and reduced unbiased RMSE after assimilation. Finally, the climatology of soil moisture over the different irrigation fractions reveals that data assimilated outputs over irrigated grid cells tend to have higher soil moisture during dry winter season, demonstrating the ability to capture irrigation signals. These findings quantify the value of data assimilation in improving soil moisture estimates and the ability to capture unmodeled processes such as irrigation, which lays the science groundwork for upcoming space missions such as NASA ISRO Synthetic Aperture Radar (NISAR).

Statistical insights of polarization speckle via von Mises–Fisher distribution on the Poincaré sphere

Polarization speckles generated via random scattering of light are ubiquitous in natural and engineered systems. They not only manifest intensity fluctuations but also reveal a spatially fluctuating, random polarization distribution. The precise morphology of the polarization speckle pattern serves as a deterministic signature of the light’s state of polarization fluctuation within a scattering medium. Given the inherent randomness of polarization speckle patterns, a statistical approach emerges as the most pragmatic method for their analysis. Stokes parameters, implemented as temporal or spatial averages, are utilized for this purpose. However, within a polarization speckle field featuring a specific spatial average of Stokes parameters, the polarization state exhibits spatial variations across the speckle pattern. These random polarization fluctuations can be effectively modeled using a particular probability density function (PDF), visually represented on the Poincaré sphere. In this work, von Mises–Fisher (vMF) distribution on the Poincaré sphere is extended and applied to demonstrate a statistical insight of polarization speckle fields. A complete theoretical basis is established to investigate the spatial fluctuation of the state of polarization in the polarization speckle using vMF distribution on the Poincaré sphere, including the spatial mean direction, and spatial concentration parameter. Behavior of the marginal vMF distribution on the axes of the Poincaré sphere and its association with the probability density function of the normalized at-the-point Stokes parameters for three different polarization speckles are examined by experiment and simulation. The experimental results are in good agreement with the simulation results and confirm the usefulness of the established theoretical framework for the analysis of the polarization speckles. Characterization of spatial polarization fluctuation offers significant applications, such as in polarimetric analysis and optical sensing, and the same analogy can be used in quantum optics.

On a generalization of the Cahn-Hilliard type equation with logarithmic nonlinearities for formation of islands

Abstract In this paper, main objective is to demonstrate the existence of solutions for an equation resembling the Cahn-Hilliard model, featuring a proliferation term and a logarithmic nonlinear term. This equation has been conceptualized within the context of interactions in liquid-gas systems, particularly in the context of island formation. The primary challenge lies in the departure from the original Cahn-Hilliard equation, as we no longer maintain conservation of the spatial average mean of the order parameter. This departure introduces the complexity in establishing uniform estimates for the solutions of the approximated problems concerning the regularization parameter, as it may potentially result in finite-time blow-up.

Long-Term Response of Floodplain Meadow Normalized Difference Vegetation Index to Hydro-Climate and Grazing Pressure: Tamir River Plains, Mongolia

The greenery of floodplain meadows in arid regions, such as Mongolia, is influenced by climate, hydrology, and land use. In this study, we analyzed the NDVI (Normalized Difference Vegetation Index) of two floodplain meadows located along the South Tamir and Tamir Rivers using LANDSAT images. Our goal was to observe NDVI spatial changes, variations, and mean values in mid-August every six years from 1991 to 2015 and to identify the factors driving these differences. To achieve this, we conducted variance analysis to identify changes in NDVI and implemented Principal Component Analysis to determine the influence of hydro-meteorological factors and grazing intensity. Our findings indicate a significant decrease in greenness, as measured by pixel-scale NDVI, during the late summer period. This decrease was consistently observed, except for a series of harsh winters that followed relatively dry summers, resulting in a disastrous event called dzud, which led to the death of livestock. The decrease in NDVI was amplified by lower precipitation in June, higher temperatures and wind speed in July, and increased precipitation in August, along with a higher frequency of days with convective rain. Our findings have important implications for managing grazing in Mongolia’s grasslands, promoting sustainable land use, and mitigating sandstorms. The variance and average values of NDVI at the pixel level can serve as reliable markers of sustainable pasture management in areas where other vegetation measures are limited.

Color constancy mechanisms in virtual reality environments.

Prior research has demonstrated high levels of color constancy in real-world scenarios featuring single light sources, extensive fields of view, and prolonged adaptation periods. However, exploring the specific cues humans rely on becomes challenging, if not unfeasible, with actual objects and lighting conditions. To circumvent these obstacles, we employed virtual reality technology to craft immersive, realistic settings that can be manipulated in real time. We designed forest and office scenes illuminated by five colors. Participants selected a test object most resembling a previously shown achromatic reference. To study color constancy mechanisms, we modified scenes to neutralize three contributors: local surround (placing a uniform-colored leaf under test objects), maximum flux (keeping the brightest object constant), and spatial mean (maintaining a neutral average light reflectance), employing two methods for the latter: changing object reflectances or introducing new elements. We found that color constancy was high in conditions with all cues present, aligning with past research. However, removing individual cues led to varied impacts on constancy. Local surrounds significantly reduced performance, especially under green illumination, showing strong interaction between greenish light and rose-colored contexts. In contrast, the maximum flux mechanism barely affected performance, challenging assumptions used in white balancing algorithms. The spatial mean experiment showed disparate effects: Adding objects slightly impacted performance, while changing reflectances nearly eliminated constancy, suggesting human color constancy relies more on scene interpretation than pixel-based calculations.

Dual-camera off-axis holographic particle tracking velocimetry: Development and application to air-blast swirl spray measurement

Quantifying the droplet field near the atomizer exit poses a challenge due to its three-dimensional (3D), multiscale and turbulent characteristics. An optical measurement method named dual-camera high-resolution holographic particle tracking velocimetry (DCHR-HPTV) is proposed. This method enables the extraction of near-field spatial Sauter mean diameter (SMD) distribution and three-dimensional two-component (3D-2C) velocity field, from off-axis hologram pairs with a frame interval of 6 μs, recorded using two synchronized industrial cameras and pulsed lasers. The system achieves a resolution of 9344 × 7000 pixels with an individual pixel size (dpix) of 3.2 μm. The droplet size measurement range is from 10 μm to above 500 μm with an absolute error within 3 μm. The velocity measurement range is adjustable and is set to be 0 to 38.4 m/s in this study, with errors of 0.23 m/s and 0.15 m/s for horizontal and axial components, respectively. The method is applied to investigate a spray region of 29 mm × 20 mm with a depth of 35 mm (equivalent to 20.3 cm3) at the exit of an air-blast atomizer. The spatial distribution of circumferentially integrated SMD along radial and axial directions is obtained. Statistical analysis is performed on both spatial and quantity distributions of droplet 3D-2C velocities. Concurrently acquired droplet size and velocity in the central toroidal recirculation zone (CTRZ) indicate that both the increasing air pressure and enlarged size range lead to a weakened droplet backflow motion. This method, distinguished by its large field of view and high resolution for particle velocimetry, is suited for dynamic analysis of near-nozzle droplet fields and can be employed in various testing scenarios involving 3D motion of micron-sized particles within multiscale flow fields.

IMU-Based Real-Time Estimation of Gait Phase Using Multi-Resolution Neural Networks.

This work presents a real-time gait phase estimator using thigh- and shank-mounted inertial measurement units (IMUs). A multi-rate convolutional neural network (CNN) was trained to estimate gait phase for a dataset of 16 participants walking on an instrumented treadmill with speeds varying between 0.1 to 1.9 m/s, and conditions such as asymmetric walking, stop-start, and sudden speed changes. One-subject-out cross-validation was used to assess the robustness of the estimator to the gait patterns of new individuals. The proposed model had a spatial root mean square error of 5.00±1.65%, and a temporal mean absolute error of 2.78±0.97% evaluated at the heel strike. A second cross-validation was performed to show that leaving out any of the walking conditions from the training dataset did not result in significant performance degradation. A 2-sample Kolmogorov-Smirnov test showed that there was no significant increase in spatial or temporal error when testing on the abnormal walking conditions left out of the training set. The results of the two cross-validations demonstrate that the proposed model generalizes well across new participants, various walking speeds, and gait patterns, showcasing its potential for use in investigating patient populations with pathological gaits and facilitating robot-assisted walking.

Contribution of regional versus trans-regional anthropogenic sources to the particulate matter over western India derived from high-resolution modeling

Elevated concentrations of particulate matter (PM) significantly deteriorate the air quality; however, the contributions from regional versus remote anthropogenic sources have remained uncertain over the western Indian region. In this regard, we have performed high-resolution regional modeling (WRF-Chem v3.9.1) to quantify the contribution of regional versus trans-regional anthropogenic sources to PM2.5 (fine PM) and PM2.5-10 (coarse PM) concentrations in contrasting seasons. Seasonal variability in spatial mean Aerosol Optical Depth (AOD) derived from the WRF-Chem model (0.21–0.42) agreed reasonably with MERRA-2 reanalysis (0.29–0.54) and MODIS satellite (0.23–0.51) over western India. Variability in surface PM2.5 and PM10 concentrations were also reproduced as per the benchmarks (|Fractional Bias| ≤ 60% and |Fractional Error| ≤ 75%) at most of the stations in this region. Results from sensitivity simulations reveal the dominant contribution of both regional and trans-regional anthropogenic sources to PM2.5 concentrations over western India in winter and post-monsoon, when PM2.5 concentrations are generally high. On the other hand, contribution from background levels (due to domain-wide natural emissions, fire emissions and pollutant transport from beyond domain boundaries) is highest during pre-monsoon and monsoon with a significant contribution of mineral dust especially to PM2.5-10 (coarse PM). Analysis of PM spatial distribution at ∼900hpa pressure level reveals greater relative contributions of trans-regional emissions and background levels compared to that near the surface. Our study highlights key roles of trans-regional anthropogenic emissions and mineral dust, besides the local and regional emissions, in air pollution over western India. The quantitative analyses presented here would be useful for designing measures to minimize health and environmental impacts in line with the objectives of the National Clean Air Programme (NCAP) in India.