Field Estimation Research Articles

Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data.

Computational fluid dynamics (CFD) can be used for non-invasive evaluation of hemodynamics. However, its routine use is limited by labor-intensive manual segmentation, CFD mesh creation, and time-consuming simulation. This study aims to train a deep learning model to both generate patient-specific volume-meshes of the pulmonary artery from 3D cardiac MRI data and directly estimate CFD flow fields. This proof-of-concept study used 135 3D cardiac MRIs from both a public and private dataset. The pulmonary arteries in the MRIs were manually segmented and converted into volume-meshes. CFD simulations were performed on ground truth meshes and interpolated onto point-point correspondent meshes to create the ground truth dataset. The dataset was split 110/10/15 for training, validation, and testing. Image2Flow, a hybrid image and graph convolutional neural network, was trained to transform a pulmonary artery template to patient-specific anatomy and CFD values, taking a specific inlet velocity as an additional input. Image2Flow was evaluated in terms of segmentation, and the accuracy of predicted CFD was assessed using node-wise comparisons. In addition, the ability of Image2Flow to respond to increasing inlet velocities was also evaluated. Image2Flow achieved excellent segmentation accuracy with a median Dice score of 0.91 (IQR: 0.86-0.92). The median node-wise normalized absolute error for pressure and velocity magnitude was 11.75% (IQR: 9.60-15.30%) and 9.90% (IQR: 8.47-11.90), respectively. Image2Flow also showed an expected response to increased inlet velocities with increasing pressure and velocity values. This proof-of-concept study has shown that it is possible to simultaneously perform patient-specific volume-mesh based segmentation and pressure and flow field estimation using Image2Flow. Image2Flow completes segmentation and CFD in ~330ms, which is ~5000 times faster than manual methods, making it more feasible in a clinical environment.

Regional evaluation of simulated waves during tropical storm events in the Gulf of Mexico

High waves in the Gulf of Mexico are mainly originated in tropical cyclones and winter storms. The availability of accurate estimations of high wave characteristics is an essential prerequisite for marine spatial planning, coastal zone management, and marine developments over the Northeastern Gulf of Mexico (NeGoM). Moreover, accurate wave simulation relies upon accurate wind field estimations. The wind field data used as input for numerical wave models is typically obtained from global and regional atmospheric model outputs. However, these model outputs may exhibit higher bias relative to observations, especially during fast-traveling tropical weather events. This study evaluates wave sensitivity over the NeGoM to different input wind speed and direction sources using unstructured SWAN model simulations. Five wind data products were employed in this study from ECMWF and NCEP global and regional models to examine the impact of wind forcing on wave simulations. The quality of wind field input data from these sources was evaluated relative to available observations (wind speed and direction) at National Data Buoy Center stations. A detailed sensitivity analysis is carried out to determine optimal wave model configuration. The model's performance is evaluated by comparing its wave simulation results with observational data during selected periods, simultaneously assessing the quality and sufficiency of wind data inputs from various sources. Our study shows that wind fields from FNL and NAM datasets are better correlated with observational data, in terms of vector correlation. Simulations using ECMWF – Real-Time product, combined with Janssen white-capping scheme better estimate wave conditions over the NeGoM compared with the other wind forcings used in this study. Moreover, Janssen white-capping scheme seems to be more realistic for estimations during Hurricane Michael; while for Hurricane Ida, Janssen generation scheme tends to overestimate the wave heights and Komen provides more accurate results.

Simulation of gravity field estimation of Phobos for Martian Moon eXploration (MMX)

We study the dynamic orbit about Phobos of the Martian Moon eXploration (MMX) spacecraft and simulate gravity field estimation using Doppler, image landmarks, and LIght Detection And Ranging (LIDAR) data based on a mission plan of MMX and investigate whether the differences in the internal density structure of Phobos can be detected through this mission. Degree 2 Stokes coefficients of the Phobos gravity field, C¯20\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{C} }_{20}$$\\end{document} and C¯22\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{C} }_{22}$$\\end{document}, which are necessary to obtain the moment of inertia by combining with the libration amplitude, could be determined with an accuracy within the order of 0.1% using datasets from two-dimensional (2D) quasi-satellite orbits (QSOs). If observations from 3D-QSO could be realized at low altitude, coefficients up to degree and order 5 could be estimated, which could be used to detect regional density anomalies. Moreover, the observation data from the ascending trajectory after landing on the Phobos surface can be used to detect local density anomalies around the landing site, which could help interpret the origin of the samples and understand the internal structure of Phobos.Graphical

Improving the Reliability and Accuracy of Population Receptive Field Measures Using a Logarithmically Warped Stimulus.

The population receptive field method, which measures the region in visual space that elicits a BOLD signal in a voxel in retinotopic cortex, is a powerful tool for investigating the functional organization of human visual cortex with fMRI (Dumoulin & Wandell, 2008). However, recent work has shown that population receptive field (pRF) estimates for early retinotopic visual areas can be biased and unreliable, especially for voxels representing the fovea. Here, we show that a 'log-bar' stimulus that is logarithmically warped along the eccentricity dimension produces more reliable estimates of pRF size and location than the traditional moving bar stimulus. The log-bar stimulus was better able to identify pRFs near the foveal representation, and pRFs were smaller in size, consistent with simulation estimates of receptive field sizes in the fovea.

Preliminary field estimation of vertical displacements of prestressed concrete bridges under construction based on measured strains and inclinations

The development of continuous and automatic quantitative monitoring methods will improve bridge construction. This paper proposes a method for estimating vertical bridge displacement during construction based on the Bayesian inverse problem. The proposed method is applied to a bridge under construction in Japan. Case studies suggest that the observed rotation angle is more important than the observed strain for the estimation of vertical displacement. Based on strain and rotation angle measurements, vertical bridge displacement was estimated with an accuracy of less than 2 mm, as determined from a comparison with displacements obtained in a leveling survey. The 95 % confidence interval of the estimation error is consistent with the scatter of measured data of displacement, rotation angle, and strain.

The Influence of Temporal Disturbances in EKF Calculations on the Achieved Parameters of Flight Control and Stabilization of UAVs.

This article investigates the causes of occasional flight instability observed in Unmanned Aerial Vehicles (UAVs). The issue manifests as unexpected oscillations that can lead to emergency landings. The analysis focuses on delays in the Extended Kalman Filter (EKF) algorithm used to estimate the drone's attitude, position, and velocity. These delays disrupt the flight stabilization process. The research identifies two potential causes for the delays. First cause is magnetic field distrurbances created by UAV motors and external magnetic fields (e.g., power lines) that can interfere with magnetometer readings, leading to extended EKF calculations. Second cause is EKF fusion step implementation of the PX4-ECL library combining magnetometer data with other sensor measurements, which can become computionally expensive, especially when dealing with inconsistent magnetic field readings. This can significantly increase EKF processing time. The authors propose a solution of moving the magnetic field estimation calculations to a separate, lower-priority thread. This would prevent them from blocking the main EKF loop and causing delays. The implemented monitoring techniques allow for continuous observation of the real-time operating system's behavior. Since addressing the identified issues, no significant problems have been encountered during flights. However, ongoing monitoring is crucial due to the infrequent and unpredictable nature of the disturbances.

Optimizing Mining Ventilation Using 3D Technologies

Abstract. Ventilation systems constitute an important piece of the industrial facility ecosystems. Creating proper working environmental conditions for humans is crucial, especially in hazardous sites with presence of various gases, such as underground mines. Combined with the vast amount of space to be ventilated in large mines, designing and maintaining such a system is challenging and costly. To alleviate these issues, the EIT-RM project VOT3D (Ventilation Optimizing Technology based on 3D scanning) proposes conducting advanced airflow modeling in the underground tunnel networks, utilizing computational fluid dynamics (CFD) simulations, modern surveying and 3D modeling approaches to reverse engineer a reliable geometric model of the mine and estimate the 3D airflow field inside it. In this paper, we present the challenges to be solved in this task and the proposed workflow to address them. An example related to an active industrial mine in Poland is reported as a basis for performing experimental data processing using the full, highly automatized procedure. Developments and results of underground mobile mapping (with a drone and a handheld system), point cloud processing and filtering, surface reconstruction and CFD modeling are presented. The detailed results of airflow field estimation show the advantages of the proposed solution and promise its high practical usefulness.

Pastures as natural climate solutions: A socioecological study of tree carbon and beef production trade-offs

Sustainable management strategies that facilitate the incorporation of trees into cattle pastures are widely recognized as emerging natural climate solutions. However, their potential to dually improve ecological functioning and human livelihoods has yet to be evaluated across regions on local scales, utilizing both ecological and sociological data. Further, the relationship between landowner participation in conservation management programs (CMPS) and their management priorities (i.e. production vs sustainability) also remains unclear. Therefore, the aims of this study were to evaluate the accuracy of past global-scale remote estimations of aboveground biomass and derived tree carbon density (MgC ha−1) within pasturelands at the local scale in both temperate and tropical regions. We also sought to analyze how participation in CMPs related to the perceptions of stakeholders toward the ecological health of their system. We compared findings between farms in temperate (n = 26) and tropical (n = 16) ecosystems. We found that discrepancies exist between previous remote studies and current field estimations of tree carbon in the farms, and these differences were not regionally uniform. In contrast, stakeholders enrolled in CMPs had a higher value of trees in both regions. CMPs participants also had lower annual stocking rates than those that practiced conventional management but no differences in woody species diversity (H’) or tree carbon (MgC ha−1). We demonstrate the urgent need for local-scale studies to accurately estimate tree carbon in mosaic pasture systems and the importance of conservation management in shifting the priorities of landowners, which could eventually lead to more sustainable systems.

Estimation of electric field inside a neural spheroid by low‐frequency magnetic field exposure

AbstractExposure to time‐varying, low‐frequency and high‐intensity magnetic field (MF) induce electric field (EF) inside the human body, producing stimulus effects such as nerve fiber excitation or synaptic modulation. To measure such stimulus effects by low‐frequency MF exposure in real‐time, we developed a fluorescent recording system using optical fibers that is neither affected by the MF nor affects the MF distribution. In this study, a numerical calculation model composed of voxels with a 6.25 µm spatial resolution was developed. Using this numerical model, we evaluated the distribution of the EF generated inside three‐dimensional neuronal tissue called neural spheroid, under 50 Hz sinusoidal wave, 300 mT (root mean square) uniform MF exposure. We also investigated the influence of the optical fiber on the electric field distribution in neural spheroid. As a result, MF produced an induced EF in the neural spheroid of more than 4 V/m, well above the theoretical threshold of synaptic modulation. These results indicated that our experimental system was suitable for the evaluation of the threshold of stimulus effects using neural spheroid.

Laboratory Earthquakes Simulations—Emergence, Structure, and Evolution of Fault Heterogeneity

AbstractSeismic faults are known to exhibit a high level of spatial and temporal complexity, and the causes and consequences of this complexity have been the topic of numerous research works in the past decade. In this paper, we investigate the origins and the structure of this complexity by considering a numerical model of laboratory earthquake experiment, where we introduce a fault with homogeneous mechanical properties but allow it to evolve spontaneously to its natural level of complexity. This is achieved by coupling the elastic deformability of the off‐fault medium (and therefore allowing for heterogeneous stress fields to develop) and the discrete degradation and gouge formation at the fault plane (and therefore allowing for structural heterogeneity to develop). Numerical results show the development of persistent stress, damage, and gouge thickness heterogeneities, with a much larger variability in space than in time. Strong positive correlations are found between these quantities, which suggest a positive feedback between local normal stress and damage rate, only mildly mitigated by the mobility of the granular gouge in the interface. For a wide range of confining stresses, after a sufficient number of seismic cycles, the fault reaches a state of established disorder with a constant roughness, a certain amount of periodicity at the millimetric scale, and a power law decay of the Power Spectral Density at smaller spatial scales. The typical height‐to‐wavelength ratio of geometrical asperities and the correlations between stress and damage profiles are in good agreement with previous field or lab estimates.

Molecular line polarisation from the circumstellar envelopes of asymptotic giant branch stars

Context. Polarisation observations of masers in the circumstellar envelopes (CSEs) around asymptotic giant branch (AGB) stars have revealed strong magnetic fields. However, masers probe only specific lines of sight through the CSE. Non-masing molecular line polarisation observations can more directly reveal the large-scale magnetic field morphology and hence probe the effect of the magnetic field on AGB mass loss and the shaping of the AGB wind. Aims. Observations and models of CSE molecular line polarisation can now be used to describe the magnetic field morphology and estimate its strength throughout the entire CSE. Methods. We used observations taken with the Atacama Large Millimeter/submillimeter Array (ALMA) of molecular line polarisation in the envelope of two AGB stars: CW Leo and R Leo. We modelled the observations using the multi-dimensional polarised radiative transfer tool PORTAL. Results. We found linearly polarised emission, with maximum fractional polarisation on the order of a few percent, in several molecular lines towards both stars. Towards R Leo, we also found a high level of linear polarisation (up to ∼35%) for one of the SiO v = 1 maser transitions. We can explain the observed differences in polarisation structure between the different molecular lines by alignment of the molecules through a combination of the Goldreich-Kylafis effect and radiative alignment effects. We specifically show that the polarisation of CO traces the morphology of the magnetic field. Competition between the alignment mechanisms allowed us to describe the behaviour of the magnetic field strength as a function of the radius throughout the circumstellar envelope of CW Leo. The magnetic field strength derived using this method is inconsistent with the magnetic field strength derived using a structure-function analysis of the CO polarisation and the strength previously derived using CN Zeeman observations. In contrast with CW Leo, the magnetic field in the outer envelope of R Leo appears to be advected outwards by the stellar wind. Conclusions. The ALMA observations and our polarised radiative transfer models show the power of using multiple molecular species to trace the magnetic field behaviour throughout the circumstellar envelope. While the observations appear to confirm the existence of a large-scale magnetic field, further observations and modelling are needed to understand the apparent inconsistency of the magnetic field strength derived with different methods in the envelope of CW Leo.

A Quantitative Analysis of the Uncertainties on Reconnection Electric Field Estimates Using Ionospheric Measurements

AbstractCalculating the magnetic flux transfer across the open‐closed boundary (OCB) per unit time and distance—the reconnection electric field—is an important means of remotely monitoring magnetospheric dynamics. Ground‐based measurements of plasma convection velocities together with velocities of the OCB are commonly used to infer reconnection rates. However, this approach is limited by spatial coverage and often lacks robust uncertainty quantification. In this paper, we assimilate Super Dual Auroral Radar Network convection measurements, ground magnetometer data, and estimates of the conductance derived from the Imager for Magnetopause‐to‐Aurora Global Exploration satellite imagers, using the Local mapping of polar ionospheric electrodynamics (Lompe) framework over a region in North America. We present a new method to assess various contributions to uncertainties in the derived reconnection electric fields, including a novel approach to estimate uncertainties in conductance from global auroral imaging. Our method is demonstrated on a substorm event with an associated pseudobreakup during a period of favorable observational coverage. In this case study, the uncertainties in the reconnection electric field are ∼5–10 mV/m at the peak of substorm expansion, roughly 15% of the peak reconnection electric field. We find that the main contributor to the reconnection electric field estimates after substorm onset is the OCB motion, whereas during the pseudobreakup the main contributor is ionospheric plasma convection.

Determination of the time-variable geopotential by means of orbiting clocks

Monitoring the time-variable geopotential identifies the mass redistribution across the Earth and reveals, e.g., climate change and availability of water resources. The features of interest are characterized by spatial and temporal scales accessible only through space missions. Among the most important gravity missions are GRACE (2002–2017), its successor GRACE-FO (since 2018), and GOCE (2009–2013), which all sense the Earth’s gravity field via the geopotential derivatives. We investigate the geopotential estimation through frequency comparisons between orbiting clocks by means of the Doppler-canceling technique, describing the clocks’ behavior in the Earth’s gravitational field via Einstein’s general relativity. The novelty of this approach lies in measuring gravity by sensing the geopotential itself. The proof of principle for the measurement is achieved through an innovative mission scenario: for the first time, the observations are collected by a probing clock in LEO. We show gravity solutions obtained by simulating an estimation problem via our proposed architecture. The results suggest that we can conceivably retrieve the geopotential coefficients with accuracy comparable to the GRACE measurement concept by employing clocks with stabilities of order 10-18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${10}^{-18}$$\\end{document}. Presently, terrestrial clocks can routinely attain fractional frequency stabilities of 10-18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${10}^{-18}$$\\end{document}, whereas spaceborne clocks are still at the 10-15\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${10}^{-15}$$\\end{document} level. While our findings are promising, further analysis is needed to obtain more realistic indications on the feasibility of an actual mission, whose realization will be possible when clock technology reaches the required performance. The goal is for the technique investigated in this study to become a future staple for gravity field estimation.

Efficiency-Accuracy Trade-Off in Light Field Estimation with Cost Volume Construction and Aggregation.

The Rich spatial and angular information in light field images enables accurate depth estimation, which is a crucial aspect of environmental perception. However, the abundance of light field information also leads to high computational costs and memory pressure. Typically, selectively pruning some light field information can significantly improve computational efficiency but at the expense of reduced depth estimation accuracy in the pruned model, especially in low-texture regions and occluded areas where angular diversity is reduced. In this study, we propose a lightweight disparity estimation model that balances speed and accuracy and enhances depth estimation accuracy in textureless regions. We combined cost matching methods based on absolute difference and correlation to construct cost volumes, improving both accuracy and robustness. Additionally, we developed a multi-scale disparity cost fusion architecture, employing 3D convolutions and a UNet-like structure to handle matching costs at different depth scales. This method effectively integrates information across scales, utilizing the UNet structure for efficient fusion and completion of cost volumes, thus yielding more precise depth maps. Extensive testing shows that our method achieves computational efficiency on par with the most efficient existing methods, yet with double the accuracy. Moreover, our approach achieves comparable accuracy to the current highest-accuracy methods but with an order of magnitude improvement in computational performance.

Compact white dwarf binaries in the combined SRG/eROSITA/SDSS eFEDS survey

Context. X-ray surveys combined with optical follow-up observations are used to generate complete flux-limited samples of the main X-ray emitting source classes. eROSITA on the Spectrum-Roentgen-Gamma mission provides sufficient sensitivity to build significantly enhanced samples of rare X-ray emitting sources. Aims. We strive to identify and classify compact white dwarf binaries, cataclysmic variables (CVs), and related objects, which were detected in the sky area of eFEDS, the eROSITA Final Equatorial Depths Survey, and they were observed in the plate program of SDSS-V. Methods. Compact white dwarf binaries were selected from spectra obtained in the early SDSS-V plate program. A dedicated set of SDSS plate observations were carried out in the eFEDS field, providing spectroscopic classifications for a significant fraction of the optically bright end (r < 22.5) of the X-ray sample. The identification and subclassification rests on visual inspections of the SDSS spectra, spectral variability, color-magnitude and color-color diagrams involving optical and X-ray fluxes, optical variability, and literature work. Results. Upon visual inspection of SDSS spectra and various auxiliary data products, we have identified 26 accreting compact white dwarf binaries (aCWDBs) in eFEDS, of which 24 are proven X-ray emitters. Among those 26 objects, there are 12 dwarf novae, three WZ Sge-like disk-accreting nonmagnetic CVs with low accretion rates, five likely nonmagnetic high accretion rate nova-like CVs, two magnetic CVs of the polar subcategory, and three double degenerates (AM CVn objects). Period bouncing candidates and magnetic systems are rarer than expected in this sample, but it is too small for a thorough statistical analysis. Fourteen of the systems are new discoveries, of which five are fainter than the Gaia magnitude limit. Thirteen aCWDBs have measured or estimated orbital periods, of which five were presented here. Through a Zeeman analysis, we revise the magnetic field estimate of the polar system J0926+0105, which is likely a low-field polar at B = 16 MG. We quantified the success of X-ray versus optical/UV selection of compact white dwarf binaries which will be relevant for the full SDSS-V survey. We also identified six white dwarf main sequence (WDMS) systems, among them there is one confirmed pre-CV at an orbital period of 17.6 h and another pre-CV candidate. Conclusions. This work presents successful initial work in building large samples of all kinds of accreting and X-ray emitting compact white dwarf binaries that will be continued over the full hemisphere in the years to come.

Improving estimates of water resources availability over North Tropical South America: comparison of two satellite precipitation merging schemes

Low-density precipitation measurements impair the ability of hydrological models to estimate surface water resources accurately. Remote sensing techniques and climate models can help to improve the estimation of the space-time rainfall variability. However, they alone are not good enough to be used in surface models built to support water management. In this research, we test the improvement of rainfall field estimation by using hydrological modelling based on the premise that a higher hydrological performance generally implies that precipitation is more consistent with streamflow observations and evaporation estimates in the basin. The SWAT model was forced with two satellite and rain gauge blending techniques and with the traditional IDW deterministic interpolation method from stations. The three simulated streamflows were compared separately against observed records. We do not only focus the comparison on one hydrological performance metric but also conduct a deeper evaluation using several hydrological signatures and statistics. We included the bias, the temporal correlation, the relation of general variability, and an analysis of the Flow Duration Curves (we found that low and medium segments were estimated correctly, whereas the high segments were underestimated). We conclude that either combination technique has its advantages over the other and that both outperform the performance achieved by the IDW in most of the defined criteria, with an overall 10% improvement and with individual streamflow gauge performance enhancement up to 50%.

Interspecific selection in a diverse mycorrhizal symbiosis.

Coevolution describes evolutionary change in which two or more interacting species reciprocally drive each other's evolution, potentially resulting in trait diversification and ecological speciation. Much progress has been made in analysis of its dynamics and consequences, but relatively little is understood about how coevolution works in multispecies interactions, i.e., those with diverse suites of species on one or both sides of an interaction. Interactions among plant hosts and their mutualistic ectomycorrhizal fungi (ECM) may provide an ecologically unique arena to examine the nature of selection in multispecies interactions. Using native genotypes of Monterey pine (Pinus radiata), we performed a common garden experiment at a field site that contains native stands to investigate selection from ECM fungi on pine traits. We planted seedlings from all five native populations, as well as inter-population crosses to represent intermediate phenotypes/genotypes, and measured seedling traits and ECM fungal traits to evaluate the potential for evolution in the symbiosis. We then combined field estimates of selection gradients with estimates of heritability and genetic variance-covariance matrices for multiple traits of the mutualism to determine which fungal traits drive plant fitness variation. We found evidence that certain fungal operational taxonomic units, families and species-level morphological traits by which ECM fungi acquire and transport nutrients exert selection on plant traits related to growth and allocation patterns. This work represents the first field-based, community-level study measuring multispecific coevolutionary selection in nutritional symbioses.

Perspectives on Optimized Transcranial Electrical Stimulation Based on Spatial Electric Field Modeling in Humans.

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies.

Training-induced changes in population receptive field properties in visual cortex: Impact of eccentric vision training on population receptive field properties and the crowding effect.

This study aimed to investigate the impact of eccentric-vision training on population receptive field (pRF) estimates to provide insights into brain plasticity processes driven by practice. Fifteen participants underwent functional magnetic resonance imaging (fMRI) measurements before and after behavioral training on a visual crowding task, where the relative orientation of the opening (gap position: up/down, left/right) in a Landolt C optotype had to be discriminated in the presence of flanking ring stimuli. Drifting checkerboard bar stimuli were used for pRF size estimation in multiple regions of interest (ROIs): dorsal-V1 (dV1), dorsal-V2 (dV2), ventral-V1 (vV1), and ventral-V2 (vV2), including the visual cortex region corresponding to the trained retinal location. pRF estimates in V1 and V2 were obtained along eccentricities from 0.5° to 9°. Statistical analyses revealed a significant decrease of the crowding anisotropy index (p = 0.009) after training, indicating improvement on crowding task performance following training. Notably, pRF sizes at and near the trained location decreased significantly (p = 0.005). Dorsal and ventral V2 exhibited significant pRF size reductions, especially at eccentricities where the training stimuli were presented (p < 0.001). In contrast, no significant changes in pRF estimates were found in either vV1 (p = 0.181) or dV1 (p = 0.055) voxels. These findings suggest that practice on a crowding task can lead to a reduction of pRF sizes in trained visual cortex, particularly in V2, highlighting the plasticity and adaptability of the adult visual system induced by prolonged training.

Flow Field Estimation with Distortion Correction Based on Multiple Input Deep Convolutional Neural Networks and Hartmann–Shack Wavefront Sensing

The precise estimation of fluid motion is critical across various fields, including aerodynamics, hydrodynamics, and industrial fluid mechanics. However, refraction at complex interfaces in the light path can cause image deterioration and lead to severe measurement errors if the aberration changes with time, e.g., at fluctuating air–water interfaces. This challenge is particularly pronounced in technical energy conversion processes such as bubble formation in electrolysis, droplet formation in fuel cells, or film flows. In this paper, a flow field estimation algorithm that can perform the aberration correction function is proposed, which integrates the flow field distribution estimation algorithm based on the Particle Image Velocimetry (PIV) technique and the novel actuator-free adaptive optics technique. Two different multi-input convolutional neural network (CNN) structures are established, with two frames of distorted PIV images and measured wavefront distortion information as inputs. The corrected flow field results are directly output, which are divided into two types based on different network structures: dense estimation and sparse estimation. Based on a series of models, a corresponding dataset synthesis model is established to generate training datasets. Finally, the algorithm performance is evaluated from different perspectives. Compared with traditional algorithms, the two proposed algorithms achieves reductions in the root mean square value of velocity residual error by 84% and 89%, respectively. By integrating both flow field measurement and novel adaptive optics technique into deep CNNs, this method lays a foundation for future research aimed at exploring more intricate distortion phenomena in flow field measurement.