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The transient electromagnetic (TEM) method is a crucial tool for subsurface exploration, providing essential insights into the electrical resistivity structures beneath the Earth's surface. Traditional forward modeling approaches, such as the finite-difference time-domain (FDTD) method and the finite-element method (FEM), are computationally intensive, limiting their practicality for real-time, high-resolution, or large-scale investigations. To address these challenges, we present Deep-TEMNet, an advanced deep learning framework specifically designed for two-dimensional TEM forward modeling. Deep-TEMNet integrates the U-Net architecture with a tailored two-dimensional long short-term memory (2D LSTM) module, allowing it to effectively capture complex spatial-temporal relationships in TEM data. The U-Net component enables high-resolution spatial feature extraction, while the 2D LSTM module enhances temporal modeling by processing spatial sequences in two dimensions, thereby optimizing the representation of electromagnetic field dynamics over time. Trained on high-fidelity FEM-generated datasets, Deep-TEMNet achieves exceptional accuracy in reproducing electromagnetic field distributions across diverse geological scenarios, with a mean squared error of 0.00000134 and a root mean square percentage error of 0.002373019. The framework offers over 150 times the computational speed of traditional FEMs, with an average inference time of just 3.26 s. Extensive validation across varied geological conditions highlights Deep-TEMNet's robustness and adaptability, establishing its potential for efficient, large-scale subsurface mapping and real-time data processing. By combining U-Net's spatial resolution capabilities with the sequential processing strength of the 2D LSTM module, Deep-TEMNet significantly advances computational efficiency and accuracy, positioning it as a valuable tool for geophysical exploration, environmental monitoring, and other applications requiring scalable, real-time TEM analyses that are easily integrated into remote sensing workflows. [ABSTRACT FROM AUTHOR]

Compliant manipulation has long been a major constraint for grappling in robotic manipulators. To adopt robotic manipulators in space for the prospect of capturing space junk and transforming them into salvageable assets for re‐use, robust adaptive manipulation would be key. We believe that a bio‐inspired approach could provide human‐like tactility required for robustness and adaptability in robotic manipulation. Given the similarity in form and dynamics between earth‐based and space‐based robotic manipulators, we first explored the transfer learning of neural network controllers as an avenue to address the challenges of limited computation resources onboard the spacecraft (space manipulator). We introduced a pre‐trained and learned feedforward neural network for modelling the control error a priori. While the results were encouraging, there are major limitations of neural networks' capability to ensuring the transfer learning of similar earth‐based dynamics to space‐based dynamics, given that the parameters of contrast are fairly straightforward. We have demonstrated these limitations by presenting a novel approach that is inspired by human motor control. We explored the adaptability through a practical problem of transferring a neuro‐controller from earth to space. With the results not as plausible as expected, an alternative adaptive controller has been learned to demonstrate a viable solution. The controller was trained entirely in simulation via rapid online adaptation of the robot's controller to the object's properties and environmental dynamics using only proprioception history. As a notable step, we have shown that appropriate models can be learned in this manner by training the control policy via reinforcement learning, which provides avenue for transferring the learned model from earth to space environments. [ABSTRACT FROM AUTHOR]

Motor imagery and execution often indicate a similar trend in the temporal characteristics of movements. This finding supports the notion of functional equivalence, whereby imagery and execution use a common neural representation. However, there is comparatively limited evidence related to the spatial characteristics of movements; no doubt owing to the absence of an actual spatial trajectory during imagery. Therefore, we adapted the trajectory priming paradigm involving an obstacle, where the trajectory adopted in a trial (n) is directly contaminated by a previous trial (n-1). If imagery accurately represents the spatial characteristics, then we would predict a similar priming effect as execution. Participants completed a series of trial blocks under different imagery/execution protocols, where the test trial (n) comprised execution alone, while the previous trial (n-1) involved imagery or execution. Each block comprised pairs of trials with alternate or consistent presentations of a virtual obstacle (O) or no obstacle (N): N-N, N-O, O-N, O-O. For trial n-1 (imagery/execution), there was a more prolonged reaction and movement time for imagery compared execution. Most importantly for trial n (execution), there was an increase in early angular and peak deviation following an obstacle compared to no obstacle in trial n-1, but only when it was execution and not imagery. These findings suggest imagery holds a limited representation of the spatial characteristics, while functional equivalence may be limited to the temporal characteristics. [ABSTRACT FROM AUTHOR]

After its arrival at Jupiter in July 2016, Juno conducted a global survey of Jupiter's magnetosphere (especially the equatorial plasma disk region) with its highly eccentric polar orbit. Since then, the JADE instrument has accumulated a large amount of plasma measurements. Using a developed forward modeling method and the Alpine supercomputer cluster at CU Boulder, we fit all ion measurements between 10 and 50 RJ ${\mathrm{R}}_{\mathrm{J}}$ from PJ5 to PJ56, obtaining a dataset with 70,487 good fits that consists of the following set of plasma parameters: abundances of different heavy ions, density, temperature, and 3‐D bulk flow velocity of heavy ions. An overview of the dataset and an example use on flux tube interchange are presented to illustrate its effectiveness and usefulness. In the investigated interchange event that occurred at ∼ ${\sim} $14 RJ ${\mathrm{R}}_{\mathrm{J}}$, the plasma disk is variable and heated, with alternating hotter inflows and colder outflows, accompanied by small perturbations in the magnetic fields. This dataset has the potential for many applications. Key Points: A dataset from thermal plasma observations of Juno using a forward modeling methodThe dataset includes ion abundances, density, temperature, and 3‐D bulk flow velocityThe dataset consists of 70,487 data points obtained between 10 and 50 RJ from Juno orbits PJ5 to PJ56 [ABSTRACT FROM AUTHOR]

Previous studies have found electroencephalogram (EEG) amplitude and scalp topography differences between neurotypical and neurological/neurosurgical groups, being interpreted at the cognitive level. However, these comparisons are invariably accompanied by anatomical changes. Critical to EEG are the so‐called volume currents, which are affected by the spatial distribution of the different tissues in the head. We investigated the effect of cerebrospinal fluid (CSF)‐filled cavities on simulated EEG scalp data. We simulated EEG scalp potentials for known sources using different volume conduction models: a reference model (i.e., unlesioned brain) and models with realistic CSF‐filled cavities gradually increasing in size. We used this approach for a single source close or far from the CSF‐lesion cavity, and for a scenario with a distributed configuration of sources (i.e., a "cognitive event‐related potential effect"). The magnitude and topography errors between the reference and lesion models were quantified. For the single‐source simulation close to the lesion, the CSF‐filled lesion modulated signal amplitude with more than 17% magnitude error and topography with more than 9% topographical error. Negligible modulation was found for the single source far from the lesion. For the multisource simulations of the cognitive effect, the CSF‐filled lesion modulated signal amplitude with more than 6% magnitude error and topography with more than 16% topography error in a nonmonotonic fashion. In conclusion, the impact of a CSF‐filled cavity cannot be neglected for scalp‐level EEG data. Especially when group‐level comparisons are made, any scalp‐level attenuated, aberrant, or absent effects are difficult to interpret without considering the confounding effect of CSF. Previous studies have found electroencephalogram (EEG) amplitude and scalp topography differences between neurotypical and neurological/neurosurgical groups (whose brain damage leads to the presence of a cerebrospinal fluid‐filled cavity) being interpreted at the cognitive level. Via simulations of scalp‐level EEG patterns, we show that attenuated, aberrant, or absent effects in these comparisons are difficult to interpret without considering the confounding effect of CSF. [ABSTRACT FROM AUTHOR]

Previous research has shown that action effects of self-generated movements are internally predicted before outcome feedback becomes available. To test whether these sensorimotor predictions are used to facilitate visual information uptake for feedback processing, we measured eye movements during the execution of a goal-directed throwing task. Participants could fully observe the effects of their throwing actions (ball trajectory and either hitting or missing a target) in most of the trials. In a portion of the trials, the ball trajectory was not visible, and participants only received static information about the outcome. We observed a large proportion of predictive saccades, shifting gaze toward the goal region before the ball arrived and outcome feedback became available. Fixation locations after predictive saccades systematically covaried with future ball positions in trials with continuous ball flight information, but notably also in trials with static outcome feedback and only efferent and proprioceptive information about the movement that could be used for predictions. Fixation durations at the chosen positions after feedback onset were modulated by action outcome (longer durations for misses than for hits) and outcome uncertainty (longer durations for narrow vs. clear outcomes). Combining both effects, durations were longest for narrow errors and shortest for clear hits, indicating that the chosen locations offer informational value for feedback processing. Thus, humans are able to use sensorimotor predictions to direct their gaze toward task-relevant feedback locations. Outcome-dependent saccade latency differences (miss vs. hit) indicate that also predictive valuation processes are involved in planning predictive saccades. NEW & NOTEWORTHY: We elucidate the potential benefits of sensorimotor predictions, focusing on how the system actually uses this information to optimize feedback processing in goal-directed actions. Sensorimotor information is used to predict spatial parameters of movement outcomes, guiding predictive saccades toward future action effects. Saccade latencies and fixation durations are modulated by outcome quality, indicating that predictive valuation processes are considered and that the locations chosen are of high informational value for feedback processing. [ABSTRACT FROM AUTHOR]

A shared mechanism across species heralds the arrival of self-generated sensations, helping the brain to anticipate, and therefore distinguish, self-generated from externally generated sensations. In mammals, this sensory prediction mechanism is supported by communication within a cortico-ponto-cerebellar-thalamo-cortical loop. Schizophrenia is associated with impaired sensory prediction as well as abnormal structural and functional connections between nodes in this circuit. Despite the pons principal role in relaying and processing sensory information passed from the cortex to cerebellum, few studies have examined pons connectivity in schizophrenia. Here, we first briefly describe how the pons contributes to sensory prediction. We then summarize schizophrenia-related abnormalities in the cortico-ponto-cerebellar-thalamo-cortical loop, emphasizing the dearth of research on the pons relative to thalamic and cerebellar connections. We conclude with recommendations for advancing our understanding of how the pons relates to sensory prediction failures in schizophrenia.

Traditional associative learning tasks focus on the formation of associations between salient events and arbitrary stimuli that predict those events. This is exemplified in cerebellar-dependent delay eyeblink conditioning, where arbitrary cues such as a tone or light act as conditioned stimuli (CSs) that predict aversive sensations at the cornea (unconditioned stimulus [US]). Here, we ask if a similar framework could be applied to another type of cerebellar-dependent sensorimotor learning - sensorimotor adaptation. Models of sensorimotor adaptation posit that the introduction of an environmental perturbation results in an error signal that is used to update an internal model of a sensorimotor map for motor planning. Here, we take a step toward an integrative account of these two forms of cerebellar-dependent learning, examining the relevance of core concepts from associative learning for sensorimotor adaptation. Using a visuomotor adaptation reaching task, we paired movement-related feedback (US) with neutral auditory or visual contextual cues that served as CSs. Trial-by-trial changes in feedforward movement kinematics exhibited three key signatures of associative learning: differential conditioning, sensitivity to the CS-US interval, and compound conditioning. Moreover, after compound conditioning, a robust negative correlation was observed between responses to the two elemental CSs of the compound (i.e. overshadowing), consistent with the additivity principle posited by theories of associative learning. The existence of associative learning effects in sensorimotor adaptation provides a proof-of-concept for linking cerebellar-dependent learning paradigms within a common theoretical framework.

Determining, understanding, and predicting the so-called structure–property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn–Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this.

As activity in Earth orbit continues to grow, it is important to characterize the environment of near‐Earth space. One means of remotely sensing lower thermospheric neutrals is by measurement of O and N2 density through the observation of far‐ultraviolet (FUV) airglow of atomic oxygen at 135.6 nm and the N2 Lyman‐Birge‐Hopfield (LBH) bands (~130–180 nm), as has been done on the Ionospheric Connection Explorer (ICON), Global‐scale Observations of the Limb and Disk (GOLD), and Thermosphere Ionosphere Mesosphere Energetics and Dynamics (TIMED) missions. This technique is not without limitations, however, as the FUV measurements suffer from contamination by ionospheric emissions at low latitudes and auroral emissions excited by precipitating energetic electrons and protons at high latitudes. Previous work has shown the potential for making measurements of O and N2 density in the lower‐middle thermosphere using observations of extreme‐ultraviolet (EUV) airglow. This measurement approach has a potential advantage in that it does not have an inherent ionospheric emission that must be accounted for. Additionally, these emissions are primarily excited directly by solar UV rather than electron impact and thus have the potential to enable expansion of neutral density observations into the auroral zone and polar cap where the FUV measurement cannot be applied. This article demonstrates a new approach and algorithm designed to retrieve thermospheric O and N2 density from 150 to 400 km using measurements from the ICON EUV instrument. The retrieval results throughout 2020 are summarized and compared to measurements from ICON FUV, GOLD, and SWARM. Plain Language Summary: The region of space where most satellites orbit is almost empty ‐ but not completely empty. The small amount of nitrogen and oxygen gas present can cause satellites to gradually fall out of orbit through drag. A failure to understand and evaluate changes in the density of the upper atmosphere led to the loss of 38 Starlink satellites in February 2022, for example, This is one reason that it is important to understand how much gas is in near‐Earth space and how the amount changes over time, but orbit decay due to drag makes it difficult to make direct measurements. One common solution is to measure the amount of gas from afar, taking advantage of the fact that these gases emit small amounts of light in the far‐UV spectrum. There are some limitations to this approach, one of which being that charged particles in the same region can create an extra and unrelated signal, which requires correction. This paper presents a novel approach which uses more energetic extreme‐UV light as an alternative to the far‐UV. We show the results of this new algorithm and demonstrate that the measurements line up well with those from other instruments and spacecraft. Key Points: We present an algorithm concept for remote sensing thermospheric O and N2, novel in its use of EUV airglow measurementsComparison to related measurements during conjunctions with ICON FUV, GOLD, and Swarm are favorableRetrieval results from 2020 indicate cooler atmospheres than those predicted by MSIS [ABSTRACT FROM AUTHOR]

Self‐initiated movements are accompanied by an efference copy, a motor command sent from motor regions to the sensory cortices, containing a prediction of the movement's sensory outcome. Previous studies have proposed pre‐motor event‐related potentials (ERPs), including the readiness potential (RP) and its lateralized sub‐component (LRP), as potential neural markers of action feedback prediction. However, it is not known how specific these neural markers are for voluntary (active) movements as compared to involuntary (passive) movements, which produce much of the same sensory feedback (tactile, proprioceptive) but are not accompanied by an efference copy. The goal of the current study was to investigate how active and passive movements are distinguishable from premotor electroencephalography (EEG), and to examine if this change of neural activity differs when participants engage in tasks that differ in their expectation of sensory outcomes. Participants made active (self‐initiated) or passive (finger moved by device) finger movements that led to either visual or auditory stimuli (100 ms delay), or to no immediate contingency effects (control). We investigated the time window before the movement onset by measuring pre‐movement ERPs time‐locked to the button press. For RP, we observed an interaction between task and movement. This was driven by movement differences in the visual and auditory but not the control conditions. LRP conversely only showed a main effect of movement. We then used multivariate pattern analysis to decode movements (active vs. passive). The results revealed ramping decoding for all tasks from around −800 ms onwards up to an accuracy of approximately 85% at the movement. Importantly, similar to RP, we observed lower decoding accuracies for the control condition than the visual and auditory conditions, but only shortly (from −200 ms) before the button press. We also decoded visual vs. auditory conditions. Here, task is decodable for both active and passive conditions, but the active condition showed increased decoding shortly before the button press. Taken together, our results provide robust evidence that pre‐movement EEG activity may represent action‐feedback prediction in which information about the subsequent sensory outcome is encoded. [ABSTRACT FROM AUTHOR]

When we perform an action, its sensory outcomes usually follow shortly after. This characteristic temporal relationship aids in distinguishing self‐ from externally generated sensory input. To preserve this ability under dynamically changing environmental conditions, our expectation of the timing between action and outcome must be able to recalibrate, for example, when the outcome is consistently delayed. Until now, it remains unclear whether this process, known as sensorimotor temporal recalibration, can be specifically attributed to recalibration of sensorimotor (action‐outcome) predictions, or whether it may be partly due to the recalibration of expectations about the intersensory (e.g., audio‐tactile) timing. Therefore, we investigated the behavioral and neural correlates of temporal recalibration and differences in sensorimotor and intersensory contexts. During fMRI, subjects were exposed to delayed or undelayed tones elicited by actively or passively generated button presses. While recalibration of the expected intersensory timing (i.e., between the tactile sensation during the button movement and the tones) can be expected to occur during both active and passive movements, recalibration of sensorimotor predictions should be limited to active movement conditions. Effects of this procedure on auditory temporal perception and the modality‐transfer to visual perception were tested in a delay detection task. Across both contexts, we found recalibration to be associated with activations in hippocampus and cerebellum. Context‐dependent differences emerged in terms of stronger behavioral recalibration effects in sensorimotor conditions and were captured by differential activation pattern in frontal cortices, cerebellum, and sensory processing regions. These findings highlight the role of the hippocampus in encoding and retrieving newly acquired temporal stimulus associations during temporal recalibration. Furthermore, recalibration‐related activations in the cerebellum may reflect the retention of multiple representations of temporal stimulus associations across both contexts. Finally, we showed that sensorimotor predictions modulate recalibration‐related processes in frontal, cerebellar, and sensory regions, which potentially account for the perceptual advantage of sensorimotor versus intersensory temporal recalibration. [ABSTRACT FROM AUTHOR]

Introduction: It has been suggested that the positive symptoms of schizophrenic patients (hallucinations, delusions, and passivity experience) are caused by dysfunction of their internal and external sensory prediction errors. This is often discussed as related to dysfunction of the forward model that executes selfmonitoring. Several reports have suggested that dysfunction of the forward model in schizophrenia causes misattributions of self-generated thoughts and actions to external sources. There is some evidence that the forward model can be measured using the electroencephalography (EEG) and magnetoencephalography (MEG) components such as N1 (m) and mismatch negativity (MMN) (m). The objective in this MEG study is to investigate differences in the N1m and MMNm-like activity generated in motor-auditory cross-modal tasks in normal control (NC) subjects and schizophrenic (SC) patients, and compared that activity with N1m and MMNm in the auditory unimodal task. Methods: The N1m and MMNm/MMNm-like activity were recorded in 15 SC patients and 12 matched NC subjects. The N1m-attenuation effects and peak amplitude of MMNm/MMNm-like activity of the NC and SC groups were compared. Additionally, correlations between MEG measures (N1m suppression rate, MMNm, and MMNm-like activity) and clinical variables (Positive and Negative Syndrome Scale (PANSS) scores and antipsychotic drug (APD) dosages) in SC patients were investigated. Results: It was found that (i) there was no significant difference in N1m-attenuation for the NC and SC groups, and that (ii) MMNm in the unimodal task in the SC group was significantly smaller than that in the NC group. Further, the MMNm-like activity in the cross-modal task was smaller than that of the MMNm in the unimodal task in the NC group, but there was no significant difference in the SC group. The PANSS positive symptoms and general psychopathology score were moderately negatively correlated with the amplitudes of the MMNm-like activity, and the APD dosage was moderately negatively correlated with the N1m suppression rate. However, none of these correlations reached statistical significance. Discussion: The findings suggest that schizophrenic patients perform altered predictive processes differently from healthy subjects in latencies reflecting MMNm, depending on whether they are under forward model generation or not. This may support the hypothesis that schizophrenic patients tend to misattribute their inner experience to external agents, thus leading to the characteristic schizophrenia symptoms. [ABSTRACT FROM AUTHOR]

Touch generated by our voluntary movements is attenuated both at the perceptual and neural levels compared with touch of the same intensity delivered to our body by another person or machine. This somatosensory attenuation phenomenon relies on the integration of somatosensory input and predictions about the somatosensory consequences of our actions. Previous studies have reported increased somatosensory attenuation in elderly people, proposing an overreliance on sensorimotor predictions to compensate for age-related declines in somatosensory perception; however, recent results have challenged this direct relationship. In a preregistered study, we used a force-discrimination task to assess whether aging increases somatosensory attenuation and whether this increase is explained by decreased somatosensory precision in elderly individuals. Although 94% of our sample (n ¼ 108, 21–77 yr old) perceived their self-generated touches as weaker than externally generated touches of identical intensity (somatosensory attenuation) regardless of age, we did not find a significant increase in somatosensory attenuation in our elderly participants (65–77 yr old), but a trend when considering only the oldest subset (69–77 yr old). Moreover, we did not observe a significant age-related decline in somatosensory precision or a significant relationship of age with somatosensory attenuation. Together, our results suggest that aging exerts a limited influence on the perception of self-generated and externally generated touch and indicate a less direct relationship between somatosensory precision and attenuation in the elderly individuals than previously proposed. NEW & NOTEWORTHY Self-generated touch is attenuated compared with externally generated touch of identical intensity. This somatosensory attenuation has been previously shown to be increased in elderly participants, but it remains unclear whether it is related to age-related somatosensory decline. In our preregistered study, we observed a trend for increased somatosensory attenuation in our oldest participants (69 yr), but we found no evidence of an age-related decline in somatosensory function or a relationship of age with somatosensory attenuation. [ABSTRACT FROM AUTHOR]

This paper presents a framework for efficient and accurate computation of X-ray optics, a key ingredient in optimization-based computed tomography (CT) reconstruction algorithms. Based on an algebraic framework for directional convolution in image space and detector space, we construct forward models for X-ray imaging whose computational cost can be optimized for each specific CT geometry. While the framework allows for modeling various sources of blur in the X-ray imaging process for any CT geometry, we demonstrate and characterize its effectiveness in fan-beam and cone-beam geometries with flat detectors. The experiments show improvements in computational efficiency as well as accuracy, in optics calculations and reconstruction error, of the proposed projector compared to the state-of-the-art methods used in forward- and back-projection algorithms. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM) waves, traditionally used for purposes such as geophysical characterization, impact properties to be measured. This paper describes the effects of radio frequency (RF) waves on the hydraulic conductivity of glass beads and natural sand. A series of tests was conducted using a customized, rigid-wall, cylindrical permeameter inside a resonant cavity made of Plexiglas covered with electrically conductive transparent films. Constant-head ASTM-D2434 tests were performed to measure the samples’ hydraulic conductivity. RF stimulation was performed using a magnetically coupled loop antenna at various frequencies and input RF-power levels. The hydraulic conductivity of both natural sand and glass-bead samples increased with RF stimulation. Furthermore, the measurement of the electric field component of RF waves was also performed to illustrate the pattern of the electric field, as well as evaluate RF’s impact on the hydraulic conductivity tests. The electric field was numerically simulated and validated against experimentally measured electric fields. A finite-difference numerical model was developed in MATLAB to analyze the seepage flow, which was then validated against the experimental results. An optimization scheme was then used to develop a governing equation for RF’s impact on hydraulic conductivity.

In this article, our aim is to estimate synthetic aperture radar (SAR) observables, such as backscatter in VV and VH polarizations, as well as the VH/VV ratio, cross ratio, and interferometric coherence in VV, from agricultural fields. In this study, we use the decision support system for agrotechnology transfer (DSSAT) crop-growth simulation model to simulate parcel-level phenological and growth parameters for over 1500 parcels of silage maize in the Netherlands. The crop model was calibrated using field data, including silage maize phenological phases, leaf area index, and above-ground dry biomass (AGB). The simulations incorporate fine-resolution gridded precipitation data and soil parameters to model the interaction between soil–plant–atmosphere and genotype in DSSAT. The crop variables produced by DSSAT are then used as inputs to a support vector regression model. This model is trained to simulate SAR observables in 2017, 2018, and 2019, and its performance is evaluated using independent fields in each of these years. The results show a close fit between modeled and observed SAR C-band observables. The importance of vegetation variables in the estimation of SAR observables is assessed. The AGB showed significant importance in the estimation of backscatter. This study demonstrates the potential value of combining crop-growth simulation models and machine learning to simulate SAR observables. For example, the SVR model developed here could be used as an observation operator in an assimilation context to constrain vegetation and soil water dynamics in a crop-growth model.

Electromagnetic (EM) waves, traditionally used for purposes such as geophysical characterization, impact properties to be measured. This paper describes the effects of radio frequency (RF) waves on the hydraulic conductivity of glass beads and natural sand. A series of tests was conducted using a customized, rigid-wall, cylindrical permeameter inside a resonant cavity made of Plexiglas covered with electrically conductive transparent films. Constant-head ASTM-D2434 tests were performed to measure the samples' hydraulic conductivity. RF stimulation was performed using a magnetically coupled loop antenna at various frequencies and input RF-power levels. The hydraulic conductivity of both natural sand and glass-bead samples increased with RF stimulation. Furthermore, the measurement of the electric field component of RF waves was also performed to illustrate the pattern of the electric field, as well as evaluate RF's impact on the hydraulic conductivity tests. The electric field was numerically simulated and validated against experimentally measured electric fields. A finite-difference numerical model was developed in MATLAB to analyze the seepage flow, which was then validated against the experimental results. An optimization scheme was then used to develop a governing equation for RF's impact on hydraulic conductivity. [ABSTRACT FROM AUTHOR]

Efference copy‐based forward model mechanisms may help us to distinguish between self‐generated and externally‐generated sensory consequences. Previous studies have shown that self‐initiation modulates neural and perceptual responses to identical stimulation. For example, event‐related potentials (ERPs) elicited by tones that follow a button press are reduced in amplitude relative to ERPs elicited by passively attended tones. However, previous EEG studies investigating visual stimuli in this context are rare, provide inconclusive results, and lack adequate control conditions with passive movements. Furthermore, although self‐initiation is known to modulate behavioral responses, it is not known whether differences in the amplitude of ERPs also reflect differences in perception of sensory outcomes. In this study, we presented to participants visual stimuli consisting of gray discs following either active button presses, or passive button presses, in which an electromagnet moved the participant's finger. Two discs presented visually 500–1250 ms apart followed each button press, and participants judged which of the two was more intense. Early components of the primary visual response (N1 and P2) over the occipital electrodes were suppressed in the active condition. Interestingly, suppression in the intensity judgment task was only correlated with suppression of the visual P2 component. These data support the notion of efference copy‐based forward model predictions in the visual sensory modality, but especially later processes (P2) seem to be perceptually relevant. Taken together, the results challenge the assumption that N1 differences reflect perceptual suppression and emphasize the relevance of the P2 ERP component. Previous sensory suppression EEG experiments lack optimal control conditions and behavioral measures of perception. We employed an electromagnetic button to produce involuntary movements. With this improved control, we demonstrated cortical suppression of self‐generated stimuli in early visual (N1 and P2) ERP components. Furthermore, suppression of perceived intensity was correlated with suppression of visual P2, demonstrating the behavioral relevance of this component. [ABSTRACT FROM AUTHOR]

Several theories of predictive processing propose reduced sensory and neural responses to anticipated events. Support comes from magnetoencephalography/electroencephalography (M/EEG) studies, showing reduced auditory N1 and P2 responses to self‐generated compared to externally generated events, or when the timing and form of stimuli are more predictable. The current study examined the sensitivity of N1 and P2 responses to statistical speech regularities. We employed a motor‐to‐auditory paradigm comparing event‐related potential (ERP) responses to externally and self‐triggered pseudowords. Participants were presented with a cue indicating which button to press (motor–auditory condition) or which pseudoword would be presented (auditory‐only condition). Stimuli consisted of the participant's own voice uttering pseudowords that varied in phonotactic probability and syllable stress. We expected to see N1 and P2 suppression for self‐triggered stimuli, with greater suppression effects for more predictable features such as high phonotactic probability and first‐syllable stress in pseudowords. In a temporal principal component analysis (PCA), we observed an interaction between syllable stress and condition for the N1, where second‐syllable stress items elicited a larger N1 than first‐syllable stress items, but only for externally generated stimuli. We further observed an effect of syllable stress on the P2, where first‐syllable stress items elicited a larger P2. Strikingly, we did not observe motor‐induced suppression for self‐triggered stimuli for either the N1 or P2 component, likely due to the temporal predictability of the stimulus onset in both conditions. Taking into account previous findings, the current results suggest that sensitivity to syllable stress regularities depends on task demands. [ABSTRACT FROM AUTHOR]

Due to the increase in population and the need for water supply, preservation and protection of surface water and groundwater resources has been considered by governments. One of the pollutant sources in rivers is entering salinity from groundwater into the river, that in this research is considered as distributed (nonpoint) sources. The goal is to identify the salinity intensity, location and length of sources by measuring the temporal distribution of concentration in one observation point. For this purpose, the inverse solution of advection-dispersion equation in the river was employed using the simulation-optimization approach. MIKE11 numerical model was used to simulate flow and transfer of salinity in the river, and genetic algorithm was employed for optimization. In the proposed model, considering only one observation point with some measured intensity data for recovering several sources, unknown location and length of the sources, in addition to their intensities is the most significant advantage of the present study. The model verified by using hypothetical examples, 40 km section of the Karun River and also by applying five and 15 percent noise to the observation data. The results confirm the ability of the model to recover the specifications of several distributed sources using only one observation point. With five percent of noise in the observation data, all three specifications of sources can be recovered with the desired accuracy. While at 15 percent of noise, the accuracy of the model in recovering the location and length of sources was decreased. Also, to recover the specifications of each source, employing only three points of the measured data in the ascending part are sufficient. [ABSTRACT FROM AUTHOR]

Generating realistic volume conductor models for forward calculations in electroencephalography (EEG) is not trivial and several factors contribute to the accuracy of such models, two of which are its anatomical accuracy and the accuracy with which electrode positions are known. Here, we investigate effects of anatomical accuracy by comparing forward solutions from SimNIBS, a tool which allows state-of-the-art anatomical modeling, with well-established pipelines in MNE-Python and FieldTrip. We also compare different ways of specifying electrode locations when digitized positions are not available such as transformation of measured positions from standard space and transformation of a manufacturer layout.Substantial effects of anatomical accuracy were seen throughout the entire brain both in terms of field topography and magnitude with SimNIBS generally being more accurate than the pipelines in MNE-Python and FieldTrip. Topographic and magnitude effects were particularly pronounced for MNE-Python which uses a three-layer boundary element method (BEM) model. We attribute these mainly to the coarse representation of the anatomy used in this model, in particular differences in skull and cerebrospinal fluid (CSF). Effects of electrode specification method were evident in occipital and posterior areas when using a transformed manufacturer layout whereas transforming measured positions from standard space generally resulted in smaller errors.We suggest modeling the anatomy of the volume conductor as accurately possible and we hope to facilitate this by making it easy to export simulations from SimNIBS to MNE-Python and FieldTrip for further analysis. Likewise, if digitized electrode positions are not available, a set of measured positions on a standard head template may be preferable to those specified by the manufacturer.

BackgroundAlthough they form a unitary phenomenon, the relationship between extracranial M/EEG and transmembrane ion flows is understood only as a general principle rather than as a well-articulated and quantified causal chain.MethodWe present an integrated multiscale model, consisting of a neural simulation of thalamus and cortex during stage N2 sleep and a biophysical model projecting cortical current densities to M/EEG fields. Sleep spindles were generated through the interactions of local and distant network connections and intrinsic currents within thalamocortical circuits. 32,652 cortical neurons were mapped onto the cortical surface reconstructed from subjects' MRI, interconnected based on geodesic distances, and scaled-up to current dipole densities based on laminar recordings in humans. MRIs were used to generate a quasi-static electromagnetic model enabling simulated cortical activity to be projected to the M/EEG sensors.ResultsThe simulated M/EEG spindles were similar in amplitude and topography to empirical examples in the same subjects. Simulated spindles with more core-dominant activity were more MEG weighted.Comparison with existing methodsPrevious models lacked either spindle-generating thalamic neural dynamics or whole head biophysical modeling; the framework presented here is the first to simultaneously capture these disparate scales.ConclusionsThis multiscale model provides a platform for the principled quantitative integration of existing information relevant to the generation of sleep spindles, and allows the implications of future findings to be explored. It provides a proof of principle for a methodological framework allowing large-scale integrative brain oscillations to be understood in terms of their underlying channels and synapses.

In magnetoencephalography (MEG) research there are a variety of inversion methods to transform sensor data into estimates of brain activity. Each new inversion scheme is generally justified against a specific simulated or task scenario. The choice of this scenario will however have a large impact on how well the scheme performs. We describe a method with minimal selection bias to quantify algorithm performance using human resting state data. These recordings provide a generic, heterogeneous, and plentiful functional substrate against which to test different MEG recording and reconstruction approaches. We used a Hidden Markov model to spatio-temporally partition data into self-similar dynamic states. To test the anatomical precision that could be achieved, we then inverted these data onto libraries of systematically distorted subject-specific cortical meshes and compared the quality of the fit using cross validation and a Free energy metric. This revealed which inversion scheme was able to identify the least distorted (most accurate) anatomical models, and allowed us to quantify an upper bound on the mean anatomical distortion accordingly. We used two resting state datasets, one recorded with head-casts and one without. In the head-cast data, the Empirical Bayesian Beamformer (EBB) algorithm showed the best mean anatomical discrimination (3.7 mm) compared with Minimum Norm/LORETA (6.0 mm) and Multiple Sparse Priors (9.4 mm). This pattern was replicated in the second (conventional dataset) although with a marginally poorer (non-significant) prediction of the missing (cross-validated) data. Our findings suggest that the abundant resting state data now commonly available could be used to refine and validate MEG source reconstruction methods and/or recording paradigms.

EEG/MEG source localization requires a subject's brain MRI to compute the sourcemodel and headmodel. As part of this computation, co‐registration of the digitized head information and brain MRI scan is the essential step. However, in the absence of a brain MRI scan, an approximated sourcemodel and headmodel can be computed from the subject's digitized head information and brain MRI scans from other subjects. In the present work, we compared the fiducial (FID)‐ and iterative closet point (ICP)‐based co‐registration approaches for computing an approximated sourcemodel using single and multiple available brain MRI scans. We also evaluated the two different template MRI selection strategies: one is based on objective registration error, and another on sourcemodel approximation error. The outcome suggests that averaged approximated solutions using multiple template brain MRI scans showed better performance than single‐template MRI‐based solutions. The FID‐based approach performed better than the ICP‐based approach for co‐registration of the digitized head surface and brain MRI scan. While selecting template MRIs, the selection approach based on objective registration error showed better performance than a sourcemodel approximation error‐based criterion. Cross‐dataset performance analysis showed a higher model approximation error than within‐dataset analysis. In conclusion, the FID‐based co‐registration approach and objective registration error‐based MRI selection criteria provide a simple, fast and more accurate solution to compute averaged approximated models compared with the ICP‐based approach. The demography of brain MRI scans should be similar to that of the query subject whose brain MRI scan was unavailable. [ABSTRACT FROM AUTHOR]

A forward model for optical fiber strain was established based on a planar 3D multi-fracture model. Then the forward method calculating distributed fiber strain induced by multi-fracture growth was proposed. Based on this method, fiber strain evolution during fracturing of the horizontal well was numerically simulated. Fiber strain evolution induced by fracture growth can be divided into three stages: strain increasing, shrinkage convergence, and straight-line convergence, whereas the evolution of fiber strain rate has four stages: strain rate increasing, shrinkage convergence, straight-line convergence, and strain rate reversal after pumping stops. Fiber strain does not flip after pumping stop, while the strain rate flips after pumping stop so that strain rate can reflect injection dynamics. The time when the fracture extends to the fiber and inter-well pressure channeling can be identified by the straight-line convergence band of distributed fiber strain or strain rate, and the non-uniform growth of multiple fractures can be evaluated by using the instants of fractures reaching the fiber monitoring well. When the horizontal section of the fiber monitoring well is within the height range of a hydraulic fracture, the instant of the fracture reaching the fiber can be identified; otherwise, the converging band is not apparent. In multi-stage fracturing, under the influence of stress shadow from previous fracturing stages, the tensile region of fiber strain may not appear, but the fiber strain rate can effectively show the fracture growth behavior in each stage. The evolution law of fiber strain rate in single-stage fracturing can be applied to multi-stage fracturing.

In the paper, we propose a procedure to be used for the validation of software for forward modeling of rat electroencephalogram with scalp potentials measured on rat head phantoms. Measurements are performed on a cuboidal phantom, a simplified shape of a rat brain, and an anatomically realistic, computed tomography (CT)-based phantom considering the brain and the skull. The physical phantoms are composed of an agar mixture to mimic the rat brain, excitation dipoles for modeling the neural activity of the brain, electrodes for monitoring the surface electric potential and a 3D printed skull. To ensure correct positions of dipoles and electrodes for numerical simulations, the phantoms are scanned by a computed tomography. After that, reconstructed 3D models are simulated in three EM solvers and results are compared with EEG measurements. Differences between simulations and measurements are further analyzed by parametric simulations and discussed. Obtained results provide the software validation method for rat brain forward modeling. Properly validated computation of electric potentials is essential for development of electrical brain stimulation protocols as well as in optimization of electrode placement.

Human brain activity generates scalp potentials (electroencephalography – EEG), intracranial potentials (iEEG), and external magnetic fields (magnetoencephalography – MEG). These electrophysiology (e-phys) signals can often be measured simultaneously for research and clinical applications. The forward problem involves modeling these signals at their sensors for a given equivalent current dipole configuration within the brain. While earlier researchers modeled the head as a simple set of isotropic spheres, today's magnetic resonance imaging (MRI) data allow for a detailed anatomic description of brain structures and anisotropic characterization of tissue conductivities. We present a complete pipeline, integrated into the Brainstorm software, that allows users to automatically generate an individual and accurate head model based on the subject's MRI and calculate the electromagnetic forward solution using the finite element method (FEM). The head model generation is performed by integrating the latest tools for MRI segmentation and FEM mesh generation. The final head model comprises the five main compartments: white-matter, gray-matter, CSF, skull, and scalp. The anisotropic brain conductivity model is based on the effective medium approach (EMA), which estimates anisotropic conductivity tensors from diffusion-weighted imaging (DWI) data. The FEM electromagnetic forward solution is obtained through the DUNEuro library, integrated into Brainstorm, and accessible with either a user-friendly graphical interface or scripting. With tutorials and example data sets available in an open-source format on the Brainstorm website, this integrated pipeline provides access to advanced FEM tools for electromagnetic modeling to a broader neuroscience community.