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Abstract Background Early Mobilization in Intensive Care Units (ICUs) enhances patients’ evolution, but has been rarely studied in neurological ICUs. The aim of this study was to assess gait training with body-weight support (BWS) in neuroICU, and to report on its safety, feasibility and on delays before walking with and without BWS. Methods This study was an observational one-year single-center study. Inclusion criteria were adults with a neurological injury requiring mechanical ventilation. Exclusion criteria were early death or ICU transfer. After weaning from ventilation, patients were screened for indications of BWS walking using predefined criteria. Results Patients’ conditions were mostly brain injuries: 32% subarachnoid hemorrhages, 42% focal strokes, and 12% traumatic brain injuries. Out of 272 admissions, 136 patients were excluded, 78 were eligible, and 33 performed BWS walking. Among non-eligible patients, 36 walked unsuspended upon ventilation weaning, 17 presented too severe impairments. Among the 45 eligible patients who did not receive BWS training, main reasons were workload and weekends (31%), medical barriers (29%), and early ICU discharge (22%). 78 BWS sessions were performed on the 33 beneficiaries (median sessions per patient 2, max 10). Pre-session, most patients had inadequate response to pain, orders, or simple orientation questions. Sitting without support was impossible for 74%. Most pre-post changes in hemodynamic, respiratory, and pain parameters were small, and recovered spontaneously after the session. Eight sessions were interrupted; reasons were pain, fatigue or major imbalance (4), syncope (1), occurrence of stool (2), and battery failure (1). None of these adverse events required medical intervention, patients recovered upon session interruption. Median session duration was 31 min, patients walked on median 17 m. First BWS session occurred on median 3 days after ventilation weaning, and 11 days before patients were able to walk unsuspended. Conclusions Verticalization and walking using a suspension device in patients in neuroICU allows early gait training, despite challenging neurological impairments. It is safe and generally well tolerated. Trial registration: ClinicalTrials database (ID: NCT04300491).

According to the industry, the value of wood logs is heavily influenced by their internal structure, particularly the distribution of knots within the trees. Nowadays, CT scanners combined with classical computer vision approach are the most common tool for obtaining reliable and accurate images of the interior structure of trees. Knowing where the tree semantic features, especially knots, contours and centers are within a tree could improve the efficiency of the overall tree industry by minimizing waste and enhancing the quality of wood-log by-products. However, this requires to automatically process the CT-scanner images so as to extract the different elements such as tree centerline, knot localization and log contour, in a robust and efficient manner. In this paper, we propose an effective methodology based on deep learning for performing these different tasks by processing CT-scanner images with deep convolutional neural networks. To meet this objective, three end-to-end trainable pipelines are proposed. The first pipeline is focused on centers detection using CNNs architecture with a regression head, the second and the third one address contour estimation and knot detection as a binary segmentation task based on an Encoder-Decoder architecture. The different architectures are tested on several tree species. With these experiments, we demonstrate that our approaches can be used to extract the different elements of trees in a precise manner while preserving good performances of robustness. The main objective was to demonstrate that methods based on deep learning might be used and have a relevant potential for segmentation and regression on CT-scans of tree trunks.

A central problem in magnetized fusion plasmas is to understand whether the plasma edge sustains a turbulent state and transitions to improved confinement. The authors simulate the detailed organization of turbulence from the primitive flux-driven kinetic equations including plasma-solid interaction and identify basic mechanisms which allow to cure the oft-reported shortfall of turbulence power in the plasma edge and to describe spontaneous onset of a transport barrier.

The mission of WEST (tungsten-W Environment in Steady-state Tokamak) is to explore long pulse operation in a full tungsten (W) environment for preparing next-step fusion devices (ITER and DEMO) with a focus on testing the ITER actively cooled W divertor in tokamak conditions. Following the successful completion of phase 1 (2016-2021), phase 2 started in December 2022 with the lower divertor made entirely of actively cooled ITER-grade tungsten mono-blocks. A boronization prior the first plasma attempt allowed for a smooth startup with the new divertor. Despite the reduced operating window due to tungsten, rapid progress has been made in long pulse operation, resulting in discharges with a pulse length of 100 s and an injected energy of around 300 MJ per discharge. Plasma startup studies were carried out with equatorial boron nitride limiters to compare them with tungsten limiters, while Ion Cyclotron Resonance Heating assisted startup was attempted. High fluence operation in attached regime, which was the main thrust of the first campaigns, already showed the progressive build up of deposits and appearance of dust, impacting the plasma operation as the plasma fluence increased. In total, the cumulated injected energy during the first campaigns reached 43 GJ and the cumulated plasma time exceeded 5 h. Demonstration of controlled X-Point Radiator regime is also reported, opening a promising route for investigating plasma exhaust and plasma-wall interaction issues in more detached regime. This paper summarises the lessons learned from the manufacturing and the first operation of the ITER-grade divertor, describing the progress achieved in optimising operation in a full W environment with a focus on long pulse operation and plasma wall interaction.

The interaction between a plasma and a solid surface is studied in a (1D–1V) kinetic framework using a localized particle and convective energy source. Matching the quasineutral plasma region and sheath horizon is addressed in the fluid framework with a zero heat flux closure. It highlights non-polytropic nature of the physics of parallel transport. Shortfalls of this approach compared to a reference kinetic simulation highlight the importance of the heat flux as the measure of kinetic effects. Non-collisional closure and higher moment closure are used to determine the sound velocity. Within these frameworks, no gain in the fluid predictive capability is obtained. The kinetic constraint at the sheath horizon is discussed and modified to account for conditions that are actually met in simulations, namely quasineutrality with a small but finite charge density. Analyzing the distribution functions shows that collisional transfer is mandatory to achieve steady-state self-organization on the open field lines.

The interaction between a plasma and a solid surface is studied in a (1D-1V) kinetic approach using immersed boundary conditions and penalization to model the wall. Two solutions for the penalized wall region are investigated that either allow currents to flow within the material boundary or not. Essential kinetic aspects of sheath physics are recovered in both cases and their parametric dependencies investigated. Importantly, we show how the two approaches can be reconciled when accounting for relevant kinetic effects. Non-Maxwellian features of the ion and electron distribution functions are essential to capture the value of the potential drop in the sheath. These features lead to a sheath heat transmission factor for ions 60% larger than usually predicted and 35% for electrons. The role of collisions is discussed and means of incorporating minimally-relevant kinetic sheath physics in the gyrokinetic framework are discussed.

A reduced model for trapped electron mode stability has been developed, which incorporates the basic effects of magnetic surface shaping, in particular, elongation and triangularity. This model shows that while elongation is stabilising, though weakly, negative triangularity usually leads to a more unstable plasma. This is in marked contrast with the experimental evidence of a better confinement at negative triangularity, and with recent gyrokinetic linear simulations. This paradox is solved when finite orbit and/or finite mode extent along field lines (mode ballooning) effects are included. These effects give more weight to particles trapped at low bounce angles, which are those that exhibit lower precession frequencies at negative—compared to positive—triangularity. As a result, the interchange growth rate becomes lower at negative triangularity and large temperature gradients, so that negative triangularity appears to have an overall stabilising effect. Mode ballooning appears to play the most important role in this reversal of stability.

Abstract We present a generalized mapping framework that can withstand the challenges incurred by working in unstructured outdoor environments, such as a snowy forest. The proposed method takes advantage of a sensor fusion scheme, where sensors such as cameras and lidars are used in order to reconstruct the surrounding natural environment. Although mapping techniques such as SLAM and ICP cannot themselves properly handle the complexity of natural scenes, they do have the potential to contribute to the global solution in a proposed sensor fusion scheme, based on a factor graph architecture. In this paper, we propose an innovative map registration scheme for visual maps, and show how it can improve the reconstruction quality after data fusion. We also analyze the behavior and sensitivity of factor graphs to uncertainties, by comparing the residual error with different parameter combinations such as variances, using an exhaustive grid search with ground truth comparison. Finally, we suggest an ICP-inferred loop closure, capable of compensating position and attitude drift. The experiments are carried out by recording in a snowy forest using a wearable sensor suite. In the experiments, ground truth was acquired using a millimeter-accurate total station. The proposed framework is shown to be robust and likewise capable of providing estimates that are otherwise unattainable using classic techniques, such as visual SLAM and ICP for lasers. Finally, a visible improvement in the map reconstruction quality is shown, and the proposed framework achieves a translation error of 0.36 m.

Historical charcoal production is one of the significant factors affecting today’s forest dynamics. A key challenge is to develop tools to investigate historical charcoal production over large areas, allowing a more comprehensive understanding of past impacts and history of charcoal production over a given landscape. In this study, high-resolution remote-sensing airborne LiDAR images over a large woodland area were used to compare manual on-screen versus algorithm-based automatic methods to inventory charcoal kilns with inputs of field-validated data. The results revealed that (1) the on-screen detection method provided less false-positives, (2) the automatic method detects a higher number of kilns and (3) kiln distribution seemed to be connected mostly to land ownership rather than to environmental variables. This study validates a new method of charcoal kilns’ inventory and spatial analysis that can be applied to other areas to better understand the effect of past biomass harvesting for charcoal production on forest dynamics.

This study is motivated by experiments on Tore Supra and WEST tokamaks where a deepening of the E × B velocity—governed by the radial electric field Er —near the edge is observed when the safety factor decreases in L-mode plasmas. Flux-driven global simulations of ion temperature gradient turbulence recover qualitatively the trend observed in the experiments, i.e. the E × B velocity increases when decreasing the safety factor. From these simulations, multiple clues point out the role of turbulence in the establishment of the radial electric field even though the turbulent intensity increases with the safety factor. The proposed mechanism to elucidate this phenomenon, backed up by a reduced model, is that the damping of the poloidal flow, governed by the neoclassical friction, increases more strongly with the safety factor than the turbulent drive for Er, due to the (r , θ) component of the Reynolds stress. [ABSTRACT FROM AUTHOR]

The inspection and maintenance of large-scale industrial structures are major challenges that require time-efficient and reliable solutions to ensure the healthy condition of structures during operation. Autonomous robots may provide a promising solution for this purpose. In particular, they could lead to faster and more reliable inspection and maintenance without direct intervention from human operators. In this paper, we present a custom magnetic crawler system, and sensor suit and sensing modalities to enable such robotic operation. We first describe a localization framework based on a mesh created from a point cloud coupled with Inertial Measurement Unit (IMU) and Ultra-Wide Band (UWB) readings. Next, we introduce a mapping framework that relies on a 3D laser, and explicitly state how autonomous navigation and obstacle avoidance can be developed. Lastly, we present how ultrasonic guided waves (UGWs) are integrated into the system to provide accurate robot localization and structural feature mapping by relying on acoustic reflections in combination with the other systems. It is envisioned that long-range inspection capabilities that are not yet available in current industrial mobile platforms could emerge from the designed robotic system.

Gaps often occur in eddy covariance flux measurements, leading to data loss and necessitating accurate gap-filling. Furthermore, gaps in evapotranspiration (ET) measurements of annual field crops are particularly challenging to fill because crops undergo rapid change over a short season. In this study, an innovative deep learning (DL) gap-filling method was tested on a database comprising six datasets from different crops (cotton, tomato, and wheat). For various gap scenarios, the performance of the method was compared with the common gap-filling technique, marginal distribution sampling (MDS), which is based on lookup tables. Furthermore, a predictor importance analysis was performed to evaluate the importance of the different meteorological inputs in estimating ET. On the half-hourly time scale, the deep learning method showed a significant 13.5% decrease in nRMSE (normalized root mean square error) throughout all datasets and gap durations. A substantially smaller standard deviation of mean nRMSE, compared with marginal distribution sampling, was also observed. On the whole-gap time scale (half a day to six days), average nMBE (normalized mean bias error) was similar to that of MDS, whereas standard deviation was improved. Using only air temperature and relative humidity as input variables provided an RMSE that was significantly smaller than that resulting from the MDS method. These results suggest that the deep learning method developed here is reliable and more consistent than the standard gap-filling method and thereby demonstrates the potential of advanced deep learning techniques for improving dynamic time series modeling.

In streams, phototrophic biofilms are considered to be a good‐quality resource for consumers and are essential to support secondary production. However, with the increasing occurrence of flow intermittency as a consequence of global climate change, limited information exists regarding the impact of drying and rewetting events on biofilm nutritional quality indicators and their consequences for consumers. This study aims at understanding how river intermittency affects the nutritional quality of phototrophic biofilms. Specifically, we examine the effects of drying and rewetting events on their capacity to support secondary production.Our hypothesis was that the capacity of biofilms to support secondary production relies on their nutritional quality: biofilms characterised by higher contents of long‐chain fatty acids, nitrogen and phosphorus are expected to provide a better‐quality resource for consumers. We also hypothesised that the nutritional quality of biofilms undergoes changes over time during drying events, and that these changes are influenced by their initial algal composition. This is because the algal composition within biofilms may shift in response to drying events, subsequently impacting the nutritional quality of the biofilms.We grew four phototrophic biofilms in flowing water, each with a different nutritional quality, and then exposed them to a short (3 days) or a long dry period (14 days). Biofilms were sampled 3 days and 18 days after rewetting (post‐disturbance and post‐recovery) to assess alterations in nutritional indicators relative to their pre‐disturbance state through pigment, fatty acid and stoichiometric analyses. We fed these biofilms to Gammarids (Gammarus fossarum) for 29 days and measured individual growth, feeding rate and locomotor activity. We also calculated a secondary production index to assess the biofilms' capacity to support higher trophic levels.Our findings revealed that the nutritional quality of biofilms was significantly reduced during the post‐disturbance phase. The duration of the dry period had minimal effect on this decline. Subsequently, during the recovery phase, nutritional quality indicators improved for biofilms initially dominated by cyanobacteria, while they either remained unchanged or decreased for biofilms initially dominated by diatoms, in comparison to the pre‐disturbance state. As a result, biofilms that initially exhibited a high nutritional quality were disrupted by the dry period, depending on the duration. However, the overall effects of dry period on gammarid's response and on secondary production were less pronounced, which is likely to have resulted from changes in the quantity of available resources.Our study demonstrates that a disturbance can modify the expected and effective qualities of biofilms. It highlights that biochemical parameters cannot reliably predict biofilm capacity to support secondary production. Biofilm history of disturbance, among other parameters, must be taken into account. [ABSTRACT FROM AUTHOR]

Approximations for closing the kinetic equation for the one particle distribution function are calculated by using propagators. These provide the formal structure of the collision term in the Landau approximation. The method allows one to investigate the effect of inhomogeneities at the Debye scale and to analyse magnetised collisions, when the Larmor radius is smaller than the Debye length. This method also allows one developing a simple renormalisation scheme to derive the Lenard-Balescu collision operator.

Starting from the given passive particle equilibrium particle cylindrical profiles, we built self-consistent stationary conditions of the Maxwell-Vlasov equation at thermodynamic equilibrium with non-flat density profiles. The solutions to the obtained equations are then discussed. It appears that the presence of an azimuthal (poloidal) flow in the plasma can ensure radial confinement, while the presence of a longitudinal (toroidal) flow can enhance greatly the confinement. Moreover in the global physically reasonable situation, we find that no unstable point can emerge in the effective integrable Hamiltonian of the individual particles, hinting at some stability of the confinement when considering a toroidal geometry in the large aspect ratio limit.

Two time slices in linear and saturated Ohmic confinement (LOC and SOC) regimes in a Tore Supra Ohmic L-mode discharge are analyzed using nonlinear global full-f gyrokinetic simulations, and qualitative features of the LOC–SOC transition are reproduced. The exhaust of carbon impurity ions is caused by ion mixing, which is driven by the toroidal field stress. The intrinsic rotation develops in the opposite direction between the LOC and SOC phases, which is characterized by different features of the mode asymmetry between trapped electron modes (TEMs) in the LOC phase and ion temperature gradient-driven modes in the SOC phase, leading to the change of the profile shear stress. Here, the mode asymmetry or the ballooning angle depends not only on the profile shear and the Er shear but also on the radial electric field Er itself. The energy fluxes of electrons and deuterium ions are dominant in the LOC and SOC phases, respectively, and the ratio of the energy confinement time between the two phases agree with the experimental value. Turbulent frequency spectra are characterized by quasi-coherent modes (QCMs) and broad-band spectra in the LOC and SOC phases, respectively. The QCMs are produced by a split of the toroidal mode number spectra of TEMs induced by the Doppler shift of poloidal E × B rotation due to Er, which is sustained in the electron diamagnetic direction by the ripple induced counter-current rotation, and enhanced by higher temperature in the LOC phase. [ABSTRACT FROM AUTHOR]