Single Shot Transverse Emittance Measurement of multi-MeV Electron Beams Using a Long Pepper-Pot

We present a pepper-pot design in which we address the problem of penetration by high energy particle, deriving analytical expressions and performing GEANT4 simulations for the estimation of the error introduced by a long (thick) pepper-pot. We also show that a careful design allows to measure the emittance of electron beam of several hundred MeV and beyond

Footprint of the host restriction factors APOBEC3 on the genome of human viruses

peer reviewe

To set up pedagogical experiments in a virtual lab: methodology and first results

This paper concerns a methodology for setting up web based experiments by distinguishing two perspectives: the learning perspective (which tasks will be scheduled for pupils and tutors, how they are planned and what learning objectives they may attempt), and separately, the experiment perspective (how teachers or researchers may use trails of this experiment, the first to improve their teaching, the last to various research objectives). The both are described by computable “scenarios” expressed with an educational modeling language, Learning Design Language (LDL). Scenarios are then implemented on a platform (LearningLab platform) to be played by pupils and tutors and further analyzed by exploiting trails of each run

Le LiDAR aérien au service des inventaires forestiers : Application à la futaie feuillue ardennaise

Ce poster présente les premiers résultats d’une étude dont l’objectif est d’évaluer la capacité du lidar aérien à être utilisé pour estimer des données dendrométriques au niveau des forêts feuillues d’Ardenne. Les résultats présentés correspondent à la cartographie et à l’estimation du recouvrement par stade de développement de la régénération ainsi qu’à l’estimation de la distribution du nombre de tiges par classes de grosseur.Regiowood

Modeling distribution of trees stem circumference by species using LiDAR data in mixed deciduous temperate forests

Regiowood

Description of a new procedure to estimate the carbon stocks of all forest pools and impact assessment of methodological choices on the estimates

Forest ecosystems play a major role in atmospheric carbon sequestration and emission. Comparable organic carbon stock estimates at temporal and spatial scales for all forest pools are needed for scientific investigations and political purposes. Therefore, we developed a new carbon stock (CS) estimation procedure that combines forest inventory and soil and litter geodatabases at a regional scale (southern Belgium). This procedure can be implemented in other regions and countries on condition that available external carbon soil and litter data can be linked to forest inventory plots. The presented procedure includes a specific CS estimation method for each of the following forest pools and subpools (in brackets): living biomass (aboveground and belowground), deadwood (dead trees and snags, coarse woody debris and stumps), litter, and soil. The total CS of the forest was estimated at 86 Tg (185 Mg ha-1). Soil up to 0.2 m depth, living biomass, litter, and deadwood CSs account, respectively, for 48, 47, 4, and 1 % of the total CS. The analysis of the CS variation within the pools across ecoregions and forest types revealed in particular that: (1) the living biomass CS of broadleaved forests exceeds that of coniferous forests, (2) the soil and litter CSs of coniferous forest exceed those of broadleaved forests, and (3) beech stands come at the top in carbon stocking capacity. Because our estimates differ sometimes significantly from the previous studies, we compared different methods and their impacts on the estimates. We demonstrated that estimates may vary highly, from -16 to ?12 %, depending on the selected methods. Methodological choices are thus essential especially for estimating CO2 fluxes by the stock change approach. The sources of error and the accuracy of the estimates were discussed extensively

Intraoperative MRI for the microsurgical resection of meningiomas close to eloquent areas or dural sinuses: patient series

BACKGROUND: Meningiomas are the most commonly encountered nonglial primary intracranial tumors. The authors report on the usefulness of intraoperative magnetic resonance imaging (iMRI) during microsurgical resection of meningiomas located close to eloquent areas or dural sinuses and on the feasibility of further radiation therapy. OBSERVATIONS: Six patients benefited from this approach. The mean follow-up period after surgery was 3.3 (median 3.2, range 2.1–4.6) years. Five patients had no postoperative neurological deficit, of whom two with preoperative motor deficit completely recovered. One patient with preoperative left inferior limb deficit partially recovered. The mean interval between surgery and radiation therapy was 15.8 (median 16.9, range 1.4–40.5) months. Additional radiation therapy was required in five cases after surgery. The mean preoperative tumor volume was 38.7 (median 27.5, range 8.6–75.6) mL. The mean postoperative tumor volume was 1.2 (median 0.8, range 0–4.3) mL. At the last follow-up, all tumors were controlled. LESSONS: The use of iMRI was particularly helpful to (1) decide on additional tumor resection according to iMRI findings during the surgical procedure; (2) evaluate the residual tumor volume at the end of the surgery; and (3) judge the need for further radiation and, in particular, the feasibility of single-fraction radiosurgery

Estimating Species-Specific Stem Size Distributions of Uneven-Aged Mixed Deciduous Forests Using ALS Data and Neural Networks

peer reviewedSustainable forest management requires accurate fine-scale description of wood resources. Stem size distribution (SSD) by species is used by foresters worldwide as a representative overview of forest structure and species composition suitable for informing management decisions at shorter and longer terms. In mixed uneven-aged deciduous forests, tree data required for SSD estimation are most often collected in the field through traditional forest management inventories (FMIs), but these are time-consuming and costly with respect to the sampled area. Combining FMIs with remote sensing methods such as airborne laser scanning (ALS), which has high potential for predicting forest structure and composition, and is becoming increasingly accessible and affordable, could provide cheaper and faster SSD data across large areas. In this study, we developed a method for estimating species-specific SSDs by combining FMIs and dual-wavelength ALS data using neural networks (NNs). The proposed method was tested and validated using 178 FMI plots within 22,000 ha of a mixed uneven-aged deciduous forest in Belgium. The forest canopy was segmented, and metrics were derived from the ALS point cloud. A NN with a custom architecture was set up to simultaneously predict the three components required to compute species-specific SSDs (species, circumference, and number of stems) at segment level. Species-specific SSDs were thereafter estimated at stand level by aggregating the estimates for the segments. A robustness test was set up using fully independent plots to thoroughly assess the method precision at stand-level on a larger area. The global Reynolds index for the species-specific SSDs was 21.2 for the training dataset and 54.0 for the independent dataset. The proposed method does not require allometric models, prior knowledge of the structure, or the predefinition of variables; it is versatile and thus potentially adaptable to other forest types having different structures and compositions

Mapping tree species proportions from satellite imagery using spectral–spatial deep learning

peer reviewedRemote sensing can be used to collect information related to forest management. Previous studies demonstratedthe potential of using multispectral satellite imagery for classifying tree species. However, methods that canmap tree species in mixed forest stands on a large scale are lacking. We propose an innovative method formapping the proportions of tree species using Sentinel-2 imagery. A convolutional neural network was usedto quantify the per-pixel basal area proportions of tree species considering the neighbouring environment(spectral–spatial deep learning). A nested U-shaped neural network (UNet++) architecture was implemented.We produced a map of the entire Wallonia Region (southern Belgium). Nine species or groups of species wereconsidered: Spruce genus, Oak genus, Beech, Douglas fir, Pine genus, Poplar genus, Larch genus, Birch genus, andremaining species. The training dataset for the convolutional neural network model was prepared using a mapof forest parcels extracted from the public forest administration’s geodatabase of Wallonia. The accuracy of thepredicted map covering the region was independently assessed using data from the regional forest inventoryof Wallonia. A robust assessment method for tree species proportions maps was proposed for assessing the(1) majority species, (2) species composition (presence or absence), and (3) species proportions (proportionvalues). The achieved value of indicator OA (0.73) shows that our approach can map the majority treespecies in mixed and pure forest stands. Indicators MS (0.89), MPS (0.72) and MUS (0.83) support that themodel can predict the species composition in most cases in the study area. Spruce genus, Oak genus, Beech,and Douglas fir achieved the best results, with PAs and UAs close to or higher than 0.70. Particularly, highperformance was achieved for detecting Oak genus and Beech in low area proportions: PAs and UAs higherthan 0.70 from the 0.4 proportion. Predicted proportions had a R2of 0.50. The proposed method, which usesspectral–spatial deep learning to map the proportions of tree species, is innovative because it was adapted tothe complexity of mixed forests and spatial resolution of current satellite imagery. Additionally, it optimisesthe use of available forest data in the model conception by considering all pixels from pure stands to highlymixed forest stands. When forest inventories are available in a broad sense, that is, georeferenced areas withthe proportions of tree species, this method is highly reproducible and applicable at a large scale, offeringpotential for use in forest management