Computing wildfire behaviour metrics from CFD simulation data

In this article, we demonstrate a new post-processing methodology which can be used to analyse CFD wildfire simulation outputs in a model-independent manner. CFD models produce a great deal of quantitative output but require additional post-processing to calculate commonly used wildfire behaviour metrics. Such post-processing has so far been model specific. Our method takes advantage of the 3D renderings that are a common output from such models and provides a means of calculating important fire metrics such as rate of spread and flame height using image processing techniques. This approach can be applied similarly to different models and to real world fire behaviour datasets, thus providing a new framework for model validation. Furthermore, obtained information is not limited to average values over the complete domain but spatially and temporally explicit metric distributions are provided. This feature supports posterior statistical analyses, ultimately contributing to more detailed and rigorous fire behaviour studies.Peer ReviewedPostprint (published version

Geochemical characterization of alkaline gneissic rocks of Alentejo (Portugal)

Geochemical characterization of alkaline gneissic rocks of Alentejo (Portugal) J. Carrilho Lopes1, J. Munhá2, C. Pin3, J. Mata2 1Departamento de Geociências, Universidade de Évora & Centro de Geologia da Universidade de Lisboa, Portugal. 2Departamento de Geologia & Centro de Geologia da Universidade de Lisboa, Portugal. 3Département de Géologie, C.N.R.S., Université Blaise Pascal, France. [email protected] This study presents and interprets, on a petrological/petrogenetic point of view, a set of mineral and whole-rock geochemical data collected from the so called “Alkaline Province of Northeast Alentejo”, a group of gneisses that outcrops in lithostratigraphic domains known as Ossa-Morena Zone and Blastomilonitic Belt. It’s composed by felsic gneissic rocks of (per)alkaline type, represented by syenites with sodic inossilicates (riebeckite and/or aegirine), nefelinic syenites and hastingsitic syenites, as well as hedenbergitic granites. Most of riebeckitic syenites presents (Zr/Nb)<10, (Y/Nb)<0.7 e (Th/Nb)<0.3, while hastingsitic ones and hedenbergitic granites reveal higher values of these ratios (15.0, 2.0 e 0.6, respectively). The highest contents of Zr (4800 ppm) are also observed on peralkaline terms, with minimum values measured on alkaline granites (135 ppm). Maximum contents of F (6100 ppm) and Cl (7233 ppm) have been determined on riebeckitic and nefelino-sodalitic syenites, respectively, and seems that halogenous contents may be correlated with devolatilization processes, deformation/micro-fracturation and REE mobility. Even though irregular crystallization of phases which consume high contents of REE (e.g. allanite) can disturbe the correspondent geochemical signatures, it is still possible to identify, in most of the cases, distinctions between maximum values of (La/Sm)N , (La/Lu)N and (Gd/Lu)N of peralkaline rocks (29.6, 11.6, 2.4), hastingsitic syenites (14.8, 4.8, 2.0) and alkaline granites (4.0, 3.1, 1.7). Riebeckitic and nefelinic facies present, simultaniously, the sharpest negative anomalies of Ti and the less marked negative anomalies of Nb (means of 0.9 and 0.7), which can be interpreted as a result of differentiation processes with small to moderate contributions of crustal contamination; comparatively, this anomaly is higher in hastingsitic (0.6) and granitic terms (0.4). Obtained in a small set of mafic and felsic samples, Rb-Sr and Sm-Nd isotopic data, show the vulnerability of the first system to post-magmatic processes. Peralkaline rocks show (+2.5<eNd480<+4.9) values which reflect the origin of these magmas from timeintegrated depleted mantle sources, that were enriched in LREE at the time of, or shortly before, the igneous episode in an intracontinental rift setting. Sr-Nd petrogenetic modelling adds complementary information: i.) intracontinental alkaline character of (primary) basaltic magmas as precursors of this alkaline province; ii.) low to moderate crustal contamination during differentiation processes, namely 7% to 20% for peralkaline syenites and about 26% for alkaline granites

Feasibility of Using the Two-Source Energy Balance Model (TSEB) with Sentinel-2 and Sentinel-3 Images to Analyze the Spatio-Temporal Variability of Vine Water Status in a Vineyard

In viticulture, detailed spatial information about actual evapotranspiration (ETa) and vine water status within a vineyard may be of particular utility when applying site-specific, precision irrigation management. Over recent decades, extensive research has been carried out in the use of remote sensing energy balance models to estimate and monitor ETa at the field level. However, one of the major limitations remains the coarse spatial resolution in the thermal infrared (TIR) domain. In this context, the recent advent of the Sentinel missions of the European Space Agency (ESA) has greatly improved the possibility of monitoring crop parameters and estimating ETa at higher temporal and spatial resolutions. In order to bridge the gap between the coarse-resolution Sentinel-3 thermal and the fine-resolution Sentinel-2 shortwave data, sharpening techniques have been used to downscale the Sentinel-3 land surface temperature (LST) from 1 km to 20 m. However, the accurate estimates of high-resolution LST through sharpening techniques are still unclear, particularly when intended to be used for detecting crop water stress. The goal of this study was to assess the feasibility of the two-source energy balance model (TSEB) using sharpened LST images from Sentinel-2 and Sentinel-3 (TSEB-PTS2+3) to estimate the spatio-temporal variability of actual transpiration (T) and water stress in a vineyard. T and crop water stress index (CWSI) estimates were evaluated against a vine water consumption model and regressed with in situ stem water potential (Ψstem). Two different TSEB approaches, using very high-resolution airborne thermal imagery, were also included in the analysis as benchmarks for TSEB-PTS2+3. One of them uses aggregated TIR data at the vine+inter-row level (TSEB-PTairb), while the other is based on a contextual method that directly, although separately, retrieves soil and canopy temperatures (TSEB-2T). The results obtained demonstrated that when comparing airborne Trad and sharpened S2+3 LST, the latter tend to be underestimated. This complicates the use of TSEB-PTS2+3 to detect crop water stress. TSEB-2T appeared to outperform all the other methods. This was shown by a higher R2 and slightly lower RMSD when compared with modelled T. In addition, regressions between T and CWSI-2T with Ψstem also produced the highest R2.info:eu-repo/semantics/publishedVersio

A flexible, smart and self-evolving actuator based on polypropylene mesh for hernia repair and a thermo-sensitive gel

Here, a smart mesh actuator, able to self-evolve under temperature and humidity control,has been developed. Thermo-responsive poly(N-isopropylacrylamide) (PNIPAAm)-based materialsare widely appliedin biomedical field owingto theirexcellent biocompatibility and abrupt conformational change at a critical temperature very close to that of human body(~32 °C) [1-2]. The actuator is based on PNIPAAmgrafted on a commercial polypropylene (PP)mesh used for hernia repair[3].Flexible devices composed of PP-g-PNIPAAm arranged inmonolayer (one layer of PNIPAAm) and bilayer (two layers of PNIPAAm) conformationswere synthesized. The microstructureof the gel chains (chain length measurements) and the macromotion(unfolding angle observations) behavior of the composite mesh in water and air at different temperatures were studied. The motion is affected by the amount and the position of the gel (upper fibers or among them) and by the crosslinking degree. For the first time,a self-evolving motion sensor based on commercial hernia repair mesh has beenproduced by using a biocompatible hydrogel. The strategy can be easily extrapolated to complex mesh architecturesPostprint (published version

Comparison of automatic prostate zones segmentation models in MRI images using U-net-like architectures

Prostate cancer is the second-most frequently diagnosed cancer and the sixth leading cause of cancer death in males worldwide. The main problem that specialists face during the diagnosis of prostate cancer is the localization of Regions of Interest (ROI) containing a tumor tissue. Currently, the segmentation of this ROI in most cases is carried out manually by expert doctors, but the procedure is plagued with low detection rates (of about 27-44%) or overdiagnosis in some patients. Therefore, several research works have tackled the challenge of automatically segmenting and extracting features of the ROI from magnetic resonance images, as this process can greatly facilitate many diagnostic and therapeutic applications. However, the lack of clear prostate boundaries, the heterogeneity inherent to the prostate tissue, and the variety of prostate shapes makes this process very difficult to automate.In this work, six deep learning models were trained and analyzed with a dataset of MRI images obtained from the Centre Hospitalaire de Dijon and Universitat Politecnica de Catalunya. We carried out a comparison of multiple deep learning models (i.e. U-Net, Attention U-Net, Dense-UNet, Attention Dense-UNet, R2U-Net, and Attention R2U-Net) using categorical cross-entropy loss function. The analysis was performed using three metrics commonly used for image segmentation: Dice score, Jaccard index, and mean squared error. The model that give us the best result segmenting all the zones was R2U-Net, which achieved 0.869, 0.782, and 0.00013 for Dice, Jaccard and mean squared error, respectively

FAU-Net: An Attention U-Net Extension with Feature Pyramid Attention for Prostate Cancer Segmentation

This contribution presents a deep learning method for the segmentation of prostate zones in MRI images based on U-Net using additive and feature pyramid attention modules, which can improve the workflow of prostate cancer detection and diagnosis. The proposed model is compared to seven different U-Net-based architectures. The automatic segmentation performance of each model of the central zone (CZ), peripheral zone (PZ), transition zone (TZ) and Tumor were evaluated using Dice Score (DSC), and the Intersection over Union (IoU) metrics. The proposed alternative achieved a mean DSC of 84.15% and IoU of 76.9% in the test set, outperforming most of the studied models in this work except from R2U-Net and attention R2U-Net architectures.Comment: This paper has been accepted at the 22nd Mexican International Conference on Artificial Intelligence (MICAI 2023

Image similarity metrics suitable for infrared video stabilization during active wildfire monitoring : a comparative analysis

Aerial Thermal Infrared (TIR) imagery has demonstrated tremendous potential to monitor active forest fires and acquire detailed information about fire behavior. However, aerial video is usually unstable and requires inter-frame registration before further processing. Measurement of image misalignment is an essential operation for video stabilization. Misalignment can usually be estimated through image similarity, although image similarity metrics are also sensitive to other factors such as changes in the scene and lighting conditions. Therefore, this article presents a thorough analysis of image similarity measurement techniques useful for inter-frame registration in wildfire thermal video. Image similarity metrics most commonly and successfully employed in other fields were surveyed, adapted, benchmarked and compared. We investigated their response to different camera movement components as well as recording frequency and natural variations in fire, background and ambient conditions. The study was conducted in real video from six fire experimental scenarios, ranging from laboratory tests to large-scale controlled burns. Both Global and Local Sensitivity Analyses (GSA and LSA, respectively) were performed using state-of-the-art techniques. Based on the obtained results, two different similarity metrics are proposed to satisfy two different needs. A normalized version of Mutual Information is recommended as cost function during registration, whereas 2D correlation performed the best as quality control metric after registration. These results provide a sound basis for image alignment measurement and open the door to further developments in image registration, motion estimation and video stabilization for aerial monitoring of active wildland fires

Bioinspired Design for Lightweighting and Vibration Behavior Optimization in Large-Scale Aeronautical Tooling: A Comparative Study

A comparative study is presented, focusing on three different bioinspired design methodologies applied to a large-scale aeronautical tooling use case. The study aims to optimize the structure in terms of the first vibration mode, minimizing mass, and supporting operational loads. The development of lightweight metallic components is of great importance for industries such as aerospace, automotive, and energy harvesting, where weight reduction can lead to significant improvements in performance, efficiency, and sustainability. Bioinspired design offers a promising approach to achieving these goals. The study begins with an introduction to natural selection and various bioinspired concepts. It proceeds with a thorough review of the selected bioinspired design methodologies and tools, which are then applied to the chosen use case. The outcomes for each methodology were explored with respect to the design requirements. Subsequently, the most suitable design was selected according to the success criteria defined and its validation is explained. The manufacturing of this design was carried out using an advanced and novel approach specifically tailored to accommodate the large dimensions and complexity of the structure. Finally, modal testing was performed to validate the entire process, and the results obtained demonstrate the potential effectiveness of bioinspired design methodologies in achieving lightweighting and optimizing vibration modes for large-scale aeronautical tooling.The Government of the Basque Country and the Aquitane Euskadi Network in Green Manufacturing and Ecodesign (LTC ÆNIGME) are acknowledged for their support through the project EKOHEGAZ, grant KK-2021/00092. In the same way, the OASIS consortium in the frame of the European Union’s Horizon 2020 research and innovation programme under grant agreement No 814581 is acknowledged for support through the project BioFLY

Thermal infrared video stabilization for aerial monitoring of active wildfires

Measuring wildland fire behavior is essential for fire science and fire management. Aerial thermal infrared (TIR) imaging provides outstanding opportunities to acquire such information remotely. Variables such as fire rate of spread (ROS), fire radiative power (FRP), and fireline intensity may be measured explicitly both in time and space, providing the necessary data to study the response of fire behavior to weather, vegetation, topography, and firefighting efforts. However, raw TIR imagery acquired by unmanned aerial vehicles (UAVs) requires stabilization and georeferencing before any other processing can be performed. Aerial video usually suffers from instabilities produced by sensor movement. This problem is especially acute near an active wildfire due to fire-generated turbulence. Furthermore, the nature of fire TIR video presents some specific challenges that hinder robust interframe registration. Therefore, this article presents a software-based video stabilization algorithm specifically designed for TIR imagery of forest fires. After a comparative analysis of existing image registration algorithms, the KAZE feature-matching method was selected and accompanied by pre- and postprocessing modules. These included foreground histogram equalization and a multireference framework designed to increase the algorithm's robustness in the presence of missing or faulty frames. The performance of the proposed algorithm was validated in a total of nine video sequences acquired during field fire experiments. The proposed algorithm yielded a registration accuracy between 10 and 1000x higher than other tested methods, returned 10x more meaningful feature matches, and proved robust in the presence of faulty video frames. The ability to automatically cancel camera movement for every frame in a video sequence solves a key limitation in data processing pipelines and opens the door to a number of systematic fire behavior experimental analyses. Moreover, a completely automated process supports the development of decision support tools that can operate in real time during an emergency