Herein, a facile approach toward transforming a 2D polypropylene flexible mesh material into a 4D dynamic system is presented. The versatile platform, composed by a substrate of knitted fibers of isotactic polypropylene (iPP) mesh and a coating of thermosensitive poly(N‐isopropylacrylamide‐co‐N,N’‐methylene bis(acrylamide) (PNIPAAm‐co‐MBA) hydrogel, covalently bonded to the mesh surface, after cold‐plasma surface treatment and radical polymerization, is intended to undergo variations in its geometry via its reversible folding/unfolding behavior. The study is the first to trace the 3D movement of a flat surgical mesh, intended to repair hernia defects, under temperature and humidity control. An infrared thermographic camera and an optical microscope are used to evaluate the macroscopic and microscopic structure stimulus response. The presence of the PP substrate and the distribution of the gel surrounding the PP threads, affect both the PNIPAAM gel expansion/contraction as well as the time of folding/unfolding response. Furthermore, PP‐g‐PNIPAAm meshes show an increase in the bursting strength of ≈16% with respect to the uncoated mesh, offering a strongest and adaptable system for its future implantation in human body. The findings reported offer unprecedented application possibilities in the biomedical field.

Fires can be an important hazard for the safety of chemical and process industries. Particularly, pool fires are the most frequent fire scenarios in such facilities and can affect other equipment of the plant with severe consequences due to the domino effect. During the last decades, simplified fire modelling tools were used to predict some of the harmful effects that hydrocarbon pool fires may entail. Although these can be applied to limited number of scenarios, they cannot cover the overall characteristics governing the fire behaviour. Computational Fluid Dynamics (CFD) modelling may provide more detailed insights of the related fire effects, may consider complex geometries and may represent from small to large-scale fires. However, simulation results should be firstly compared to experimental measurements in order to assess the predictive capabilities of these tools. This paper investigates the predictive capabilities of CFD modelling when performing a priori simulations of large-scale hydrocarbon pool fires. The main objective is to assess the fire effects prediction performance of two CFD codes that may be used to evaluate the hazard of hydrocarbon pool fires. FLACS-Fire and FDS codes have been used to simulate large-scale pool fires (1.5, 3, 4, 5 and 6 m-diameter) of diesel and gasoline fuels in unconfined environments. Given the notable differences between the mathematical methods applied to solve the CFD sub-models, the mesh resolution and the boundary conditions in each investigated tool, this study is not aimed at directly comparing both codes (i.e. using identical sub-models choices). However, the present CFD analysis is intended to reveal the potential of each software separately by applying the most appropriate modelling options for each tool. Based on a qualitative assessment of the predictions and a quantitative error estimation of the variables measured (i.e. flame temperature, burning rate, heat flux, flame height, flame surface, and surface emissive power), the main strengths and weaknesses of FLACS-Fire and FDS are identified when modelling hydrocarbon pool fires.

ProstateAnalyzer enables experts to manage prostate cancer patient data set more efficiently. The tool allows delineating annotations by experts and displays all the required information for use in diagnosis. According to the current European Society of Urogenital Radiology guidelines, it also includes the PI-RADS structured reporting scheme.

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Abstract-Web-based applications in computational medicine have become increasingly important during the last years. The rapid growth of the World Wide Web supposes a new paradigm in the telemedicine and eHealth areas in order to assist and enhance the prevention, diagnosis and treatment of patients. Furthermore, training of radiologists and management of medical databases are also becoming increasingly important issues in the field. In this paper, we present MammoApplet, an interactive Java applet interface designed as a web-based tool. It aims to facilitate the diagnosis of new mammographic cases by providing a set of image processing tools that allow a better visualization of the images, and a set of drawing tools, used to annotate the suspicious regions. Each annotation allows including the attributes considered by the experts when issuing the final diagnosis. The overall set of overlays is stored in a database as XML files associated with the original images. The final goal is to obtain a database of already diagnosed cases for training and enhancing the performance of novice radiologists.

Measuring wildland fire behaviour 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 fire line intensity may be measured explicitly both in time and space, providing the necessary data to study the response of fire behaviour 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 inter-frame registration. Therefore, this paper presents a software-based video stabilization algorithm specifically designed for thermal infrared 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 post-processing 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. 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 1000 times 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 behaviour experimental analyses. Moreover, a completely automated process supports the development of decision support tools that can operate in real time during an emergency.

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.

Jet fires are relatively small and have the least severe effects among the diverse fire accidents that can occur in industrial plants; however, they are usually involved in a process known as the domino effect, that leads to more severe events, such as explosions or the initiation of another fire, making the analysis of such fires an important part of risk analysis. This research work explores the application of deep learning models in an alternative approach that uses the semantic segmentation of jet fires flames to extract the flame's main geometrical attributes, relevant for fire risk assessments. A comparison is made between traditional image processing methods and some state-of-the-art deep learning models. It is found that the best approach is a deep learning architecture known as UNet, along with its two improvements, Attention UNet and UNet++. The models are then used to segment a group of vertical jet flames of varying pipe outlet diameters to extract their main geometrical characteristics. Attention UNet obtained the best general performance in the approximation of both height and area of the flames, while also showing a statistically significant difference between it and UNet++. UNet obtained the best overall performance for the approximation of the lift-off distances; however, there is not enough data to prove a statistically significant difference between Attention UNet and UNet++. The only instance where UNet++ outperformed the other models, was while obtaining the lift-off distances of the jet flames with 0.01275 m pipe outlet diameter. In general, the explored models show good agreement between the experimental and predicted values for relatively large turbulent propane jet flames, released in sonic and subsonic regimes; thus, making these radiation zones segmentation models, a suitable approach for different jet flame risk management scenarios.