A micromechanics-based recurrent neural networks model for path-dependent cyclic deformation of short fiber composites

The macroscopic response of short fiber reinforced composites (SFRCs) is dependent on an extensive range of microstructural parameters. Thus, micromechanical modeling of these materials is challenging and in some cases, computationally expensive. This is particularly important when path-dependent plastic behavior is needed to be predicted. A solution to this challenge is to enhance micromechanical solutions with machine learning techniques such as artificial neural networks. In this work, a recurrent deep neural network model is trained to predict the path-dependent elasto-plastic stress response of SFRCs, given the microstructural parameters and the strain path. Micromechanical mean-field simulations are conducted to create a database for training the validating the model. The model gives very accurate predictions in a computationally efficient manner when compared with independent micromechanical simulations

UOLO - automatic object detection and segmentation in biomedical images

We propose UOLO, a novel framework for the simultaneous detection and segmentation of structures of interest in medical images. UOLO consists of an object segmentation module which intermediate abstract representations are processed and used as input for object detection. The resulting system is optimized simultaneously for detecting a class of objects and segmenting an optionally different class of structures. UOLO is trained on a set of bounding boxes enclosing the objects to detect, as well as pixel-wise segmentation information, when available. A new loss function is devised, taking into account whether a reference segmentation is accessible for each training image, in order to suitably backpropagate the error. We validate UOLO on the task of simultaneous optic disc (OD) detection, fovea detection, and OD segmentation from retinal images, achieving state-of-the-art performance on public datasets.Comment: Publised on DLMIA 2018. Licensed under the Creative Commons CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0

Durability and wear resistance of laser-textured hardened stainless steel surfaces with hydrophobic properties

Hydrophobic surfaces are of high interest to industry. While surface functionalization has attracted significant interest, from both industry and research, the durability of engineered surfaces remains a challenge, as wear and scratches deteriorate their functional response. In this work, a cost-effective combination of surface engineering processes on stainless steel was investigated. Low-temperature plasma surface alloying was applied to increase surface hardness from 172 to 305 HV. Then, near-infrared nanosecond laser patterning was deployed to fabricate channel-like patterns that enabled superhydrophobicity. Abrasion tests were carried out to examine the durability of such engineered surfaces during daily use. In particular, the evolution of surface topographies, chemical composition, and water contact angle with increasing abrasion cycles were studied. Hydrophobicity deteriorated progressively on both hardened and raw stainless steel samples, suggesting that the major contributing factor to hydrophobicity was the surface chemical composition. At the same time, samples with increased surface hardness exhibited a slower deterioration of their topographies when compared with nontreated surfaces. A conclusion is made about the durability of laser-textured hardened stainless steel surfaces produced by applying the proposed combined surface engineering approach

Infrastructure for Retinal Image Analysis

This paper introduces a retinal image analysis infrastructure for the automatic assessment of biomarkers related to early signs of diabetes, hypertension and other systemic diseases. The developed application provides several tools, namely normalization, vessel enhancement and segmentation, optic disc and fovea detection, junction detection, bifurcation/crossing discrimination, artery/vein classification and red lesion detection. The pipeline of these methods allows the assessment of important biomarkers characterizing dynamic properties of retinal vessels, such as tortuosity, width, fractal dimension and bifurcation geometry features

Network-Based Approach for Modeling and Analyzing Coronary Angiography

Significant intra-observer and inter-observer variability in the interpretation of coronary angiograms are reported. This variability is in part due to the common practices that rely on performing visual inspections by specialists (e.g., the thickness of coronaries). Quantitative Coronary Angiography (QCA) approaches are emerging to minimize observer's error and furthermore perform predictions and analysis on angiography images. However, QCA approaches suffer from the same problem as they mainly rely on performing visual inspections by utilizing image processing techniques. In this work, we propose an approach to model and analyze the entire cardiovascular tree as a complex network derived from coronary angiography images. This approach enables to analyze the graph structure of coronary arteries. We conduct the assessments of network integration, degree distribution, and controllability on a healthy and a diseased coronary angiogram. Through our discussion and assessments, we propose modeling the cardiovascular system as a complex network is an essential phase to fully automate the interpretation of coronary angiographic images. We show how network science can provide a new perspective to look at coronary angiograms

Effects of mould wear on hydrophobic polymer surfaces replicated using plasma treated and laser-textured stainless steel inserts

YesThe mass production of polymeric parts with functional surfaces requires economically viable manufacturing routes. Injection moulding is a very attractive option however wear and surface damage can be detrimental to the lifespan of replication masters. In this research, the replication of superhydrophobic surfaces is investigated by employing a process chain that integrates surface hardening, laser texturing and injection moulding. Austenitic stainless steel inserts were hardened by low temperature plasma carburising and three different micro and nano scale surface textures were laser fabricated, i.e. submicron triangular LaserInduced Periodic Surface Structures (LIPSS), micro grooves and Lotus-leaf like topographies. Then, a commonly available talc-loaded polypropylene was used to produce 5000 replicas to investigate the evolution of surface textures on both inserts and replicas together with their functional response. Any wear orsurface damage progressively built up on the inserts during the injection moulding process had a clear impact on surface roughness and peak-to-peak topographies of the replicas. In general, the polymer replicas produced with the carburised inserts retained the wetting properties of their textured surfaces for longer periods compared with those produced with untreated replication masters.European Union’s H2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 675063 (www.laser4fun.eu). The work was also supported by three other H2020 projects, i.e. “HighImpact Injection Moulding Platform for mass-production of 3D and/or large micro-structured surfaces with Antimicrobial, Self-cleaning, Anti-scratch, Anti-squeak and Aesthetic functionalities” (HIMALAIA, No. 766871), “Process Fingerprint for Zero-defect Net-shape Micromanufacturing” (MICROMAN, No. 674801) and “Modular laser based additive manufacturing platform for large scale industrial applications” (MAESTRO, No. 723826). Further support was provided by the UKIERI DST programme “Surface functionalisation for 18/20 Accepted in the journal Tribology – Materials, Surfaces & Interfaces. food, packaging, and healthcare applications

Discerning natural and anthropogenic organic matter inputs to salt marsh sediments of Ria Formosa lagoon (South Portugal)

Sedimentary organic matter (OM) origin and molecular composition provide useful information to understand carbon cycling in coastal wetlands. Core sediments from threors' Contributionse transects along Ria Formosa lagoon intertidal zone were analysed using analytical pyrolysis (Py-GC/MS) to determine composition, distribution and origin of sedimentary OM. The distribution of alkyl compounds (alkanes, alkanoic acids and alkan-2-ones), polycyclic aromatic hydrocarbons (PAHs), lignin-derived methoxyphenols, linear alkylbenzenes (LABs), steranes and hopanes indicated OM inputs to the intertidal environment from natural-autochthonous and allochthonous-as well as anthropogenic. Several n-alkane geochemical indices used to assess the distribution of main OM sources (terrestrial and marine) in the sediments indicate that algal and aquatic macrophyte derived OM inputs dominated over terrigenous plant sources. The lignin-derived methoxyphenol assemblage, dominated by vinylguaiacol and vinylsyringol derivatives in all sediments, points to large OM contribution from higher plants. The spatial distributions of PAHs (polyaromatic hydrocarbons) showed that most pollution sources were mixed sources including both pyrogenic and petrogenic. Low carbon preference indexes (CPI > 1) for n-alkanes, the presence of UCM (unresolved complex mixture) and the distribution of hopanes (C-29-C-36) and steranes (C-27-C-29) suggested localized petroleum-derived hydrocarbon inputs to the core sediments. Series of LABs were found in most sediment samples also pointing to domestic sewage anthropogenic contributions to the sediment OM.EU Erasmus Mundus Joint Doctorate fellowship (FUECA, University of Cadiz, Spain)EUEuropean Commission [FP7-ENV-2011, 282845, FP7-534 ENV-2012, 308392]MINECO project INTERCARBON [CGL2016-78937-R]info:eu-repo/semantics/publishedVersio

Optic disc segmentation using the sliding band filter,

Background: The optic disc (OD) centre and boundary are important landmarks in retinal images and are essential for automating the calculation of health biomarkers related with some prevalent systemic disorders, such as diabetes, hypertension, cerebrovascular and cardiovascular diseases. Methods: This paper presents an automatic approach for OD segmentation using a multiresolution sliding band filter (SBF). After the preprocessing phase, a low-resolution SBF is applied on a downsampled retinal image and the locations of maximal filter response are used for focusing the analysis on a reduced region of interest (ROI). A high-resolution SBF is applied to obtain a set of pixels associated with the maximum response of the SBF, giving a coarse estimation of the OD boundary, which is regularized using a smoothing algorithm. Results: Our results are compared with manually extracted boundaries from public databases (ONHSD, MESSIDOR and INSPIRE-AVR datasets) outperforming recent approaches for OD segmentation. For the ONHSD, 44% of the results are classified as Excellent, while the remaining images are distributed between the Good (47%) and Fair (9%) categories. An average overlapping area of 83%, 89% and 85% is achieved for the images in ONHSD, MESSIDOR and INSPIR-AVR datasets, respectively, when comparing with the manually delineated OD regions. Discussion: The evaluation results on the images of three datasets demonstrate the better performance of the proposed method compared to recently published OD segmentation approaches and prove the independence of this method when from changes in image characteristics such as size, quality and camera field of view