1,721,192 research outputs found
Mode I translaminar fracture toughness of high performance laminated biocomposites reinforced by sisal fibers: Accurate measurement approach and lay-up effects
Full text linkThe present work performs a systematic experimental analysis of the translaminar fracture behavior of high performance biocomposites constituted by green epoxy reinforced by sisal fibers, by varying the main influence parameters as fiber concentration and lay-up. Despite the corrective function properly introduced to take into account the anisotropy as well as the use of the equivalent crack length, the study shows that the LEFM does not give accurate estimations of the fracture toughness, because the extension of the near tip damaged zone is higher than the singular dominated one. Accurate estimations can be obtained instead by the proposed modified area method that takes into account both the local damage and the fiber bridging that occurs during crack propagation, that lead to R-curves whose asymptotic values constitute the true fracture toughness of the biocomposites examined. The constancy of the damage mechanisms observed by varying the fiber concentration, allows the user to compute the fracture toughness of a generic laminate from the specific fracture energy of the unidirectional lamina. Finally, the relatively high fracture toughness of the examined laminates allows to state that they can advantageously replace not only other composites having lower toughness, but also metals as steel, aluminum and titanium
Applications of imaging processing to MRgFUS treatment for fibroids: A review
No full textMagnetic resonance guided focused ultrasound (MRgFUS) is an innovative technology that can treat many oncological diseases. Among these, uterine fibroids are well suited to be treated by focused ultrasound, because the treatment, unlike traditional surgical resection, is non-invasive and thus preserves the desired reproductive capacity of patients. There are some methodological issues in MRgFUS treatment that should be addressed. First, there is operator dependence; this is related to the use of manual approaches for the segmentation of regions of interest (ROI) for treatment, both in the initial stages of treatment planning and in the post-treatment evaluation of the ablated area. From this scenario, we understand the need to integrate MRgFUS technology with methods for the automatic detection of the regions affected by the treatment. Temperature monitoring techniques, based on proton resonance shift (PRF), although not always able to provide correct measurements, are the most used in the treatments guided by magnetic resonance. The dependence on a reference image makes the thermal maps obtained through PRF subject to artefacts and, consequently, to temperature measurement errors. It is therefore crucial to develop new techniques, perhaps based on referenceless thermometry approaches, in order to avoid overheating in unwanted areas that could lead to patients suffering burns. Closely linked to the two above mentioned problems, there is the motion compensation that would improve the current method (this requires re-planning MRgFUS treatment in cases of patient movement) as well as temperature monitoring since it would overcome limitations related to a fixed baseline image, uncorrelated to the real patient position. This paper addresses these important issues, providing for each of them a high-level discussion able to give a brief overview. Specific technical details are provided about one of the approaches in the literature, in order to make the discussed problem more complete and easier to understand
RESISTENZA ALLA FRATTURA TRANSLAMINARE DI BIOCOMPOSITI RINFORZATI CON FIBRE DI AGAVE
No full textData la crescente attenzione nei confronti dell’ambiente, le sempre più restrittive norme in materia di
salvaguardia ambientale e di riciclo dei materiali hanno portato ad un notevole interesse dei ricercatori
verso i biocompositi, materiali costituiti da rinforzi di origine naturale e matrici a basso impatto
ambientale. Nonostante molteplici studi siano stati indirizzati a tali materiali innovativi, allo stato
attuale poche ricerche hanno riguardato l’analisi della tenacità alla frattura di laminati biocompositi. Il
presente lavoro propone pertanto uno studio sperimentale del comportamento alla frattura
translaminare in modo I di laminati biocompositi in fibre di agave e matrice epossidica green,
valutando in particolare l’effetto della variazione percentuale volumetrica del rinforzo e della sequenza
di impacchettamento (unidirezionali, cross-ply, angle-ply ecc.). Le prove di resistenza alla frattura a
trazione in modo I sono state eseguite su campioni con configurazione Compact Tension (CT) al fine
di determinare sia il fattore critico di intensificazione delle tensioni, sia la Critical Strain Energy
Release Rate (CSERR). In particolare, i diversi provini CT sono stati ottenuti attraverso laminazione
manuale e successivo processo di compression-moulding, a partire da tessuti unidirezionali di tipo
stitched appositamente prodotti in laboratorio. Al fine di individuare il metodo ottimale per una
accurata valutazione sperimentale della CSERR, sono stati confrontati i risultati ottenuti con diversi
metodi tra cui l’Area Method ed il Compliance CalibrationThe recent attention toward the environmental protection, as well as to the restrictive regulations in term of material recycling, have led to a noticeable interest of the scientific research for biocomposites, i.e. materials constituted by natural fibers and eco-friendly matrix. Although various research works have been focused on such innovative materials, only a few articles have been devoted to the their fracture strenght (thoughness).
The present work regards the experimental study of the translaminar fracture behavior (in mode I) of biocomposite laminates constituted by green epoxy matrix reinforced by optimized agave sisalana fibers, varying the main influence parameters as the fiber concentration and the lay-up (unidirectional, cross-ply, angle-ply etc.).
The fracture tests in mode I, have been performed by using Compact Tension (CT) specimens, in order to determine both the critical Stress Intensity Factor (SIFc) and the so called Critical Strain Energy Release Rate (CSERR).
In more detail, several CT specimens have been manufactured by hand lay-up and successive compression-moulding process whose parameters have been optimized in previous study of the same authors. To this aim proper stitched fabrics have been previously manufactured in laboratory. In order to detect the optimal method for an accurate experimental analysis of the CSERR, the results provided by the Area Method and the Compliance Calibration, have been compared
VALUTAZIONE DEL COMPORTAMENTO ALL’IMPATTO DI BIOCOMPOSITI RINFORZATI CON FIBRE DI AGAVE
No full textThe growing attention on environmental issues has led to a recent research interest in eco-sustainable and renewable materials, among which biocomposites play an important role. Biocomposites are materials consisting of a matrix with low environmental impact or renewable, reinforced with natural fibres. Several research activities reported in literature, deal mainly with the static mechanical properties. Just few works are instead devoted to the assessment of their dynamic properties, such as fatigue strength, impact strength, etc. In order to give a contribution to the knowledge of the impact behaviour of green epoxy matrix biocomposites reinforced with agave fibres, in this paper a systematic study is carried out to evaluate the impact strength of different laminates (single layer, cross-ply, quasi-isotropic), by low velocity impact test. The various selected laminates, allowed to assess the effects of the main influence parameters as fibre distribution (unidirectional and random), fibre concentration and lay-up
Image biomarkers and explainable AI: handcrafted features versus deep learned features
No full textAbstract Feature extraction and selection from medical data are the basis of radiomics and image biomarker discovery for various architectures, including convolutional neural networks (CNNs). We herein describe the typical radiomics steps and the components of a CNN for both deep feature extraction and end-to-end approaches. We discuss the curse of dimensionality, along with dimensionality reduction techniques. Despite the outstanding performance of deep learning (DL) approaches, the use of handcrafted features instead of deep learned features needs to be considered for each specific study. Dataset size is a key factor: large-scale datasets with low sample diversity could lead to overfitting; limited sample sizes can provide unstable models. The dataset must be representative of all the “facets” of the clinical phenomenon/disease investigated. The access to high-performance computational resources from graphics processing units is another key factor, especially for the training phase of deep architectures. The advantages of multi-institutional federated/collaborative learning are described. When large language models are used, high stability is needed to avoid catastrophic forgetting in complex domain-specific tasks. We highlight that non-DL approaches provide model explainability superior to that provided by DL approaches. To implement explainability, the need for explainable AI arises, also through post hoc mechanisms. Relevance statement This work aims to provide the key concepts for processing the imaging features to extract reliable and robust image biomarkers. Key Points The key concepts for processing the imaging features to extract reliable and robust image biomarkers are provided. The main differences between radiomics and representation learning approaches are highlighted. The advantages and disadvantages of handcrafted versus learned features are given without losing sight of the clinical purpose of artificial intelligence models. Graphical Abstrac
A Novel Bio-Inspired Approach for High-Performance Management in Service-Oriented Networks
No full textService-continuity in distributed computing can be enhanced by designing self-organized systems, with a non-fixed structure, able to modify their structure and organization, as well as adaptively react to internal and external environment changes. In this paper, an architecture exploiting a bio-inspired management approach, i.e., the functioning of cell metabolism, for specialized computing environments in Service-Oriented Networks (SONs) is proposed. Similar to the processes acting in metabolic networks, the nodes communicate to each other by means of stimulation or suppression chains giving rise to emergent behaviors to defend against foreign invaders, attacks, and malfunctioning. The main contribution of this work is a novel bio-inspired methodology for SON analysis to improve the network reliability and robustness for maintaining service-continuity. To show the effectiveness of the proposed computational framework, an embedded Field-Programmable Gate Array (FPGA) prototyped SON for a relevant healthcare imaging application is also outlined. In particular, our case study extracts and analyzes the Cerebral Vascular Tree from Magnetic Resonance Angiography series via a Maximum Intensity Projection algorithm; the proposed solution addresses and implements some basic issues of an interesting diagnosis tool for cerebral aneurysm detection. The prototyped system was tested and evaluated in terms of execution time and used resource analysis, by achieving a 4× speed-up factor compared to the software counterpart
Fingerprint classification based on deep learning approaches: Experimental findings and comparisons
Full text linkBiometric classification plays a key role in fingerprint characterization, especially in the identification process. In fact, reducing the number of comparisons in biometric recognition systems is essential when dealing with large-scale databases. The classification of fingerprints aims to achieve this target by splitting fingerprints into different categories. The general approach of fingerprint classification requires pre-processing techniques that are usually computationally expensive. Deep Learning is emerging as the leading field that has been successfully applied to many areas, such as image processing. This work shows the performance of pre-trained Convolutional Neural Networks (CNNs), tested on two fingerprint databases—namely, PolyU and NIST—and comparisons to other results presented in the literature in order to establish the type of classification that allows us to obtain the best performance in terms of precision and model efficiency, among approaches under examination, namely: AlexNet, GoogLeNet, and ResNet. We present the first study that extensively compares the most used CNN architectures by classifying the fingerprints into four, five, and eight classes. From the experimental results, the best performance was obtained in the classification of the PolyU database by all the tested CNN architectures due to the higher quality of its samples. To confirm the reliability of our study and the results obtained, a statistical analysis based on the McNemar test was performed
Advanced computational methods for oncological image analysis
Full text link: The Special Issue "Advanced Computational Methods for Oncological Image Analysis", published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...]
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