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The ship's resistance and manoeuvrability in shallow waters can be adversely influenced by the presence of fluid mud layers on the seabed of ports and waterways. Fluid mud exhibits a complex non-Newtonian rheology that is often described using the Herschel–Bulkley model. The latter has been recently implemented in a maritime finite-volume CFD code to study the manoeuvrability of ships in the presence of muddy seabeds. In this paper, we explore the accuracy and robustness of the CFD code in simulating the flow of Herschel–Bulkley fluids, including power-law, Bingham and Newtonian fluids as particular cases. As a stepping stone towards the final maritime applications, the study is carried out on a classic benchmark problem in non-Newtonian fluid mechanics: the laminar flow around a sphere. The aim is to test the performance of the non-Newtonian solver before applying it to the more complex scenarios. Present results could also be used as reference data for future testing. Flow simulations are carried out at low Reynolds numbers in order to compare our results with an extensive collection of data from the literature. Results agree both qualitatively and quantitatively with literature. Difficulties in the convergence of the iterative solver emerged when simulating Bingham and Herschel–Bulkley flows. A simple change in the interpolation of the apparent viscosity has mitigated such difficulties. The results of this work, combined with our previous code verification exercises, suggest that the non-Newtonian solver works as intended and it can be thus employed on more complex applications. [ABSTRACT FROM AUTHOR]

Development of vascular collaterals in a lesion area is one of the key factors that determine not only the choice of treatment for ischemic stroke (IS) patients, but also outcome and therapy effectiveness. The main method for examining the vessels’ ramification is CT angiography (CTA). CTA analysis may be improved by incorporating filters designed to extract more features about vessels and quantify their level of development. This work suggests the usage of radiomics methods in the analysis of vesselness measure calculated from CTA images. Vesselness measurement is based on the analysis of the Hessian matrix with a few modifications dictated by practical aspects of this issue. The developed algorithm was implemented as a filter that generates a new 3D image, every voxel of which has the probability of belonging to a vessel-like structure. Further analysis of the distribution of vesselness in the lesion area and in the intact contralateral area was conducted with the methods from the open library PyRadiomics. A set of radiomics features was calculated. Preliminary analysis on a sample of 30 IS patients showed the presence of significant differences between afflicted and intact hemispheres. [ABSTRACT FROM AUTHOR]

The main cause of non-traumatic subarachnoid haemorrhage is an intracranial aneurysm's rupture. The choice of treatment approach is exceptionally difficult in cases of aneurysms with additional branches on the aneurysm's dome or neck. The impact of the arterial branches on local hemodynamics is still unclear and controversial question. At the same time, up-to-date methods of image processing and mathematical modeling provide a way to investigate the hemodynamic environment of aneurysms. The paper discusses hemodynamic aspects of aneurysms harboring arterial branch through the use of patient-specific 3D models and computational fluid dynamics (CFD) methods. The analysis showed that the presence of the arterial branches has a great influence on flow streamlines and wall shear stress, particularly for side wall aneurysm. [ABSTRACT FROM AUTHOR]

The crucial issue in many classification applications is how to achieve the best possible classifier with a limited number of labeled data for training. Training data selection is one method which addresses this issue by selecting the most informative data for training. In this work, we propose three data selection mechanisms based on fuzzy clustering method: center-based selection, border-based selection and hybrid selection. Center-based selection selects the samples with high degree of membership in each cluster as training data. Border-based selection selects the samples around the border between clusters. Hybrid selection is the combination of center-based selection and border-based selection. Compared with existing work, our methods do not require much computational effort. Moreover, they are independent with respect to the supervised learning algorithms and initial labeled data. We use fuzzy c-means to implement our data selection mechanisms. The effects of them are empirically studied on a set of UCI data sets. Experimental results indicate that, compared with random selection, hybrid selection can effectively enhance the learning performance in all the data sets, center-based selection shows better performance in certain data sets, border-based selection does not show significant improvement. [ABSTRACT FROM AUTHOR]