FINITE ELEMENT SIMULATION OF LCF BEHAVIOUR OF SA 333 C-MN STEEL

Finite Element simulation to characterize the LCF behavior of Sa 333 C-Mn Steel is studied in this paper. Experiment and Finite Element simulation are done together. LCF parameters of the material are calibrated and tuned from the experimental results. Non linear version of Ziegler kinematic hardening material model is used to address the stable hysteresis cycles of the material. Cyclic hardening phenomenon is addressed by introducing cyclic hardening in the material model. The elastic plastic FE code ABAQUS is used for finite element simulation of LCF behavior. The plastic modulus formulation with zeigler kinematic hardening rule and exponential isotropic hardening rule has been used for simulation. Using the incremental plasticity theories the cyclic plastic stress-strain responses were analyzed and the results obtained from FE simulations have been compared with the experimental results at different strain amplitudes. Variation of cyclic yield stress with strain amplitudes has also been studied in this paper

Peristaltic Transport of a Physiological Fluid in an Asymmetric Porous Channel in the Presence of an External Magnetic Field

The paper deals with a theoretical investigation of the peristaltic transport of a physiological fluid in a porous asymmetric channel under the action of a magnetic field. The stream function, pressure gradient and axial velocity are studied by using appropriate analytical and numerical techniques. Effects of different physical parameters such as permeability, phase difference, wave amplitude and magnetic parameter on the velocity, pumping characteristics, streamline pattern and trapping are investigated with particular emphasis. The computational results are presented in graphical form. The results are found to be in perfect agreement with those of a previous study carried out for a non-porous channel in the absence of a magnetic field

A skeletonization algorithm for gradient-based optimization

The skeleton of a digital image is a compact representation of its topology, geometry, and scale. It has utility in many computer vision applications, such as image description, segmentation, and registration. However, skeletonization has only seen limited use in contemporary deep learning solutions. Most existing skeletonization algorithms are not differentiable, making it impossible to integrate them with gradient-based optimization. Compatible algorithms based on morphological operations and neural networks have been proposed, but their results often deviate from the geometry and topology of the true medial axis. This work introduces the first three-dimensional skeletonization algorithm that is both compatible with gradient-based optimization and preserves an object's topology. Our method is exclusively based on matrix additions and multiplications, convolutional operations, basic non-linear functions, and sampling from a uniform probability distribution, allowing it to be easily implemented in any major deep learning library. In benchmarking experiments, we prove the advantages of our skeletonization algorithm compared to non-differentiable, morphological, and neural-network-based baselines. Finally, we demonstrate the utility of our algorithm by integrating it with two medical image processing applications that use gradient-based optimization: deep-learning-based blood vessel segmentation, and multimodal registration of the mandible in computed tomography and magnetic resonance images.Comment: Accepted at ICCV 202

Navigating Copper-Atom-Pair Structural Effect inside a Porous Organic Polymer Cavity for Selective Hydrogenation of Biomass-Derived 5-Hydroxymethylfurfural

In recent times, selective hydrogenation of biomass-derived 5-hydroxymethylfurfural (5-HMF) to produce the novel difuranic polyol scaffold 2,5-dihydroxymethylfuran (DHMF) has attracted the interest of the many researchers due to its peculiar symmetrical structure and its widespread application as a monomer for the preparation of cross-linked polyesters and polyurethane. Copper-based catalysts have been explored for selective catalytic hydrogenation; however, hurdles are still associated with the strongly reducing H2 atmosphere and oxidizing C–O bond that make the Cu0 and Cux+ surface active species unstable, limiting the rational design of highly efficient integrated catalyst systems. To address this, herein, we built catalytic systems for 5-HMF hydrogenation with stable and balanced Cu0 and Cux+ active surface species inside the nanocage of a catechol-based porous organic polymer (POP) endowed with large surface areas, impressive stabilities, and spatial restriction inhibiting nanoparticle aggregation. Batch reactor screening identified that a superior catalytic performance (DHMF selectivity of 98%) has been achieved with our newly designed Cu@C-POP at 150 °C temperature and 20 bar H2 pressure, which was also higher than that of other reported copper catalysts. Comprehensive characterization understanding with H2-TPR and X-ray photoelectron spectroscopy (XPS) study revealed that substantially boosted activity is induced by the presence of the bulk CuOx phase and atomically dispersed Cu species incorporating isolated Cu ions, which are further confirmed through the positive binding energy shift of Cu 2p3/2 XPS spectra (∼0.4 eV). The Cu environment in our catalytic systems comprises a predominantly square planar geometry (probably Jahn–Teller distorted OH), which we gleaned from the extended X-ray absorption for fine structure (EXAFS) analysis featuring two adjacent copper atoms with the valence state in between of 0 and +2, as validated by XANES absorption edge positions. EXAFS studies further revealed a lowering of the Cu coordination number for the most active Cu@C-POP-B catalyst, suggesting the presence of metal vacancies. Density functional theory calculations showed that the presence of Cu metal vacancies stabilized the reaction intermediates formed during 5-HMF hydrogenation and decreased the hydrogenation barriers, resulting in an enhanced catalytic activity of the Cu@C-POP-B catalyst

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor

Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling

Current treatment planning of patients diagnosed with a brain tumor, such as glioma, could significantly benefit by accessing the spatial distribution of tumor cell concentration. Existing diagnostic modalities, e.g. magnetic resonance imaging (MRI), contrast sufficiently well areas of high cell density. In gliomas, however, they do not portray areas of low cell concentration, which can often serve as a source for the secondary appearance of the tumor after treatment. To estimate tumor cell densities beyond the visible boundaries of the lesion, numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. Alongside, various parametric inference schemes were developed to perform an efficient tumor model personalization, i.e. solving the inverse problem. However, the unifying drawback of all existing approaches is the time complexity of the model personalization which prohibits a potential integration of the modeling into clinical settings. In this work, we introduce a deep learning based methodology for inferring the patient-specific spatial distribution of brain tumors from T1Gd and FLAIR MRI medical scans. Coined as Learn-Morph-Infer the method achieves real-time performance in the order of minutes on widely available hardware and the compute time is stable across tumor models of different complexity, such as reaction-diffusion and reaction-advection-diffusion models. We believe the proposed inverse solution approach not only bridges the way for clinical translation of brain tumor personalization but can also be adopted to other scientific and engineering domains

Electro-osmotic flow of couple stress fluids in a microchannel propagated by peristalsis

A mathematical model is developed for electro-osmotic peristaltic pumping of a non-Newtonian liquid in a deformable micro-channel. Stokes’ couple stress fluid model is deployed to represent realistic working liquids. The Poisson-Boltzmann equation for electric potential distribution is implemented owing to the presence of an electrical double layer (EDL) in the micro-channel. Using long wavelength, lubrication theory and Debye-Huckel approximations, the linearized transformed dimensionless boundary value problem is solved analytically. The influence of electro-osmotic parameter (inversely proportional to Debye length), maximum electro-osmotic velocity (a function of external applied electrical field) and couple stress parameter on axial velocity, volumetric flow rate, pressure gradient, local wall shear stress and stream function distributions is evaluated in detail with the aid of graphs. The Newtonian fluid case is retrieved as a special case with vanishing couple stress effects. With increasing couple stress parameter there is a significant elevation in axial pressure gradient whereas the core axial velocity is reduced. An increase in electro-osmotic parameter induces both flow acceleration in the core region (around the channel centreline) and also enhances axial pressure gradient substantially. The study is relevant to simulation of novel smart bio-inspired space pumps, chromatography and medical microscale devices

Electroosmosis modulated peristaltic biorheological flow through an asymmetric microchannel : mathematical model

A theoretical study is presented of peristaltic hydrodynamics of an aqueous electrolytic nonNewtonian Jeffrey bio-rheological fluid through an asymmetric microchannel under an applied axial electric field. An analytical approach is adopted to obtain the closed form solution for velocity, volumetric flow, pressure difference and stream function. The analysis is also restricted under the low Reynolds number assumption and lubrication theory approximations. Debye-Hückel linearization (i.e. wall zeta potential ≤ 25mV) is also considered. Streamline plots are also presented for the different electro-osmotic parameter, varying magnitudes of the electric field (both aiding and opposing cases) and for different values of the ratio of relaxation to retardation time parameter. Comparisons are also included between the Newtonian and general non-Newtonian Jeffrey fluid cases. The results presented here may be of fundamental interest towards designing lab-on-a-chip devices for flow mixing, cell manipulation, micro-scale pumps etc. Trapping is shown to be more sensitive to an electric field (aiding, opposing and neutral) rather than the electro-osmotic parameter and viscoelastic relaxation to retardation ratio parameter. The results may also help towards the design of organ-on-a-chip like devices for better drug design

Molecular Characterization of Branchial aquaporin 1aa and Effects of Seawater Acclimation, Emersion or Ammonia Exposure on Its mRNA Expression in the Gills, Gut, Kidney and Skin of the Freshwater Climbing Perch, Anabas testudineus

10.1371/journal.pone.0061163PLoS ONE84