Multiple-relaxation-time Finsler-Lagrange dynamics in a compressed Langmuir monolayer

In this paper an information geometric approach has been proposed to describe the two-dimensional (2d) phase transition of the first order in a monomolecular layer (monolayer) of amphiphilic molecules deposited on air/water interface. The structurization of the monolayer was simulated as an entropy evolution of a statistical set of microscopic states with a large number of relaxation times. The electrocapillary forces are considered as information constraints on the statistical manifold. The solution curves of Euler-Lagrange equations and the Jacobi field equations point out contracting pencils of geodesic trajectories on the statistical manifold, which may change into spreading ones, and converse. It was shown that the information geometrodynamics of the first-order phase transition in the Langmuir monolayer finds an appropriate realization within the Finsler-Lagrange framework

A novel application of deep learning with image cropping: a smart city use case for flood monitoring

© 2020, The Author(s). Event monitoring is an essential application of Smart City platforms. Real-time monitoring of gully and drainage blockage is an important part of flood monitoring applications. Building viable IoT sensors for detecting blockage is a complex task due to the limitations of deploying such sensors in situ. Image classification with deep learning is a potential alternative solution. However, there are no image datasets of gullies and drainages. We were faced with such challenges as part of developing a flood monitoring application in a European Union-funded project. To address these issues, we propose a novel image classification approach based on deep learning with an IoT-enabled camera to monitor gullies and drainages. This approach utilises deep learning to develop an effective image classification model to classify blockage images into different class labels based on the severity. In order to handle the complexity of video-based images, and subsequent poor classification accuracy of the model, we have carried out experiments with the removal of image edges by applying image cropping. The process of cropping in our proposed experimentation is aimed to concentrate only on the regions of interest within images, hence leaving out some proportion of image edges. An image dataset from crowd-sourced publicly accessible images has been curated to train and test the proposed model. For validation, model accuracies were compared considering model with and without image cropping. The cropping-based image classification showed improvement in the classification accuracy. This paper outlines the lessons from our experimentation that have a wider impact on many similar use cases involving IoT-based cameras as part of smart city event monitoring platforms

FeCuNbSiB Thin Films Deposited by Pulsed Laser Deposition: Structural and Magnetic Properties

Using the pulsed laser ablation technique, Fe73.5Cu1Nb3Si15.5B7 amorphous thin films, with smooth and uniform surfaces, have been deposited on glass and silicon substrates. Based on the information provided by the thermomagnetic analysis, the nanocrystalline state was achieved after the thermal treatment per-formed at 460 C. In nanocrystalline state, the samples present an 80 % lower coercive magnetic field and a 3.5 times higher saturation magnetization with respect to the as-deposited state. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3515

Kinetic Monte Carlo and Cellular Particle Dynamics Simulations of Multicellular Systems

Computer modeling of multicellular systems has been a valuable tool for interpreting and guiding in vitro experiments relevant to embryonic morphogenesis, tumor growth, angiogenesis and, lately, structure formation following the printing of cell aggregates as bioink particles. Computer simulations based on Metropolis Monte Carlo (MMC) algorithms were successful in explaining and predicting the resulting stationary structures (corresponding to the lowest adhesion energy state). Here we present two alternatives to the MMC approach for modeling cellular motion and self-assembly: (1) a kinetic Monte Carlo (KMC), and (2) a cellular particle dynamics (CPD) method. Unlike MMC, both KMC and CPD methods are capable of simulating the dynamics of the cellular system in real time. In the KMC approach a transition rate is associated with possible rearrangements of the cellular system, and the corresponding time evolution is expressed in terms of these rates. In the CPD approach cells are modeled as interacting cellular particles (CPs) and the time evolution of the multicellular system is determined by integrating the equations of motion of all CPs. The KMC and CPD methods are tested and compared by simulating two experimentally well known phenomena: (1) cell-sorting within an aggregate formed by two types of cells with different adhesivities, and (2) fusion of two spherical aggregates of living cells.Comment: 11 pages, 7 figures; submitted to Phys Rev