Modelling mechanical percolation in graphene-reinforced elastomer nanocomposites

Graphene is considered an ideal filler for the production of multifunctional nanocomposites; as a result, considerable efforts have been focused on the evaluation and modeling of its reinforcement characteristics. In this work, we modelled successfully the mechanical percolation phenomenon, observed on a thermoplastic elastomer (TPE) reinforced by graphene nanoplatelets (GNPs), by designing a new set of equations for filler contents below and above the percolation threshold volume fraction (Vp). The proposed micromechanical model is based on a combination of the well-established shear-lag theory and the rule-of-mixtures and was introduced to analyse the different stages and mechanisms of mechanical reinforcement. It was found that when the GNPs content is below Vp, reinforcement originates from the inherent ability of individual GNPs flakes to transfer stress efficiently. Furthermore, at higher filler contents and above Vp, the nanocomposite materials displayed accelerated stiffening due to the reduction of the distance between adjacent flakes. The model derived herein, was consistent with the experimental data and the reasons why the superlative properties of graphene cannot be fully utilized in this type of composites, were discussed in depth.Comment: 29 pages, 12 figure

Seeking the Truth Beyond the Data. An Unsupervised Machine Learning Approach

Clustering is an unsupervised machine learning methodology where unlabeled elements/objects are grouped together aiming to the construction of well-established clusters that their elements are classified according to their similarity. The goal of this process is to provide a useful aid to the researcher that will help her/him to identify patterns among the data. Dealing with large databases, such patterns may not be easily detectable without the contribution of a clustering algorithm. This article provides a deep description of the most widely used clustering methodologies accompanied by useful presentations concerning suitable parameter selection and initializations. Simultaneously, this article not only represents a review highlighting the major elements of examined clustering techniques but emphasizes the comparison of these algorithms' clustering efficiency based on 3 datasets, revealing their existing weaknesses and capabilities through accuracy and complexity, during the confrontation of discrete and continuous observations. The produced results help us extract valuable conclusions about the appropriateness of the examined clustering techniques in accordance with the dataset's size.Comment: This paper has been accepted for publication in the proceedings of the 3rd International Scientific Forum on Computer and Energy Sciences (WFCES 2022

Genetic and Geo-Epidemiological Analysis of the Zika Virus Pandemic; Learning Lessons from the Recent Ebola Outbreak

“Outbreak” is a term referring to a virus or a parasite that is transmitted very aggressively and therefore could potentially cause fatalities, as the recent Ebola and Zika epidemics did. Nevertheless, looking back through history, quite a few outbreaks have been reported, which turned out so deadly that essentially changed, molded and literally re-shaped the society as it is today. In the present chapter, differences and similarities between the two most recent outbreaks have been studied, in order to pinpoint and design a trace model that will allow us to draw some conclusions for the connection of those two epidemics. Due to the high dimensionality of the problem, modern and state of the art geo-epidemiological methods have been used in an effort to provide the means necessary to establish the abovementioned model. It is only through geo-epidemiological analysis that it is possible to analyze concurrently a multitude of variables, such as genetic, environmental, behavioral, socioeconomic and a series of related infection risk factors

The effect of three cognitive variables on students' understanding of the particulate nature of matter and its changes of state

In this study, students' understanding of the structure of matter and its changes of state, such as, melting, evaporation, boiling and condensation was investigated in relation to three cognitive variables: logical thinking, field-dependence/ field-independence and convergence/ divergence dimension. The study took place in Greece with the participation of 329 ninth-grade junior high school pupils (age 14-15). A stepwise multiple regression analysis revealed that all of the above mentioned cognitive variables were statistically significant predictors of the students' achievement. Among the three predictors, logical thinking was found to be the most dominant one. In addition, students’ understanding of the structure of matter, along with the cognitive variables, were shown to have an effect on their understanding the changes of states and on their competence to interpret these physical changes. Path analyses were implemented to depict these effects. Moreover, a theoretical analysis is provided that associates logical thinking and cognitive styles with the nature of mental tasks involved when learning the material concerning the particulate nature of matter and its changes of state. Implications for science education are also discussed