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Water flow simulations play a key role in determining how fluid properties are affected due to geometrical and flow conditions, establishing a theoretical basis so as to guide technological applications of nanofluidics for desalination and water purification. In this work, we investigate liquid water properties when downsizing a Poiseuille-flow system at the nanoscale with Molecular Dynamics simulations. Water is modelled through the well-known SPC and SPC/E models. To establish the theoretical background, we present number density profiles to reveal fluid ordering close to the walls, calculate velocity and temperature profiles for various channel widths and magnitudes of the external driving force and comment on slip length issues. As a multi-parameter study, we aim to contribute on theoretical analysis oriented on water applications design. [ABSTRACT FROM AUTHOR]

Bioinformatics is a relatively new discipline where Mathematics are applied in the analysis of genetic sequences. The analysis of the genetic material of living organisms which consist of nucleic acids DNA and RNA is of great importance for diagnosis and taxonomy reasons. In the present paper we propose a new methodology for the representation of genetic sequences as fuzzy sets in the I12 space which can significantly improve the results of Sadegh-Zadeh and Torres & Nieto. An important characteristic of our proposed methodology is that the location of Amino acids along the genetic sequences play an important role thus extending in a significant way the computational efficiency advantage of genetic sequence representation. We present some characteristic examples using the new proposed methodology where we calculate the distance and similarity degree of given polynucleotides. [ABSTRACT FROM AUTHOR]

Background and Objective: Serious side effects are occurred during the cancer therapy. Magnetic driving of nanoparticles is a novel method for the elimination of these effects by supplying with anticancer drug or increase the temperature of the infected area. For this reason, a numerical model for optimal guidance of nanoparticles, through the gradient magnetic field, inside the human artery system is presented in this study., Methods: The present method couples Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) techniques. In addition, the optimum magnetic intensity each time is evaluated by using the covariance matrix adaptation evolution strategy (CMA-ES). Under five feature blood flow velocities in cardiac cycle, the developed method evaluate and select the optimum gradient magnetic field in order to eliminate the deviation of the guided nanoparticles from a pre-described trajectory., Results: Results of the simulations indicate both the influence of the blood flow and the volume of nanocarriers in the magnetic driving process in real conditions. Specifically, the blood flow and the volume of particles are inversely proportional parameters in the magnetic navigation process. As the blood flow is decreased, the deviation of nanoparticles compared to the desired path is minimized. On the contrary, the decrease of the volume of nanocarriers increase the distance of particles from the described trajectory. However, greater magnetic gradient values are needed as the blood flow is increased. Furthermore, the imposed gradient magnetic values are strongly connected with the position of the nanoparticles and the blood blow velocity., Conclusions: Based on the results of the present study, the most important parameter in the navigation process is the magnetic volume of particles. Under real conditions, the effect of the blood flow is insignificant compared to the volume of particles in the navigation process. In addition, great differences in the optimized magnetic sequence are presented both among the different blood flows and the volume of particles., Competing Interests: Declaration of Competing Interest The author declare no conflict of interest, (Copyright © 2022 Elsevier B.V. All rights reserved.)

In this study, we present results of the application of nonlinear time series analysis on traffic data for incident detection. More specifically, we analyze daily volume records of Attica Tollway (Greece) collected from sensors located at various locations. The analysis was performed using the Recurrence Plot (RP) and Recurrence Quantification Analysis (RQA) method of the volume data of the lane closest to the median. The results show that it is possible to identify, through the abrupt change of the dynamics of the system revealed by RPs and RQA, the occurrence of incidents on the freeway and differentiate from recurrent traffic congestion. The proposed methodology could be of interest for big data traffic analysis.

In the present study, we applied the methodology of the complex network-based time series analysis to experimental temperature time series from a vertical turbulent heated jet. More specifically, we approach the hydrodynamic problem of discriminating time series corresponding to various regions relative to the jet axis, i.e., time series corresponding to regions that are close to the jet axis from time series originating at regions with a different dynamical regime based on the constructed network properties. Applying the transformation phase space method (k nearest neighbors) and also the visibility algorithm, we transformed time series into networks and evaluated the topological properties of the networks such as degree distribution, average path length, diameter, modularity, and clustering coefficient. The results show that the complex network approach allows distinguishing, identifying, and exploring in detail various dynamical regions of the jet flow, and associate it to the corresponding physical behavior. In addition, in order to reject the hypothesis that the studied networks originate from a stochastic process, we generated random network and we compared their statistical properties with that originating from the experimental data. As far as the efficiency of the two methods for network construction is concerned, we conclude that both methodologies lead to network properties that present almost the same qualitative behavior and allow us to reveal the underlying system dynamics.

The study of genetic sequences is of great importance in biology and medicine. Sequence analysis and taxonomy are two major fields of application of bioinformatics. In the present paper we extend the notion of entropy and clarity to the use of different metrics and apply them in the case of the Fuzzy Polynuclotide Space (FPS). Applications of these notions on selected polynucleotides and complete genomes both in the I(12×k) space, but also using their representation in FPS are presented. Our results show that the values of fuzzy entropy/clarity are indicative of the degree of complexity necessary for the description of the polynucleotides in the FPS, although in the latter case the interpretation is slightly different than in the case of the I(12×k) hypercube. Fuzzy entropy/clarity along with the use of appropriate metrics can contribute to sequence analysis and taxonomy., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

In this paper we present a study of classification of the 20 amino acids via a fuzzy clustering technique. In order to calculate distances among the various elements we employ two different distance functions: the Minkowski distance function and the NTV metric. In the clustering procedure we take into account several physical properties of the amino acids. We examine the effect of the number and nature of properties taken into account to the clustering procedure as a function of the degree of similarity and the distance function used. It turns out that one should use the properties that determine in the more important way the behavior of the amino acids and that the use of the appropriate metric can help in defining the separation into groups.