Entropy generation of peristaltic Eyring–Powell nanofluid flow in a vertical divergent channel for biomedical applications

Exploring the movement of blood in a blood vessel has been fascinated by clinicians and biomedical researchers because it is predominant in cell tissue engineering, drug targeting and various treatments like hypothermia, hyperthermia, and cancer. It is noticed that numerous non-Newtonian rheological fluids like Carreau fluid, tangent hyperbolic fluid, Eyring–Powell fluid and viscoelastic fluid manifest the characteristics of blood flow. Further, the investigation of entropy generation can be used to raise the performance of medical equipments. Consequently, the present mathematical model scrutinizes the transport characteristics and entropy generation of the peristaltic Eyring–Powell nanofluid in a permeable vertical divergent channel in the presence of dissipation and linear radiation. The non-similar variables are employed to convert the dimensional partial differential equations into dimensionless form which are tackled by the Homotopy perturbation method. The impacts of emerging parameters like Eyring–Powell parameters, left and right wall amplitudes, thermophoresis, mean flow rate, radiation, permeability parameter, Brownian motion, Eckert number, Hartman number on Eyring–Powell nanofluid axial velocity, temperature, and concentration are manifested. Present results disclose that the thermal Grashof number highly inflates the pressure rise. Eyring–Powell nanofluid temperature reduces for uplifting the linear radiation parameter. Growing values of the non-uniform parameter lead to move the trapping bolus towards the left and right wall. The total entropy generation diminishes for magnifying the temperature difference parameter.

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic traveling salesman problem (DTSP). The DTSP can be modeled as a sequence of static sub-problems, each of which is an instance of the TSP. In the proposed DPSO algorithm, the information gathered while solving a sub-problem is retained in the form of a pheromone matrix and used by the algorithm while solving the next sub-problem. We present a method for automatically setting the values of the key DPSO parameters (except for the parameters directly related to the computation time and size of a problem).We show that the diversity of parameters values has a positive effect on the quality of the generated results. Furthermore, the population in the proposed algorithm has a higher level of entropy. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

A Self-Adaptive Discrete PSO Algorithm with Heterogeneous Parameter Values for Dynamic TSP

This paper presents a discrete particle swarm optimization (DPSO) algorithm with heterogeneous (non-uniform) parameter values for solving the dynamic travelling salesman problem (DTSP). The DTSP can be modelled as a sequence of static sub-problems, each of which is an instance of the TSP. We present a method for automatically setting the values of the DPSO parameters without three parameters, which can be defined based on the size of the problem, the size of the particle swarm, the number of iterations, and the particle neighbourhood size. We show that the diversity of parameter values has a positive effect on the quality of the generated results. We compare the performance of the proposed heterogeneous DPSO with two ant colony optimization (ACO) algorithms. The proposed algorithm outperforms the base DPSO and is competitive with the ACO.

Effects of Slip, Brownian Motion and Thermophoresis on Peristaltic Pumping of Nano Fluid in an Asymmetric Channel with Porous Medium

This paper deals with peristaltic motion of electrically conducting nanofluid in a tapered asymmetric channel through a porous medium in presence of heat and mass transfer under the effect of slip conditions. The problem is reduced mathematically by a set of nonlinear partial differential equations which describe the conservation of mass, momentum, energy and concentration of nanoparticles. The non-dimensional form of these equations is simplified under the assumption of long wavelength and low Reynolds number. The coupled governing equations are solved analytically. The expressions for velocity, stream function, temperature and concentration are derived. The results have been presented graphically for the various interested emerging parameters and the obtained results are discussed in detail. It is observed that the magnitude of the velocity decreases in the middle of the channel while it increases near the channel walls with an increase in the non-uniform parameter It is also noticed that the nanoparticle temperature increases with increasing thermal slip parameter . The present result coincides with the findings of Kothandapani and Prakash [19].

Density-Based Penalty Parameter Optimization on C-SVM

The support vector machine (SVM) is one of the most widely used approaches for data classification and regression. SVM achieves the largest distance between the positive and negative support vectors, which neglects the remote instances away from the SVM interface. In order to avoid a position change of the SVM interface as the result of an error system outlier, C-SVM was implemented to decrease the influences of the system’s outliers. Traditional C-SVM holds a uniform parameterCfor both positive and negative instances; however, according to the different number proportions and the data distribution, positive and negative instances should be set with different weights for the penalty parameter of the error terms. Therefore, in this paper, we propose density-based penalty parameter optimization of C-SVM. The experiential results indicated that our proposed algorithm has outstanding performance with respect to both precision and recall.

Min And Max Uniform Extreme Interval Values And Statistics

<p class="MsoNormal" style="text-align: justify; line-height: normal; margin: 0in 0.5in 0pt;"><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt;">The min and max uniform extreme interval values and statistics; ie expected value, standard deviation, mode, median, and coefficient of variation, are discussed.<span style="mso-spacerun: yes;">&nbsp;&nbsp; </span>An extreme interval value </span><span style="position: relative; line-height: 115%; font-family: &quot;Calibri&quot;,&quot;sans-serif&quot;; font-size: 11pt; top: 4pt; mso-fareast-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-text-raise: -4.0pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"></span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt;">is defined as a numerical bound where a specified percentage &alpha; of the data is less than </span><span style="position: relative; line-height: 115%; font-family: &quot;Calibri&quot;,&quot;sans-serif&quot;; font-size: 11pt; top: 4pt; mso-fareast-font-family: Calibri; mso-ascii-theme-font: minor-latin; mso-fareast-theme-font: minor-latin; mso-hansi-theme-font: minor-latin; mso-bidi-font-family: 'Times New Roman'; mso-bidi-theme-font: minor-bidi; mso-text-raise: -4.0pt; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA;"></span><span style="font-family: &quot;Times New Roman&quot;,&quot;serif&quot;; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-fareast-theme-font: minor-fareast;">. A numerical example and an analysis of the min and max extreme interval values and statistics are provided.<span style="mso-spacerun: yes;">&nbsp; </span>In addition, a procedure for finding the min and max extreme interval values for different uniform parameter values, and an application of this research are presented.</span></p>

SYNCHRONIZATION OF CELL DIVISION

Most living organisms display many types of biological rhythms. We describe how a growing population of cells may be distributed between age classes or cell types, and define conditions necessary to produce synchronous population development. A probabilistic model describing the changes in cell numbers during proliferation is presented. The model predicts that during cell reproduction with constant parameters any cell population approaches a stationary behavior. According to this model, synchronization of cell growth is possible if there is a uniform parameter set for cell division. This point is illustrated by a set of graphs showing snapshots of model simulations with different parameter sets for transient and stationary behaviors.