The Influence of Electrolyte Flow Hydrodynamics on the Performance of a Microfluidic Dye-Sensitized Solar Cell

The dye-sensitized solar cells microfluidically integrated with a redox flow battery (µDSSC-RFB) belong to a new emerging class of green energy sources with an inherent opportunity for energy storage. The successful engineering of microfluidically linked systems is, however, a challenging subject, as the hydrodynamics of electrolyte flow influences the electron and species transport in the system in several ways. In the article, we have analyzed the microflows hydrodynamics by means of the lattice-Boltzmann method, using the algebraic solution of the Navier-Stokes equation for a duct flow and experimentally by the micro particle image velocimetry method. Several prototypes of µDSSC were prepared and tested under different flow conditions. The efficiency of serpentine µDSSC raised from 2.8% for stationary conditions to 3.1% for electrolyte flow above 20 mL/h, while the fill factor increased about 13% and open-circuit voltage from an initial 0.715 V to 0.745 V. Although the hexagonal or circular configurations are the straightforward extensions of standard photo chambers of solar cells, those configurations are hydrodynamically less predictable and unfavorable due to large velocity gradients. The serpentine channel configuration with silver fingers would allow for the scaling of the µDSSC-RFB systems to the industrial scale without loss of performance. Furthermore, the deterioration of cell performance over time can be inhibited by the periodic sensitizer regeneration, which is the inherent advantage of µDSSC.

Algebraic Solution of Tropical Polynomial Optimization Problems

We consider constrained optimization problems defined in the tropical algebra setting on a linearly ordered, algebraically complete (radicable) idempotent semifield (a semiring with idempotent addition and invertible multiplication). The problems are to minimize the objective functions given by tropical analogues of multivariate Puiseux polynomials, subject to box constraints on the variables. A technique for variable elimination is presented that converts the original optimization problem to a new one in which one variable is removed and the box constraint for this variable is modified. The novel approach may be thought of as an extension of the Fourier–Motzkin elimination method for systems of linear inequalities in ordered fields to the issue of polynomial optimization in ordered tropical semifields. We use this technique to develop a procedure to solve the problem in a finite number of iterations. The procedure includes two phases: backward elimination and forward substitution of variables. We describe the main steps of the procedure, discuss its computational complexity and present numerical examples.

The Lattice-Boltzmann Modeling of Microflows in a Cell Culture Microdevice for High-Throughput Drug Screening

The aim of our research was to develop a numerical model of microflows occurring in the culture chambers (CC) of a microfluidic device of our construction for high-throughput drug screening. The incompressible fluid flow model is based on the lattice-Boltzmann equation, with an external body force term approximated by the He-Shan-Doolen scheme and the Bhatnagar-Gross-Krook approximation of the collision operator. The model accuracy was validated by the algebraic solution of the Navier–Stokes equation (NSE) for a fully developed duct flow, as well as experimentally. The mean velocity prediction error for the middle-length cross-section of CC was 1.0%, comparing to the NSE algebraic solution. The mean error of volumetric flow rate prediction was 6.1%, comparing to the experimental results. The analysis of flow hydrodynamics showed that the discrepancies from the plug-flow-like velocity profile are observed close to the inlets only, and do not influence cell cultures in the working area of CC. Within its workspace area, the biochip provides stable and homogeneous fully developed laminar flow conditions, which make the procedures of gradient generation, cell seeding, and cell-staining repeatable and uniform across CC, and weakly dependent on perturbations.

An Algorithm for Solving Robot Inverse Kinematics Based on FOA Optimized BP Neural Network

The solution of robot inverse kinematics has a direct impact on the control accuracy of the robot. Conventional inverse kinematics solution methods, such as numerical solution, algebraic solution, and geometric solution, have insufficient solution speed and solution accuracy, and the solution process is complicated. Due to the mapping ability of the neural network, the use of neural networks to solve robot inverse kinematics problems has attracted widespread attention. However, it has slow convergence speed and low accuracy. This paper proposes the FOA optimized BP neural network algorithm to solve inverse kinematics. It overcomes the shortcomings of low convergence accuracy, slow convergence speed, and easy to fall into local minima when using BP neural network to solve inverse kinematics. The experimental results show that using the trained FOA optimized BP neural network to solve the inverse kinematics, the maximum error range of the output joint angle is [−0.04686, 0.1271]. The output error of the FOA optimized BP neural network algorithm is smaller than that of the ordinary BP neural network algorithm and the PSO optimized BP neural network algorithm. Using the FOA optimized BP neural network algorithm to solve the robot kinematics can improve the control accuracy of the robot.

KINEMATICS ANALYSIS AND IMPLEMENTATION OF THREE DEGREES OF FREEDOM ROBOTIC ARM BY USING MATLAB

The kinematics modeling of the robot arm plays an important role in robot control. This paper presents the kinematic model of a three-degree of freedom articulated robot arm, which is designed for picking and placing an application with hand gripper, where a robot has been manufactured for that purpose. The forward kinematic model has been presented in order to determine the end effector’s poses using the Denavit-Hartenberg (DH) convention. For inverse kinematics, an algebraic solution based on trigonometric formulas mixed with geometric method was adopted for a 3 DOF modular manipulator taking into account the existence of a shoulder offset. MATLAB software was used as a tool to simulate and implement the motional characteristics of the robot arm, by creating a 3D visual software package under designing a Graphical User Interface "GUI" with a support simulation from robotic Toolbox (Rtb 10.3). Finally, an electronic interfacing circuit between the GUI program and the robot arm was developed using Arduino microcontroller to control the robot motion. The presented work can be applicable for learning the reality interface design methodology of the other kinds of robot manipulators and achieve a suitable solution for the motional characteristics

Development of a Five-Parameter Model to Facilitate the Estimation of Additive, Dominance, and Epistatic Effects with a Mediating Using Bootstrapping in Advanced Generations of Wheat (Triticum aestivum L.)

As a result of two crosses among three local varieties of wheat, five populations (P1, P2, F5, F6 and F7) were used as parents and grown during two successive seasons; 2016/2017 and 2017/2018. To estimate five types of gene action (e.g., mean effects, additive, dominance, additive × additive, and dominance × dominance), five formulas were developed from with algebraic solution, algebraic proof, and mathematical proof. Besides, to test adequate of a simple additive-dominance model, three formulas A, B, and C scaling test were developed. The path analysis method by PROCESS Macro, AMOS, and Bootstrapping was employed to assess the relationships between grain yield/plant (GYP) as the dependent variable and each one of the number of spikes (NS) and 1000-grain weight (TW) as the independent variables. The results show that there are eight validated equations used to estimate the scaling test (A, B and C) and five types of gene effects (m, a, D, I and L), respectively. Confidence interval using Bootstrapping results indicate that TW was played as the partial mediator between NS as an exogenous variable and GYP as an endogenous variable. Generation means analysis is a relatively simple and statistically reliable tool suitable for the fundamental estimation of different genetic influences.

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The results are presented and compared, and PSO achieved the best results.