Electrochemical sensors for hemoglobin and myoglobin detection based on methylene blue-multiwalled carbon nanotubes nanohybrid-modified glassy carbon electrode

An effective electrochemical sensors for hemoglobin (Hb) and myoglobin (Mb) detection was firstly developed using a simple procedure of self-assembled methylene blue-multiwalled carbon nanotubes (MB-MWNTs) nanohybrid modified on glassy carbon electrode without using any enzymes immobilization. The direct electrochemical and electrocatalytic behaviors of the modified electrode were studied using cyclic voltammetry (CV) and flow injection analysis (FIA) with amperometry. The performance of the sensor was investigated and optimized and the system was evaluated by monitoring Hb and Mb concentrations. The developed MB-MWNTs nanohybrid modified electrode showed excellent electrocatalytic activity for reduction of Hb and Mb with good stability, sensitivity and reproducibility (RSD = 3.05% and 4.5% for 50 successive injections of Hb and Mb, respectively). Under optimal conditions, the catalytic currents are linearly proportional to the concentrations of Hb and Mb in the wide range from 5 nM to 2 μM and 0.1 to 3 μM, and the corresponding detection limits are 1.5 nM and 20 nM (S/N = 3), respectively. This approach provides improved detection limit over other previous works and may provide a novel and efficient platform for the fabrication of sensors for other heme proteins.     

Supervised learning based on the self-organizing maps for forward kinematic modeling of Stewart platform

In this study, we propose an alternative technique for solving the forward kinematic problem of parallel manipulator which is designed based on generalized Stewart platform. The focus of this work is to predict a pose vector of a moving plate from a given set of six leg lengths. Since the data of parallel kinematics are usually available in the form of nonlinear dynamic system, several methods of system identification have been proposed in order to construct the forward kinematic model and approximate the pose vectors. Although these methods based on a multilayer perceptron (MLP) neural network provide acceptable results, MLP training suffers from convergence to local optima. Thus, we propose to use an alternative supervised learning algorithm called vector-quantized temporal associative memory (VQTAM) instead of MLP-based methods. VQTAM relying on self-organizing map architecture is used to build the mapping from the input space to the output space such that the training/testing datasets are generated from inverse kinematic model. The solutions from standard VQTAM are improved by an autoregressive (AR) model and locally linear embedding (LLE). The experimental results indicate that VQTAM with AR/LLE gives the outputs with nearly 100% prediction accuracy in the case of smooth data, while VQTAM + LLE provides the most accurate prediction on noisy data. Therefore, VQTAM + LLE is a very robust estimation method and can practically be used for solving the forward kinematic problem.