Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool

In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non‐linear controllers. The related gains of the designed controllers are optimised using genetic algorithm and simultaneously are adapted by employing the Fuzzy scheduling method. The cancer dynamic model is extracted with five differential equations, including normal cells, endothelial cells, cancer cells, and the amount of two chemotherapy and anti‐angiogenic drugs left in the body as the engaged state variables, while double drug injection is considered as the corresponding controlling signals of the mentioned state space. This treatment aims to reduce the tumour cells by providing a timely schedule for drug dosage. In chemotherapy, not only the cancer cells are killed but also other healthy cells will be destroyed, so the rate of drug injection is highly significant. It is shown that the simultaneous application of chemotherapy and anti‐angiogenic therapy is more efficient than single chemotherapy. Two different non‐linear controllers are employed and their performances are compared. Simulation results and comparison studies show that not only adding the anti‐angiogenic reduce the side effects of chemotherapy but also the proposed robust controller of sliding mode provides a faster and stronger treatment in the presence of patient parametric uncertainties in an optimal way. As a result of the proposed closed‐loop drug treatment, the tumour cells rapidly decrease to zero, while the normal cells remain healthy simultaneously. Also, the injection rate of the chemotherapy drug is very low after a short time and converges to zero.     

Use of radial basis functions for estimating monthly mean daily solar radiation

The present study utilizes the radial basis functions technique for the estimation of monthly mean daily values of solar radiation falling on horizontal surfaces and compares its performance with that of the multilayer perceptrons network and a classical regression model. In this work, we use solar radiation data from 41 stations that are spread over the Kingdom of Saudi Arabia. The solar radiation data from 31 locations are used for training the neural networks and the data from the remaining 10 locations are used for testing the estimated values. However, the testing data were not used in the modeling or training of the networks to give an indication of the performance of the system at unknown locations. Results indicate the viability of the radial basis for this kind of problem.     

Evolving Fuzzy Min–Max Neural Network Based Decision Trees for Data Stream Classification

Learning from data streams is a challenging task which demands a learning algorithm with several high quality features. In addition to space complexity and speed requirements needed for processing the huge volume of data which arrives at high speed, the learning algorithm must have a good balance between stability and plasticity. This paper presents a new approach to induce incremental decision trees on streaming data. In this approach, the internal nodes contain trainable split tests. In contrast with traditional decision trees in which a single attribute is selected as the split test, each internal node of the proposed approach contains a trainable function based on multiple attributes, which not only provides the flexibility needed in the stream context, but also improves stability. Based on this approach, we propose evolving fuzzy min–max decision tree (EFMMDT) learning algorithm in which each internal node of the decision tree contains an evolving fuzzy min–max neural network. EFMMDT splits the instance space non-linearly based on multiple attributes which results in much smaller and shallower decision trees. The extensive experiments reveal that the proposed algorithm achieves much better precision in comparison with the state-of-the-art decision tree learning algorithms on the benchmark data streams, especially in the presence of concept drift.     

Stochastic modeling and generation of synthetic sequences of hourly global solar irradiation at Quetta, Pakistan

Using hourly global radiation data at Quetta, Pakistan for 10 yr, an Autoregressive Moving Average (ARMA) process is fitted. Markov Transition Matrices have also been developed. These models are used for generating synthetic sequences for hourly radiations in MJ/m² and that the generated sequences are compared with the observed data. We found the MTM approach relatively better as a simulator compared to ARMA modeling.     

Optimal Automatic Generation Control of Asynchronous Power Systems Using Output Feedback Control Strategy with Dynamic Participation of Wind Turbines

Abstract—This article presents the design of optimal output feedback automatic generation control regulators for an interconnected power system with dynamic participation of doubly fed induction generator based wind turbines. The power systems consist of plants with hydro-thermal turbines and are interconnected via parallel AC/DC links. Efforts have been made to propose optimal automatic generation control regulators based on feedback of output state variables, which are easily accessible and available for the measurement. The designed optimal output feedback automatic generation control regulators are implemented, and the system dynamic responses for various system states are obtained considering 1% load perturbation in one of the areas. The dynamic performance is compared with that obtained with optimal automatic generation control regulators designed using full state vector feedback. The pattern of closed-loop eigenvalues is also determined to test the system stability.     

Mathematical modelling of mass transfer in the concentration polarisation layer of flat‐sheet membranes during clarification of pomegranate juice

Evaluation of membrane clarification of pomegranate juice showed that a cake layer is created at the beginning of the process. This evaluation was used as the basis for modelling the solute concentration in the concentration polarisation layer to control the fouling of the membrane. The model of changes in concentration as a function of time and distance (in its dimensionless form) was solved numerically using the Forward‐Time/Central‐Space (FTCS) method. Programming was performed using MATLAB software, and the predicted concentration on the membrane surface was compared with experimental data; the prediction fit well with the experimental data. The model showed that the solute concentration increased as distance to the membrane surface decreased, time increased, transmembrane pressure increased and velocity decreased. Also, the model matched the experimental data in that it showed the cake layer to be created in the early stages of the membrane clarification process.     

Band structure effect on the electron current oscillation in ultra-scaled GaSb Schottky MOSFET: tight-binding approach

This paper explores band structure effect on the quantum transport of a low-dimensional GaSb Schottky MOSFET (SBFET) for the implementation of III–V transistor with a low series resistance. Precise treatment of the full band structure is employed applying sp ³ d ⁵ s ^? tight-binding (TB) formalism. A remarkable distinction between the thickness dependent effective masses extracted from the TB and the bulk values imply that the quantum confinement modifies the device performance. Strong transverse confinement leads to the effective Schottky barrier height increment. Owing to the adequate enhanced Schottky barriers at low drain voltages, a double barrier gate modulated potential well is formed along the channel. The double barrier profile creates a longitudinal quantum confinement and induces drain current oscillation at low temperatures. Significant factors that may affect the current oscillation are thoroughly investigated. Current oscillation is gradually smoothed out as the gate length shrinks down in ultra scaled structure. The results in this paper are paving a way to clarify the feasibility of this device in nanoscale regime.     

Using plasmonic cloaking method on infinite cylindrical structures and its applications

Journal of Computational Electronics - In recent years, cloaking using materials with negative electric permittivity or magnetic permeability has been studied and researched. It has been...     

Mechanochemical sulfidization of a mixed oxide-sulphide copper ore by co-grinding with sulfur and its effect on the flotation efficiency

Mechanochemical sulfidization of a mixed sulfide/oxide copper ore by co-grinding with sulfur and additives including Mg(NO₃)₂ and Fe(NO₃)₃ salts and iron, aluminum and magnesium powders was investigated for the first time. Also, the influence of sulfidization during the wet-milling process was examined on the separation efficiency and recovery of copper in detail. The results demonstrated that co-grinding with sulfur solely had the best flotation performance at the value of 0.5 wt.% and it was attributed to the possible existence of S-O bonding on copper oxides surfaces. In addition, adding magnesium nitrate salt, magnesium powder, iron nitrate salt and aluminum powder as additive associated with 0.5 wt% sulfur into ball milling caused the flotation improvement at the amounts of 0.2 wt%, 0.2 wt%, 0.5 wt% and 0.5 wt%, respectively. Also, the effect of grinding time and sulfidization pH with 0.5 wt% sulfur solely was determined and pHs of 7.5 to 8.5 gave the best results. The highest recovery (75.76%) and separation efficiency (63.44%) were achieved at pH of 7.5 and 8.5, respectively.