Analysis of bioheat transfer equation for hyperthermia cancer treatment

Magnetic fluid hyperthermia is a new subclass of hyperthermia cancer treatment that can selectively heat up a tumor without damaging the surrounding healthy tissues. Some authors studied the temperature distribution of a magnetically mediated tumor assuming a homogeneous distribution of nanoparticles inside the tumor. Practically speaking, the injected nanoparticles do not usually distribute uniformly throughout the entire tumor, thus leaving some parts of the tumor without nanoparticles. In this study, an inhomogeneous dispersion of nanoparticles inside the tumor is assumed to investigate the tissues’ temperature profiles. The problem is solved for polar coordinate. Also in this study, the heating effect of magnetic fluid in a porcine liver tissue is experimentally examined. Numerical transient solutions are found to be in good agreement with experimental data.     

Well-conditioned configurations of fault-tolerant manipulators

Fault-tolerant motion of redundant manipulators can be obtained by joint velocity reconfiguration. For fault-tolerant manipulators, it is beneficial to determine the configurations that can tolerate the locked-joint failures with a minimum relative joint velocity jump, because the manipulator can rapidly reconfigure itself to tolerate the fault. This paper uses the properties of the condition numbers to introduce those optimal configurations for serial manipulators. The relationship between the manipulator’s locked-joint failures and the condition number of the Jacobian matrix is indicated by using a matrix perturbation methodology. Then, it is observed that the condition number provides an upper bound of the required relative joint velocity change for recovering the faults which leads to define the optimal fault-tolerant configuration from the minimization of the condition number. The optimization problem to obtain the minimum condition number is converted to three standard Eigen value optimization problems. A solution is for selected optimization problem is presented. Finally, in order to obtain the optimal fault-tolerant configuration, the proposed method is applied to a 4-DoF planar manipulator.     

Effect of different levels of fat and inulin on the microbial growth and metabolites in probiotic yogurt containing nonviable bacteria

Effects of different levels of fat and inulin on bacterial cell counts, degree of proteolysis and concentrations of organic acids in the yogurt containing inactivated cells of probiotic strains Bifidobacterium animalis and Lactobacillus acidophilus were investigated. Results showed that both L. acidophilus and B. animalis grew well in the yogurt samples reaching cell counts higher than 10⁶ CFU mL^?1 at the final pH of 4.5. Inulin at the concentration of 1% had no significant effects on the production of organic acids and cell counts of L. acidophilus, but promoted the growth of B. animalis with a reduction in the degree of proteolysis. Generally, different fat levels showed significant effects on the production of organic acids and nonsignificant effects on the cell counts of probiotic bacteria and degree of proteolysis. In case of lactic acid, the ratio of L‐ (+)to D‐ (?) isomer ranged from 50/50 to 80/20 in yogurt samples.     

Photoluminescence emission of nanoporous anodic aluminum oxide films prepared in phosphoric acid

The photoluminescence emission of nanoporous anodic aluminum oxide films formed in phosphoric acid is studied in order to explore their defect-based subband electronic structure. Different excitation wavelengths are used to identify most of the details of the subband states. The films are produced under different anodizing conditions to optimize their emission in the visible range. Scanning electron microscopy investigations confirm pore formation in the produced layers. Gaussian analysis of the emission data indicates that subband states change with anodizing parameters, and various point defects can be formed both in the bulk and on the surface of these nanoporous layers during anodizing.     

A combinatorial approach to evaluation of monomer conversion and particle size distribution in vinyl chloride emulsion polymerization

This work is targeted to study emulsion polymerization of vinyl chloride monomer (VCM) using experimental and mathematical methods. To fulfill this goal, a computer code was developed on the basis of zero–one population balance by which the effects of initiator and emulsifier concentration on the evolution of VCM conversion were investigated in the course of polymerization. The model was also trained to capture the coagulation of the particles. This enabled to adopt a reliable way of evaluating the particle size distribution (PSD). In particular, the rates of homogeneous and micellar nucleation mechanisms were simulated and reasonably predicted alterations in the PSD and the number of polymer particles under the influence of aforementioned parameters. The results from modeling were satisfactorily consistent with the experimental outputs and obviously visualized the impact of initiator and surfactant concentration on the PSD of the prepared PVC latexes.     

Reductive removal of hexavalent chromium from aqueous solution using sepiolite-stabilized zero-valent iron nanoparticles: Process optimization and kinetic studies

We studied the optimization of hexavalent chromium (Cr(VI)) removal from aqueous solution using the synthesized zero-valent iron nanoparticles stabilized with sepiolite clay (S-ZVIN), under various parameters such as reaction time (min), initial solution pH and concentration of S-ZVIN (g·L^?1) using response surface methodology (RSM). The kinetic study of Cr(VI) was conducted using three types of the most commonly used kinetic models including pseudo zero-order, pseudo first-order, and pseudo second-order models. The rate of reduction reaction showed the best fit with the pseudo first-order kinetic model. The process optimization results revealed a high agreement between the experimental and the predicted data (R²=0.945, Adj-R²=0.890). The results of statistical analyses showed that reaction time was the most impressive factor influencing the efficiency of removal process. The optimum conditions for maximum response (98.15%) were achieved at the initial pH of 4.7, S-ZVIN concentration of 1.3 g·L^?1 and the reaction time of 75 min.     

Performance analysis of three advanced controllers for polymerization batch reactor: An experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC.     

A thermodynamically consistent framework for non-isothermal modelling of local heat generation and electromotive force in SOFC single electrodes

Mathematical models for single electrode reversible heat and non-isothermal electromotive force (EMF) of a solid oxide fuel cell (SOFC) are developed. These models estimate the volumetric reversible heat generation and EMF of electrochemical reactions, within each electrode at local conditions of temperature and pressure, based on entropy change of half reactions. The resulting equations are thermodynamically consistent. They inherently obey the conservation of energy law as the electrochemical energy released added to the heat of reactions at each electrode equate the enthalpy change of the reacted species. The equations are implemented to model electrodes in a tubular micro- solid oxide fuel cell (TμSOFC). The thermodynamic consistency of the model is numerically confirmed as the enthalpy of the reactants equates the electric energy released by the cell plus the sum of electrode heats plus electrolyte Ohmic heat. The effect of thermal gradients on the cell's overall EMF is found to be negligible. The reversible and irreversible heat generation of each electrode are distinguished. Overall, the anode is found to be endothermic, and the cathode exothermic.     

Modelling qualitative and quantitative parameters of groundwater using a new wavelet conjunction heuristic method: wavelet extreme learning machine versus wavelet neural networks

In recent years, as a result of climate change as well as rainfall reduction in arid and semi‐arid regions, modelling qualitative and quantitative parameters belonging to aquifers has become crucially important. In Iran, as aquifers are treated as the most commonly used drinking water resources, modelling their qualitative and quantitative parameters is enormously important. In this paper, for the first time, values of salinity, total dissolved solids (TDS), groundwater level (GWL) and electrical conductivity (EC) of the Arak Plain, located in Markazi Province, Iran, are simulated by means of four modern artificial intelligence models including extreme learning machine (ELM), wavelet extreme learning machine (WELM), online sequential extreme learning machine (OSELM) and wavelet online sequential extreme learning machine (WOSELM) as well as the MODFLOW software for a 15‐year period monthly. To develop the hybrid artificial intelligence models, the wavelet is employed. First, the effective lags in estimating the qualitative and quantitative parameters of the groundwater are identified using the autocorrelation function (ACF) and the partial autocorrelation function (PACF) analysis. After that, four different models are developed by the selected input combinations and also the ACF and the PACF in the form of different lags for each of ELM, WAELM, OSELM and WOSELM methods. Then, the superior models in simulating the groundwater qualitative and qualitative parameters are detected by conducting a sensitivity analysis. To forecast the electrical conductivity (EC) by the best WOSELM model, the values of the Nash–Sutcliffe efficiency coefficient (NSC), Mean Absolute Error (MAE) and the scatter index (SI) are obtained to be 0.991, 18.005 and 4.28E‐03, respectively. In addition, the most effective lags in estimating these parameters are introduced. Subsequently, the results found by the MODFLOW model are compared with those of the artificial intelligence models and it is concluded that the latter are more accurate. For instance, the scatter index and Nash–Sutcliffe efficiency coefficient values calculated by WOSELM for TDS, respectively, are 5.34E‐03 and 0.991. Finally, an uncertainty analysis is conducted to evaluate the performance of different numerical models. For example, MODFLOW has an underestimated performance in simulating the salinity parameter.