A fuzzy logic based-method for prognostic decision making in breast and prostate cancers

Accurate and reliable decision making in oncological prognosis can help in the planning of suitable surgery and therapy, and generally, improve patient management through the different stages of the disease. In recent years, several prognostic markers have been used as indicators of disease progression in oncology. However, the rapid increase in the discovery of novel prognostic markers resulting from the development in medical technology, has dictated the need for developing reliable methods for extracting clinically significant markers where complex and nonlinear interactions between these markers naturally exist. The aim of this paper is to investigate the fuzzy k-nearest neighbor (FK-NN) classifier as a fuzzy logic method that provides a certainty degree for prognostic decision and assessment of the markers, and to compare it with: 1) logistic regression as a statistical method and 2) multilayer feedforward backpropagation neural networks an artificial neural-network tool, the latter two techniques having been widely used for oncological prognosis. In order to achieve this aim, breast and prostate cancer data sets are considered as benchmarks for this analysis. The overall results obtained indicate that the FK-NN-based method yields the highest predictive accuracy, and that it has produced a more reliable prognostic marker model than both the statistical and artificial neural-network-based methods.     

Optimal Control of the Primary Drying Stage of Freeze Drying of Solutions in Vials Using Variational Calculus

The problem of operating freeze drying of pharmaceutical products in vials placed in trays of a freeze dryer to remove free water (in frozen state) at a minimum time was formulated as an optimal control problem. Two different types of freeze dryer designs were considered. In type I freeze dryer design, upper and lower plate temperatures were controlled together, while in type II freeze dryer design, upper and lower plate temperatures were controlled independently. The heat input to the material being dried and the drying chamber pressure were considered as control variables. Constraints were placed on the system state variables by the melting and scorch temperatures during primary drying stage. Necessary conditions of optimality for the primary drying stage of freeze drying process in vials are derived and presented. Furthermore, an approach for constructing the optimal control policies that would minimize the drying time for the primary drying stage was given. In order to analyze optimal control policy for the primary drying stage of the freeze-drying process in vials, a rigorous multi-dimensional unsteady state mathematical model was used. The theoretical approach presented in this work was applied in the freeze drying of skim milk. Significant reductions in the drying times of primary drying stage of freeze drying process in vials were obtained, as compared to the drying times obtained from conventional operational policies.     

Viscoelastic State for the Solutions of Spherically Symmetric Viscoelasticity Problems

The solutions of the spherically symmetric, linear, isothermal, and transient viscoelasticity problems via reciprocity theorem have been investigated for a specific material. The integral form of stress–strain relations has been used. The Laplace transform of a viscoelastic state, which is necessary for the integral equation arising as a result of reciprocity theorem, has been derived. This integral equation has been solved by Laplace transform. A sample problem has been solved to test the presented formulation. A numerical application of the analytic solution of this problem has been given.     

Electrical and optical properties of fluorine-doped CdO films deposited by ultrasonic spray pyrolysis

The CdO:F samples have been deposited onto microscope glass substrates at 250 °C by ultrasonic spray pyrolysis method. With the incorporation of fluorine into CdO, the direct optical transition has shifted towards the shorter wavelengths, and the transparency of the material has increased at a given wavelength above the fundamental absorption edge. The shift in the absorption edge is explained by means of the Moss–Burstein effect, which is also supported with the results of the current–voltage characteristics. Here, a correlation has been established between the band broadening and the increase in conductivity due to the increase in carrier density.     

PHASE EQUILIBRIUM FOR TERNARY LIQUID SYSTEMS OF WATER CARBOXYLIC ACID CHLORINATED HYDROCARBON: THERMODYNAMIC MODELING THROUGH SERLAS

Liquid-liquid equilibrium data of the solubility (binodal) curves and tie-line end compositions are presented for mixtures of [water (1) + formic acid, or acetic acid, or propanoic acid (2) + chlorobenzene, or 1,2-dichloroethane (3)] at T = 293.2 K and P = 101.3 ± 0.7 kPa. A log-basis approach, SERLAS (solvation energy relation for liquid-associated system), has been proposed to estimate the properties and liquid-liquid equilibrium (LLE) of associated systems containing proton-donating and -accepting and polar components capable of a physical interaction through hydrogen bonding or dipole-dipole interaction. The tie lines were also correlated using the UNIFAC-original model. The reliability of the models has been analyzed against the LLE data with respect to the distribution coefficient and separation factor. The proposed model appears to be an improvement in data fit for the ternary systems, yielding a mean relative error of 10.1% for all the systems considered.     

Hydrothermal derived nanostructure rare earth (Er,Yb)-doped ZnO: structural,optical and electrical properties

The structural, optical and electrical properties of undoped and rare-earth (Er, Yb) doped zinc oxide (ZnO) nanopowder samples synthesized by hydrothermal method were investigated. The obtained samples were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and energy-dispersive spectroscopy. The optical properties of undoped and rare-earth (Er, Yb) doped ZnO were carried out with UV–visible diffuse reflectance spectroscopy techniques. XRD results reveal that Yb and Er doped ZnO nanopowders have single phase hexagonal (Wurtzite) structure without any impurities. SEM analysis indicate that dopants with different radii affected the surface morphology of ZnO nanostructures. The optical band gap of all samples were calculated from UV–Vis diffuse reflectance spectroscopy data. We have obtained band gap values of undoped, Er and Yb doped ZnO as 3.24, 3.23, 3.22 eV, respectively. Electrical characterization of the samples were made in the 280–350 K temperature range using Van der Pauw method based on Hall effect measurement. The carrier concentrations decreased for both Er and Yb doping while the Hall mobility and electrical resistivity increased with Yb, Er doping compared to undoped ZnO nanopowder at room temperature. The temperature dependent resistivity measurements of Er doped ZnO showed a metal–semiconductor transition at about 295 K, while Yb doped ZnO showed characteristic semiconductor behavior.     

Routing table updating by using intelligent agents

In a network, one of the important problems is making an efficient routing decision. Many studies have been carried out on making a decision and several routing algorithms have been developed. In a network environment, every node has a routing table and these routing tables are used for making routing decisions. Nowadays, intelligent agents are used to make routing decisions. Intelligent agents have been inspired by social insects such as ants. One of the intelligent agent types is self a cloning ant. In this study, a self cloning ant colony approach is used. Self cloning ants are a new synthetic ant type. This ant assesses the situation and multiplies through cloning or destroying itself. It is done by making a routing decision and finding the optimal path. This study explains routing table updating by using the self cloning ant colony approach. In a real net, this approach has been used and routing tables have been created and updated for every node.     

On-line neuro-expert monitoring system for Borssele Nuclear Power Plant

A new method for an on-line monitoring system for the nuclear power plants has been developed utilizing the neural networks and the expert system. The integration of them is expected to enhance a substantial potential of the functionality as operators support.

The recurrent neural network and the feed-forward neural network with adaptive learning are selected for the plant modeling and anomaly detection because of the high capability of modeling for dynamic behavior. The expert system is used as a decision agent, which works on the information space of both the neural networks and the human operators. The information of other sensory signals is also fed to the expert system, together with the outputs that the neural networks generate from the measured plant signals. The expert system can treat almost all known correlation between plant status patterns and operation modes as a priori set of rules.

From the off-line test at Borssele Nuclear Power Plant (PWR 480 MWe) in the Netherlands, it was shown that the neuro-expert system successfully monitored the plant status. The expert system worked satisfactorily in diagnosing the system status by using the outputs of the neural networks and a priori knowledge base from the PWR simulator. The electric power coefficient is simultaneously monitored from the measured reactive and active electric power signals.