Synthesis,characterization, and antitumor activity of poly(maleic anhydride‐co‐vinyl acetate‐co‐acrylic acid)

The ternary copolymerization of maleic anhydride (MA), vinyl acetate (VA), and acrylic acid (AA) [P(MA‐co‐VA‐co‐AA)], which is considered to be an acceptor–donor–acceptor system, was carried out in 1,4‐dioxane with benzoyl peroxide as an initiator at 70°C under a nitrogen atmosphere. Constants of complex formation for the monomer systems in the study were determined by UV–visible (hydrogen‐bonding complex) and ¹H‐NMR (charge transfer complex) methods, respectively. The results show that polymerization of the P(MA‐co‐VA‐co‐AA) system proceeds by an alternating terpolymerization mechanism. It is shown that the synthesized copolymers have typical polyelectrolyte behavior, ability for reversible hydrolysis–anhydrization reactions, and semicrystalline structures. In these cases, including radical polymerization, and formation of semicrystalline structures, the hydrogen‐bonding effect plays a significant role. The in vitro cytotoxicities of the synthesized terpolymer and alternating copolymer were evaluated using Raji cells (human Burkitt lymphoma cell line). The antitumor activities of prepared anion‐active copolymers were studied using methyl–thiazol–tetrazolium colorimetric assay and 50% of the cytotoxic dose of each copolymer and terpolymer were calculated. Hydrolyzed P(MA‐co‐VA‐co‐AA) and P(MA‐alt‐AA) copolymers have sufficiently high antitumor activity, which depends on the amount of hydrogen‐bonding carboxylic groups and their regular distribution in the side chain of functional macromolecules. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 100: 3425–3432, 2006     

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.     

Exploring comprehensible classification rules from trained neural networks integrated with a time-varying binary particle swarm optimizer

Purpose

Extracting comprehensible classification rules is the most emphasized concept in data mining researches. In order to obtain accurate and comprehensible classification rules from databases, a new approach is proposed by combining advantages of artificial neural networks (ANN) and swarm intelligence.

Method

Artificial neural networks (ANNs) are a group of very powerful tools applied to prediction, classification and clustering in different domains. The main disadvantage of this general purpose tool is the difficulties in its interpretability and comprehensibility. In order to eliminate these disadvantages, a novel approach is developed to uncover and decode the information hidden in the black-box structure of ANNs. Therefore, in this paper a study on knowledge extraction from trained ANNs for classification problems is carried out. The proposed approach makes use of particle swarm optimization (PSO) algorithm to transform the behaviors of trained ANNs into accurate and comprehensible classification rules. Particle swarm optimization with time varying inertia weight and acceleration coefficients is designed to explore the best attribute-value combination via optimizing ANN output function.

Results

The weights hidden in trained ANNs turned into comprehensible classification rule set with higher testing accuracy rates compared to traditional rule based classifiers.     

Classification rule discovery for the aviation incidents resulted in fatality

Data mining methods have been successfully applied to different fields. Aviation industry is one of them. There is a large amount of knowledge and data accumulation in aviation industry. These data could be stored in the form of pilot reports, maintenance reports, incident reports or delay reports. This paper explains the data mining application on the incident reports of the Federal Aviation Administration (FAA) Accident/Incident Data System database, contains incident data records for all categories of civil aviation between the years of 2000 and 2006. In this study, we applied data mining methods on the incident reports. Moreover rough sets concept is used to reduce the attributes of data set. The purpose of this application is to find out the effective attributes in order to reduce the number of the fatality in the incidents. The categorization tools and decision trees are used to find the relations and rules about the incidents resulted in fatality. For this purpose data-mining analysis is conducted. As a result some rules about the fatality are obtained and also the parameters that affect the fatality in the incident have determined. The rules found are tested in terms of their accuracy and reliability, and these results are seen to be meaningful.     

Sodium and ultrafiltration profiling in hemodialysis: A long-forgotten issue revisited

Sodium and ultrafiltration profiling are method of dialysis in which dialysate sodium concentration and ultrafiltration rate are altered during the course of the dialysis session. Sodium and ultrafiltration profiling have been used, commonly simultaneously, to improve hemodynamic stability during hemodialysis. Sodium profiling is particularly effective in decreasing the incidence of intradialytic hypotension, while ultrafiltration profiling is suggested to decrease subclinical repeated end organ ischemia during dialysis. However, complications such as increased interdialytic weight gain and thirst due to sodium excess have prevented widespread use of sodium profiling. Evidence suggest that different sodium profiling techniques may lead to different clinical results, and preferring sodium balance neutral sodium profiling may mitigate adverse effects related to sodium overload. However, evidence is lacking on the long-term clinical outcomes of different sodium profiling methods. Optimal method of sodium profiling as well as the utility of sodium/ultrafiltration profiling in routine practice await further clinical investigation.     

Solving fuzzy multiple objective generalized assignment problems directly via bees algorithm and fuzzy ranking

In this paper, a direct solution approach for solving fuzzy multiple objective generalized assignment problems is proposed. In the problem, the coefficients and right hand side values of the constraints and the objective function coefficients are defined as fuzzy numbers. The addressed problem also has a multiple objective structure where the goals are determined so as to minimize the total cost and the imbalance between the workload of the agents. The direct solution approach utilizes the fuzzy ranking methods to rank the objective function values and to determine the feasibility of the constraints within a metaheuristic search algorithm, known as bees algorithm. Different fuzzy ranking methods, namely signed distance, integral value and area based approach are used in bees algorithm. For the computational study, the effects of these fuzzy ranking methods on the quality of the solutions are also analyzed.     

7 T renal MRI: challenges and promises

The progression to 7 Tesla (7 T) magnetic resonance imaging (MRI) yields promises of substantial increase in signal-to-noise (SNR) ratio. This increase can be traded off to increase image spatial resolution or to decrease acquisition time. However, renal 7 T MRI remains challenging due to inhomogeneity of the radiofrequency field and due to specific absorption rate (SAR) constraints. A number of studies has been published in the field of renal 7 T imaging. While the focus initially was on anatomic imaging and renal MR angiography, later studies have explored renal functional imaging. Although anatomic imaging remains somewhat limited by inhomogeneous excitation and SAR constraints, functional imaging results are promising. The increased SNR at 7 T has been particularly advantageous for blood oxygen level-dependent and arterial spin labelling MRI, as well as sodium MR imaging, thanks to changes in field-strength-dependent magnetic properties. Here, we provide an overview of the currently available literature on renal 7 T MRI. In addition, we provide a brief overview of challenges and opportunities in renal 7 T MR imaging.     

A modified dual current mode control method with an adaptive current bandwidth

In this paper, a new adaptive dual current mode control method (ADCMC) is presented, being a result of the modification of existing dual current mode control (DCMC) by introducing an adaptive current bandwidth. The ADCMC offers several important advantages over DCMC, such as no peak‐to‐average error in the inductor current, better transient response of current loop, and improved line regulation. A detailed analysis of the proposed ADCMC is performed for three types of DC–DC power electronics converters: buck, boost, and non‐inverting buck–boost converter. The performances of the ADCMC are tested with simulations and experiments. The obtained results confirm the analysis and validity of the proposed ADCMC method. Copyright © 2015 John Wiley & Sons, Ltd.     

Attractancy of diethyl ether extracts of Arachis hypogaea (Linnaeus) (Papilionaceae), Citrullus lanatus (Thunberg) (Cucurbitaceae) and Irvingia gabonensis var. excelsa (Baillon) (Irvingiaceae) to Oryzaephilus mercator (Fauvel) (Coleoptera: Silvanidae)

Volatiles of the diethyl ether extracts of I. gabonensis, C. lanatus and A. hypogaea attracted 1–7 and 28–42-day-old adult O. mercator when tested in a two-choice pitfall Petri dish bioassay. For both age groups, a significant difference was observed in response between fed and starved adults to the different extracts. Differences in starvation time did not affect responses of individuals in the 1–7-day-old group; a significant effect was observed in the 28–42-day-old group.

Adults, 28–42-day-old were significantly more responsive to virtually all extracts than 1–7-day-old adults. Responses of adults in each age group to the different extracts were not significantly different. The time of trial within the period 0900–1900 h did not significantly affect the response of adults in both age groups, starved for 2 days, to any extract.