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.     

Utilization of Catalytic Properties of the Encapsulated Lipase with Calix[4]arene-Adorned Sporopollenin

The dihydrazide calix[4]arene was immobilized onto sporopollenin in order to encapsulate Candida rugosa lipase (CRL) via sol-gel entrapment. The kinetic resolution of the new encapsulated lipase was investigated for enantioselective hydrolysis of racemic naproxen methyl ester and 2-phenoxypropionic acid methyl ester. The results demonstrated that the activity and enantioselectivity of the lipase were improved when the lipase was encapsulated in the presence of calix[4]arene-immobilized sporopollenin. The encapsulated lipase showed an excellent rate of enantioselectivity against the (R/S)-naproxen methyl and (R/S)-2-phenoxypropionic acid methyl esters, with E = 350 and 295, respectively, compared to the free enzyme.     

艾哈迈德·阿德南·萨伊贡艺术中心,伊兹密尔,土耳其

<正>一座建筑的性能应当如何来衡量?除了通过统治当代建筑舆论的美学、技术以及经济价值来进行描述之外,我们还能够如何来阐释一座建筑,尤其是一座致力于庇护那些渴望展现自己的艺术家的建筑?有人会问成功的表演是如何实现的,表演者是如何进行伪装的、又是如何展现自己的?根据海德格尔的理论,当我们看不到钉子的时候,铁锤的表现才是成功的;钉子本身的出现通常表明出现了问题。     

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.     

Novel synthesis medium for poly(aniline-co-o-anisidine)

Aqueous sodium oxalate (NaOX) solutions both without (1) and with (2) p-toluenesulfonic acid (p-TSA) were used to synthesize a poly(aniline-co-o-anisidine) (PANI-co-POA) film on mild steel (MS). Polarization of the substrate in p-TSA containing NaOX solution mixture led to the passivation of the surface mainly via the formation of an iron(II) oxalate layer. Copolymer coatings showed that p-TSA + NaOX solution mixture was a suitable medium for the deposition of a uniform, compact and strongly adherent PANI-co-POA coating on MS electrode. Similar coating was achieved when NaOX solution was used free of p-TSA. It was observed that addition of p-TSA in NaOX solution provided also an increase in electropolymerization rate. Evaluation of the anticorrosion performance of copolymer coatings in 3.5% NaCl solution was investigated by using AC impedance spectroscopy (EIS) technique and anodic polarization curves. Both copolymer coatings exhibited a significant physical barrier property against the attack of corrosive products on mild steel, in longer periods.