Electro-copolymerisation of indene and styrene

Electroinitiated cationic copolymerisation of indene and styrene was investigated by constant potential electrolysis. Effects of copolymerisation potential and temperature on the copolymer composition and the reactivity ratios are discussed. The reactivity ratios of the monomers were calculated according to the integrated Lewis-Mayo equation.     

Fuzzy rule interpolation for multidimensional input spaces in determining d50c of hydrocyclones

Fuzzy rule-based systems have been very popular in many engineering applications. In mineral engineering, fuzzy rules are normally constructed using some fuzzy rule extraction techniques to establish the determination model in predicting the d50c of hydrocyclones. However, when generating fuzzy rules from the available information, it may result in a sparse fuzzy rule base. The use of more than one input variable is also common in hydrocyclone data analysis. This paper examines the application of fuzzy interpolation to resolve the problems using sparse fuzzy rule bases, and to perform analysis of fuzzy interpolation in multidimensional input spaces.     

On-Line Shape recognition with incremental training using binary synaptic weights algorithm

Recognition of hand drawn shapes is beneficial in drawing packages and automated sketch entry in handheld computers. In this paper, we propose a new approach to on-line geometric shape recognition with incremental training function, which utilizes a heuristic function to reduce noise and a neural network for classification and on-line training. Instead of recognizing segments of a drawing and then performing syntactical analysis to match with a predefined shape, which is weak in terms of generalization and dealing with noise, we examine the shape as a whole. The main concept of the recognition method is derived from the fact that internal angles are very important in the perceived shape of outlines. Our application's aim is to recognize elliptic, rectangular, and triangular shapes in a way similar to human cognition of these shapes. Human beings recognize such basic shapes regardless of the variations in size, noise on the shape border, rotation and in the case of triangles, regardless of the type of the triangle. The key concept is that the neural network learns the relationships between the internal angles of a shape and its classification, therefore only a few training samples which represent the class of the shape are sufficient. Fast meremental training, which is performed on-line, is accomplished by the use of the Binary Synaptic Weights algorithm, a one pass, feedforward neural network training algorithm. Incremental training offers the advantage of adjusting the recognition capability of the system to the user's drawings. the results are very successful, such that the neural network correctly classified shapes that did not have any resemblance to the shapes in the initial training set.     

A new deep intuitionistic fuzzy time series forecasting method based on long short-term memory

The Journal of Supercomputing - In recent years, deep artificial neural networks can have better forecasting performance than many other artificial neural networks. The long short-term memory...     

Effects of gamma irradiation on aflatoxins

Summary The effects of gamma irradiation on a mixture of aflatoxins B₁, B₂, G₁, G₂ were studied. Standard solutions of A and B were irradiated at 5, 10, and 20 kGy in a solution of water/DMSO (9+1, v/ v) by using a¹³⁷Cs source. The control (0 kGy) and irradiated samples were subjected to RP-HPLC analyses with methanol/water (4+5, v/v) as the mobile phase. Aflatoxin B₁ (AFB₁) was the most radio-sensitive of the four compounds. The radiosensitivity of the other aflatoxins, was in increasing order: G₂, B₂, G₁. Only about 5% of AFB₁ remained after irradiation of solution A at 5 kGy. When the concentration of solution B was increased two-fold, trace amounts of AFB₁ remained after irradiation doses of 10 and 20 kGy. Irradiation was found to be suitable for the destruction of aflatoxins in solution.

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Einfluß von Gammastrahlung auf die Aflatoxine

Zusammenfassung Der EinfluB von -Strahlen auf die Aflatoxinmischung B₁, B₂, G₁ und G₂ wurde untersucht. Standardlösungen A und B wurden mit -Strahlung von 5; 10; bzw. 20 kGy in der Lösung von Wasser/DMSO (9+1, v/v) unter Verwendung einer¹³⁷Cs-Quelle bestrahlt. Kontroll(0 kGy)-und bestrahlte Proben wurden der RP-HPLC Analyse unter Verwendung von Methanol/Wasser (4+5, v/v) als mobile Phase unterworfen. Aflatoxin B₁ (AFB₁) war die strahlenempfindlichste von den vier, die Strahlenempfindlichkeit der anderen Aflatoxine nahm bei 5 kGy in der Reihenfolge: G₂, B₂, G₁ zu. Nur ca. 5% von AFB₁ ist nach der Bestrahlung üibriggeblie-ben. Wenn sich die Konzentration zweifach erhöht (Lösung B), ist von AFB₁ nur eine Spur nach der 10 kGy- und 20 kGy-Bestrahlung übriggeblieben. Es wurde gefunden, daß die Bestrahlung zur Zerstörung der Aflatoxine in der Lösung geeignet ist.

     

Simultaneous afterclearing and decolorisation by ozonation after disperse dyeing of polyester

A set of trials have been conducted to examine the efficiency of ozonation on afterclearing of disperse dyed poly(ethylene terephthalate) fibres. Ozonation was performed in the cooled dyebath after the completion of the dyeing cycle. The ozone concentration used was 12.8 ± 0.3 mg/min at a gas flow rate of 400 ml/min. The results indicate that 3 min ozonation time is appropriate to achieve wash fastness results comparable to conventional reduction clearing. These results were obtained with simultaneous dyebath decolorisation ratios up to 67% and without significant colour yield ( K/S value) losses of the dyed fabric. Ozonation periods exceeding 3 min caused significant colour yield ( K/S value) losses, although dyebath decolorisation ratios increased up to 82% and wash fastness properties further improved. The chemical oxygen demand of the dyeing process decreased up to 62% by the ozonation afterclearing. The advantages of the ozonation afterclearing process are savings in terms of water, energy and time and reduction in environmental load.     

A parallel algorithm with enhancements via partial objective value cuts for cluster-based wireless sensor network design

In this paper, we develop a parallel algorithm for the solution of an integrated topology control and routing problem in Wireless Sensor Networks (WSNs). After presenting a mixed-integer linear optimization formulation for the problem, for its solution, we develop an effective parallel algorithm in a Master–Worker model that incorporates three parallelization strategies, namely low-level parallelism, domain decomposition, and multiple search (both cooperative and independent) in a single Master–Worker framework.     

A modified genetic algorithm for forecasting fuzzy time series

Fuzzy time series approaches are used when observations of time series contain uncertainty. Moreover, these approaches do not require the assumptions needed for traditional time series approaches. Generally, fuzzy time series methods consist of three stages, namely, fuzzification, determination of fuzzy relations, and defuzzification. Artificial intelligence algorithms are frequently used in these stages with genetic algorithms being the most popular of these algorithms owing to their rich operators and good performance. However, the mutation operator of a GA may cause some negative results in the solution set. Thus, we propose a modified genetic algorithm to find optimal interval lengths and control the effects of the mutation operator. The results of applying our new approach to real datasets show superior forecasting performance when compared with those obtained by other techniques.     

Metamodeling live sequence charts for code generation

This article presents a metamodeling study for Live Sequence Charts (LSCs) and Message Sequence Charts (MSCs) with an emphasis on code generation. The article discusses specifically the following points: the approach to building a metamodel for MSCs and LSCs, a metamodel extension from MSC to LSC, support for model-based code generation, and finally action model and domain-specific data model integration. The metamodel is formulated in metaGME, the metamodel language for the Generic Modeling Environment.

The Training of Pi-Sigma Artificial Neural Networks with Differential Evolution Algorithm for Forecasting

Computational Economics - Looking at the artificial neural networks’ literature, most of the studies started with feedforward artificial neural networks and the training of many feedforward...