Development of a micro-punching machine and study on the influence of vibration machining in micro-EDM

This paper describes the development of a novel micro-punching machine that is capable of producing precision micro-holes. A significant feature of this machine is to fabricate the micro-punch and then the micro-die in the same machine, totally eliminating the eccentricity between the punch and the die when punching is proceeded. By applying vibration machining technique, we can decrease the possibility of electric short-circuiting during the micro-EDM process. The utilization of a proportional solenoid as the power unit of the micro-punching machine and as the source of vibration is found to be a successful attempt. Experiments to punch micro-holes with diameters of 0.1 and 0.2 mm on an SUS 304 stainless steel strip with 0.1 mm in thickness were carried out. The results show that the performance of this machine and the geometry of punched micro-holes are satisfactory.     

Cyclopolymerization of cinnamate ester derivatives of alkyl α-(hydroxymethyl) acrylates

Summary Three new monomers for cyclopolymerization were synthesized using phase transfer catalysis of ethyl -(chloromethyl) acrylate (ECMA), t-butyl -(bromomethyl) acrylate (TBBMA) and isobornyl -(bromomethyl)acrylate (IBBMA) with cinnamic acid sodium salt. Bulk and solution polymerization at 70–80°C using AIBN gave soluble cyclopolymers with Mn=13650 and Mw=36540 for the ethyl ester, Mn=47700 and Mw=86900 for the t-butyl ester and Mn=3500 and Mw=4650 for the isobornyl ester monomer. The ester polymerizabilities decreased with increasing substituent bulkiness. FTIR spectra showed ca 30 to 93% cyclic units depending on the concentration of the monomer used in polymerizations. DSC thermograms showed that alkyl group size had little effect on Tg's, with values of 151°C, 156°C, and 164°C for the ethyl, t-butyl and isobornyl esters, respectively.     

Oil Content,Oil Yield and Fatty Acid Profile of Groundnut Germplasm in Mediterranean Climates

A high amount of good-quality vegetable oil in seeds has an overwhelming contribution to the groundnut (Arachis hypogaea L.) cultivation throughout the world. In order to take into account great variation in oil characteristics in Arachis subspecies and botanical varieties, 256 groundnut genotypes including ICRISAT’s mini core collection were investigated. Significant variability in oil content (31.7–57.0%) was detected among groundnut genotypes. Oil yield varied from 9.5 to 179.3 kg da^?1 with the average being 67.7 kg da^?1. Significant genotypic differences were also observed for all the fatty acids studied. Oleic and linoleic acids accounted for the major fraction with mean values of 45.3 and 32.1% in the ranges of 35.3–60.9% and 16.1–43.6%, respectively. Significant negative correlation was observed between oleic and linoleic acid. In the present investigation, desirable values were obtained for oil traits which would be useful to develop nutritional and health-beneficial cultivars.     

Synthesis and Properties of Polyimides from 1,3-di(p- dimethylamino benzyl)-imidazolidine-2-thion

Summary A synthetic strategy is presented that allows the preparation of dimethylamino functionalized heterocyclic system 1,3-di (p-dimethylaminobenzy1)-imidazolidine-2-thion that was then used to prepare polyimides by the two-stage polycondensation method. The key to this success was the development of an efficient procedure leading highly pure dimethylamino monomer. Polyimides derived from 1,3-di(p-dimethylaminobenzyl)-imidazolidine-2-thion and commercial dianhydrides exhibited excellent solubility in various polar solvents. These polymers showed glass transition temperatures between 290–360°C, and initial decomposition temperature 490–485 °C and 10% mass loss ranging from 505–75°C in air. Received: 17 April 2002/Revised version: 25 January 2003/ Accepted: 14 March 2003 Correspondence to Turgay Seckin     

Synthesis and photopolymerizations of new hydroxyl-containing dimethacrylate crosslinkers

Two new hydroxyl-containing di(meth)acrylate monomers were synthesized from the reaction of methyl α-chloromethylacrylate (MCMA) and of ethyl α-chloromethylacrylate (ECMA) with glycerol. The monomers were obtained as mixtures of two isomers in different ratios and in combination with the analogous trimethacrylate monomers. Each monomer was isolated by column chromatography. The photopolymerization of these isomer mixtures and the trimethacrylate monomers was investigated individually by photodifferential scanning calorimetry (photoDSC) at room temperature using 2,2′-dimethoxy-2-phenyl-acetophenone (DMPA) as a photoinitiator. The effect of hydrogen bonding on the rates of polymerizations and conversions was examined. The results obtained for the synthesized monomers were compared to the values obtained for commercial monomers. It was observed that the hydroxyl-containing dimethacrylates polymerize much faster and to considerably higher conversion than the trimethacrylate monomers. The maximum rates of polymerization of the hydroxyl-containing monomers were higher than that of the hexanediol dimethacrylate (HDDMA), comparable to glycerol dimethacrylate and lower than hexanediol diacrylate (HDDA) and 3-(acryloyloxy)-2-hydroxypropyl methacrylate (AHM).     

A new method for expert target recognition system: Genetic wavelet extreme learning machine (GAWELM)

In last year’s, the expert target recognition has been become very important topic in radar literature. In this study, a target recognition system is introduced for expert target recognition (ATR) using radar target echo signals of High Range Resolution (HRR) radars. This study includes a combination of an adaptive feature extraction and classification using optimum wavelet entropy parameter values. The features used in this study are extracted from radar target echo signals. Herein, a genetic wavelet extreme learning machine classifier model (GAWELM) is developed for expert target recognition. The GAWELM composes of three stages. These stages of GAWELM are genetic algorithm, wavelet analysis and extreme learning machine (ELM) classifier. In previous studies of radar target recognition have shown that the learning speed of feedforward networks is in general much slower than required and it has been a major disadvantage. There are two important causes. These are: (1) the slow gradient-based learning algorithms are commonly used to train neural networks, and (2) all the parameters of the networks are fixed iteratively by using such learning algorithms. In this paper, a new learning algorithm named extreme learning machine (ELM) for single-hidden layer feedforward networks (SLFNs) Ahern et al., 1989, Al-Otum and Al-Sowayan, 2011, Avci et al., 2005a, Avci et al., 2005b, Biswal et al., 2009, Frigui et al., in press, Cao et al., 2010, Guo et al., 2011, Famili et al., 1997, Han and Huang, 2006, Huang et al., 2011, Huang et al., 2006, Huang and Siew, 2005, Huang et al., 2009, Jiang et al., 2011, Kubrusly and Levan, 2009, Le et al., 2011, Lhermitte et al., in press, Martínez-Martínez et al., 2011, Matlab, 2011, Nelson et al., 2002, Nejad and Zakeri, 2011, Tabib et al., 2009, Tang et al., 2011, which randomly choose hidden nodes and analytically determines the output weights of SLFNs, to eliminate the these disadvantages of feedforward networks for expert target recognition area. Then, the genetic algorithm (GA) stage is used for obtaining the feature extraction method and finding the optimum wavelet entropy parameter values. Herein, the optimal one of four variant feature extraction methods is obtained by using a genetic algorithm (GA). The four feature extraction methods proposed GAWELM model are discrete wavelet transform (DWT), discrete wavelet transform–short-time Fourier transform (DWT–STFT), discrete wavelet transform–Born–Jordan time–frequency transform (DWT–BJTFT), and discrete wavelet transform–Choi–Williams time–frequency transform (DWT–CWTFT). The discrete wavelet transform stage is performed for optimum feature extraction in the time–frequency domain. The discrete wavelet transform stage includes discrete wavelet transform and calculating of discrete wavelet entropies. The extreme learning machine (ELM) classifier is performed for evaluating the fitness function of the genetic algorithm and classification of radar targets. The performance of the developed GAWELM expert radar target recognition system is examined by using noisy real radar target echo signals. The applications results of the developed GAWELM expert radar target recognition system show that this GAWELM system is effective in rating real radar target echo signals. The correct classification rate of this GAWELM system is about 90% for radar target types used in this study.     

Determination of ECoG information flow activity based on Granger causality and Hilbert transformation

Analysis of directional information flow patterns among different regions of the brain is important for investigating the relation between ECoG (electrocorticographic) and mental activity. The objective is to study and evaluate the information flow activity at different frequencies in the primary motor cortex. We employed Granger causality for capturing the future state of the propagation path and direction between recording electrode sites on the cerebral cortex. A grid covered the right motor cortex completely due to its size (approx. 8 cm × 8 cm) but grid area extends to the surrounding cortex areas. During the experiment, a subject was asked to imagine performing two activities: movement of the left small finger and/or movement of the tongue. The time series of the electrical brain activity was recorded during these trials using an 8 × 8 (0.016–300 Hz band with) ECoG platinum electrode grid, which was placed on the contralateral (right) motor cortex. For detection of information flow activity and communication frequencies among the electrodes, we have proposed a method based on following steps: (i) calculation of analytical time series such as amplitude and phase difference acquired from Hilbert transformation, (ii) selection of frequency having highest interdependence for the electrode pairs for the concerned time series over a sliding window in which we assumed time series were stationary, (iii) calculation of Granger causality values for each pair with selected frequency. The information flow (causal influence) activity and communication frequencies between the electrodes in grid were determined and shown successfully. It is supposed that information flow activity and communication frequencies between the electrodes in the grid are approximately the same for the same pattern. The successful employment of Granger causality and Hilbert transformation for the detection of the propagation path and direction of each component of ECoG among different sub-cortex areas were capable of determining the information flow (causal influence) activity and communication frequencies between the populations of neurons successfully.     

Investigation of bismuth doped bioglass/graphene oxide nanocomposites for bone tissue engineering

In this study, bismuth doped 45S5 nanobioactive bioglass (nBG) and graphene oxide (GO) nanocomposites were developed and characterized in terms of microstructural, mechanical, bioactivity and biological properties. Bismuth (Bi) - doped nBG was synthesized by sol-gel method and sintered at 600 °C for 2 h. Nanosized GO was homogeneously mixed with Bi doped bioglass at various ratios to prepare nanocomposites. Addition of Bi increased the density of nBG samples while a considerable decrease in density was observed for nanocomposites with GO incorporation. Bi improved the diametral tensile strength of nBG and addition of 2.5% GO to the composite also increased the diametral tensile strength of the nanocomposites. However, addition of more than 2.5% GO had negative effect on the diametral tensile strength of the composites. Bi doping to bioglass and its composite with GO increased the biocompatibility of 45S5 nBG in which 96.5BG1Bi2.5GO (containing 96.5% BG 1% Bi 2.5% GO in weight ratio) showed highest cell viability. Overall, it can be concluded that composites of Bi doped 45S5 nBG with GO hold promise as biomaterial for biomedical applications.     

Formation of dynamic virtual enterprises and enterprise networks

This paper addresses the preparation and set up of virtual enterprises and enterprise networks. A virtual enterprise (VE) can be perceived as a customer solution delivery system created by a temporary and re-configurable information and communications technology (ICT) enabled aggregation of competencies. The main achievements of the research include: (1) Clarification and definition of the concept for virtual enterprises and enterprise networks including preparation of these. (2) Development of a framework and a reference architecture for virtual enterprises named as Structured Methodology and ICT Reference Architecture respectively. Structured methodology structures the body of knowledge related to preparation, setup, and operation of virtual enterprises and enterprise networks. ICT reference architecture consists of three levels with seven layers to portray in a diagrammatic fashion how different enterprises may exchange and use information between their respective organizations’ specific proprietary systems and a central server. (3) Development of a methodology for virtual enterprise named as virtual enterprise methodology (VEM). VEM consists of a set of guidelines, which systematically describes activities that enterprises should consider in relation to set up and preparation of own enterprise networks with the aim to set up virtual enterprises. (4) Testing and validation of the developed VEM with the realization of a virtual case study. Virtual case study demonstrates and validates the application of the developed VE methodology with the illustration of the key activities related to setting up breeding environment, setting up and operating VE and dissolution of VE.