A new metal chelate sorbent for glucose oxidase: Cu(II)- and Co(II)-chelated poly(N-vinylimidazole) gels

Poly(N-vinylimidazole) (PVIm) gels were prepared by irradiating a binary mixture of N-vinylimidazole (VIm)–water in a ⁶⁰Co-γ source having 4.5 kGy/h dose rate. In the glucose oxidase (GOx) adsorption studies, affinity gels with a swelling ratio of 1100% for PVIm and 40 and 55% for Cu(II)- and Co(II)-chelated PVIm gels, respectively, at pH 6.5 in phosphate buffer were used. FTIR spectra were taken for PVIm and Cu(II)- and Co(II)-chelated PVIm, and glucose oxidase adsorption on these gels, to characterize the nature of the interactions in each species. The results show that PVIm–glucose oxidase interaction is mainly electrostatic and metal ion–chelated PVIm gel–glucose oxidase interaction is of coordinate covalent nature. Cu(II) and Co(II) ions were chelated within the gels via amine groups on the imidazole ring of the gel. Different amounts of Cu(II) and Co(II) ions [maximum 3.64 mmol/g dry gel for Cu(II) and 1.72 mmol/g dry gel for Co(II)] were loaded on the gels by changing the initial concentration of Cu(II) and Co(II) ions at pH 7.0. GOx adsorption on these gels from aqueous solutions containing different amounts of GOx at different pH was investigated in batch reactors. GOx adsorption capacity was further increased when Cu(II) and Co(II) ions were attached [up to 0.53 g GOx/g dry Co(II)-chelated PVIm gels]. More than 90% of the adsorbed GOx was desorbed in 5 h in desorption medium containing 1.0M KSCN at pH 7.0 for plain gel and 0.05M EDTA at pH 4.9 for metal-chelated gel. Nonspecific glucose oxidase adsorption on/in the metal ion–chelated PVIm gel was investigated using 0.02M of phosphate buffer solution. The nonspecific GOx adsorption was determined to be about 18% for PVIm and 8% for the metal ion–chelated PVIm gels. The ionic strength effect was investigated both on PVIm and on the metal ion–chelated PVIm gels for the glucose oxidase adsorption. It was found that ionic strength was more effective on the PVIm gel because of the electrostatic interaction between protonated gel and the deprotonated glucose oxidase side chain. © 2001 John Wiley & Sons, Inc. J Appl Polym Sci 82: 446–453, 2001     

通用螺旋立铣刀的力学和动力学模型(二)

4模拟和试验结果 使用本文提出的广义方法,用各种刀具几何参数和材料,进行了超过300个切削试验.这里介绍螺旋和镶齿立铣实例,用以证明所提出模型的柔性.     

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.     

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     

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.