Radiation polymerization of acrylonitrile in a viscous system with styrene

Radiation polymerization of acrylonitrile in a viscous system with styrene was performed at ambient temperature by using γ‐rays. It is found that the overall rate of polymerization was accelerated after critical conversion due to the gel effect. As the molar fraction of styrene in monomer feed (f_St) is increased, both the total polymer conversion and molar fraction of acrylonitrile in the copolymer feed (F_AN) were decreased. The monomer reactivity ratios for acrylonitrile and styerne were determined to be r₁ (AN) = 0.25 and r₂ (St) = 2.0, respectively. The copolymers obtained were characterized by Fourier transformed infrared spectra (FTIR), X‐ray diffraction (XRD), scanning electron microscopy (SEM), ¹H‐NMR, and pyrolysis mass spectrometry (PMS). It was found that the slight addition of styrene to acrylonitrile strongly changes crystallinity, morphology, and thermal decomposition of the resulting polymer. ¹H‐NMR measurment of AN/St copolymer showed the appearance of aromatic proton signals and shifted the resonance of the methylene proton to lower chemical shifts. The mass spectra of AN/St copolymers showed fragments of pyrolysates corresponding to oligonitriles with styrene end groups. © 2002 Wiley Periodicals, Inc. J Appl Polym Sci 84: 268–275, 2002; DOI 10.1002/app.10324     

Low-temperature unit-cell parameters of La1−x Pr x CaBaCu3O7 compounds

The suppression of the superconductivity by praseodymium in La_1−x Pr _x CaBaCu₃O₇ may be explained in terms of the difference in the electronic or crystal structure between these compounds. Three compositions with x=0.0, 0.5, 1.0 were, therefore, investigated by X-ray diffraction in order to determine the unit-cell dimensions at room temperature as well as at lower temperatures down to 5 K. The results showed no considerable changes in the orthorhombic unit-cell dimensions on decreasing the temperature to 5 K. Unit-cell volumes at low temperatures were 0.99 of the room temperature values. The intensity sequence of reflections in the diffraction pattern was the same for all samples throughout the range of temperature studied.     

MC-CDMA with exponential power profile in Nakagami fading channel

A refined study of multi-carrier code division multiple access transmission over a Nakagami fading channel is proposed. The channel power is modeled with an exponential power profile rather than the uniform profile prevalent in other papers. An expression for the bit error rate (BER) is then derived. Numerical results show that MC-CDMA performances depend on the power dispersion of interferers around the desired user power.     

New mechanisms in photo-assisted MOVPE of II-VI semiconductors

The first detailed comparison has been made of the metalorganic vapor phase epitaxy growth rates of CdTe, ZnTe, and ZnSe, measured in situ with laser reflectometry. The comparison also includes the photo-assisted growth with visible radiation from an argon ion laser. Using a standard Group II precursor (DMCd or DMZn.TEN) partial pressure of 1.5 × 10⁻⁴ atm, VI/II ratio of 1 and DIPM (M = Te, Se) the maximum growth rates are in the region of 10 to 15 AU/ s. Decrease in growth rates of ZnTe at higher temperatures or higher laser powers have been attributed to the desorption from the substrate of unreacted Te precursor. The behavior of DTBSe is quite different from DIPSe for both pyrolytic and photo-assisted growth. The maximum growth rate is around 1 AU/ s with very little photo-enhancement, except at 300°C. Secondary ion mass spectroscopy analysis of hydrogen concentration in the ZnSe layers shows high concentrations, up to 5.9 × 10¹⁹ atoms cm⁻³ for DTBSe grown ZnSe under pyrolytic conditions. These results show that the growth kinetics play an important part in the incorporation of hydrogen and passivation of acceptor doped material.     

The electrical properties of post-deposition annealed and as-deposited In-doped CdTe thin films

The electrical properties of CdTe thin film have been studied and discussed, including, the conduction mechanism, and the effect of temperature and indium doping on the current passing through the CdTe film and hence on the film conductivity. It is observed that the CdTe film is of the modified Poole–Frenkel conduction mechanism and the resistivity of the film can be lowered by more than one order of magnitude due to indium doping.     

A General Reliable Quadratic Form: An Extension of Affine Arithmetic

In this article, a new extension of affine arithmetic is introduced. This technique is based on a quadratic form named general quadratic form. We focus here on the computation of reliable bounds of a function over a hypercube by using this new tool. Some properties of first quadratic functions and then polynomial ones are reported. In order to show the efficiency of such a method, ten polynomial global optimization problems are presented and solved by using an interval branch-and-bound based algorithm. The work of the first author was also supported by the Laboratoire de Mathématiques Appliquées CNRS–FRE 2570, Université de Pau et des Pays de l'Adour, France, and by the Laboratoire d'Electrotechnique et d'Electronique Industrielle CNRS–UMR5828, Group EM³, INPT–ENSEEIHT.     

Tree-based partitioning of date for association rule mining

The most computationally demanding aspect of Association Rule Mining is the identification and counting of support of the frequent sets of items that occur together sufficiently often to be the basis of potentially interesting rules. The task increases in difficulty with the scale of the data and also with its density. The greatest challenge is posed by data that is too large to be contained in primary memory, especially when high data density and/or low support thresholds give rise to very large numbers of candidates that must be counted. In this paper, we consider strategies for partitioning the data to deal effectively with such cases. We describe a partitioning approach which organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds. Shakil Ahmed received a first class BSc (Hons) degree from Dhaka University, Bangladesh, in 1990; and an MSc (first class), also Dhaka University, in 1992. He received his PhD from The University of Liverpool, UK, in 2005. From 2000 onwards he is a member of the Data Mining Group at the Department of Computer Science of the University of Liverpool, UK. His research interests include data mining, Association Rule Mining and pattern recognition. Frans Coenen has been working in the field of Data Mining for many years and has written widely on the subject. He received his PhD from Liverpool Polytechnic in 1989, after which he took up a post as a RA within the Department of Computer Science at the University of Liverpool. In 1997, he took up a lecturing post within the same department. His current Data Mining research interests include Association rule Mining, Classification algorithms and text mining. He is on the programme committee for ICDM'05 and was the chair for the UK KDD symposium (UKKDD'05). Paul Leng is professor of e-Learning at the University of Liverpool and director of the e-Learning Unit, which is responsible for overseeing the University's online degree programmes, leading to degrees of MSc in IT and MBA. Along with e-Learning, his main research interests are in Data Mining, especially in methods of discovering Association Rules. In collaboration with Frans Coenen, he has developed efficient new algorithms for finding frequent sets and is exploring applications in text mining and classification.     

On the free convection boundary layer on a vertical plate with prescribed surface heat flux

The free convection boundary layer on a vertical plate with a prescribed surface heat flux proportional to (1 +x ²)^µ (µ a constant) is discussed. For µ > –12 the boundary-layer solution develops from a similarity solution valid forx small to the one valid forx large. However, with µ –12 the similarity equations forx large are not solvable and the behaviour for largex in this case is discussed. It is found that there are two cases to consider, namely µ < –12 and µ = –12. In both cases the leading-order problem is homogeneous involving an arbitrary constant which is determined from an integral property of the full boundary-layer problem. However, in the former case the asymptotic behaviour is algebraic, with the perturbation to the leading-order solution, arising from the heat flux boundary condition, being ofO[x ^1+2µ]. The latter case also involves logarithmic terms, with the perturbation to be leading-order solution now being ofO[(logx)^–1].     

Automatic Channel Detection Using DNN on 2D Seismic Data

Geologists interpret seismic data to understand subsurface properties and subsequently to locate underground hydrocarbon resources. Channels are among the most important geological features interpreters analyze to locate petroleum reservoirs. However, manual channel picking is both time consuming and tedious. Moreover, similar to any other process dependent on human intervention, manual channel picking is error prone and inconsistent. To address these issues, automatic channel detection is both necessary and important for efficient and accurate seismic interpretation. Modern systems make use of real-time image processing techniques for different tasks. Automatic channel detection is a combination of different mathematical methods in digital image processing that can identify streaks within the images called channels that are important to the oil companies. In this paper, we propose an innovative automatic channel detection algorithm based on machine learning techniques. The new algorithm can identify channels in seismic data/images fully automatically and tremendously increases the efficiency and accuracy of the interpretation process. The algorithm uses deep neural network to train the classifier with both the channel and non-channel patches. We provide a field data example to demonstrate the performance of the new algorithm. The training phase gave a maximum accuracy of 84.6% for the classifier and it performed even better in the testing phase, giving a maximum accuracy of 90%.