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.     

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.     

Differential cellular response within the rabbit tendon unit following tendon injury

Since 1975, 340 patients were treated by tracheal sleeve resection for tracheal or subglottic laryngeal iatrogenic stenoses in our unit. Preoperative iterative Nd YAG laser sessions have usually been performed, without success. The length of the sleeve specimen was an average of 3 1/4 cm. Twelve patients died on the post operative course (3.5%), 3 more patients died later after failure of the procedure (0.9%) and nineteen had recurrent stenoses treated with use of a tracheostomy tube, a permanent Montgomery tube, or an endotracheal stent (5.6%). Three hundred and six patients are definitely cured (90%), at the first attempt for 265 patients, after a laser session for granulomas for 20 patients, after a second tracheal resection for 6 patients and after a temporary Montgomery tube for 15 patients. Providing there is a good selection of the patients, tracheal sleeve resection is the best treatment for iatrogenic stenosis.     

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.     

Macrophage formation of angiostatin during inflammation. A byproduct of the activation of plasminogen

Angiostatin is a potent inhibitor of tumor angiogenesis and the growth of metastatic foci. Recent studies have indicated that neoplastic cells can generate angiostatin directly or in cooperation with tumor-associated macrophages. In studies reported here, we determined whether angiostatin is generated in mice under non-neoplastic settings. Utilizing murine RAW264.7 macrophages and thioglycollate-elicited peritoneal macrophages, we demonstrate that angiostatin-like fragments are generated as a byproduct of the proteolytic regulation of membrane-bound plasmin. Plasmin proteolysis and subsequent loss in membrane-bound plasmin activity requires active plasmin but was unaffected by inhibitors of metalloproteinases. Lysine binding fragments of plasmin, isolated from macrophage-conditioned media utilizing affinity chromatography, appeared as a major (48 kDa) and two minor bands (42 and 50 kDa) in SDS-polyacrylamide gel electrophoresis and were immunoreactive with anti-kringle 1-3 IgG. Each peptide begins with Lys77 and contains the entire sequence of angiostatin. The affinity isolated plasmin fragments inhibited bFGF-induced endothelial cell proliferation. Lavage fluid recovered from the peritoneal cavities of mice previously injected with thioglycollate contained angiostatin-like plasmin fragments similar to those generated in vitro. This is the first demonstration that angiostatin-like plasmin fragments are generated in a non-neoplastic inflammatory setting. Thus, in addition to regulating pericellular plasmin activity, proteolysis of plasmin generates inactive kringle-containing fragments expressing angiostatic properties.     

The effect of composition,substrate temperature and annealing on the paramagnetic centres in SiO x /SnO co-evaporated thin films

The effects of composition and substrate temperature during evaporation and annealing, on the electron spin resonance spectra of co-evaporated SiO _x /SnO (300 nm) thin films have been investigated. The spin density is observed to decrease with increasing SnO content. This effect is attributed to the saturation of some of the paramagnetic defects such as dangling bonds and other structural defects like E₁ centres. The decrease in spin density in the composite thin films of SiO _x /SnO of constant thickness and composition with annealing and with increase in the substrate temperature during evaporation, is related to the conversion of some of the paramagnetic E₁ centres to E₁ (probable non-paramagnetic precursor of E₁) centres as well as to the saturation of some of the dangling bonds.     

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%.     

The Bivariate Transmuted Family of Distributions: Theory and Applications

The bivariate distributions are useful in simultaneous modeling of two random variables. These distributions provide a way to model models. The bivariate families of distributions are not much widely explored and in this article a new family of bivariate distributions is proposed. The new family will extend the univariate transmuted family of distributions and will be helpful in modeling complex joint phenomenon. Statistical properties of the new family of distributions are explored which include marginal and conditional distributions, conditional moments, product and ratio moments, bivariate reliability and bivariate hazard rate functions. The maximum likelihood estimation (MLE) for parameters of the family is also carried out. The proposed bivariate family of distributions is studied for the Weibull baseline distributions giving rise to bivariate transmuted Weibull (BTW) distribution. The new bivariate transmuted Weibull distribution is explored in detail. Statistical properties of the new BTW distribution are studied which include the marginal and conditional distributions, product, ratio and conditional momenst. The hazard rate function of the BTW distribution is obtained. Parameter estimation of the BTW distribution is also done. Finally, real data application of the BTW distribution is given. It is observed that the proposed BTW distribution is a suitable fit for the data used.