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.     

Transmission line analysis of nonlinear slot coupled microstrip antenna

The transmission line method is used for the analysis of microstrip antennas fed by several nonlinear slots with 'H' shapes or new geometries. This analysis allows the input impedance of nonlinear slot coupled microstrip antennas to be calculated for the four different structures.<>     

Single-sided ventilation — Part II. further considerations

This paper extends an earlier report of measurements and computer predictions of flows in a cubical cavity exposed on one side to the scavenging effect of an external stream. Further measurements and photographs are presented; they are consistent with each other and with the predictions of the computational model. Specifically, comparisons are made between measured and predicted mean velocities and turbulent shear stresses, and photographs are used to elucidate features of the shear layers generated by accelerating and decelerating external flows. The computer model is used to deduce approximate relations between internal turbulent activity (measured by mean velocity, turbulence energy and shear stress) and external velocity, percentage ‘window’ opening and relative position of the plane walls upstream and downstream of the cavity.     

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.     

Extended-spectrum beta-lactamase-producing Klebsiella pneumoniae strains causing nosocomial outbreaks of infection in the United Kingdom

Representative isolates from 10 distinct extended-spectrum beta-lactamase-producing strains of Klebsiella pneumoniae that caused hospital outbreaks in the United Kingdom from 1991 to 1994 were examined for relationships between their enzymes and plasmids. The beta-lactamases were identified by a combination of isoelectric focusing and gene sequencing. SHV-2 beta-lactamase was produced by isolates from four outbreaks, SHV-5 was involved in three, and SHV-4, TEM-15, and TEM-26 were involved in one outbreak each. All of the extended-spectrum beta-lactamases were encoded by self-transmissible plasmids, with sizes ranging from about 70 to 160 kb. No similarities between the restriction digest patterns of the extended-spectrum beta-lactamase-encoding plasmids were detected, except to some extent between those that produced TEM-15 and TEM-26. Thus, outbreaks of hospital infection with these organisms in the United Kingdom from 1991 to 1994 involved distinct organisms and resistance plasmids and appeared to be unrelated.     

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.     

A dynamic variability management approach working with agile product line engineering practices for reusing features

The Journal of Supercomputing - Agile software development (ASD) and software product line (SPL) have shown significant benefits for software engineering processes and practices. Although both...