Explaining IT governance disclosure through the constructs of IT governance maturity and IT strategic role

This study investigates the relation between the maturity of IT governance processes and the IT governance disclosure of firms. Furthermore, it examines whether the strategic role of IT in an industry induces systematic variation in IT governance disclosure. Based on a content analysis of annual reports and a field survey on the maturity of the implementation of COBIT processes, the results demonstrate a role of IT governance frameworks in stimulating accountability and transparency via enhanced external reporting of relevant IT information to external stakeholders, in particular in settings where the strategic role of IT is high.     

Survey of GPU Based Sorting Algorithms

Parallel sorting algorithms are widely studied nowadays. After the introduction of parallel processors such as graphics processing unit (GPU) and easy to use parallel programming languages such as CUDA and OpenCL, literature on parallel sorting algorithms has become vast and richer with new ideas and techniques applied to solve the famous problem of sorting. This paper presents a survey of GPU based sorting algorithms. Four sorting algorithms have been selected for this survey: Radix sort, Merge sort, Sample sort and Quick sort. Methods used in those algorithms are described in brief. The performance of these algorithms as claimed by their authors is also presented. A comparative analysis based on the literature is depicted.     

Sparse Adaptive Parameterization of Variability in Image Ensembles

This paper introduces a new parameterization of diffeomorphic deformations for the characterization of the variability in image ensembles. Dense diffeomorphic deformations are built by interpolating the motion of a finite set of control points that forms a Hamiltonian flow of self-interacting particles. The proposed approach estimates a template image representative of a given image set, an optimal set of control points that focuses on the most variable parts of the image, and template-to-image registrations that quantify the variability within the image set. The method automatically selects the most relevant control points for the characterization of the image variability and estimates their optimal positions in the template domain. The optimization in position is done during the estimation of the deformations without adding any computational cost at each step of the gradient descent. The selection of the control points is done by adding a L ¹ prior to the objective function, which is optimized using the FISTA algorithm.     

Spatio-temporal polygonal clustering with space and time as first-class citizens

Detecting spatio-temporal clusters, i.e. clusters of objects similar to each other occurring together across space and time, has important real-world applications such as climate change, drought analysis, detection of outbreak of epidemics (e.g. bird flu), bioterrorist attacks (e.g. anthrax release), and detection of increased military activity. Research in spatio-temporal clustering has focused on grouping individual objects with similar trajectories, detecting moving clusters, or discovering convoys of objects. However, most of these solutions are based on using a piece-meal approach where snapshot clusters are formed at each time stamp and then the series of snapshot clusters are analyzed to discover moving clusters. This approach has two fundamental limitations. First, it is point-based and is not readily applicable to polygonal datasets. Second, its static analysis approach at each time slice is susceptible to inaccurate tracking of dynamic cluster especially when clusters change over both time and space. In this paper we present a spatio-temporal polygonal clustering algorithm known as the Spatio-Temporal Polygonal Clustering (STPC) algorithm. STPC clusters spatial polygons taking into account their spatial and topological properties, treating time as a first-class citizen, and integrating density-based clustering with moving cluster analysis. Our experiments on the drought analysis application, flu spread analysis and crime cluster detection show the validity and robustness of our algorithm in an important geospatial application.     

Propagation of QSH (quasi shear horizontal) acoustic waves in piezoelectric plates

The characteristics of QSH (quasi shear horizontal) acoustic waves propagating in thin plates of Y-cut, X-propagation lithium niobate are investigated theoretically and experimentally. The fractional velocity change (Deltanu/nu) produced by electrical shorting of the surface is calculated as a function of the normalized plate thickness h/lambda (h=plate thickness, lambda=acoustic wavelength). It was found that values of Deltanu/nu as high as 0.18 could be obtained. Experimental measurements show good agreement with theory. The properties of QSH waves propagating in the presence of a perfectly conducting electrode separated from the piezoelectric plate by a small air gap have been studied theoretically and experimentally. It was found that by varying the height of the gap, the phase shift through a 3.2-MHz QSH wave delay line can be varied by more than 230 degrees . We have also theoretically investigated the influence of a thin layer of arbitrary conductivity on the velocity and attenuation of the QSH wave. Calculations show that the variations in these parameters can be as high as 18% and 5 dB per wavelength for a change in layer surface conductance from 10(-7) to 10(-5) S. Results obtained in this paper confirm the attractive properties of QSH waves for a variety of sensing and signal processing applications.     

Effective visualization of complex vascular structures using a non-parametric vessel detection method

The effective visualization of vascular structures is critical for diagnosis, surgical planning as well as treatment evaluation. In recent work, we have developed an algorithm for vessel detection that examines the intensity profile around each voxel in an angiographic image and determines the likelihood that any given voxel belongs to a vessel; we term this the "vesselness coefficient" of the voxel. Our results show that our algorithm works particularly well for visualizing branch points in vessels. Compared to standard Hessian based techniques, which are fine-tuned to identify long cylindrical structures, our technique identifies branches and connections with other vessels. Using our computed vesselness coefficient, we explore a set of techniques for visualizing vasculature. Visualizing vessels is particularly challenging because not only is their position in space important for clinicians but it is also important to be able to resolve their spatial relationship. We applied visualization techniques that provide shape cues as well as depth cues to allow the viewer to differentiate between vessels that are closer from those that are farther. We use our computed vesselness coefficient to effectively visualize vasculature in both clinical neurovascular x-ray computed tomography based angiography images, as well as images from three different animal studies. We conducted a formal user evaluation of our visualization techniques with the help of radiologists, surgeons, and other expert users. Results indicate that experts preferred distance color blending and tone shading for conveying depth over standard visualization techniques.     

COMPARATIVE STUDY BETWEEN THE KINETICS OF RETORTING OF OHIO ANO COLORADO SHALE

A comparative study between the kinetics of oil generation from Ohio and Colorado shales has been done. To facilitate comparison and to eliminate systematic errors, the same retorting system was used to study the kinetics of both Ohio and Colorado shales. Temperatures ranging to 923° K and nitrogen and carbon dioxide as sweep gases were used for this study. Both the isothermal and non-isothermal retorting techniques were employed for the kinetic measurement and the results obtained compared favorably with each other. The pyrolysis of Ohio shale was also found to comply with first order kinetics within the limits of experimental error. The study gave an apparent activation energy of 149.1 ± 2.8 KJ/mol for Ohio shale (25 cm³/kg) and 182.5 ± 3.5 KJ/mol for Colorado shale (110  cm³/kg).