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    Volume 1, Issue 1
    Editorial
    Noe Lopez-Benitez*
    Workflows describe not only a collection of component functions, but also their dependencies, which predefine a constrained order of execution. Scientific workflows are used to describe not only computational and service requirements but also the location of such services or computational units. Instruments in scientific laboratories, robots in remote inaccessible areas, a satellite unit in outer space, a set of databases, storage units as well as computational units, all provide services that must be orchestrated to satisfy an overall scientific objective. In this paper functional representations of workflows are discussed as a convenient alternative abstractions that can provide the basis for dynamic management of service requests; furthermore they can be regarded as a paradigm to organize and easily develop entire applications via the use of functional languages to explore not only fine-grained parallelisms, but also functional dynamic parallelisms suitable for grid and cloud execution environments.
    Research Article
    Delbert Bonner and Akbar Siami Namin*
    Abstract: Rising energy costs, the shrinking size of mobile devices and political influences have begun to force device and software developers to look at ways in which they can reduce their energy usage. While most energy savings models can be found in how hardware is designed, software plays a key role in how devices can be more energy efficient because software is what ultimately controls the hardware it runs on. At the same time a careful balance between performance and energy savings must be maintained. In order to examine this balance, researchers have begun to put forth energy models and metrics that rely on dynamic voltage and frequency scaling to optimize performance and energy usage. The problem with these models and measurements being that while most software is ran on devices capable of changing their processor frequency and voltage, most developers do not have the ability to change these settings due to the operating system safety and security restrictions. We present an alternative energy ratio that uses the work and idle times of the processor to examine energy efficiency gain by parallelization of software systems. Using this ratio we show how software developers can examine their parallelization efforts and decide not only which method will provide them with the performance they seek while not sacrificing energy usage, but also when it is expedient to reduce the amount of processors used by their application. The model is evaluated through a number of scheduling algorithms and case studies.
    Destiny EO Anyaiwe, George D. Wilson, Timothy J. Geddes, and Gautam B. Singh*
    Abstract: Diverse algorithms and methods are needed to answer the ever increasing need of adequately harnessing Mass Spectrometer generated data. The unique nature and structure of this data, requires a high level of expertise and rigorous algorithms to harness its full benefits. The methodology of this study discusses feature selection based on direct observations of variables and their inter-relationships, Jackknife technique for data sampling, matrix to vector decomposition and successfully classifies Alzheimer’s disease patients into three disease stages; age-matched controls without any evidence of dementia, patients with mild cognitive impairment and patients with clinical symptoms of Alzheimer’s disease (AD). Our model extends the use and principle of K-nearest neighbor (KNN) algorithm and also presents a modification of Euclidean distance formula. Hitherto, there exists no clinical diagnostic tool for AD, in lieu of this, patient cognitive abilities are clinically followed-up over a period of time (may be months) to make a diagnosis. This practice usually leads to inconclusive diagnosis and results obtained from it are not generalizable. This study, provides a platform for immediate classification and correctly indicates test data sets predisposed to AD with 75% accuracy (giving a probability of 0.13 for committing type II error) without collaborating clinical records.
    Kuocheng Wang* and Thenkurussi Kesavadas*
    Data registration is a common process in medical image analysis. The goal of data registration is to solve the transformation problem with multiple images’ alignment. Conventionally, diagnosing the tumors periodically requires understanding the growth and spread of tumor which is performed by doctors by visual inspections of multiple MRI scans taken over different stages in time series. Due to the misalignment of patient’s posture, comparison of these multiple MRI scans is tedious. This problem is addressed often using image registration of non-rigid body. However, this can be slow and hard to implement. On the other hand, rigid body registration is sometimes faster and easier to implement. The downside is that rigid body registration doesn’t usually take deformation into consideration. In this paper, two rigid body registration methods were explored, which are the BFP method and the PA method. Those results are later compared with the ICP method.
    Short Communication
    André Menolli*
    Abstract: Organizational learning assists the companies to significantly improve their processes by means of experiences reuse, making knowledge accessible to the whole organization. In the software engineering area it is important that the knowledge is stored and systematically reused. Over the years, many researches have focused on improving organizational learning in software engineering area, addressing different techniques and topics. However, in software engineering always are being proposed new methods and techniques that can be applied to improve organizational learning. An instance is the Model-Driven Development, which allows the development of codes from high-level models. We think that Model-Driven Development may help improve some aspects of organizational learning, so we present a brief reflection about this issue.
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