Browsing by Subject "protocols"

Now showing 1 - 3 of 3
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    Applying Phenomenography to Develop a Comprehensive Understanding of Ethics in Engineering Practice
    (IEEE, 2018-10) Brightman, Andrew O.; Fila, Nicholas D.; Hess, Justin L.; Kerr, Alison J.; Kim, Dayoung; Loui, Michael C.; Zoltowski, Carla B.; Technology and Leadership Communication, School of Engineering and Technology
    This Work-in-Progress Research paper describes (1) the contemporary research space on ethics education in engineering; (2) our long-term research plan; (3) the theoretical underpinnings of Phase 1 of our research plan (phenomenography); and (4) the design and developmental process of a phenomenographic interview protocol to explore engineers' experiences with ethics. Ethical behavior is a complex phenomenon that is complicated by the institutional and cultural contexts in which it occurs. Engineers also have varied roles and often work in a myriad of capacities that influence their experiences with and understanding of ethics in practice. We are using phenomenography, a qualitative research approach, to explore and categorize the ways engineers experience and understand ethical engineering practice. Specifically, phenomenography will allow us to systematically investigate the range and complexity of ways that engineers experience ethics in professional practice in the health products industry. Phenomenographic data will be obtained through a specialized type of semi-structured interview. Here we introduce the design of our interview protocol and its four sections: Background, Experience, Conceptual, and Summative. We also describe our iterative process for framing questions throughout each section.
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    Diabetes in sub-Saharan Africa – from policy to practice to progress: targeting the existing gaps for future care for diabetes
    (DovePress, 2017-06-22) Pastakia, Sonak D; Pekny, Chelsea R; Manyara, Simon M; Fischer, Lydia; Pediatrics, School of Medicine
    The global prevalence and impact of diabetes has increased dramatically, particularly in sub-Saharan Africa. This region faces unique challenges in combating the disease including lack of funding for noncommunicable diseases, lack of availability of studies and guidelines specific to the population, lack of availability of medications, differences in urban and rural patients, and inequity between public and private sector health care. Because of these challenges, diabetes has a greater impact on morbidity and mortality related to the disease in sub-Saharan Africa than any other region in the world. In order to address these unacceptably poor trends, contextualized strategies for the prevention, identification, management, and financing of diabetes care within this population must be developed. This narrative review provides insights into the policy landscape, epidemiology, pathophysiology, care protocols, medication availability, and health care systems to give readers a comprehensive summary of many factors in these domains as they pertain to diabetes in sub-Saharan Africa. In addition to providing a review of the current evidence available in these domains, potential solutions to address the major gaps in care will be proposed to reverse the negative trends seen with diabetes in sub-Saharan Africa.
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    Exploring a Service-Based Normal Behaviour Profiling System for Botnet Detection
    (IEEE, 2017-05) Chen, Weikeng; Luo, Xiao; Zincir-Heywood, A. Nur; Computer Information and Graphics Technology, School of Engineering and Technology
    Effective detection of botnet traffic becomes difficult as the attackers use encrypted payload and dynamically changing port numbers (protocols) to bypass signature based detection and deep packet inspection. In this paper, we build a normal profiling-based botnet detection system using three unsupervised learning algorithms on service-based flow-based data, including self-organizing map, local outlier, and k-NN outlier factors. Evaluations on publicly available botnet data sets show that the proposed system could reach up to 91% detection rate with a false alarm rate of 5%.