Graduate and Undergraduate Catalog, 2014-2015

Eastern Washington University\u27s course catalog for the 2014-2015 academic year.https://dc.ewu.edu/catalogs/1001/thumbnail.jp

Graduate and Undergraduate Catalog, 2012-2013

Eastern Washington University\u27s course catalog for the 2012-2013 academic year.https://dc.ewu.edu/catalogs/1003/thumbnail.jp

Graduate and Undergraduate Catalog, 2011-2012

Eastern Washington University\u27s course catalog for the 2011-2012 academic year.https://dc.ewu.edu/catalogs/1004/thumbnail.jp

Graduate and Undergraduate Catalog, 2010-2011

Eastern Washington University\u27s course catalog for the 2010-2011 academic year.https://dc.ewu.edu/catalogs/1005/thumbnail.jp

Graduate and Undergraduate Catalog, 2009-2010

Eastern Washington University\u27s course catalog for the 2009-2010 academic year.https://dc.ewu.edu/catalogs/1006/thumbnail.jp

Graph-based, systems approach for detecting violent extremist radicalization trajectories and other latent behaviors, A

2017 Summer.Includes bibliographical references.The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. Recent terrorist attack successes highlight the real possibility of missed signals from, or continued radicalization by, individuals whom the authorities had formerly investigated and even interviewed. Additionally, beyond considering just the behavioral dynamics of a person of interest is the need for investigators to consider the behaviors and activities of social ties vis-à-vis the person of interest. We undertake a fundamentally systems approach in addressing these challenges by investigating the need and feasibility of a radicalization detection system, a risk assessment assistance technology for law enforcement and intelligence agencies. The proposed system first mines public data and government databases for individuals who exhibit risk indicators for extremist violence, and then enables law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. In this thesis, we first identify the operational deficiencies of current law enforcement and intelligence agency efforts, investigate the environmental conditions and stakeholders most salient to the development and operation of the proposed system, and address both programmatic and technical risks with several initial mitigating strategies. We codify this large effort into a radicalization detection system framework. The main thrust of this effort is the investigation of the technological opportunities for the identification of individuals matching a radicalization pattern of behaviors in the proposed radicalization detection system. We frame our technical approach as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific data sets, a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists, and a large, real-world BlogCatalog dataset serving as a proxy for the type of intelligence or law enforcement data networks that could be utilized to track the radicalization of violent extremists. We also extended INSiGHT by developing a non-combinatorial neighbor matching technique to enable analysts to maintain visibility of potential collective threats and conspiracies and account for the role close social ties have in an individual's radicalization. This enhancement was validated on small, synthetic radicalization-specific datasets as well as the large BlogCatalog dataset with real social network connections and tagging behaviors for over 80K accounts. The results showed that our algorithm returned whole and partial subgraph matches that enabled analysts to gain and maintain visibility on neighbors' activities. Overall, INSiGHT led to consistent, informed, and reliable assessments about those who pose a significant risk for some latent behavior in a variety of settings. Based upon these results, we maintain that INSiGHT is a feasible and useful supporting technology with the potential to optimize law enforcement investigative efforts and ultimately enable the prevention of individuals from carrying out extremist violence. Although the prime motivation of this research is the detection of violent extremist radicalization, we found that INSiGHT is applicable in detecting latent behaviors in other domains such as on-line student assessment and consumer analytics. This utility was demonstrated through experiments with real data. For on-line student assessment, we tested INSiGHT on a MOOC dataset of students and time-stamped on-line course activities to predict those students who persisted in the course. For consumer analytics, we tested the performance on a real, large proprietary consumer activities dataset from a home improvement retailer. Lastly, motivated by the desire to validate INSiGHT as a screening technology when ground truth is known, we developed a synthetic data generator of large population, time-stamped, individual-level consumer activities data consistent with an a priori project set designation (latent behavior). This contribution also sets the stage for future work in developing an analogous synthetic data generator for radicalization indicators to serve as a testbed for INSiGHT and other data mining algorithms

Towards high performing hospital enterprise architectures : elevating hospitals to lean enterprise thinking

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 608-630).This research is motivated by the National Academy of Engineering and the Institute of Medicine's joint call for research in healthcare, promoting the application of principles, tools, and research from engineering disciplines, and complex systems in particular. In 2005, the US healthcare expenditure represented 16% of its GDP, with hospitals representing the largest source of expenditure, as is the case in the United Kingdom. Consequently, the strategies and operations developed and implemented by hospitals have a significant impact on healthcare. Today, it would be hard to find a hospital that is not implementing a Lean initiative or who isn't familiar with its concepts. However, more often than not, their approach has narrowly focused at a process level and inside individual service units like an emergency department. This research seeks to elevate traditionally narrow hospital definitions of lean and explore the broader concepts of lean enterprise principles and Enterprise Architecture (EA) while enhancing our knowledge of hospitals' socio-technical complexity and enriching an emerging EA Framework (EAF) developed at the Massachusetts Institute of Technology (MIT). Following an extensive longitudinal multidisciplinary literature review, a number of expert interviews, and preliminary empirical findings, an exploratory inductive and deductive hybrid study was designed to collect and concurrently analyze both qualitative and quantitative empirical data from multiple hospital settings over two main phases: * The first phase consisted of recorded interviews with the Chief Executive Officers of seven leading Massachusetts hospitals, who also provided sensitive internal strategy and operations documents. We explored how hospitals currently measure their hospital performance and how their explicit and implicit practices may be improved using lean enterprise principles. e The second phase comprised two in-depth case studies of large leading multidisciplinary hospitals, one located in the US and other in the United Kingdom, and included a total of 13 embedded units of analysis. Multiple sources of evidence were collected including electronic medical records, 54 interviews, observation, and internal documents. Findings were categorized and sorted, as phenomena of interest consistently emerged from the data, and enriched both the EAF, and our understanding of hospitals' EA in particular. In both in-depth hospital cases we found that their EA consisted of multiple internal architectural configurations, and in particular, those with an enriched understanding of EA had made decisions which had improved not only their local performance, but also enhanced their interactions with other service units upstream and downstream. Conversely, worse performing configurations demonstrated a limited understanding of their hospital's EA. We conclude that hospital performance can be improved through an enriched understanding of hospital EA. Furthermore, whilst considering all hospitals included in this study, we propose general and specific recommendations, as well as diagnostic questions, performance dimensions, and metrics, to assist senior hospital leaders in architecting and managing their enterprise.by Jorge Miguel dos Santos Fradinho.Ph.D

CIM: Revolution in Progress (Proceedings of the Final IIASA Conference on Computer Integrated Manufacturing: Technologies, Organizations and People in Transition)

The Final Conference of the IIASA Project on Computer Integrated Manufacturing was held at the headquarters of IIASA in Laxenburg, Austria, on July 1-4, 1990. The Conference itself was co-sponsored by the Ford and Alfred P. Sloan Foundations, though much of the earlier research work owes its existence to funding by the Finnish Sitra Organization and the American National Science Foundation. In addition to these primary funders, much of the work carried out by individual researchers was funded by their own governments, research institutes, etc., this included major inputs from Japan and Czechoslovakia. The aim of the research was to examine CIM from various perspectives including: technological characteristics, the diffusion process, managerial and organizational aspects, and the social and economic implications. The Conference was attended by 105 people from 22 countries, including representatives from the OECD, UNIDO, the ECE, and the ILO. Of these participants 28 came from Eastern Europe and the rest from Japan or Western countries. This Volume contains selected papers presented at the Conference, and transcripts of key parts of the policy discussion. The papers are organized in the following way: Part 1. Overviews; Part 2. Strategies and Models for CIM; Part 3. CIM Diffusion Studies; Part 4. CIM Technologies; Part 5. Organizational and Social Impacts; Part 6. Keynote Policy Panel Discussion; Part 7. CIM Implications for Industry and Government

The HBP1 tumor suppressor is a negative epigenetic regulator of MYCN driven neuroblastoma through interaction with the PRC2 complex

C