A new zone was added to the existing Livestock Early Warning System (LEWS), which is a subproject of the USAID Global Livestock Collaborative Research Support Program. LEWS uses the PHYGROW model and satellite imagery of weather and vegetation to estimate the availability of forage to livestock and wildlife. Drought advisories are then distributed to governments, development organizations, and pastoralists via the Internet, satellite radios, and written reports. The Laikipia zone was established in 2001 to provide drought early warning for the arid pastoral rangelands of the Ewaso Ngiro ecosystem in the Laikipia and southern Samburu Districts, Kenya. Field verification of PHYGROW estimates of standing crop was conducted in 2002. In addition, research was conducted to determine the ability of the warning system to provide significant advance notice of emerging drought conditions. Results of this study indicate that LEWS is capable of providing accurate estimates of forage availability on East African rangelands. There is also evidence that the use of LEWS advisories could accelerate drought response by pastoralists as much as three to seven weeks.

Intrastate conflicts in Sub-Saharan Africa are a development tragedy and a security dilemma that requires more prevention and better intervention from the international community. Such engagement necessitates a robust early warning system, which can determine, with a sufficient degree of accuracy, the countries most at risk of experiencing intrastate conflict. This research summarizes and critiques current efforts to conceptualize intrastate conflict in Sub-Saharan Africa and determine what factors best explain the likelihood of intrastate conflict onset. The research examines the challenges of empirically modelling the human behaviour that underlies intrastate conflict, as well as some promising avenues for overcoming challenges posed by data issues and existing methodological shortcomings. The research concludes that with improved data and research design, and more attention being paid to how statistical significance reflects pathway(s) to violence, the development of an intrastate conflict early warning system is possible.

Failure to rescue (FTR) outcomes may be one consequence of the relationship be-tween healthcare provider behaviors and attitudes, organizational factors, and environ-mental factors that intersect to potentially threaten patient safety. Early warning systems (EWS) were designed as surveillance systems to reduce failure to rescue events and avoid morbidity and mortality. Challenges with EWS include lack of standardization, organiza-tional barriers, such as culture and supports, and human factor attributes such as intuition, expertise, and experience. The experience and perceptions of nurses using EWS technolo-gy as it relates to their clinical assessment, critical thinking, and decision-making skills has yet to be undertaken. This study adds to the body of EWS and FTR literature and the broader culture of safety literature in acute care environments. The purposes of this exploratory qualitative descriptive study was to explore the experiences of nurses using EWS in acute care practice settings and how they perceive it impacts on their critical thinking and clinical decision-making processes. The study identi-fied three informative findings: a) EWS has added value particularly with novice nurses or nurses new to practice settings, b) EWS provides benefits to nurses working in acute clinical environments that experience high volumes and high acuity of patients by alerting or reminding them about potential FTR situations, and c) Existing EWS may require mod-ification to improve adequacy, reduce redundancy, and reduce alarm fatigue. Based on the evidence reviewed, a qualitative study to increase our understanding of the experi-ence of nurses and their perception of the impact of EWS and related technology on their critical thinking and other nursing practice processes has the potential to contribute to a wider evaluation of EWS systems and to improve patient outcomes. / Thesis / Master of Science (MSc) / Before patients on general medical or surgical hospital units become so ill that they need to be transferred to an intensive care unit, they have abnormal vital signs and other physiological changes that can go unnoticed for 2448 hours. As these changes can be hard to detect and serious illness can begin very slowly at first, early warning systems have been developed to help health care professionals respond to patient’s conditions before they need to be in an intensive care unit. These early warning systems can be in the form of new technology and assist nurses with preventing a serious illness from becoming critical. It is not well research or understood how nurses experience such early warning systems technology and it is not well understood how nurses think this technology impacts their nursing practice. The overall aim of the study is to learn more about the experience and perception of nurses who use this technology, how they believe it informs their nursing practice and how it supports them in making clinical decisions about patient care.

Risk indicators are metrics that are widely used in risk management to indicate how risky an activity is. Among different types of risk indicators, early warning systems are designed to help decision makers predict and be prepared for catastrophic events. Especially, in complex systems where outcomes are often difficult to predict, early warnings can help decision makers manage possible risks and take a proactive approach. Early prediction of catastrophic events and outcomes are at the heart of risk management, and help decision makers take appropriate actions in order to mitigate possible effects of such events. For example, physicians would like to prevent any adverse events for their patients and like to use all pieces of information that help accurate early diagnosis and interventions. In this research, first we study risk assessment for occupational injuries using accident severity grade as an early warning indicator. We develop a new severity scoring system which considers multiple injury severity factors, and can be used as a part of a novel three-dimensional risk assessment matrix which includes an incident's severity, frequency, and preventability. Then we study the predictability of health outcome based on early risk indicators. A systems model of patient health outcomes and hospital length of stay is presented based on initial health risk and physician assessment of risk. The model elaborates on the interdependent effects of hospital service and a physician's subjective risk assessment on length of stay and mortality. Finally, we extend our research to study the predictive power of early warning systems and prognostic risk indicators in predicting different outcomes in health such as mortality, disease diagnosis, adverse outcomes, care intensity, and survival. This study provides a theoretical framework on why risk indicators can or cannot predict healthcare outcomes, and how better predictors can be designed. Overall, these three essays shed light on complexities of risk assessments, especially in health domain, and in the contexts where individuals continuously observe and react to the risk indicators. Furthermore, our multi-method research approach provides new insights into improving the design and use of the risk measures. / Ph. D.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The popularization of the Internet has provided an increase in the number of web applications that work with critical information. Parallel to this, attacks that exploit the vulnerabilities of these applications has also grown. This scenario has stimulated companies to invest in tools to monitor their network infrastructure in order to detect malicious activity. One of the main tools used by companies to monitor their network infrastructures and identifying attacks are Intrusion Detection Systems. However, due to expansion of the volume of data in computer networks, these systems are becoming limited. In contrast, researchers have explored the construction of Internet Early Warning Systems to monitor malicious activities on the Internet. This work proposes a data model of a knowledge base for Internet EarlyWarning Systems. The model represents the data of different aspects of the network with a focus on events related to intrusion detection, such as data of alerts generated by intrusion detection systems, information on response measures, traffic statistics and signatures of known attacks. A case study on a real network infrastructure demonstrates the applicability of the data model of knowledge base and identifies the advantages of its use. Furthermore, the data stored in the knowledge base potentializes the construction of situational awareness of monitored environment, directing the activities of the security team and helping in the decision process responses to potential attacks. / A popularização da Internet tem proporcionado um aumento no número de aplicações web que trabalham com informações críticas. Em paralelo a isso, os ataques que exploram as vulnerabilidades dessas aplicações também tem crescido. Esse cenário tem estimulado as empresas a investir em ferramentas para monitorar sua infraestrutura de rede, visando a detecção de atividades mal-intencionadas. Uma das principais ferramentas utilizadas pelas empresas para o monitoramento de suas infraestruturas de redes e identificação de ataques são os Sistemas de Detecção de Intrusão. No entanto, devido a expansão do volume de dados que trafegam nas redes de computadores, estes sistemas estão tornando-se limitados. Em contrapartida, pesquisadores têm explorado a construção de Internet Early Warning Systems para o monitoramento de atividades maliciosas na Internet. Este trabalho propõe a modelagem de dados de uma base de conhecimento para Internet Early Warning Systems. O modelo representa os dados de diferentes aspectos da rede com foco em eventos relacionados a detecção de intrusão, tais como: dados de alertas gerados por sistemas de detecção de intrusão, informações sobre medidas de respostas, estatísticas do tráfego e assinaturas de ataques já conhecidos. Um estudo de caso em uma infraestrutura de rede real demonstra a aplicabilidade do modelo de dados da base de conhecimento e permite identificar as vantagens de sua utilização. Além disso, os dados armazenados na base de conhecimento potencializam a construção de uma consciência situacional do ambiente monitorado, direcionando as atividades da equipe de segurança e auxiliando no processo de decisão de respostas a ataques em potencial.

Vi har undersökt hur Scandinavian Airlines Systems (SAS) årsredovisningar kanstuderas utifrån ord respektive siffror och vilket av de två som ger bäst informationför en extern intressent. Vidare har vi studerat hur väl redovisningen överrensstämmermed företagets situation. I studien har nyckeltal och analysmetoden Content analysisanvänts för att studera siffror i årsredovisningen, medans stycket ”VD har ordet” stårtill grund för analysen av text. Vårt syfte med uppsatsen är att bidra med kunskap omhur årsredovisningar kan användas av externa intressenter, det vill säga hurårsredovisningar kan belysa ett företags situation utifrån olika perspektiv.Studien bygger på både en kvalitativ och en kvantitativ metod på grund av uppsatsensuppdelning mellan att studera siffror samt tolka text. Vidare har en abduktiv processanvänts, det vill säga att teori och empiri har skapats i växelverkan. Endastsekundärdata har använts då vi har studerat SAS årsredovisningar mellan år 1992 tillår 2008.Vi har i studien kommit fram till att det bästa sättet för en extern intressent att förståett företags situation är att utföra en analys som kombinerar flera olika metoder, därnyckeltal och nyckelord fungerar som indikatorer medans tolkning av text ger endjupare förståelse för företaget. Vi har även funnit att redovisningen inte alltidstämmer överrens med företagets situation. Detta beror på att siffror endast gerindikationer på problem men inte visar orsakerna till dessa. Ytterligare är en text oftasubjektiv och beror även på läsarens tolkningsförmåga av den, vilket gör det svårareatt få en korrekt bild av företagets situation.

Institutions have implemented many campus interventions to address student persistence/retention, one of which is Early Warning Systems (EWS). However, few research studies show evidence of interventions that incorporate noncognitive factors/skills, and psychotherapy/psycho-educational processes in the EWS. A qualitative study (phenomenological interview and document analysis) of EWS at both a public and private 4-year Florida university was conducted to explore EWS through the eyes of the administrators of the ways administrators make sense of students’ experiences and the services they provide and do not provide to assist students. Administrators’ understanding of noncognitive factors and the executive skills subset and their contribution to retention and the executive skills development of at-risk students were also explored. Hossler and Bean’s multiple retention lenses theory/paradigms and Perez’s retention strategies were used to guide the study. Six administrators from each institution who oversee and/or assist with EWS for first time in college undergraduate students considered academically at-risk for attrition were interviewed. Among numerous findings, at Institution X: EWS was infrequently identified as a service, EWS training was not conducted, numerous cognitive and noncognitive issues/deficits were identified for students, and services/critical departments such as EWS did not work together to share students’ information to benefit students. Assessment measures were used to identify students’ issues/deficits; however, they were not used to assess, track, and monitor students’ issues/deficits. Additionally, the institution’s EWS did address students’ executive skills function beyond time management and organizational skills, but did not address students’ psychotherapy/psycho-educational processes. Among numerous findings, at Institution Y: EWS was frequently identified as a service, EWS training was not conducted, numerous cognitive and noncognitive issues/deficits were identified for students, and services/critical departments such as EWS worked together to share students’ information to benefit students. Assessment measures were used to identify, track, and monitor students’ issues/deficits; however, they were not used to assess students’ issues/deficits. Additionally, the institution’s EWS addressed students’ executive skills function beyond time management and organizational skills, and psychotherapy/psycho-educational processes. Based on the findings, Perez’s retention strategies were not utilized in EWS at Institution X, yet were collectively utilized in EWS at Institution Y, to achieve Hossler and Bean’s retention paradigms. Future research could be designed to test the link between engaging in the specific promising activities identified in this research (one-to-one coaching, participation in student success workshops, academic contracts, and tutoring) and student success (e.g., higher GPA, retention). Further, because this research uncovered some concern with how to best handle students with physical and psychological disabilities, future research could link these same promising strategies for improving student performance for example among ADHD students or those with clinical depression.

Organisations such as governments and healthcare bodies are increasingly responsible for managing large amounts of personal information, and the increasing complexity of modern information systems is causing growing concerns about the protection of these assets from insider threats. Insider threats are very difficult to handle, because the insiders have direct access to information and are trusted by their organisations. The nature of insider privacy breaches varies with the organisation’s acceptable usage policy and the attributes of an insider. However, the level of risk that insiders pose depends on insider breach scenarios including their access patterns and contextual information, such as timing of access. Protection from insider threats is a newly emerging research area, and thus, only few approaches are available that systemise the continuous monitoring of dynamic insider usage characteristics and adaptation depending on the level of risk. The aim of this research is to develop a formal framework for an adaptive early warning and response system for insider privacy breaches within dynamic software systems. This framework will allow the specification of multiple policies at different risk levels, depending on event patterns, timing constraints, and the enforcement of adaptive response actions, to interrupt insider activity. Our framework is based on Usage Control (UCON), a comprehensive model that controls previous, ongoing, and subsequent resource usage. We extend UCON to include interrupt policy decisions, in which multiple policy decisions can be expressed at different risk levels. In particular, interrupt policy decisions can be dynamically adapted upon the occurrence of an event or over time. We propose a computational model that represents the concurrent behaviour of an adaptive early warning and response system in the form of statechart. In addition, we propose a Privacy Breach Specification Language (PBSL) based on this computational model, in which event patterns, timing constraints, and the triggered early warning level are expressed in the form of policy rules. The main features of PBSL are its expressiveness, simplicity, practicality, and formal semantics. The formal semantics of the PBSL, together with a model of the mechanisms enforcing the policies, is given in an operational style. Enforcement mechanisms, which are defined by the outcomes of the policy rules, influence the system state by mutually interacting between the policy rules and the system behaviour. We demonstrate the use of this PBSL with a case study from the e-government domain that includes some real-world insider breach scenarios. The formal framework utilises a tool that supports the animation of the enforcement and policy models. This tool also supports the model checking used to formally verify the safety and progress properties of the system over the policy and the enforcement specifications.

This diploma thesis deals with the topic of Early Warning Systems (EWS), a key part of conflict prevention. It applies a model of quantitative analysis of international media outputs on the case of the Mali insurgency in January 2012. As an EWS tool, it analyzes international media represented by the major global press agencies. The main goal of the thesis is to answer the following research question: Did the international media manage to anticipate the outbreak of the conflict in Mali? The answer should also show whether international media can detect growing tensions leading to a conflict and therefore whether they could be used as an EWS tool. The application of the model should, observing the period from August 2011 to the beginning of the insurgency on January 17, 2012, prove whether the conflict could have been anticipated. In order to contextualize the model, structural factors of instability were identified in the discussion of the dynamics of the conflict that has been repeating itself for dozens of years. A discourse analysis of international media during the observed period was also presented on the background of the securitization theory of the Copenhagen school of security studies. The discourse analysis and the quantitative EWS model have both shown that international media have not...

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