Purpose: The aim of this project was to evaluate data from a survey sent to nurses in regards to the current practices and workflow of the Early Warning System (EWS) currently in use at Banner Health. Methods: A descriptive cross-sectional design was used to collect feedback from Registered Nurses. The survey was open between February and March of 2016. Likert style and open-ended questions demonstrate evidence supporting the following study questions: 1) What are the barriers to documentation that triggers the EWS? 2) To what extent is the EWS useful and usable? 3) What were the features of the EWS implementation? Results: Findings demonstrated three barriers associated with EWS protocol compliance to include increased workload (78%), previous negative responses from providers (62%), and alert fatigue (48%). Provider responsiveness to notification of the Early Warning Score was shown to be effective "most of the time" and "about half of the time" at 71%, with 12% indicating "sometimes" and "never". Deployment of the Rapid Response Team (RRT) when indicated by EWS algorithm showed only 9 (14%) of nurses always call the RRT, 7 (11%) call about half the time, and 16% indicated they never use the RRT. "Real time" charting occurred 50-75% or less than 50% of the time for 96% of respondents. Open ended questions support recommendations for future practice to include: implementation of a pop up alert for easy recognition of changes in EWS, tailoring parameters based on individual patient characteristics, automatic direct paging to medical providers, and elimination of the level of consciousness parameter. A validated usability survey provided data with a mean response rate (n=58). Nurses (84%) agree the EWS is useful and usable. Ease of use, efficiency, and comfort with EWS software showed 90% agree. System interface responses demonstrate 23% dislike using the interface, and 21% felt the system interface was unpleasant.Conclusions: Findings demonstrate EWS system usability and usefulness. Recommendations for improvement include implementation of a "pop up" alert for easy recognition of changes in the Early Warning Score and/or automatic direct paging to medical providers and nursing will increase effective use. Barriers to EWS protocol documentation include increased workload, previous negative response from providers, and alert fatigue. "Real time" documentation of physiological parameters is essential to successful triggering of an Early Warning Score.

Nowadays, the trend towards globalization and internationalization of business has strong impact on companies’ strategy. When a company is thinking about strategy development, it needs to pay attention to variable aspects on micro- and macro-level. This study includes the overview of the factors which need to be considered while developing a strategy. Moreover, a significant emphasize is made on the contemporary deriving challenges at the market. The purpose of the study is to investigate a new approach to the strategic planning, named “Early warning system”. For that purpose, it was conducted qualitative research at the example of the Ukrainian telecommunication company – MTS Ukraine. I identified factors which influence the company and analyzed how company copes with deriving challenges. Moreover, it was found out the company’s strategic planning process is significantly influenced by specific market characteristics such as high level of bureaucracy, specific behavior of some competitors, growing market.

The nursing culture of an inpatient pediatric unit was resistant to activating pediatric rapid response team (PRRT) alerts despite guidelines for activation. Nurses routinely assessed patients and assigned a pediatric early warning score (PEWS); however, the level of illness severity was not interpreted consistently among nurses and a PEWS action algorithm did not exist to guide nurses' minimal actions based on the PEWS score. Guided by 3 adult learning theories (Knowles, Kolb, and Bandura) and 1 evaluation model (Kirkpatrick), this staff education project sought to educate pediatric nurses on a PEWS action algorithm and determine whether this project improved nurses' knowledge, situational awareness, and attitude toward activating PRRT alerts. A convenience sample of 30 pediatric nurses completed a preeducation knowledge survey (EKS), attended an interactive PEWS education class, and completed a postEKS. After participating in the class, correct responses on the EKS increased from 43% to 82% and, using the Wilcoxon-signed rank test, a significant increase was noted in nurses' responses to questions related to self-efficacy, factual knowledge, and application. The overall increase in the nurses' self-efficacy and knowledge about the PEWS might enhance critical-thinking skills, foster identification of patients at risk for clinical deterioration, and empower nurses to follow the PEWS action algorithm including activation of PRRT alerts when indicated. This project has the potential to effect positive social change by supporting nurses' actions designed to improve pediatric patient outcomes.

Over one million high school students drop out of school each year in this country. Dropping out of school is a serious problem for the student, community, and the nation. Often dropouts are unable to compete in an increasingly technological society and face numerous consequences from their decision to leave school early including higher levels of poverty, unemployment, public assistance, incarceration, and poor health. Dropping out is a gradual process of school disengagement and related to individual, family, and school factors. In the past, it has been difficult to track individual student's progress through school and to determine accurate dropout and graduation rates. In 2005, the National Governors Association made a commitment to implement a uniform method to calculate and report graduates and dropouts as well as better data collections systems.This study intended to replicate aspects of other major studies around the county to determine the best early predictors of dropping out of school in this large school district in southern Arizona and use this information to build an early warning system. Student data were obtained from the district's Research and Accountability office for a cohort of students (n=6751) who began the ninth grade in fall 2006 and graduated or should have graduated in 2010. Data collected included general demographic information, academic data, number of schools attended, and school withdrawal codes.The intent of this research was to determine if there were statistically significant differences between dropouts and graduates in the variables collected and which variables yielded the highest effect sizes and should be included in the district's early warning system.Two analyses were used to determine significance differences between dropouts and graduates. Then four analyses were performed to determine the highest-yield variables for this district. Consistent with recent research in the field, the variables of ninth grade attendance, ninth grade English and Math grades, and GPA were the strongest predictors of student dropouts.Local educators can use this early warning information to help identify potential high school dropouts as early as possible and intervene more efficiently and effectively with these students.

There are several methods to predict financial crises. There are also several types of indicators used by financial institutions. These indicators, which are estimated in different ways, often show various developments, although it is not possible to directly assess which is the most suitable. Here, we still try to find what characteristics that industry group has and forecast financial crises In this paper, our data started from monthly of 1977 January to 2008 December in S&P100. We consider Fama-French and Cluster Analysis to process data to make data with same characteristic within a group. Then, we use GARCH type models and apply it to VaR predicting stock turmoil. In conclusion, we found that the group which has high kurtosis value is the key factor for predicting stock crises instead of volatility. Moreover, the characteristics of this industry which can predict stock crises is a great scale. On the other hand, we can through this model to double check the reaction for anticipating. Therefore, people can do some actions to control risk to reduce the loss.

In recent year, the securities firms had suffered form the turmoil of the financial crisis in Taiwan. Although the Taiwan Stock Exchange Corporation and the Securities and Futures Commission have their own early warning systems (EWS), the EWS based on financial statements and the "capital adequacy ratio", respectively for the risks that the brokers and dealers assume, still have some defects: (1) EWS based on financial statements are static and time-lagged in the rapid-moving market, and (2) the calculation rules in the capital adequacy ratio are inelastic and inefficient. This research emphasizes on the stock positions of the dealers, and calculate the "Value at Risk" (VaR) for these positions. In this way, we hope to know whether the EWS based on VaR can detect the risks of the dealers in time, and improve the drawbacks of the EWS based on financial statements and capital adequacy ratio. We found that: (1) the EWS based on VaR can effectively reflect the market risk of the dealers, and (2) the "historical simulation" method might distort the real portfolio risk, thus we suggest that "delta-normal" is a better method, and (3) the EWS based on VaR can discriminate the risk level of different securities dealers. In conclusion, we have the suggestion of the EWS for securities firms in the future. For firm-wide operation, the EWS based on financial statements is suitable; for the credit risks the securities firms may assume, the capital adequacy ratio is better; as for the market risk of the positions, VaR, undoubtedly, is a good alternative.

BACKGROUND: Late identification of deteriorating children undermines timely implementation of life-saving measures to prevent cardiopulmonary arrest (CPA) or death. The Pediatric Early Warning System (PEWS) has been validated for use in pediatric acute care settings for early identification of children at increased risk of physiologic deterioration, yet there is a dearth of evidence of the use of PEWS in primary care. Implementing the PEWS in primary care could guide rural primary care practitioners to early detection and prompt management of deteriorating children. This DNP project evaluated the attitudes and perceptions of rural practitioners towards the implementation of the PEWS scoring tool. METHODS: A cross-sectional descriptive design was conducted using an anonymous online survey via an email listserv. RESULTS: Seventeen practitioners responded to the survey, but only 14 participants met criteria for inclusion – 2 males and 11 females. The sex of one participant was not reported. Participants areas of specialization include 79% specialized in family practice, 79% pediatric specialists 14% and (7%) listed as "Other." Thirty-one percent of participants reported a travel distance of over 60 miles, while 39% reported a travel distance of over 60 miles lasting over 60 minutes via ground from a place of care to a hospital that specializes in the pediatric emergency care, and pediatric care respectively. Although 92% reported they have not heard of the PEWS tool prior to this survey, 54% strongly agree that the PEWS could help prevent cardiopulmonary arrest or death. Similarly, 54% of respondents reported they strongly agree that the PEWS can help identify deteriorating children, while 39% somewhat agree. Over 62% strongly agree that implementing the PEWS is appropriate in primary care, while 31% somewhat agree. Fifty-four percent of participants strongly agree they could use the PEWS tool in their practice. DISCUSSION: Participants have a positive view of the PEWS tool and perceive implementation of the PEWS to be a vital clinical decision support tool that could lead pediatric primary care providers to early detection of deteriorating children before the occurrence of an adverse event. Further study could determine the generalizability of implementing the PEWS in primary care.

In-ground slope monitoring is an essential part of landslide early warning systems. Precise movement data from borehole monitors can detect emerging hazards near critical infrastructure. Typically, monitoring is done with inclinometers, but lower-cost alternatives have emerged which have yet to be tested in Tennessee. Time domain reflectometry (TDR) records magnitudes and depths of movements along a buried coaxial cable. When paired with a remote data logger, TDR can wirelessly transmit high resolution movement data in real time, making it promising for landslide early warning systems. Tennessee Department of Transportation (TDOT) has proposed a one-year feasibility study to test TDR for use in unstable soil slopes near highways. The study area is a well-known landslide site along Interstate 40 in Roane County, TN. Careful siting of borehole instrumentation is crucial for accurate monitoring. The goal of this study is to optimize TDR installation, with three specific aims: (i) evaluate landslide morphology, (ii) pinpoint locations and depths with greatest movement, and (iii) assess spatiotemporal patterns across the site. Statistical analysis of prior data from 13 inclinometers showed ongoing slope movement over the 21-acre complex landslide. Spatial interpolation suggested an asymmetrical failure surface with both shallow and deep motion. Space-time cube analysis indicated varying movement rates and timing across the site, suggesting separate landslide bodies. Based on these results, three optimal borehole depths and locations were proposed for TDR instruments. This analysis will ensure accuracy in tests of TDR for early warning system feasibility in Tennessee.

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Intrusion Detection Systems (IDS) are designed to monitor the computer network infrastructure against possible attacks by generating security alerts. With the increase of components connected to computer networks, traditional IDS are not capable of effectively detecting malicious attacks. This occurs either by the distributed amount of data that traverses the network or the complexity of the attacks launched against the network. Therefore, the design of Internet Early Warning Systems (IEWS) enables the early detection of threats in the network, possibly avoiding eventual damages to the network resources. The IEWS works as a sink that collects alerts from different sources (for example, from different IDS), centralizing and correlating information in order to provide a holistic view of the network. This way, the current dissertation describes an IEWS architecture for correlating alerts from (geographically) spread out IDS using the Case-Based Reasoning (CBR) technique together with IP Georeferencing. The results obtained during experiments, which were executed over the implementation of the developed technique, showed the viability of the technique in reducing false-positives. This demonstrates the applicability of the proposal as the basis for developing advanced techniques inside the extended IEWS architecture. / Sistemas de Detecção de Instrução (Intrusion Detection Systems IDS) são projetados para monitorar possíveis ataques à infraestruturas da rede através da geração de alertas. Com a crescente quantidade de componentes conectados na rede, os IDS tradicionais não estão sendo suficientes para a efetiva detecção de ataques maliciosos, tanto pelo volume de dados como pela crescente complexidade de novos ataques. Nesse sentido, a construção de uma arquitetura Internet Early Warning Systems (IEWS) possibilita detectar precocemente as ameaças, antes de causar algum perigo para os recursos da rede. O IEWS funciona como um coletor de diferentes geradores de alertas, possivelmente IDS, centralizando e correlacionado informações afim de gerar uma visão holística da rede. Sendo assim, o trabalho tem como objetivo descrever uma arquitetura IEWS para a correlação de alertas gerados por IDS dispersos geograficamente utilizando a técnica Case-Based Reasoning (CBR) em conjunto com Georreferenciamento de endereços IP. Os resultados obtidos nos experimentos, realizados sobre a implementação da técnica desenvolvida, mostraram a viabilidade da técnica na redução de alertas classificados como falsos-positivos. Isso demonstra a aplicabilidade da proposta como base para o desenvolvimento de técnicas mais apuradas de detecção dentro da arquitetura de IEWS estendida.

Published Article / Early indication of possible drought can help in developing suitable drought mitigation strategies and measures in advance. Therefore, drought forecasting plays an important role in the planning and management of water resource in such circumstances. In this study, a non-linear streamflow forecasting model was developed using Artificial Neural Network (ANN) modeling technique at the Melka Sedi stream gauging station, Ethiopia, with adequate lead times. The available data was divided into two independent sets using a split sampling tool of the neural network software. The first data set was used for training and the second data set, which is normally about one fourth of the total available data, was used for testing the model. A one year data was set aside for validating the ANN model. The streamflow predicted using the model on weekly time step compared favorably with the measured streamflow data (R2 = 75%) during the validation period. Application of the model in assessing appropriate agricultural water management strategies for a large-scale irrigation scheme in the Awash River Basin, Ethiopia, has already been considered for publication in a referred journal.