996 resultados para Tornado warning systems


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Cybercriminals ramp up their efforts with sophisticated techniques while defenders gradually update their typical security measures. Attackers often have a long-term interest in their targets. Due to a number of factors such as scale, architecture and nonproductive traffic however it makes difficult to detect them using typical intrusion detection techniques. Cyber early warning systems (CEWS) aim at alerting such attempts in their nascent stages using preliminary indicators. Design and implementation of such systems involves numerous research challenges such as generic set of indicators, intelligence gathering, uncertainty reasoning and information fusion. This paper discusses such challenges and presents the reader with compelling motivation. A carefully deployed empirical analysis using a real world attack scenario and a real network traffic capture is also presented.

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Wheat (Triticum aestivum L.) blast caused by Pyricularia grisea is a new disease in Brazil and no resistant cultivars are available. The interactions between temperature and wetness durations have been used in many early warning systems. Hence, growth chamber experiments to assess the effect of different temperatures (10, 15, 20, 25, 30 and 35ºC) and the duration of spike-wetness (0, 5, 10, 15, 20, 25, 30, 35 and 40 hours) on the intensity of blast in cultivar BR23 were carried out. Each temperature formed an experiment and the duration of wetness the treatments. The highest blast intensity was observed at 30°C and increased as the duration of the wetting period increased while the lowest occurred at 25°C and 10 hours of spike wetness. Regardless of the temperature, no symptoms occurred when the wetting period was less than 10 hours but at 25°C and a 40 h wetting period blast intensity exceeded 85%. These variations in blast intensity as a function of temperature are explained by a generalized beta model and as a function of the duration of spike wetness by the Gompertz model. Disease intensity was modeled as a function of both temperature and the durations of spike wetness and the resulting equation provided a precise description of the response of P. grisea to temperatures and the durations of spike wetness. This model was used to construct tables that can be used to predict the intensity of P. grisea wheat blast based on the temperatures and the durations of wheat spike wetness obtained in the field.

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Most warning systems for plant disease control are based on Vinho, in Bento Gonçalves - RS, during the growing seasons 2000/ weather models dependent on the relationships between leaf wetness 01, 2002/03 and 2003/2004, using the grape cultivar Isabel. The duration and mean air temperature in this period considering the conventional system used by local growers was compared with the target disease intensity. For the development of a warning system to new warning system by using different cumulative daily disease severity control grapevine downy mildew, the equation generated by Lalancette values (CDDSV) as the criterion to schedule fungicide application and et al. (7) was used. This equation was employed to elaborate a critical reapplication. In experiments conducted in 2003/04, CDDSV of 12 - period table and program a computerized device, which records, though 14 showed promising to schedule the first spraying and the interval electronic sensors, leaf wetness duration, mean temperature in this between fungicide applications, reducing by 37.5% the number of period and automatically calculates the daily value of probability of applications and maintaining the same control efficiency in leaves infection occurrence. The system was validated at Embrapa Uva e and bunches, similarly to the conventional system.

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Drought is a global problem that has far-reaching impacts and especially 47 on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF) and the recent progress made towards its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global real-time drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental to global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in-situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress towards meeting these challenges and developing a global system.

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Given the high levels of uncertainty and substantial variability in local weather and climate, what constitutes successful adaptation for the 800 million food-insecure people in Africa? In this context there is a need for building climate resilience through effective early warning systems, bringing real-time monitoring and decision-making together with stakeholders. The chapter presents two effective operational early warning systems in Africa: The Radio and Internet (RANET) network and the Rainwatch project. These examples were developed in partnership with local climate scientists and tailored to local development needs, enabled and encouraged with only modest international support. They deliver important lessons about how to prepare for crises using simple real-time monitoring. They also help us identify characteristics of managing for resilience in practice. The chapter concludes that successful adaptation requires adaptive, flexible, linked institutions, together with ground-based collaboration and practical tools. In the context of early warning three features stand out that make these systems successful: effective communication of current weather and climate information, a key individual within a bridging organization with the ability to navigate the governance systems, and sufficient time for innovation development.

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This report provides case studies of Early Warning Systems (EWSs) and risk assessments encompassing three main hazard types: drought; flood and cyclone. The case studies are taken from ten countries across three continents (focusing on Africa, South Asia and the Caribbean). The case studies have been developed to assist the UK Department for International Development (DFID) to prioritise areas for Early Warning System (EWS) related research under their ‘Science for Humanitarian Emergencies and Resilience’ (SHEAR) programme. The aim of these case studies is to ensure that DFID SHEAR research is informed by the views of Non-Governmental Organisations (NGOs) and communities engaged with Early Warning Systems and risk assessments (including community-based Early Warning Systems). The case studies highlight a number of challenges facing Early Warning Systems (EWSs). These challenges relate to financing; integration; responsibilities; community interpretation; politics; dissemination; accuracy; capacity and focus. The case studies summarise a number of priority areas for EWS related research: • Priority 1: Contextualising and localising early warning information • Priority 2: Climate proofing current EWSs • Priority 3: How best to sustain effective EWSs between hazard events? • Priority 4: Optimising the dissemination of risk and warning information • Priority 5: Governance and financing of EWSs • Priority 6: How to support EWSs under challenging circumstances • Priority 7: Improving EWSs through monitoring and evaluating the impact and effectiveness of those systems

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Floods are the most frequent of natural disasters, affecting millions of people across the globe every year. The anticipation and forecasting of floods at the global scale is crucial to preparing for severe events and providing early awareness where local flood models and warning services may not exist. As numerical weather prediction models continue to improve, operational centres are increasingly using the meteorological output from these to drive hydrological models, creating hydrometeorological systems capable of forecasting river flow and flood events at much longer lead times than has previously been possible. Furthermore, developments in, for example, modelling capabilities, data and resources in recent years have made it possible to produce global scale flood forecasting systems. In this paper, the current state of operational large scale flood forecasting is discussed, including probabilistic forecasting of floods using ensemble prediction systems. Six state-of-the-art operational large scale flood forecasting systems are reviewed, describing similarities and differences in their approaches to forecasting floods at the global and continental scale. Currently, operational systems have the capability to produce coarse-scale discharge forecasts in the medium-range and disseminate forecasts and, in some cases, early warning products, in real time across the globe, in support of national forecasting capabilities. With improvements in seasonal weather forecasting, future advances may include more seamless hydrological forecasting at the global scale, alongside a move towards multi-model forecasts and grand ensemble techniques, responding to the requirement of developing multi-hazard early warning systems for disaster risk reduction.

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As a highly urbanized and flood prone region, Flanders has experienced multiple floods causing significant damage in the past. In response to the floods of 1998 and 2002 the Flemish Environment Agency, responsible for managing 1 400 km of unnavigable rivers, started setting up a real time flood forecasting system in 2003. Currently the system covers almost 2 000 km of unnavigable rivers, for which flood forecasts are accessible online (www.waterinfo.be). The forecasting system comprises more than 1 000 hydrologic and 50 hydrodynamic models which are supplied with radar rainfall, rainfall forecasts and on-site observations. Forecasts for the next 2 days are generated hourly, while 10 day forecasts are generated twice a day. Additionally, twice daily simulations based on percentile rainfall forecasts (from EPS predictions) result in uncertainty bands for the latter. Subsequent flood forecasts use the most recent rainfall predictions and observed parameters at any time while uncertainty on the longer-term is taken into account. The flood forecasting system produces high resolution dynamic flood maps and graphs at about 200 river gauges and more than 3 000 forecast points. A customized emergency response system generates phone calls and text messages to a team of hydrologists initiating a pro-active response to prevent upcoming flood damage. The flood forecasting system of the Flemish Environment Agency is constantly evolving and has proven to be an indispensable tool in flood crisis management. This was clearly the case during the November 2010 floods, when the agency issued a press release 2 days in advance allowing water managers, emergency services and civilians to take measures.

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The objective of this study is to develop a Pollution Early Warning System (PEWS) for efficient management of water quality in oyster harvesting areas. To that end, this paper presents a web-enabled, user-friendly PEWS for managing water quality in oyster harvesting areas along Louisiana Gulf Coast, USA. The PEWS consists of (1) an Integrated Space-Ground Sensing System (ISGSS) gathering data for environmental factors influencing water quality, (2) an Artificial Neural Network (ANN) model for predicting the level of fecal coliform bacteria, and (3) a web-enabled, user-friendly Geographic Information System (GIS) platform for issuing water pollution advisories and managing oyster harvesting waters. The ISGSS (data acquisition system) collects near real-time environmental data from various sources, including NASA MODIS Terra and Aqua satellites and in-situ sensing stations managed by the USGS and the NOAA. The ANN model is developed using the ANN program in MATLAB Toolbox. The ANN model involves a total of 6 independent environmental variables, including rainfall, tide, wind, salinity, temperature, and weather type along with 8 different combinations of the independent variables. The ANN model is constructed and tested using environmental and bacteriological data collected monthly from 2001 – 2011 by Louisiana Molluscan Shellfish Program at seven oyster harvesting areas in Louisiana Coast, USA. The ANN model is capable of explaining about 76% of variation in fecal coliform levels for model training data and 44% for independent data. The web-based GIS platform is developed using ArcView GIS and ArcIMS. The web-based GIS system can be employed for mapping fecal coliform levels, predicted by the ANN model, and potential risks of norovirus outbreaks in oyster harvesting waters. The PEWS is able to inform decision-makers of potential risks of fecal pollution and virus outbreak on a daily basis, greatly reducing the risk of contaminated oysters to human health.

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Mode of access: Internet.

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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.

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The observation chart is for many health professionals (HPs) the primary source of objective information relating to the health of a patient. Information Systems (IS) research has demonstrated the positive impact of good interface design on decision making and it is logical that good observation chart design can positively impact healthcare decision making. Despite the potential for good observation chart design, there is a paucity of observation chart design literature, with the primary source of literature leveraging Human Computer Interaction (HCI) literature to design better charts. While this approach has been successful, this design approach introduces a gap between understanding of the tasks performed by HPs when using charts and the design features implemented in the chart. Good IS allow for the collection and manipulation of data so that it can be presented in a timely manner that support specific tasks. Good interface design should therefore consider the specific tasks being performed prior to designing the interface. This research adopts a Design Science Research (DSR) approach to formalise a framework of design principles that incorporates knowledge of the tasks performed by HPs when using observation charts and knowledge pertaining to visual representations of data and semiology of graphics. This research is presented in three phases, the initial two phases seek to discover and formalise design knowledge embedded in two situated observation charts: the paper-based NEWS chart developed by the Health Service Executive in Ireland and the electronically generated eNEWS chart developed by the Health Information Systems Research Centre in University College Cork. A comparative evaluation of each chart is also presented in the respective phases. Throughout each of these phases, tentative versions of a design framework for electronic vital sign observation charts are presented, with each subsequent iteration of the framework (versions Alpha, Beta, V0.1 and V1.0) representing a refinement of the design knowledge. The design framework will be named the framework for the Retrospective Evaluation of Vital Sign Information from Early Warning Systems (REVIEWS). Phase 3 of the research presents the deductive process for designing and implementing V0.1 of the framework, with evaluation of the instantiation allowing for the final iteration V1.0 of the framework. This study makes a number of contributions to academic research. First the research demonstrates that the cognitive tasks performed by nurses during clinical reasoning can be supported through good observation chart design. Secondly the research establishes the utility of electronic vital sign observation charts in terms of supporting the cognitive tasks performed by nurses during clinical reasoning. Third the framework for REVIEWS represents a comprehensive set of design principles which if applied to chart design will improve the usefulness of the chart in terms of supporting clinical reasoning. Fourth the electronic observation chart that emerges from this research is demonstrated to be significantly more useful than previously designed charts and represents a significant contribution to practice. Finally the research presents a research design that employs a combination of inductive and deductive design activities to iterate on the design of situated artefacts.

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Abstract The purpose of this study was to examine how four high schools used an Early Warning Indicator Report (EWIR) to improve ninth grade promotion rates. Ninth grade on-time promotion is an early predictor of a student’s likelihood to graduate (Bornsheuer, Polonyi, Andrews, Fore, & Onwuegbuzie, 2011; Leckrone & Griffith, 2006; Roderick, Kelley-Kemple, Johnson, & Beechum, 2014; Zvoch, 2006). The analysis revealed both similarities and differences in the ways that the four schools used the EWIR. The research took place in a large urban school district in the Mid-Atlantic. Sixteen participants from four high schools and the district’s central office voluntarily participated in face-to-face interviews. The researcher utilized a qualitative case study method to examine the implementation of the EWIR system in Wyatt School District. The interview data was transcribed and analyzed, along with district documents, to identify categories in this cross case analysis. Three primary themes emerged from the data: (1) targeted school structures for EWIR implementation, (2) the EWIR identified necessary supports for students, and (3) the central office support for school staff. The findings revealed the various ways that the target schools implemented the EWIR in their buildings and the level of support that they received from the central office that aided them in using the EWIR to improve ninth grade promotion rates. Based on the findings of this study, the researcher provided a number of key recommendations: (1) Districts should provide professional development to schools to ensure that schools have the support they need to implement the EWIR successfully; (2) There should be increased accountability from the central office for schools using the EWIR to identify impactful interventions for ninth graders; and (3) The district needs to assign dedicated central office staff to support the implementation of the EWIR in high schools across the district. As schools continue to face the challenge of improving ninth grade promotion rates, effective use of an Early Warning Indicator Report is recommended to provide school and district staff with data needed to impact overall student performance.