An early-warning system for TNE

This report for Jisc1 is based on feedback from the UK higher education (HE) sector on current (2014) transnational education (TNE) activities and future plans, including the locations of such activity. The exercise includes feedback on current and future TNE delivery modes. It is further based on feedback of a more technical nature, for example, on what the network is used for in TNE and how such IT operations are managed abroad. The resulting narrative is a synthesis of these two distinct voices from within UK higher education institutions (HEIs).

Could the Asian Crisis Have Been More Accurately Predicted? Improving and Early Warning System Model.

Rapport de recherche

Statistical monitoring of student performance: an early warning system

Weekly monitoring of profiles of student performances on formative and summative coursework throughout the year can be used to quickly identify those who need additional help, possibly due to acute and sudden-onset problems. Such an early-warning system can help retention, but also assist students in overcoming problems early on, thus helping them fulfil their potential in the long run. We have developed a simple approach for the automatic monitoring of student mark profiles for individual modules, which we intend to trial in the near future. Its ease of implementation means that it can be used for very large cohorts with little additional effort when marks are already collected and recorded on a spreadsheet.

Science for Humanitarian Emergencies and Resilience (SHEAR) scoping study: Annex 3 - Early warning system and risk assessment case studies

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

The uptake of an early warning system in an Australian emergency department : a pilot study

Objectives: To evaluate the uptake of an emergency department early warning system (ED EWS) for recognition of, and response to, clinical deterioration.

Design, setting and participants: A descriptive exploratory study conducted in an urban district hospital in Melbourne, Australia. Systematic sampling was used to identify every 10th patient for whom the ED EWS was activated from May 2009 to May 2011.

Main outcome measures: Patient characteristics, ED system data and ED EWS activation characteristics.

Results: ED EWS activation occurred in 1.5% of ED patients; 204 patients were included in this pilot study. The median age was 65.1 years (interquartile range [IQR], 47.8-77.5 years), 89.2% of patients were classified as triage category 2 or 3, and 82.4% of patients were seen by medical staff before ED EWS activation. Hypotension (27.7%) and tachycardia (23.7%) were the most common reasons for ED EWS activation. Median duration of clinical instability was 39 minutes (IQR, 5- 129 minutes). Nurses made 93.1% of ED EWS activations. Median time between documenting physiological abnormalities and ED EWS activation was 5 minutes (IQR, 0- 20). Most patients (57.8%) required hospital admission: 4.4% of patients required intensive care unit admission.

Conclusions: The ED EWS resulted in at least two formal reports of clinical deterioration in ED patients per day, indicating reasonable uptake by clinicians. A greater understanding of clinical deterioration in ED patients is warranted to inform an evidence-based approach to recognition of, and response to, clinical deterioration in ED patients.

Development Of Water Pollution Early Warning System For Oyster Harvesting Areas

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.

An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models

Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia/hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy.

An Early-Warning System for Hypo-/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Introduction: Early warning of future hypoglycemic and hyperglycemic events can improve the safety of type 1 diabetes mellitus (T1DM) patients. The aim of this study is to design and evaluate a hypoglycemia / hyperglycemia early warning system (EWS) for T1DM patients under sensor-augmented pump (SAP) therapy. Methods: The EWS is based on the combination of data-driven online adaptive prediction models and a warning algorithm. Three modeling approaches have been investigated: (i) autoregressive (ARX) models, (ii) auto-regressive with an output correction module (cARX) models, and (iii) recurrent neural network (RNN) models. The warning algorithm performs postprocessing of the models′ outputs and issues alerts if upcoming hypoglycemic/hyperglycemic events are detected. Fusion of the cARX and RNN models, due to their complementary prediction performances, resulted in the hybrid autoregressive with an output correction module/recurrent neural network (cARN)-based EWS. Results: The EWS was evaluated on 23 T1DM patients under SAP therapy. The ARX-based system achieved hypoglycemic (hyperglycemic) event prediction with median values of accuracy of 100.0% (100.0%), detection time of 10.0 (8.0) min, and daily false alarms of 0.7 (0.5). The respective values for the cARX-based system were 100.0% (100.0%), 17.5 (14.8) min, and 1.5 (1.3) and, for the RNN-based system, were 100.0% (92.0%), 8.4 (7.0) min, and 0.1 (0.2). The hybrid cARN-based EWS presented outperforming results with 100.0% (100.0%) prediction accuracy, detection 16.7 (14.7) min in advance, and 0.8 (0.8) daily false alarms. Conclusion: Combined use of cARX and RNN models for the development of an EWS outperformed the single use of each model, achieving accurate and prompt event prediction with few false alarms, thus providing increased safety and comfort.

Multi-Model Data Fusion to Improve an Early Warning System for Hypo-/Hyperglycemic Events

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed. Data from 23 T1D subjects under sensor-augmented pump (SAP) therapy were used in two adaptive data-driven models (an autoregressive model with output correction - cARX, and a recurrent neural network - RNN). Data fusion techniques based on i) Dempster-Shafer Evidential Theory (DST), ii) Genetic Algorithms (GA), and iii) Genetic Programming (GP) were used to merge the complimentary performances of the prediction models. The fused output is used in a warning algorithm to issue alarms of upcoming hypo-/hyperglycemic events. The fusion schemes showed improved performance with lower root mean square errors, lower time lags, and higher correlation. In the warning algorithm, median daily false alarms (DFA) of 0.25%, and 100% correct alarms (CA) were obtained for both event types. The detection times (DT) before occurrence of events were 13.0 and 12.1 min respectively for hypo-/hyperglycemic events. Compared to the cARX and RNN models, and a linear fusion of the two, the proposed fusion schemes represents a significant improvement.

Provision of an airborne early warning system for Pakistan [microform] : hearings before the Subcommittee on Asian and Pacific Affairs of the Committee on Foreign Affairs, House of Representatives, One Hundredth Congress, first session, May 21 and June 11, 1987.

CIS Microfiche Accession Numbers: CIS 89 H381-7

Hand receipt covering contents of components of end item (COEI), basic issue items (BII), and additional authorization list (ALL) for platoon early warning system AN/TRS-2(V)1 (NSN 5895-01-063-8103) ... AN/TRS-2(V)6 (NSN 5895-01-068-6749).

"20 July 1981."

Organizational and direct support maintenance manual for platoon early warning system AN/TRS-2(V)1 (NSN 5895-01-063-8103) ... AN/TRS-2(V)6 (NSN 5895-01-068-6749).

"8 September 1980"

Reflections on the development and implementation of an early warning system for ecstasy and related drug markets in Australia

Regular and systematic monitoring of drug markets provides the basis for evidence-based policy. In Australia, trends in ecstasy and related drug (ERD) markets have been monitored in selected jurisdictions since 2000 and nationally since 2003, by the Party Drugs Initiative (PDI). The PDI maximises the validity of conclusions by triangulating information from (a) interviews with regular ecstasy users (REU), (b) interviews with key experts and (c) indicator data. There is currently no other system in Australia for monitoring these markets systematically; however, the value of the PDI has been constrained by the quality of available data. Difficulties in recruiting and interviewing appropriate consumers (REU) and key experts have been experienced, but largely overcome. Limitations of available indicator data from both health and law enforcement continue to present challenges and there remains considerable scope for enhancing existing routine data collection systems, to facilitate monitoring of ERD markets. With an expanding market for ecstasy and related drugs in Australia, and in the context of indicator data that continue to be limited in scope and detail, there is a strong argument for the continued collection of annual, comparable data from a sentinel group of REU, such as those recruited for the PDI.