Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate ...timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.
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•The early warning index system contains 3-level and 5 composite indexes.•The index weights were determined by subjective and objective combination weighting.•GM (1,1) early warning ...model for iron and steel enterprises was established.•The comprehensive early warning index was defined.
In order to prevent the occurrence of accidents in iron and steel enterprises, it is essential to change the risk management pattern from post-emergency response to hazard control and prevention. Based on the characteristics of iron and steel enterprises, this paper investigates the early warning system for accidents for iron and steel enterprises, aiming for the adoption of accident prevention and hazard control. An early warning index system and an early warning model were constructed based on production types and accident statistics of the enterprises. On account of the factors that influence accidents, this early warning index system contains 3 hierarchies with 5 composite indexes and 22 thematic indexes. The indexes have been quantified, regularized, and their weights were determined using a combination weighting method based on the Analytic Hierarchy Process and the Entropy Weight Method. The early warning index model was established according to Grey System Theory GM (1,1), and the comprehensive early warning indexes were calculated by Multi-objective Linear Weighted Function. The thresholds were then determined, the early warning levels were identified, and the early warning signals were output accordingly. The feasibility and validity of the proposed early warning model was tested and verified through its application in a functioning industrial plant.
In this article, we propose a multichannel sea clutter model in a spaceborne early warning radar system and analyze the influence of the sea clutter motion characteristics on the space-time adaptive ...processing (STAP) performance. To establish a multichannel sea clutter model, the 3-D Gerstner wave model is applied to construct the sea surface. Then the Pierson-Moskowitz wave spectrum and the stereo wave observation project (SWOP) directional spectrum are combined to describe the amplitude distribution of waves in different frequencies and directions. At the same time, the two-scale model is applied to obtain the specific backscattering coefficients of sea clutter at different time and positions. In addition, breaking waves are added in sea clutter returns with the form of false targets. Finally, the space-time distribution characteristics of sea clutter in a spaceborne multichannel array system and the influences of sea clutter under different wind speeds and directions on STAP performance are analyzed based on the simulation processing results. Processing results of some real-measured radar data are also exhibited to verify the theoretical analyses.
This study aims to investigate whether combining scoring systems with monocyte distribution width (MDW) improves early sepsis detection in older adults in the emergency department (ED).
In this ...prospective observational study, we enrolled older adults aged ≥60 years who presented with confirmed infectious diseases to the ED. Three scoring systems-namely quick sepsis-related organ failure assessment (qSOFA), Modified Early Warning Score (MEWS), and National Early Warning Score (NEWS), and biomarkers including MDW, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein (CRP), were assessed in the ED. Logistic regression models were used to construct sepsis prediction models.
After propensity score matching, we included 522 and 2088 patients with and without sepsis in our analysis from January 1, 2020, to September 30, 2021. NEWS ≥5 and MEWS ≥3 exhibited a moderate-to-high sensitivity and a low specificity for sepsis, whereas qSOFA score ≥2 demonstrated a low sensitivity and a high specificity. When combined with biomarkers, the NEWS-based, the MEWS-based, and the qSOFA-based models exhibited improved diagnostic accuracy for sepsis detection without CRP inclusion (c-statistics=0.842, 0.842, and 0.826, respectively). Of the three models, MEWS ≥3 with white blood cell (WBC) count ≥11 × 10
/L, NLR ≥8, and MDW ≥20 demonstrated the highest diagnostic accuracy in all age subgroups (c-statistics=0.886, 0.825, and 0.822 in patients aged 60-74, 75-89, and 90-109 years, respectively).
Our novel scoring system combining MEWS with WBC, NLR, and MDW effectively detected sepsis in older adults.
Geographical landslide early warning systems Guzzetti, Fausto; Gariano, Stefano Luigi; Peruccacci, Silvia ...
Earth-science reviews,
January 2020, 2020-01-00, Letnik:
200
Journal Article
Recenzirano
Odprti dostop
The design, implementation, management, and verification of landslide early warning systems (LEWSs) are gaining increasing attention in the literature and among government officials, decision makers, ...and the public. Based on a critical analysis of nine main assumptions that form the rationale for landslide forecasting and early warning, we examine 26 regional, national, and global LEWSs worldwide from 1977 to August 2019. We find that currently only five nations, 13 regions, and four metropolitan areas benefit from LEWSs, while many areas with numerous fatal landslides, where landslide risk to the population is high, lack LEWSs. Operational LEWSs use information from rain gauge networks, meteorological models, weather radars, and satellite estimates; and most systems use two sources of rainfall information. LEWSs use one or more types of landslide forecast models, including rainfall thresholds, distributed slope stability models, and soil water balance models; and most systems use landslide susceptibility zonations. Most LEWSs have undergone some form of verification, but there is no accepted standard to check the performance and forecasting skills of a LEWS. Based on our review, and our experience in the design, implementation, management, and verification of geographical LEWSs in Italy, we conclude that operational forecast of weather-induced landslides is feasible, and it can help reduce landslide risk. We propose 30 recommendations to further develop and improve geographical LEWSs, and to increase their reliability and credibility. We encourage landslide forecasters and LEWSs managers to propose open standards for geographical LEWSs, and we expect our work to contribute to this endeavour.
Earthquake early warning (EEW) is the delivery of ground shaking alerts or warnings. It is distinguished from earthquake prediction in that the earthquake has nucleated to provide detectable ground ...motion when an EEW is issued. Here we review progress in the field in the last 10 years. We begin with EEW users, synthesizing what we now know about who uses EEW and what information they need and can digest. We summarize the approaches to EEW and gather information about currently existing EEW systems implemented in various countries while providing the context and stimulus for their creation and development. We survey important advances in methods, instrumentation, and algorithms that improve the quality and timeliness of EEW alerts. We also discuss the development of new, potentially transformative ideas and methodologies that could change how we provide alerts in the future.
Earthquake early warning (EEW) is the rapid detection and characterization of earthquakes and delivery of an alert so that protective actions can be taken.
EEW systems now provide public alerts in Mexico, Japan, South Korea, and Taiwan and alerts to select user groups in India, Turkey, Romania, and the United States.
EEW methodologies fall into three categories, point source, finite fault, and ground motion models, and we review the advantages of each of these approaches.
The wealth of information about EEW uses and user needs must be employed to focus future developments and improvements in EEW systems.
Systematic adoption of early warning systems in healthcare settings is dependent on the optimal and reliable application by the user. Psychosocial issues and hospital culture influence clinicians' ...patient safety behaviours.
(i) To examine the sociocultural factors that influence nurses' EWS compliance behaviours, using a theory driven behavioural model and (ii) to propose a conceptual model of sociocultural factors for EWS compliance behaviour.
A cross-sectional survey.
Nurses employed in public hospitals across Queensland, Australia.
Using convenience and snowball sampling techniques eligible nurses accessed a dedicated web site and survey containing closed and open-ended questions. 291 nurses from 60 hospitals completed the survey.
Quantitative data were analysed using ANOVA or t-tests to test differences in means. A series of path models based on the theory were conducted to develop a new model. Directed or theory driven content analysis informed qualitative data analysis.
Nurses report high levels of previous compliance behaviour and strong intentions to continue complying in the future (M=4.7; SD 0.48). Individual compliance attitudes (β 0.29, p<.05), perceived value of escalation (β 0.24, p<.05) and perceived ease or difficulty complying with documentation (β −0.31, p<.05) were statistically significant, predicting 24% of variation in compliance behaviour. Positive personal charting beliefs (β 0.14, p<.05) and subjective norms both explain higher behavioural intent indirectly through personal attitudes. High ratings of peer charting beliefs indirectly explain attitudes through subjective norms (β 0.20, p<.05). Perceptions of control over one's clinical actions (β −0.24, p<.05) and early warning system training (β −0.17, p<.05) directly contributed to fewer difficulties complying with documentation requirements. Prior difficulties when escalating care (β −0.31, p<.05) directly influenced the perceived value of escalating.
The developed theory-based conceptual model identified sociocultural variables that inform compliance behaviour (documenting and escalation protocols). The model highlights areas of clinical judgement, education, interprofessional trust, workplace norms and cultural factors that directly or indirectly influence nurses' intention to comply with EWS protocols. Extending our understanding of the sociocultural and system wide factors that hamper nurses' use of EWSs and professional accountability has the potential to improve the compliance behaviour of staff and subsequently enhance the safety climate attitudes of hospitals.
A newly developed model reports nurse's personal attitudes, peer influence, perceived difficulties encountered documenting and escalation beliefs all predict early warning system compliance behaviour.
Performance of earthquake early warning systems suffers from false alerts caused by local impulsive noise from natural or anthropogenic sources. To mitigate this problem, we train a generative ...adversarial network (GAN) to learn the characteristics of first‐arrival earthquake P waves, using 300,000 waveforms recorded in southern California and Japan. We apply the GAN critic as an automatic feature extractor and train a Random Forest classifier with about 700,000 earthquake and noise waveforms. We show that the discriminator can recognize 99.2% of the earthquake P waves and 98.4% of the noise signals. This state‐of‐the‐art performance is expected to reduce significantly the number of false triggers from local impulsive noise. Our study demonstrates that GANs can discover a compact and effective representation of seismic waves, which has the potential for wide applications in seismology.
Plain Language Summary
Earthquake early warning systems are sometimes accidentally triggered by impulsive noise signals, rather than by real earthquake signals, which leads to false alerts. This may cause unnecessary economic loss and public concern. Here we use machine learning tools to determine if the waveforms are generated by earthquakes or local noise sources. We train the algorithms with about 700,000 waveforms recorded by southern California and Japan. We demonstrate that the trained machine learning discriminator can recognize 99.2% of the earthquakes and 98.4% of the noise. This discriminator can reduce a large number of false alerts and significantly improve the robustness of early warning systems.
Key Points
We train machine learning algorithms with a large data set to discriminate earthquake P waves from local impulsive noise
The trained discriminator achieves accuracy of 99.2% for P waves and 98.4% for impulsive noise
The discriminator can significantly reduce false alerts in earthquake early warning systems
Since the introduction of the UK’s National Early Warning Score (NEWS) and its modification, NEWS2, coronavirus disease 2019 (COVID-19), has caused a worldwide pandemic. NEWS and NEWS2 have good ...predictive abilities in patients with other infections and sepsis, however there is little evidence of their performance in COVID-19.
Using receiver-operating characteristics analyses, we used the area under the receiver operating characteristic (AUROC) curve to evaluate the performance of NEWS or NEWS2 to discriminate the combined outcome of either death or intensive care unit (ICU) admission within 24 h of a vital sign set in five cohorts (COVID-19 POSITIVE, n = 405; COVID-19 NOT DETECTED, n = 1716; COVID-19 NOT TESTED, n = 2686; CONTROL 2018, n = 6273; CONTROL 2019, n = 6523).
The AUROC values for NEWS or NEWS2 for the combined outcome were: COVID-19 POSITIVE, 0.882 (0.868−0.895); COVID-19 NOT DETECTED, 0.875 (0.861−0.89); COVID-19 NOT TESTED, 0.876 (0.85−0.902); CONTROL 2018, 0.894 (0.884−0.904); CONTROL 2019, 0.842 (0.829−0.855).
The finding that NEWS or NEWS2 performance was good and similar in all five cohorts (range = 0.842−0.894) suggests that amendments to NEWS or NEWS2, such as the addition of new covariates or the need to change the weighting of existing parameters, are unnecessary when evaluating patients with COVID-19. Our results support the national and international recommendations for the use of NEWS or NEWS2 for the assessment of acute-illness severity in patients with COVID-19.
Earthquake early warning (EEW) is a relatively new strategy for reducing disaster risk and increasing resilience to seismic hazard in urban settings. EEW systems provide real-time information about ...ongoing earthquakes, enabling individuals, communities, governments, businesses and others located at distance to take timely action to reduce the probability of harm or loss before the earthquake-induced ground shaking reaches them. Examples of potential losses mitigated by EEW systems include injuries and infrastructure downtime. These systems are currently operating in nine countries, and are being/have been tested for implementation in 13 more. This paper reviews state-of-the-art approaches to EEW around the world. We specifically focus on the various algorithms that have been developed for the rapid calculation of seismic-source parameters, ground shaking, and potential consequences in the wake of an event. We also discuss limitations of the existing applied methodologies, with a particular emphasis on the lack of engineering-related (i.e., risk and resilience) metrics currently used to support decision-making related to the triggering of alerts by various end users. Finally, we provide a number of suggestions for future end-user-orientated advances in the field of EEW. For example, we propose that next-generation EEW systems should incorporate engineering-based, application-specific models/tools for more effective risk communication. They should operate within robust probabilistic frameworks that explicitly quantify uncertainties at each stage of the analysis, for more informed stakeholder decision-making. These types of advancements in EEW systems would represent an important paradigm shift in current approaches to issuing early warnings for natural hazards.
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