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There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies. [ABSTRACT FROM AUTHOR]

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes' destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas. [ABSTRACT FROM AUTHOR]

In Durban, South Africa, heavy rainfall leading to flooding has become more frequent over the past decade, resulting in devastating consequences for households. However, studies in South Africa have predominantly focused on understanding the impacts of flooding on rural livelihoods. With intense rainfall events that could result in localized flash flooding to be expected to increase in Durban in the near future, failure to understand how households prepare for flooding and the interventions implemented by local authorities could jeopardize the achievement of Sustainable Development Goal (SDG) 11 – resilient and sustainable cities. To address this gap, primary data were collected from purposively selected residents of the Amanzimtoti community in Durban using questionnaires that comprised both closed and open-ended questions. Additionally, secondary data were analyzed to determine the measures implemented by the eThekwini Municipality to mitigate the adverse effects of flooding. The findings indicate that flooding has triggered psychological trauma among residents. Regarding interventions to mitigate flooding impacts, some homeowners and business owners purchased insurance. Despite the municipality providing early warnings, a severe flood event in 2019 had a significant negative impact on households partly due to the municipality's failure to utilize preferred communication channels – such as X (formally known as Twitter) – that resonate with residents. The study also found that adoption of early warnings may be partly hinged on the extent to which they remember the last flood episode that resulted in disaster. The findings are discussed in terms of their implications for achieving SDG 11 in Durban.

Given the abrupt global disruption caused by SARS-CoV-2, one might think that the COVID pandemic was an unpredictable event. But in the years leading up to the emergence of the COVID pandemic, several documents had already been warning of the increasing occurrences of new disease outbreaks with pandemic potential and lack of corresponding policies to promote pandemic preparedness and response. In this article, we call these documents "early warnings". We argue that a survey of early warnings can help science communicators to promote the public understanding of evidence-based pandemic preparedness and response policies at local or international level. Our proposal differs from other approaches to pandemic preparedness and response in that it highlights the relevance of documents published before the COVID outbreak. We show that the early warnings did not become outdated after the COVID outbreak, but, rather, that they are even more pressing now. [ABSTRACT FROM AUTHOR]

One in every five occupational deaths occurs in the construction sector. A proactive approach for improving on-site safety is identifying and analyzing accident precursors, such as near-misses, that provide early warnings of accidents. Despite the importance of near-misses, they are frequently left unreported and unrecorded in the construction sector. The adoption of modern technologies can prevent accidents by automated data collection and analysis. This study aims to develop near-miss detection metrics to facilitate the automated detection of near-misses through sensors. The study adopted a mixed method approach including both qualitative and quantitative approaches. First, a quantifiable definition of near-misses was developed from the literature. Hazards, accidents, and the causes of accidents were identified. Through empirical and statistical analyses of accidents from the OSHA repository, combinations of unsafe acts and conditions responsible for a near-miss were identified. The identified factors were analyzed using a frequency analysis, correlation, and a lambda analysis. The results revealed twelve significant near-misses, such as A1—approach to restricted areas and C2—unguarded floor/roof openings, A5—equipment and tool inspection was incomplete and C8—unsafely positioned ladders and scaffolds, A2—no or improper use of PPE and C2—unguarded floor or roof openings, etc. Lastly, measurable data required by sensors for autonomous detection of near-misses were determined. The developed metric set the basis for automating near-miss reporting and documentation using modern sensing technology to improve construction safety. This study contributes to improving construction safety by addressing the underreporting of near-miss events. Overall, the developed metrics lay the groundwork for enhancing construction safety through automated near-miss reporting and documentation. Furthermore, it helps for the establishment of safety management schemes in the construction industry, specifically in identifying near-misses. This research offers valuable insight into developing guidelines for safety managers to improve near-miss reporting and detection on construction sites. In sum, the findings can be valuable for other industries also looking to establish or assess their own safety management systems. [ABSTRACT FROM AUTHOR]

There has been growing interest in the application of smart technologies for hazard management. However, very limited studies have reviewed the trends of such technologies in the context of flash floods. This study reviews innovative technologies such as artificial intelligence (AI)/machine learning (ML), the Internet of Things (IoT), cloud computing, and robotics used for flash flood early warnings and susceptibility predictions. Articles published between 2010 and 2023 were manually collected from scientific databases such as Google Scholar, Scopus, and Web of Science. Based on the review, AI/ML has been applied to flash flood susceptibility and early warning prediction in 64% of the published papers, followed by the IoT (19%), cloud computing (6%), and robotics (2%). Among the most common AI/ML methods used in susceptibility and early warning predictions are random forests and support vector machines. However, further optimization and emerging technologies, such as computer vision, are required to improve these technologies. AI/ML algorithms have demonstrated very accurate prediction performance, with receiver operating characteristics (ROC) and areas under the curve (AUC) greater than 0.90. However, there is a need to improve on these current models with large test datasets. Through AI/ML, IoT, and cloud computing technologies, early warnings can be disseminated to targeted communities in real time via electronic media, such as SMS and social media platforms. In spite of this, these systems have issues with internet connectivity, as well as data loss. Additionally, Al/ML used a number of topographical variables (such as slope), geological variables (such as lithology), and hydrological variables (such as stream density) to predict susceptibility, but the selection of these variables lacks a clear theoretical basis and has inconsistencies. To generate more reliable flood risk assessment maps, future studies should also consider sociodemographic, health, and housing data. Considering future climate change impacts, susceptibility or early warning studies may be projected under different climate change scenarios to help design long-term adaptation strategies.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes’ destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.

The authors of the study put forward a hypothesis that it is possible to extend the forecast period for the models of discriminant analysis used to assess the risk of enterprise bankruptcy, focusing on the components of these functions in the form of one-dimensional predictors, i.e. the indicators most frequently included in the discriminant functions developed in Poland. Early warning about the growing risk of bankruptcy would be very valuable for any company. The dataset was constructed from all enterprises in Poland that went bankrupt in the years 2007-2013, which was the end of the research project period. Out of the 4,750 business entities that went bankrupt at that time, 2,739 filed financial statements with commercial courts. The main objective was realized using dynamic assessment of the variability of selected one-dimensional predictors of bankruptcy for all of these enterprises. Assessment of the time variability of the indicators under analysis allows conclusions on the predictive possibilities associated with early warning against insolvency of business entities. The results constitute input to the discussion on determination of the longest prognostic horizon that can be adopted in the models of discriminant analysis used to assess the risk of enterprise bankruptcy. Most of them cover an annual forecasting horizon. Only a few authors have attempted to construct models based on data from the two, three, or even four years preceding bankruptcy. The study showed that the main symptoms of the growing risk of bankruptcy in most of the surveyed enterprises are visible much earlier than one year before bankruptcy. This provides an opportunity to correct the predictive models and more time to restructure the company, to prevent bankruptcy. Therefore, the authors of the study have assessed the possibility of extending this forecast period. [ABSTRACT FROM AUTHOR]

Abstract Ecosystems are complex and sparsely observed making inference and prediction challenging. Empirical dynamic modelling (EDM) circumvents the need for a parametric model and complete observations of all system variables. Classical univariate approaches, which require time‐series observations of only a single focal variable, can produce verifiable out‐of‐sample forecasts; however, they can sometimes require long time series that may be difficult to obtain. More importantly, classical approaches limit the depth of mechanistic understanding that can be gained and the generalizability of forecasts to non‐analogue futures. We review the main ideas of EDM and more recent extensions that expand their capabilities for improving forecasts and understanding mechanism. Algorithms are now available that allow for missing data, unequal sampling intervals and combining short time series, which increase the number of datasets that can be used. Recent extensions of EDM to multivariate time series substantially expand the range of applications and mechanistic questions that can be addressed, including detecting causal coupling, tracking changing interactions in real time, leveraging short time series from information shared in coupled variables, modelling dynamically changing stability, scenario exploration, and management applications involving optimal control.

One in every five occupational deaths occurs in the construction sector. A proactive approach for improving on-site safety is identifying and analyzing accident precursors, such as near-misses, that provide early warnings of accidents. Despite the importance of near-misses, they are frequently left unreported and unrecorded in the construction sector. The adoption of modern technologies can prevent accidents by automated data collection and analysis. This study aims to develop near-miss detection metrics to facilitate the automated detection of near-misses through sensors. The study adopted a mixed method approach including both qualitative and quantitative approaches. First, a quantifiable definition of near-misses was developed from the literature. Hazards, accidents, and the causes of accidents were identified. Through empirical and statistical analyses of accidents from the OSHA repository, combinations of unsafe acts and conditions responsible for a near-miss were identified. The identified factors were analyzed using a frequency analysis, correlation, and a lambda analysis. The results revealed twelve significant near-misses, such as A1—approach to restricted areas and C2—unguarded floor/roof openings, A5—equipment and tool inspection was incomplete and C8—unsafely positioned ladders and scaffolds, A2—no or improper use of PPE and C2—unguarded floor or roof openings, etc. Lastly, measurable data required by sensors for autonomous detection of near-misses were determined. The developed metric set the basis for automating near-miss reporting and documentation using modern sensing technology to improve construction safety. This study contributes to improving construction safety by addressing the underreporting of near-miss events. Overall, the developed metrics lay the groundwork for enhancing construction safety through automated near-miss reporting and documentation. Furthermore, it helps for the establishment of safety management schemes in the construction industry, specifically in identifying near-misses. This research offers valuable insight into developing guidelines for safety managers to improve near-miss reporting and detection on construction sites. In sum, the findings can be valuable for other industries also looking to establish or assess their own safety management systems.

Ecosystems are complex and sparsely observed making inference and prediction challenging.Empirical dynamic modelling (EDM) circumvents the need for a parametric model and complete observations of all system variables. Classical univariate approaches, which require time‐series observations of only a single focal variable, can produce verifiable out‐of‐sample forecasts; however, they can sometimes require long time series that may be difficult to obtain. More importantly, classical approaches limit the depth of mechanistic understanding that can be gained and the generalizability of forecasts to non‐analogue futures.We review the main ideas of EDM and more recent extensions that expand their capabilities for improving forecasts and understanding mechanism.Algorithms are now available that allow for missing data, unequal sampling intervals and combining short time series, which increase the number of datasets that can be used. Recent extensions of EDM to multivariate time series substantially expand the range of applications and mechanistic questions that can be addressed, including detecting causal coupling, tracking changing interactions in real time, leveraging short time series from information shared in coupled variables, modelling dynamically changing stability, scenario exploration, and management applications involving optimal control. [ABSTRACT FROM AUTHOR]

This article establishes the design of an early warning system for flooding in the Arauca River, in the municipality of Arauca, Colombia. The information corresponding to this study is extracted from the IDEAM and is processed obtaining a model through the variables that intervene such as precipitation, level and flow. This information model supplies the data to the mathematical model corresponding to the river channel, which is obtained from three kinds of trends: linear, power and potential relationships. This model is compared with an observer based on intelligent techniques such as neural networks and ANFIS, which make the difference of their outputs and a residue is obtained that is in charge of supplying the information that provides the current state of the river level under study, which in turn generates alerts that are addressed by government entities dedicated to risk management. [ABSTRACT FROM AUTHOR]

This article sought to review the literature on the effectiveness of the disaster preparedness plans in two provinces that were struck by climate-induced disasters in South Africa in 2022. Making use of a qualitative desktop study, the effects of urbanisation and infrastructure development on the deadly disasters of April 2022 were examined, using thematic analyses. The review reveals a complex combination of the causes of disaster in both KwaZulu-Natal and the Eastern Cape, which included unplanned urbanisation; ineffective warning systems; inadequate infrastructure, and houses being built on floodplains, in wetland, and in coastal areas. The lack of climate knowledge among government officials and communities prone to climateinduced disasters was considered to have led to the severe devastating effects of the April 2022 disasters. These challenges were compounded by the failure of municipal officials to harness learnings from the rich indigenous knowledge systems of the vulnerable groups and to reverse impacts of the disasters. The article summarises the causes, challenges, impacts, and solutions that can be considered to plan for disaster preparedness. [ABSTRACT FROM AUTHOR]

To minimize the impacts of large losses and optimize the emergency response when a large earthquake occurs, an accurate early warning of an earthquake or tsunami is crucial. One important parameter that can provide an accurate early warning is the earthquake's magnitude. This study proposes a method for estimating the magnitude, and some of the source parameters, of an earthquake using genetic algorithms (GAs). In this study, GAs were used to perform an inversion of Okada's model from earthquake displacement data. In the first stage of the experiment, the GA was used to inverse the displacement calculated from the forward calculation in Okada's model. The best performance of the GA was obtained by tuning the hyperparameters to obtain the most functional configuration. In the second stage, the inversion method was tested on GPS time series data from the 2011 Tohoku Oki earthquake. The earthquake's displacement was first estimated from GPS time series data using a detection and estimation formula from previous research to calculate the permanent displacement value. The proposed method can estimate an earthquake's magnitude and four source parameters (i.e., length, width, rake, and slip) close to the real values with reasonable accuracy. [ABSTRACT FROM AUTHOR]

Some historical aspects on late lessons from early warnings on cancer risks with lost time for prevention are discussed. One current example is the cancer-causing effect from radiofrequency (RF) radiation. Studies since decades have shown increased human cancer risk. The fifth generation, 5G, for wireless communication is about to be implemented world-wide despite no comprehensive investigations of potential risks to human health and the environment. This has created debate on this technology among concerned people in many countries. In an appeal to EU in September 2017, currently endorsed by more than 400 scientists and medical doctors, a moratorium on the 5G deployment was required until proper scientific evaluation of negative consequences has been made (www.5Gappeal.eu). That request has not been taken seriously by EU. Lack of proper unbiased risk evaluation of the 5G technology makes adverse effects impossible to be foreseen. This disregard is exemplified by the recent report from the International Commission on non-ionizing radiation protection (ICNIRP) whereby only thermal (heating) effects from RF radiation are acknowledged despite a large number of reported non-thermal effects. Thus, no health effects are acknowledged by ICNIRP for non-thermal RF electromagnetic fields in the range of 100 kHz–300 GHz. Based on results in three case-control studies on use of wireless phones we present preventable fraction for brain tumors. Numbers of brain tumors of not defined type were found to increase in Sweden, especially in the age group 20–39 years in both genders, based on the Swedish Inpatient Register. This may be caused by the high prevalence of wireless phone use among children and in adolescence taking a reasonable latency period and the higher vulnerability to RF radiation among young persons. [ABSTRACT FROM AUTHOR]

Severe weather events associated with strong winds and flooding can cause fatalities, injuries and damage to property. Detailed and accurate weather forecasts that are issued and communicated timeously, and actioned upon, can reduce the impact of these events. The responsibility to provide such forecasts usually lies with government departments or state-owned entities; in South Africa that responsibility lies with the South African Weather Service (SAWS). SAWS is also a regional specialised meteorological centre and therefore provides weather information to meteorological services within the Southern African Development Community (SADC). We evaluated SAWS weather information using near real-time observations and models on the nowcasting to short-range forecasting timescales during two extreme events. These are the Idai tropical cyclone in March 2019 which impacted Mozambique, Zimbabwe and Malawi resulting in over 1000 deaths, and the floods over the KwaZulu-Natal (KZN) province in April 2019 that caused over 70 deaths. Our results show that weather models gave an indication of these systems in advance, with warnings issued at least 2 days in advance in the case of Idai and 1 day in advance for the KZN floods. Nowcasting systems were also in place for detailed warnings to be provided as events progressed. Shortcomings in model simulations were shown, in particular on locating the KZN flood event properly and over/-underestimation of the event. The impacts experienced during the two events indicate that more needs to be done to increase weather awareness, and build disaster risk management systems, including disaster preparedness and risk reduction. Significance: This paper is relevant for all South Africans and the SADC region at large because it provides information on: • the weather forecasting processes followed at the South African Weather Service, • available early warning products in South Africa and for the SADC region made possible through the public purse, • the performance of nowcasting and modelling systems in the case of predicting two extreme weather events that had adverse impacts on southern African society, and • the dissemination of warnings of future extreme weather events.

Severe weather events associated with strong winds and flooding can cause fatalities, injuries and damage to property. Detailed and accurate weather forecasts that are issued and communicated timeously, and actioned upon, can reduce the impact of these events. The responsibility to provide such forecasts usually lies with government departments or state-owned entities; in South Africa that responsibility lies with the South African Weather Service (SAWS). SAWS is also a regional specialised meteorological centre and therefore provides weather information to meteorological services within the Southern African Development Community (SADC). We evaluated SAWS weather information using near real-time observations and models on the nowcasting to short-range forecasting timescales during two extreme events. These are the Idai tropical cyclone in March 2019 which impacted Mozambique, Zimbabwe and Malawi resulting in over 1000 deaths, and the floods over the KwaZulu-Natal (KZN) province in April 2019 that caused over 70 deaths. Our results show that weather models gave an indication of these systems in advance, with warnings issued at least 2 days in advance in the case of Idai and 1 day in advance for the KZN floods. Nowcasting systems were also in place for detailed warnings to be provided as events progressed. Shortcomings in model simulations were shown, in particular on locating the KZN flood event properly and over/underestimation of the event. The impacts experienced during the two events indicate that more needs to be done to increase weather awareness, and build disaster risk management systems, including disaster preparedness and risk reduction. [ABSTRACT FROM AUTHOR]

Risk communication is a critical component in disaster risk reduction, especially in designing and implementing early warning systems. The ability to communicate hazard forecasts and risk information to vulnerable communities and stakeholders successfully is crucial for effective disaster preparedness and response, to reduce impact, and to prevent loss of life. Successful risk communication, however, is challenged constantly. Even the most sophisticated forecasting models and advanced early warning systems can be rendered ineffective if the information is not communicated clearly, on time, and in a way that allows the end-user to consider options and act appropriately. These challenges not only apply to fast onset hazards such as cyclones and tsunamis, but also to slower onset hazards such as the current COVID-19 pandemic. Around the world, governments' responses to the current pandemic provide examples of how complex and difficult successful risk communication is. This paper discusses the importance of risk communication as a critical component of early warning systems and explores the constant challenges that vulnerable communities face, how early warning systems sit within the wider Sendai Framework, and what governments have learned from the COVID-19 pandemic, and discusses how we can communicate more effectively in the future to reduce harm.

Ecosystems around the world are at risk of critical transitions due to increasing anthropogenic pressures and climate change. Yet it is unclear where the risks are higher or where in the world ecosystems are more vulnerable. Here I measure resilience of primary productivity proxies for marine and terrestrial ecosystems globally. Up to 29% of global terrestrial ecosystem, and 24% marine ones, show symptoms of resilience loss. These symptoms are shown in all biomes, but Arctic tundra and boreal forest are the most affected, as well as the Indian Ocean and Eastern Pacific. Although the results are likely an underestimation, they enable the identification of risk areas as well as the potential synchrony of some transitions, helping prioritize areas for management interventions and conservation.

To minimize the impacts of large losses and optimize the emergency response when a large earthquake occurs, an accurate early warning of an earthquake or tsunami is crucial. One important parameter that can provide an accurate early warning is the earthquake’s magnitude. This study proposes a method for estimating the magnitude, and some of the source parameters, of an earthquake using genetic algorithms (GAs). In this study, GAs were used to perform an inversion of Okada’s model from earthquake displacement data. In the first stage of the experiment, the GA was used to inverse the displacement calculated from the forward calculation in Okada’s model. The best performance of the GA was obtained by tuning the hyperparameters to obtain the most functional configuration. In the second stage, the inversion method was tested on GPS time series data from the 2011 Tohoku Oki earthquake. The earthquake’s displacement was first estimated from GPS time series data using a detection and estimation formula from previous research to calculate the permanent displacement value. The proposed method can estimate an earthquake’s magnitude and four source parameters (i.e., length, width, rake, and slip) close to the real values with reasonable accuracy.

Real-time supervision of shifts in inflation expectations is an important issue for monetary policy makers, especially in the presence of economic uncertainty. In this paper, we elaborate tools for on-line monitoring of such shifts by extracting valuable information from noisy daily financial market data. For this purpose, first, we suggest a new risk adjustment for observable proxies of medium and long run inflation expectations assuming that the latter are well-anchored. Second, we propose an econometric methodology for sequential monitoring of level changes in the associated proxies at daily frequency. Our empirical evidence shows that the on-line surveillance of risk adjusted US forward breakeven inflation rates by means of the cumulative sum (CUSUM) detector appears to be helpful to extract timely signals on potential shifts. In particular, the obtained signals indicate important turning points in market-based measures of inflation expectations, which also tend to materialize in lower frequency experts' surveys. [ABSTRACT FROM AUTHOR]

Cyber threats are on the increase. Authorities need to respond to growing challenges by increasing cooperation. Information sharing or information exchange in the EU level and between the countries is a main facility when the objective is to prevent hybrid threats. Intensifying relationships with private sector companies has become very important function and operating model to authorities to provide cyber-safe atmosphere. The main purpose of this study is to find out separating and combining factors concerning cyber information sharing models. The aim is also to find out nation level factors, which affect the utilization of a common Early Warning system by the ECHO stakeholders. Summary of findings: unclear allocation of responsibilities in national government departments prevents authorities from fighting together against cyber and physical threats. Cybersecurity responsibilities have been spread too widely. Operational work concerning cyber threat prevention between European public safety authorities should be more standardized, with more centralized management. When the purpose is to protect vital functions of society, public safety organizations in EU member states need proactive features in their information systems. An essential factor in information exchange is the place of registration of organizations or companies. Unclear standardization concerning cyber emergency procedures between authorities and organizations and lack of co-operation between cyber situation centres and cyber emergency response centres prevent common situational awareness. [ABSTRACT FROM AUTHOR]

Abstract Contrary to the general belief, systemic risk does not only regard the risk posed by balance sheet relationships and interdependencies among institutions. It also features a temporal dimension related to the inappropriate responses of financial market participants to changes in risk over time. This paper proposes a method to simultaneously address the cross-sectional and the time dimension in which systemic risk materializes. The method is based on the TOPHITS algorithm. It provides three scores, namely borrowing, lending and time scores: the first two represent the systemic importance of the borrowing and the lending activity associated with each financial institution,while the third represents an empirical Early Warning Signal of the financial crisis. Our findings reveal that the identification of the time score as an indicator for an incoming market distress could be relevant to design macro prudential policies. [ABSTRACT FROM AUTHOR]

Objective: This study was to explore the main influencing factors of attempted suicide and establish an early warning model, so as to put forward prevention strategies for attempted suicide.Method: Data came from a large-scale case-control epidemiological survey. A sample of 659 serious suicide attempters was randomly recruited from 13 rural counties in China. Each case was matched by a community control for gender, age, and residence location. Face to face interviews were conducted for all the cases and controls with the same structured questionnaire. Univariate logistic regression was applied to screen the factors and multivariate logistic regression was used to excavate the predictors.Results: There were no statistical differences between suicide attempters and the community controls in gender, age, and residence location. The Cronbach`s coefficients for all the scales used were above 0.675. The multivariate logistic regressions have revealed 12 statistically significant variables predicting attempted suicide, including less education, family history of suicide, poor health, mental problem, aspiration strain, hopelessness, impulsivity, depression, negative life events. On the other hand, social support, coping skills, and healthy community protected the rural residents from suicide attempt.Conclusions: The excavated warning predictors are significant clinical meaning for the clinical psychiatrist. Crisis intervention strategies in rural China should be informed by the findings from this research. Education, social support, healthy community, and strain reduction are all measures to decrease the likelihood of crises. [ABSTRACT FROM AUTHOR]

Abstract: Hysteresis linked to alternative stable states may explain delays in water quality recovery despite reduced nutrient loadings in shallow lakes. Because deep lakes are assumed to be less prone to critical transitions, similar delays are attributed to the confounding effects of additional environmental disturbances, such as climate warming. Herein, we hypothesised that the lack of evidence of nutrient‐driven alternative stable states in a deep lake arises from the fact that the nutrient threshold that causes the critical transition is lower than the nutrient threshold in shallow lakes. Thereby, it might have been crossed much earlier in the lake history. To test this hypothesis, we focused on the palaeo‐ecological trajectory of Lake Varese, which is a deep, hypereutrophicated peri‐alpine lake undergoing restoration. Proxies for drivers of ecological state (i.e. total phosphorus—TP—through diatoms and pigments) and ecological responses (Cladocera), as well as a repeatable analysis, were used to identify transitions and to distinguish hysteretic delays from those of the ecosystems responding to additional constraints over the past century. Our results suggest spatial heterogeneity in the ecological response. The littoral habitats changed abruptly and prematurely for a low TP threshold, causing a shift that met many criteria of a flickering‐type critical transition. Soon after the littoral shift, a striking increase in the lake phosphorous concentration was recorded and drove the pelagic assemblage towards a new state. This transition was abrupt, and the pelagic communities exhibited limited evidence of recovery; however, we found no evidence of hysteresis. Therefore, the modern ecological trajectory of the pelagic communities is currently driven by climate warming. This detailed analysis allowed us to go beyond the general pattern that links ecological responses to drivers and suggest that a nonlinear transition following eutrophication can take place in a deep lake synchronously with linear transitions. Instead of triggering a new regime shift, climate warming, to which pelagic habitats are more sensitive than littoral ones, has driven the lake further from its safe operating space. [ABSTRACT FROM AUTHOR]

Abstract: Ecosystem regime shifts are abrupt changes from one dynamical state to another, such as the shift from a clear‐water state to an algal bloom state in lakes. These transitions are hard to forecast but theory suggests that early warning indicators can predict impending regime shifts that may allow for management intervention to prevent or mitigate an unwanted change. The efficacy of early warning indicators has been demonstrated in modeling and laboratory experiments, but rarely in the field, where environmental drivers are numerous and interacting. It is unclear if early warning indicators are observable or timely enough to allow for intervention under these conditions. We performed six whole‐lake experimental nutrient additions to test the utility of early warning indicators for predicting the regime shift from a clear‐water state to a cyanobacteria‐dominated state. The lakes were monitored for increases in resilience indicators including rises in standard deviation and autocorrelation of algal pigments and dissolved oxygen saturation. A statistical method, quickest detection, determined when resilience indicators in manipulated lakes deviated substantially from those in a reference ecosystem. Blooms occurred in five of the six lake‐years. Although there was substantial variability in bloom size and timing, at least one indicator foreshadowed the peak chlorophyll a concentration in all instances. Early warnings occurred 1–57 d prior to a bloom, which in some instances, may allow managers to notify the public or intervene to prevent blooms. The resilience indicators generally identified changes in resilience over time within a lake and also ranked large differences in resilience among lakes. Our findings suggest that resilience indicators can be useful for classifying ecosystems on a landscape and across time with respect to proximity to a critical threshold. [ABSTRACT FROM AUTHOR]

Seagrasses are globally declining and often their loss is due to synergies among stressors. We investigated the interactive effects of eutrophication and burial on the Mediterranean seagrass, Posidonia oceanica. A field experiment was conducted to estimate whether shoot survival depends on the interactive effects of three levels of intensity of both stressors and to identify early changes in plants (i.e., morphological, physiological and biochemical, and expression of stress-related genes) that may serve to detect signals of imminent shoot density collapse. Sediment burial and nutrient enrichment produced interactive effects on P. oceanica shoot survival, as high nutrient levels had the potential to accelerate the regression of the seagrass exposed to high burial (HB). After 11 weeks, HB in combination with either high or medium nutrient enrichment caused a shoot loss of about 60%. Changes in morphology were poor predictors of the seagrass decline. Likewise, few biochemical variables were associated with P. oceanica survival (the phenolics, ORAC and leaf δ34S). In contrast, the expression of target genes had the highest correlation with plant survival: photosynthetic genes (ATPa, psbD and psbA) were up-regulated in response to high burial, while carbon metabolism genes (CA-chl, PGK and GADPH) were down-regulated. Therefore, die-offs due to high sedimentation rate in eutrophic areas can only be anticipated by altered expression of stress-related genes that may warn the imminent seagrass collapse. Management of local stressors, such as nutrient pollution, may enhance seagrass resilience in the face of the intensification of extreme climate events, such as floods. [ABSTRACT FROM AUTHOR]

The need for effective early detection and timely surveillance for a robust pandemic and epidemic early warning and preparedness has been widely discussed amidst the Covid-19 pandemic, which suddenly erupted worldwide. This need is further established by various other hazards reported in many countries amidst the COVID-19 pandemic. In addition, the failure of early detection of pathogens and their source of origin has been largely connected with global transmission and severe outbreaks in many contexts. Therefore, effective early detection , timely surveillance and early warning are key aspects of a successful response to an epidemic or pandemic.. Hence, this paper aims to identify key elements and stages of an effective epidemic and pandemic early warning (EW) and response system. Further, the paper analyses inter-connections of the elements of the early warning system, focusing on the COVID-19 and multi-hazard context. The systematic literature review method was used to collect data from electronic databases. Results suggest that epidemiological surveillance & detection, primary screening of raw data & information, risk and vulnerability assessments, prediction and decision-making, alerts & early warnings are critical components of epidemic and pandemic EW. In addition, response-control & mitigation, preparedness-preventive strategies, and reducing transmission , elimination and eradication of the disease are integrated components of the early warning and response ecosystem that largely depend on effective early warnings. The significance of integrating epidemic and pandemic EW with other EWs to operate as multi-hazard early warning systems is also analysed. [ABSTRACT FROM AUTHOR]

This paper employs a recently developed statistical algorithm in order to build an early warning model for banking crises in emerging markets. The procedure creates many 'artificial' samples by iteratively perturbing the original data set and estimates many models from these samples. The final model is constructed by aggregation, so that, by construction, it is flexible enough to accommodate new data for out-of-sample prediction. Out of a large number (540) of candidate explanatory variables, ranging from macroeconomic variables to balance sheet indicators, our procedure selects a handful of indicators (and their combinations) that is sufficient to generate accurate out-of-sample predictions of banking crises. Using data covering emerging markets from 1980 to 2010, the model identifies two banking crisis' 'danger-zones', e.g. economic configurations that are conducive to crises. The first occurs when high interest rates on bank deposits, possibly reflecting liquidity risks and solvency fears, interact with credit-booms and capital flights; the second occurs when an investment boom is financed by a large rise in banks' net foreign exposure. We compare our model to models derived by standard econometric techniques, and find that our approach delivers much better out-of-sample predictions. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper is the extension of the paper stated in [1].In the extended paper, elaboration of outlier entities are discovered based on the behavioral dissimilarities. Fifteen different features were proposed for this process using a variant of Kth nearest neighborhood (KNN) algorithm. Outliers were the convicted email users in ENRON, US based company which were already been declared convicted. The results in the papers proved to be matched with the 80% of the convicted email users because few of the users were not detected due to the constraints of the algorithm. In top 19 outliers detection 3 convicted users were found out. It means 15% of the result was achieved in top 20 users. The bench marking is made with the existing convicted and declared email users. The proposed features proved to helpful in the detection of outliers in email communication network and can be further implemented for various other kind of communication networks. [ABSTRACT FROM AUTHOR]

Emerging risks may arise from process intensification and new scenarios due to the innovative technologies and higher potentialities of new LNG regasification facilities. In the conventional hazard identification process it is difficult to include new scenarios related to innovative technologies or facilities, for which limited or no operational experience is available. In the present study, a new technique for HAZard IDentification (HAZID), named Dynamic Procedure for Atypical Scenarios Identification (DyPASI), was applied to identify atypical accident scenarios in LNG terminals. The technique aims to make easier and more systematic the process of learning from early warnings and identify atypical accident scenarios otherwise disregarded by common HAZID techniques. The comparison with a survey of the accident scenarios typically considered in available Environmental Impact Assessment (EIA) studies evidences that DyPASI is a valuable tool to obtain a complete and updated overview of potential hazards in particular for new or innovative technologies, where limited operational experience is available. [ABSTRACT FROM AUTHOR]

The European FP7 SafeWind Project aims at developing research towards a European vision of wind power forecasting, which requires advanced meteorological support concerning extreme wind events. This study is focused mainly on early warnings of extreme winds in the early medium-range. Three synoptic stations (airports) of North Germany (Bremen, Hamburg and Hannover) were considered for the construction of time series of daily maximum wind speeds. All daily wind extremes were found to be linked to very intense surface cyclonic circulation systems being advected mainly by southwest and northwest flow regimes. Overall, it becomes clear that the first indications of an extreme wind event might come from the ECMWF deterministic and/or probabilistic components capturing very intense weather systems (possible windstorms) in the medium term. For early warnings, all available EPS Extreme Forecast Index (EFI) formulations were used, by linking daily maximum wind speeds to EFI values for different forecast horizons. From all possible EFI schemes deployed for issuing early warnings, the highest skill was found for the Gust Factor formulation (EFI-10FGI). Using EFI-10FGI, the corresponding 99% threshold could provide an early warning for a considerable portion of the > 99% wind extremes, but not for all. By lowering this threshold the number of hits is increased until all extremes are captured (zero misses), although by doing so the number of false alarms increased significantly. Consequently, an optimal trade-off between hits and false alarms has to be made when setting different (critical) EFI thresholds. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Education is often considered a means for enhancing adaptive capacity, based on the consideration that formal education is likely to improve the ability of individuals to evaluate risks and respond to warning information. We explore the relation between the level and nature of education and enhanced ability to respond to tropical cyclone risk. We make a distinction between formal school-based education and nonformal education in the form of traditional knowledge of environmental precursors and conditions that may be associated with tropical cyclone occurrence. We evaluate two possible routes through which education could lead to enhanced ability to respond to tropical cyclone risk; first, education, both formal and nonformal, may lead to a better ability to access, understand, and interpret warning information and hence lead to an appropriate response to the warning; and second, formal education may be associated with greater income levels and socioeconomic status and thus with greater resources for evacuating in response to cyclone warning. We find that the hypotheses regarding the link between formal education and adaptive capacity are actually not well supported by empirical data. On the other hand nonformal education in the form of traditional knowledge for predicting cyclones based on environmental precursors emerged as a significant determinant of the ability to understand and interpret warning information and provides a strong case for preserving and promoting a hazard-specific traditional knowledge base along with formal education. [ABSTRACT FROM AUTHOR]