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The globe has recently seen several terrifying pandemics and outbreaks, underlining the ongoing danger presented by infectious microorganisms. This literature review aims to explore the wide range of infections that have the potential to lead to pandemics in the present and the future and pave the way to the conception of epidemic early warning systems. A systematic review was carried out to identify and compile data on infectious agents known to cause pandemics and those that pose future concerns. One hundred and fifteen articles were included in the review. They provided insights on 25 pathogens that could start or contribute to creating pandemic situations. Diagnostic procedures, clinical symptoms, and infection transmission routes were analyzed for each of these pathogens. Each infectious agent's potential is discussed, shedding light on the crucial aspects that render them potential threats to the future. This literature review provides insights for policymakers, healthcare professionals, and researchers in their quest to identify potential pandemic pathogens, and in their efforts to enhance pandemic preparedness through building early warning systems for continuous epidemiological monitoring. [ABSTRACT FROM AUTHOR]

Can a P-wave detection algorithm enhance the performance of an Earthquake Early Warning System (EEWS), particularly in community-engaged networks of low-cost ground motion sensors susceptible to noise? If so, what P-wave detection algorithm would perform the best? This study analyses the performance of four different P-wave detection algorithms using a community-engaged Earthquake Early Warning (EEW) network. The ground motion data from a 48-hour time window around a M5.8 earthquake on 22 September 2022 were used as the basis for this case study, where false and missed detections were analysed for each P-wave detection algorithm. The results indicate that a wavelet transformation-based P-wave picker is the most suitable algorithm for detecting an earthquake with minimal missed and false detections for a community-engaged EEWS. Our results show that a citizen seismology-based EEWS is capable of detecting events of interest to EEW when selecting an appropriate earthquake detection algorithm. The study also suggests future research areas for community-engaged EEWSs, including dynamically changing P-wave detection thresholds and improving citizen seismologists' user experience and involvement. [ABSTRACT FROM AUTHOR]

What is the most effective P-wave detection algorithm for an Earthquake Early Warning (EEW) system that minimises false, late and missed detections? This study evaluates the performance of four distinct P-wave detection algorithms in terms of their detection accuracy. Utilising a comprehensive MEMS-based ground motion dataset from the GeoNet network, this study analyses the algorithms’ performances by introducing four distinct pick deviation categories. Among the evaluated algorithms, the wavelet-based P-wave picker is identified as the most suitable and accurate for EEW systems, achieving a 98.3% success rate with a mean deviation of 0.12 s and a standard deviation of 0.63 s compared to the manual pick. This algorithm proves effective for both community-engaged and traditional EEW systems. The methodology used for performance comparison in this research is applicable to other regions and datasets, aiding in selecting more accurate and reliable P-wave detection algorithms. The study suggests extending this performance analysis to encompass a broader spectrum of traditional and modern algorithms in future research. [ABSTRACT FROM AUTHOR]

Objective: This study investigated drivers' move-over behavior when receiving an Emergency Vehicle Approaching (EVA) warning. Furthermore, the possible effects of false alarms, driver experience, and modality on move-over behavior were explored. Background: EVA warnings are one solution to encourage drivers to move over for emergency vehicles in a safe and timely manner. EVA warnings are distributed based on the predicted path of the emergency vehicle causing a risk of false alarms. Previous EVA studies have suggested a difference between inexperienced and experienced drivers' move-over behavior. Method: A driving simulator study was conducted with 110 participants, whereof 54 inexperienced and 56 experienced drivers. They were approached by an emergency vehicle three times. A control group received no EVA warnings, whereas the experimental groups received either true or false warnings, auditory or visual, 15 seconds before the emergency vehicle overtook them. Results: Drivers who received EVA warnings moved over more quickly for the emergency vehicle compared to the control group. Drivers moved over more quickly for each emergency vehicle interaction. False alarms impaired move-over behavior. No difference in driver behavior based on driver experience or modality was observed. Conclusion: EVA warnings positively affect drivers' move-over behavior. However, false alarms can decrease drivers' future willingness to comply with the warning. Application: The findings regarding measurements of delay can be used to optimize the design of future EVA systems. Moreover, this research should be used to further understand the effect of false alarms in in-car warnings. [ABSTRACT FROM AUTHOR]

This study addresses the critical question of predicting the amplitude of S-waves during earthquakes in Aotearoa New Zealand (NZ), a highly earthquake-prone region, for implementing an Earthquake Early Warning System (EEWS). This research uses ground motion parameters from a comprehensive dataset comprising historical earthquakes in the Canterbury region of NZ. It explores the potential to estimate the damaging S-wave amplitude before it arrives, primarily focusing on the initial P-wave signals. The study establishes nine linear regression relationships between P-wave and S-wave amplitudes, employing three parameters: peak ground acceleration, peak ground velocity, and peak ground displacement. Each relationship's performance is evaluated through correlation coefficient (R), coefficient of determination (R²), root mean square error (RMSE), and 5-fold Cross-validation RMSE, aiming to identify the most predictive empirical model for the Canterbury context. Results using a weighted scoring approach indicate that the relationship involving P-wave Peak Ground Velocity (Pv) within a 3-second window strongly correlates with S-wave Peak Ground Acceleration (PGA), highlighting its potential for EEWS. The selected empirical relationship is subsequently applied to establish a P-wave amplitude (Pv) threshold for the Canterbury region as a case study from which an EEWS could benefit. The study also suggests future research exploring complex machine learning models for predicting S-wave amplitude and expanding the analysis with more datasets from different regions of NZ. [ABSTRACT FROM AUTHOR]

On-site earthquake early warning (EEW) requires the best estimates of earthquake magnitude and distance parameters within a few seconds after the P-wave arrival for estimating the subsequent S-wave parameters. The errors in the estimates of the earthquake P-wave parameters will propagate into the estimates of the S-wave parameters. To solve this problem, we used the methodology by Zhao and Zhao (2019), which uses the spectral ratio between the response spectral values of the first 3 s of the S wave and that of the first 3 s of the P wave, referred to as the R3P model. The modeling presented here was based on strong-motion records from the Next Generation Attenuation (NGA)-West2 dataset. We also used the spectral ratio between the response spectral values from the full records from the S-wave arrival time to the end of the record and that of the first 3 s P wave to develop a second EEW model (the RFP model). An advantage of these two models is that the magnitude and hypocentral distance are not required in considerable magnitude and distance ranges. This means that the errors in the estimated source and path parameters from the first 3 s of the P wave will not affect the model predictions. A theoretical justification for these results is that the magnitude and the distance scaling rates for the first 3 s of the P wave are similar to those of the first 3 s of the S wave. This may also apply to the full S-wave window within useful EEW magnitude and distance ranges. We also found that when the estimated magnitude and distance for a record are necessary, the effect of the corresponding errors would be smaller than using a ground-motion prediction equation (GMPE), because the magnitude and distance scaling rates from this study are smaller than those of many GMPEs. [ABSTRACT FROM AUTHOR]

The objective functions adopted by earthquake early warning (EEW) location algorithms are inadequate for out-of-network earthquakes. As a result, the real-time locations of these earthquakes are often erroneous. The consequences of mislocating out-of-network earthquakes are that their magnitudes are miscalculated, and the loci of their shaking predictions map are shifted. Given that the largest earthquakes occur in subduction settings, improving real-time out-of-network earthquake location is of great importance. In this study, the Virtual Dynamically Assembled Array Algorithm (ViDA³) is introduced, which addresses the location issue of offshore and off-network earthquakes. The guiding principle underlying the new EEW location algorithm is that standard seismic networks may be viewed as a collection of medium-sized seismic arrays, with each array consisting of three or more network stations. The potential of array seismology for EEW against out-of-network earthquakes stems primarily from the slowness vector, which points at the direction of the epicentral region. Thus, this region may be constrained merely by intersecting two or more such vectors. In addition, the length of the slowness vector depends on the hypocentral distance and depth and is thus vital for addressing an acute problem in a subduction setting--discriminating between upper crust and deep slab earthquakes. Furthermore, when the slowness of the P phase is known, the slowness of the S phase is deduced, and the S-phase arrival is searched for using the shift-and-sum practice. What makes ViDA³ so attractive is that, in locations where a real-time network is already in place, these added values may be achieved without extra hardware or substantial budget requirements. We present the result of ViDA³ real-time operation on a shallow earthquake offshore Vancouver Island and the result of its replay on a deep slab earthquake in northern Chile. performance is further assessed using a dataset of seismograms from the Mendocino Triple Junction area. It is concluded that ViDA³ location scheme outperforms currently available EEW location algorithms for out-of-network earthquakes. [ABSTRACT FROM AUTHOR]

Abstract Landslides are a major concern for hilly regions worldwide, claiming lives and livelihoods. Early detection and warning systems are crucial in mitigating the impact. This paper aims to identify and analyse the existing landslides early warning systems (LEWS) by analysing the community awareness and assessing the perception of the respondents toward the effectiveness of existing warning systems in Bududa District, Uganda. LEWS are integrated systems designed to monitor, assess, and provide timely alerts about potential landslides. Through mixed methods with sample size of 199 participants, the study revealed that majority of the respondents’ (48.2%) lacked awareness about existing landslide early warning systems, while 28.2% were none committal, with only 23.7%, indicating awareness of some of these systems. Further identified that weather stations were the most popular (44.9%), and community radios (41%). Additionally, majority of the respondents (51.3%) ranked the systems effectiveness in terms of providing early detection and timely warning at 25%, and only 9.6% of the respondents ranked their effectiveness at 75%. The study recommends that Bududa district officials should increase community awareness of the installed landslide early detection and warning systems through sensitization programs, the Government should develop customized landslide detection and early warning system.

The Colorado River has experienced a significant streamflow reduction in recent decades due to climate change, resulting in pronounced hydrological droughts that pose challenges to the environment and human activities. However, current models struggle to accurately capture complex drought patterns, and their accuracy decreases as the lead time increases. Thus, determining the reliability of drought forecasting for specific months ahead presents a challenging task. This study introduces a robust approach that utilizes the Beluga Whale Optimization (BWO) algorithm to train and optimize the parameters of the Regularized Extreme Learning Machine (RELM) and Random Forest (RF) models. The applied models are validated against a KNN benchmark model for forecasting drought from one- to six-month ahead across four hydrological stations distributed over the Colorado River. The achieved results demonstrate that RELM-BWO outperforms RF-BWO and KNN models, achieving the lowest root-mean square error (0.2795), uncertainty (U95 = 0.1077), mean absolute error (0.2104), and highest correlation coefficient (0.9135). Also, the current study uses Global Multi-Criteria Decision Analysis (GMCDA) as an evaluation metric to assess the reliability of the forecasting. The GMCDA results indicate that RELM-BWO provides reliable forecasts up to four months ahead. Overall, the research methodology is valuable for drought assessment and forecasting, enabling advanced early warning systems and effective drought countermeasures. [ABSTRACT FROM AUTHOR]

Objective: This study explored the impact of in-vehicle messages relative to roadside messages to alert drivers to events within a simulated work zone, in order to determine if these messages can improve driving performance within the work zone. Background: Safety risks in work zones are usually mitigated by design standards and clear signage to communicate work zone information to drivers. Due to distraction and other driving task demands, these signs are not always noticed by motorists, nor are they always followed when they are noticed. Method: The driving simulation tested drivers in two different types of work zones, shoulder work, and lane closure. Participants drove through these work zones three times, each with different messaging interfaces to communicate hazardous events to the driver. The interfaces included a roadside, portable changeable message sign, a smartphone presenting only auditory messages, and a smartphone presenting audio-visual messages. Results: There was significantly better driving performance on key metrics including lane deviation for the in-vehicle message conditions relative to the roadside signs. Furthermore, drivers directed visual attention toward the roadway for the in-vehicle message conditions relative to the roadside sign condition. Conclusion: The results indicate that in-vehicle messaging could provide benefits to primary task performance in driving if the message content is appropriately designed. [ABSTRACT FROM AUTHOR]

The organizational dimension of a today society is often modified by distinct and very specific risk, crisis and/or emergency events. Those aspects are also built culturally by promoting actions, practices and processes typical of the society in which they take place. In the contemporary temporal dimension, we observe not only the risk, but also the social and cultural impacts generated by the crises or emergencies that may result from it. And it is precisely on emergencies, or rather on the way of communicating emergencies in the digital society, that this article focus on, by considering the cases of Japan and Italy to support the reflection. [ABSTRACT FROM AUTHOR]

We develop Attenuated ProPagation of Local Earthquake Shaking (APPLES), a new configuration for the United States West Coast version of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm that incorporates attenuation into its ground‐motion prediction procedures. Under APPLES, instead of using a fixed radius to forward‐predict observed peak ground shaking to the area surrounding a seismic station, the forward‐predicted intensity at a location depends on the distance from the station using an intensity prediction relationship. We conduct conceptual tests of maximum intensity distribution predictions in APPLES and PLUM using a catalog of ShakeMaps to confirm that the attenuation relationship in APPLES is appropriately modeling shaking distributions for West Coast earthquakes. Then, we run APPLES and PLUM in simulated real‐time tests to determine warning time performance. Finally, we compare real‐time alert behavior during the 2022 M6.4 Ferndale, California, earthquake and other recent events. We find that APPLES presents two potential improvements to PLUM by reducing over‐alerting during smaller magnitude earthquakes and by increasing warning times in some locations during larger earthquakes. APPLES can produce missed and late alerts in locations that experience shaking intensities close to the level used to issue alerts, so preferred alerting strategies with APPLES would use alert thresholds that are lower than the intensities targeted for EEW alerts. We find alerts using APPLES are also similar to those for the source‐based approaches currently used in the ShakeAlert EEW system, which will make APPLES easier to integrate into the system. Plain Language Summary: Earthquake early warning systems aim to provide a few seconds notice of incoming shaking from an earthquake before shaking arrives at the alerted location. There are many ways to go about creating early warning alerts. The approach we use, called PLUM, calculates alert regions directly from station observations, where the level of shaking estimated for a location is simply the maximum‐observed shaking at stations within a specified distance of that location. The value of this specified distance causes trade‐offs between the accuracy of the estimated shaking and the amount of warning time that PLUM can provide. In this work, we modify the PLUM approach to vary its shaking estimates based on where the location is compared to the station: locations near stations have shaking estimates that are similar to the station observations, and locations that are farther away have lower shaking estimates than the station observations. We find this new approach improves the accuracy of estimated shaking while maintaining (and sometimes increasing) warning times compared to PLUM. This new approach also produces shaking estimates that are similar to those produced by the current United States earthquake early warning system, which will make it easier to combine them in the future. Key Points: We added intensity attenuation with distance into the Propagation of Local Undamped Motion (PLUM) algorithm's prediction scheme (which we call Attenuated ProPagation of Local Earthquake Shaking (APPLES))APPLES reduces over‐alerting for smaller‐magnitude events and can increase warning times in some areasOptimized alerting strategies with APPLES show comparable performance to PLUM during large‐magnitude events [ABSTRACT FROM AUTHOR]

The advancement of technology is projected to result in the creation of efficient tsunami detection early warning systems to aid individuals, especially those disabilities-friendly, in raising their consciousness and preparing for the worst-case disaster scenarios before they occur. This qualitative descriptive study uses data-gathering procedures based on the library research method. The numerous TEWS has been developed as an effort to recover, rehabilitate, and reconstruct and are carried out in such a way as to anticipate and prepare residents to be more alert and alert to the occurrence of tsunami. IoT based on IMU devices can be utilized as TEWS sensors with minimum limitation. IDSL information concerning elevation is highly correlated with the BIG forecast information. The Android-based received a response time of fewer than five seconds to start receiving with retrieving the tsunami and earthquake data. In conclusion, the EWS needs to be developed along with professional sign- language translators in all catastrophe knowledge as required by the National Regulation on the fundamental rights of individuals with disabilities as part of disclosing information for deaf citizens. Hence, recommendations for further research are needed to develop the TEWS integrated with VBEWS, ViBEWS, ViSEWS, and sign language.

Abstract We develop Attenuated ProPagation of Local Earthquake Shaking (APPLES), a new configuration for the United States West Coast version of the Propagation of Local Undamped Motion (PLUM) earthquake early warning (EEW) algorithm that incorporates attenuation into its ground‐motion prediction procedures. Under APPLES, instead of using a fixed radius to forward‐predict observed peak ground shaking to the area surrounding a seismic station, the forward‐predicted intensity at a location depends on the distance from the station using an intensity prediction relationship. We conduct conceptual tests of maximum intensity distribution predictions in APPLES and PLUM using a catalog of ShakeMaps to confirm that the attenuation relationship in APPLES is appropriately modeling shaking distributions for West Coast earthquakes. Then, we run APPLES and PLUM in simulated real‐time tests to determine warning time performance. Finally, we compare real‐time alert behavior during the 2022 M6.4 Ferndale, California, earthquake and other recent events. We find that APPLES presents two potential improvements to PLUM by reducing over‐alerting during smaller magnitude earthquakes and by increasing warning times in some locations during larger earthquakes. APPLES can produce missed and late alerts in locations that experience shaking intensities close to the level used to issue alerts, so preferred alerting strategies with APPLES would use alert thresholds that are lower than the intensities targeted for EEW alerts. We find alerts using APPLES are also similar to those for the source‐based approaches currently used in the ShakeAlert EEW system, which will make APPLES easier to integrate into the system.

Automatic P‐wave arrival detection is the first task in an earthquake early warning systems. This study proposes a novel detection method for this based on a fractal slope (FS). We improved the calculation method of the fractal dimension to increase the calculation speed and proposed a continuous algorithm. Furthermore, we applied FS in conjunction with the short‐term average over the long‐term average (STA/LTA), named STA/LTA + FS. We designed orthogonal experiments with different parameters and selected a total of 40,020 sets of seismic waves from the Japanese dataset to test the best parameters. A total of 45,302 sets of seismic waves from the STanford EArthquake dataset and the Chinese dataset were selected to test the generality of the proposed method. The results show that the mean error in detection time of the proposed method is +0.042 s for different datasets. In addition, STA/LTA + FS is robust over a wide range of signal‐to‐noise ratio, epicentral distance, and magnitude, with the percentage of timing errors below 0.5 s higher than 95%. [ABSTRACT FROM AUTHOR]

The advancement of technology is projected to result in the creation of efficient tsunami detection early warning systems to aid individuals, especially those disabilities-friendly, in raising their consciousness and preparing for the worst-case disaster scenarios before they occur. This qualitative descriptive study uses data-gathering procedures based on the library research method. The numerous TEWS has been developed as an effort to recover, rehabilitate, and reconstruct and are carried out in such a way as to anticipate and prepare residents to be more alert and alert to the occurrence of tsunami. IoT based on IMU devices can be utilized as TEWS sensors with minimum limitation. IDSL information concerning elevation is highly correlated with the BIG forecast information. The Android-based received a response time of fewer than five seconds to start receiving with retrieving the tsunami and earthquake data. In conclusion, the EWS needs to be developed along with professional sign-language translators in all catastrophe knowledge as required by the National Regulation on the fundamental rights of individuals with disabilities as part of disclosing information for deaf citizens. Hence, recommendations for further research are needed to develop the TEWS integrated with VBEWS, ViBEWS, ViSEWS, and sign language. [ABSTRACT FROM AUTHOR]

Between 2017 and 2019, California experienced a series of devastating wildfires that together led over one million people to be ordered to evacuate. Due to the speed of many of these wildfires, residents across California found themselves in challenging evacuation situations, often at night and with little time to escape. These evacuations placed considerable stress on public resources and infrastructure for both transportation and sheltering. In the face of these clear challenges, transportation and emergency management agencies across California have widely varying levels of preparedness for major disasters, and nearly all agencies do not have the public resources to adequately and swiftly evacuate all populations in danger. To holistically address these challenges and bolster current disaster and evacuation planning, preparedness, and response in California, we summarize the evacuations of eleven major wildfires in California between 2017 and 2019 and offer a cross-comparison to highlight key similarities and differences. We present results of new empirical data we collected via an online survey of individuals impacted by: 1) the 2017 October Northern California Wildfires (n=79), 2) the 2017 December Southern California Wildfires (n=226), and 3) the 2018 Carr Wildfire (n=284). These data reveal the decision-making of individuals in these wildfires including choices related to evacuating or staying, departure timing, route, sheltering, destination, transportation mode, and reentry timing. We also present results related to communication and messaging, non-evacuee behavior, and opinion of government response. Using the summarized case studies and empirical evidence, we present a series of recommendations for agencies to prepare for, respond to, and recover from wildfires.

In considering projections that flooding will increase in the future years due to factors such as climate change and urbanization, the need for dependable and accurate water sensors systems is greater than ever. In this study, the performance of four different water level sensors, including ultrasonic, infrared (IR), and pressure sensors, is analyzed based on innovative characterization and comparative analysis, to determine whether or not these sensors have the ability to detect rising water levels and flash floods at an earlier stage under different conditions. During our exhaustive tests, we subjected the device to a variety of conditions, including clean and contaminated water, light and darkness, and an analogue connection to a display. When it came to monitoring water levels, the ultrasonic sensors stood out because of their remarkable precision and consistency. To address this issue, this study provides a novel and comparative examination of four water level sensors to determine which is the most effective and cost-effective in detecting floods and water level fluctuations. The IR sensor delivered accurate findings; however, it demonstrated some degree of variability throughout the course of the experiment. In addition, the results of our research show that the pressure sensor is a legitimate alternative to ultrasonic sensors. This presents a possibility that is more advantageous financially when it comes to the development of effective water level monitoring systems. The findings of this study are extremely helpful in improving the dependability and accuracy of flood detection systems and, eventually, in lessening the devastation caused by natural catastrophes. [ABSTRACT FROM AUTHOR]

This research develops an innovative terror threat advisory system capable of visually communicating variations in the terrorism levels to policymakers or the public and forecasts future levels. Earlier attempts to create similar advisory systems by policymakers were either discontinued or lost their relevance due to a trust deficit in the system. We propose a novel approach for creating a color scheme and utilize it to develop an intuitive caterpillar diagram summarizing various stages of terrorism. It incorporates Global Terrorism Impact Scores for a nation or region using the Global Terrorism Database (GTD). Further, color transitions in the caterpillar diagram between consecutive periods mimic a Markovian process, thereby enabling us to develop the forecasting model. We successfully demonstrated the effectiveness of the proposed caterpillar diagram and forecasting model for India, Iraq, and their respective regions. The forecasting model suggests that the aggressive terrorism stage depicted by red color and transient stages of ascent (yellow) and descent (cyan) are the most probable in these nations and their regions. The proposed caterpillar diagram is an innovative visualization approach to identify terrorism patterns, from which a Markovian forecasting model is developed to aid policymakers. Our approach applies to any event-based database like GTD. Finally, the caterpillar diagram is a domain-independent framework that can visualize variations in any univariate data series, thereby assisting in system monitoring. [ABSTRACT FROM AUTHOR]

Inclusive, participatory governance is a key enabler of effective responses to natural hazard risks exacerbated by climate change. This paper describes a community-level co-design process among academic, state, and federal scientists and the community of Sitka, Alaska to develop a novel landslide warning system for this small coastal town. The decentralized system features an online dashboard which displays current and forecast risk levels to help residents make their own risk management decisions. The system and associated risk communications are informed by new geoscience, social, and information science generated during the course of the project. This case study focuses on our project team's activities and addresses questions including: what activities did the project team conduct, what did these activities intend to accomplish, and did these activities accomplish what they intended? The paper describes the co-design process, the associated changes in system design and research activities, and formal and informal evaluations of the system and process. Overall, the co-design process appears to have generated a warning system the Sitka community finds valuable, helped to align system design with local knowledge and community values, significantly modified the scientists' research agendas, and helped navigate sensitivities such as the effect of landslide exposure maps on property values. Other communities in SE Alaska are now adopting this engagement approach. The paper concludes with broader implications for the role of community-level, participatory co-design and risk governance for climate services. [ABSTRACT FROM AUTHOR]

Earthquake early warning system (EEWS) plays an important role in detecting ground shaking during an earthquake and alerting the public and authorities to take appropriate safety measures, reducing possible damages to lives and property. However, the cost of high-end ground motion sensors makes most earthquake-prone countries unable to afford an EEWS. Low-cost Microelectromechanical systems (MEMS)-based ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust EEWS. This paper contributes to advancing Earthquake early warning (EEW) research by conducting a literature review investigating different methods and approaches to building a low-cost EEWS using MEMS-based sensors in different territories. The review of 59 articles found that low-cost MEMS-based EEWSs can become a feasible solution for generating reliable and accurate EEW, especially for developing countries and can serve as a support system for high-end EEWS in terms of increasing the density of the sensors. Also, this paper proposes a classification for EEWSs based on the warning type and the EEW algorithm adopted. Further, with the support of the proposed EEWS classification, it summarises the different approaches researchers attempted in developing an EEWS. Following that, this paper discusses the challenges and complexities in implementing and maintaining a low-cost MEMS-based EEWS and proposes future research areas to improve the performance of EEWSs mainly in 1) exploring node-level processing, 2) introducing multi-sensor support capability, and 3) adopting ground motionbased EEW algorithms for generating EEW. [ABSTRACT FROM AUTHOR]

Earthquake early warning system (EEWS) plays an important role in detecting ground shaking during an earthquake and alerting the public and authorities to take appropriate safety measures, reducing possible damages to lives and property. However, the cost of high-end ground motion sensors makes most earthquake-prone countries unable to afford an EEWS. Low-cost Microelectromechanical systems (MEMS)-based ground motion sensors are becoming a promising solution for constructing an affordable yet reliable and robust EEWS. This paper contributes to advancing Earthquake early warning (EEW) research by conducting a literature review investigating different methods and approaches to building a low-cost EEWS using MEMS-based sensors in different territories. The review of 59 articles found that low-cost MEMS-based EEWSs can become a feasible solution for generating reliable and accurate EEW, especially for developing countries and can serve as a support system for high-end EEWS in terms of increasing the density of the sensors. Also, this paper proposes a classification for EEWSs based on the warning type and the EEW algorithm adopted. Further, with the support of the proposed EEWS classification, it summarises the different approaches researchers attempted in developing an EEWS. Following that, this paper discusses the challenges and complexities in implementing and maintaining a low-cost MEMS-based EEWS and proposes future research areas to improve the performance of EEWSs mainly in 1) exploring node-level processing, 2) introducing multi-sensor support capability, and 3) adopting ground motion-based EEW algorithms for generating EEW.

Abstract Background Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere. Methods The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality. Results We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season. Conclusions This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July–August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics.

Individualized Patient Monitoring Under Alarm Fatigue Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. "Individualized Dynamic Patient Monitoring Under Alarm Fatigue" by Piri, Huh, Shechter, and Hudson studies the problem of personalizing alarm thresholds for vital signs at a hospital while considering the "boy who cried wolf" effect of false alarms. The authors create a model that learns patients' personal alarm thresholds during their hospital stay and updates their alarm settings dynamically. They formulate the problem as a partially observable Markov decision process. They provide structural properties of the optimal policy and perform a numerical case study based on clinical data from an intensive care unit. They show that dynamic methods of alarm settings that explicitly consider the feedback loop of false positives can significantly reduce patient harm when compared with current methods of alarm settings. Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. We develop a partially observable Markov decision process model for recommending dynamic, patient-specific alarms in which we incorporate a cry-wolf feedback loop of repeated false alarms. Our model takes into account patient heterogeneity in safety limits for vital signs and learns a patient's safety limits by performing Bayesian updates during a patient's hospital stay. We develop structural results of the optimal policy and perform a numerical case study based on clinical data from an intensive care unit. We find that compared with current approaches of setting patients' alarms, our dynamic patient-centered model significantly reduces the risk of patient harm. [ABSTRACT FROM AUTHOR]

Earthquake early warning (EEW) systems aim to warn end-users of incoming ground shaking from earthquakes that have ruptured further afield, potentially reducing risks to lives and properties. EEW is a socio-technical system involving technical and social processes. This paper contributes to advancing EEW research by conducting a literature review investigating the social science knowledge gap in EEW systems. The review of 70 manuscripts found that EEW systems could benefit society, and the benefits may go beyond its direct function for immediate earthquake response. The findings also show that there are social processes involved in designing, developing, and implementing people-centered EEW systems. Therefore, social science research should not just be concerned with the end-user response but also investigate various stakeholders' involvement throughout the development process of EEW systems. Additionally, EEW is a rapidly evolving field of study, and social science research must take a proactive role as EEW technological capacities improve further and becomes more accessible to the public. To improve EEW effectiveness, further research is needed, including (1) advancing our understanding of why people take protective action or not, and ways to encourage appropriate action when alerted; (2) enhancing public understanding, investigating best practices for communicating, educating, and engaging with the public about EEW and overall earthquake resilience; and (3) keeping up with technological advances and societal changes and investigating how these changes impact communities' interactions with EEW from various standpoints including legal perspectives.

In considering projections that flooding will increase in the future years due to factors such as climate change and urbanization, the need for dependable and accurate water sensors systems is greater than ever. In this study, the performance of four different water level sensors, including ultrasonic, infrared (IR), and pressure sensors, is analyzed based on innovative characterization and comparative analysis, to determine whether or not these sensors have the ability to detect rising water levels and flash floods at an earlier stage under different conditions. During our exhaustive tests, we subjected the device to a variety of conditions, including clean and contaminated water, light and darkness, and an analogue connection to a display. When it came to monitoring water levels, the ultrasonic sensors stood out because of their remarkable precision and consistency. To address this issue, this study provides a novel and comparative examination of four water level sensors to determine which is the most effective and cost-effective in detecting floods and water level fluctuations. The IR sensor delivered accurate findings; however, it demonstrated some degree of variability throughout the course of the experiment. In addition, the results of our research show that the pressure sensor is a legitimate alternative to ultrasonic sensors. This presents a possibility that is more advantageous financially when it comes to the development of effective water level monitoring systems. The findings of this study are extremely helpful in improving the dependability and accuracy of flood detection systems and, eventually, in lessening the devastation caused by natural catastrophes.

Abstract We use a suite of historical earthquakes to quantitatively determine earthquake early warning (EEW) alert threshold strategies for a range of shaking intensity targets for EEW along the United States West Coast. The current method for calculating alert regions for the ShakeAlert EEW System does not take into account variabilities and uncertainties in shaking distribution. As a result, if the modified Mercalli intensity (MMI) level used to determine the extent of the alert region (the alert threshold) is the same as the target intensity threshold, the alert region will be too small to include all locations that require alerts even if earthquake source parameters are estimated accurately. Missed alerts can be reduced by using a lower alert threshold than the target threshold. This expands the alert region, increasing the number of precautionary alerts issued to people who experience shaking below the target level. We determine alert thresholds that optimize this tradeoff between missed and precautionary alerts for target thresholds of MMI 4.0–6.0 using 143 M5.0–7.3 earthquake ShakeMaps as ground truth. We examine the quality of each alerting strategy relative to the target MMI, where we define alert quality metrics in terms of both the area and population alerted. Optimal alert thresholds maximize correct alerts while limiting most precautionary alerts to regions that are likely to still feel some shaking. We find these optimal alert thresholds also maximize warning times. This analysis presents a quantitative framework ShakeAlert can use to communicate alerting strategies and performance expectations to ShakeAlert users.

Objective: We explore users' and observers' subjective assessments of human and automation capabilities and human causal responsibility for outcomes. Background: In intelligent systems and advanced automation, human responsibility for outcomes becomes equivocal, as do subjective perceptions of responsibility. In particular, actors who actively work with a system may perceive responsibility differently from observers. Method: In a laboratory experiment with pairs of participants, one participant (the "actor") performed a decision task, aided by an automated system, and the other (the "observer") passively observed the actor. We compared the perceptions of responsibility between the two roles when interacting with two systems with different capabilities. Results: Actors' behavior matched the theoretical predictions, and actors and observers assessed the system and human capabilities and the comparative human responsibility similarly. However, actors tended to relate adverse outcomes more to system characteristics than to their own limitations, whereas the observers insufficiently considered system capabilities when evaluating the actors' comparative responsibility. Conclusion: When intelligent systems greatly exceed human capabilities, users may correctly feel they contribute little to system performance. They may interfere more than necessary, impairing the overall performance. Outside observers, such as managers, may overweigh users' contribution to outcomes, holding users responsible for adverse outcomes when they rightly trusted the system. Application: Presenting users of intelligent systems and others with performance measures and the comparative human responsibility may help them calibrate subjective assessments of performance, reducing users' and outside observers' biases and attribution errors. [ABSTRACT FROM AUTHOR]

We use a suite of historical earthquakes to quantitatively determine earthquake early warning (EEW) alert threshold strategies for a range of shaking intensity targets for EEW along the United States West Coast. The current method for calculating alert regions for the ShakeAlert EEW System does not take into account variabilities and uncertainties in shaking distribution. As a result, if the modified Mercalli intensity (MMI) level used to determine the extent of the alert region (the alert threshold) is the same as the target intensity threshold, the alert region will be too small to include all locations that require alerts even if earthquake source parameters are estimated accurately. Missed alerts can be reduced by using a lower alert threshold than the target threshold. This expands the alert region, increasing the number of precautionary alerts issued to people who experience shaking below the target level. We determine alert thresholds that optimize this tradeoff between missed and precautionary alerts for target thresholds of MMI 4.0–6.0 using 143 M5.0–7.3 earthquake ShakeMaps as ground truth. We examine the quality of each alerting strategy relative to the target MMI, where we define alert quality metrics in terms of both the area and population alerted. Optimal alert thresholds maximize correct alerts while limiting most precautionary alerts to regions that are likely to still feel some shaking. We find these optimal alert thresholds also maximize warning times. This analysis presents a quantitative framework ShakeAlert can use to communicate alerting strategies and performance expectations to ShakeAlert users. Plain Language Summary: In the ShakeAlert Earthquake Early Warning (EEW) System, ground‐motion models are used to rapidly calculate the distribution of shaking caused by an earthquake, where the resulting shaking distribution is used to determine the size of the EEW alert region. However, because these ground‐motion models cannot account for shaking variabilities, if the EEW alert region is determined using the same shaking level as the minimum shaking level that requires an alert (the target level), the alert region will be too small to include all locations that experience target level shaking, resulting in missed alerts. One solution is to expand the size of the alert region by using a shaking level that is lower than the target level in the alert region calculation. This action comes with a tradeoff: missed alerts cannot be decreased without also increasing over‐alerting, that is, increasing alerts to locations that experience lower than target shaking levels. Here, we determine the preferred alerting levels for a range of target shaking levels by examining this tradeoff using a catalog of United States West Coast earthquakes. We find the preferred alerting levels can reduce missed alerts while keeping over‐alerting to locations that will still feel some shaking from the earthquake. Key Points: We determine optimal alert thresholds for the ShakeAlert Earthquake Early Warning System for a range of target shaking intensity levelsEarly warning alert regions produced using an intensity threshold at the target level cannot alert most (>60%) people who need alertsOptimal alert thresholds can achieve >95% correct alerts with nearly all precautionary alerts to places that still feel some shaking [ABSTRACT FROM AUTHOR]

• Training programs had a positive effect on drivers' response to vehicle cyberattacks. • Warning systems had a positive effect on drivers' response to vehicle cyberattacks. • The type of cyberattack greatly influenced drivers' behavior. • The training programs had a more efficient effect than the warning systems. Modern vehicles are vulnerable to cyberattacks and the consequences can be severe. While technological efforts have attempted to address the problem, the role of human drivers is understudied. This study aims to assess the effectiveness of training and warning systems on drivers' response behavior to vehicle cyberattacks. Thirty-two participants completed a driving simulator study to assess the effectiveness of training and warning system according to their velocity, deceleration events, and count of cautionary behaviors. Participants, who held a valid United States driving license and had a mean age of 20.4 years old, were equally assigned to one of four groups: control (n = 8), training-only (n = 8), warning-only (n = 8), training and warning groups (n = 8). For each drive, mixed ANOVAs were implemented on the velocity variables and Poisson regression was conducted on the normalized time with large deceleration events and cautionary behavior variables. Overall, the results suggest that drivers' response behaviors were moderately affected by the training programs and the warning messages. Most drivers who received training or warning messages responded safely and appropriately to cyberattacks, e.g., by slowing down, pulling over, or performing cautionary behaviors, but only in specific cyberattack events. Training programs show promise in improving drivers' responses toward vehicle cyberattacks, and warning messages show rather moderate improvement but can be further refined to yield consistent behavior. [ABSTRACT FROM AUTHOR]

Background: Many countries have developed heat-health watch and warning systems (HHWWS) or early-warning systems to mitigate the health consequences of extreme heat events. HHWWS usually focuses on the four hottest months of the year and imposes the same threshold over these months. However, according to climate projections, the warm season is expected to extend and/or shift. Some studies demonstrated that health impacts of heat waves are more severe when the human body is not acclimatized to the heat. In order to adapt those systems to potential heat waves occurring outside the hottest months of the season, this study proposes specific health-based monthly heat indicators and thresholds over an extended season from April to October in the northern hemisphere.Methods: The proposed approach, an adoption and extension of the HHWWS methodology currently implemented in Quebec (Canada). The latter is developed and applied to the Greater Montreal area (current population 4.3 million) based on historical health and meteorological data over the years. This approach consists of determining excess mortality episodes and then choosing monthly indicators and thresholds that may involve excess mortality.Results: We obtain thresholds for the maximum and minimum temperature couple (in °C) that range from (respectively, 23 and 12) in April, to (32 and 21) in July and back to (25 and 13) in October. The resulting HHWWS is flexible, with health-related thresholds taking into account the seasonality and the monthly variability of temperatures over an extended summer season.Conclusions: This adaptive and more realistic system has the potential to prevent, by data-driven health alerts, heat-related mortality outside the typical July-August months of heat waves. The proposed methodology is general and can be applied to other regions and situations based on their characteristics. [ABSTRACT FROM AUTHOR]

In order to assess the seismic risk of the energy infrastructure on the Făgăraş-Câmpulung seismic area, the accelerations of seismic vibrations produced by 7 earthquakes were measured (in two distinct locations, with different morphostructural characteristics of the soil) and compared. Thus, were analysed 3 subcrustal earthquakes, with epicentres in Vrancea area, a crustal earthquake with epicentre in Făgăraş-Câmpulung seismic area (approximately 30 km from the measurement locations), 2 crustal earthquakes and a subcrustal earthquake with epicentres located at a longer distance (over 650 km) from the measurement locations. Following the data processing, it was found that the intensity of local vibrations produced by the analysed earthquakes is determined by the earthquake intensity, by the distance between epicentre and measurement point and by the geological layers morphological structure in the epicentre-measurement direction. The measurements showed that the mountains in the seismic waves direction, especially those generated by crustal earthquakes (with hypocentre h˂10km), produce a significant attenuation of the measured vibrations. Based on measurement results and their analysis, it is considered that the energy infrastructure on the Făgăraş-Câmpulung seismic area presents an appreciable seismic risk. Thus, the earthquakes in the Făgăraş-Câmpulung seismic area can cause significant damage: the subcrustal earthquakes of Mw> 7.2 with epicentres in the Vrancea seismic area on large scale, and surface earthquakes, near the epicentre zone, on limited areas. Therefore, in order to ensure the safety of the hydroelectric dams, with high risk in operation, it is necessary to have them constructed and maintained so as to withstand vibrations with accelerations of at least 3.5-4 m/s². [ABSTRACT FROM AUTHOR]