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Rainfall-induced shallow landslides can occur rapidly after intense or prolonged heavy rainfall, especially in mountainous areas. In this study, failure behaviors of rainfall-induced shallow landslides are investigated using a novel large angle-adjustable plane shear (LAPS) apparatus. Details on the conception, features, and setup of the LAPS apparatus are first illustrated. Two series of tests are conducted with varying slope angles and water intensities to demonstrate the test processes. The results of the LAPS tests are analyzed according to the changes in displacement behaviors and water contents, as well as predictions of slope failure. The results show that the behaviors of rainfall-induced landslides were clearly observed during the LAPS tests, exhibiting three distinct stages: initial deformation, constant speed deformation, and accelerated deformation, which are consistent with the phenomenon from field cases. Moreover, the switch points of water content from stable to rapid growth stages gradually advanced with an increase in water intensity. The velocity behaviors observed in these tests displayed two distinct patterns, with earlier turning points observed as the slope angle or rainfall intensity increased. The LAPS apparatus provides a new tool for studying the failure behaviors of rainfall-induced shallow landslides. [ABSTRACT FROM AUTHOR]

Cyber security is the most studied in recent years by the association of metaheuristic techniques thanks to their ability to solve optimization problems in a limited time and NP-hard problems. The Early Warning System (EWS) can identify malicious exercises and anomalies in the CPS and provide robust network systems protection. In addition, grouping attacks in EWS is important for defining defense policies. Information security in the CPS architecture is about securing information during data aggregation, large-scale processing and sharing in the network environment, especially low-linkage open networks. Motivated by these considerations, we propose a new security approach based on the Artificial Bee Colony (ABC) metaheuristic algorithm to strengthen the security framework of the CPS architecture against the system and within CPS attacks. In this work, we propose a new Early Warning System approach based on metaheuristics. Based on two fundamental concepts of clustering, coherence and separation, an integrated index was proposed based on Davies-Bouldin (DB) and Silhouette Index (SI). An improved ABC algorithm was proposed based on these indices. After having identified two different types of an objective function, based on the above concepts, and after having tested the algorithm on DBSI, the results of the experiments found are auspicious. [ABSTRACT FROM AUTHOR]

Wastewater-based environmental surveillance (WBES) has been proven as proxy tool for monitoring nucleic acids of pathogens shed by infected population before clinical outcomes. The poor sewershed network of low to middle-income countries (LMICs) leads to most of the wastewater flow through open drains. We studied the effectiveness of WBES using open drain samples to monitor the emergence of the SARS-CoV-2 variants in 2 megacities of India having dense population through zonation approach. Samples from 28 locations spanned into 5 zones of Pune region, Maharashtra, India, were collected on a weekly basis during October 2021 to July 2022. Out of 1115 total processed samples, 303 (~ 27%) tested positive for SARS-CoV-2. The periodical rise and fall in the percentage positivity of the samples was found to be in sync with the abundance of SARS-CoV-2 RNA and the reported COVID-19 active cases for Pune city. Sequencing of the RNA obtained from wastewater samples confirmed the presence of SARS-CoV-2. Of 337 sequences, lineage identification for 242 samples revealed 265 distinct SARS-CoV-2 variants including 10 highly transmissible ones. Importantly, transition from Delta to Omicron variant could be detected in wastewater samples 2 weeks prior to any clinically reported Omicron cases in India. Thus, this study demonstrates the usefulness of open drain samples for real-time monitoring of a viral pathogen's evolutionary dynamics and could be implemented in LMICs. [ABSTRACT FROM AUTHOR]

Intravenous (IV) infusion is a pervasive medical intervention, administered to approximately 90% of hospitalized patients. Phlebitis, characterized by inflammation of the veins resulting from infusion, stands as a prevalent complication, ranking fourth among hospitalacquired infections globally. This research investigates the efficacy of a Smartphone Early Warning System (EWS) display in mitigating the incidence of phlebitis within the Safa treatment room at Aisyiyah Hospital. Employing a pre-experimental research design with a Static-group Comparison approach, 16 respondents were allocated to treatment and control groups. The Mann-Whitney Test, a statistical analysis, unveiled a significant difference (P Value = 0.001 < 0.05) in phlebitis incidence between the treatment group, utilizing the Smartphone EWS display, and the control group, which relied on conventional monitoring methods. Notably, the average rank of phlebitis incidence in the control group (21.12) exceeded that in the treatment group (9.78). This study sheds light on the potential of the Smartphone EWS display to curtail phlebitis during infusion, emphasizing its role in advancing nursing care quality through real-time monitoring and early prevention strategies. [ABSTRACT FROM AUTHOR]

The observation of precursory signals of the 2021 Chamoli rock–ice avalanche provides an opportunity to investigate the multidisciplinary analysis approach of rock failure. On 7 February 2021, a huge rock–ice mass detached from the Raunthi peak at Chamoli district in Uttarakhand, India. The tragic catastrophe resulted in more than 200 deaths and significant economic losses. Here, we analyse radon concentration and seismic signals to characterise the potential precursory anomalies prior to the detachment. Continuous peaks of radon anomalies were observed from the afternoon of 5 to 7 February and decreased suddenly after the event, while a cumulative number of seismic tremors and amplitude variations are more intensified ~2.30 h before the main event, indicating a static to dynamic phase change within the weak zone. This study not only characterises abnormal signals but also models the rock failure mechanisms. The analysis unveils three time‐dependent nucleation phases, physical mechanisms of signal generation and a complete scenario of physical factors that affected the degree of criticality of slope failure. The results of this study suggest gradual progression of rock cracks/joints, subsequent material creep and slip advancement acceleration preceded the final failure. Furthermore, the study highlights the importance of an early warning system to mitigate the impact of events like the 2021 Chamoli rock–ice avalanche. [ABSTRACT FROM AUTHOR]

Currency crises, recurrent events in the economic history of developing, emerging, and developed countries, have disastrous economic consequences. This paper proposes an early warning system for currency crises using sophisticated recurrent neural networks, such as long short‐term memory (LSTM) and gated recurrent unit (GRU). These models were initially used in language processing, where they performed well. Such models are increasingly being used in forecasting financial asset prices, including exchange rates, but they have not yet been applied to the prediction of currency crises. As for all recurrent neural networks, they allow for taking into account nonlinear interactions between variables and the influence of past data in a dynamic form. For a set of 68 countries including developed, emerging, and developing economies over the period of 1995–2020, LSTM and GRU outperformed our benchmark models. LSTM and GRU correctly sent continuous signals within a 2‐year warning window to alert for 91% of the crises. For the LSTM, false signals represent only 14% of the emitted signals compared with 23% for logistic regression, making it an efficient early warning system for policymakers. [ABSTRACT FROM AUTHOR]

Wheat contributes one‐fifth of the global food supply with an estimated 29% of global production in low and lower‐middle income countries. As production expands across southern Asia, yields are often negatively impacted by outbreaks of fungal rust diseases. A wheat rust early warning and advisory system comprising surveillance, near real‐time disease risk forecasts and advisory dissemination has been established in two target countries in South Asia, including Nepal and Bangladesh. However, as wheat rust spores can be aerially transmitted over long distances, near real‐time estimates of disease incidence are required from sources of infection in neighbouring regions. To address this challenge, we developed and tested a novel algorithm to generate proxy observations of infection sources using online media reports in two neighbouring countries, India and Pakistan. Media sampling could provide an effective alternative where data from ground surveys are not readily available in near real‐time. Our results show that west Nepal was exposed to a substantial inoculum pressure from aerially dispersed stripe rust spores originating from India and Pakistan. There were no outbreaks of stripe rust disease in Bangladesh with only very low levels of cross‐border dispersion and generally unfavourable environmental conditions for infection. We further describe how proxy observations informed farmer decision‐making in near real‐time in Nepal and filled a knowledge gap in identifying early sources of infection for a major outbreak of stripe rust during 2020 in Nepal. Our results highlight the importance of international cooperation in mitigating transboundary plant pathogens. [ABSTRACT FROM AUTHOR]

We performed a passive seismic monitoring of the La Praz ∼14,000 m3 unstable slope (French Alps) spanning over 10 years. During the last 6 months prior to collapse, we detected a clear 24% decrease in the slope's fundamental resonance frequency, f0, caused by a reduction in overall rock mass stiffness. The combined study of f0 and slope deformation suggested the alternating importance of sudden brittle failure processes versus more ductile phases with possible sliding. Seismic monitoring revealed slope damage that remained ambiguous or undetected with ground surface deformation monitoring, and highlighted critical periods with intense damage. Only some of these critical damage periods could be related to clear external forcing factors such as intense rainfall episodes. These new insights into rock slope's structural condition at depth represent an asset for future monitoring systems. Surface deformation and passive seismic stiffness tracking combined could reveal active slopes with ongoing damage processes. Plain Language Summary: Forecasting the time of rockfalls is of critical importance for risk mitigation operators in order to preserve the safety of persons and the integrity of infrastructure. Most monitoring systems are based on ground deformation measurements, which may fail when the surface motion does not accurately reflect changes in slope stiffness with time. In this work, we used a seismic sensor that passively recorded ground vibrations on top of a ∼14,000 m3 unstable slope. We detected a significant decrease in the slope's first resonance frequency (−24%) during the 6 months preceding complete collapse. We also revealed details of slope damage processes acting within the slope, showing sudden breakage phases alternating with smoother deformation and sliding phases. These processes would not have been suspected with slope deformation monitoring alone. The use of a new parameter that combines passive seismic tracking and surface deformation measurements could help revealing active slopes with ongoing damage processes. This inovative approach represents an asset for future practical rock slope monitoring. Key Points: We observed a 24% decrease in rock slope fundamental frequency during the 6 months preceding a ∼14,000 m3 collapseNew insights into slope degradation processes revealed the alternate control of two damage regimes with contrasting rateResults revealed slope damage phases that would not have been suspected on the basis of slope deformation monitoring alone [ABSTRACT FROM AUTHOR]

Bedload transport is a natural process that strongly affects the Earth's surface system. An important component of quantifying bedload transport flux and establishing early warning systems is the identification of the onset of bedload motion. Bedload transport can be monitored with high temporal resolution using passive acoustic methods, for example, hydrophones. Yet, an efficient method for identifying the onset of bedload transport from long‐term continuous acoustic data is still lacking. Benford's Law defines a probability distribution of the first‐digit of data sets and has been used to identify anomalies. Here, we apply Benford's law to continuous acoustic recordings from Baiyang hydrometric station, a tributary of Liwu River, Taroko National Park, Taiwan at the frequency of 32 kHz from stationary hydrophones deployed for 3 years since 2019. We construct a workflow to parse sound combinations of bedload transportation and analyze them in the context of hydrometric sensing constraining the onset, and recession of bedload transport. We identified three separate sound classes in the data related to the noise produced by the motion of pebbles, water flow, and air. We identify two bedload transport events that lasted 17 and 45 hr, respectively, covering about 0.35% of the total recorded time. The workflow could be transferred to other different catchments, events, or data sets. Due to the influence of instrument and background noise on the regularity of the residuals of the first‐digit, we recommend identifying the first‐digit distribution of the background noise and ruling it out before implementing this workflow. Plain Language Summary: Long‐term, high‐frequency monitoring of Earth surface processes brings huge data sets that can be of high quality. Benford's Law defines the specific probability distribution of the first‐digit of the data sets and has been used to identify anomalies and high‐energy events. We provide a workflow for applying Benford's Law to identify the onset of the motion of coarse sediment along the river bed at a time resolution of seconds. Since Benford's Law has demonstrated usefulness in acoustic amplitude analysis in this study, it could serve as a tool for identifying anomalous events in any kind of real‐time data series, which could be beneficial for generating event samples for machine learning applications. Key Points: Long‐term, high‐frequency acoustic monitoring constitutes huge‐volume data sets with a low signal‐to‐noise ratioThe distinct first‐digit distribution between signal and noise can used to filter out 99% of background noise from acoustic recordingsWe applied the method to a three‐year‐long acoustic data set in Baiyang, identifying two bedload transportation events [ABSTRACT FROM AUTHOR]

In the wake of the largest-ever recorded outbreak of mpox in terms of magnitude and geographical spread in human history since May 2022, we innovatively developed an automated online sewage virus enrichment and concentration robot for disease tracking. Coupled with an artificial intelligence (AI) model, our research aims to estimate mpox cases based on the concentration of the monkeypox virus (MPXV) in wastewater. Our research has revealed a compelling link between the levels of MPXV in wastewater and the number of clinically confirmed mpox infections, a finding that is reinforced by the ability of our AI prediction model to forecast cases with remarkable precision, capturing 87 % of the data’s variability. However, it is worth noting that this high precision in predictions may be related to the relatively high frequency of data acquisition and the relatively non-mobile isolated environment of the hospital itself. In conclusion, this study represents a significant step forward in our ability to track and respond to mpox outbreaks. It has the potential to revolutionize public health surveillance by utilizing innovative technologies for disease surveillance and prediction.

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system for predicting blackout events in smart grids is presented. The system is based on deep learning models: convolutional neural networks (CNN) and deep self-organizing maps (DSOM), and is designed to analyse data from various sources, such as power demand, generation, transmission, distribution and weather forecasts. The system performance is evaluated using a dataset of time windows and labels, where the labels indicate whether a blackout event occurred within a given time window. It is found that the system is able to achieve an accuracy of 98.71% and a precision of 98.65% in predicting blackout events. The results suggest that the early warning system presented in this paper is a promising tool for improving the resilience and reliability of electrical grids and for mitigating the impacts of blackout events on communities and businesses.

Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1–3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks.

Portugal is the country in Europe where the death rate in winter and summer has the highest correlation with outdoor temperatures. The Portuguese National Institute of Public Health Ricardo Jorge has developed a national warning system for heat waves called ICARO, which has been in place since 1999 (and is the oldest in Europe). However, it presents some limitations, namely, the low spatial resolution (five regions in Portugal's mainland), the low temporal forecasting period (one day), and the fact that it was only accessible to health authorities until very recently. This work describes the development of a new public dashboard that uses a new early warning index for extreme weather events, the CLIMAEXTREMO index, which extends the current warning system by improving the current forecasting models for risk by integrating new sources of public data and increasing the spatial and time resolution of the warnings to the municipality or the parish level. The new index is a combination of a new model to estimate the relative mortality increase (updating the model used in ICARO) together with a model of the indoor temperature of building archetypes for all municipalities and a vulnerability index that considers socio-demographic economic indicators. This work discusses the results of the new risk indicator for the heat waves that occurred in Portugal at the end of June and mid-August 2023, and it shows that the index was able to indicate a high risk for the municipalities that had an increase in the number of deaths during that period. [ABSTRACT FROM AUTHOR]

Background Aim/s Study Design Results Conclusions Relevance to Clinical Practice Internationally, there is an increasing trend in using Rapid Response Systems (RRS) to stabilize in‐patient deterioration. Despite a growing evidence base, there remains limited understanding of the processes in place to aid the early recognition and response to deteriorating children in hospitals across Europe.To describe the processes in place for early recognition and response to in‐patient deterioration in children in European hospitals.A cross‐sectional opportunistic multi‐centre European study, of hospitals with paediatric in‐patients, using a descriptive self‐reported, web‐based survey, was conducted between September 2021 and March 2022. The sampling method used chain referral through members of European and national societies, led by country leads. The survey instrument was an adaptation to the survey of Recognition and Response Systems in Australia. The study received ethics approval. Descriptive analysis and Chi‐squared tests were performed to compare results in European regions.A total of 185 questionnaires from 21 European countries were received. The majority of respondents (n = 153, 83%) reported having written policies, protocols, or guidelines, regarding the measurement of physiological observations. Over half (n = 120, 65%) reported that their hospital uses a Paediatric Early Warning System (PEWS) and 75 (41%) reported having a Rapid Response Team (RRT). Approximately one‐third (38%) reported that their hospital collects specific data about the effectiveness of their RRS, while 100 (54%) reported providing regular training and education to support it. European regional differences existed in PEWS utilization (North = 98%, Centre = 25%, South = 44%, p < .001) and process evaluation (North = 49%, Centre = 6%, South = 36%, p < .001).RRS practices in European hospitals are heterogeneous. Differences in the uptake of PEWS and RRS process evaluation emerged across Europe.It is important to scope practices for the safe monitoring and management of deteriorating children in hospital across Europe. To reduce variance in practice, a consensus statement endorsed by paediatric and intensive care societies could provide guidance and resources to support PEWS implementation and for the operational governance required for continuous quality improvement. [ABSTRACT FROM AUTHOR]

Deep-seated landslides, becoming increasingly frequent due to changing climate patterns, pose significant risks to human life and infrastructure. This research contributes to developing predictive early warning systems for deep-seated slope displacements, employing advanced computational models for environmental risk management. Our novel framework integrates machine learning, time series deep learning, and convolutional neural networks (CNN), enhanced by the Age of Exploration-Inspired Optimizer (AEIO) algorithm. Our approach demonstrates exceptional forecasting capabilities by utilizing eight years of comprehensive data—including displacement, groundwater levels, and meteorological information from the Lushan Mountain region in Taiwan. The AEIO-MobileNet model stands out for its precision in predicting imminent slope displacements with a mean absolute percentage error (MAPE) of 2.81 %. These advancements significantly enhance geohazard informatics by providing reliable and efficient landslide risk assessment and management tools. These safeguard road networks, construction projects, and infrastructure within vulnerable slope areas. [ABSTRACT FROM AUTHOR]

Background and Aims: Clinical deterioration within the first 24 h of patient admission triggering a Medical Emergency Team (MET) call is a common occurrence. A greater understanding of these events, with a focus on the recognition and management of sepsis, could lead to quality improvement interventions. Methods: A retrospective observational review of general and subspecialty medical admissions triggering a MET call within 24 h of admission at a quaternary Australian hospital. Results: 2648 MET calls occurred (47.9/1000 admissions), 527 (20% of total MET events, 9.5/1000 admissions) within 24 h of admission, with the trigger more likely to be hypotension (odds ratio: 1.5, P = 0.0013). There were 263 MET calls to 217 individual medical patients within 24 h of admission, of which 84 (38.7%) were admitted with suspected infection, 69% of which fulfilled sepsis criteria. Of these, 36.2% received antimicrobial therapy within the recommended timeframe and 39.6% received antibiotics in line with hospital guidelines. Sepsis was initially missed in 11% of patients. Afferent limb failure occurred in 29% of patients with 40.5% experiencing a failure of the ward‐based response to deterioration prior to MET call. Median hospital length of stay was increased in patients admitted with suspected infection (7 vs 5 days, P = 0.015) and in those with sepsis not receiving antimicrobial therapy within guideline timeframes (9 vs 4 days, P = 0.017). Conclusion: There is a significant opportunity to improve care for patients who trigger a MET within 24 h of admission. This study supports the implementation of a hospital sepsis management guideline. [ABSTRACT FROM AUTHOR]

Policy research established that it is possible to predict a student will drop out of school based on academic, attendance, behavior indicators. Little is known about the processes that put Early Warning Systems (EWS) in place. This case study of the Montana EWS describes the characteristics of a statewide implementation, the efficiency of the EWS to predict graduation, and effectiveness of the tool among adopters. By painting a picture of high adopters, low adopters, and non-adopters, we can distinguish the demand for the use of the tool, how users respond to the Montana EWS, how the tool is used to intervene with students, and what are the outcomes for the schools that use the tool. Students in high adoption schools who received an EWS score are more likely to graduate in comparison to students in low adoption schools. [ABSTRACT FROM AUTHOR]

Owing to undesired hydraulic effects, bridge collapse accidents have become increasingly frequent during the flooding season. Considering hydraulic effects in bridge design is paramount to controlling and mitigating hydrological damage. Despite the efforts in previous studies on bridges under floods, a proper partial factor of hydraulic effects considering foundation scour is still lacking for bridge design against floods. To bridge the gap, this study proposed an inverse reliability analysis framework to generate a proper partial factor of hydraulic effects and a corresponding partial factor of resistance. First, failure modes of typical short- to medium-span bridges were summarized to construct limit state functions. Then, an inverse reliability analysis framework considering hydraulic effects was proposed. The optimum partial factors of hydraulic effects and corresponding partial factors of resistance were calculated for various design specifications. Finally, a classification early warning system of foundation scour was built to forecast hydraulic failure accurately. This paper reveals the necessity of considering the hydraulic effects of floods in bridge design. The proposed framework and warning system can effectively enhance bridge safety against floods. [ABSTRACT FROM AUTHOR]

Flooding is a natural disaster that has a serious impact on humans, the environment, and the economy. To reduce the risk and adverse impacts of flooding, this research aims to design an Internet of Things (IoT) based early detection system integrated with a decision support system. The proposed system uses various types of sensors, such as DHT22 to monitor air temperature and humidity, an Ombrometer to measure rainfall, a Water Flow Sensor to measure water flow, and an Ultrasonic Sensor to detect changes in water level. Data from these sensors will be collected in real time and analyzed to predict potential flooding. In addition, the system will have a user interface that facilitates monitoring and decision-making by authorities. The decision support system will use sensor data and weather information to warn decision-makers early of potential flooding and appropriate action recommendations. This research is expected to improve the ability to detect and respond to floods more effectively, thereby assisting in protecting human lives, protecting the environment, and reducing the economic impact of floods. In addition, this research contributes to the development of IoT-based technologies and decision support systems in the context of natural disaster mitigation. [ABSTRACT FROM AUTHOR]

Recent environmental change exacerbates living conditions especially in coastal areas due to increases of frequency and intensity of typhoons. A storm surge early warning system (SEWS) can be a useful non-structural measure to protect the public living in coastal areas. The establishment of sea levels for the SEWS has a critical role. Thorough ground surveying is desired to establish sea levels by finding the most vulnerable point. Meanwhile, remote sensing techniques have been addressed for the coastal vulnerability especially with UAV photogrammetry. Therefore, a UAV photogrammetry procedure is proposed for a SEWS to estimate the alarming sea levels in the current study combining historical tide data for storm surge events. The procedure is comprised of five steps: 1) UAV surveying of the target area and producing the DSM data; 2) definition of the vulnerable points with DSM and ground surveying; 3) estimation of the inundation area from the DSM with sea level rising; 4) analysis of the precedent storm surge events; and 5) estimation of sea levels with the inundation area and historical sea levels of the vulnerable points. Wolpo Beach in South Korea was employed, since its coastal area has been damaged from storm surge events. Results indicated that the UAV-based procedure can be a useful method to establish sea levels for the SEWS. [ABSTRACT FROM AUTHOR]

Copyright of Al Bashaer Economic Journal is the property of Al Bashaer Economic Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

During recent decades there has been an increase in extreme flood events and their intensity in most regions, mainly driven by climate change. Furthermore, these critical events are expected to intensify in the future. Therefore, the improvement of preparedness, mitigation, and adaptation counterparts is mandatory. Many scientific fields are involved in this task, but from a meteorological and hydrological perspective, one of the main tools that can contribute to mitigating the impact of floods is the development of Early Warning Systems. In this sense, this paper presents a scientific literature review of some of the most representative Flood Early Warning Systems worldwide, many of which are currently fully operational, with a special focus on the numerical modeling component when it is developed and integrated into the system. Thus, from basic to technically complex, and from basin or regional to continental or global scales of application, these systems have been reviewed. In this sense, a brief description of their main features, operational procedures, and implemented numerical models is also depicted. Additionally, a series of indications regarding the key aspects of the newly developed FEWSs, based on recent trends and advancements in FEWSs development found in the literature, are also summarized. Thus, this work aims to provide a literature review useful to scientists and engineers involved in flood analysis to improve and develop supporting tools to assist in the implementation of mitigation measures to reduce flood damage for people, goods, and ecosystems and to improve the community resilience. [ABSTRACT FROM AUTHOR]

Aim Design Method Results Conclusion Impact Patient or Public Contribution To evaluate registered nurses' perceptions of whether the mandated use of the early warning system vital signs tool impacts the development of nurses' higher‐order thinking skills.A concurrent mixed methods study design.Using an online survey, registered nurses' perceptions were elucidated on whether early warning system algorithmic tools affected the development of their higher‐order thinking. Likert‐type matrix questions with additional qualitative fields were used to obtain information on nurse's perceptions of the tool's usefulness, clinical confidence in using the tool, compliance with escalation protocols, work environment and perceived compliance barriers.Most of the 305 (91%) participants included in the analysis had more than 5 years of nursing experience. Most nurses supported the early warning tool and were happy to comply with escalation protocols if the early warning score concurred with their assessment of the patient (63.6%). When the score and the nurse's higher‐order thinking did not align, some had the confidence to override the escalation protocol (40.0%), while others omitted (69.4%) or inaccurately documented vital signs (63.3%) to achieve the desired score. Very few nurses (3.6%) believe using early warning tools did not impede the development of higher‐order thinking.Although experienced nurses appreciate the support of early warning tools, most value patient safety above the tools and rely on their higher‐order thinking. The sustained development and use of nurses' higher‐order thinking should be encouraged, possibly by adding a critical thinking criterion to existing algorithmic tools.The study has implications for all nurses who utilize algorithmic tools, such as early warning systems, in their practice. Relying heavily on algorithmic tools risks impeding the development of higher‐order thinking. Most experienced nurses prioritize their higher‐order thinking in decision‐making but believe early warning tools can impede higher‐order thinking.Registered nurses participated as survey respondents. [ABSTRACT FROM AUTHOR]

In response to the adverse effects of climate change-induced frequent extreme disasters on agricultural production and supply stability, this study develops a comprehensive agricultural disaster prevention system based on current adaptation strategies for mitigating agricultural meteorological disasters. The primary goal is to enhance disaster preparedness and recovery through three core platforms: a fine-scale weather forecast service system, a crop disaster early warning system, and an agricultural information service platform for disasters. The results show that every major agricultural production township in Taiwan now has dedicated agricultural weather stations and access to refined weather forecasts. Additionally, a disaster prevention calendar for 76 important crops is established, integrating cultivation management practices and critical disaster thresholds for different growth periods. Utilizing this calendar, the crop disaster early warning system can provide timely disaster-related information and pre-disaster prevention assistance to farmers through various information dissemination tools. As a disaster approaches, the agricultural information service platform for disasters provides updates on current crop growth conditions. This service not only pinpoints areas at higher risk of disasters and vulnerable crop types but also offers mitigation suggestions to prevent potential damage. Administrative efficiency is then improved with a response mechanism incorporating drones and image analysis for early disaster detection and rapid response. In summary, the collaborative efforts outlined in this study demonstrate a proactive approach to agricultural disaster prevention. By leveraging technological advancements and interdisciplinary cooperation, the aim is to safeguard agricultural livelihoods and ensure food security in the face of climate-induced challenges. [ABSTRACT FROM AUTHOR]

In response to the challenges introduced by global warming and increased eutrophication, this paper presents an innovative modeling and simulation (M&S)-driven model for developing an automated inland water monitoring system. This system is grounded in a layered Internet of Things (IoT) architecture and seamlessly integrates cloud, fog, and edge computing to enable sophisticated, real-time environmental surveillance and prediction of harmful algal and cyanobacterial blooms (HACBs). Utilizing autonomous boats as mobile data collection units within the edge layer, the system efficiently tracks algae and cyanobacteria proliferation and relays critical data upward through the architecture. These data feed into advanced inference models within the cloud layer, which inform predictive algorithms in the fog layer, orchestrating subsequent data-gathering missions. This paper also details a complete development environment that facilitates the system lifecycle from concept to deployment. The modular design is powered by Discrete Event System Specification (DEVS) and offers unparalleled adaptability, allowing developers to simulate, validate, and deploy modules incrementally and cutting across traditional developmental phases. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aquaculture provided 43% of the aquatic animal food consumed by humans in 2007, and it is anticipated to expand even more to meet the increasing future demand. Marine Science Techno Park (MSTP) is one of the Techno Parks in Indonesia and is located in Teluk Awur, Jepara. MSTP has an intensive system of Vannamei shrimp farming activities. The challenge that has been faced annually is the organic matter of the remaining shrimp feed that accumulates in the waters can cause a decrease in pond water quality. Ammonia-N anions derived from the decomposition of feed residues can cause toxins in shrimp culture ponds which can further interfere with shrimp survival. The objective of this study is to design a self-controlled water quality monitoring system that is equipped with an early warning system based on internet of things (IoT) and to implement the design for analyzing the water quality including dissolved oxygen (DO), pH, and temperature in the Vannamei farming pond of MSTP UNDIP through integrating IoT which is equipped with an early warning system. If the water quality reaches a threshold value, the monitoring system will send a sound signal to the buzzer followed by sending an alert notification in real-time conditions automatically to a smartphone. [ABSTRACT FROM AUTHOR]

Background and Aims: Vital sign monitoring needs to be timely and correct to recognize deteriorating patients early and trigger the relevant clinical response. The aim of this study is to retrospectively evaluate compliance specifically toward the regional vital sign monitoring protocol the so called early warning score protocol (EWS‐protocol) 72 h before a medical emergency team response (MET‐response) and thereby illuminate whether poor compliance translates into a worse patient outcome. Methods: It was investigated all eligible patients that underwent MET responses during the calendar year 2019. The inclusion criteria encompassed somatic patients above 18 years of age admitted to the hospital and detailed evaluations of the medical records of the included patients were conducted. Results: Four hundred and twenty‐nine MET‐responses were included in the final analysis. EWS‐protocol failure was observed for more than half the patients within all the time frames assessed. Thirty‐day mortality was significantly higher for patients subject to EWS protocol failure in the timeframes 24−16, 16−8, 8−0 h before MET response. Adjusting for admission length, age, and gender, patients subject to EWS‐protocol failure had an odds ratio (OR) of 1.9, 2.0, 2.1, 2.3 for mortality in the time frames 72−48, 24−16, 16−8, and 8−0 h before the MET‐response, respectively. The adjusted OR for ICU‐admission was 1.7, and 1.6 for patients subject to EWS‐protocol failure in the time frames 16−8 and 8−0 h before MET‐response, respectively. Conclusion: According to all the data analysis in this article, there is evidence that compliance toward the NEWS‐protocol is poor. EWS‐protocol failure is associated with a significant higher mortality and ICU‐admission rate. [ABSTRACT FROM AUTHOR]

The subsidiarity principle divides competences between the European Union and its Member States. The Lisbon Treaty suggests a connection between ex ante and ex post subsidiarity review. Ex ante, national parliaments were given a role via the early warning system (EWS). Ex post, legislation is subject to review by the Court of Justice of the European Union (CJEU). More than 10 years after Lisbon, this article is the first to theorize and evaluate whether there is indeed a connection. Our theory predicts a conditional connection. Quantitatively, we provide a two‐way classification of Member States based on their use of the EWS and CJEU subsidiarity cases. Qualitatively, we look back from court cases to the Council stage and analyse the Advocate General's opinions in subsidiarity cases. We find a weak conditional connection. A key explanation appears to be the Court's limited willingness to enforce subsidiarity and hence the limited incentives to go to court. [ABSTRACT FROM AUTHOR]

This paper investigates the student dropout phenomenon in a technical Italian university from a time-to-event perspective. Shared frailty Cox time-dependent models are applied to analyse the careers of students enrolled in different engineering programs with the aim of identifying the determinants of student dropout through time, predicting the time to dropout as soon as possible and to observe how the dropout phenomenon varies across time and degree programs. The innovative contributions of this work are methodological and managerial. First, the adoption of shared frailty Cox models with time-varying covariates is relatively new to the student dropout literature and it allows to consider the student career evolution and the heterogeneity across degree programs. Second, understanding the dropout pattern over time and identifying the earliest moment for obtaining its accurate prediction allow policy makers to set timely interventions for students at risk of dropout. [ABSTRACT FROM AUTHOR]

Disease transmission is dependent on a variety of factors, including the characteristics of an event, such as crowding and shared accommodations, the potential of participants having prolonged exposure and close contact with infectious individuals, the type of activities, and the characteristics of the participants, such as their age and immunity to infectious agents [1–3]. Effective control of outbreaks of infectious diseases requires rapid diagnosis and intervention in high-risk settings. As a result, syndromic and event-based surveillance may be used to enhance the responsiveness of the surveillance system [1]. In public health, surveillance is collecting, analyzing, and interpreting data across time to inform decision-making and aid policy implementation [1]. In this review article we aimed to provide an overview of the principles, types, uses, advantages, and limitations of surveillance systems and to highlight the importance of early warning systems in response to the information received by disease surveillance. The study conducted a comprehensive literature search using several databases, selecting, and reviewing 78 articles that covered different types of surveillance systems, their applications, and their impact on controlling infectious diseases. The article also presents a case study from the Hajj gathering, which highlighted the development, evaluation, and impact of early warning systems on response to the information received by disease surveillance. The study concludes that ongoing disease surveillance should be accompanied by well-designed early warning and response systems, and continuous efforts should be invested in evaluating and validating these systems to minimize the risk of reporting delays and reducing the risk of outbreaks. [ABSTRACT FROM AUTHOR]

This study aims to provide empirical evidence regarding the comparison of the value of the Early Warning System and Risk-Based Capital in measuring the financial performance of insurance companies before and during the COVID-19 pandemic. The Early Warning System measurement indicator uses the claim expense ratio. Sampling using purposive sampling method and obtained a sample of 18 insurance companies listed on the Indonesia Stock Exchange (IDX) for the period 2017-2022. The type of data used is secondary data in the form of financial reports collected through the company's official website and the Indonesia Stock Exchange (IDX). The data analysis method uses non-parametric statistics, namely the Wilcoxon Sign Rank Test with the help of SPSS version 27. The results prove that there is a significant increase in the value of the Early Warning System during the pandemic compared to before the Covid-19 pandemic. The Risk-Based Capital value did not experience a significant decrease during the pandemic compared to before the Covid-19 pandemic. In addition, in this study, the Early Warning System is considered more accurate than Risk-Based Capital because it has a smaller standard error. [ABSTRACT FROM AUTHOR]

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Biological monitoring and assessments are commonly used for sustainable ecosystem management. Oligochaetes are found in various freshwater ecosystems and have been used as indicators of water quality and for the biological assessment of aquatic ecosystems. Among aquatic oligochaetes, the sludge worm Tubifex tubifex (Oligochaeta, Naididae) is tolerant to organic pollution and has been used as a biomonitoring indicator of toxicity and organic pollution. In this study, we investigated the response of worm colonies to copper (CuSO4) treatments (0.01, 0.05, 0.1, 0.5, and 1.0 mg/L) in an observation cage (100 mL beaker) for 30 min. Using a digital image analysis approach, we measured the changes in the colony image area between pre- and post-copper treatment. After copper treatment, the colony image area tended to decrease, even at low copper concentrations. In addition, the colony areas did not recover to their original levels at high concentrations, although those at low concentrations did. Area decreased proportional to the logarithm of the copper concentration. Finally, our results present the possible use of the retraction responses of Tubifex tubifex colonies to chemical disturbances as early biological warning systems. [ABSTRACT FROM AUTHOR]

Abstract Wheat contributes one‐fifth of the global food supply with an estimated 29% of global production in low and lower‐middle income countries. As production expands across southern Asia, yields are often negatively impacted by outbreaks of fungal rust diseases. A wheat rust early warning and advisory system comprising surveillance, near real‐time disease risk forecasts and advisory dissemination has been established in two target countries in South Asia, including Nepal and Bangladesh. However, as wheat rust spores can be aerially transmitted over long distances, near real‐time estimates of disease incidence are required from sources of infection in neighbouring regions. To address this challenge, we developed and tested a novel algorithm to generate proxy observations of infection sources using online media reports in two neighbouring countries, India and Pakistan. Media sampling could provide an effective alternative where data from ground surveys are not readily available in near real‐time. Our results show that west Nepal was exposed to a substantial inoculum pressure from aerially dispersed stripe rust spores originating from India and Pakistan. There were no outbreaks of stripe rust disease in Bangladesh with only very low levels of cross‐border dispersion and generally unfavourable environmental conditions for infection. We further describe how proxy observations informed farmer decision‐making in near real‐time in Nepal and filled a knowledge gap in identifying early sources of infection for a major outbreak of stripe rust during 2020 in Nepal. Our results highlight the importance of international cooperation in mitigating transboundary plant pathogens.

Abstract Bedload transport is a natural process that strongly affects the Earth's surface system. An important component of quantifying bedload transport flux and establishing early warning systems is the identification of the onset of bedload motion. Bedload transport can be monitored with high temporal resolution using passive acoustic methods, for example, hydrophones. Yet, an efficient method for identifying the onset of bedload transport from long‐term continuous acoustic data is still lacking. Benford's Law defines a probability distribution of the first‐digit of data sets and has been used to identify anomalies. Here, we apply Benford's law to continuous acoustic recordings from Baiyang hydrometric station, a tributary of Liwu River, Taroko National Park, Taiwan at the frequency of 32 kHz from stationary hydrophones deployed for 3 years since 2019. We construct a workflow to parse sound combinations of bedload transportation and analyze them in the context of hydrometric sensing constraining the onset, and recession of bedload transport. We identified three separate sound classes in the data related to the noise produced by the motion of pebbles, water flow, and air. We identify two bedload transport events that lasted 17 and 45 hr, respectively, covering about 0.35% of the total recorded time. The workflow could be transferred to other different catchments, events, or data sets. Due to the influence of instrument and background noise on the regularity of the residuals of the first‐digit, we recommend identifying the first‐digit distribution of the background noise and ruling it out before implementing this workflow.

Due to the continuing large number of malaria-related deaths in tropical Africa, the need to develop a robust Malaria Early Warning System (MEWS) for effective action is growing to guide cost-effective implementation of interventions. This study employs a two-stage hierarchical evaluation technique to evaluate the ability of the VECTRI malaria model to simulate malaria dynamics at seasonal time scale (1 - 7 months) over Nigeria and West Africa. Two sets of malaria simulations are considered. The first set is based on VECTRI simulations driven by observed rainfall and temperature datasets (hereafter referred to as control run). The second is based on malaria simulations driven by the European Centre for Medium-Range Weather Forecasting (ECMWF) System5 ensemble seasonal forecasting system (hereafter referred to as Forecast run). Different metrics are employed to assess the skill of the VECTRI malaria model. Results based on the control run indicate that the model can reproduce hyper-endemic zones and the evolution of malaria cases, particularly the observed increase in cases with decreasing population density. Despite having significant biases and low correlation, the model successfully predicts annual anomalies in malaria cases across Nigeria, particularly in the savannah region that experience large malaria burden. Annual correlations between the VECTRI Forecast run and the VECTRI Control run are relatively low at all lead times (LT) and for each start date (SD) across West Africa, although correlation generally increases from the Gulf of Guinea to the Sahel. Despite low correlations, the Rank Probability Skill Score (RPSS) reveals that the model has a statistically significant skill in predicting malaria occurrences across all categories of malaria cases, regardless of start date or lead time. While the Guinea Forest has the strongest RPSS, the increase or decrease in skill from the first to seventh lead time varies significantly across the region. In addition, the VECTRI malaria model has a good ability to discriminate variability in malaria cases across all regions, with an average Area Under the Relative Operating Characteristics (ROC) Curve (AUC) of approximately 0.62. Our findings suggest that the VECTRI malaria model could be used as a reliable Malaria Early Warning System (MEWS), particularly for identifying malaria hyper-endemic zones in West Africa at seasonal time scale.

Abstract We performed a passive seismic monitoring of the La Praz ∼14,000 m3 unstable slope (French Alps) spanning over 10 years. During the last 6 months prior to collapse, we detected a clear 24% decrease in the slope's fundamental resonance frequency, f0, caused by a reduction in overall rock mass stiffness. The combined study of f0 and slope deformation suggested the alternating importance of sudden brittle failure processes versus more ductile phases with possible sliding. Seismic monitoring revealed slope damage that remained ambiguous or undetected with ground surface deformation monitoring, and highlighted critical periods with intense damage. Only some of these critical damage periods could be related to clear external forcing factors such as intense rainfall episodes. These new insights into rock slope's structural condition at depth represent an asset for future monitoring systems. Surface deformation and passive seismic stiffness tracking combined could reveal active slopes with ongoing damage processes.

Introduction: Protonitazene is an opioid belonging to the 2-benzylbenzimidazole structural class. We describe two cases of opioid toxicity involving the reported inhalation of a delta-9-tetrahydrocannabinol vape product in which protonitazene was detected. Case reports: Case 1 was a young male found unconscious after the reported use of a delta-9-tetrahydrocannabinol vape. He suffered two subsequent apnoeic episodes requiring bag-valve-mask ventilation before eventual recovery. Only protonitazene was detected in blood at a concentration of 0.74 µg/L. Case 2 was a young male who died shortly after being found unresponsive. The postmortem femoral blood concentrations of protonitazene and delta-9-tetrahydrocannabinol were 0.33 µg/L and 2 µg/L, respectively. Analysis of a pod vaping device found in the decedent's hand and a separate e-liquid bottle labelled as delta-9-tetrahydrocannabinol showed a mixture of protonitazene and delta-9-tetrahydrocannabinol. Discussion: The opioid effects of protonitazene are mediated through β-arrestin2 and mu opioid receptor signalling pathways. Benzimidazole opioids are lipophilic and, when mixed with a suitable solvent, can be used in a vape device. It is anticipated that naloxone would have provided effective reversal of toxicity in our cases. Conclusions: Novel routes of opioid administration, like vaping, may appear relatively innocuous in comparison to intravenous administration, but opioids may still be absorbed at high concentrations, resulting in severe opioid toxicity or death. [ABSTRACT FROM AUTHOR]