Warning System Research Articles

The SNEWS 2.0 alert software for the coincident detection of neutrinos from core-collapse supernovae

The neutrino signal from the next galactic core-collapse supernova will provide an invaluable early warning of the explosion. By combining the burst trigger from several neutrino detectors, the location of the explosion can be triangulated minutes to hours before the optical emission becomes visible, while also reducing the rate of false-positive triggers. To enable multi-messenger follow-up of nearby supernovae, the SuperNova Early Warning System 2.0 (SNEWS 2.0) will produce a combined alert using a global network of neutrino detectors. This paper describes the trigger publishing and alert formation framework of the SNEWS 2.0 network. The framework is built on the HOPSKOTCH publish-subscribe system to easily incorporate new detectors into the network, and it implements a coincidence system to form alerts and estimate a false-positive rate for the combined triggers. The paper outlines the structure of the SNEWS 2.0 software and the initial testing of coincident signals.

Machine Learning Application to Classify Asteroids Based on Orbital Parameters

Abstract A machine learning application was developed to detect Potentially Hazardous Asteroid and mitigate asteroid collision risk with the Earth by applying three classifiers: the K-Nearest Neighbors, Naïve Bayes, and Random Forest. The study determined the most effective classifier for developing an asteroid classification program based on orbital motion. The machine learning classifier was then evaluated by its precision, accuracy, F1-score, and recall in determining Potentially Hazardous Asteroids and non-Potentially Hazardous Asteroids. The result presented Random Forest as the most appropriate classifier with the highest accuracy score of 99.53%, followed by the Naive Bayes classifier with an accuracy score of 92.00%, and the KNN classifier with an accuracy score of 84.45%. The study provided information on the most accurate machine learning classifier with the impact parameters for asteroid classification in an early warning system. By improving an embedded real-time detection system for Potentially Hazardous Asteroids, the study contributes to more effective strategies for mitigating the risk of asteroid impacts and enhancing planetary defence.

An Empirical Model for Skillful Rogue Wave Forecasts

Abstract One of the leading goals of rogue wave research is to develop a robust rogue wave warning system to mitigate the danger they pose. One such system has been developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), called the freak wave warning system (FWWS), based on nonlinear wave effects. The FWWS predicts maximum expected wave envelope height as a risk parameter for forecasts. Recently, a data-driven alternative has been proposed by Häfner et al., which was distilled from a neural network using wave buoy observations. However, it has yet to be evaluated by a spectral wave model for application to operational wave forecasting. The data-driven, learned model emphasizes bandwidth-controlled linear superposition as the predominant mechanism in crest-to-trough rogue wave generation, while nonlinear effects are a secondary term. The present work evaluates the performance of the empirical model using output from an ECMWF global wave hindcast. We find that the prediction models based on bandwidth effects have the highest log likelihood scores, with the empirical model outperforming all other tested models. In contrast, the expected maximum envelope wave height from the FWWS does not predict the occurrence of rogue waves. These results indicate that the empirical model with wave model input is a skillful predictor and should be considered for operational implementation to improve rogue wave forecasting. Significance Statement Rogue waves are unexpectedly large and unpredictable waves. Encounters with rogue waves can result in damage to marine vessels and offshore infrastructures. Research is devoted to developing systems to predict the risk of rogue wave events. A novel predictive model has been shown to perform well in predicting rogue wave occurrences based on wave buoy observations. This model is a symbolic expression distilled from an artificial neural network that incorporates known rogue wave dynamics and that can be evaluated alongside traditional risk estimates with minor adjustments to operational forecasting systems. This study evaluates the newly proposed empirical model in a forecasting setting. Our work demonstrates the efficacy of the empirical model for operational forecasting purposes.

Machine Learning approach in the prediction of Fog: An Early Warning System.

The aviation sector is extremely vulnerable to fog. Thus, accurate fog predictions are essential foraviation sector efficiency, particularly airport management and flight scheduling. Even with numerical weather predictionmodels and guiding systems, fog prediction is challenging. The difficulty of fog prediction is due to the inability to graspthe micro-scale factors that cause fog to form, intensify, augment and dissipate in the boundary layer. This study looks athow well machine learning (ML) tools can predict fog (Visibility <1000 m) and dense fog (Visibility <200 m) at India'sJay Prakash Narayan International Airport (ICAO Index-VEPT), a representative station of the Indo-Gangetic Plains(IGP). The proposed ensemble ML-based model was trained using hourly synoptic data from 2014 to 2020 and testedusing data from 2021 to 2022 (December to February). Once the features are chosen and the forecasters' local knowledgeis taken into account, the dry bulb temperature (°C), dew point temperature (°C), relative humidity (%), cloud amount(octa), wind direction (degrees from true north) and wind speed (knots) are used to build the proposed ML models. MLalgorithms were trained on meteorological data from 1500 to 2200 UTC to predict fog (Visibility <1000 m) and densefog (Visibility <200 m) for the next day at 0000 UTC, with a two-hour lead time. For fog forecasting at 0000 UTC with a 4-hour lead time, ML models were trained with data from 1300 to 2000 UTC and so on. This study evaluates parameter tuning in six level 0 ML models: distributed random forest (DFR), deep learning (DL), gradient boosting machine (GBM), generalized linear model (GLM), extremely randomized tree (XRT), XG Boost and stacked ensemble at level 1. The performance metrics and statistical skill scores indicate that DRF and DL models perform well for lead times of 2 and 4 hours at level 0 for fog (visibility <1000 m) and dense fog (visibility <200 m). But the proposed ensemble models outperform all the base models at level 0 and are recognized as the best instrument for predicting fog at Patna Airport.

Avian Influenza: Lessons from Past Outbreaks and an Inventory of Data Sources, Mathematical and AI Models, and Early Warning Systems for Forecasting and Hotspot Detection to Tackle Ongoing Outbreaks.

The ongoing avian influenza (H5N1) outbreak, one of the most widespread and persistent in recent history, has significantly impacted public health and the poultry and dairy cattle industries. This review covers lessons from past outbreaks, risk factors for transmission, molecular epidemiology, clinical features, surveillance strategies, and socioeconomic impacts. Since 1997, H5N1 has infected over 900 individuals globally, with a fatality rate exceeding 50%. Key factors influencing infection rates include demographic, socioeconomic, environmental, and ecological variables. The virus's potential for sustained human-to-human transmission remains a concern. The current outbreak, marked by new viral clades, has complicated containment efforts. This review discusses how to integrate technological advances, such as mathematical modeling and artificial intelligence (AI), to improve forecasting, hotspot detection, and early warning systems. We provide inventories of data sources, covering both conventional and unconventional data streams, as well as those of mathematical and AI models, which can be vital for comprehensive surveillance and outbreak responses. In conclusion, integrating AI, mathematical models, and technological innovations into a One-Health approach is essential for improving surveillance, forecasting, and response strategies to mitigate the impacts of the ongoing avian influenza outbreak. Strengthening international collaboration and biosecurity measures will be pivotal in controlling future outbreaks and protecting both human and animal populations from this evolving global threat.

A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm.

Due to the flammable and explosive nature of explosives, there are significant potential hazards and risks during transportation. During the operation of explosive transport vehicles, there are often situations where the vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance and collision, leading to serious consequences such as explosions and fires. Therefore, in response to the above issues, this article has developed an explosive transport vehicle driving warning system based on object detection algorithms. Consumer-level cameras are flexibly arranged around the vehicle body to monitor surrounding vehicles. Using the YOLOv4 object detection algorithm to identify and distance surrounding vehicles, using a game theory-based cellular automaton model to simulate the actual operation of vehicles, simulating the driver's decision-making behavior when encountering other vehicles approaching or changing lanes abnormally during actual driving. The cellular automaton model was used to simulate two scenarios of explosive transport vehicles equipped with and without warning systems. The results show that when explosive transport vehicles encounter the above-mentioned dangerous situations, the warning system can timely issue warnings, remind drivers to make decisions, avoid risks, ensure the safety of vehicle operation, and verify the effectiveness of the warning system.

What drives household protective actions in an industrial crisis? Insights from the East Palestine train derailment

Complex industrial disasters illustrate the challenges of underdeveloped public warning systems. Unlike most natural disasters, quickly identifying hazardous materials and assessing their threats is crucial for developing protective action recommendations (PARs) that guide household response in industrial crises. The 2023 East Palestine, Ohio (USA) train derailment, chemical spill, and fires revealed that gaps in rapidly identifying hazardous materials, and the threats they present, can severely impact the public warning system. As the crisis unfolded, responding agencies left crucial questions unanswered, leaving community members uncertain about their safety, the extent of environmental contamination, and what protective actions to take. It is imperative to study the drivers of household protective actions in the absence of a developed warning system and well-established PARS. To achieve this, we conducted a community survey in Ohio, Pennsylvania, and West Virginia (n = 259) in response to the East Palestine crisis. We used multivariate logistic regressions to identify statistically significant explanatory factors that predict protective action response. Our findings reveal gaps in response, where challenges identifying and communicating hazards created environmental justice concerns. We provide policy recommendations to strengthen hazard identification and outline further work to include equity as a pillar of environmental disaster response.

An Overview of Traditional and Next-Generation Earthquake Early Warning Systems

The Earthquake Early Warning System generates rapid and effective warnings after detecting an earthquake, before the arrival of destructive waves to the areas that may be affected. In this way, it is aimed to minimize the loss of life and property. In this study, traditional early warning systems that are frequently used in the world are discussed. The details of the next-generation early warning system, which has recently produced successful outputs, are discussed, and its advantages over traditional early warning systems are mentioned. The recent developments of this system are also analyzed. Next-generation early warning systems consist of a new array-based algorithm composed of two modules for real-time epicenter detection. The detection of an earthquake occurring in the Marmara Sea (Türkiye) with a next-generation early warning system using array-based location methodology is given as an example. Next-generation early warning systems have advantages over traditional ones, such as lower cost and time gains of up to minutes.

Data Analysis and Algorithm Innovation in Power System IntelligentMonitoring and Early Warning Technology

Data analysis and algorithm innovation play a pivotal role in enhancing power system intelligent monitoring and early warning technology. With the increasing complexity of modern power grids, the integration of advanced data analytics enables real-time monitoring, fault detection, and predictive maintenance. By leveraging machine learning algorithms, anomaly detection techniques, and big data analytics, power systems can efficiently identify potential risks and failures before they escalate into serious issues. These innovations not only improve grid reliability and resilience but also optimize resource utilization. Early warning mechanisms based on intelligent algorithms provide timely alerts, allowing for preventive measures that ensure the stability and safety of the power network. This approach fosters a smarter, more adaptive power infrastructure capable of meeting growing energy demands while minimizing downtime and disruptions.This paper presents a comprehensive investigation into the development and efficacy of an intelligent early warning system for power systems. Leveraging machine learning algorithms, IoT sensors, and cloud computing frameworks, the system aims to enhance real-time monitoring capabilities and facilitate proactive intervention and maintenance. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems. Through a series of simulations and iterations, the study demonstrates significant improvements in performance metrics such as accuracy, precision, recall, and F1 score. Specifically, the system achieves an average accuracy of 95%, precision of 92%, recall of 94%, and F1 score of 92% across multiple iterations. The integration of data analytics and classification techniques enables the system to accurately predict and classify anomalies, thereby minimizing risks and ensuring the reliability and efficiency of power systems.

The Influence of Claystone Type on Landslide Hazard: A Case Study

The landslide is more common in locations with steep slopes and is unstable. Developing an early warning system is the aim of this study, which focuses on Nanggulan, Yogyakarta Special Region-Indonesia. The Nanggulan has a wavy shape with a minor slope and is made up of Eocene sedimentary rocks. The landslide indicators, such as curved walls, cracked roads, fractured building walls, subsided building floors, sinkholes, and the creation of crown scarps, were discovered at various sites. X-Ray Diffraction (XRD) analysis and soil index properties of rock and soil samples, were used in the research. The XRD examination revealed the presence of smectite-group clay minerals (monmorillonite and saponite). Calculating the soil index parameters yielded that the soil has a specific gravity of 2.173–2.62, water content of 18.207–47.653%, liquid limit of 45–77%, plastic limit of 21–43%, plasticity index of 18–42%, cohesiveness of 1–91 kPa, and a friction angle of 10°–54°. The direction of the slope is more than 20°, so there is little possibility of planar failure, and the Nanggulan is stable. However, from the smectite-group clay minerals, the plasticity index and the liquid limit value indicate that the rock is cohesive and easy to move when it is wet. This form of landslide typically occurs on gently sloping terrain, is controlled by expansive lithology, has a relatively slow movement rate, and indicates that the landslide type was creep.

IoT and AI: a panacea for climate change-resilient smart agriculture

The application of Internet of Things (IoT) and Artificial Intelligence (AI) for disaster preparedness and sustainable agriculture has been a topic of great interest lately. In the last few years, extreme weather swings due to climate change caused by global warming have caught the farming community off guard, especially in the developing world. One of the key objectives of smart agriculture is optimal use of freshwater, which has become an increasingly scarce resource around the world. Reference Evapotranspiration (ETo), an estimation of total flux of water evaporating from a reference surface is an important parameter for irrigation management. IoT & AI-based location-specific estimation of ETo for crop water requirements augments the decision-making process. In this work, we utilize the Hargeaves and Samani (H–S) model and six regression algorithms for the estimation of ETo. We create a location-specific dataset with locally sensed IoT data from a flood warning system and remotely sensed meteorological data, spanning over 5 years. We train and test Linear Regression (LR), Multilayer Perceptron (MLP), Radial Basis Function (RBF), Support Vector Regression (SVR), Bagging and Random Forest (RF) algorithms on the locally curated dataset with 20 basic, extracted, and derived attributes. We gradually reduce number of attributes in the dataset from 20 to 3 and compare performance of the six algorithms using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Coefficient of Determination R2, Kendall Tau and Spearman Rho metrics. SVR shows superior performance with an MAE of 0.03 and an RMSE of 0.05, followed closely by MLP with an MAE of 0.04 and RMSE of 0.06 with a dataset of 12 attributes. The performance of Bagging and RF algorithms remains relatively unchanged with feature reduction whereas RBF shows slight improvement in performance when number of attributes is reduced to 3. Finally, we develop a novel ensemble hybrid model using the Stacked Generalization technique, which outperforms all individual models in prediction accuracy when using reduced-feature datasets. This work clearly delineates the performances of a diverse set of ML algorithms for feature-rich and feature-scarce scenarios and demonstrates the efficacy of our hybrid ensemble ML algorithm for estimating ETo under limited availability of data in resource-constrained environments.

Spatial-Temporal Visualization of Dengue Fever Vulnerability in Kediri Using Hierarchy Clustering Based on Mobile Devices

In Indonesia, Dengue Fever (DF) is a contagious disease that is a significant issue in public health. The Kediri Regency in East Java, as reported by the Ministry of Health in 2019, had the highest number of DF cases. If not addressed promptly, DF can lead to outbreaks, creating a health emergency. The lack of a thorough investigation into the diversity of risk within a spatial and temporal region exacerbates this issue. Therefore, spatial-temporal analysis is crucial in developing a warning system to prevent and control DF. This paper proposes a method that combines the Euclidean Distance calculation with the Hierarchical Clustering method. We collected data from the Kediri Regency health department and conducted pre-processing and classification processes, considering the number of DF victims, death rate, population, rainfall, and public facilities. The hierarchical clustering algorithm was used to categorize the 344 village analyses into low, medium, and high vulnerability categories. This method allows for a comparison of yearly single, average, complete, and centroid linkage in DF vulnerability levels. We also employed spatial-temporal visualization based on cellular applications to create a clear picture of areas vulnerable to DF. The experimental results in clustering showed a satisfactory level of matching, with variant values calculated using the hierarchical clustering method. The variants for single linkages were 0.113; for average linkages, they were 0.120; for complete linkages, they were 0.178; and for centroid linkages, they were 0.106. The grouping validation results indicated that the centroid linkage method produced the best variant level. We suggest further enhancing the methods with better process steps using other pre-processing methods to improve the validation quality.

Diurnal temperature range and cardiopulmonary health in Taiwan: Evaluating impacts, thresholds, and vulnerable groups

The health impacts of the diurnal temperature range (DTR), which may be affected by climate change, have received little attention. The objectives of this study were (1) to evaluate the association of DTR and cardiopulmonary outcomes, (2) to select the proper thresholds for a DTR warning system, and (3) to identify vulnerable groups. The weather and health records in Taiwan from 2000 to 2019, with a maximum DTR of 12.8 °C, were analyzed using generalized additive models. The health outcomes included cardiovascular (CVD) and respiratory disease (RD) categories and several sub-categories, such as ischemic heart disease, stroke, pneumonia, asthma, and chronic obstructive pulmonary disease. The results showed that the associations of DTR and cardiopulmonary outcomes were as significant as, and sometimes even stronger than, those of the daily maximum temperature and daily minimum apparent temperature in the warm and cold seasons, respectively. The significant association began at DTR of 6 °C, lower than previously reported. The identified DTR warning thresholds were 8.5 and 11 °C for the warm and cold seasons, respectively. DTR is statistically significantly associated with a 5–36% and a 9–20% increase in cardiopulmonary emergency and hospitalized cases in the warm season with a 1 °C increase above 8.5 °C, respectively. In the cold season, DTR is significantly associated with 7–41%, 4–30%, and 36–100% increases in cardiopulmonary emergency, hospitalized, and mortality with a 1 °C increase above 11 °C, respectively. People with hypertension, hyperglycemia, and hyperlipidemia had even higher risks. Vulnerable age and sex groups were identified if they had a lower DTR–health threshold than the general population, which can be integrated into a warning system. In conclusion, DTR may be increased on a local or city scale under climate change; a DTR warning system and vulnerable group identification may be warranted in most countries for health risk reduction.

Prediction of Tsunamis in Indonesia Using an Optimized Neural Network with SMOTE

Tsunamis have the potential to have a large impact on the environment, therefore early detection and preparation for tsunami need to be carried out to reduce the impact of casualties and losses incurred. This research aims to predict tsunami events due to large earthquakes in Indonesia as a form of early detection. The optimized neural network method is used in research to classify tsunami events in Indonesia in 2000-2023 for large earthquakes with strength more than 5 magnitudes. The research results show that the neural network structure formed consists of an input layer, a hidden layer, and an output layer. The results of the evaluation of the neural network model with SMOTE obtained an accuracy value of 99.43%, precision of 96.31%, and an F1 score of 97.86%, which means the resulting model is good. Therefore, an optimized neural networks can be applied as a warning system in various regions to detect potential tsunami events in the future.

Implementation of INEWS to Improve Patient Outcomes in a Community Hospital

Abstract Background It is imperative to identify at risk older patients with clinical deterioration as soon as possible. The Irish National Early Warning System (INEWS)1 is used to detect clinical deterioration at an early stage based on each patient's vital sign records. It can then initiate a communication mechanism between nurses and doctors to enable early patient care. Data on the effectiveness of the INEWS in patients admitted to community rehabilitation wards, the barriers and difficulties encountered during implementation, are still scarce, despite the development and implementation in acute settings. There are not enough standardised tool available to identify patients' deterioration for community hospitals. Methods This pilot quality improvement programme was conducted in a community hospital's rehabilitation section. Data were gathered seven weeks after the deployment of INEWS and fourteen weeks before. Issues were studied to determine the underlying cause, created a driver diagram to comprehend the factors that lead to unexpected outcomes, educated and gathered feedback from the clinical care teams involved, measured adherence to the INEWS during the six-week pilot project implementation period, and assessed results by looking back at the deteriorating patient data and INEWS compliance using Plan, Do, Study, Act cycles. Results During the pre-INEWS introduction phase (14 weeks), there were 11.9 events per week (7.8 events with infection or septic workups, 9.6 events with onsite management, 1.0 acute care transfers, 0.28 per week unexpected deaths). In the post-INEWS piloting phase (6 weeks), there were 3.6 events per week (2.5 infection or septic workup, 1.8 onsite management and 1.1 acute care transfers per week with no unexpected deaths). Conclusion Conclusion Early detection and rapid management through the INEWS can enhance patient care; however, increased stakeholder engagement in escalation response may enhance patient outcome in resource limited community setting. Reference https://www2.healthservice.hse.ie/organisation/qps-improvement/deteriorating-patient-improvement-programme-partnership-dpipp/

Factors Influencing Farmers’ Adoption of Climate Smart Agriculture Practices in the Case of Ilu Aba Bora Zone, South West Ethiopia

To overcome the challenges of climate change, this study was intended to identify factors that affect the farmers’ adoption of CSA in the Ilu Aba Bora Zone of Southwest Ethiopia across varying agro-ecological zones. A mixed research design was used for this study. During this study, representative districts (Bure, Hurumu, and Nono Sale) were selected purposely based on their differences in agro-ecology, while rural kebeles and respondents were selected randomly from the districts to minimize the biases of the data. Data collection tools employed were a questionnaire, KKI, FGD, and field observations. A binomial logistic regression model was used to identify the interaction of response and explanatory variables and to draw a conclusion. The findings of this study revealed the majority (63.1%) of farmers did not have an understanding of the meaning, function, and goals of CSA practices. Existing CSA options in the study area were conservation agriculture, crop diversification, agro-forestry, early warning system, livelihood diversification, and improved livestock feed, which were practiced at different levels and by a few households. In particular, the early warning system and improved livestock feed were the least implemented CSA options, whereas livelihood diversification, conservation agriculture, and agro-forestry were the highly adopted practices in the area. Independent variables including on-farm income, farming experience, weather information, agro-ecology, farm land size, age, extension service, marital status, and off-farm income were the factors significantly affecting the adoption of CSA practices in the study area. Experts, governments, and nongovernmental organizations should improve farmers’ awareness of the benefits of CSA practices in ensuring food security, climate mitigation and adaptation through training and capacity building. The result of this study will show the direction for farmers, policy makers, planners, and other stakeholders to set up a solution in order to promote CSA practices and technologies based on agro-ecology.

Mapping Species Distributions of Latoia consocia Walker under Climate Change Using Current Geographical Presence Data and MAXENT (CMIP 6).

Latoia consocia Walker is an important phytophagous pest that has rapidly spread across North China in recent years, posing a severe threat to related plants. To study the impact of climatic conditions on its distribution and to predict its distribution under current and future climate conditions, the MaxEnt niche model and ArcGIS 10.8 software were used. The results showed that the MaxEnt model performs well in predicting the distribution of L. consocia, with an AUC value of 0.913. The annual precipitation (Bio12), the precipitation of the driest month (Bio14), the temperature annual range (Bio7), and the minimum temperature of the coldest month (Bio6) are key environmental factors affecting the potential distribution of L. consocia. Under current climate conditions, L. consocia has a highly suitable growth area of 2243 km2 in China, among which Taiwan has the largest high-suitable area with a total area of 1450 km2. With climate warming, the potential habitat area for L. consocia shows an overall decreasing trend in future. This work provides a scientific basis for research on pest control and ecological protection. A "graded response" detection and early warning system, as well as prevention and control strategies, can be developed for potentially suitable areas to effectively address this pest challenge.

The Current Epizootiological Situation of Three Major Viral Infections Affecting Cattle in Egypt.

One of the major factors hindering efficient livestock production is the presence of high-impact infectious animal diseases, such as foot and mouth disease (FMD), lumpy skin disease (LSD), and bovine ephemeral fever (BEF), which are notable viral infections affecting cattle in Egypt, leading to significant economic losses. FMD is caused by the foot and mouth disease virus (FMDV) of the genus Aphthovirus in the Picornaviridae family. LSD is caused by lumpy skin disease virus (LSDV) of Capripox genus within the Poxviridae family, subfamily Chordopoxvirinae. BEF is caused by bovine ephemeral fever virus (BEFV) of genus Ephemerovirus in the Rhabdoviridae family. FMD is a highly contagious viral infection of domestic and wild cloven-hooved animals and can spread through the wind. On the other hand, LSD and BEF are arthropod-borne viral diseases that mainly affect domestic cattle and water buffalo. Despite government vaccination efforts, these three viral diseases have become widespread in Egypt, with several reported epidemics. Egypt's importation of large numbers of animals from different countries, combined with unregulated animal movements through trading and borders between African countries and Egypt, facilitates the introduction of new FMDV serotypes and lineages not covered by the current vaccination plans. To establish an effective control program, countries need to assess the real epizootic situation of various infectious animal diseases to develop an efficient early warning system. This review provides information about FMD, LSD, and BEF, including their economic impacts, causative viruses, global burden, the situation in Egypt, and the challenges in controlling these diseases.

Deception mechanisms of FDA‒AWACS against passive monopulse angle measurements

AbstractAirborne warning and control systems (AWACS) serve as critical command and control centres in air combat operations, making them prime targets for strategic attacks. These enemy attacks typically rely on the accurate determination of AWACS combat positions using different direction‐finding devices. In particular, passive monopulse angle measurement systems locate AWACS by measuring the angles of signals emitted by AWACS radiation sources, thus rendering them vulnerable to attacks. Given the criticality of the airborne radars of AWACS in battle command and control operations, they must function continuously to monitor air and sea targets. Hence, AWACS cannot effectively evade electronic reconnaissance systems through tactics such as radar shutdown. To explore alternative measures, the authors investigate the deception mechanisms of an integrated frequency diverse array AWACS (FDA‒AWACS) against passive monopulse angle measurements. Using an established FDA signal model, the principles underlying two common monopulse angle measurement methods are first outlined. Subsequently, the angle estimation formulae typically used by these monopulse angle measurement systems to interpret received FDA radiation signals are derived. Additionally, the transmit beampatterns, amplitude patterns, and angle measurement deception capabilities of several typical FDAs are examined. Simulation results indicate that the FDA‒AWACS can theoretically deceive passive monopulse angle measurement systems to a certain extent. However, one‐dimensional uniform linear FDAs and other arrays using sinusoidal frequency offsets exhibit limited deception abilities. In contrast, arrays utilising cubic and quartic frequency offset achieve angle measurement errors exceeding 2° in far‐field scenarios.

Prediction model of in-hospital cardiac arrest using machine learning in the early phase of hospitalization.

In hospitals, the deterioration of a patient's condition leading to death is often preceded by physiological abnormalities in the hours to days beforehand. Several risk-scoring systems have been developed to identify patients at risk of major adverse events; however, such systems often exhibit low sensitivity and specificity. To identify the risk factors associated with in-hospital cardiac arrest (IHCA), we conducted a retrospective cohort study at a tertiary medical center in Taiwan. Four machine learning algorithms were employed to identify the factors most predictive of IHCA. The support vector machine model was discovered to be the most effective at predicting IHCA. The ten most critical physiological parameters at 8 h prior to the event were pulse rate, age, white blood cell count, lymphocyte count, body temperature, body mass index, systolic and diastolic blood pressure, platelet count, and use of central nervous system-active medication. Using these parameters, we can enhance early warning and rapid response systems in our hospital, potentially reducing the incidence of IHCA in clinical practice.