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This manuscript introduces the convergence Epidemic Volatility Index (cEVI), a modification of the recently introduced Epidemic Volatility Index (EVI), as an early warning tool for emerging epidemic waves. cEVI has a similar architectural structure as EVI, but with an optimization process inspired by a Geweke diagnostic-type test. Our approach triggers an early warning based on a comparison of the most recently available window of data samples and a window based on the previous time frame. Application of cEVI to data from the COVID-19 pandemic data revealed steady performance in predicting early, intermediate epidemic waves and retaining a warning during an epidemic wave. Furthermore, we present two basic combinations of EVI and cEVI: (1) their disjunction cEVI + that respectively identifies waves earlier than the original index, (2) their conjunction cEVI- that results in higher accuracy. Combination of multiple warning systems could potentially create a surveillance umbrella that would result in early implementation of optimal outbreak interventions.

Data Availability Statement: All data are included in the Supporting Information for publication online. Supporting Information is available online at https://setac.onlinelibrary.wiley.com/doi/10.1002/etc.5620#support-information-section . Copyright © 2023 Crown copyright and The Authors. While chemicals are vital to modern society through materials, agriculture, textiles, new technology, medicines, and consumer goods, their use is not without risks. Unfortunately, our resources seem inadequate to address the breadth of chemical challenges to the environment and human health. Therefore, it is important we use our intelligence and knowledge wisely to prepare for what lies ahead. The present study used a Delphi-style approach to horizon-scan future chemical threats that need to be considered in the setting of chemicals and environmental policy, which involved a multidisciplinary, multisectoral, and multinational panel of 25 scientists and practitioners (mainly from the United Kingdom, Europe, and other industrialized nations) in a three-stage process. Fifteen issues were shortlisted (from a nominated list of 48), considered by the panel to hold global relevance. The issues span from the need for new chemical manufacturing (including transitioning to non-fossil-fuel feedstocks); challenges from novel materials, food imports, landfills, and tire wear; and opportunities from artificial intelligence, greater data transparency, and the weight-of-evidence approach. The 15 issues can be divided into three classes: new perspectives on historic but insufficiently appreciated chemicals/issues, new or relatively new products and their associated industries, and thinking through approaches we can use to meet these challenges. Chemicals are one threat among many that influence the environment and human health, and interlinkages with wider issues such as climate change and how we mitigate these were clear in this exercise. The horizon scan highlights the value of thinking broadly and consulting widely, considering systems approaches to ensure that interventions appreciate synergies and avoid harmful trade-offs in other areas. We recommend further collaboration between researchers, industry, regulators, and policymakers to perform horizon scanning to inform policymaking, to develop our ability to meet these challenges, and especially to extend the approach to consider also concerns from countries with developing economies. Environ Toxicol Chem 2023;00:1–17. © 2023 Crown copyright and The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland. Department for Environment, Food and Rural Affairs

The novel coronavirus SARS-CoV-2 has spread at an unprecedented rate since late 2019, leading to the global COVID-19 pandemic. During the pandemic, being able to detect SARS-CoV-2 in human populations with high coverage quickly is a huge challenge. As SARS-CoV-2 is excreted in human excreta and thus exposed to the aqueous environment through sewers, the goal is to develop an ideal, non-invasive, cost-effective epidemiological method for detecting SARS-CoV-2. Wastewater surveillance has gained widespread interest and is increasingly being investigated as an effective early warning tool for monitoring the spread and evolution of the virus. This review emphasizes important findings on SARS-CoV-2 wastewater-based epidemiology (WBE) in different continents and techniques used to detect SARS-CoV-2 in wastewater during the period 2020-2022. The results show that WBE is a valuable population-level method for monitoring SARS-CoV-2 and is a valuable early warning alert. It can assist policymakers in formulating relevant policies to avoid the negative impacts of early or delayed action. Such strategy can also help avoid unnecessary wastage of medical resources, rationalize vaccine distribution, assist early detection, and contain large-scale outbreaks.

From ancient times to the present, infestations of the Central American locust (CAL) [Schistocerca piceifrons piceifrons (Walker, 1870)] have occurred periodically and with varying intensities in the Yucatan Peninsula (YP), Mexico. Despite efforts to survey the recession zone, an upsurge is still difficult to predict and prevent, and high economic costs are incurred in controlling this pest. For this study, two models were developed to determine the probability of an upsurge in the YP. The first was the Markov chain (MC) with transition probability matrix, which estimates probability by determining the proportion of times that the system moved from one state to another (n2) over 71, 33, and 24 years in Yucatan, Campeche, and the Quintana Roo States, respectively, divided into different periods; a correlation of the matrix and probability (n2) of the next period was performed to evaluate the accuracy of the estimation. The other method is the classic probabilistic (CP) model, which uses the number of times the upsurge could happen and the number of possible events. In the MC model, great variation was found in CAL upsurge probabilities between periods, with a similar number of upsurges from the past to the present but with varying intensity. In recent years, the treated area with insecticides has been less than that of the past. The CP model revealed that the locust population reached its maximum peak every four years, with the migration of swarms to neighboring states at the end/start of the year. Validation of the MC and CP models was performed considering information on areas treated in 2019 and 2020, and good accuracy was obtained. Both models provide information on the probability of an upsurge in the YP. This information can be incorporated into economic models to improve management decisions, such as when to announce early warnings, and to implement preventive control strategies.

Underground coal mining can cause the deformation, failure, and collapse of the overlying rock mass of a coal seam. If the mining design, monitoring, early warning, or emergency disposal are improper, in that case, it can often lead to mining disasters such as roof falls, water inrush, surface collapse, and ground fissures, seriously threatening the safety of mine engineering and the geological environment protection in mining areas. To ensure the intrinsic security of the entire coal mining process, aspace–time continuous sensing system of overburden deformation and failure was developed, which breaks through the limitations of traditional monitoring methods that characterize the evolution process of overlying rock deformation and ground subsidence. This paper summarizes the classification of typical overburden deformation and failure modes. It researches the space–time continuous sensing of rock–soil mass above the coal seam based on Distributed Fiber Optic Sensing (DFOS). A multi-range strain optical fiber sensing neural series from micron to meter was developed to achieve synchronous sensing of overburden separation, internal micro–cracks, and large rock mass deformation. The sensing cable–rock mass coupling test verified the reliability of the optical fiber monitoring data. The sensing neural network of overburden deformation was constructed using integrated optical fiber layout technology on the ground and underground. Different sensing nerves’ performance and application effects in overburden deformation and failure monitoring were compared and analyzed with field monitoring examples. A physical model was used to carry out the experimental study on the overburden subsidence prediction during coal mining. The results showed that the optical fiber monitoring data were reliable and could be used to predict overburden subsidence. The reliability of the calculation model for overlying rock subsidence based on space–time continuous optical fiber sensing data was verified in the application of mining subsidence evaluation. A systematic review of the shortcomings of current overburden deformation observation technology during coal mining was conducted, and a space–time continuous sensing system for overburden deformation and failure was proposed. This system integrated sensing, transmission, processing, early warning, decision-making, and emergency response. Based on the fusion of multi-parameter sensing, multi-method transmission, multi-algorithm processing, and multi-threshold early warning, the system realized the real-time acquisition of space–time continuous information for the overburden above coal seams. This system utilizes long-term historical monitoring data from the research area for data mining and modeling, realizing the prediction and evaluation of the evolution process of overburden deformation as well as the potential for mining subsidence. This work provides a theoretical reference for the prevention and control of mining disasters and the environmental carrying capacity evaluation of coal development.

Examining a particular tradition of visual inspection methodologies based on empirical evidence represents the basis for developing a method of evaluating simple masonry structural damages during post-seismic emergencies. Integrating these observation codes allows the calibration and analysis of the survey and its measurement techniques. By taking advantage of the technologies widely available, the digital assistant (App) integrates both specialized knowledge and traditional techniques that derive from directly surveying as an analytical basis for assessing the damage. The representation of this analytical dimension of the damage is a critical aspect of establishing the correlations between the visual codes and the algorithm’s scaled assessment that determine the seismic risk and the degree of the structural integrity of the houses., Examinar una tradición de levantamientos de inspección visual fundamentados en evidencia empírica representa la base para el desarrollo de un asistente digital (App) como instrumento de diagnóstico y valoración de estructuras en albañilería simple durante la post-emergencia sísmica. La integración de los códigos observacionales derivados de esta tradición permitió la calibración y el análisis de las técnicas de levantamiento y medición utilizadas. El asistente aprovecha las tecnologías ampliamente disponibles en la población e integra los conocimientos especializados y las técnicas tradicionales derivadas del levantamiento directo como base analítica para la valoración de los daños. Esta dimensión analítica de la representación del daño es uno de los aspectos críticos que establece las correlaciones entre los códigos visuales y las escalas de valoración del algoritmo que determina el riesgo sísmico y el grado de habitabilidad estructural de las viviendas.

The Shuangjiangkou hydropower station in China has complex geological conditions with high in situ stress. During the tunnel excavation, rockbursts occurred frequently, which seriously affected construction progress. Microseismic (MS) monitoring technology was used to explore rock MS activities to predict rockbursts. The MS monitoring system can capture a large number of MS signals. Based on Hilbert–Huang transform (HHT) instantaneous frequency analysis technology, using MATLAB software (R2022a) to write a program to convert the MS waveform, the frequency and energy characteristics of MS signals at a certain time can be obtained. By analyzing the frequency and energy characteristics of every event, the microseism active areas can be determined, and then rockbursts can be predicted scientifically. This paper selected two different construction sites, which were the main powerhouse and the access tunnel in the main powerhouse, as the research background. Introducing HHT instantaneous time–frequency analysis technology conducted MS event dynamic analysis and predicted rockbursts. The HHT spectrum scientifically and comprehensively displayed MS signal frequency characteristics at a certain time and reflected the change laws of signal instantaneous energy and local abrupt change information. The results indicated that some parameter anomalies in the event spectrum can predict rockbursts. For complex tunnel construction conditions, the HHT time–frequency analysis technology can realize a new idea of using a single-channel signal to predict rockbursts, which was very meaningful.

Coronavirus disease 2019 (COVID-19) is as an emerging infectious disease (EID) that has caused the worst public health catastrophe of the 21st century thus far. In terms of impact, the COVID-19 pandemic is second only to the Spanish Flu pandemic of 1918 in modern world history. As of 7 September 2021, there have been 220 million confirmed cases of COVID-19 and more than 4.5 million deaths. EIDs pose serious public health and socio-economic risks, and 70% of EIDs originate from wildlife. Preventing development of EIDs such as COVID-19 is a pressing concern. Here, taking the COVID-19 pandemic as an example, we illustrate the disastrous effects of EIDs and assess their emergence and evolution from a One Health perspective. We propose a One Health strategy, centered on 'moving the gates forward', for EID prevention and control at the human-animal-environment interface. This strategy may be instructive and provide early warnings of EIDs in the future.

Few studies explore extreme wintertime European cold waves (CW) despite their huge economic, and social impacts and a recent decade punctuated by CW (February 2012, January 2017 or March 2018), while Europe bets on mild winter to avoid an energy crisis in 2023. Here, we investigate potential early CW warning signals and model biases over the winters (November-to-March) of the 1950–2005 period, calculating the atmospheric and surface conditions of key climate variables (e.g. snow, incoming radiation, cloudiness, pressure, winds and sea surface temperatures variables) both in reanalyses and climate models, before and during CW events. We show that global coupled climate models systematically overestimate the number of CW in the present-day period. Until 30 days before CW, some robust and significant early patterns emerge, both in models and reanalyses: weak atmospheric blocking, anomalously negative North Sea surface temperatures (SST) and a deficit of incoming longwave radiation. Downward shortwave radiation is anomalously positive during and before CW which weakens arguments for direct negative solar forcing on winter extreme cold events in Europe. Climate models share in their great majority (> 80%) these patterns (for dynamical and radiative-related variables, spatial correlation r > 0.7) and can correctly simulate the sign of climate variables anomalies during and at least 7 days before CW. We find that excess of European snow cover and snow depth are unlikely to cause occurrences of CW, but the advection of cold air masses (North-East) emerges as a potential precursor signal of a majority of CW. In a context of climate change, fossil fuels scarcity and increased uncertainty due to geopolitical events, it is crucial to study the sensitivity of the energy, health and agriculture sectors to compound extreme weather, including cold waves.

One of the pieces of equipment used in the combustion-based process of creating ceramic tiles is a kiln production machine or oven. To make sure the combustion process runs smoothly, 39 parameters on the kiln engine are closely monitored, and it is manually determined if the value of these parameters exceeds the minimum or maximum limits based on the data produced by the image on the kiln engine. Losses for the company are caused by human error, carelessness, and other characteristics while these metrics are being monitored. In order to minimize losses, a monitoring system that can automate the settings of ceramic tile combustion engines is required. The initial analysis's findings are obtained using a LAN (Local Area Network) sensor recorder, which displays parameter data that can be accessed as an image rather than as digital alphanumeric data that cannot be processed directly and must instead be converted into alphanumeric data as a data source. The image is turned into alphanumeric data using the template matching technique introduced with optical character recognition (OCR), and the generated data is then compared with the given minimum or maximum parameter values. This gadget will issue an early warning message if the conversion result is more than the minimum or maximum limit. The system prototype used in this study for converting image data into alphanumeric data achieved an accuracy of 100.00% and was able to send early warning notifications in accordance with parameters that were above the established minimum or maximum limits.

The landslide early warning system (LEWS) relies on various models for data processing, prediction, forecasting, and warning level discrimination. The potential different programming implementations and dependencies of these models complicate the deployment and integration of LEWS. Moreover, the coupling between LEWS and models makes it hard to modify or replace models rapidly and dynamically according to changes in business requirements (such as updating the early warning business process, adjusting the model parameters, etc.). This paper proposes a framework for dynamic management and integration of models in LEWS by using WebAPIs and Docker to standardize model interfaces and facilitate model deployment, using Kubernetes and Istio to enable microservice architecture, dynamic scaling, and high availability of models, and using a model repository management system to manage and orchestrate model-related information and application processes. The results of applying this framework to a real LEWS demonstrate that our approach can support efficient deployment, management, and integration of models within the system. Furthermore, it provides a rapid and feasible implementation method for upgrading, expanding, and maintaining LEWS in response to changes in business requirements.

Drought is one of the most hazardous natural disasters in terms of the number of people directly affected. An important characteristic of drought is the prolonged absence of rainfall relative to the long-term average. The intrinsic persistence of drought conditions continuing from one month to the next can be utilized for drought monitoring and early warning systems. This study sought to better understand drought probabilities and baselines for two agriculturally important rainfall regions in the Western Cape, South Africa – one with a distinct rainfall season and one which receives year-round rainfall. The drought indices, Standardised Precipitation and Evapotranspiration Index (SPEI) and Standardised Precipitation Index (SPI), were assessed to obtain predictive information and establish a set of baseline probabilities for drought. Two sets of synthetic time-series data were used (one where seasonality was retained and one where seasonality was removed), along with observed data of monthly rainfall and minimum and maximum temperature. Based on the inherent persistence characteristics, autocorrelation was used to obtain a probability density function of the future state of the various SPI start and lead times. Optimal persistence was also established. The validity of the methodology was then examined by application to the recent Cape Town drought (2015–2018). Results showed potential for this methodology to be applied in drought early warning systems and decision support tools for the province.&nbsp

The ecological security of cultivated land critically depends on maintaining the quality of the land under cultivation. For the security of the nation’s grain supply, the evaluation and early warning of cultivated land quality (CLQ) are essential. However, previous studies on the assessment of the ecological safety of CLQ only rigidly standardized the assessment indicators and failed to investigate the positive and negative trends and spatiotemporal driving factors of the indicators. The main objective of this study was to develop a drive–pressure–state–response (DPSR) model to identify the hierarchical structure of indicators, using an improved matter–element model to assess the CLQ in the black soil region of northeastern China from 2001 to 2020. A panel data model was employed to explore the crucial drivers of CLQ warnings. The findings reveal that socioeconomic development has a potential impact on the improvement of CLQ. CLQ is generally in a secure state, with 69.71% of cities with no warnings and only 3.46% and 0.13% of cities under serious and extreme warnings, respectively. Compared with 2001, the CLQ in 2020 effectively improved by socioeconomic development and the conservation and reasonable utilization of arable land. According to the early warning results, the cultivated land in the northern regions was of higher quality than that in the southern regions. Moreover, the CLQ was significantly positively correlated with the agricultural GDP growth rate, grain yield per unit of cultivated land area, annual precipitation, and the habitat quality index, and was significantly negatively correlated with land carrying capacity. The findings of this study can provide a scientific and targeted basis for black soil conservation and utilization.

Affected by an insufficient understanding of oxidation characteristics and the CO production mechanism in low-rank coal goaf, the safety management of coal spontaneous combustion (CSC) faces severe challenges. In this study, in-depth research was conducted using ambient temperature oxidation (ATO), temperature-programmed, in situ FTIR experiments and DFT simulation after analyzing the oxidation scenario characteristics of low-metamorphic coal goaf. The results show the oxidation of low-rank coal goaf includes two processes of ATO in the dissipation zone and CSC in the oxidation zone. The CO production of ATO increases with a decrease in coal metamorphic degree, and the risk of CSC is influenced by ATO, with an inhibitory effect before the critical temperature, and an encouraging effect after that. The CO production mechanism of low metamorphic coal goaf from ATO to CSC is established. Before the critical temperature, CO mainly comes from the primary aldehyde functional groups, then peroxide-free radicals participate in the reaction, resulting in the production of a large number of secondary aldehyde functional groups, which leads to the sudden change in CO output. The problem of the abnormal, continuous exceedance of CO in the tailgate corner can be solved by developing an ATO inhibitor, which plays an inhibiting role at ambient temperature and decomposes in the event of CSC.

Establishing an effective real-time monitoring and early warning system for glacier lake outburst floods (GLOFs) requires a full understanding of their occurrence mechanism. However, the harsh conditions and hard-to-reach locations of these glacial lakes limit detailed fieldwork, making satellite imagery a critical tool for monitoring. Lake Mercbacher, an ice-dammed lake in the central Tian Shan mountain range, poses a significant threat downstream due to its relatively high frequency of outbursts. In this study, we first monitored the daily changes in the lake area before the 2022 Lake Mercbacher outburst. Additionally, based on historical satellite images from 2014 to 2021, we calculated the maximum lake area (MLA) and its changes before the outburst. Furthermore, we extracted the proportion of floating ice and water area during the period. The results show that the lake area of Lake Mercbacher would first increase at a relatively low speed (0.01 km2/day) for about one month, followed by a relatively high-speed increase (0.04 km2/day) until reaching the maximum, which would last for about twenty days. Then, the lake area would decrease slowly until the outburst, which would last five days and is significant for early warning. Moreover, the floating ice and water proportion provides more information about the outburst signals. In 2022, we found that the floating ice area increased rapidly during the early warning stage, especially one day before the outburst, accounting for about 50% of the total lake area. Historical evidence indicates that the MLA shows a decreasing trend, and combining it with the outburst date and climate data, we found that the outburst date shows an obvious advance trend (6 days per decade) since 1902, caused by climate warming. Earlier melting results in an earlier outburst. This study provides essential references for monitoring Lake Mercbacher GLOFs and building an effective early warning system.

Flash flood early warning requires accurate rainfall forecasts with a high spatial and temporal resolution. As the first few hours ahead are already not sufficiently well captured by the rainfall forecasts of numerical weather prediction (NWP) models, radar rainfall nowcasting can provide an alternative. Because this observation-based method quickly loses skill after the first 2 hr of the forecast, it needs to be combined with NWP forecasts to extend the skillful lead time of short-term rainfall forecasts, which should increase decision-making times. We implemented an adaptive scale-dependent ensemble blending method in the open-source pysteps library, based on the Short-Term Ensemble Prediction System scheme. In this implementation, the extrapolation (ensemble) nowcast, (ensemble) NWP, and noise components are combined with skill-dependent weights that vary per spatial scale level. To constrain the (dis)appearance of rain in the ensemble members to regions around the rainy areas, we have developed a Lagrangian blended probability matching scheme and incremental masking strategy. We describe the implementation details and evaluate the method using three heavy and extreme (July 2021) rainfall events in four Belgian and Dutch catchments. We benchmark the results of the 48-member blended forecasts against the Belgian NWP forecast, a 48-member nowcast, and a simple 48-member linear blending approach. Both on the radar domain and catchment scale, the introduced blending approach predominantly performs similarly or better than only nowcasting (in terms of event-averaged continuous ranked probability score and critical success index values) and adds value compared with NWP for the first hours of the forecast, although the difference, particularly with the linear blending method, reduces when we focus on catchment-average cumulative rainfall sums instead of instantaneous rainfall rates. By properly combining observations and NWP forecasts, blending methods such as these are a crucial component of seamless prediction systems.

Lava flows are one of the hazards involved in a volcanic eruption, and although they rarely cause the loss of human life, they are highly destructive in terms of damage to property and economic activity. Therefore, the management of volcanic disasters requires fast and accurate information on the behaviour and evolution of the flows, mainly related to their extension, displacement, and trajectory. This was the case during the disaster linked to the volcanic event that occurred on the island of La Palma in the Cumbre Vieja area at the end of 2021, which lasted eighty-five days. This paper describes part of the work performed by many different groups to provide predictive information aimed at feeding the early warning system set up during the disaster. This case shows the experience in the use of a probabilistic simulation algorithm implemented in the Q-LavHA plugin for the QGIS software, which is both easily accessible and applicable, to analyze its features in detail, as well as its predictive capacity. The results show that the model can efficiently and quickly satisfy the demand for this type of information, and its high similarity value is also validated by the Kappa index.

Recent outbreaks of Zika, chikungunya and dengue fever worldwide highlight the importance of understanding the spread of pathogen-carrying mosquitoes. Citizen or community science initiatives can contribute towards early warning rapid response systems and complement at spatial and temporal scales traditional mosquito surveillance which often relies on limited resources. Herein, we provide information regarding a first attempt of involving citizens in mosquito monitoring on the island of Cyprus. During the 2018-2019 season, an island wide network of citizen scientists was formed as part of The Three Mosquiteers initiative and the Mosquitoes Without Borders project that aims at educating and engaging citizens of all ages regarding mosquitoes. The new network was named Aedes Watch Out and it aims to provide information on island wide mosquito presence overcoming jurisdictional barriers and cost constraints. Aedes Watch Out aims to serve as an early warning information source about the non-native Asian tiger mosquito (Aedes albopictus) arrival to Cyprus, complementing current capacities and any work that the authorities undertake. The initial outcomes of the project demonstrate that even a small number of dedicated citizen scientists can generate important information and greatly facilitate, COST Action; [CA 17122], Many thanks to our dedicated volunteer citizen-scientists of the Aedes watch out network: Nilufer Rahmioglu Ramiz, Semahat Aytug, Olgun Yetismis, Ahmet Essizer, Hasan Ergueler, Sinan Ergueler, Cemal Guerkan, Nurcan Guenduez, Olkan Ergueler, Cansu Karsili and Hasan Beyatli. We would like to thank Professor George Christophides, Imperial College, UK for his comments on the manuscript. We thank COST Action CA 17122 Alien CSI for funding K.Y. during her short-term scientific mission to Cyprus. Also, we would like to thank two anonymous reviewers for their constructive comments on the manuscript

We modified an automated method capable of quantifying behaviors which we then applied to the changes associated with the post-weaning transition in pigs. The method is data-driven and depends solely on video-captured image data without relying on sensors or additional pig markings. It was applied to video images generated from an experiment during which post-weaned piglets were subjected to treatments either containing or not containing in-feed antimicrobials (ZnO or antibiotics). These treatments were expected to affect piglet performance and health in the short-term by minimizing the risk from post-weaning enteric disorders, such as diarrhea. The method quantified total group feeding and drinking behaviors as well as posture (i.e., standing and non-standing) during the first week post-weaning, when the risk of post-weaning diarrhea is at its highest, by learning from the variations within each behavior using data manually annotated by a behavioral scientist. Automatically quantified changes in behavior were consistent with the effects of the absence of antimicrobials on pig performance and health, and manifested as reduced feed efficiency and looser feces. In these piglets both drinking and standing behaviors were increased during the first 6 days post-weaning. The correlation between fecal consistency and drinking behavior 6 days post weaning was relatively high, suggesting that these behaviors may have a diagnostic value. The presence or absence of in-feed antimicrobials had no effect on feeding behavior, which, however, increased over time. The approach developed here is capable of automatically monitoring several different behaviors of a group of pigs at the same time, and potentially this may be where its value as a diagnostic tool may lie.

Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2. Wastewater surveillance and quantitative analysis of SARS-CoV-2 RNA are increasingly used to monitor the spread of COVID-19 in the community. We studied the feasibility of applying the surveillance data for early detection of local outbreaks. A Monte Carlo simulation model was constructed, applying data on reported variation in RNA gene copy concentration in faeces and faecal masses shed. It showed that, even with a constant number of SARS-CoV-2 RNA shedders, the variation in concentrations found in wastewater samples will be large, and that it will be challenging to translate viral concentrations into incidence estimates, especially when the number of shedders is low. Potential signals for early detection of hypothetical outbreaks were analysed for their performance in terms of sensitivity and specificity of the signals. The results suggest that a sudden increase in incidence is not easily identified on the basis of wastewater surveillance data, especially in small sampling areas and in low-incidence situations. However, with a high number of shedders and when combining data from multiple consecutive tests, the performance of wastewater sampling is expected to improve considerably. The developed modelling approach can increase our understanding of the results from wastewater surveillance of SARS-CoV-2.

Despite repeated attempts to reform the Romanian health system, as an important part of the national security system, after 30 years of reforms, except for large university centers, the Romanian health system does not have enough family doctors or specialists, not enough local medical services and units, no available information, early warning and prevention programs at the level of the authorities in the field. As a reliable solution to the real situation of the health system in Romania, this paper comes up with a technical and medical solution in the event of a pandemics, case study for COVID 19 pandemic, which can substantially contribute to national security system. Building an integrated open source portal with access to geoformation and telemedicine technologies for public health management is intended to act as an early warning system by communicating and exchanging data accurately, efficiently, securely and consistently with various computer systems, software applications and networks, ensuring the operational work processes of the institutions involved (Ministry of Health, Ministry of Internal Affairs, Ministry of National Defense, Public Health Directorates, Hospitals, Epidemiologists, family physicians, specialists physicians, pharmacies and last but not least patients), while maintaining data confidentiality.

The prediction of an economic crisis is the most critical area of study for all actors related to the economy. Crises, a sign of uncertainty, do not have a specific timeline, but they can be predicted by analyzing particular indications. Studies on predicting the crisis are commonly related to macroeconomic variables. This study addresses an alternative approach to predicting crisis periods, which involves analyzing changes in the trading volumes of companies listed on Borsa Istanbul (BIST) instead of relying solely on macroeconomic variables. The study aims to examine the transaction volume data from 169 firms that regularly traded in BIST between 2000 and 2018. The predictability of economic crises in Türkiye has been investigated by applying binary logistic regression analysis, a methodology commonly employed in the literature as a signal approach for detecting economic crises. Some statistically significant parameters were discovered positive, and some were found negative in estimated logistic regression models, and the companies to which the statistically insignificant parameters belonged were evaluated as companies that did not give a signal for the economic crisis model. The findings suggest that changes in the trading volume of many companies, not just a few ones, can be a valuable predictor of crises.

The nature and character of conflicts in Africa have remained intractable. This is as a result of an unending agitations and fights among parties to the conflict, struggling to win at all cost. In Nigeria, the extent of conflict, especially communal has taken a dangerous shape as losses are counted unceasingly. Interestingly, the degree of eruptions and spread of violent communal conflicts, and claim of ownership of land by communal groupings in Ebonyi State have created a state of perceptible fear among Ebonyians with the attendant loss of lives and property and displacement of thousands of people. Violent communal conflicts have caused huge disarray in the state’s internal security situation and reached critical proportions, yet no systematic effort has been made to know the nexus between management of land in the state and persistence of violent communal conflict in the state. Thus, it was on this premise that the study examined how undocumented method of customary management of land account for the persistent violent communal conflicts in Ebonyi state, and how persistent occurrence of violent communal land contestation lead to forced population displacement in Ebonyi state. Methodologically, the study utilized documentary method, and data were analyzed using content analysis. The theoretical framework of analysis for the study was anchored on the theory of Eco-violence theory. The study found that predominance of communal land regimes which makes for management of land undocumented, and subsistence agricultural system where greater percentage of the people depended on were responsible for the continuous violent communal conflict in the state. The study recommended that Ebonyi State government should develop a land law in line with the 1978 Land Use Act that will make land available to peasant farmers in different communities, and also to develop a proactive mechanism that will detect early warning signals where violent communal conflict may likely occur in the State and set up a special panel to hear all land issues in different communities.

Rockburst is a major disaster in deep mining, restricting the safety and the production efficiency of the Laohutai Coal Mine in Fushun, Liaoning Province. To predict and prevent coalmine rockbursts, a comprehensive method based on multi-instrument monitoring is proposed by using a YDD16 acoustic-electromagnetic monitor and microseismic monitoring system, including microseismic (MS) monitoring, electromagnetic radiation (EMR) monitoring, and acoustic emission (AE) monitoring. Field investigation shows that MS, AE, and EMR signals have abnormal precursors before rockbursts in a new working face. Based on the fluctuation theory and D-S evidence theory, the multi-index geophysical monitoring and early warning technology for rockburst disasters in the Laohutai Coal Mine are established. The method has been applied to the prediction of rockbursts in the Laohutai Coal Mine. The application shows that the acoustic-electromagnetic synchronous monitoring and early warning technology can accurately identify the potential rockburst risk and trigger an early warning, which is more reliable than a single method. The case study of the Laohutai rockburst shows that the joint early warning method of multi-instrument comprehensive monitoring can predict the possibility of rockbursts.

Armed conflict increases people's vulnerability to climate extremes. Since many of these climate extremes are predictable beforehand, Early Warning Early Action (EWEA) can help protect people's lives, livelihoods, and wellbeing. While such EWEA systems exist in several countries, there is limited scientific knowledge about EWEA in conflict-affected countries where communities experience the compounding effects of both disaster and conflict risks. By doing a retrospective analysis of the most severe disaster events and historical forecast information, this paper examines whether global forecast models predicted historical floods in conflict-affected regions and whether forecast information was communicated for droughts. It analyses the historical forecast availability and communication for the most severe disaster events in 20 countries affected by protracted conflict over the last 20 years. These 72 disaster events were reported to affect approximately 150 million individuals and more than 150,000 were reported dead. The results show that heavy rainfall was predicted in advance for 48 out of 50 flood events, with lead times of more than three days and probabilities between 10 and 90%. In addition, in 16 out of 20 major drought events a low rainfall forecast was communicated in advance of the disaster declaration. We conclude that forecasts exist and could be used to provide early warnings in conflict-affected areas. Further research is needed as to what extent forecast warnings did lead to early action to protect populations affected by conflict.

Resumen Esta investigación se enfoca en el estudio de las precipitaciones detonantes de procesos de remoción en masa (PRM), con el fin de proponer umbrales críticos que puedan ser empleados con fines de alerta temprana en las zonas con mayor recurrencia del Estado de México. Se generó un inventario de PRM para, una vez ubicados los eventos y seleccionados los pluviómetros, proceder a registrar los parámetros de lluvia a graficar (lluvia diaria y lluvia acumulada de 3 y 5 días, respectivamente). El inventario lo integraron un total de 140 PRM, concentrados en tres zonas principale del estado. Se propone un umbral mínimo uno máximo y uno intermedio, basados en los valores de la lluvia diaria y la acumulada de 3 días. Estos indican un rango de lluvia diaria de entre 52 y 105 milímetros capaces de detonar algún PRM, en condiciones de lluvia acumulada ausente. Por otro lado, se observa una tendencia en la cual, mientras mayores sean los valores de lluvia acumulada, menor será la cantidad de lluvia diaria necesaria para detonar un evento de este tipo. Abstract This research focuses on the study of precipitation triggering mass movement processes (MMP) to propose critical thresholds for early warning purposes in areas with the highest recurrence in the State of Mexico. An inventory of MMP was generated so that, once the events were located and the rain gauges were selected, the rain parameters to be plotted were recorded (daily and three-day accumulated rainfall, respectively). The inventory resulted in spotting 140 landslides, concentrated in three main areas of the state. A minimum threshold, a maximum threshold, and an intermediate threshold are proposed, based on daily and three-day accumulated rainfall values. These indicate a daily rainfall range between 52 and 105 millimeters capable of triggering some MMP in conditions of absent accumulated rain. On the other hand, a trend is observed in which the higher the accumulated rainfall values are, the lower the amount of daily rainfall to trigger an event of this type will be needed.

This study measured the impacts of failure in Crisis and Emergency Risk Communication (CERC) during the outbreak of a contagious Corona viral disease. The study measured the impacts by the number of individuals and hospitals exposed to the virus. The 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea was used to investigate the consequences of CERC failure, where the names of hospitals exposed to MERS‐CoV were withheld from the public during the early stage of virus diffusion. Empirical data analyses and simulated model tests were conducted. The findings of analyses and tests show that an early announcement of the hospital names and publicizing the necessary preventive measures could have reduced the rate of infection by approximately 85% and the number of contaminated healthcare facilities by 39% at maximum. This level of reduction is comparable to that of vaccination and of social distancing.

River floods are distinct because not all of them are destructive and typically affect the people who are living in riverine areas. Therefore, people often refuse to evacuate even when they face imminent danger. River floods are a recurrent phenomenon in Bangladesh. This research aims to analyse the flood evacuation behaviour of riverine people in Bangladesh. A total of 377 households were selected for the questionnaire survey and were interviewed from April 2019 to May 2019. Bivariate and multivariate statistics were employed to analyse riverine people's evacuation behaviour based on their socio-demographic and economic characteristics. This study found that although 82% of the households had received flood warning messages, only 40% had evacuated. Results from multivariate analysis suggested that the age of household heads, their education, whether they are disabled/chronically ill members, their income, the height of floodwater inside the house, and the type of warning messages they receive appear to be key determinants that influenced their decisions regarding evacuation. Elderly household heads had a lower likelihood to evacuate. The results showed a negative association between early warning messages and evacuation. Household with disabled/chronically ill member(s) was associated with a higher likelihood of evacuation. Similarly, higher water depth in the home was associated with evacuation. These findings will be helpful for policymakers to enhance awareness of riverine households.

A rip current is a strong, localized current of water which moves along and away from the shore. Recent studies have suggested that drownings due to rip currents are still a major threat to beach safety. Identification of rip currents is important for lifeguards when making decisions on where to designate patrolled areas. The public also require information while deciding where to swim when lifeguards are not on patrol. In the present study we present an artificial intelligence (AI) algorithm that both identifies whether a rip current exists in images/video, and also localizes where that rip current occurs. While there have been some significant advances in AI for rip current detection and localization, there is a lack of research ensuring that an AI algorithm can generalize well to a diverse range of coastal environments and marine conditions. The present study made use of an interpretable AI method, gradient-weighted class-activation maps (Grad-CAM), which is a novel approach for amorphous rip current detection. The training data/images were diverse and encompass rip currents in a wide variety of environmental settings, ensuring model generalization. An open-access aerial catalogue of rip currents were used for model training. Here, the aerial imagery was also augmented by applying a wide variety of randomized image transformations (e.g., perspective, rotational transforms, and additive noise), which dramatically improves model performance through generalization. To account for diverse environmental settings, a synthetically generated training set, containing fog, shadows, and rain, was also added to the rip current images, thus increased the training dataset approximately 10-fold. Interpretable AI has dramatically improved the accuracy of unbounded rip current detection, which can correctly classify and localize rip currents about 89% of the time when validated on independent videos from surf-cameras at oblique angles. The novelty also lies in the ability to capture some shape characteristics of the amorphous rip current structure without the need of a predefined bounding box, therefore enabling the use of remote technology like drones. A comparison with well-established coastal image processing techniques is also presented via a short discussion and easy reference table. The strengths and weaknesses of both methods are highlighted and discussed.

The rainfall-induced landslide early warning model (LEWM) is an important means to mitigate property loss and casualties, but the conventional discriminant matrix-based LEWM (DLEWM) leaves room for subjectivity and limits warning accuracy. Additionally, it is important to employ appropriate indicators to evaluate warning model performance. In this study, a new method for calculating the spatiotemporal probability of rainfall-induced landslides based on a Bayesian approach is proposed, and a probabilistic-based LEWM (PLEWM) at the regional scale is developed. The method involves four steps: landslide spatial probability modeling, landslide temporal probability modeling, coupling of spatial and temporal probability models, and the conversion method from the spatiotemporal probability index to warning levels. Each step follows the law of probability and is tested with real data. At the same time, we propose the idea of using economic indicators to evaluate the performance of the multilevel LEWM and reflect its significant and unique aspects. The proposed PLEWM and the conventional DLEWM are used to conduct simulate warnings for the study area day-by-day in the rainy season (July-September) from 2016 to 2020. The results show that the areas of the 2nd-, 3rd-, and 4th-level warning zones issued by the PLEWM account for 60.23%, 45.99%, and 43.98% of those of the DLEWM, respectively. The investment in issuing warning information and the losses caused by landslides account for 54.54% and 59.06% of those of the DLEWM, respectively. Moreover, under extreme rainfall conditions, the correct warning rate of the PLEWM is much higher than that of the DLEWM.

The colour preference of the plum psyllid,Cacopsylla pruni(Hemiptera, Psyllidae), is yet poorly studied. This species is the only known vector of the ‘CandidatusPhytoplasma prunorum’, the agent of European stone fruit yellows (ESFY), a devastating disease of several cultivatedPrunusspecies in Europe. As ESFY is still uncurable, vector control, thus vector monitoring, is pivotal to protect these trees.Cacopsylla pruniis a univoltine, host-shelter-alternating species; overwintered adults migrate from conifer to wild or cultivatedPrunusspecies (family Rosaceae) in late winter-early spring. To select the most effective colour indicating the arrivals of the immigrants, yellow, fluorescent yellow, white, red and transparent sticky traps were deployed in an apricot orchard in Hungary. The two most abundant species in sticky traps wereC. pruniandC. melanoneura. Catches of white traps were significantly biased towardsC. prunias compared toC. melanoneuraspecimens. Moreover, white sticky traps were better at catching plum psyllids than the other colours. Attraction to white was strongest when immigrants from shelter plants kept arriving in the orchard, coinciding with the blooming principal phenophase of apricot trees. When the host flowering growth stage was over, catches ofC. pruniin white traps declined sharply to the level of yellow traps that was highest during this post-blooming period. We recommended white sticky traps for promptly monitoringC. pruniin apricot orchards because it is more potent and more selective than yellow ones during the critically important early flowering interval.

Pourquoi la Terre tremble ? Cette question, que se posent les hommes – probablement – depuis toujours, a trouvé, à la fin des années 1960, une réponse partielle avec la théorie de la tectonique des plaques : des plaques rigides, entraînées par un manteau asthénosphérique en mouvement, glissent les unes par rapport aux autres. Leur mouvement relatif est accommodé lors d’évènements rares mais violents : les séismes. Si ce cadre conceptuel simple a permis de comprendre l’origine des tremblements de terre, il ne permet pas de répondre aux deux questions fondamentales qui permettraient de limiter le coût humain de ces catastrophes : Où et Quand se produisent les grands séismes ? Ce mémoire synthétise mes travaux de recherche réalisés depuis la soutenance de mon doctorat qui, par divers angles, ont tenté de répondre à ces questions.

In recent years, local governments have boosted their local economies by raising large amounts of debt. Even though the state further strictly controls local government debt, the hidden debt formed by the local government borrowing in disguised form can infect systemic financial risks, creating an urgent need to carry out risk warning based on local government hidden debt. The paper uses the macro indicators of local government implicit debt risk at the prefecture-level city level, and introduces the micro indicators of PPP projects, financing platform bank debt, and urban investment debt to establish a BP neural network model. We not only study the contagion effect of local government hidden debt on systemic financial risks, but also predict the systemic financial risks in 2019 and construct an early warning risk system based on the prefecture-level city data from 2015 to 2018. In addition, the early warning effect of local government implicit debt on systemic financial risk under different stress scenarios is investigated. The study found that the implicit debt risk of local governments, the scale of financing platform bank debt, the scale of PPP, and the scale of urban investment bonds have a significant impact on systemic financial risks. The neural network model constructed by introducing these four variables at the same time can better predict the level of systemic financial risk. The model can also accurately predict the changes in systemic financial risks under the stress test of the increase in hidden debt of different local governments, and has a good early warning effect.

En el presente estudio se tratará de abordar de qué forma los mecanismos de alerta temprana podrían ser una fuente de información útil para el desarrollo del análisis del principio de empresa en funcionamiento por parte del auditor. This study will deal with how early warning mechanisms could be a useful source of information for the development of the auditor’s engagement Universidad de Sevilla. Master en Auditoría y Contabilidad Superior

The accuracy of the wave signal is key to studying physical information inside the soil using bender-element-type piezoelectric transducers. There is too much noise during the elastic wave signal collected by bender elements, which is caused by factors such as fluid current and infiltration. At present, the mainstream method is the superposition method, which superposes multiple tested waveform data to obtain a clear waveform. However, the superposition method is limited by the number of signals during the collection, and the denoised waveform still contains high-frequency noise. A combination method combining superposition and the wavelet threshold is proposed in this work to improve the accuracy of the elastic waveform signal. Three different signal denoising simulation tests and one model box test are conducted to verify the method’s feasibility from two aspects. The results show that the combined method can effectively remove high-frequency noise and display clear waveforms based on overcoming the number of signals. This work provides a new means of signal denoising in the case of studying soil properties by bender-element-type piezoelectric transducers.

SummaryBackgroundECDC performs epidemic intelligence activities to systematically collate information from a variety of sources, including Twitter, to rapidly detect public health events. The lack of a freely available, customisable and automated early warning tool using Twitter data, prompted ECDC to develop epitweetr.The specific objectives are to assess the performance of the geolocation and signal detection algorithms used by epitweetr and to assess the performance of epitweetr in comparison with the manual monitoring of Twitter for early detection of public health threats.MethodsEpitweetr collects, geolocates and aggregates tweets to generate signals and email alerts. Firstly, we evaluated manually the tweet geolocation characteristics of 1,200 tweets, and assessed its accuracy in extracting the correct location and its performance in detecting tweets with available information on the tweet geolocation. Secondly, we evaluated signals generated by epitweetr between 19 October and 30 November 2020 and we calculated the positive predictive value (PPV). Then, we evaluated the sensitivity, specificity and timeliness of epitweetr in comparison with Twitter manual monitoring.FindingsThe epitweetr geolocation algorithm had an accuracy of 30.1% and 25.9% at national and subnational levels, respectively. General and specific PPV of the signal detection algorithm was 3.0% and 74.6%, respectively. Epitweetr and/or manual monitoring detected 570 signals and 454 events. Epitweetr had a sensitivity of 78.6% [75.2% - 82.0%] and PPV of 74.6% [70.5% - 78.6%]; and the manual monitoring had a sensitivity of 47.9% [43.8% - 52.0%] and PPV of 97.9% [95.8% - 99.9%]. The median validation time difference between sixteen common events detected by epitweetr and manual monitoring was −48.6 hours [(−102.8) - (−23.7) hours].InterpretationEpitweetr has shown to have sufficient performance as an early warning tool for public health threats using Twitter data. Having developed epitweetr as a free, open-source tool with several configurable settings and a strong automated component, it is expected to increase its usability and usefulness to public health experts.FundingNot applicableResearch in contextEvidence before this studyPrevious reviews have shown how social media, including Twitter, have been used for public health purposes. Most recent studies, in relation to the COVID-19 pandemic, have shown the added value of early warning tools based on Twitter and other social media platforms. They also noted the lack of an open-source tool for real-time monitoring and surveillance.Added value of this studyEpitweetr is a free, open-source and R-based early warning tool for automatic Twitter data monitoring that will support public health experts in rapidly detecting public health threats. The evaluation of epitweetr presented in this study shows the strengths of the tool which include having good performance, high degree of automation, being a near-real-time tool and being publicly available with various customisable settings. Furthermore, it shows which are the areas of improvement for the next versions of epitweetr.Implications of all the available evidenceThis tool can be further developed to include more automation and machine learning components to increase usability and information processing time by users.

International audience; Passive Radio-Frequency Identification (RFID) has been used to monitor landslide displacement since approximately 5 years. This method allows soil displacement estimation at a high spatio-temporal resolution, and at a relatively low cost. In perspective of the previous years, this paper proposes to summarize the various challenges encountered with the longterm outdoor RFID localization method, and presents solutions that were implemented to overcome these challenges. Finally, displacement results from three monitored sites are shown in order to validate the implemented solutions.

[Problem] The risks of hosting large-scale sports events are very difficult to evaluate and often directly affected by natural environment risks, events management risks, and social environment risks. Before hosting the events, accurately assessing these risks can effectively minimize the occurrence of risks and reduce the subsequent losses. [Aim] In this article, we advocate the use of a back propagation neural network (BPNN) model for risk evaluation and early warning of large-scale sports events. [Methods] We first use expert surveys to assess the risks of 28 large-scale sports events using 12 indicators associated with climate conditions, events management, and natural disasters. We then apply the BPNN model to evaluate the risks of 28 large-scale sports events with sufficient samples by adding white noise with mean zero and small variance to the small actual samples. We provide a general rule to establish a BPNN model with insufficient and small samples. [Results] Our research results show that the recognition accuracy of the established BPNN model is 86.7% for the 15 simulation samples and 100% for the 28 actual samples. Based on this BPNN model, we determined and ranked the risk level of the events and the importance of each indicator. Thus, sample S8 had the highest risk and the second highest was sample S14, and indicator nine was the most important and indicator one the least important. [Conclusions] We can apply the established BPNN model to conveniently evaluate the risk of hosting a large-scale sports event. By analyzing the nonlinear relationship between each indicator and the risk of the sports event, and applying the established BPNN model, we can propose more targeted and effective measures and suggestions for eliminating and decreasing the risks of hosting a large-scale sports event, and ensure large-scale sports events can be successfully hosted.

形態: カラー図版あり, Physical characteristics: Original contains color illustrations, Accepted: 2021-08-07, 資料番号: PA2210062000

Real-time unstable slope monitoring is essential for recognition of landslide occurrence, as well as for early warning to reduce landslide-induced damages. This study investigated an unstable slope monitoring system that consists of tilt sensors aiming to establish an advanced time prediction model (TPM) for landslide early warning. The monitoring process utilized additional support devices (e.g. pipe strain gauges, water level gauges and a rain gauge) installed on a natural cut slope site. The tilt sensors could detect movements (in tilt angle) within the slope generated by heavy rains. Analysis of the recorded data revealed that the rate of movement, or tilt, was influenced by groundwater table fluctuations and antecedent rainfalls. A relationship between the tilt rate and the horizontal displacement calculated from pipe strain value has been established. Subsequently, a new classification of slope movement was proposed according to the tilt rates obtained from the deformation process of the slope. Considering the movement characteristics, two warning levels were identified such as warning and evacuation. Further, an advanced TPM was proposed in relation to realtime slope surface tilt rates. The TPM could provide efficient results at the continuous acceleration phase of the landslide occurrence. Therefore, we suggest this technique can be applied for monitoring and early warning of rainfall-induced shallow landslides.

Over time, even in the modern world, epidemics and pandemics have caused millions of deaths. Moreover, Europe and, implicitly Romania, are facing an aging population, which will be a crucial challenge in the fight against viruses for the coming years. Therefore, in the case of pandemics, as is the case Covid-19 pandemic, launching an artificial intelligence-assisted program, which could help pre-sort patients arriving in hospitals, has become crucial. This pilot study is a first phase of the UEFISCDI Project, T1 - Approaches to public health management in the context of the COVID-19 pandemic, in which "Carol I" National University is involved. The pilot study has the purpose and expected outcome to develop and implement an artificial intelligence-assisted early warning system able to make warnings and projections on the diagnostic and treatment capacity of COVID-19 cases in hospitals from different areas.

Rockburst is a phenomenon where the elastic deformation may be emitted suddenly because of rock fragmentation, ejection, launching, and even earthquake. This may result in casualties, failure or deformation of the support structure, and damage to field equipment. Therefore, early warning of rockburst is significant. In this paper, a dynamic early warning model of rockburst using microseismic multi-parameters based on Bayesian network is proposed. Taking the moment magnitude, seismic energy, source radius, apparent stress, and dynamic stress drop as Bayesian network parameters input. 114 sets of parameters required for Bayesian network structure learning are obtained by pre-processing the historical data of rockburst, and belief update is performed by the Junction Tree algorithm. Besides, the model passes self-validation, 6-fold cross-validation, ROC curve analysis and using new historical data to real-time early warning analysis. The results indicate that the proposed model has good precision and can effectively realize early warning of rockbursts. Furthermore, through the strength of the influence of parent–child nodes and sensitivity analysis, it can find that moment magnitude and seismic energy are the most influential parameters. They can as a significant reference for the early warning of rockburst.

When using the flipped classroom method, students are required to come to the lesson after having prepared the basic concepts. Thus, the effectiveness of the lecture depends on the students’ preparation. With the ongoing COVID-19 pandemic, it has become difficult to examine student preparations and to predict student course failures with limiting variables. Learning analytics can overcome this limitation. In this study, we aimed to develop a predictive model for at-risk students who are at risk of failing their final exam in an introductory anatomy course. In a five-week online flipped anatomy course, students’ weekly interaction metrics, quiz scores, and pretest scores were used to design a predictive model. We also compared the performances of different machine learning algorithms. According to the results, the Naïve Bayes algorithm showed the best performance for predicting student grades with an overall classification accuracy of 68% and with at-risk prediction accuracy of 71%. These results can be used as a traffic light project wherein the “at-risk” group will receive the red light, and thus, will require more effort to engage with the content and they might need to solve the quiz tests after an individual study period.

Early warnings are an indispensable part of emergency management, which is a powerful way to eliminate or reduce the negative impacts caused by emergencies in advance. Early warning problems have been discussed from different perspectives and have obtained fruitful results. Information plays a critical role in all kinds of decision problems, with no exception for the early warning problem. There are various information types related to emergencies in real-world situations; however, existing early warning studies only considered a single information type, which might not describe the problem properly and comprehensively. To enrich existing early warning studies, a novel early warning method considering non-homogeneous information together with experts’ hesitation is proposed, in which numerical values, interval values, linguistic terms, and hesitant fuzzy linguistic terms are considered. To facilitate the computations with non-homogeneous information, a transformation process needs to be conducted. On such a basis, a fuzzy TOPSIS method based on alpha-level sets is employed to handle the transformed fuzzy information due to its superiority in obtaining information and its capacity to contain as much information as possible during the early warning process. Additionally, two different options are provided to analyze the status and tendency of early warning objects. Finally, an illustrative example about early warnings about landslides and a related comparison are conducted to demonstrate the novelty, superiority, and feasibility and validity of the proposed method.

A crucial component for the success of any early warning system for flood and extreme weather phenomena is understanding people’s perceptions and views of the warning processes and approaches. This paper aims to explore public perceptions on flood and extreme weather warnings as well as factors that influence these perceptions in Greece, a characteristic example of a country that has suffered several climate-related disasters in the recent past. To this end, a survey of 427 residents of the country was conducted between April 2021 and June 2021. The collected data were analyzed by using both descriptive and inductive statistics. The results showed that certain factors affect participants’ views on early warnings, including demographics, perceived knowledge on floods, flood risk perception, and perceived self-efficacy. The above factors present statistically significant correlations with the perceived reliability and effectiveness of warnings, as well the degree to which participants perceived the expected phenomena as a threat to their well-being or a signal to take preventive actions. These correlations are described in detail in the present study, together with certain exceptions that exist. The findings are a strong indication that public perception has the potential to impact early warning systems’ actual effectiveness, leading to certain practical implications for their improvement, particularly in multi-hazard, climate change-sensitive areas like the Mediterranean region.

Forecasts of precipitation type are of high priority, as they have a large influence on human safety, the economy and the environment. In recent decades, methods of statistical post-processing of numerical weather prediction (NWP) outputs were only applied beside the experience of the operational forecasters. In the last few years, numerical models developed significantly; thus, precipitation type has become a variable directly calculated in some models. In the European Centre for Medium-Range Weather Forecasts (ECMWF) integrated forecast system (IFS) model, a new method has been used since 2015 to predict the type of precipitation. In this study, we examine the forecast of the ECMWF IFS ensemble model concerning precipitation type through ensemble verification and a case study on a freezing-rain situation for the territory of Hungary. We put emphasis on the investigation of the usability of ensemble forecasts. We introduce the developed forms of visualization supporting the interpretation of ensemble precipitation-type forecasts.

Multi-dimensional, long-term time series displacement monitoring is crucial for generating early warnings for active landslides and for mitigating geohazards. The synthetic aperture radar (SAR) interferometry method has been widely applied to achieve small-gradient landslide displacement monitoring; however, measuring the landslide displacement with a steep gradient has posed certain challenges. In comparison, the SAR offset tracking method is a powerful tool for mapping large-gradient landslide displacement in both the slant-range and azimuth directions. Nevertheless, there are some limitations in the existing SAR offset tracking approaches: (i) the measurement accuracy is greatly reduced by the extreme topography relief in high mountain areas, (ii) a fixed matching window from expert experience is usually adopted in the calculation of cross-correlation, (iii) estimating the long-term displacements between the SAR images from cross-platforms and with longer spatiotemporal baselines is a challenging task, and (iv) it is difficult to calculate the three-dimensional (3D) landslide displacements using a single SAR dataset. Additionally, only a few studies have focused on how to realize early warning of landslide deformation using SAR measurements. To address these issues, this paper presents an improved cross-platform SAR offset tracking method, which can not only estimate high-precision landslide displacements in two and three dimensions but also calculate long-term time series displacements over a decade using cross-platform SAR offset tracking measurements. Initially, we optimize the traditional SAR offset tracking workflow to estimate high-precision ground displacements, in which three improvements are highlighted: (i) an “ortho-rectification” operation is applied to restrain the effect of topography relief, (ii) an “adaptive matching window” is adopted in the cross-correlation computation, and (iii) a new strategy is proposed to combine all the possible offset pairs and optimally design the displacement inversion network based on the “optimization theory” of geodetic inversion. Next, robust mathematical equations are built to estimate the two-dimensional (2D) and 3D long-term time series landslide displacements using cross-platform SAR observations. The M-estimator is introduced into the 2D displacement inversion equation to restrain the outliers, and the total least squares criterion is adopted to estimate the 3D displacements considering the random errors in both the design matrix and observations. We take the Laojingbian landslide, Wudongde Reservoir Area (China), as an example to demonstrate the proposed method using ALOS/PALSAR-1 and ALOS/PALSAR-2 images. The results reveal that the proposed method significantly outperforms traditional methods. We also retrieve the movement direction of each pixel of the Laojingbian landslide using the proposed method, thus allowing us to understand the fine-scale landslide kinematics. Finally, we capture and analyze the acceleration characteristics of the landslide, perform an early warning of hazard, and forecast the future displacement evolution based on the 3D displacement time series coupled with the physical models of the rocks. This work was supported by the National Natural Science Foundation of China (Grant Nos. 41874005 and 41929001), the Fundamental Research Funds for the Central Universities, CHD (Grant Nos. 300102269712 and 300102269303), and by the China Geological Survey Projects (Grant Nos. DD20190637 and DD20190647). This research was also supported by a Chinese Scholarship Council studentship awarded to Xiaojie Liu (Grant No. 202006560031).