Search Results (35)

Traditional academic monitoring practices rely on retrospective data analysis, generally identifying at-risk students too late to take meaningful action. To address this, this paper proposes a real-time, rule-based decision support system designed to increase student achievement by early detection of disengagement, meeting the growing demand for prompt academic intervention in online and blended learning contexts. The study uses the Open University Learning Analytics Dataset (OULAD), comprising over 32,000 students and millions of virtual learning environment (VLE) interaction records, to simulate weekly assessments of engagement through clickstream activity. Students were flagged as “at risk” if their participation dropped below defined thresholds, and these flags were associated with assessment performance and final course results. The system demonstrated 72% precision and 86% recall in identifying failing and withdrawn students as major alert contributors. This lightweight, replicable framework requires minimal computing power and can be integrated into existing LMS platforms. Its visual and statistical validation supports its role as a scalable, real-time early warning tool. The paper recommends integrating real-time engagement dashboards into institutional LMS and suggests future research explore hybrid models combining rule-based and machine learning approaches to personalize interventions across diverse learner profiles and educational contexts. Full article

In increasingly complex Engineer-to-Order (EtO) supply chains, accurately predicting supplier delivery delays is essential for ensuring operational resilience. This study proposes an intelligent Internet of Things (IoT)-enhanced probabilistic framework for early warning and dynamic prediction of supplier lead times in smart manufacturing contexts. Within this framework, three novel Early Warning Systems (EWS) are introduced: the Baseline Probabilistic Alert System (BPAS) based on fixed thresholds, the Smart IoT-Calibrated Alert System (SIoT-CAS) leveraging IoT-driven calibration, and the Adaptive IoT-Driven Risk Alert System (AID-RAS) featuring real-time threshold adaptation. Supplier lead times are modeled using statistical distributions and dynamically adjusted with IoT data to capture evolving disruptions. A comprehensive Monte Carlo simulation was conducted across varying levels of lead time uncertainty (σ), alert sensitivity ($P_{threshold}$), and delivery constraints ($L_{max}$), generating over 1000 synthetic scenarios per configuration. The results highlight distinct trade-offs between predictive accuracy, sensitivity, and robustness: BPAS minimizes false alarms in stable environments, SIoT-CAS improves forecasting precision through IoT calibration, and AID-RAS maximizes detection capability and resilience under high-risk conditions. Overall, the findings advance theoretical understanding of adaptive, data-driven risk modeling in EtO supply chains and provide practical guidance for selecting appropriate EWS mechanisms based on operational priorities. Furthermore, they offer actionable insights for integrating predictive EWS into MES (Manufacturing Execution System) and digital control tower platforms, thereby contributing to both academic research and industrial best practices. Full article

This paper investigates the utilization of artificial neural networks (ANNs) for the proactive identification of gearbox failures in wind turbines, boosting the use of operational SCADA data for predictive analysis. Avoiding gearbox failures, which can strongly impact the functioning of wind turbines, is crucial for ensuring high reliability and efficiency within wind farms. Early detection can be achieved though the development of a normal behavior model based on ANNs, which are trained with data from healthy conditions derived from selected SCADA variables that are closely associated with gearbox operations. The objective of this model is to forecast deviations in the gear bearing temperature, which serve as an early warning alert for potential failures. The research employs extensive SCADA data collected from January 2018 to February 2022 from a wind farm with multiple turbines. The study guarantees the robustness of the model through a thorough data cleaning process, normalization, and splitting into training, validation, and testing sets. The findings reveal that the model is able to effectively identify anomalies in gear bearing temperatures several months prior to failure, outperforming simple data processing methods, thereby offering a significant lead time for maintenance actions. This early detection capability is highlighted by a case study involving a gearbox failure in one of the turbines, where the proposed ANN model detected the issue months ahead of the actual failure. The present paper is an extended version of the work presented at the 5th International Conference of IFToMM ITALY 2024. Full article

The displacement of bridge towers is relatively large under strong wind action. Changes in tower displacement can reflect the usage status of the bridge towers. Therefore, it is necessary to conduct performance warning research on tower displacement under strong wind action. In this paper, the triple standard deviation method, multiple linear regression method, and interpolation method are used to preprocess monitoring data with skipped points and missing anomalies. An improved multi-rate data fusion method, validated using simulated datasets, was applied to correct monitoring data at bridge tower tops. The fused data were used to feed predictive models and generate structural performance alerts. Spectral analysis confirmed that the fused displacement measurements achieve high precision by effectively merging the low-frequency GPS signal with the high-frequency accelerometer signal. Structural integrity monitoring of wind-loaded bridge towers used modeling residuals as alert triggers. The efficacy of this proactive monitoring strategy has been quantitatively validated through statistical evaluation of alarm accuracy rates. Full article

Tunnel construction poses significant safety challenges due to confined spaces, limited visibility, and the dynamic movement of labourers and machinery. This study addresses a critical gap in real-time, bidirectional proximity monitoring by developing and validating a prototype early-warning system that integrates real-time location systems (RTLS) with long-range (LoRa) wireless communication and ultra-wideband (UWB) positioning. The system comprises Arduino nano microcontrollers, organic light-emitting diode (OLED) displays, and piezo buzzers to detect and signal proximity breaches between workers and equipment. Using an action research approach, three pilot case studies were conducted in a simulated tunnel environment to test the system’s effectiveness in both static and dynamic risk scenarios. The results showed that the system accurately tracked proximity and generated timely alerts when safety thresholds were crossed, although minor delays of 5–8 s and slight positional inaccuracies were noted. These findings confirm the system’s capacity to enhance situational awareness and reduce reliance on manual safety protocols. The study contributes to the tunnel safety literature by demonstrating the feasibility of low-cost, real-time monitoring solutions that simultaneously track labour and machinery. The proposed RTLS framework offers practical value for safety managers and informs future research into automated safety systems in complex construction environments. Full article

After a series of rainfall-related slope incidents that threatened immediately protected entities, the Taipei City government initiated a slope deformation monitoring and investigation program for potential landslides in its administrative districts and a review of its current rainfall thresholds for landslide occurrences, which is the aim of this study, in 2021 for better preparedness in facing the extreme weather- and climate-related natural hazards. Due to the limited availability of historical data, this study employed a physically based method to derive rainfall thresholds for landslide occurrences by integrating different rainfall distribution patterns into infiltration and slope stability analyses. The study examined four rainfall patterns—uniform, intermediate, advanced, and delayed—to assess their impact on slope failure mechanisms. Results indicate that advanced rainfall patterns (where peak rainfall occurs early) trigger the fastest failures, while delayed rainfall patterns lead to gradual groundwater accumulation, causing slope destabilization over longer durations. The study also found that short-duration rainfall (24 h) mainly triggers shallow landslides, whereas prolonged rainfall (72 h) leads to deep landslides. The study’s findings are crucial for early landslide warning systems, which provide different mitigation strategies based on the expected rainfall duration and provide a scientific basis for authorities to revise and integrate new rainfall thresholds into their real-time landslide warning systems. Full article

While extreme oceanic phenomena can often be accurately predicted, sudden abnormal waves along the shore (surge) are often difficult to foresee; therefore, an immediate sensing system was developed to monitor sudden and extreme events to take necessary actions to prevent further risks and damage. Real-time images from coastal surveillance video and meteorological data were used to construct a warning model for incoming waves using long short-term memory (LSTM) machine learning. This model can predict the wave magnitude that will strike the destination area seconds later and issue an alarm before the surge arrives. The warning model was trained and tested using 110 h of historical data to predict the wave magnitude in the destination area 6 s ahead of its arrival. If the forecasting wave magnitude exceeds the threshold value, a warning will be issued, indicating that a surge will strike in 6 s, alerting personnel to take the necessary actions. This configuration had an accuracy of 60% and 88% recall. The proposed prediction model could issue a surge alarm 5 s ahead with an accuracy of 90% and recall of 80%. For surge caused by a typhoon, this approach could offer 10 s of early waring with recall of 76% and an accuracy of 74%. Full article

An earthquake early-warning system (EEWS) is an indispensable tool for mitigating loss of life caused by earthquakes. The ability to rapidly assess the severity of an earthquake is crucial for effectively managing earthquake disasters and implementing successful risk-reduction strategies. In this regard, the utilization of an Internet of Things (IoT) network enables the real-time transmission of on-site intensity measurements. This paper introduces a novel approach based on machine-learning (ML) techniques to accurately and promptly determine earthquake intensity by analyzing the seismic activity 2 s after the onset of the p-wave. The proposed model, referred to as 2S1C1S, leverages data from a single station and a single component to evaluate earthquake intensity. The dataset employed in this study, named “INSTANCE,” comprises data from the Italian National Seismic Network (INSN) via hundreds of stations. The model has been trained on a substantial dataset of 50,000 instances, which corresponds to 150,000 seismic windows of 2 s each, encompassing 3C. By effectively capturing key features from the waveform traces, the proposed model provides a reliable estimation of earthquake intensity, achieving an impressive accuracy rate of 99.05% in forecasting based on any single component from the 3C. The 2S1C1S model can be seamlessly integrated into a centralized IoT system, enabling the swift transmission of alerts to the relevant authorities for prompt response and action. Additionally, a comprehensive comparison is conducted between the results obtained from the 2S1C1S method and those derived from the conventional manual solution method, which is considered the benchmark. The experimental results demonstrate that the proposed 2S1C1S model, employing extreme gradient boosting (XGB), surpasses several ML benchmarks in accurately determining earthquake intensity, thus highlighting the effectiveness of this methodology for earthquake early-warning systems (EEWSs). Full article

This study builds upon deterministic evaluations of the extensive cumulative rainfall thresholds associated with shallow landslides in the Gaoping River Basin, with a specific focus on the necessary response times during typhoon and intense rainfall events. Following the significant impact of Typhoon Morakot on the Liugui area, our investigation enhances previous research by employing a downscaled approach. We aim to establish early warning models for village-level, intermediate-scale landslide cumulative rainfall thresholds and to create action thresholds for small-scale, key landslide-prone slopes. Our inquiry not only combines various analytical models but also validates their reliability through comprehensive case studies. Comparative analysis with the empirical values set by the Soil and Water Conservation Bureau (SWCB) and the National Center for Disaster Reduction (NCDR) provides a median response time of 6 h, confirming that our findings are consistent with the response time frameworks established by these institutions, thus validating their effectiveness for typhoon-related landslide alerts. The results not only highlight the reference value of applying downscaled cumulative rainfall thresholds at the village level but also emphasize the significance of the evaluated warning thresholds as viable benchmarks for early warnings in landslide disaster management during Taiwan’s flood and typhoon seasons. This research offers a refined methodological approach to landslide early warning systems and provides scientific support for decision making by local governments and disaster response teams. Full article

The proposed climate monitoring system aims to address the substantial risks to human health, climate stability, and ecological balance posed by air pollution, utilizing Raspberry Pi as a central procession unit and integrating various sensors which also incorporate sensors to measure the concentrations of PM1, PM2.5, PM10, and black carbon. This method meets the need for effective and immediate air quality monitoring and offers useful information to communities, academics, and policy makers. Through IoT connectivity, the gathered data are sent to a cloud-based platform for analysis and visualization. The system offers a user-friendly interface that presents actionable insights for informed decision making. Its warning capabilities alert users when pollution levels exceed thresholds, and this system also contributes to a comprehensive understanding of air pollution. By measuring particulate matter and black carbon levels, it supports the development of effective air quality management strategies. The system helps to take proactive measures and create cleaner and healthier environments. In conclusion, the proposed climate monitoring system utilizing Raspberry Pi, sensors, IoT connectivity, and machine learning techniques offers an effective and real-time solution for monitoring air quality. The integration of IoT connectivity allows for remote access to air quality data, while machine learning algorithms analyze the data and initiate alerts. Full article

The integration of early warning and evacuation systems (EWES) with estimations for mitigating the loss of life in flood risk assessments marks an advancement towards developing robust emergency action plans for dam breaks. Through the simulation of diverse EWES scenarios, the impact of these systems, coupled with community preparedness, on minimising the potential for loss of life could be calculated. This study was conducted in the theoretical context of a dam break located upstream from a densely populated urban region in Brazil. Hydrodynamic and agent-based models were utilised to estimate potential loss of life across various scenarios and simulations. The Monte Carlo approach, in combination with the LifeSim model, was applied to assess how factors such as warning issuance timing, evacuation strategies and community responses impact the model’s outcomes. Sensitivity analysis was performed considering the overall exposed area and specific areas at risk for different spatiotemporal EWES strategies. The results of simulations highlighted the EWES’ great potential for risk mitigation and displayed optimal times for warning issuance. The warning diffusion and the protective action initiation parameters proved crucial for improving EWES. The spatiotemporal sectorisation of the alert and evacuation was also an effective strategy to optimise EWES. This methodology should allow for further similar tests and incite EWES improvements based on consistent loss of life alleviation simulations. Full article

Groundwater contamination due to municipal solid waste landfills’ leachate is a serious environmental threat. Deuterium (²H) and oxygen (¹⁸O) isotopes have been successfully applied to identify groundwater contamination processes, due to interactions with municipal solid waste landfills’ leachate, including significant organic amounts. A parameter influencing the isotope content of deuterium and oxygen18 is the deuterium excess (d or d-excess). This paper presents a d-isotope-based model, defined early-warning model, depending on the assessment of the deuterium excess variations in groundwater samples. The isotopic results are corroborated with the trace elements’ concentrations (Fe, Mn, Ni, Co and Zn), suggesting that the methanogenic activity diminished under trace element limitation. This model provides the determination of an index, F, as the percentage variation of d-excess, which makes it possible to define an alert level system to assess and check groundwater contamination by leachate. The procedure shows that values of F index higher than 1.1 highlight possible contamination phenomena of groundwater due to leachate and, therefore, actions by the municipal solid waste landfill management are required. This early-warning model is presented by the application to a case study in Central Italy in order to evaluate innovative aspects and opportunities to optimize the model. The application of the procedure to the case study highlighted anomalous values of the F index for the samples AD16 (F_max = 2.069) and AD13 (F_max = 1.366) in January, April, July and October surveys as well as the boundary values (1 ≤ F ≤ 1.1) for samples AD73 (F = 1.229) and AD68 (F = 1.219) in the April survey. The proposed model can be a useful management tool for monitoring the potential contamination process of groundwater due to the presence of landfills with municipal solid waste, including a significant organic component. Full article

The eutrophication of aquatic ecosystems caused by rapid human urbanization has led to an increased production of potentially hazardous bacterial populations, known as blooms. One of the most notorious forms of these aquatic blooms are cyanobacteria, which in sufficiently large quantities can pose a hazard to human health through ingestion or prolonged exposure. Currently, one of the greatest difficulties in regulating and monitoring these potential hazards is the early detection of cyanobacterial blooms, in real time. Therefore, this paper presents an integrated microflow cytometry platform for label-free phycocyanin fluorescence detection, which can be used for the rapid quantification of low-level cyanobacteria and provide early warning alerts for potential harmful cyanobacterial blooms. An automated cyanobacterial concentration and recovery system (ACCRS) was developed and optimized to reduce the assay volume, from 1000 mL to 1 mL, to act as a pre-concentrator and subsequently enhance the detection limit. The microflow cytometry platform utilizes an on-chip laser-facilitated detection to measure the in vivo fluorescence emitted from each individual cyanobacterial cell, as opposed to measuring overall fluorescence of the whole sample, potentially decreasing the detection limit. By applying transit time and amplitude thresholds, the proposed cyanobacteria detection method was verified by the traditional cell counting technique using a hemocytometer with an R² value of 0.993. It was shown that the limit of quantification of this microflow cytometry platform can be as low as 5 cells/mL for Microcystis aeruginosa, 400-fold lower than the Alert Level 1 (2000 cells/mL) set by the World Health Organization (WHO). Furthermore, the decreased detection limit may facilitate the future characterization of cyanobacterial bloom formation to better provide authorities with ample time to take the appropriate actions to mitigate human risk from these potentially hazardous blooms. Full article

According to the EU Early Warning System (EWS), synthetic cathinones (SCs) are the second largest new psychoactive substances (NPS) class, with 162 synthetic cathinones monitored by the EU EWS. They have a similar structure to cathinone, principally found in Catha Edulis; they have a phenethylamine related structure but also exhibit amphetamine-like stimulant effects. Illegal laboratories regularly develop new substances and place them on the market. For this reason, during the last decade this class of substances has presented a great challenge for public health and forensic toxicologists. Acting on different systems and with various mechanisms of action, the spectrum of side effects caused by the intake of these drugs of abuse is very broad. To date, most studies have focused on the substances’ cardiac effects, and very few on their associated neurotoxicity. Specifically, synthetic cathinones appear to be involved in different neurological events, including increased alertness, mild agitation, severe psychosis, hyperthermia and death. A systematic literature search in PubMed and Scopus databases according to PRISMA guidelines was performed. A total of 515 studies published from 2005 to 2022 (350 articles from PubMed and 165 from Scopus) were initially screened for eligibility. The papers excluded, according to the criteria described in the Method Section (n = 401) and after full text analyses (n = 82), were 483 in total. The remaining 76 were included in the present review, as they met fully the inclusion criteria. The present work provides a comprehensive review on neurotoxic mechanisms of synthetic cathinones highlighting intoxication cases and fatalities in humans, as well as the toxic effects on animals (in particular rats, mice and zebrafish larvae). The reviewed studies showed brain-related adverse effects, including encephalopathy, coma and convulsions, and sympathomimetic and hallucinogenic toxidromes, together with the risk of developing excited/agitated delirium syndrome and serotonin syndrome. Full article

One of the United Nations (UN) Sustainable Development Goals is climate action (SDG-13), and wildfire is among the catastrophic events that both impact climate change and are aggravated by it. In Australia and other countries, large-scale wildfires have dramatically grown in frequency and size in recent years. These fires threaten the world’s forests and urban woods, cause enormous environmental and property damage, and quite often result in fatalities. As a result of their increasing frequency, there is an ongoing debate over how to handle catastrophic wildfires and mitigate their social, economic, and environmental repercussions. Effective prevention, early warning, and response strategies must be well-planned and carefully coordinated to minimise harmful consequences to people and the environment. Rapid advancements in remote sensing technologies such as ground-based, aerial surveillance vehicle-based, and satellite-based systems have been used for efficient wildfire surveillance. This study focuses on the application of space-borne technology for very accurate fire detection under challenging conditions. Due to the significant advances in artificial intelligence (AI) techniques in recent years, numerous studies have previously been conducted to examine how AI might be applied in various situations. As a result of its special physical and operational requirements, spaceflight has emerged as one of the most challenging application fields. This work contains a feasibility study as well as a model and scenario prototype for a satellite AI system. With the intention of swiftly generating alerts and enabling immediate actions, the detection of wildfires has been studied with reference to the Australian events that occurred in December 2019. Convolutional neural networks (CNNs) were developed, trained, and used from the ground up to detect wildfires while also adjusting their complexity to meet onboard implementation requirements for trusted autonomous satellite operations (TASO). The capability of a 1-dimensional convolution neural network (1-DCNN) to classify wildfires is demonstrated in this research and the results are assessed against those reported in the literature. In order to enable autonomous onboard data processing, various hardware accelerators were considered and evaluated for onboard implementation. The trained model was then implemented in the following: Intel Movidius NCS-2 and Nvidia Jetson Nano and Nvidia Jetson TX2. Using the selected onboard hardware, the developed model was then put into practice and analysis was carried out. The results were positive and in favour of using the technology that has been proposed for onboard data processing to enable TASO on future missions. The findings indicate that data processing onboard can be very beneficial in disaster management and climate change mitigation by facilitating the generation of timely alerts for users and by enabling rapid and appropriate responses. Full article