Search Results (144)

Drought is a recurring agricultural hazard in Northeast Thailand’s floodplain environments, yet the actual values of remote sensing indices at confirmed drought locations remain poorly characterized. This study characterized six remote sensing indices (NDVI, VCI, SMI, NDMI, MNDWI, NSMI) at 541 agricultural drought-reporting locations in the Chi River Basin, Maha Sarakham Province, across three years representing different ENSO phases (La Niña 2020, El Niño 2023, neutral 2024). Drought-reporting frequency was classified based on village-level alert frequency: high frequency (six alerts, n = 100 villages) and low–moderate frequency (≤3 alerts, n = 441 villages). Sentinel-2 imagery was processed for the January–May dry season. Due to non-independence of observations (repeated measurements and spatial autocorrelation), analyses focused on descriptive statistics and effect sizes (Cohen’s d) rather than formal hypothesis testing. Results revealed remarkably small mean differences between frequency classes (0.008–0.024), with uniformly small effect sizes (Cohen’s d = 0.20–0.22). VCI and MNDWI showed negligible differences (Cohen’s d = 0.124 and 0.094, respectively). Index values at high-frequency locations showed stability across years (CV < 7% for all indices except NDMI), with limited year-to-year variation. SMI and NSMI were perfectly correlated (r = 1.00), indicating mathematical redundancy. Provisional descriptive reference points were derived from the three-year dataset (NDVI ≈ 0.21, VCI ≈ 0.52, SMI ≈ 0.41, MNDWI ≈ −0.33 at high-frequency locations), but these are descriptive summaries only and require validation with longer time series before they can be considered for operational use. These findings demonstrate that individual remote sensing indices have limited discriminatory power in this sandy soil floodplain environment, where local factors—soil properties, topography, and irrigation access—dominate over regional climate forcing. Five policy-relevant observations are proposed, including re-evaluation of threshold-based early warning systems and prioritized irrigation investments based on static vulnerability factors. This study contributes to SDG 2 (Zero Hunger), SDG 6 (Clean Water), and SDG 13 (Climate Action) through improved understanding of drought monitoring limitations in floodplain environments. Full article

The floods triggered by the 29 October 2024 cut-off low in Valencia (Spain) expose a persistent challenge in disaster risk reduction: extensive meteorological and territorial data do not automatically become timely, trusted or actionable public guidance. This conceptual synthesis uses the Valencia event as a diagnostic case and reconstructs selected evidence on rainfall, hydrological escalation and alert timing to develop a data-driven framework spanning observation, modelling, impact assessment, communication, decision-making and post-event learning. Here, “data-driven” denotes an end-to-end governance and translation process, not the development of a new forecasting model. The framework integrates four dimensions: data governance, user-centred visualization, uncertainty communication and school-based education. It also introduces territorial translation as the link between impact forecasts and place-specific infrastructures, routines, vulnerabilities and responsibilities. Its novelty lies in connecting the Early Warnings for All pillars and impact-based, people-centred warning approaches with an explicit educational and territorial learning loop. Its practical contribution is a responsibility matrix, a minimum governance package and an implementation roadmap with indicators for latency, reach, comprehension and protective action. The framework is intended for adaptation, rather than statistical generalization, across Mediterranean and other fast-onset flood contexts. Improved forecasts remain necessary but insufficient: loss reduction requires interoperable records, accessible impact-based messages and inclusive educational programmes that convert scientific information into situated collective competence. Full article

The concept of the “cannabidiol (CBD) Hazard Triangle” reflects the unique position of CBD at the intersection of three overlapping dimensions: CBD as a substance associated with medicinal and pharmacological effects (“Drug”); CBD as a food ingredient subject to the EU Novel Food regulatory framework (“Novel Food”); and CBD as a potential source of food safety concerns (“Hazard”). This study investigates the growing presence of CBD-containing food products, their associated regulatory challenges, safety concerns, and market dynamics through an analysis of notifications reported in the EU Rapid Alert System for Food and Feed (RASFF), complemented by evidence from the scientific literature and authoritative regulatory sources. During the eight years (2018–2025), more than 400 CBD-related notifications were reported, predominantly involving food supplements (66.7%) and confectionery products, particularly gummies (12.6%). Significant discrepancies between the labelled and actual CBD content were frequently identified, along with unauthorized health claims implying therapeutic benefits. CBD-containing products were also found to contain other cannabinoids, most notably tetrahydrocannabinol (THC), which was reported in 26.7% of CBD-related hazard notifications. In several cases, THC concentrations exceeded legally permitted limits. Furthermore, these products are often marketed in forms that may promote casual or unintentional consumption, including by children. Overall, the widespread availability of CBD-containing food products raises important safety and regulatory concerns, particularly for vulnerable population groups. The CBD food market remains highly heterogeneous, characterized by inconsistent labelling practices, strong consumer demand, and increasing regulatory pressure. These findings underscore the need for clearer regulatory frameworks, improved market surveillance, and harmonized standards. Further research is essential to address unresolved issues related to product safety, quality, and market integrity. Full article

The COVID-19 pandemic exposed critical vulnerabilities in healthcare systems worldwide, placing healthcare workers (HCWs) at severe infection risk through direct patient contact. Epidemiological data confirm that HCWs were approximately seven times more likely to develop severe COVID-19 than other occupations, with over 7000 HCW deaths recorded globally by mid-2020. This paper presents the design and laboratory proof-of-concept validation of an IoT-based remote patient-monitoring system prototype—the IoT-Based Isolation Ward Monitoring System Prototype—designed to eliminate unnecessary patient-to-HCW physical contact while maintaining continuous, real-time physiological surveillance. The system integrates multi-sensor hardware comprising an AD8232 ECG module, a MAX30100 pulse oximeter, an NTC thermistor, and an MQ-135 ${\mathrm{C}\mathrm{O}}_2$ sensor. These sensors interface with an Arduino UNO for data acquisition, while localized edge computing is executed on a Raspberry Pi 3B. A convolutional neural network (CNN) trained on the MIT-BIH Arrhythmia Database classifies heartbeats into five distinct categories. By utilizing SMOTE resampling on 109,446 samples, the network achieves an on-device inference latency of under 200 ms. The sensor data are transmitted to a Firebase Realtime Database via an authenticated REST API, which synchronizes data across dual front-end interfaces: a LabVIEW desktop dashboard for clinical oversight and a cross-platform Flutter mobile application for mobile monitoring. End-to-end technical validation under controlled laboratory conditions confirmed round-trip cloud latencies between 300 and 800 ms, error-free threshold alert generation, and sub-second latency for the integrated chat utility. The proposed system uniquely combines hardware sensing, ML-based ECG classification, cloud storage, a LabVIEW physician dashboard, and bidirectional doctor–patient mobile communication into a single unified, low-cost platform. Full article

In 2025, the Democratic Republic of the Congo (DRC) experienced its 16th Ebola Virus Disease (EVD) outbreak, centered in the Bulape Health Zone of Kasai Province, amid multiple concurrent epidemics and limited health infrastructure. Genomic sequencing revealed a novel zoonotic spillover genetically related to the 1976 Yambuku strain. A Root Cause Analysis (RCA) using the “5 Whys” framework, integrating epidemiological data, genomic analysis, and surveillance reports, identified key contributors to delayed detection and response, with comparative insights drawn from the 2018–2020 North Kivu outbreak. The Mweka outbreak resulted in 28 confirmed, probable, or suspected cases and 15 deaths, including four healthcare workers. Root causes included inadequate ecological surveillance, weak community alert systems, diagnostic delays due to reliance on centralized laboratories, health system overload from concurrent outbreaks, and structural underfunding of preparedness and coordination. Unlike North Kivu, where security issues drove response delays, systemic and ecological vulnerabilities predominated in Mweka. These findings highlight how ecological and structural weaknesses facilitate novel Ebola spillovers and their escalation, emphasizing the need for sustained investment in One Health surveillance, decentralized diagnostics, and resilient public health governance to strengthen outbreak response capacity. Full article

Global climate change and extreme precipitation events increasingly challenge urban infrastructure resilience, particularly in topographically vulnerable regions like Taiwan. Traditional flood monitoring relies heavily on the manual visual interpretation of extensive surveillance networks, a process that imposes high cognitive loads and risks delayed emergency responses. This study presents a comprehensive Cyber-Physical System (CPS) architecture for an automated Water Image Monitoring Platform. Integrating approximately 10,000 cameras and multi-modal data—including precipitation records and spatial alerts—the platform leverages advanced semantic segmentation (DeepLabV3+ with Xception71) to delineate inundation boundaries. To ensure robustness under adverse conditions such as low illumination, fog, and specular glare, we implemented targeted optimizations, including HSV pre-processing, Deblur GAN architectures, and attention mechanisms. Results demonstrate a significant performance evolution, with the event recall rate rising from 88% in 2022 to 99.7% by 2025. A key driver of this success is the synergy between stationary nodes and vehicle-mounted CCTV units, which provide critical dynamic geographic coverage. Furthermore, the deployment of edge computing reduced warning latency 10 times—from 19.2 to 2 s—while virtual water level gauges maintained a mean error within ±10 cm. Despite these gains, a Human-in-the-Loop (HITL) architecture remains strategically necessary for ethical accountability and error filtering. This CPS provides a foundational model for autonomous, resilient urban disaster management. Full article

Background: In March 2026, a meningococcal cluster centred on the University of Kent, England, caused two deaths and resulted in over 20 reported cases within the first week, including confirmed and suspected invasive cases. Subsequent UKHSA updates in early April 2026 reported 21 laboratory-confirmed MenB cases (18 linked to the outbreak strain) and two deaths, with the outbreak subsequently spreading to a second Canterbury university, Canterbury Christ Church University, and confirmed as Neisseria meningitidis serogroup B (MenB). Sub-Saharan Africa (SSA) bears a disproportionate global burden of meningococcal disease, yet university settings remain a critically understudied outbreak amplifier. This narrative review extracts epidemiological and policy lessons from the Kent event and applies them to the SSA context. Methods: We conducted a narrative review following the SANRA criteria, searching PubMed, Embase, Scopus, Google Scholar, and African Journals Online (2000–2026), with supplementary grey literature retrieved from World Health Organisation (WHO), Africa Centre for Disease Control, and United Kingdom Health Security Agency (UKHSA). Outbreak data were drawn from official UKHSA public-health statements (grey literature, archived), the University of Kent communications, and peer-reviewed expert commentary. Results: The Canterbury outbreak exposed six reproducible vulnerabilities: unprotected serogroup circulation (confirmed MenB, not covered for the current university-age cohort), nightlife-linked transmission amplification, delayed serogroup identification, poor student symptom-recognition, inadequate institutional response capacity, and, critically, multi-institutional spread via shared nightlife venues (confirmed extension to Canterbury Christ Church University within five days). Each vulnerability is demonstrably more severe in SSA universities, which face a broader multi-serogroup threat environment (NmA, B, C, W, X), virtually no university-entry vaccination requirement, and critical evidence gap of campus-specific meningococcal evidence in the published literature. Conclusions: This review proposes a five-pillar preparedness framework for SSA tertiary institutions, derived from a synthesis of the Kent outbreak and broader epidemiological evidence, intended to inform policy discussion and future research. Moreover, these should be embedded within a broader age-linked prevention strategy that begins before university entry, particularly during the transition into secondary school in high-risk settings. Priority measures include meningococcal vaccination at key educational transition points, prophylactic antibiotic pre-positioning, serogroup-capable surveillance, symptom-recognition training, and pan-continental alert A predominantly reactive response may carry substantial risk in SSA settings. Full article

In current 5G systems, broadcast messages such as System Information (SI) and Public Warning System (PWS) notifications are processed outside the established UE-network security context before initial access, leaving their integrity structurally unprotected. This vulnerability enables overshadowing attacks where adversaries inject manipulated SI/PWS messages, potentially causing large-scale service disruption and false public alerts. To attend to this gap, we propose a SHA-256-based lightweight integrity protocol that operates consistently across Radio Resource Control (RRC) Connected, Inactive, and Idle states without relying on Public Key Infrastructure (PKI). The User Equipment (UE) computes a hash of received PWS-related SIB content and attaches it to existing RRC/Non-Access Stratum (NAS) state-transition control signaling, enabling the Next Generation NodeB (gNB) to validate broadcast content integrity and feedback verification results to the UE. Security protocols often harbor non-intuitive vulnerabilities that deviate from designer intent, even in standardized protocols where authentication, integrity, and freshness assumptions are repeatedly challenged. Thus, we formally verify our proposed protocol using SVO-Logic and Scyther to establish trustworthiness results, confirming that it satisfies integrity, mutual authentication, freshness, and replay resistance under an active attacker model. Performance evaluation against public-key- and Message Authentication Code (MAC)-based alternatives demonstrates that our hash-based approach achieves significantly lower computational load on gNB while maintaining moderate signaling overhead, making it suitable for large-scale 5G/6G PWS deployments. These results position the protocol as a promising candidate for future 3rd Generation Partnership Project (3GPP) broadcast integrity enhancements. Full article

Natural disasters represent a critical intersection of environmental degradation, climate change, and societal vulnerability, posing a severe threat to sustainable development. Building a resilient communication infrastructure is therefore paramount for environmental sustainability and community survival. This paper addresses the shortcomings of traditional systems—such as high latency, limited coverage, and unreliable infrastructure—by proposing a novel integrated disaster management system built on Internet Radio technology. The framework combines a robust early warning system with an efficient emergency information broadcaster, offering global reach, real-time capabilities, and significantly reduced resource requirements. Its low-power consumption and minimal physical infrastructure make it an environmentally sustainable and cost-effective solution, aligning with goals for reducing the ecological footprint of critical services. A comprehensive 6-month case study for the Dodecanese Islands, Greece—with focused implementation on Symi Island—was conducted to validate the system. IoT-based meteorological stations and machine learning models (Random Forest) achieved a temperature prediction RMSE of 1.5 °C (a 35% improvement over traditional models), a wind velocity RMSE of 3.1 km/h, and an F1-Score of 0.80 for rainfall prediction. The integrated system demonstrated end-to-end latency of 10–25 s (210× faster than traditional systems), 98% coverage, 94% user comprehension, and a 70% reduction in operational costs. System-wide testing confirmed an alert accuracy of 92%, a false alarm rate of 12%, and a missed event rate of 10%, all within acceptable thresholds. The system achieved 99.2% overall uptime with redundant components ensuring continuous operation. Comparative analysis shows the proposed system outperforms traditional Greek EWS by 210× in latency, improves coverage by 327%, and reduces costs by 70% while maintaining three UN SDG alignments. The research fills a critical gap by integrating sustainable communication technology with modern predictive analytics, offering a replicable model for island communities worldwide. Full article

Current flood early warning and risk communication approaches are often characterized by simple and/or alarmist messages, which can promote non-protective behaviours—either through overreliance on defence structures or emergency management organizations. In response, we propose and develop an early warning system (EWS) prototype aimed at fostering “flood literacy” within communities. This system seeks to empower individuals and local populations to better understand their flood risk by recognizing their personal vulnerability and the characteristics of potential floods affecting them. Such understanding enables timely and appropriate self-protective actions. The proposed EWS comprises an Internet of Things (IoT)-based camera network for monitoring rainfall, water depth, and water velocity based on Artificial Intelligence (AI) techniques. These AI algorithms have been used also to analyze and assess historical flood events in the study area, i.e., the heritage city of Matera (Basilicata Region, Italy). The monitoring system is integrated with AI-driven flood modelling to generate impact scenarios at the local scale. These forecasted scenarios can be compared with historical flood data to contextualize current measurements of rainfall and water levels and therefore the citizens can judge how significant a flood might be. The system incorporates threshold-based alerts related to flood instability for pedestrians, along with signals and symbols designed for quick interpretation and communication of self-protection measures to improve citizen resilience and response. Full article

Viticulture is highly vulnerable to weather variability and climate change. Growers increasingly face risks associated with extreme weather events, water scarcity, and emerging pests and diseases. To address these challenges, this study presents the development and implementation of the first operational digital decision support platform (DSP) tailored to Montenegrin vineyards within the MONTEVITIS project. The platform integrates IoT sensor data, national meteorological records and high-resolution global climate datasets to provide real-time monitoring and climate projections for vineyard management. The system was piloted in four vineyards representing diverse microclimatic and soil conditions of Montenegro. Key functionalities include phenology, irrigation and disease alerts supported by a user-friendly dashboard, map-based visualisation tools and data export functions. The pilot deployment demonstrated that combining heterogeneous data streams increases the reliability of outputs and enables timely, site-specific recommendations. Challenges identified during implementation include connectivity limitations, gaps in data and variable levels of digital expertise among growers; however, lessons learned point to the importance of continuous stakeholder engagement and institutional support for sustained use. The MONTEVITIS experience demonstrates how digital agriculture tools can bridge tradition and innovation in viticulture. By fostering collaboration between growers, researchers and policy makers, the platform enables adaptive strategies for climate resilience and sustainable vineyard management. Although the platform has been successfully deployed and tested under pilot conditions, a comprehensive long-term validation of its performance and impact on vineyard decision-making remains part of ongoing future work. Full article

Modern cyber–physical infrastructures rely heavily on alarm and notification systems to direct human attention when abnormal conditions occur. These mechanisms support timely and safe responses by informing operators and occupants about potential hazards. At the same time, research in human factors has shown that repeated or excessive alerts can weaken vigilance, slow reactions, and reduce confidence in warning systems. This behavioral pattern is commonly described as alarm fatigue. This paper examines how that vulnerability can be exploited intentionally. We refer to this adversarial strategy as alarm poisoning: the deliberate injection of false or misleading alerts in order to increase alarm pressure, erode trust in the monitoring infrastructure, and degrade organizational responsiveness over time. To study this process, we develop a stochastic Cybersecurity Dynamics model representing the interaction among attackers, defenders, alarm infrastructure, and a population of employees. Employee behavior is modeled through evolving trust and fatigue levels, while the overall system is formulated as a continuous–time Markov chain and simulated using the Gillespie Stochastic Simulation Algorithm. A Monte–Carlo campaign is used to analyze the resulting socio–technical dynamics under alternative attacker strategies. The study evaluates time-dependent trust, fatigue, and alarm-pressure trajectories, the distribution of times to behavioral collapse, and defender timing through Trust–Resilience–Agility–Mitigation (TRAM) metrics. The revised analysis also includes replication-sufficiency diagnostics, one-at-a-time sensitivity analysis, and threshold-robustness checks for the collapse criterion. The results show that false alarms with high perceived severity drive alarm pressure upward and degrade trust faster than nuisance-dominated campaigns, even when the total fake-alarm intensity is held constant across strategies. Collapse timing remains highly variable across stochastic realizations, and a non-negligible fraction of runs do not reach the collapse threshold within the simulation horizon. Sensitivity analysis indicates that the main qualitative ranking of attacker strategies is robust across most tested perturbations, with fatigue recovery and defender escalation emerging as particularly influential mechanisms. Overall, the findings support the view that alarm poisoning is a credible socio–technical attack vector and highlight the importance of rapid mitigation, robust alarm management, and human-centered defensive design in cyber–physical security systems. Full article

Healthcare artificial intelligence (AI) is moving from the laboratory into the infrastructure of care. As these systems become embedded in imaging, electronic health records, triage, and clinical decision support, their failures can affect not only individual encounters but also institutions and patient populations. Yet governance still centers on model development, local validation, and one-time compliance, with limited attention to cross-site failure after deployment. This article examines how public health preparedness can help close that gap. It presents a conceptual analysis grounded in two cases: a pneumonia-screening convolutional neural network that learned institutional confounders rather than portable clinical signals, and a widely deployed sepsis prediction model whose external performance and alert burden fell short of developer claims. Together, these cases reveal five governance features of systemic healthcare AI risk: population-level exposure, cascade effects across shared infrastructures, unequal vulnerability, delayed recognition, and coordination needs beyond any single institution. In response, we propose a tripartite framework combining stronger pre-deployment assurance, post-deployment surveillance with escalation thresholds, and tertiary response through investigation, rollback, remediation, and cross-site learning. The argument is not that AI failures are epidemics, but that high-impact clinical AI systems now function as critical digital health infrastructure requiring preparedness alongside lifecycle oversight. Full article

Introduction: Early warning systems reduce losses when risk knowledge, forecasting, communication, and response planning operate as an end-to-end chain, yet Gulf Coast warning practice often treats hazard dynamics, ecosystem change, and social vulnerability as separate domains. This study mapped operational early warning systems for climate-relevant hazards across Louisiana, Texas, Mississippi, Alabama, and Florida and examined whether ecosystem protective functions and social vulnerability were integrated into warning thresholds, dissemination design, and preparedness planning. Methods: I conducted a scoping review using the Web of Science Core Collection and Scopus for publications from 2020 through 18 January 2026 and targeted searches of NOAA/NWS/NHC, FEMA IPAWS, CDC/ATSDR SVI, IOOS/GCOOS, USGS, and state coastal agency portals between 15 September 2025 and 18 January 2026. Of 861 identified records, 440 duplicates were removed, 421 titles and abstracts were screened, 121 full texts were assessed, and 25 sources were included in the final charting and synthesis. Results: The review identified 11 operational systems and related platforms spanning the four early warning pillars, but routine socio-ecological integration remained limited. Louisiana showed the strongest documentation of ecosystem monitoring through CPRA and CRMS, while Florida and Texas showed more developed evacuation and dissemination interfaces. Mississippi and Alabama were represented by thinner monitoring and implementation records in the included sample. Across states, ecosystem loss and social vulnerability were used more often as planning context than as repeatable inputs to thresholds, message tailoring, or assistance triggers. Discussion: Gulf Coast practices can be strengthened through formal protocols that connect ecosystem condition and vulnerability indicators to impact-based briefings, multilingual and accessible alert workflows, and tract-sensitive preparedness actions. The findings indicate that implementation can advance by linking existing datasets to defined operational decisions and by evaluating warning performance through reach, accessibility, comprehension, and action feasibility, as well as technical accuracy. Full article

Severe wintertime particulate pollution (PM₁₀ and PM_2.5) affects the Santiago Metropolitan Region in Chile and is intensified by basin topography and frequent thermal inversions. Local authorities rely on the Critical Episodes Management (CEM) forecasting system, yet its predictive performance is variable. This study assesses CEM to identify operational vulnerabilities and propose data-driven improvements for urban air-quality governance. About ~1.2 million hourly meteorological and air-quality records (2017–2022) were analyzed using Generalized Additive Models (GAMs) to characterize key nonlinear relationships, and we evaluated the operational skill of the Cassmassi-1 PM₁₀ model and the WRF-Chem-based PM_2.5 forecasting component used by the system. Cassmassi-1 missed more than 50% of critical episodes and showed a false-alarm rate above 60%, consistent with limitations associated with static or incomplete emission representations. By contrast, the WRF-Chem-based component achieved episode prediction accuracy above 70%. GAM results indicate that wind speeds below 2 m s⁻¹, high diurnal temperature range, and relative humidity below 65% are strongly associated with extreme events. Considering the results, we recommend transitioning to nonlinear forecasting approaches that explicitly incorporate these meteorological thresholds and vertical stability indicators to improve alert reliability, strengthen urban resilience, and reduce population exposure. Full article