Search Results (149)

Drug recall is a critical regulatory mechanism implemented to protect public health by removing defective, unsafe, or non-compliant pharmaceutical products from the market. Despite stringent regulatory approval processes, issues related to manufacturing defects, contamination, labeling errors, stability failures, and post-marketing safety concerns may lead to drug recalls. Regulatory authorities across the world, including the Central Drugs Standard Control Organization (CDSCO), the United States Food and Drug Administration (US FDA), the European Medicines Agency (EMA), and other national agencies, have developed structured recall guidelines and rapid alert systems to ensure timely withdrawal of defective products. Drug recalls are typically classified based on the level of health risk and may be executed at different levels of the distribution chain, including wholesale, retail, and consumer levels. Effective recall management involves risk assessment, recall communication, product traceability, documentation, and recall effectiveness checks. Pharmacovigilance systems also play an important role in identifying adverse drug reactions and quality defects that may lead to product recalls. This review article provides a comprehensive overview of drug recall systems, including causes of recalls, regulatory frameworks in India and other countries, recall classification, recall procedures, rapid alert systems, and global recall trends. The article also discusses challenges in recall implementation and provides recommendations to strengthen drug recall systems and regulatory coordination worldwide. The review additionally summarizes major official sources of recall information, including recall alerts, safety communications, and regulatory databases maintained by the Food and Drug Administration (FDA), EMA, CDSCO, Medicines and Healthcare products Regulatory Agency (MHRA), and World Health Organization (WHO), and provides a comparative global perspective on contemporary pharmaceutical recall practices. 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

Objective: On 28 February 2026, Israel entered direct large-scale hostilities with Iran under Operation Roaring Lion. The opening phase was characterized by repeated missile alerts, civilian protected-space instructions, and rapid reorganization of hospital activity into protected areas. We observed an apparent increase in strict spontaneous intracerebral hemorrhage admissions during March 2026 within our linked neurology/neurosurgery services dataset. The aim of this preliminary single-center study was to determine whether March 2026 was temporally associated with a higher proportional burden of strict spontaneous intracerebral hemorrhage admissions compared with March cohorts from the preceding decade and whether this pattern was also observed for acute ischemic stroke or non-traumatic subarachnoid hemorrhage. Methods: We performed a retrospective observational cohort study of all unique March admissions captured within a linked neurology/neurosurgery services dataset from 2016 through 2026. Hospitalizations were deduplicated by admission number. March 2026 was treated as the first full March occurring after the onset of direct Israel–Iran hostilities on 28 February 2026. Strict spontaneous ICH was defined using diagnosis-text phenotyping that included intraparenchymal or intracerebral hemorrhage terminology while excluding trauma, subarachnoid hemorrhage, subdural hematoma, aneurysm, arteriovenous malformation, tumor-related hemorrhage, cavernoma, venous sinus thrombosis, dissection, and other clearly secondary etiologies. Comparator phenotypes included acute ischemic stroke and non-traumatic subarachnoid hemorrhage (SAH). Results: Across 3855 unique March admissions, 68 met criteria for strict spontaneous ICH. In March 2026, 9 of 223 admissions (4.0%) were classified as strict spontaneous ICH, compared with 59 of 3632 admissions (1.6%) across March 2016–2025, yielding a rate ratio of 2.48 (95% CI 1.25–4.94; p = 0.015). Patients with strict spontaneous ICH in March 2026 were older (mean age 72.3 vs. 65.8 years), and 7 of 9 cases (77.8%) occurred in patients aged ≥70 years compared with 25 of 59 (42.4%) historically (p = 0.073). Acute ischemic stroke did not increase in March 2026 (7.6% vs. 9.4%; p = 0.475), and non-traumatic SAH showed only a non-significant numerical increase (2.7% vs. 1.4%; p = 0.147). Sensitivity analyses showed a directionally consistent but statistically non-significant increase when March 2026 was compared with March 2025 alone (4.0% vs. 1.2%; rate ratio 3.36, 95% CI 0.92–12.27; p = 0.076) and with a rolling 3-year March baseline from 2023 through 2025 (4.0% vs. 2.1%; rate ratio 1.93, 95% CI 0.88–4.23; p = 0.143). In-hospital mortality among strict spontaneous ICH patients was 1 of 9 (11.1%) in March 2026 versus 4 of 59 (6.8%) in March 2016–2025. Conclusions: In this preliminary single-center neurology/neurosurgery services cohort, March 2026 showed a higher proportional burden of strict spontaneous intracerebral hemorrhage admissions than March cohorts from the preceding decade, while acute ischemic stroke did not increase. Sensitivity analyses using March 2025 alone and a rolling 3-year March baseline were directionally consistent but did not reach statistical significance. These findings should therefore be interpreted as a hypothesis-generating temporal association rather than evidence of causality or population-level incidence. Wartime-related psychological stress, sleep disruption, altered healthcare access, blood pressure dysregulation, and medication nonadherence are biologically plausible contributors, but individual-level blood pressure, medication exposure, body mass index, time-to-admission, direct stress exposure, and detailed outcome data were not available in the present dataset. Multicenter, hospital-wide, and registry-based validation with seasonal and systems-level sensitivity analyses is required. Full article

Despite the rapid expansion of photovoltaic (PV) installations over the past decade, challenges such as curtailments of renewable energy sources (RESs) and grid constraints continue to limit the capacity of Cyprus’ power system to accommodate higher solar penetration. In this context, grid reliability, defined as the ability to maintain stable operation by balancing supply and demand, minimizing curtailment, and reducing stress on the island network, has emerged as a critical concern. The deployment of PV-plus-storage systems offers a viable solution to enhance grid reliability while alleviating operational constraints. This paper presents a real-world case study of the first commercially deployed grid-connected PV-powered, battery-integrated electric vehicle (EV) charging station in Cyprus. Commissioned in May 2025, the system integrates a 60.32 kW_p rooftop PV array, a 100 kW/97 kWh battery energy storage system (BESS), and a 160 kW DC fast charger. A custom cloud-based energy management platform enables real-time monitoring, forecasting, and optimization under a zero-export scheme. High-resolution operational and weather data were collected between 15 May and 30 November 2025. Over this period, the integrated PV-battery system supplied 29% of the site’s total energy demand (self-sufficiency rate of 28.97%) and achieved a self-consumption rate of 98.69%. Such rates would not have been attainable with a pure PV system, given the depot’s evening-concentrated EV charging demand profile, which requires the BESS to time-shift daytime solar generation. The system reduced depot electricity costs by approximately 29%, generating €16,010 in savings and avoiding 26.47 tonnes of carbon dioxide (CO₂) emissions compared to a grid-only baseline. Beyond site-level performance, the system contributed to grid stress reduction by absorbing excess PV generation that would otherwise have been curtailed/wasted. Operational insights indicate minimal temperature-related issues, highlight the importance of automated fault detection and alerting to minimize downtime, and demonstrate how periodic operation strategies can optimize system performance and mitigate curtailment in Cyprus’s isolated grid. Full article

This paper presents a low-cost Internet-of-Things (IoT) telemetry and alerting system for monitoring and protecting sea turtle nests. The proposed platform integrates temperature, humidity, vibration, ultrasonic proximity, and ambient light sensors into an autonomous sensing node based on the ESP8266 microcontroller. Measurements are transmitted wirelessly to a cloud backend for real-time visualization and rule-based alert generation. The system is designed to support continuous nest-level monitoring and rapid response to environmental and anthropogenic threats such as overheating, artificial light exposure during hatching, and physical disturbance. In contrast to approaches that require extensive historical datasets or machine-learning models, the proposed solution employs transparent threshold-based rules that provide reliable operation without training data. The platform emphasizes low cost, ease of deployment, and scalability, making it suitable for large-scale conservation deployments across multiple nesting sites. It provides conservation practitioners with actionable situational awareness that complements existing field-based monitoring and protection strategies. Full article

Earthquake Early Warning Systems (EEWS) represent one of the most effective technological solutions for mitigating the impacts of strong ground motion in seismically active regions. This study presents the design, implementation, and comprehensive evaluation of a real-time earthquake early warning system for Izmir-a region in Western Anatolia characterized by complex tectonic structures and high seismic hazard-using multi-station seismic acceleration data. The proposed framework integrates multi-threaded data acquisition, signal preprocessing, Min-Max normalization, and Euclidean distance-based similarity analysis to enable rapid detection of anomalous seismic patterns during the early P-wave phase. The system architecture consists of distributed sensor inputs, centralized real-time processing, similarity-based anomaly detection, and user-oriented visualization and alerting modules. The performance of the system was evaluated using both real and synthetic seismic datasets. Instrumental earthquake catalog from the 12 June 2017 Karaburun (Mw 6.2) and 30 October 2020 Samos (Mw 6.6) earthquakes demonstrate that the system can generate early warnings 18 s and 13 s prior to strong ground shaking, respectively. In addition, synthetic seismic scenarios were employed to assess system robustness under varying noise levels, station configurations, and signal conditions. The results indicate that the proposed framework maintains stable detection performance and low false-positive rates across diverse operational scenarios. The methodology emphasizes computational efficiency and inter-station waveform coherence analysis, providing a lightweight alternative to conventional magnitude-based approaches. By avoiding computationally intensive source inversion, the system achieves low-latency performance while preserving detection reliability. The proposed EEWS demonstrates strong generalization capability, scalability, and practical applicability for real-time deployment in earthquake-prone urban environments. 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

Despite relatively low per capita rice consumption in the European Union (EU), averaging approximately 9 kg annually between 2010 and 2023, imports from Asian countries have shown a sustained upward trend since 2013. This study assessed hazards associated with rice available on the EU market. Data were obtained from Faostat, Eurostat, the Rapid Alert System for Food and Feed (RASFF), and the Web of Science. Pivot tables and a two-way joining cluster analysis were applied to examine temporal and geographical patterns in reported notifications. Notifications primarily concerned genetically modified rice (32%), pesticide residues (21%), and mycotoxins (17%). During 2006–2014, notifications mainly related to unauthorized genetic modifications in rice originating from China and the United States, whereas between 2017 and 2023, they predominantly involved excessive pesticide residues and mycotoxin contamination in rice from India and Pakistan. Most hazards were classified as border rejections (37%), reflecting the effectiveness and vigilance of EU food safety authorities. While rice is generally considered low risk for European consumers, rising cultural integration and the growing popularity of Asian cuisine may increase consumption in the future. Continuous monitoring, rigorous risk assessment, and collaboration with exporting countries are therefore essential to maintain high food safety standards and ensure consumer protection across the EU market. Full article

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM_2.5 and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m³. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

We developed a real-time alert and data management framework that integrates programmable logic controllers, RS-485 industrial communication, Structured Query Language Server, Message Queuing Telemetry Transport (MQTT), and the nodemation (n8n) automation platform, using a filling machine production line as a case study. The system collects and analyzes the operational status and production line data of the filling machine in real time, storing all information in a database for preservation. Through MQTT, the data is sent to n8n for automated processing. When equipment anomalies occur or data exceed predefined thresholds, the system automatically notifies maintenance personnel via communication software APIs. Additionally, users can query daily production capacity or related data using n8n’s AI functions. This architecture offers low cost, rapid deployment, cross-platform integration, and high flexibility. It not only improves anomaly handling efficiency but also preserves complete historical records, supporting trend analysis, report generation, and decision optimization, thereby assisting the filling production line in achieving long-term stable and intelligent management. Full article

The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams with false positives, leading to severe “alert fatigue.” To address this critical bottleneck, this paper introduces an enhanced, probability-driven risk-prioritization framework utilizing an XGBoost classifier integrated with Bayesian Optimization (BO-XGBoost). By optimizing directly for the Area Under the Precision–Recall Curve (PR-AUC), the model is specifically tailored to rank high-risk anomalies under severe class imbalance. We validate the proposed approach on a rigorously resampled transaction dataset simulating a realistic 5% laundering rate. The BO-XGBoost model demonstrates exceptional prioritization capability, achieving an ROC-AUC of 0.9686 and a PR-AUC of 0.7253. Most notably, it attains a near-perfect Precision@1%, meaning the top 1% of flagged transactions are 100% true illicit activities, entirely eliminating false positives at the highest priority tier. Comparative and SHAP-based interpretability analyses confirm that BO-XGBoost easily outperforms sequence-heavy deep learning baselines. Crucially, it matches computationally expensive stacking ensembles in peak predictive precision while significantly surpassing them in operational efficiency, indicating its immense promise for resource-optimized, real-world compliance screening. Full article

The rapid expansion of crypto-asset markets and the introduction of the Markets in Crypto-Assets Regulation (MiCAR) pose novel supervisory challenges. Existing blockchain intelligence platforms focus predominantly on on-chain surveillance, leaving gaps in off-chain documentary due diligence automation. This paper presents a Multi-Agent System (MAS) integrating Large Language Model (LLM) capabilities with rule-based compliance frameworks. The architecture comprises seven specialized agents: a Coordinator Agent for orchestration; data acquisition agents (Searcher, Crawler); three parallel analytical agents—Heuristic Agent (LLM-powered qualitative risk assessment), Compliance Agent (hybrid-AI MiCAR asset classification and regulatory requirement verification), and On-Chain Agent (machine learning-based fraud detection); and a Reconciliator Agent synthesizing findings into unified alerts. Component-level empirical validation on 150 projects indicates 95% output reproducibility (identical alert tier and score deviation $\le 0.05$ across five reruns) and 210 s mean latency, providing proof-of-concept evidence for the integrated pipeline. A pilot user evaluation (six researchers/master students and two experts from regulatory authorities) provides preliminary usability evidence and surfaces domain-specific feedback from regulatory-authority experts. The architecture advances proactive regulatory technology by enabling scalable analysis combining off-chain documentary evidence with on-chain forensics. Full article

Out-of-hospital cardiac arrest (OHCA) remains a leading cause of sudden death worldwide, with wide variation in reported incidence and outcomes driven by heterogeneity in registries, emergency medical services (EMS) organization, and case definitions. Despite substantial advances in resuscitation systems, survival after EMS-treated OHCA generally remains below 10%, and outcomes are critically time dependent. Delays in emergency call activation, bystander cardiopulmonary resuscitation (CPR), and—most importantly—early defibrillation are associated with a rapid decline in return of spontaneous circulation and favorable neurological recovery. This narrative review synthesizes current evidence and implementation strategies aimed at reducing “time-to-CPR” and “time-to-shock,” with a specific focus on public-access defibrillation (PAD) as a tool to mitigate avoidable delay. Randomized trials and large registry studies consistently demonstrate that automated external defibrillator (AED) use before EMS arrival is a key determinant of survival in patients with shockable rhythms. However, the real-world effectiveness of PAD remains limited by suboptimal AED placement, restricted 24/7 accessibility, low public awareness, and underutilization driven by fear and lack of confidence. We compare different PAD delivery models—including EMS-based, police and first-responder-based, and fully integrated community systems—and summarize evidence supporting targeted, high-yield AED deployment and cost-effectiveness. In addition, we review emerging strategies to reduce avoidable delay and strengthen the early links of the chain of survival, such as school-based training programs, smartphone- and SMS-based citizen-responder networks, improved dispatch recognition of cardiac arrest (including artificial intelligence–supported tools), and drone-enabled AED delivery. Across these approaches, patient benefit critically depends on system integration, alert performance, and true AED accessibility. Finally, we describe the Italian “Progetto Vita” experience as a community-integrated model explicitly designed to minimize avoidable delay through widespread AED deployment, lay responder training, and real-time integration with EMS. We conclude by outlining future priorities, including the development of robust national OHCA registries and scalable solutions for the high burden of cardiac arrests occurring at home, such as population-level deployment of low-cost, ultra-portable AEDs. Full article

Rip currents pose a serious maritime safety hazard, as they can quickly carry swimmers away from the shore, often leading to drownings caused by panic. Traditional beach flags and signs often fall short due to the complexities involved in issuing real-time warnings. In this study, a framework for rip current warning based on deep learning was introduced and evaluated. The framework consists of automated object detection, adaptive time-averaged image generation, and expert validation protocols. The YOLOv4 deep learning model was trained and evaluated using three distinct datasets derived from two primary sources: a publicly available dataset sourced from peer-reviewed literature and a custom-built dataset compiled for this study. The results indicate that the models performed effectively, even under challenging environmental conditions, such as fluctuating lighting, camera motion, and varying wave dynamics. A significant novelty of this framework is the adaptable time-averaging feature, which filters out potential false positives generated by the deep learning model. This feature also allows for rapid detection in emergency situations while identifying persistent rip channel patterns for long-term risk assessments. Furthermore, the rip current alerts are not solely activated by automated results. Rather, they are contingent on the verification of dangerous conditions by trained personnel, such as lifeguards or beach management officers. The results of implementing a pilot version of this framework demonstrate its practical viability for real-world deployment, marking a significant advancement in transitioning deep learning-based rip current detection from controlled environments to practical, real-time warning systems. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article