Search Results (181)

Food adulteration is a critical global threat to public health, economic stability, and social welfare. It primarily occurs in two forms: intentional and incidental. Intentional adulteration, or economically motivated adulteration, involves deliberately adding fraudulent or toxic substances such as hazardous ripening agents and harmful coloring to increase profitability. Substitution of food species is also a widely practiced form. Such adulteration can cause severe acute and chronic health issues, ranging from gastrointestinal distress to cancer and organ failure. Incidental adulteration happens through accidental contamination during processing, storage, or distribution, involving hazards such as heavy metals, mycotoxins, and pesticides. Such contaminants also lead to severe chronic diseases and dangerous allergic reactions. Food adulteration also triggers massive socio-economic consequences. Food safety incidents such as the melamine milk crisis and the horsemeat scandal caused billions of dollars in losses due to healthcare costs, product recalls, damaged brand reputation, and disrupted international trade. Global entities such as the WHO, WTO, and Codex Alimentarius have established robust legislative frameworks. However, effective enforcement remains a significant challenge in many countries. To mitigate such harmful impacts, it is essential to strengthen global surveillance systems, enhance rapid analytical detection methods, and strictly enforce compliance with existing food safety regulations. Full article

Mango (Mangifera indica L.), particularly the ‘Nam Dok Mai’ cultivar, is an economically important fruit crop in Thailand and is widely produced in the Lower Northern Region for domestic consumption and export. However, pesticide residues in mango remain a major food safety concern because of their potential effects on consumer health and compliance with national and international maximum residue limits (MRLs). This study investigated pesticide residue contamination and assessed the dietary health risks associated with mango consumption among different age groups. A total of 255 composite mango samples were collected from production areas in Phichit, Phitsanulok, and Phetchabun provinces, Thailand. Pesticide residues were analyzed using a validated modified QuEChERS extraction method combined with gas chromatography–tandem mass spectrometry (GC–MS/MS). Multiple pesticide residues were frequently detected in the samples analyzed. Of the detected residues, 22 pesticides exceeded Thai MRL standards and 14 exceeded Codex Alimentarius MRLs. Dietary exposure was evaluated using acute and chronic hazard quotients (HQa and HQc) and cumulative hazard indices (HIa and HIc). The results showed that acute exposure to several insecticides, particularly carbofuran, lambda-cyhalothrin, and fenpropathrin, exceeded acceptable risk thresholds in all age groups. At the pesticide-class level, cumulative acute exposure to organophosphates, carbamates, and pyrethroids showed HIa values greater than 1 in several age groups, suggesting potential short-term health concerns, especially among children. In contrast, chronic exposure to individual pesticides and cumulative pesticide groups remained below the level of concern for all age groups, with HQc and HIc values below 1. These findings indicate that although long-term dietary risk from mango consumption is within acceptable levels, acute exposure to certain pesticide residues may pose non-negligible health risks. Continuous residue monitoring, stricter enforcement of good agricultural practices, and evidence-based risk management are essential to ensure mango safety and protect consumers. Full article

Precise estimation of lithium-ion battery State of Health (SOH) is highly demanded for reliable battery management systems, lifetime prediction, and safety assurance in electric vehicle and energy storage applications. Traditional data-driven approaches such as multilayer perceptron (MLP) often suffer from poor generalization and may produce non-physical degradation trends due to the absence of domain knowledge constraints. To address these limitations, this work proposes a monotonic Physics-Informed Residual MLP neural network framework for SOH estimation using the NASA battery dataset (B0005, B0006, B0007, and B0018). The proposed model incorporates a physics-based monotonic degradation constraint by penalizing positive gradients of SOH with respect to cycle index, thereby enforcing physically consistent capacity fade behavior. A loss function is employed to improve robustness and enhance late-cycle learning. Experimental results demonstrate that the proposed approach achieves an RMSE of 0.0287, MAE of 0.0181, and MAPE of 2.69%, indicating accurate and stable SOH prediction across multiple degradation patterns. The use of physics-informed constraints markedly enhances deterioration consistency and diminishes overfitting relative to solely data-driven models. The proposed structure offers a faithful solution for State of Health estimation in practical battery management systems. Full article

The fragmentation of clinical information across health systems, community pharmacies, and specialty providers continues to undermine medication safety and emergency care, particularly when patients are unconscious or otherwise unable to communicate their history. The dominant response to this fragmentation has been the construction of a centralized data infrastructure—health information exchanges, prescription drug monitoring programs (PDMPs), and federated electronic health record (EHR) networks—that aggregates clinical information into institutional databases that are queryable by providers, insurers, regulators, and, in many jurisdictions, law enforcement. This article argues that the same care-coordination problems can be addressed through an architecturally different approach in which the patient, not the institution, holds the integrative artifact. The proposed design, here labeled the Guardian Card (a conceptual architecture, not a commercial product), pairs an HL7 Fast Healthcare Interoperability Resources (FHIR) clinical payload with the SMART Health Cards verifiable-credential framework and a dual-modality (QR code plus near-field communication) physical carrier. After describing the technical architecture, hardware options, and a five-phase deployment roadmap, the design is situated within the surveillance-critical scholarship that has documented PDMP function creep, third-party doctrine erosion, racial disparities in algorithmic prescribing oversight, and the surveillance-instrumentarian repackaging of nominally de-identified prescription data. The Guardian Card is offered as one operational implementation of a patient-controlled medication-record architecture, with community pharmacy and long-term post-acute care, where the Pharmacist eCare Plan integration is most feasible as a recommended first-deployment venue. Full article

The safety of non-food products is embedded in the United Nations 2030 Agenda for Sustainable Development and the European Union (EU) framework, supporting health protection, responsible production and consumption, and market surveillance. The EU Rapid Alert System for dangerous non-food products, known as Safety Gate (formerly RAPEX), was established in 2005 to facilitate the exchange of information on products posing risks within the internal market. The aim of this study was to present the interdependencies reported in the Safety Gate system/RAPEX in 2005–2025, considering: product category, type of risk, country of origin, notifying country and year, as well as measures taken. The VOSviewer 1.6.20 and Statistica 13.3 were used. The results highlighted the following problems: toys from China with chemical, choking and injury risks; electrical appliances also from China with electric shock hazards; motor vehicles from Germany with injury risks; cosmetics from Italy with chemical and microbiological risks; and clothing from Turkey with suffocation risks. Reporting is expected to continue under existing regulatory frameworks, although changing the name of the system from RAPEX to “Safety Gate” may reduce its recognition. The findings highlight the need for targeted enforcement, improved risk profiling by product category and origin, and ongoing monitoring of emerging safety risks. Full article

Continuous detection of critical events such as falls and health deterioration is enabled by Internet of Things (IoT)-enabled monitoring systems in elderly care. However, system reliability is undermined by real-world sensor degradation, which produces high false-alarm rates and missed incidents. Existing systems lack differentiated governance mechanisms for acute decisions (e.g., fall detection, requiring high sensitivity and low latency) versus cumulative decisions (e.g., health deterioration monitoring, requiring stability and specificity). Conventional approaches treat data quality as a preprocessing concern rather than as a formal determinant of decision admissibility, creating a gap between data availability and decision reliability. In this paper, Decision-Critical Data Quality Contracts are proposed as a governance paradigm in which decision analytics is explicitly separated from admissibility. Symmetric (uniform) and asymmetric (adaptive) enforcement strategies are explored and implemented through a hierarchical Decision Quality Tree framework for context-aware quality assessment. A simulation-based evaluation was conducted over 72 h periods across three degradation scenarios: controlled (5% missingness), realistic (15%), and stress (30% with sensor failures). The no-contract, symmetric, asymmetric, and Decision Quality Tree approaches were compared on metrics including missed alarms, coverage, stability, false alarms, and audit trail completeness. The results demonstrate that missed fall alarms are reduced by up to 71% by the Decision Quality Tree compared to asymmetric enforcement (from 28.57% to 8.20%). Coverage improved to 97.80% and stability to 95.20%. The lowest false-alarm rates are achieved by the Decision Quality Tree (0.90% for acute decisions, 2.80% for cumulative decisions). Audit trail completeness shows a 70.6% improvement over the best baseline (score: 0.87 vs. 0.51). Ablation studies confirm that these improvements stem from synergistic combinations of fallback paths and context awareness. The Decision Quality Tree framework establishes a new balance between system availability and decision safety, providing a foundation for trustworthy IoT governance in elderly care. Full article

Artificial intelligence (AI) is rapidly transforming healthcare through applications in clinical decision support, diagnostic imaging, population health management, and workflow optimization. Despite these advances, real-world deployment continues to expose critical challenges related to safety, bias, transparency, and integration into clinical workflows. Algorithmic bias can exacerbate health disparities, limited explainability may undermine clinician trust, and insufficient validation and post-deployment monitoring can compromise patient safety. Although the World Health Organization (WHO) has established six ethical principles for AI in health, including autonomy, well-being and safety, transparency, accountability, equity, and sustainability, translating these high-level principles into practical and enforceable governance mechanisms remains a persistent challenge. This narrative review synthesizes insights from bioethics, health policy, computer science, and clinical medicine to identify gaps in current AI governance approaches and proposes a lifecycle-aligned governance-by-design framework that operationalizes WHO ethical principles across key stages of the healthcare AI lifecycle, including data collection, model development, validation, deployment, and post-deployment monitoring. The framework integrates concrete governance mechanisms such as consent governance, fairness evaluation, external validation, explainability, clinician oversight, and continuous performance monitoring. Overall, this work advances a practical, lifecycle-integrated approach to AI governance and provides a structured foundation for developing safe, equitable, and trustworthy AI systems in healthcare. Full article

Porcine cysticercosis, caused by the larval stage of the zoonotic parasite Taenia solium, poses a public health and economic burden in endemic regions. This study explored stakeholder perspectives on porcine cysticercosis control and risk factors in Busia County and documented proposed control measures with relevance to endemic regions. A qualitative design was used, involving eight key informant interviews (KIIs) and twelve focus group discussions (FGDs). Data were analyzed qualitatively by identifying emerging themes. The study found that smallholder semi-confined pig farming is the dominant system in the area, driven mainly by economic constraints, which elevates the risk of porcine cysticercosis. Poor hygiene, inadequate sanitation, and lack of safe water also contribute, alongside informal pig slaughter and weak meat inspection, compromising pork safety. Low public awareness limits preventive practices and hinders effective public health interventions. These findings highlight the need for integrated control measures, including community education on preventive behaviors and practices; strengthened veterinary services; improved sanitation; and enforced meat inspection. Coordinated One Health actions across public health, veterinary services, and communities are crucial to mitigate these interconnected health risks. The findings may guide interventions in comparable endemic settings and offer insights for managing other zoonotic diseases with similar transmission dynamics. Full article

Road traffic crashes remain a global public health burden and a persistent resource allocation problem that undermines progress toward the sustainable development of safe, equitable mobility systems. Saudi Arabia’s Vision 2030 targets fewer than 10 fatalities per 100,000 population, a goal aligned with United Nations Sustainable Development Goal 3.6 (halving road traffic deaths) and SDG 11.2 (safe and sustainable transport), yet a gap persists between crash prediction research and how agencies deploy enforcement resources. This paper builds a closed-loop predict–optimize–evaluate framework connecting Long Short-Term Memory (LSTM) neural networks to a goal-distance gap metric and constrained optimization, feeding forecast outputs directly into enforcement scheduling decisions. Using monthly casualty data from official Saudi sources covering the entire kingdom (all 13 administrative regions) from 2010 through 2024 (N = 42,856 fatal and serious injuries across 180 monthly observations), we validate LSTM forecasting against five benchmarks plus a GRU and a Transformer baseline, apply gap analysis as a standardized goal-distance metric, optimize enforcement allocation with triangulated elasticity estimates, and evaluate past policy reforms through multi-method counterfactual analysis. A headline finding is that roughly 28% of fatal and serious injuries cluster within only about 6% of weekly hours, creating an unusually concentrated target for enforcement reallocation. The LSTM achieves RMSE = 2.47 with MASE = 0.83, beating ARIMA by 35% while maintaining robustness during COVID disruptions (RMSE = 2.38 in the post-acute period 2022–2024 versus 2.61 in the acute period 2020–2021). Temporal analysis confirms 28% of fatalities (95% CI: 26.0–30.0%) cluster within 6% of weekly hours. Enforcement elasticity triangulated from three independent sources converges at α ≈ 0.31 (90% CI: 0.25–0.40). The optimization model allocates 56% of enforcement resources to Thursday–Friday midnight-to-4 AM windows, projecting a 17.1% casualty reduction (90% CI: 13.5–20.6% under Monte Carlo uncertainty in α). Monte Carlo sensitivity analysis with 10,000 iterations confirms a median benefit-cost ratio of 1.88 (90% CI: 1.18–2.97), with P (BCR > 1.0) = 98.9%, using locally calibrated VSL = SAR 4.2 million (equivalent to approximately USD 1.12 million at the SAMA-pegged rate of 3.75 SAR/USD, in constant 2024 prices). Counterfactual evaluation finds that the post-2018-reform period was associated with a 22.1% casualty reduction (95% CI: 16.4–27.8%), with magnitude robust across four methods (LSTM counterfactual, Bayesian Structural Time-Series, Synthetic Control, and an inverse-variance-weighted synthesis of the three); we stress, however, that attribution to the driving reform itself cannot be cleanly separated from concurrent Saher camera expansion, public awareness campaigns, and trauma-care improvements. By translating prediction into evidence-based, resource-efficient enforcement, the framework supports sustainable road safety policy in middle-income and rapidly motorizing settings. Full article

Counterfeit and unsafe medicines pose significant risks to patient safety and undermine trust in healthcare systems. This paper presents ACTMeds, a blockchain-supported pharmaceutical traceability and recall platform that considers pharmaceutical supply chain requirements and public health operational needs relevant to the Australian Capital Territory (ACT). The system integrates Ethereum smart contracts, developed using Ganache, with a React-based web application providing regulator, operator, pharmacy, and auditor interfaces, alongside a public verification portal leveraging QR and GS1 barcodes. In addition, role-based access control is enforced across the medicine lifecycle, including manufacture, custody transfer, dispensing, and recall, with immutable on-chain events generated to support auditability and accountability. To balance transparency with confidentiality, the platform prototypes a zero-knowledge (ZK) recall mechanism in which regulators can cryptographically prove that recall conditions meet predefined policy requirements without disclosing sensitive incident details. Threat modeling was conducted using the STRIDE framework, and security evaluation combined static application security testing (Solhint and ESLint) and dynamic testing. The paper further discusses deployment options, cost considerations, ZK recall performance analysis, ethical implications, and future enhancements. Security testing validated the platform’s resilience, with no high-severity vulnerabilities identified and medium-severity issues related to HTTP security headers addressed. The results indicate that a regulator-led, privacy-preserving, tamper-evident ledger can improve medicine authenticity verification and recall responsiveness while maintaining compliance and data protection obligations. Full article

Vegetables constitute an essential component of the daily diet in Albania; however, they also represent a major pathway of human exposure to pesticide residues. This study investigates the presence of pesticide residues in widely used vegetables, including leafy, fruity, root, and bulb types, and evaluates the potential dietary health risks associated with their consumption. Vegetable samples were analyzed using gas chromatography–tandem mass spectrometry (GC-MS/MS) and liquid chromatography–tandem mass spectrometry (LC-MS/MS), for the presence of 417 pesticide analytes, ensuring high analytical sensitivity and reliability. Pesticide residues were present, with 42 distinct compounds, including metabolites, found in all the analyzed samples. Notably, some of the detected substances are not currently authorized for use as plant protection products, suggesting either environmental persistence or regulatory non-compliance. Exceedances of European Union maximum residue limits (MRLs) were most frequently detected in leafy vegetables (42.31%), followed by fruity vegetables (18.75%), whereas no MRL exceedances were observed in root and bulb vegetables. According to the dietary exposure assessment conducted using European Food Safety Authority Pesticide Residue Intake Model (EFSA PRIMo model v.3.1), chronic dietary exposure to pesticide residues was below the acceptable daily intake (ADI). According to this assessment, the acute exposure exceeded the acute reference dose (ARfD) for several pesticide–vegetable combinations, particularly among children. This highlights the need for ongoing monitoring and better agricultural management techniques to reduce potential health risks related to pesticide residues in vegetables. The study results indicate the need to strengthen national monitoring programs, enforce pesticide regulations more strictly, and promote the wider adoption of integrated pest management strategies to reduce dietary pesticide exposure and protect public health in Albania. Full article

Automobile spray painters in small informal workshops in developing countries face high occupational exposure to organic solvents. Although health effects are well known, the influence of workers’ knowledge, attitudes, and practices (KAP) on these effects is less well understood. This study examined spray painters’ KAP regarding organic solvents and health symptoms and assessed workplace safety compliance. A cross-sectional study in Region F, Johannesburg, collected data among 152 spray painters across 47 workshops using a questionnaire and checklist. KAP scores were analysed with multivariable logistic regression to identify associations with eye, skin, respiratory, and CNS symptoms, while controlling for confounders. Workplace controls were inadequate: 64% of workshops conducted spray painting outdoors, while only 17% had a functioning spray booth. Although knowledge scores were high (45.29/50 ± 6.025), practice scores remained low (9.01/20 ± 5.275). After adjustment, higher knowledge was significantly associated with reduced odds of eye (AOR = 0.846), skin (AOR = 0.915), and respiratory symptoms (AOR = 0.890). Better practice scores also correlated with fewer skin symptoms (AOR = 0.891). No KAP construct was linked to CNS symptoms. In the absence of engineering controls, workers’ knowledge is strongly linked to lower reporting of solvent-related symptoms affecting the eyes, skin, and respiratory system. However, knowledge does not appear to influence CNS symptoms, which are probably driven by ambient solvent concentrations that individual behavioural measures cannot effectively manage. Therefore, knowledge acts as a supplementary, rather than a substitute, safeguard where engineering controls are lacking. Interventions should include education and enforceable regulations to empower workers and ensure the use of engineering controls, especially in spray booths. Full article

This five-month epidemiological investigation evaluated pre-slaughter welfare, carcass-processing practices, and post-mortem lesion prevalence in 1012 cattle and 413 pigs slaughtered in Enugu State, Nigeria. Direct observations and post-mortem inspections were conducted following OIE standards. Animal welfare was markedly compromised. Cattle were dragged from the lairage to kill floor, restrained in lateral recumbency for over 30 min before bleeding, and slaughtered without stunning. Pigs were transported tied to motorcycles and processed on unsanitary floors. The lairages lacked roofing, clean water, and adequate drainage. Carcass handling was unhygienic, with meat processed near maggot-infested drains and transported in open vans or motorized tricycles used to commute passengers and cement. Of all cattle examined, 45.3% (458/1012) exhibited gross lesions attributable to contagious bovine pleuropneumonia (CBPP, 15.5%), fasciolosis (18%), liver abscessation (6.6%), ascariasis (4.6%), and bovine tuberculosis (0.5%). No lesions were detected in pigs. Lesion occurrence differed significantly (p < 0.05) by sex (males = 44.1%, females = 66.7%), age (<4 years = 54.1%, ≥4 years = 45.4%), breed (White Fulani = 45.5%, others = 36.7%), slaughterhouse location, and season (rainy = 45.2%, dry = 45.5%). Temporal analysis showed the highest lesion rate in April (68.3%), declining to 37.7% in May. Lesions of CBPP and fasciolosis were significantly more frequent in young cattle and during the rainy months (p < 0.05). These findings reveal systemic welfare violations and disease endemicity within the municipal abattoirs surveyed. The combination of poor pre-slaughter welfare, unhygienic meat handling, and high prevalence of zoonotic and economically important livestock disease lesions highlights urgent public health concerns. Strengthening abattoir infrastructure, enforcing pre-slaughter animal welfare and hygiene regulations, mechanizing slaughter processes, and instituting continuous surveillance within the One Health framework are essential for ensuring meat safety and public health security in Nigeria and beyond. Full article

Urban radio spectrum monitoring is becoming increasingly complex due to the rapid growth of wireless devices, unauthorized emissions, and dynamic electromagnetic environments in smart cities. Traditional spectrum analysis approaches, based on manual operation or static detection techniques, are no longer sufficient to ensure scalable, autonomous, and secure monitoring. The convergence of two emergent technologies—Large Language Models (LLMs) and the Model Context Protocol (MCP)—facilitates a fundamental shift in radio monitoring. We define this as the AICEBERG paradigm: a novel, stratified architecture where a high-level, intelligent agentic interface (the peak) abstracts the underlying complexity of SCPI-driven hardware integration and radio governance protocols (the foundational base). This autonomous framework provides the necessary objective rigor to audit the stochastic ‘ocean of electromagnetic waves’ characteristic of modern smart cities, ensuring a stable platform for regulatory enforcement amidst high-density signal interference. The proposed system implements a three-layer processing flow, enabling high-level natural language commands to be translated into validated and secure hardware actions on RF spectrum analyzers. A dual-server design separates operational execution from safety validation, ensuring controlled SCPI command handling, parameter verification, and instrument health monitoring. Experimental validation demonstrates the feasibility of autonomous measurement execution. The results show that the proposed architecture reduces human dependency, enhances reproducibility and lowers the expertise barrier required for RF spectrum surveillance. To the best of our knowledge, AICEBERG represents one of the first integrated frameworks to bridge LLMs with SCPI-compliant hardware through the MCP for autonomous radio governance. Full article

Road traffic injuries represent one of the most critical public health challenges in the Gulf region. Predicting traffic accident severity is therefore a critical component of evidence-based road safety management. In this study, we develop machine learning frameworks for predicting traffic accident severity using Qatar’s national dataset (2020–2025), addressing extreme class imbalance and interpretability. A dataset of 588,023 accident records was systematically preprocessed from 1,000,500 raw reports. We compare three approaches: multi-class (four severity levels), binary (Safe vs. Severe), and cascaded two-stage (combining both). Six classifiers were evaluated across two encoding methods and three balancing strategies. Systematic hyperparameter tuning with 5-fold stratified cross-validation was performed for all models. The binary LightGBM classifier achieved BA = 71.04%, AUC-ROC = 0.772, Sensitivity = 61.03%, and Specificity = 81.05%, demonstrating superior performance over multi-class approaches. Temporal validation on 2025 data (trained on 2020–2024 data) supported good temporal generalization. Analysis of 10,000 test instances identified the time period as the dominant predictor of accident severity. The binary LightGBM framework provides an interpretable and effective approach for severe accident identification and risk prioritization, with SHAP findings supporting targeted temporal enforcement and pedestrian safety as evidence-based policy priorities. Full article