Search Results (219)

LoRaWAN’s role in global maritime logistics has allowed for efficient monitoring of ships and cargo, but it also comes with critical cybersecurity vulnerabilities. Experimental validation of three attack vectors—replay attacks, narrowband jamming and metadata inference—is conducted using a reproducible digital-twin LoRaWAN dataset reflecting Rotterdam port-like operational patterns (N = 20,000 baseline transmissions). Using controlled simulations and Kolmogorov–Smirnov statistical analysis, we show that: (1) replay attacks are feasible under Activation by Personalization (ABP) configurations lacking enforced frame-counter validation and exhibit no univariate separation from legitimate traffic under Kolmogorov–Smirnov analysis (p > 0.46 for all evaluated radio features); (2) narrowband jamming leads to significant SNR degradation (p = 2.36 × 10⁻⁵) on targeted channels without inducing broad distributional anomalies across other radio features; and (3) metadata-only analysis supports elevated metadata-based re-identification susceptibility (median $R_d=0.834$), indicating high predictability under passive observation which can reveal operationally relevant signals even when AES-128 is employed. Our proposed layered mitigation framework consists of mandatory Over-the-Air Activation (OTAA), cryptographic key rotation, channel diversity incorporating Adaptive Data Rate (ADR), gateway hardening, and protocol-level enforcement considerations, customized for maritime LPWAN scenarios. We provide experiment-backed evidence and actionable recommendations to connect academic LPWAN security research to that of industrial maritime practice. Full article

Building on prior evidence that biomass cooking drives personal air pollution in rural and peri-urban Bangladesh, we measured kitchen pollution alongside personal exposure and examined the influence of outdoor industrial and traffic emissions on personal and indoor air quality. In an mHealth based-behavior change communication (BCC) intervention study (NCT05570552), 400 women were enrolled from rural Matlab and peri-urban Araihazar in Bangladesh. We measured 24 h personal exposure to fine particulate matter 2.5 (PM_2.5) and black carbon (BC) using personal monitors (UPAS V2), and 72–120 h PM_2.5 in 200 kitchens and outdoors of households using air quality sensors (PurpleAir Flex). Compared to clean fuel users, biomass users showed greater personal and kitchen exposure to PM_2.5, showing good correlation between personal and indoor PM_2.5 measurements (R² = 0.722). Daily average personal PM_2.5 and kitchen PM_2.5 during both cooking and non-cooking periods were higher in rural than peri-urban areas. Geographic information system mapping revealed that personal PM_2.5 was inversely related to the distance of factories from households when below <300 m in both rural and urban areas. Only in Araihazar, personal BC was higher in households located near factories or roads (<200–300 m) compared to those situated further away. Higher personal BC exposure was found in peri-urban women than rural women (p < 0.001). Higher levels of PM_2.5 and increased BC were found in rural and peri-urban households, respectively, which were located in close proximities to formal/informal factories and main roads. These findings highlight the need for sustainable household energy transitions and improved air quality management to reduce air pollution exposure in Bangladesh. Full article

Emerging contaminants (ECs) are a diversely mounting group of chemicals and biological compounds found in air, water, and soil, which include pharmaceuticals, personal care products, per- and poly-fluoroalkyl substances (PFASs), microplastics, endocrine-disrupting chemicals, and various other industrial compounds. Unlike conventional pollutants, ECs are usually unregulated, found in very small amounts, and can persist and build up in living organisms, resulting in toxic risks for both ecosystems and human health. These contaminants originate from various anthropogenic activities and enter the environment through wastewater, stormwater, landfill leaching, and atmospheric deposition. This article documents a holistic literature review of ECs available from the last five years, covering classification, sources and pathways of contamination, and environmental behavior, while assessing their ecological, human health, and socioeconomic impacts. Advances in detection, including high-resolution mass spectrometry, non-target screening, real-time sensors, and AI-assisted monitoring, are addressed. Management strategies including advanced oxidation, membrane filtration, electrochemical treatments, and nature-based solutions are explored. It also analyses global and regional policy frameworks, highlighting regulatory gaps and the need for standardized monitoring. The study emphasizes integrated, multidisciplinary approaches combining scientific innovation, sustainable chemical design, predictive modeling, and public engagement. Synergizing technology, governance, and prevention could reduce the risks related to ECs and protect the environment. The novel contribution is an end-to-end, decision-oriented synthesis that links what monitoring can reliably infer to be feasible, integrated control strategies and sustainability outcomes, supporting risk-based prioritization, targeted pollution treatment, and prevention-focused management. Full article

Remote tower (rTWR) operations are reshaping air traffic control but introduce significant human-factor risks, notably cognitive fatigue induced by prolonged screen-based visual surveillance. To mitigate these risks in a safety-critical domain where missed detections can be catastrophic, we propose a non-intrusive, multimodal fatigue detection framework fusing ocular and cardiac signals. A high-fidelity simulation study with 36 controllers was conducted to collect eye-tracking and electrocardiogram (ECG) data, from which a 12-dimensional feature vector—integrating gaze entropy and heart rate variability (HRV)—was extracted. Addressing the severe class imbalance and scarcity of fatigue samples in physiological data, we developed a cost-sensitive XGBoost classifier combining SMOTE oversampling with a dynamically weighted loss function. Experimental results show that the proposed framework performed well under mixed-subject evaluation and improved sensitivity to fatigue events. Although a marked performance drop was observed under LOSO evaluation, personalized calibration partially alleviated this limitation, indicating the potential of the framework for real-time fatigue monitoring in remote tower operations. Full article

This study presents a hybrid digital twin system designed for real-time indoor air quality (IAQ) monitoring and filtration optimization within a residential environment. Using a network of low-cost sensors, physics-based simulations, and machine learning models, the system dynamically replicates the indoor environment to enable continuous assessment and optimization of key pollutants, including particulate matter, volatile organic compounds, and carbon dioxide. The system architecture integrates mass balance and decay models, computational fluid dynamics simulations, regression models, and neural network algorithms, all evaluated under both filtering and non-filtering conditions. A graphical user interface allows users to interact with the system, test air purifier placements, and visualize air quality dynamics in real time. The results demonstrate that, within this system, simpler models, such as linear regression, outperform more complex architectures under data-limited conditions, achieving test-set coefficients of determination ranging from 0.97 to 0.99 across multiple IAQ parameters. At the same time, the hybrid modelling approach enhances interpretability and robustness. Overall, this digital twin system contributes to smart building management by offering a scalable, interpretable, and cost-effective solution for proactive IAQ control and personalized decision-making. Full article

This paper presents PLAM (Plovdiv Air Monitoring)—a regional multi-agent platform for air quality monitoring, semantic reasoning, and forecasting. The platform uses a hybrid architecture that combines two types of intelligent agents: classic BDI (Belief-Desire-Intention) agents for complex, goal-oriented behavior and planning, and ReAct agents based on large language models (LLM) for quick response, analysis, and interaction with users. The system integrates data from heterogeneous sources, including local IoT sensor networks and public external services, enriching it with a specialized OWL ontology of environmental norms. Based on this data, the platform performs comparative analysis, detection of anomalies and inconsistencies between measurements, as well as predictions using machine learning models. The results are visualized and presented to users via a web interface and mobile application, including personalized alerts and recommendations. The architecture demonstrates essential properties of an intelligent agent such as autonomy, proactivity, reactivity, and social capabilities. The implementation and testing in the city of Plovdiv demonstrate the system’s ability to provide a more objective and comprehensive assessment of air quality, revealing significant differences between measurements from different institutions. The platform offers a modular and adaptive design, making it applicable to other regions, and outlines future development directions, such as creating a specialized small language model and expanding sensor capabilities. Full article

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations. Full article

Background: Aging in Western societies poses a growing challenge, placing increasing pressure on healthcare costs. Early identification of infections in elderly nursing home residents is crucial to reduce complications, mortality, and the burden on emergency departments. Methods: We performed a comparative analysis of machine learning models using XGBoost classifiers for infection detection, addressing incomplete daily physiological measurements (Heart Rate, Oxygen Saturation, Body Temperature, and Electrodermal Activity) through strict imputation protocols. We evaluated three model variants—Basic (clinical only), Air Pollution-added, and Social Media-integrated—while incorporating a novel Basal Module to personalize physiological baselines for each resident. Results: Results from the binary model indicate that physiological data provides a necessary baseline for immediate screening. Notably, social media integration emerged as a powerful forecasting tool, extending the predictive horizon to a 6-day lead time with an F1-score of 0.97. Complementarily, air pollution data ensured robust immediate detection (“nowcasting”). In the multiclass scenario, external data resolved the “semantic gap” of vital signs, improving sensitivity for specific infections (e.g., acute respiratory and urinary tract infections) to over 90%. Conclusions: These findings highlight that the strategic integration of environmental and digital signals transforms the system from a reactive monitor into a proactive early warning tool for long-term care facilities. Full article

High-density urban villages pose significant environmental challenges to the aging population. Beyond traditional exposures such as noise and air pollution, older adults may experience heightened physiological stress due to visual exposure within street environments, yet the precise micro-environmental triggers of physiological stress remain poorly understood. This study investigates how street-level visual elements relate to elderly walkers’ physiological stress. We conducted on-site walking experiments and monitored the Skin Conductance Level (SCL) of 81 elderly participants walking through two typical urban villages in Lingnan, China. We used a semantic segmentation algorithm to quantify visual environmental elements from first-person-view images and employed a CatBoost (Categorical Boosting) model to predict stress levels. The explainable model (SHAP, SHapley Additive exPlanations) was then used to interpret the complex relationships. The model demonstrated strong predictive power (e.g., R² = 0.72). SHAP analysis revealed roads and sidewalks as the most dominant predictors of SCL changes, exhibiting significant non-linear effects. Their influence surpassed that of environmental aesthetics like vegetation, which showed a more complex, at times even negative, association with stress reduction. The presence of buildings also exhibited a stress-reducing effect, though less so than roads and sidewalks. Key findings revealed the following: (1) Foundational walking infrastructure is the primary determinant of physiological well-being for elderly pedestrians in high-density environments. (2) The stress-reducing effects of vegetation are context-dependent, while buildings function as a form of “social infrastructure” in mitigating stress. Our findings provide crucial, evidence-based guidance for prioritizing interventions in age-friendly urban renewal projects. Our framework offers a transferable tool for human-centered environmental assessment. Full article

The convergence of biometric and environmental sensing represents a transformative advancement in wearable technology, moving beyond single-parameter tracking towards a holistic, context-aware paradigm for health monitoring. This review comprehensively examines the landscape of multi-modal wearable devices that simultaneously capture physiological data, such as electrodermal activity (EDA), electrocardiogram (ECG), heart rate variability (HRV), and body temperature, alongside environmental exposures, including air quality, ambient temperature, and atmospheric pressure. We analyze the fundamental sensing technologies, data fusion methodologies, and the critical importance of contextualizing physiological signals within an individual’s environment to disambiguate health states. A detailed survey of existing commercial and research-grade devices highlights a growing, yet still limited, integration of these domains. As a central case study, we present an integrated prototype, which exemplifies this approach by fusing data from inertial, environmental, and physiological sensors to generate intuitive, composite indices for stress, fitness, and comfort, visualized via a polar graph. Finally, we discuss the significant challenges and future directions for this field, including clinical validation, data security, and power management, underscoring the potential of convergent sensing to revolutionize personalized, predictive healthcare. Full article

Volatile organic compounds (VOCs) are a major occupational concern in polyurethane foam production, where exposure may impact worker health. This study identified key VOCs and evaluated their concentrations across different sections of a polyurethane manufacturing facility. Area (n = 5) air samples were collected during routine full-load production using short-duration active sampling and analyzed by thermal desorption gas chromatography–mass spectrometry (TD-GC-MS). The results revealed marked spatial variability in VOC concentrations, with the curing section showing the highest totals. Dichloromethane (DCM) constituted the dominant VOC in high-emission zones. All measured concentrations of DCM and other regulated substances remained well below European and Polish short-term exposure limits. Quantitative health risk assessment demonstrated that lifetime cancer risk values for DCM and benzene were in the 10⁻⁶ range, far below the regulatory threshold of concern (10⁻⁴). Non-carcinogenic risk indices (HQ) were generally low; however, a markedly elevated HQ was identified for 1-hexanol, 2-ethyl- in the cutting area (HQ = 5.7), indicating a potential localized non-cancer health concern. Overall, existing protective measures appear effective, but additional targeted precautions are warranted in zones with elevated emissions. Enhanced ventilation, strengthened personal protective equipment, and routine air monitoring are recommended to minimize potential health risks. Regular updates of occupational safety standards should reflect evolving toxicological evidence to ensure sustainable protection of workers in polyurethane foam production. Full article

Background/Objectives: Asthma exacerbations are among the most frequent causes of pediatric emergency department (ED) visits, with over 700,000 annual cases in the United States and a significant number in Europe. Children under five years of age are particularly vulnerable to hospitalization. Methods: As timely assessment of exacerbation severity in the ED is critical, this review synthetizes data about tools such as the Pediatric Respiratory Assessment Measure (PRAM) and the Asthma Severity Score (ASS) aid in evaluating clinical status based on respiratory rate, oxygen saturation, accessory muscle use, and response to treatment. We also analyzed the proper management following established guidelines from GINA, NAEPP and other articles. Results: First-line therapy includes oxygen supplementation, short-acting beta-agonists (SABAs) administered frequently during the first hour, and early systemic corticosteroids. In moderate to severe cases, ipratropium bromide is added. For refractory or life-threatening presentations, intravenous magnesium sulfate, epinephrine, or ventilatory support may be required. Discharge is appropriate when symptoms resolve, oxygen saturation remains >94% on room air, and the child demonstrates adequate inhaler use. Hospitalization is indicated in cases of persistent hypoxemia, poor response, feeding difficulties, or social concerns. Post-discharge care includes thorough caregiver education, medication access, and a personalized asthma action plan to reduce recurrence risk. Conclusions: The effective diagnosis, appropriate exacerbation treatment, monitoring of patients in the post-attack period, as well as successful preventive medication play a key role in the management of pediatric patients with asthma. Full article

Wearable, rectifiable aerosol photometers (WRAPs), instruments with combined nephelometer and on-board filter-based sampling capabilities, generally show strong correlations with reference instruments across a range of ambient and household PM_2.5 concentrations. However, limited data exist on their performance when challenged by mixed aerosol exposures, such as those found in dusty occupational environments. Understanding how these instruments perform across a spectrum of environments is critical, as they are increasingly used in human health studies, including those in which concurrent PM_2.5 and coarse dust exposures occur simultaneously. The authors collected co-located, ~24 h. breathing zone gravimetric and nephelometer PM_2.5 measures using the MicroPEM v3.2A (RTI International) and the UPAS v2.1 PLUS (Access Sensor Technologies). Samples were collected from adult brick workers (n = 93) in Nepal during work and non-work activities. Median gravimetric/arithmetic mean (AM) PM_2.5 concentrations for the MicroPEM and UPAS were 207.06 (interquartile range [IQR]: 216.24) and 737.74 (IQR: 1399.98) µg/m³, respectively (p < 0.0001), with a concordance correlation coefficient (CCC) of 0.26. The median stabilized inverse probability-weighted nephelometer PM_2.5 concentrations, after gravimetric correction, for the MicroPEM and UPAS were 169.16 (IQR: 204.98) and 594.08 (IQR: 1001.00) µg/m³, respectively (p-value < 0.0001), with a CCC of 0.31. Digital microscope photos and electron micrographs of filters confirmed large particle breakthrough for both instruments. A possible explanation is that the miniaturized pre-separators were overwhelmed by high dust exposures. This study was unique in that it evaluated personal PM_2.5 monitors in a high dust occupational environment using both gravimetric and nephelometer-based measures. Our findings suggest that WRAPs may substantially overestimate personal PM_2.5 exposures in environments with concurrently high PM_2.5 and coarse dust levels, likely due to large particle breakthrough. This overestimation may obscure associations between exposures and health outcomes. For personal PM_2.5 monitoring in dusty environments, the authors recommend traditional pump and cyclone or impaction-based sampling methods in the interim while miniaturized pre-separators for WRAPs are designed and validated for use in high dust environments. Full article

Styrene is an aromatic compound widely used as a reactive monomer in polyester resins, which are among the most utilized resins in cured-in-place pipe (CIPP) technology, the most widely used trenchless pipe renewal method. Given that styrene is classified as a suspected human carcinogen, this study aims to evaluate styrene concentrations emitted into the air during sewer pipe rehabilitation using CIPP. This study included developing a comprehensive methodology to collect data from six different CIPP installations across the U.S. and document styrene emissions before, during, and after the curing process. The air samples were collected and analyzed using the USEPA method TO-15 and TO-17. Measured styrene emissions were then compared with exposure limits established by USEPA, NIOSH, and OSHA to assess potential occupational and worker health impacts. The result confirmed that high styrene concentrations, exceeding the established threshold, can be observed within the CIPP work zone. The result also indicated a considerable reduction in styrene concentration within five feet downwind of the work zone. In conclusion, while the health risk to the public appears to be low, there is a potential for health impact for the CIPP crew. Therefore, implementing real-time air quality monitoring during CIPP installation to mitigate these health risks is recommended. Additionally, the use of appropriate personal protective equipment (PPE) by the crew is essential. Full article

Occupational asthma (OA) and rhinitis are health problems occurring in facilities employing animals for medical and scientific reasons. We have compared the UK trends (2006–2023) in these outcomes reported to the SWORD scheme with changes in routine and personal air monitoring for the major mouse (Mus m 1) and rat (Rat n 1) allergens. The exposure data contained 1540 and 688 mouse and rat results, respectively, expressed in ng.m⁻³. The median, 75th and 90th percentiles were used as exposure characteristics, and annually incrementing three-yearly rolling data slices compared exposure and health outcomes by linear regression. The median, P75 and P90 for Mus m 1 all showed annual declines of around 5–6% (p < 0.001), suggesting general improvements in controlling mouse allergen exposure, but without evidence of a decline in rat allergen levels (p > 0.05), although control measures for both species are largely identical. An annual mean decline in OA of 2.9% (p = 0.021) was identified, but without a significant decline in rhinitis (−1.4%; p = 0.21). Over 16 years, reductions in exposure to the predominant rodent species were accompanied by a concomitant but smaller reduction in OA. These data confirm the immediate value of controlling relevant allergen exposure in reducing the incidence of IgE-E mediated OA. Full article