Search Results (629)

The IoE forms the foundation of the modern digital ecosystem by enabling seamless connectivity and data exchange among smart devices, sensors, and systems. However, the inherent nature of this structure, characterized by high heterogeneity, distribution, and resource constraints, renders traditional security approaches insufficient in areas such as data privacy, authentication, access control, and scalable protection. Moreover, centralized security systems face increasing fragility due to single points of failure, various AI-based attacks, including adversarial learning, model poisoning, and deepfakes, and the rising threat of quantum computers to encryption protocols. This study systematically examines the individual and integrated solution potentials of technologies such as Blockchain, Edge Computing, Artificial Intelligence, and Quantum-Resilient Cryptography within the scope of IoE security. Comparative analyses are provided based on metrics such as energy consumption, latency, computational load, and security level, while centralized and decentralized models are evaluated through a multi-layered security lens. In addition to the proposed multi-layered architecture, the study also structures solution methods and technology integrations specific to IoE environments. Classifications, architectural proposals, and the balance between performance and security are addressed from both theoretical and practical perspectives. Furthermore, a future vision is presented regarding federated learning-based privacy-preserving AI solutions, post-quantum digital signatures, and lightweight consensus algorithms. In this context, the study reveals existing vulnerabilities through an interdisciplinary approach and proposes a holistic framework for sustainable, scalable, and quantum-compatible IoE security. Full article

With the diversification of information retrieval methods, news retrieval tasks have gradually evolved towards multimodal retrieval. Existing methods often encounter issues such as inaccurate alignment and unstable feature matching when handling cross-modal data like text and images, limiting retrieval performance. To address this, this paper proposes an innovative multimodal news retrieval method by introducing the Learnable Alignment Module (LAM), which establishes a learnable alignment relationship between text and images to improve the accuracy and stability of cross-modal retrieval. Specifically, the LAM, through trainable label embeddings (TLEs), enables the text encoder to dynamically adjust category information during training, thereby enhancing the alignment capability of text and images in the shared embedding space. Additionally, we propose three key alignment strategies: logits calibration, parameter consistency, and semantic feature matching, to further optimize the model’s multimodal learning ability. Extensive experiments conducted on four public datasets—Visual News, MMED, N24News, and EDIS—demonstrate that the proposed method outperforms existing state-of-the-art approaches in both text and image retrieval tasks. Notably, the method achieves significant improvements in low-recall scenarios (R@1): for text retrieval, R@1 reaches 47.34, 44.94, 16.47, and 19.23, respectively; for image retrieval, R@1 achieves 40.30, 38.49, 9.86, and 17.95, validating the effectiveness and robustness of the proposed method in multimodal news retrieval. Full article

Traffic-related pollutants remain a challenging global issue, with significant policy implications. Within the European Union, Romania has the highest yearly societal cost per capita due to air pollution, which kills 29,000 Romanians every year, whereas the health and economic costs are also significant. In this context, municipal authorities in the country, particularly in high-density areas, should place a strong focus on mitigating air pollution. In particular, the capital city, Bucharest, ranks among the most congested cities in the world while registering the highest pollution index in Romania, with traffic pollution responsible for two-thirds of its air pollution. Consequently, studies that assess and model pollution trends are paramount to inform local policy-making processes and assist pollution-mitigation efforts. In this paper, a generalized additive modeling (GAM) framework is employed to model hourly concentrations of nitrogen dioxide (NO₂), i.e., a relevant traffic-pollution proxy, at a busy urban traffic location in central Bucharest, Romania. All models are developed on a wide, fine-granularity dataset spanning January 2017–December 2022 and include extensive meteorological covariates. Model robustness is assured by switching between the generalized additive model (GAM) framework and the generalized additive mixed model (GAMM) framework when the residual autoregressive process needs to be specifically acknowledged. Results indicate that trend GAMs explain a large amount of the hourly variation in traffic pollution. Furthermore, meteorological factors contribute to increasing the models’ explanation power, with wind direction, relative humidity, and the interaction between wind speed and the atmospheric pressure emerging as important mitigators for NO₂ concentrations in Bucharest. The results of this study can be valuable in assisting local authorities to take proactive measures for traffic pollution control in the capital city of Romania. Full article

Rapid industrialization and urbanization have progressed in Korea, yet public attention to hazardous pollutants emitted from industrial complexes remains limited. With the increasing coexistence of industrial and residential areas, there is a growing need for real-time monitoring and management plans that account for the rapid dispersion of hazardous air pollutants (HAPs). In this study, we conducted spatiotemporal data collection and analysis for the first time in Korea using real-time measurements obtained through mobile extractive differential optical absorption spectroscopy (Me-DOAS) mounted on a solar occultation flux (SOF) vehicle. The measurements were conducted in the Saha Sinpyeong–Janglim Industrial Complex in Busan, which comprises the Sasang Industrial Complex and the Sinpyeong–Janglim Industrial Complex. BTEX compounds were selected as target volatile organic compounds (VOCs), and real-time measurements of both BTEX and fine particulate matter (PM) were conducted simultaneously. Correlation analysis revealed a strong relationship between PM₁₀ and PM_2.5 (r = 0.848–0.894), indicating shared sources. In Sasang, BTEX levels were associated with traffic and localized facilities, while in Saha Sinpyeong–Janglim, the concentrations were more influenced by industrial zoning and wind patterns. Notably, inter-compound correlations such as benzene–m-xylene and p-xylene–toluene suggested possible co-emission sources. This study proposes a GIS-based, three-dimensional air quality management approach that integrates variables such as traffic volume, wind direction, and speed through real-time measurements. The findings are expected to inform effective pollution control strategies and future environmental management plans for industrial complexes. Full article

Air traffic controllers (ATCOs) make a significant contribution to ensuring flight safety, making this profession a highly stressful job globally. Job demands–resources (JDR) theory proposes a health impairment process stemming from job demand (complexity) to mental workload, which causes job stress, resulting in compromised flight safety. This vicious cycle is evident among ATCOs and is recognized as an unsustainable management practice. To curb this process, we propose intra-functional flexibility as a conditional factor. Intra-functional flexibility refers to the flexibility in the reallocation and coordination of resources among team members to help in urgent times. This is a relatively new concept and is yet to be empirically tested in the ATCO context. ATCOs work in a dynamic environment filled with sudden surges of urgent jobs to be handled within short time limits. Intra-functional flexibility allows standby crews to be called to ease these tensions when needed. To ascertain the role of intra-functional flexibility in mitigating health impairment among ATCOs, a questionnaire was administered to 324 ATCOs distributed across Saudi Arabia. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis exhibited two critical findings: First, the study revealed the prevalence of a vicious cycle of health impairment among Saudi ATCOs, whereby job complexity leads to increased mental workload, resulting in elevated levels of job stress. Secondly, the presence of intra-functional flexibility weakened this vicious cycle by mitigating the influence exerted by mental workload on job stress. That is, the mediation-moderated model proposed in this study provides empirical evidence supporting the applicability of intra-functional flexibility in mitigating the dire suffering of ATCOs. This study discusses limitations and future research directions in the end. Full article

Background: Surgical site infections (SSIs) are among the most common postoperative complications. Environmental factors, including intraoperative traffic and door openings in the operating room (OR), have been identified as critical contributors to microbial air contamination. Nurses play a pivotal role in managing these factors, directly influencing infection control practices. Methods: An integrative review was conducted to synthesize current evidence on the association between intraoperative traffic, door openings, and SSIs. A structured methodology was employed to identify, assess, and analyze the existing literature, with a specific focus on the nursing role in infection prevention. Results: Findings from a single-center prospective cohort study indicate that ORs with more than 10 personnel present exhibit a threefold increase in SSI risk [Relative Risk (RR) = 3.12; 95% Confidence Interval (CI): 0.71–13.66] compared to ORs with fewer personnel. Additionally, every five door openings per procedure were associated with a significant increase in SSI incidence [Hazard Ratio (HR) = 2.00; 95% CI: 1.24–3.20, p = 0.005]. Conclusions: These findings underscore the importance of strict protocols to limit intraoperative traffic and unnecessary OR access. A multidisciplinary approach plays a crucial role in ensuring surgical safety and preventing SSIs by regulating OR access and adhering to infection control best practices. Full article

Soil compaction is a critical challenge in perennial fruit production, limiting root growth, water infiltration, and nutrient uptake—factors essential for climate-resilient and sustainable orchard systems. In subtropical Asian pear (Pyrus pyrifolia Nakai) orchards under the annual top-working system, intensive machinery traffic exacerbates subsurface hardpan formation and tree performance. This study evaluated the effectiveness of pneumatic subsoiling, a minimally invasive method using high-pressure air injection, in alleviating soil compaction without disturbing orchard surface integrity. Four treatments varying in radial distance from the trunk and pneumatic application were tested in a mature orchard in central Taiwan. Pneumatic subsoiling 120 cm away from the trunk significantly reduced soil penetration resistance by 15.4% at 34 days after treatment (2,302,888 Pa) compared to the control (2,724,423 Pa). However, this reduction was not sustained at later assessment dates, and no significant improvements in vegetative growth, fruit yield, and fruit quality were observed within the first season post-treatment. These results suggest that while pneumatic subsoiling can modify subsurface soil physical conditions with minimal surface disturbance, its agronomic benefits may require longer-term evaluation under varying moisture and management regimes. Overall, this study highlights pneumatic subsoiling may be a potential low-disturbance strategy to contribute to longer-term soil physical resilience. Full article

Particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and heavy metals (HMs) present in polluted air are strongly associated with an increased risk of respiratory diseases. In our study, we grouped cities based on their pollution levels using a method called Ward’s cluster analysis and looked at the increased cancer risk from PM₁₀-bound harmful substances for adult men and women living in Polish cities. The analysis was based on data from 8 monitoring stations where concentrations of PM₁₀, PAHs, and HMs were measured simultaneously between 2018 and 2022. The cluster analysis made it possible to distinguish three separate agglomeration clusters: cluster I (Upper Silesia, Wroclaw) with the highest concentrations of heavy metals and PAHs, with mean levels of lead 14.97 ± 7.27 ng·m⁻³, arsenic 1.73 ± 0.60 ng·m⁻³, nickel 1.77 ± 0.95 ng·m⁻³, cadmium 0.49 ± 0.28 ng·m⁻³, and ∑PAHs 15.53 ± 6.44 ng·m⁻³, cluster II (Warsaw, Łódź, Lublin, Cracow) with dominant road traffic emissions and low emissions, with average levels of lead 8.00 ± 3.14 ng·m⁻³, arsenic 0.70 ± 0.17 ng·m⁻³, nickel 1.64 ± 0.96 ng·m⁻³, and cadmium 0.49 ± 0.28 ng·m⁻³, and cluster III (Szczecin, Tricity) with the lowest concentration levels with favourable ventilation conditions. All calculated ILCR values were in the range of 1.20 × 10⁻⁶ to 1.11 × 10⁻⁵, indicating a potential cancer risk associated with long-term exposure. The highest ILCR values were reached in Upper Silesia and Wroclaw (cluster I), and the lowest in Tricity, which was classified in cluster III. Our findings suggest that there are continued preventive actions and stricter air quality control. The results confirm that PM₁₀ is a significant carrier of airborne carcinogens and should remain a priority in both environmental and public health policy. Full article

Climate change and air pollution are reshaping viral circulation patterns and increasing host vulnerability, amplifying the burden of respiratory illness in early childhood. This narrative review synthesizes current evidence on how environmental exposures, particularly to nitrogen dioxide, ozone, and fine particulate matter, contribute to the incidence and severity of bronchiolitis, with a focus on biological mechanisms, epidemiological trends, and public health implications. Bronchiolitis remains one of the leading causes of hospitalization in infancy, with Respiratory Syncytial Virus (RSV) being responsible for the majority of severe cases. Airborne pollutants penetrate deep into the airways, triggering inflammation, compromising mucosal defenses, and impairing immune function, especially in infants with pre-existing vulnerabilities. These interactions can intensify the clinical course of viral infections and contribute to more severe disease presentations. Children in urban areas exposed to high levels of traffic-related emissions are disproportionately affected, underscoring the need for integrated public health interventions. These include stricter emission controls, urban design strategies to reduce exposure, and real-time health alerts during pollution peaks. Prevention strategies should also address indoor air quality and promote risk awareness among families and caregivers. Further research is needed to standardize exposure assessments, clarify dose–response relationships, and deepen our understanding of how pollution interacts with viral immunity. Bronchiolitis emerges as a sentinel condition at the crossroads of climate, environment, and pediatric health, highlighting the urgent need for collaboration across clinical medicine, epidemiology, and environmental science. Full article

In complex environments, autonomous navigation for quadrotor drones presents challenges in terms of obstacle avoidance and path planning. Traditional artificial potential field (APF) methods are plagued by issues such as getting stuck in local minima and inadequate handling of dynamic obstacles. This paper introduces a layered obstacle avoidance structure that merges vortex artificial potential (VAPF) fields with reinforcement learning (RL) for motion control. This approach dynamically adjusts the target position through VAPF, strategically guiding the drone to avoid obstacles indirectly. Additionally, it employs the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to facilitate the training of the motion controller. Simulation experiments demonstrate that the incorporation of the VAPF effectively mitigates the issue of local minima and significantly enhances the success rate of drone navigation, reduces the average arrival time and the number of sharp turns, and results in smoother paths. This solution harmoniously combines the flexibility of VAPF methods with the precision of RL for motion control, offering an effective strategy for autonomous navigation of quadrotor drones in complex environments. Full article

Real-time detection of fatigue states in air traffic controllers (ATCOs) is crucial for ensuring air traffic safety. Existing methods exhibit limitations such as poor real-time performance, intrusiveness, and susceptibility to lighting and occlusion. This paper proposes FF-YOLO, an improved YOLO11-based deep learning algorithm, to detect ATCO fatigue states through facial feature analysis. A custom dataset comprising 25,154 facial images collected from 10 ATCOs was constructed for model training and validation. The FF-YOLO model introduces the CA-C3K2 module for fine-grained feature extraction under complex lighting, incorporates a spatial–channel attention mechanism for improved detection accuracy during occlusion, and MPDIoU loss for enhanced accuracy on multi-scale facial images with accelerated convergence. Experimental results show FF-YOLO achieves 94.2% mAP@50, 74.7% mAP@50–95, 83.8% precision, and 73.8% recall, with gains of +13.7%, +11.6%, +0.6%, and +5.9% over YOLO11n, respectively, thereby enabling real-time and accurate detection of ATCO fatigue states. Future work will expand datasets with larger and more diverse ATCO populations to enhance generalizability. Full article

As urban air mobility moves rapidly toward real-world deployment, accurate vehicle trajectory prediction and early collision risk detection are vital for safe low-altitude operations. This study presents a deep learning framework based on an LSTM–Attention network that captures both short-term flight dynamics and long-range dependencies in trajectory data. The model is trained on fifty-six routes generated from a UAM planned commercialization network, sampled at 0.1 s intervals. To unify spatial dimensions, the model uses Earth-Centered Earth-Fixed (ECEF) coordinates, enabling efficient Euclidean distance calculations. The trajectory prediction component achieves an RMSE of 0.2172, MAE of 0.1668, and MSE of 0.0524. The collision classification module built on the LSTM–Attention prediction backbone delivers an accuracy of 0.9881. Analysis of attention weight distributions reveals which temporal segments most influence model outputs, enhancing interpretability and guiding future refinements. Moreover, this model is embedded within the Short-Term Conflict Alert component of the Safety Nets module in the UAM traffic management system to provide continuous trajectory prediction and collision risk assessment, supporting proactive traffic control. The system exhibits robust generalizability on unseen scenarios and offers a scalable foundation for enhancing operational safety. Validation currently excludes environmental disturbances such as wind, physical obstacles, and real-world flight logs. Future work will incorporate atmospheric variability, sensor and communication uncertainties, and obstacle detection inputs to advance toward a fully integrated traffic management solution with comprehensive situational awareness. Full article

Although China has achieved substantial reductions in particulate matter pollution, continually rising ground-level ozone now constitutes the primary challenge to further air-quality improvements. A systematic assessment of the long-term spatiotemporal behavior of ozone (O₃) and its links to meteorology and emissions is still lacking. Here, we develop a novel framework that couples Kolmogorov–Zurbenko (KZ) filtering with an optimized random forest (RF) regression model to examine daily maximum 8 h average ozone (O₃-8h) in 372 Chinese cities from 2013 to 2023. The approach quantitatively disentangles meteorological and emission contributions at the national scale, overcoming the limitations of traditional linear methods in capturing non-linear processes. Long-term components explain, in general, <40% of total O₃ variance. In eastern urban agglomerations, long-term meteorological factors—particularly temperature and surface ultraviolet radiation—account for up to 80% of the trend, whereas long-term emission contributions remain modest (≈5–6%), with pronounced signals in the Beijing–Tianjin–Hebei and Fenwei Plain regions. Empirical orthogonal function analysis further reveals distinct spatial patterns of emission influence: long-term O₃ trends in mega-cities such as Beijing, Tianjin, and Shanghai are driven mainly by local emissions (1.5–3% contribution), while key transport hubs including Xi’an, Tangshan, and Langfang are markedly affected by traffic-related emissions (>2%). These findings clarify the heterogeneous mechanisms governing O₃ formation across China and provide a scientific basis for designing and implementing the next phase of region-specific, joint prevention-and-control policies. Full article

Based on uncertainty theory, this paper studies the problem of unmanned aerial vehicle (UAV) combat mission assignment under an uncertain environment. First, considering both the target value, which is the combat mission benefit gained from attacking the target, and the unit fuel consumption of UAV as uncertain variables, an uncertain UAV combat mission assignment model is established. And according to decisions under the realization of uncertain variables, the first stage generates an initial mission allocation scheme corresponding to the realization of target value, while the second stage dynamically adjusts the scheme according to the realization of unit fuel consumption; a two-stage uncertain UAV combat mission assignment (TUCMA) model is obtained. Then, because of the difficulty of obtaining analytical solutions due to uncertainty and the complexity of solving the second stage, the TUCMA model is transformed into an expected value-effective deterministic model of the two-stage uncertain UAV combat mission assignment (ETUCMA). A modified particle swarm optimization (PSO) algorithm is designed to solve the ETUCMA model to get the expected value-effective solution of the TUCMA model. Finally, experimental simulations of multiple UAV combat task allocation scenarios demonstrate that the proposed modified PSO algorithm yields an optimal decision with maximum combat mission benefits under a maximum iteration limit, which are significantly greater benefits than those for the mission assignment achieved by the original PSO algorithm. The proposed modified PSO exhibits superior performance compared with the ant colony optimization algorithm, enabling the acquisition of an optimal allocation scheme with greater benefits. This verifies the effectiveness and superiority of the proposed model and algorithm in maximizing combat mission benefits. Full article

Airspace sectorization is an effective approach to balance increasing air traffic demand and limited airspace resources. It directly impacts the efficiency and safety of airspace operations. Traditional airspace sectorization methods are often based on fixed spatial scales, failing to fully consider the complexity and interrelationships of airspace partitioning across different spatial scales. This makes it challenging to balance large-scale airspace management with local dynamic demands. To address this issue, a multi-scale airspace sectorization framework is proposed, which integrates a multi-resolution grid system and a hierarchical deep reinforcement learning algorithm. First, an airspace grid model is constructed using Quaternary Triangular Mesh (QTM), along with an efficient workload calculation model based on grid encoding. Then, a sector optimization model is developed using hierarchical deep Q-network (HDQN), where the top-level and bottom-level policies coordinate to perform global airspace control area partitioning and local sectorization. The use of multi-resolution grids enhances the interaction efficiency between the reinforcement learning model and the environment. Prior knowledge is also incorporated to enhance training efficiency and effectiveness. Experimental results demonstrate that the proposed framework outperforms traditional models in both computational efficiency and workload balancing performance. Full article