Search Results (256)

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) spreads primarily through respiratory droplets, aerosols, and contaminated surfaces. While high-traffic locations like hospitals and airports have been studied extensively, detecting significant virus levels in aerosols and on environmental surfaces, campus settings remain underexplored. This study focused on two crowded buildings at the University of North Carolina at Charlotte (UNCC). From December 2021 to March 2022, we collected 16 indoor air samples and 201 samples from high-touch surfaces. During the sampling timeframe, 44.82% of surface samples from the Student Union and 28% from the University Recreational Center (UREC) tested positive for the presence of SARS-CoV-2 RNA. Median and average viral RNA copies per swab were higher in UREC (273 and 475) than in Student Union (92 and 269). However, all air samples tested negative. Surface positivity in these high-traffic campus locations was directly correlated with COVID-19 clinical cases in Mecklenburg County. The campus COVID-19 cases, driven by the Omicron wave, peaked a week before the peak detection of surface contamination. These findings underscore the importance of surface hygiene measures and highlight environmental conditions as potential contributors to COVID-19 spread on campuses. Full article

The increase in the quantity and variety of contaminants generated during routine airport infrastructure maintenance operations leads to a wider range of pollutants entering soil and surface waters through runoff, causing soil erosion and groundwater pollution. A significant developmental challenge is ensuring that airport infrastructure meets high-quality environmental management standards. It is crucial to have effective tools for monitoring and managing the volume and quality of stormwater produced within airports and nearby coastal areas. It is necessary to develop methodologies for determining a wide range of contaminants in airport stormwater samples and assessing their toxicity to improve the accuracy of environmental status assessments. This manuscript aims to showcase the latest advancements (2010–2024 update) in developing methodologies, including green analytical techniques, for detecting a wide range of pollutants in airport runoff waters and directly assessing the toxicity levels of airport stormwater effluent. An integrated chemical and ecotoxicological approach to assessing environmental pollution in airport areas can lead to precise environmental risk assessments and well-informed management decisions for sustainable airport operations. Furthermore, this critical review highlights the latest innovations in remediation techniques and various strategies to minimize airport waste. It shifts the paradigm of soil and water pollution management towards nature-based solutions, aligning with the sustainable development goals of the 2030 Agenda. Full article

The density of fog is directly related to visibility and is one of the decision-making criteria for airport flight management and highway traffic management. Estimating fog density based on images and videos has been a popular research topic in recent years. However, the fog density estimated results based on images should be further evaluated and analyzed by combining weather information from other sensors. The data obtained by different sensors often need to be aligned in terms of time because of the difference in acquisition methods. In this paper, we propose a video and a visibility data alignment method based on temporal consistency for data alignment. After data alignment, the fog density estimation results based on images and videos can be analyzed, and the incorrect estimation results can be efficiently detected and corrected. The experimental results show that the new method effectively combines videos and visibility for fog density estimation. Full article

Aircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. Automated defect detection is a valuable tool for maintenance engineers to ensure safety and condition monitoring. The proposed approach is to process the captured feedback using various deep learning architectures to achieve the highest performance defect detections. Additionally, an algorithm is proposed to estimate the size of the detected defect. The team collaborated with TUI’s Airline Maintenance Team at Luton Airport, allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated. The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. The size estimation approach was evaluated in both controlled laboratory conditions and real-world hangar environments, providing insights into practical implementation challenges. Full article

In the study of aircraft wake target detection, as the wake evolves and develops, the detection area of the LiDAR often shows the presence of two distinct vortices, one on each side. Sometimes, only a single wake vortex may be present. This can lead to a reduction in the accuracy of wake detection and an increased likelihood of missed detections, which may have a significant impact on the flight safety. Hence, we propose an algorithm based on the YOLOv8n–CBAM–EfficientNetV2 model for wake detection. The algorithm incorporates the lightweight network of EfficientNetV2 and the Convolutional Block Attention Module (CBAM) based on the YOLOv8n model, which achieves the lightweight improvement in the YOLOv8n algorithm and the improvement in detection accuracy. First, this study classifies the wake vortices in the wake greyscale images obtained at Hong Kong International Airport, based on the Range–Height Indicator (RHI) scanning characteristics of the LiDAR and the symmetry of the wake vortex pairs. The classification is used to detect left and right vortices for more accurate wake detection in wind field images, which thereby improves the precision rate of target detection. Subsequently, experiments are conducted using a YOLOv8n–CBAM–EfficientNetV2 model for aircraft wake detection. Finally, the performance of the YOLOv8n–CBAM–EfficientNetV2 model is analysed. The results show that the algorithm proposed in this study can achieve a 96.35% precision rate, 93.58% recall rate, 95.06% F1-score, and 250 frames/s. The results show that the method proposed in this study can be effectively applied in aircraft wake detection. Full article

Droughts have negative impacts on agricultural productivity and economic growth. Effective monitoring and accurate forecasting of drought occurrences and trends are crucial for minimizing drought losses and mitigating their spatial and temporal effects. In this study, trend dynamics in monthly total rainfall time series measured at Cape Town International Airport were analyzed using the Mann–Kendall (MK) test, Modified Mann–Kendall (MMK) test and innovative trend analysis (ITA). Additionally, we utilized a hybrid prediction method that combined the model with the complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) technique, the autoregressive integrated moving average (ARIMA) model, and the long short-term memory (LSTM) network (i.e., CEEMDAN-ARIMA-LSTM) to forecast SPI values of 6-, 9-, and 12-months using rainfall data between 1995 and 2020 from Cape Town International Airport meteorological rainfall stations. In terms of trend analysis of the monthly total rainfall, the MK and MMK tests detected a significant decreasing trend with negative z-scores of −3.7541 and −4.0773, respectively. The ITA also indicated a significant downward trend of total monthly rainfall, especially for values between 10 and 110 mm/month. The SPI forecasting results show that the hybrid model (CEEMDAN-ARIMA-LSTM) had the highest prediction accuracy of the models at all SPI timescales. The Root Mean Square Error (RMSE) values of the CEEMDAN-ARIMA-LSTM hybrid model are 0.121, 0.044, and 0.042 for SPI-6, SPI-9, and SPI-12, respectively. The directional symmetry for this hybrid model is 0.950, 0.917, and 0.950, for SPI-6, SPI-9, and SPI-12, respectively. This indicates that this is the most suitable model for forecasting long-term drought conditions in Cape Town. Additionally, models that use a decomposition step and those that are built by combining independent models seem to produce improved SPI prediction accuracy. Full article

The National Airport Pavement Test Facility (NAPTF) in USA obtained the strain and deformation data of the asphalt airport pavement numbered as Track 3 under the wheel load traveling in the north area of Construction Cycle 7 (CC7). But, the classic theories and methods still could not find out the definite and essential working characteristics, such as the starting point of the asphalt pavement’s failure process and the ending point of the normal working process. This study reveals the essential working characteristics of the asphalt airport pavement by modeling the tested strain and deformation data based on structural state-of-stress theory. Firstly, the tested data are modeled as state variables to build the state-of-stress mode and the parameter characterizing the mode. Then, the slope increment criterion detects the mutation points in the evolution curve of the characteristic parameter with a wheel load traveling number increase. Correspondingly, the mutation features are verified by investigating the evolution curves of the state-of-stress modes. The mutation points define the failure starting point and the elastoplastic branch (EPB) point in the working process of the asphalt airport pavements. The strain state-of-stress mode (Δε_t) and characteristic parameters ($E_j$ and ${\Phi }_j$) presented an obvious mutation feature around the EPB point; in addition, the deformation state-of-stress mode (ΔD_t) showed that the total deformation of the pavement changed evidently before and after the failure starting point, and the characteristic parameters ($E_j$ and ${\Phi }_j$) also presented an obvious mutation feature around the failure starting point, so both characteristic points could address the classic issues in the load-bearing capacity of asphalt airport pavements. Furthermore, the EPB point could be directly taken as the design point, and the failure starting point could be taken as the limit-bearing traffic capacity. Hence, this study could open a new way to address the classic issues in the load-bearing capacity of asphalt airport pavements and provide a new reference for their safe estimation and rational design. Full article

Bird strikes are a substantial aviation safety issue that can result in serious harm to aircraft components and even passenger deaths. In response to this increased tendency, the implementation of new and more efficient detection and prevention technologies becomes urgent. The paper presents a novel deep learning model which is developed to detect and alleviate bird strike issues in airport conditions boosting aircraft safety. Based on an extensive database of bird images having different species and flight patterns, the research adopts sophisticated image augmentation techniques which generate multiple scenarios of aircraft operation ensuring that the model is robust under different conditions. The methodology evolved around the building of a spatiotemporal convolutional neural network which employs spatial attention structures together with dynamic temporal processing to precisely recognize flying birds. One of the most important features of this research is the architecture of its dual-focus model which consists of two components, the attention-based temporal analysis network and the convolutional neural network with spatial awareness. The model’s architecture can identify specific features nested in a crowded and shifting backdrop, thereby lowering false positives and improving detection accuracy. The mechanisms of attention of this model itself enhance the model’s focus by identifying vital features of bird flight patterns that are crucial. The results are that the proposed model achieves better performance in terms of accuracy and real time responses than the existing bird detection systems. The ablation study demonstrates the indispensable roles of each component, confirming their synergistic effect on improving detection performance. The research substantiates the model’s applicability as a part of airport bird strike surveillance system, providing an alternative to the prevention strategy. This work benefits from the unique deep learning feature application, which leads to a large-scale and reliable tool for dealing with the bird strike problem. Full article

Traditional methods for monitoring the foundation settlement of airport runways predominantly employ equipment such as leveling instruments, total stations, layered settlement instruments, magnetic ring settlement instruments, ground-penetrating radar (GPR), and synthetic aperture radar. These methods suffer from low automation levels, are time-consuming, labor-intensive, and can significantly disrupt airport operations. An alternative electromagnetic detection technique, Controlled Source Audio-Frequency Magnetotellurics (CSAMT), offers deep-depth detection capabilities. However, CSAMT faces significant challenges, particularly in generating high signal-to-noise ratio (SNR) signals in the far-field region (FfR). Traditional CSAMT utilizes grounded horizontal dipoles (GHDs), which radiate symmetric beams. Due to the low directivity of GHDs, only a small fraction of the radiated energy is effectively utilized in FfR observations. Enhancing the SNR in FfR typically requires either reducing the transceiving distance or increasing the transmitting power, both of which introduce substantial complications. This paper proposes an airborne-dangled monopole-antenna symmetric remote-sensing radiation source for airport runway monitoring, which replaces the conventional GHD. The analytical, simulation, and experimental verification results indicate that the energy required by the airborne-dangled symmetric source to generate the same electric field amplitude in the FfR is only one-third of that needed by traditional CSAMT. This results in significant energy savings and reduced emissions, underscoring the advantages of the airborne-dangled monopole-antenna symmetric source in enhancing energy efficiency for CSAMT. The theoretical analysis, simulations, and experimental results consistently verify the validity and efficacy of the proposed airborne-dangled monopole-antenna symmetric remote-sensing radiation source in CSAMT. This innovative approach holds substantial promise for airport runway monitoring, offering a more efficient and less intrusive solution compared to traditional methods. Full article

The airport and its surrounding areas are home to a variety of pollution sources, and air pollution is a recognized health concern for local populated regions. Submicron particulate matter (PM₁ with an aerodynamic diameter of <1 mm) is a typical pollutant at airports, and the enrichment of heavy metals (HMs) in PM₁ poses a great threat to human health. To comprehensively assess the source-specific health effects of PM₁-bound HMs in an airport community, PM₁ filter samples were collected around the Tianjin Binhai International Airport for 12 h during the daytime and nighttime, both in the spring and summer, and 10 selected HMs (V, Cr, Mn, Co, Ni, Cu, Zn, As, Cd, and Pb) were analyzed. The indicatory elements of aircraft emissions were certified as Zn and Pb, which accounted for more than 60% of the sum concentration of detected HMs. The health risks assessment showed that the total non-cancer risks ($TNCRs$) of PM₁-bound HMs were 0.28 in the spring and 0.23 in the summer, which are lower than the safety level determined by the USEPA, and the total cancer risk ($TCR$) was 2.37 × 10⁻⁵ in the spring and 2.42 × 10⁻⁵ in the summer, implying that there were non-negligible cancer risks in the Tianjin Airport Community. After source apportionment with EF values and PMF model, four factors have been determined in both seasons. Consequently, the source-specific health risks were also evaluated by combining the PMF model with the health risk assessment model. For non-cancer risk, industrial sources containing high concentrations of Mn were the top contributors in both spring (50.4%) and summer (44.2%), while coal combustion with high loads of As and Cd posed the highest cancer risk in both seasons. From the perspective of health risk management, targeted management and control strategies should be adopted for industrial emissions and coal combustion in the Tianjin Airport Community. Full article

The rapid proliferation of unmanned aircraft systems (UAS), commonly known as drones, across various industries, government applications, and recreational use necessitates a deeper understanding of their utilization and market trends. This research leverages UAS detection technology—specifically DJI’s Aeroscope—to track serial numbers and predict product usage, market penetration, and population estimation. By analyzing three years of data from Aeroscope sensors deployed around a major airport in the Southern United States, this study provides valuable insights into UAS operational patterns and platform lifespans. The findings reveal trends in platform utilization, the impact of new product releases, and the decline in legacy platform use. This offers critical data for informed decision-making in market trends, product development, and resource allocation. Full article

Violence is a serious threat to societal health; preventing violence in airports, airplanes, and spacecraft is crucial. This study proposes the Violence-YOLO model to detect violence accurately in real time in complex environments, enhancing public safety. The model is based on YOLOv9’s Generalized Efficient Layer Aggregation Network (GELAN-C). A multilayer SimAM is incorporated into GELAN’s neck to identify attention regions in the scene. YOLOv9 modules are combined with RepGhostNet and GhostNet. Two modules, RepNCSPELAN4_GB and RepNCSPELAN4_RGB, are innovatively proposed and introduced. The shallow convolution in the backbone is replaced with GhostConv, reducing computational complexity. Additionally, an ultra-lightweight upsampler, Dysample, is introduced to enhance performance and reduce overhead. Finally, Focaler-IoU addresses the neglect of simple and difficult samples, improving training accuracy. The datasets are derived from RWF-2000 and Hockey. Experimental results show that Violence-YOLO outperforms GELAN-C. [email protected] increases by 0.9%, computational load decreases by 12.3%, and model size is reduced by 12.4%, which is significant for embedded hardware such as the Raspberry Pi. Violence-YOLO can be deployed to monitor public places such as airports, effectively handling complex backgrounds and ensuring accurate and fast detection of violent behavior. In addition, we achieved 84.4% mAP on the Pascal VOC dataset, which is a significant reduction in model parameters compared to the previously refined detector. This study offers insights for real-time detection of violent behaviors in public environments. Full article

In large public places such as railway stations and airports, dense pedestrian detection is important for safety and security. Deep learning methods provide relatively effective solutions but still face problems such as feature extraction difficulties, image multi-scale variations, and high leakage detection rates, which bring great challenges to the research in this field. In this paper, we propose an improved dense pedestrian detection algorithm GR-yolo based on Yolov8. GR-yolo introduces the repc3 module to optimize the backbone network, which enhances the ability of feature extraction, adopts the aggregation–distribution mechanism to reconstruct the yolov8 neck structure, fuses multi-level information, achieves a more efficient exchange of information, and enhances the detection ability of the model. Meanwhile, the Giou loss calculation is used to help GR-yolo converge better, improve the detection accuracy of the target position, and reduce missed detection. Experiments show that GR-yolo has improved detection performance over yolov8, with a 3.1% improvement in detection means accuracy on the wider people dataset, 7.2% on the crowd human dataset, and 11.7% on the people detection images dataset. Therefore, the proposed GR-yolo algorithm is suitable for dense, multi-scale, and scene-variable pedestrian detection, and the improvement also provides a new idea to solve dense pedestrian detection in real scenes. Full article

Hybrid threats exploit vulnerabilities in digital infrastructures, posing significant challenges to democratic countries and the resilience of critical infrastructures (CIs). This study explores integrating data governance with business process management in response actions to hybrid attacks, particularly those targeting CI vulnerabilities. This research analyzes hybrid threats as a multidimensional and time-dependent problem. Using the Business Process Model and Notation, this investigation explores data governance to counter CI-related hybrid threats. It illustrates the informational workflow and context awareness necessary for informed decision making in a cross-border hybrid threat scenario. An airport example demonstrates the proposed approach’s efficacy in ensuring stakeholder coordination for potential CI attacks requiring cross-border decision making. This study emphasizes the importance of the information security lifecycle in protecting digital assets and sensitive information through detection, prevention, response, and knowledge management. It advocates proactive strategies like implementing security policies, intrusion detection software tools, and IT services. Integrating Infosec with the methodology of confidentiality, integrity, and availability, especially in the response phase, is essential for a proactive Infosec approach, ensuring a swift stakeholder response and effective incident mitigation. Effective data governance protects sensitive information and provides reliable digital data in CIs like airports. Implementing robust frameworks enhances resilience against hybrid threats, establishes trusted information exchange, and promotes stakeholder collaboration for an emergency response. Integrating data governance with Infosec strengthens security measures, enabling proactive monitoring, mitigating threats, and safeguarding CIs from cyber-attacks and other malicious activities. Full article

With the rapid development of urban air traffic, Unmanned Aerial Vehicles (UAVs) are gradually being widely used in cities. Since UAVs are prohibited over important places in Urban Air Mobility (UAM), such as government and airports, it is important to develop air–ground non-cooperative UAV surveillance for air security all day and night. In the paper, an all-time UAV detection algorithm based on visible images during the day and infrared images at night is proposed by our team. We construct a UAV dataset used in urban visible backgrounds (UAV–visible) and a UAV dataset used in urban infrared backgrounds (UAV–infrared). In the daytime, the visible images are less accurate for UAV detection in foggy environments; therefore, we incorporate a defogging algorithm with the detection network that can ensure the undistorted output of images for UAV detection based on the realization of defogging. At night, infrared images have the characteristics of a low-resolution, unclear object contour, and complex image background. We integrate the attention and the transformation of space feature maps into depth feature maps to detect small UAVs in images. The all-time detection algorithm is trained separately on these two datasets, which can achieve 96.3% and 94.7% mAP50 on the UAV–visible and UAV–infrared datasets and perform real-time object detection with an inference speed of 40.16 FPS and 28.57 FPS, respectively. Full article