Search Results (45)

Tail striking is a typical safety event in the area of civil aviation, which is directly related to the aircraft pitch angle at landing. Based on 2933 A319 flights’ non-exceedance quick access recorder (QAR) data from Dali airport, the relationship between key flight parameters during the final approach and landing pitch angle is explored. Functional data analysis and the Group Lasso method are used to select the most important flight parameters, and cluster analysis and weighted logistic regression are used to identify and predict a “high-risk” flight pattern. Here, “high risk” refers to a flight pattern associated with a higher probability of large landing pitch attitude, which is used as a proxy indicator of potential tail-strike risk rather than as evidence of an actual tail-strike event. Finally, flight operation recommendations are provided. The research results indicate that the airspeed, pitch angle and engine speed are most closely related to the landing pitch angle. An unusually high-risk flight pattern is identified, characterized by “high airspeed, high attitude, low thrust” caused by improper energy management of light-load flights. About 32.4% of flights in this pattern land with “large landing attitude”, which means the landing pitch angle is larger than the 95% sample percentile. A prediction model for the high-risk pattern is established using QAR parameters at the heights of 500 ft, 450 ft, and 400 ft, with an accuracy rate of 99.7% on the test data. The prediction in advance at 400 ft can provide pilots with sufficient time to take necessary operations. Full article

Currently, improving runway utilization under operational safety constraints has become a critical concern for small and medium airports. Existing research focuses primarily on landing-phase runway occupation time, while predictive studies on the takeoff phase remain limited. Analysis of 1749 Quick Access Recorder (QAR) records from ten airports reveals that departure runway occupation time is strongly correlated with ground speed at liftoff (0.72) and airport elevation (0.67) but weakly correlated with aircraft weight and meteorological conditions, providing guidance for feature engineering. To address the prediction of departure runway occupation time, this study proposes a TCN-FEP hybrid model. The model employs an enhanced Temporal Convolutional Network (TCN) module with multi-scale convolutions (kernel sizes 3, 5, 7) and dilated convolutions (rates 2, 4, 8) to capture multi-scale feature interactions, alongside a Feature Enhancement Projection (FEP) module that maps local features into a high-dimensional latent space for implicit relationship mining and global information integration. Experimental results demonstrate that the proposed TCN-FEP model achieves an MSE of 90.20, RMSE of 9.49, MAE of 5.84 s, MAPE of 3.80%, and R² of 0.97, outperforming Informer (MSE 117.95), Longformer (MSE 132.11), XGBoost (MSE 92.30), and LightGBM (MSE 91.45). Under 5% outlier injection, MSE increases by 7.9%, compared to 24.3% for LSTM and 18.4% for Informer. With 94% of prediction errors within ±5 s, the model’s accuracy may offer a useful reference for runway resource optimization at small and medium airports. Full article

The safety-critical nature of aircraft landing gear has led to interest in Structural Health Monitoring (SHM) and Remaining Useful Life (RUL) methodologies for the fatigue substantiation of landing gear assemblies. Due to the engineering effort that can be required to implement such approaches, it is prudent to target SHM and RUL activities at specific aircraft fleets. This paper employs air traffic data in the form of Automatic Dependent Surveillance-Broadcast (ADS-B) data to characterise the occurrence and severity of ground turns performed across fleets of differing aircraft type, location and operator characteristics. From the evaluation of 3250 flights, it was observed at the fleet level that ground turn characteristics show limited sensitivity to the aircraft’s geographical location and operator characteristics, excluding cargo aircraft and those operated by Ultra-Low-Cost Carriers. However, assessment of individual aircraft highlighted that the occurrence rate of fatigue-critical pivot turns can exceed twice that of the remaining aircraft fleet, suggesting that SHM and RUL activities should be focused on aircraft that deviate significantly from the expected fleet-wide behaviour. Finally, this paper presents an initial investigation into inferring the Nose Wheel Steering angle provided from Quick Access Recorder flight data directly from ADS-B trajectories. Full article

Accurate prediction of air traffic noise is critical for advancing environmentally sustainable operations in high density terminal areas. Conventional noise prediction models often exhibit significant limitations due to discrepancies between actual and nominal flight trajectories. To overcome this challenge, this study introduces a probabilistic framework that integrates real air-traffic-flow data to generate realistic flight trajectory distributions. The proposed methodology extracts key operational features—including trajectory distribution probabilities, and essential trajectory operation features—within a machine learning architecture. Furthermore, we develop a dedicated air traffic noise prediction model for clustered flight paths that explicitly incorporates traffic flow patterns, enabling high-fidelity simulation of noise propagation under actual air traffic operation. The framework is validated using a QAR (Quick Access Recorder) dataset from the terminal area of Changsha Huanghua International Airport. Experimental results demonstrate the model’s high predictive accuracy for both air traffic noise distribution and its influence, coupled with computational efficiency and practical applicability. The findings indicate that the proposed approach successfully addresses the challenge of predicting air traffic noise from divergent, real-world flight trajectories, offering a robust method for supporting noise-abatement strategies and sustainable aviation-planning initiatives. Full article

Agricultural mechanization and advanced technology have developed significantly in the coffee industry. However, there are still requirements for human laborers to operate, monitor crop health care, and manage production. The integration of advanced technology can significantly enhance the production efficiency and management practices of agricultural enterprises. This paper aims to address these gaps by proposing and implementing a computer vision-based attendance tracking system on mobile platforms that are suitable for the requirements and limitations of agricultural enterprises. First, the face detection process involves interpreting and locating facial structure. Next, the model transforms a photographic image of a human face into digital data based on the unique features and facial structure. We utilize the InsightFace model with the buffalo_l variant, as well as ArcFace with a ResNet backbone, as a facial recognition algorithm. After capturing a facial image, the system conducts a matching process against the existing database to verify identity. Finally, we implement a mobile application prototype on both iOS and Android platforms, ensuring accessibility for farm workers. As a result, our system achieved 95.2% accuracy on the query set, with an average processing time of <200 ms per image (including face detection, embedding extraction, and database matching). The system performs real-time attendance monitoring, automatically recording the entry and exit times of farm workers using facial recognition technology, and enables quick registration of new workers. Our work is expected to enhance transparency and fairness in the human management process, focusing on the coffee farm use case. Full article

Healthcare providers in oncology experience exceptionally high stress rates. Research emphasizes that stress management programs must be quick to implement, flexible to accommodate demanding schedules, cost-effective, accessible to all staff, and tailored to the needs of oncology personnel. Programs that fail to meet these criteria often struggle with uptake and sustainability. This mixed-methods exploratory study aimed (1) to design an online stress management program, Breathe with the Waves (BWW), based on breathing techniques; (2) to evaluate its acceptability, satisfaction, and relevance; (3) to identify perceived benefits and challenges; and (4) to generate potential outcome measures for future studies. A team of Canadian researchers and end-users co-designed the intervention. Twenty oncology professionals completed BWW, which featured pre-recorded breathing videos, and provided feedback via questionnaires and semi-structured interviews. We used t-tests and Wilcoxon rank tests to analyze quantitative data, and template analysis for qualitative data. Participants found BWW highly acceptable, satisfactory, and relevant. Participants reported three categories of benefits: stress reduction, improved work performance, and increased mindfulness. Challenges included anticipated challenges and experienced challenges. Potential outcome measures fell into six categories: physical health, mental health, relational, work, mindfulness and personal practice. BWW, available in English and French, represents a promising and accessible approach to supporting the well-being of oncology personnel. Full article

Long landings can reduce runway utilization and increase the probability of runway incursions and excursions. Previous studies on long landings often lacked support from actual operational data and primarily relied on event-triggering logic established by airlines for parameter exceedance detection and retrospective analysis. In response, a comprehensive risk prediction framework for aircraft long landings, supported by Quick Access Recorder (QAR) data, was constructed. The framework includes a data analysis pipeline, a sequence prediction model, and performance evaluation metrics for accident warning efficiency. Specifically, approximately 3 million rows of real QAR data were collected, and reasonable landing intervals were extracted based on pilots’ correct landing sightlines, attention allocation, and actual visual scenarios at departure heights. Gradient Boosting Decision Trees (GBDT) were employed to develop a method for extracting landing interval feature data, based on monitored parameters and ranges of landing distance. Additionally, the GBDT-Informer long-sequence time series prediction model was developed to forecast landing distance, accompanied by the construction of effective metrics for evaluating prediction performance. The results indicate that the GBDT-Informer model effectively models the temporal dimensions of landing intervals, accurately predicting ground speed (GS), radio altitude (RALT), and landing distance sequences. Compared to other prediction models, the GBDT-Informer model consistently achieved the smallest RMSE, MAE, and MAPE values, demonstrating high prediction accuracy. This predictive framework allows for the analysis of the coupling relationships among multiple parameters in flight data and their interrelations with exceedance anomalies. The findings can be applied in actual flight landings to promptly assess whether landing distances exceed limits, providing quick references for flight crews during landing or go-around decisions, thereby enhancing operational safety margins during the landing phase. Full article

To achieve the forward movement of the aircraft landing attitude ultra-limit, this paper builds a deep learning-based aircraft landing attitude warning system. The early warning system includes four modules: data pretreatment, feature dimensionality reduction, prediction, and judgment. Subsequently, through data pretreatment methods such as data cleaning, frequency normalization, data standardization, and feature classification, the experimental dataset is transformed into a form recognizable by machine learning algorithms and neural network models. The necessary feature parameters are extracted to form a deep learning training dataset. Then, the Max-Relevance and Min-Redundancy algorithm was applied to screen the QAR (Quick Access Recorder) parameters with the highest correlation with the predictor variables, and the LSTM network model was established to predict the pitch and roll angles of the aircraft landing, respectively. Evaluation metrics are used to determine the optimal model parameters. Finally, the confusion matrix is introduced to test the prediction effect of the model, and through the secondary indicators of the confusion matrix, the prediction accuracy of the established landing attitude warning system is 94.83% for the pitch angle and 91.18% for the roll angle. It also provides pilots with a 5 s time margin to avoid risks. The system can effectively issue early warnings for ultra-limit landing attitude events and, based on the prediction results, identify the types of risks. Full article

Reducing emissions in the aviation industry remains a critical challenge for global low-carbon transition. Accurate fuel consumption prediction is essential to achieving emission reduction targets and advancing sustainable development in aviation. Aircraft fuel consumption is influenced by numerous complex factors during flight, resulting in significant nonlinear relationships between segment-specific variables and fuel usage. Traditional statistical and econometric models struggle to capture these relationships effectively. This article first focuses on the different characteristics of QAR data and uses the Adaptive Noise Ensemble Empirical Mode Decomposition (CEEMDAN) method to obtain more significant potential features of QAR data, solving the problems of mode aliasing and uneven mode gaps that may occur in traditional decomposition methods when processing non-stationary signals. Secondly, a dynamic multidimensional particle swarm optimization algorithm (DMPSO) was constructed using an adaptive adjustment dynamic change method of inertia weight and learning factor, which solved the problem of local extremum and low search accuracy in the solution space that PSO algorithm is prone to during the optimization process. Then, a DMPSO-LSTM aircraft fuel consumption model was established to achieve fuel consumption prediction for three flight segments: climb, cruise, and descent. The final proposed model was validated on real-world datasets, and the results showed that it outperformed other baseline models such as BP, RNN, PSO-LSTM, etc. Among the results, the climbing segment MAE index decreased by more than 40%, the RMSE index decreased by more than 38%, and the R² index increased by more than 6%, respectively. The MAE index of the cruise segment decreased by more than 40%, the RMSE index decreased by more than 40%, and the R² index increased by more than 5%, respectively. The MAE index of the descending segment decreased by more than 20%, the RMSE index decreased by more than 30%, and the R² index increased by more than 5%, respectively. The improved prediction accuracy can be used to implement multi-criteria optimization in flight operations: (1) by quantifying weight–fuel relationships, it supports payload–fuel tradeoff decisions; (2) enhanced phase-specific predictions allow optimized climb/cruise profile selections, balancing time and fuel use; and (3) precise consumption estimates facilitate optimal fuel-loading decisions, minimizing safety margins. The high-precision fuel consumption prediction framework proposed in this study provides actionable insights for airlines to optimize flight operations and design low-carbon route strategies, thereby accelerating the aviation industry’s transition toward net-zero emissions. Full article

Flight Operations Quality Assurance (FOQA) is an internationally recognized solution to ensure the safety of civil aircraft flights based on Quick Access Recorder (QAR) data. The traditional approach to anomaly detection in civil aviation is to detect the over-limit values of monitoring parameters for each monitoring event based on the standards issued by civil aviation authorities. Usually, for each anomaly detection operation routine, this only works for one monitoring event. Furthermore, the causal analyses for the detected anomaly events are based on the relevant worker’s expertise. In order to improve the efficiency of FOQA, this paper proposes an automated anomaly detection and causal analysis method called $\mathsf{MAD}\text{-}\mathsf{XFP}$. Due to the unique industry characteristics of QAR data and the requirements of FOQA, feature engineering and hyper-parameter optimization techniques are utilized to enhance the machine learning model. The proposed method can monitor multiple events in one routine and provide a causal analysis. In the causal analysis process, the Shapley additive interpretation method is applied to produce analysis report for detected anomalies. Experimental evaluations are conducted on real civil aviation datasets. The experimental results show that the proposed method can efficiently and automatically detect different abnormal events with high precision in the approach phase and produce preliminary causal analysis. Full article

In situ observational data from aircraft within microbursts is rather rare in Hong Kong, and such a case is documented in this paper by comparison with the large amount of meteorological data in the vicinity of Hong Kong International Airport, in particular, from the weather radars. Three-dimensional wind field retrieval has been conducted from the radars, and the wind data so obtained are compared with the vertical velocity and eddy dissipation rate measured onboard the aircraft during the encountering of two microbursts. The two datasets are found to be generally consistent with each other. The dataset and the meteorological phenomenon studied in this paper are unique, and it is hoped that such a documented case could be useful for reference for aviation weather forecasting and alerting elsewhere in the world and the design of new aircraft. Full article

A significant challenge that hinders advancements in medical research is the sensitive and confidential nature of patient data in available datasets. In particular, sharing patients’ facial images poses considerable privacy risks, especially with the rise of generative artificial intelligence (AI), which could misuse such data if accessed by unauthorized parties. However, facial expressions are a valuable source of information for doctors and researchers, which creates a need for methods to derive them without compromising patient privacy or safety by exposing identifiable facial images. To address this, we present a quick, computationally efficient method for detecting action units (AUs) and their intensities—key indicators of health and emotion—using only 3D facial landmarks. Our proposed framework extracts 3D face landmarks from video recordings and employs a lightweight neural network (NN) to identify AUs and estimate AU intensities based on these landmarks. Our proposed method reaches a 79.25% F1-score in AU detection for the main AUs, and 0.66 in AU intensity estimation Root Mean Square Error (RMSE). This performance shows that it is possible for researchers to share 3D landmarks, which are far less intrusive, instead of facial images while maintaining high accuracy in AU detection. Moreover, to showcase the usefulness of our AU detection model, using the detected AUs and estimated intensities, we trained state-of-the-art Deep Learning (DL) models to detect pain. Our method reaches 91.16% accuracy in pain detection, which is not far behind the 93.14% accuracy obtained when employing a convolutional neural network (CNN) with residual blocks trained on actual images and the 92.11% accuracy obtained when employing all the ground-truth AUs. Full article

Background/Objectives: Decreased well-being may be a precursor to mental health challenges. Mental health visits for 5–11-year-old children increased by 24% from 2019 to 2020. COVID-19 led to record high levels of anxiety and depression in young children. This highlights the need for early identification and intervention of decreased well-being to prevent progression to potential mental health issues. The purpose of our research was to develop the Pediatric Well-Being Picture Scale© (PWBPS©), the first picture-based screening tool for ages 8–11 years, accessible to children regardless of their literacy, language skill, and developmental levels, allowing for quick screening for early referral and intervention. Methods: The mixed-methods research design included focus groups and one-on-one interviews for content and face validity, test/retest reliability, convergent validity, and exploratory factor analysis. Subjects were recruited from public elementary schools. Results: The numbers of participating subjects were as follows: N = 17 for focus groups; N = 12 for one-on-one interviews; N = 50 for test/retest reliability; and N = 237 for convergent validity. Thematic analysis resulted in a 10-item, 3-point picture-based Likert scale. The test/retest reliability demonstrated strong correlations, with an ICC of 0.823 (95% CI [0.690, 0.905]). The Cronbach’s alpha for all the administrations was 0.74, 0.74, 0.84, and 0.89. The convergent validity demonstrated correlation with the validated KIDSCREEN-10. The Spearman’s correlation was 0.64 (95% CI as [0.55, 0.71]). The cutoff for the PWBPS© was 18.5, which correlated to a score of 22 on the KIDSCREEN-10. All the items loaded on one component. Conclusions: These findings demonstrate that the PWBPS© is valid and reliable, allowing for quick and accurate assessments of children’s well-being and allowing for early intervention, which is key to reducing the negative effects of poor mental well-being. Full article

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To assess the pollutant emissions and dispersion of aircraft during the landing and takeoff (LTO) cycle, and address air pollution surrounding the airport resulting from flight operations, this study evaluated emissions throughout the LTO phase based on Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources was employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability. Specifically, a change in wind direction from 0° to 45° caused a shift of approximately 1000 m in the contaminant’s center. An increase in wind speed from 3 m/s to 5 m/s led to a decrease in concentration by about 15%. Furthermore, a transition in atmospheric stability from category ‘a’ (very unstable) to ‘f’ (very stable) resulted in a two-order-of-magnitude increase in contaminant concentrations. Full article

Snakebite envenomations (SBE) are a significant global public health threat due to their morbidity and mortality. This is a neglected public health issue in many tropical and subtropical countries. Brazil is in the top ten countries affected by SBE, with 32,160 cases reported only in 2020, posing a high burden for this population. In this paper, we describe the data structure of snakebite records from 2007 to 2020 in the Notifiable Disease Information System (SINAN), made available by the Brazilian Ministry of Health (MoH). In addition, we also provide R scripts that allow a quick and automatic updating of data from the SINAN according to its availability. The data presented in this work are related to clinical and demographic information on SBE cases. Also, data on outcomes, laboratory results, and treatment are available. The dataset is available and freely accessible; however, preprocessing, adjustments, and standardization are necessary due to incompleteness and inconsistencies. Regardless of these limitations, it provides a solid basis for assessing different aspects and the national burden of envenoming. Full article