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With growing air travel demand, weather disruptions cost millions in flight delays and cancellations. Current resilience analysis research has been focused on airports and airlines, rather than the en-route waypoints, and has failed to consider the impact of disruption scenarios. This paper analyses the resilience of the United Kingdom (UK) air traffic network to weather events that disrupt the network’s high-traffic areas. A Demand and Capacity Balancing (DCB) model is used to simulate adverse weather and re-optimise the cancellation, delay, and rerouting of flights. The model’s feasibility and effectiveness were evaluated under 20 concentrated and randomly occurring extreme disruption scenarios, lasting 2 h and 4 h. The results show that the network is vulnerable to extended weather events that target the network’s most central waypoints. However, the network demonstrates resilience to weather disruptions lasting up to two hours, maintaining operational status without any flight cancellations. As the scale of disruption increases, the network’s resilience decreases. Notably, there exists a threshold beyond which further escalation in disruption scale does not significantly impair the network’s performance.

Wind speed affects aviation performance, clean energy production, and other applications. By accurately predicting wind speed, operational delays and accidents can be avoided, while the efficiency of wind energy production can also be increased. This paper initially overviews the definition, characteristics, sensors capable of measuring the feature, and the relationship between this feature and wind speed for all Quality Indicators (QIs). Subsequently, the feature importance of each QI relevant to wind-speed prediction is assessed, and all QIs are employed to predict horizontal wind speed. In addition, we conduct a comparison between the performance of traditional point-wise machine learning models and temporally correlated deep learning ones. The results demonstrate that the Bidirectional Long Short-Term Memory (BiLSTM) neural network yielded the highest level of accuracy across three metrics. Additionally, the newly proposed set of QIs outperformed the previously utilised QIs to a significant degree.

Eye movement is an important indicator of information-seeking behavior and provides insight into cognitive strategies which are vital for decision-making. Various measures based on eye movements have been proposed to capture humans’ ability to process information in a complex environment. The effectiveness of these measures has not yet been fully explored in the field of air traffic management. This paper presents a comparative study on eye-movement measures in air traffic controllers with different levels of working experience. Two commonly investigated oculomotor behaviors, fixation and saccades, together with gaze entropy, are examined. By comparing the statistical properties of the relevant metrics, it is shown that working experience has a notable effect on eye-movement patterns. Both fixation and saccades differ between qualified and novice controllers, with the former type of controller employing more efficient searching strategies. These findings are useful in enhancing the quality of controller training and contributing to an understanding of the information-seeking mechanisms humans use when executing complex tasks

Congestion in airports and metroplexes is increasingly becoming a key bottleneck in the global air transport system. It is largely due to inefficient utilization of runway resources and its consequences of imbalance between demand and capacity. Existing studies mainly focus on runway configuration in a single airport system, and little consideration is given to the impact of demand management on runway configuration in metroplexes. Therefore, this paper proposes a methodology and assessment framework for runway configuration with a focus on the exploitation of multiple active runways in metroplex airports. The primary innovation is an integrated runway configuration management formulation with air traffic demand management options. The first is static and dynamic runway configuration management models featuring three optimization objectives, four cases of demand-capacity imbalance, three air traffic demand management options and three assessment scenarios, for eleven priority settings. The second is an efficient multi-objective evolutionary algorithm with a mechanism of objective-guided individual selection, which can obtain close-to optimal solutions with a very low computational cost within 6 seconds. Computational experiments for the real-world case of the Shanghai metroplex airports show that, the inducing strategy of minimizing the total number of adjusted flights is the best mechanism for making satisfactory tradeoffs among multiple objectives in dynamic runway configuration. The proposed model reduces the total number of adjusted flights by an average of 36% compared with the baseline static runway configuration management. Furthermore, unlike the conventional approach of giving priority to arriving aircraft, a higher priority for departures is more effective in enhancing the performance of runway systems and reducing the number of adjusted flights. The proposed framework can be applied at pre-tactical (e.g., one-day planning) as well as tactical (e.g., several-hours rolling horizon) levels.

Retroclival hemorrhages in adults are very rare and are mostly associated with trauma. Prompt diagnosis is usually very important to avoid neurological deficits and even death. We present a case of retroclival hemorrhage identified at autopsy in a 37 year old man who had a history of trauma.

In environments that are hostile to Global Navigation Satellites Systems (GNSS), the precision achieved by a mobile light detection and ranging (LiDAR) system (MLS) can deteriorate into the sub-meter or even the meter range due to errors in the positioning and orientation system (POS). This paper proposes a novel least squares collocation (LSC)-based method to improve the accuracy of the MLS in these hostile environments. Through a thorough consideration of the characteristics of POS errors, the proposed LSC-based method effectively corrects these errors using LiDAR control points, thereby improving the accuracy of the MLS. This method is also applied to the calibration of misalignment between the laser scanner and the POS. Several datasets from different scenarios have been adopted in order to evaluate the effectiveness of the proposed method. The results from experiments indicate that this method would represent a significant improvement in terms of the accuracy of the MLS in environments that are essentially hostile to GNSS and is also effective regarding the calibration of misalignment.

This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively.

Robotic air vehicles are used increasingly in delivering goods especially for safety-of-life applications. This paper discusses a guidance module for trajectory generation of such vehicles. An offline algorithm is developed using a navigation model to produce the required trajectory in the form of time-tagged longitude, latitude and altitude. The process is an essential requirement when an operator has to program a robotic vehicle to travel on the desired course. This problem is addressed scarcely in the relevant literature. The waypoints are generated for all phases of flight and then modified to cater for the wind disturbance parameters obtained from current meteorological information. The waypoints are uploaded to the vehicle's flight control system memory and reside there for the vehicle to follow. This paper also renders the generated trajectory on Google Earth® using Matlab/Simulink® to test the closed-loop performance. Furthermore, a Dryden wind model is utilized to generate a modified trajectory for turbulent conditions. An operator can make adjustments in the required initial heading angle so the vehicle lands at its destination even in turbulent weather. An empirical formula is also proposed for this purpose. Further work includes design of a control system to follow the generated waypoints.

AbstractA study on the genetic diversity of HIV-1 subtypes present along the coastal strip of Kenya, i.e., Kilifi, Mombasa, Msambweni, and Malindi districts, was carried out. DNA sequences for regions encoding a portion of the env-gp41region of the virus were generated by PCR and sequenced directly. Eighty six samples that were successfully sequenced were analyzed. From the analysis, 86% (74) were subtype A1, 5% (4) were subtype C, 8% (7) were subtype D, and 1% (1) was subtype G. This study shows that HIV-1 subtype A1 is the most dominant subtype in circulation in this region. [ABSTRACT FROM AUTHOR]

Abstract  Pseudorange-based integrity monitoring, for example receiver autonomous integrity monitoring (RAIM), has been investigated for many years and is used in various applications such as non-precision approach phase of flight. However, for high-accuracy applications, carrier phase-based RAIM (CRAIM), an extension of pseudorange-based RAIM (PRAIM) must be used. Existing CRAIM algorithms are a direct extension of PRAIM in which the carrier phase ambiguities are estimated together with the estimation of the position solution. The main issues with the existing algorithms are reliability and robustness, which are dominated by the correctness of the ambiguity resolution, ambiguity validation and error sources such as multipath, cycle slips and noise correlation. This paper proposes a new carrier phase-based integrity monitoring algorithm for high-accuracy positioning, using a Kalman filter. The ambiguities are estimated together with other states in the Kalman filter. The double differenced pseudorange, widelane and carrier phase observations are used as measurements in the Kalman filter. This configuration makes the positioning solution both robust and reliable. The integrity monitoring is based on a number of test statistics and error propagation for the determination of the protection levels. The measurement noise and covariance matrices in the Kalman filter are used to account for the correlation due to differencing of measurements and in the construction of the test statistics. The coefficient used to project the test statistic to the position domain is derived and the synthesis of correlated noise errors is used to determine the protection level. Results from four cases based on limited real data injected with simulated cycle slips show that residual cycle slips have a negative impact on positioning accuracy and that the integrity monitoring algorithm proposed can be effective in detecting and isolating such occurrences if their effects violate the integrity requirements. The CRAIM algorithm proposed is suitable for use within Kalman filter-based integrated navigation systems. [ABSTRACT FROM AUTHOR]

AbstractThe genetic diversity of HIV-1 subtypes circulating in three districts of northern Kenya, i.e., Turkana, Mandera, and Moyale, was studied. DNA sequences encoding a portion of the env-C2-V3region of the virus were amplified by PCR and sequenced directly. One hundred and fifty-nine samples were successfully sequenced in the env-C2-V3region and analyzed. From the analysis, 57 were subtype A1, 27 were subtype C, 9 were subtype D, and the remaining 7 were unclassified. This study showed that HIV-1 subtype A1 was the dominant subtype in circulation in this region, though there was a significant percentage of HIV-1 subtype C in circulation there. [ABSTRACT FROM AUTHOR]

Abstract  The Galileo integrity chain depends on a number of key factors, one of which is contamination of the signal-in-space errors with residual errors other than imperfect modelling of satellite orbits and clocks. A potential consequence of this is that the user protection limit is driven not by the errors associated with the imperfect orbit and clock modelling, but by the distortions induced by noise and bias in the integrity chain. These distortions increase the minimum bias the integrity chain can guarantee to detect, which is reflected in the user protection limit. A contributor to this distortion is the inaccuracy associated with the estimation of the offset between the Galileo sensor station (GSS) receiver clocks and the Galileo system time (GST). This offset is termed the receiver clock synchronization error (CSE). This paper describes the research carried out to determine both the CSE and its associated error using GPS data as captured with the Galileo System Test Bed Version 1 (GSTB-V1). In the study we simulate open access to a time datum using IGS data. Two methods are compared for determining CSE and the corresponding uncertainty (noise) across a global network of tracking stations. The single-epoch single-station method is an ‘averaging’ technique that uses a single epoch of data, and is carried out at individual sensor stations, without recourse to the data from other stations. The global network solution method is also single epoch based, but uses the inversion of a linearised model of the global system to solve for the CSE simultaneously at all GSS along with a number of other parameters that would otherwise be absorbed into the CSE estimate in the averaging technique. To test the effectiveness of various configurations in the two methods the estimated synchronisation errors across the GSS network (comprising 25 stations) are compared to the same values as estimated by the International GPS Service (IGS) using a global tracking network of around 150 stations, as well as precise orbit and satellite clock models determined by a combination of global analysis centres. The results show that the averaging technique is vulnerable to unmodelled errors in the satellite clock offsets from system time, leading to receiver CSE errors in the region of 12 ns (3.7 m), this value being largely driven by the satellite CSE errors. The global network approach is capable of delivering CSE errors at the level of 1.5 ns (46 cm) depending on the number of parameters in the linearised model. The International GNSS Service (IGS) receiver clock estimates were used as a truth model for comparative assessment. [ABSTRACT FROM AUTHOR]

Abstract  This is the second of two consecutive papers (Part II) in this journal on the monitoring of the integrity of integrated GPS/INS systems. Part I established that the worst class of error for an integrated system in terms of failure detection performance is that of slowly growing errors (SGEs). It was also concluded that among the integration architectures, the tightly coupled architecture provides the best mechanism to tackle SGEs due to its simpler structure and accessibility to the relevant measurements. In a subsequent comparison of the existing integrity algorithms, the multiple solution separation (MSS) and autonomous integrity monitoring by extrapolation (AIME) methods were selected for further analysis because of their representative characteristics of the current integrity algorithms. This paper carries out a detailed investigation of the capability of the MSS and AIME algorithms to deal with the SGEs, using a realistic simulation platform and limited real data analysis. Results show that although it is possible for the existing algorithms to detect SGEs, the time it takes to detect them varies inversely to the rate of growth of the SGE. A new algorithm is developed to deal with this based on the concept of rate detection. Simulation results show that the new algorithm can detect SGEs earlier than the current algorithms. This is validated by preliminary tests with real data. [ABSTRACT FROM AUTHOR]

One of the key approaches to monitor the integrity of Global Satellite Navigation Systems (GNSS) is receiver autonomous integrity monitoring (RAIM). Existing RAIM algorithms utilise two tests in the position domain (for RAIM availability) and measurement domain (for failure detection). This paper proposes an alternative RAIM algorithm, which is based entirely in the measurement domain. This algorithm can be used for sensitivity analyses to support performance specification and system design. It can also be used during actual flight operations where the trigger is the phase of flight and its required navigation performance (RNP) parameters. This is made possible by computationally efficient calculation of the chi-squared parameters. The algorithm reverts to the current approach if the phase of flight is unknown. Simulation results for non-precision approach (NPA) have been used to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]