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Traffic management in aviation, shipping, and rail transport shows similarities and dissimilarities in the work process. For example, they share the temporal aspect, but different levels of urgency in the control work set different requirements on monitoring, decisions, and actions. However, few studies have been presented that model and compare the different domains in terms of temporal decision-making. The Joint Control Framework (JCF) is an approach to analyse and temporally model operators' control processes from a cognitive systems engineering perspective. In this study, we have used JCF to map, and compare, cognitive joints, such as perceptions, decisions, and actions, in temporally challenging control situations in air traffic control, maritime vessel traffic service, and train traffic management. Data was collected collaboratively with traffic operators, focusing on (1) identifying challenging traffic situations and (2) jointly modelling the temporal decision-making patterns of these situations using simplified JCF. Post-analysis was done by breaking down the results into different processes and comparing domains to ascertain how operators maintain control. An intermediate level of activity—between general monitoring and work with specific vehicles—was identified: processes-in-focus. A shared problem arises in the shift between general monitoring and the processes-in-focus. All processes-in-focus comprise cognitive joint cycles of perceptions, decisions, and actions. However, depending on the framing of processes-in-focus, the patterns of joints, such as temporal extension and complexity, differ. In the remainder of the article, implications for the interaction design, in particular the potential for human–AI/automation teaming with higher levels of automation and cognitive autonomy, are discussed. [ABSTRACT FROM AUTHOR]

There is a significant surge in unmanned aerial vehicles' (UAVs) popularity with an ongoing increase in demand due to their multi-functional uses. The capacity of the UAVs to respond to society and industry needs account for their pervasiveness. UAVs can be strategically deployed to enhance network efficiency and support critical missions, including vital tasks like infrastructure monitoring operations. To be effective, a UAV must interact securely with its network's entities, such as ground control stations, other UAVs, air traffic control systems, and navigation satellite systems. UAVs are exposed to a dangerous and costly world of cyber dangers as a result of cyber technology and connections. The UAV and the Ground Control Station (GCS) exchange information using communication lines, which are vulnerable to cyber-attacks. As a result, detective, defensive, and preventative countermeasures are critical. The Micro Air Vehicle Link (MAVLink) protocol is a widely used lightweight communication protocol for enabling communication between UAVs and GCSs. It outlines communication exchanges between a GCS and a UAV. It carries information about the UAV's condition as well as commands for control from the GCS. Although widely used, the MAVLink protocol lacks sufficient security measures and is susceptible to various types of attacks, which can pose significant risks to public safety. UAV data should be encrypted because they are very sensitive. To give a high level of security, complex operation encryption techniques are developed. These mathematical operations bring forth issues with efficiency and power usage, though. One of these methods, the ChaCha cipher, recently gained popularity after Google used it in several applications. In the current study, a new stream cipher method with low-duty cycles is proposed for protecting data in UAVs and is compared with existing security algorithms on the basis of various factors. The research findings show that by including the suggested method in MAVLink, it is possible to maintain both message secrecy and battery life for a resource-constrained UAV. This can be done while using comparable amounts of memory and CPU and without affecting MAVLink's performance. [ABSTRACT FROM AUTHOR]

In air traffic control (ATC), Key Information Recognition (KIR) of ATC instructions plays a pivotal role in automation. The field's specialized nature has led to a scarcity of related research and a gap with the industry's cutting-edge developments. Addressing this, an innovative end-to-end deep learning framework, Small Sample Learning for Key Information Recognition (SLKIR), is introduced for enhancing KIR in ATC instructions. SLKIR incorporates a novel Multi-Head Local Lexical Association Attention (MHLA) mechanism, specifically designed to enhance accuracy in identifying boundary words of key information by capturing their latent representations. Furthermore, the framework includes a task focused on prompt, aiming to bolster the semantic comprehension of ATC instructions within the core network. To overcome the challenges posed by category imbalance in boundary word and prompt discrimination tasks, tailored loss function optimization strategies are implemented, effectively expediting the learning process and boosting recognition accuracy. The framework's efficacy and adaptability are demonstrated through experiments on two distinct ATC instruction datasets. Notably, SLKIR outperforms the leading baseline model, W2NER, achieving a 3.65% increase in F1 score on the commercial flight dataset and a 12.8% increase on the training flight dataset. This study is the first of its kind to apply small-sample learning in KIR for ATC and the source code of SLKIR will be available at: https://github.com/PANPANKK/ATC_KIR. [ABSTRACT FROM AUTHOR]

Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation. [ABSTRACT FROM AUTHOR]

With the rapid expansion of transportation demand, the number of global flights has rapidly increased, which also poses challenges to air traffic management (ATM). Considering that the radar system in ATM can no longer meet the requirements of flight safety, a very promising next-generation air traffic control technology—Automatic Dependent Surveillance Broadcast (ADS-B) technology has been introduced. However, in the event of on-board equipment failure and local area signal interference, the ADS-B's signal will disappear or be interrupted. This sudden situation can pose a danger to aviation safety. To solve this problem, this article proposes a bidirectional long short-term memory (Bi-LSTM) network prediction method combining historical ADS-B data to short-term predict the trajectory of aircraft, which can improve aviation safety in busy airspace. Firstly, the problem of frequent dynamic modeling of different types of aircraft was solved by utilizing historical ADS-B data as the data source. Secondly, the data cleansing method is proposed for ADS-B raw data. Furthermore, considering that the spatial trajectory of the aircraft is a complex time series with continuity and interactivity, a bidirectional LSTM based aircraft trajectory prediction framework is proposed to further improve prediction accuracy. Finally, a trajectory with frequent changes was selected for prediction, and compared with 7 prediction methods. The results showed that the proposed method had high prediction accuracy, thus also improving the aviation safety of the aircraft. [ABSTRACT FROM AUTHOR]

Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation. [ABSTRACT FROM AUTHOR]

The primary concern of an air traffic controller is to ensure the safety and fluidity of ever-increasing air traffic. This requires effective training through practical work supervised by instructors. Based on certain rules called separation rules, the trainee must find a solution to a traffic configuration defined by flight plans (FPL) initially containing a number of conflicts. This solution will then be compared to the one proposed by the instructor. The purpose of this article is to replace the instructor with a Geographical Information System (GIS) solution combined with a genetic algorithm which, from a set of FPLs, will find the best solution to ensure on the one hand the safety of the aircraft but also minimizing the distance and the changes to be made. The application will use the GAMA platform, very suitable for this and a set of tests composed of actual exercises will be performed to validate the work. [ABSTRACT FROM AUTHOR]

The popularity of air transportation, both for personal and commercial uses, has been growing in recent years. As a result, the traffic volume in the airspace is constantly increasing, which leads to the higher chances of conflicts among aircraft. In air traffic management, conflict detection and resolution are challenging and stressful tasks due to the highly dynamic nature of aircraft flight plans as well as the interdependency among pilots' decisions. Therefore, reliable and comprehensive decision-making techniques are necessary to deal with such conflicts in the airspace in a timely manner. In this regard, innovative technological developments are essential to assist decision-makers. In this paper, we propose a semi-decentralized, three-stage algorithm based on game theory to resolve potential conflicts among multiple aircraft traveling in a 3D shared airspace. The result from the algorithm shows that deviation costs are highly sensitive to the level of congestion in the airspace. The proposed algorithm provides useful information for air traffic control and pilots to enhance their coordination and facilitate decision-making procedures in scenarios with single and multiple conflicts during both nominal and deviated-from-nominal situations. [ABSTRACT FROM AUTHOR]

Visuospatial attention is a cognitive skill essential to the performance of air traffic control activities. We evaluated the effect of an anodic session of transcranial low-intensity direct current stimulation (tDCS) right parietal associated with cognitive training of visuospatial attention of 21 air traffic controllers. Within-subject designs were used, with all volunteers undergoing two tDCS sessions; an experimental (2 mA anodic) and control (sham) performed concomitantly with the cognitive training (2-Back). Visuospatial performance was measured using the Attention Network Test for Interactions and Vigilance pre- and post-intervention. The results indicate that after an active parietal tDCS session, the ATCOs showed faster responses, but not more accurate, for visuospatial attention in its aspects of orientation and reorientation. This result was significant when comparing baseline and post-tests in the active tDCS group. Comparing the post-tests between the tDCS active and sham groups, it is possible to infer a trend of improvement in the results based on faster and more accurate responses, which suggests a possible refinement of the ATCO's attentional orientation. However, this population may eventually have reached a plateau in the performance of this skill. From the analysis of the results we arrive at the following hypotheses: (I) the increase in cortical excitability mediated by anodic tDCS frequently recorded may not be accompanied by improvements in behavioural measures; (II) the interaction between anodic tDCS with another event of increased excitability—execution of a cognitive task, may have hindered the occurrence of neuroplasticity; (III) the air traffic control activity may be associated with a high level of attention, which may have contributed to a ceiling effect for the development of this skill; (IV) online assessments may be more relevant to identify acute effects; (V) repeated sessions may be more efficient to find cumulative effects; (VI) the analysis of interactions between attentional networks can contribute to the study of visuospatial attention; (VII) tDCS protocols aimed at ATCO need to consider the specifics of this audience, such as circadian rhythm and sleep and fatigue conditions. [ABSTRACT FROM AUTHOR]

Airport aircraft identification has essential application value in conflict early warning, anti-runway foreign body intrusion, remote command, etc. The scene video images have problems such as small aircraft targets and mutual occlusion due to the extended shooting distance. However, the detection model is generally complex in structure, and it is challenging to meet real-time detection in air traffic control. This paper proposes a real-time detection network of scene video aircraft-RPD (Realtime Planes Detection) to solve this problem. We construct the lightweight convolution backbone network RPDNet4 for feature extraction. We design a new core component CBL module(Conv (Convolution), BN (Batch Normalization), RELU (Rectified Linear Units)) to expand the range of receptive fields in the neural network. We design a lightweight channel adjustment module block by adding separable depth convolution to reduce the model's structural parameters. The loss function of GIou loss improves the convergence speed of network training. the paper designs the four-scale prediction module and the adjacent scale feature fusion technology to fuse the adjacent features of different abstract levels. Furthermore, a feature pyramid structure with low-level to high-level is constructed to improve the accuracy of airport aircraft's small target detection. The experimental results show that compared with YOLOv3, Faster-RCNN, and SSD models, the detection accuracy of the RPD model improved by 5.4%, 7.1%, and 23.6%; in terms of model parameters, the RPD model was reduced by 40.5%, 33.7%, and 80.2%; In terms of detection speed, YOLOv3 is 8.4 fps while RPD model reaches 13.6 fps which is 61.9% faster than YOLOv3. [ABSTRACT FROM AUTHOR]

We provide evidence for a power law relationship between the subjective one-dimensional Instantaneous Self Assessment workload measure (five-level ISA-WL scale) and the radio communication of air traffic controllers (ATCOs) as an objective task load variable. It corresponds to Stevens' classical psychophysics relationship between physical stimulus and subjective response, with characteristic power law exponent γ of the order of 1. The theoretical model was validated in a human-in-the loop air traffic control simulation experiment with traffic flow as environmental stimulus that correlates positively with ATCOs frequency and duration of radio calls (task load, RC-TL) and their reported ISA-WL. The theoretical predictions together with nonlinear regression-based model parameter estimates expand previously published results that quantified the formal logistic relationship between the subjective ISA measure and simulated air traffic flow (Fürstenau et al. in Theor Issues Ergon Sci 21(6): 684–708, 2020). The present analysis refers to a psychophysics approach to mental workload suggested by (Gopher and Braune in Hum Factors 26(5): 519–532, 1984) that was recently used by (Bachelder and Godfroy-Cooper in Pilot workload esimation: synthesis of spectral requirements analysis and Weber's law, SCL Tech, San Diego, 2019) for pilot workload estimation, with a corresponding power law exponent in the typical range of Stevens' exponents. Based on the hypothesis of cognitive resource limitation, we derived the power law by combination of the two logistic models for ISA-WL and communication TL characteristics, respectively. Despite large inter-individual variance, the theoretically predicted logistic and power law parameter values exhibit surprisingly close agreement with the regression-based estimates (for averages across participants). Significant differences between logistic ISA-WL and RC-TL scaling parameters and the corresponding Stevens exponents as ratio of these parameters quantify the TL/WL dissociation with regard to traffic flow. The sensitivity with regard to work conditions of the logistic WL-scaling parameter as well as the power law exponent was revealed by traffic scenarios with a non-nominal event: WL sensitivity increased significantly for traffic flow larger than a critical value. Initial analysis of a simultaneously measured new neurophysiological (EEG) load index (dual frequency head maps, DFHM, (Radüntz in Front Physiol 8: 1–15, 2017)) provided evidence for the power law to be applicable to the DFHM load measure as well. [ABSTRACT FROM AUTHOR]

An accurate recognition method of the air traffic flow pattern is proposed based on historical flight trajectory data to understand the spatial distribution of air traffic flow and improve the airspace utilization in the terminal area. Due to the high dimensionality of flight trajectory data, we establish a trajectory similarity model based on the geodesic distance and use an improved spectral clustering algorithm to classify the flight trajectory sample data. An improved algorithm based on the minimum spanning tree is presented to extract the skeleton similarity between the tracks and obtain the model of the prevalent traffic flow. The experimental results show that the method can accurately divide 1070 flight paths into 5 categories and extract 7 prevalent traffic flows, exhibiting strong robustness to abnormal trajectories and noise. [ABSTRACT FROM AUTHOR]

The ADS-B—automatic dependent surveillance-broadcast—technology requires aircraft to broadcast their position and velocity periodically. As compared to legacy radar technologies, coupled with alarming cyber security issues (the ADS-B protocol provides no encryption nor identification), the reliance on aircraft to communicate this surveillance information exposes air transport to new cyber security threats, and especially to FDIAs—false data injection attacks—where an attacker modifies, blocks, or emits fake ADS-B messages to dupe controllers and surveillance systems. This paper is part of an ongoing research initiative toward the generation of FDIA test scenarios and focuses on supporting the test design activity, i.e., supporting ATC experts to meticulously craft test cases in order to assess the resilience of surveillance systems against FDIAs. To achieve this goal, we propose a complete and powerful domain-specific language (DSL), close to natural language, that provides a large expressiveness to support ATC business experts in creating FDIA's test scenarios. We demonstrate the design capabilities of this approach and its productivity gain with respect to manually creating the FDIAs test scenarios. [ABSTRACT FROM AUTHOR]

We consider the NP-hard problem of optimizing the sequence and times of aircraft landings under necessary constraints. It is impossible to obtain an exact solution of the problem online owing to the large amount of calculations. An integrated approach is proposed to produce an approximate solution: a genetic algorithm is applied at the first stage to obtain an initial solution; this algorithm is then improved based on a heuristic algorithm. The approach proposed permits obtaining optimal or nearly optimal solutions in reasonable time. To study the algorithms developed, a simulation software tool was used. Extensive computational experiments have confirmed the efficiency of the approach. [ABSTRACT FROM AUTHOR]

This paper addresses how to describe critical episodes of interaction between human operators and autonomous, automated, and manual control systems. The first part of the paper poses three questions: (1) what levels of cognitive control are important to include in a descriptive framework for joint human-autonomy in process control; (2) how should one describe temporal developments in joint socio-technical systems; and (3) how does one analyse communication and control at the system joints. The paper proceeds by proposing a new framework for description and analysis, the Joint Control Framework (JCF), with a simple notation, the Score (JCF-S). It allows descriptions of the three previously mentioned aspects through three analytical activities: process mapping (PM), analysis of Levels of Autonomy in Cognitive Control (LACC), and temporal descriptions of human–machine interaction (T-HMI) through the Score notation. This facilitates analyses across cases and domains. The framework is discussed based on an analysis of two episodes; one work episode (from an air traffic control tower simulator); and one work procedure (from an unmanned traffic management system design concept). [ABSTRACT FROM AUTHOR]

Humans still play a key role in air traffic control but their performances limit the capacity of the airspace and are responsible for delays. At the tactical level, even though air traffic controllers (ATCO) are trained for years, their performances are limited. In this article, we first isolated the tactical horizontal deconfliction task and explained its mathematical complexity. We observed through a simple experiment conducted on trainee and experienced ATCOs its complexity on random traffic in a part-task trainer displaying two to five aircraft trajectories at the same altitude. We compared performances of trainee ATCOs with experienced ATCOs using two different displays: a basic display showing information on aircraft positions and a dynamic visualization tool that represents the conflicting portions of aircraft trajectories and the evolution of the conflict zone when the user adds a maneuver to an aircraft. The tool allows the user to dynamically check the potential conflicting zones with the computer mouse before making a maneuver decision. Results showed that in easy situations (two aircraft), performance was similar with both displays and groups. However, as the complexity of the situations grows (from three to five aircraft), the dynamic visualization tool enables users to solve the conflicts more efficiently. Using the tool leads to fewer unsolved conflicts. Even if experienced ATCOs performed much better than trainee ATCOs on complex situations, they also performed much better with the conflict visualization tool than without on such situations. [ABSTRACT FROM AUTHOR]

In the document of the Radio Technical Commission for Aeronautics (RTCA) [1], a methodology has been developed for the flight safety level assessment when using a promising automatic dependent surveillance system in broadcast mode (ADS-B) intended for certification of an air traffic control system. As the analysis presented in the article has shown, this technique is based on an erroneous hypothesis, the use of which leads to underestimated requirements for the ADS-B certified system. The example considered in the article shows that the rejection of this erroneous hypothesis can lead to tightening the requirements by an order of magnitude. To obtain correct results, the article proposes an easy-to-compute modification of the technique that is devoid of this, generally speaking, serious drawback. The use of the proposed technique is illustrated by an example. [ABSTRACT FROM AUTHOR]

Prior to the 21st century, positioning technologies had limited applications including air traffic control, air and sea navigation, satellite communications and related military uses. Today, positioning technologies have deeply merged with daily life and enabled many novel sensors, systems and services. For example, navigation systems are the enablers of road traffic prediction, assisted and autonomous driving, and several aspects of healthcare. They have also facilitated worldwide services provided by companies such as Uber and Lyft. In fact, in many aspects of modern life, localization systems are deemed essential to day-to-day living and are contributing to our general well-being, the economy, and security. Accordingly, position location technologies have become key components of many worldwide industries. These positioning technologies include the Global Positioning System (GPS), WiFi-based indoor localization, cell-phone based localization (including the fusion of GPS, cell-tower based localization and dead-reckoning), and inertial/dead-reckoning techniques. Tracking technologies are also considered key components for localization, as are the more recently integrated concepts of machine learning and artificial intelligence. This paper provides a review of the history of localization, the main technological enablers of localization and assesses the future directions of localization methods. [ABSTRACT FROM AUTHOR]

One central topic in ergonomics and human-factors research is the assessment of mental workload. Heart rate and heart rate variability are common for registering mental workload. However, a major problem of workload assessment is the dissociation among different workload measures. One potential reason could be the disregard of their inherent timescales and the interrelation between participants' individual differences and timescales. The aim of our study was to determine if different cardiovascular biomarkers exhibit different timescales. We focused on air traffic controller and investigated biomarkers' ability to distinguish between conditions with different load levels connected to prior work experience and different time slots. During an interactive real-time simulation, we varied the load situations with two independent variables: the traffic volume and the occurrence of a priority-flight request. Dependent variables for registering mental workload were the heart rate and heart rate variability from two time slots. Our results show that all cardiovascular biomarkers were sensitive to workload differences with different inherent timescales. The heart rate responded sooner than the heart rate variability features from the frequency domain and it was most indicative during the time slot immediately after the priority-flight request. The heart rate variability parameters from the frequency domain responded with latency and were most indicative during the subsequent time slot. Furthermore, by consideration of biomarkers' inherent timescales, we were able to assess a significant effect of work experience on heart rate and mid/high frequency-band ratio of the heart rate variability. Results indicated that different cardiovascular biomarkers reveal different inherent timescales. [ABSTRACT FROM AUTHOR]

Our work aims to explore novel approaches to the challenge of designing the interaction between people and automation. Through a case study within the domain of air traffic control, we focus on designing fine-grained human–automation interactions. We design a concept and develop an interactive lo-fi prototype of an assisted sketching system to enable air traffic controllers to interact with automation in a fine-grained manner and to externalize mental images. Assisted sketching seems to offer a possible way to communicate different degrees of predictive certainty using visual cues and interaction. Our insights further suggest that externalization through assisted sketching could encourage exploration of future scenarios, and support communication and collaboration between air traffic controllers and between air traffic controllers and pilots. The explorative benefits for the individual decision-making process might be more evident in situations where air traffic controllers have more time for reflection, for example during planning or debriefing and in educational settings. [ABSTRACT FROM AUTHOR]

We consider map labeling for the case that a map undergoes a sequence of operations such as rotation, zoom and translation over a specified time span. We unify and generalize several previous models for dynamic map labeling into one versatile and flexible model. In contrast to previous research, we completely abstract from the particular operations and express the labeling problem as a set of time intervals representing the labels' presences, activities and conflicts. One of the model's strength is manifested in its simplicity and broad range of applications. In particular, it supports label selection both for map features with fixed position as well as for moving entities (e.g., for tracking vehicles in logistics or air traffic control). We study the active range maximization problem in this model. We prove that the problem is NP-complete and W[1]-hard, and present constant-factor approximation algorithms. In the restricted, yet practically relevant case that no more than k labels can be active at any time, we give polynomial-time algorithms as well as constant-factor approximation algorithms. [ABSTRACT FROM AUTHOR]

Stress is a word used to describe human reactions to emotionally, cognitively and physically challenging experiences. A hallmark of the stress response is the activation of the autonomic nervous system, resulting in the "fight-freeze-flight" response to a threat from a dangerous situation. Consequently, the capability to objectively assess and track a controller's stress level while dealing with air traffic control (ATC) activities would make it possible to better tailor the work shift and maintain high safety levels, as well as to preserve the operator's health. In this regard, sixteen controllers were asked to perform a realistic air traffic management (ATM) simulation during which subjective data (i.e. stress perception) and neurophysiological data (i.e. brain activity, heart rate, and galvanic skin response) were collected with the aim of accurately characterising the controller's stress level experienced in the various experimental conditions. In addition, external supervisors regularly evaluated the controllers in terms of manifested stress, safety, and efficiency throughout the ATM scenario. The results demonstrated 1) how the stressful events caused both supervisors and controllers to underestimate the experienced stress level, 2) the advantage of taking into account both cognitive and hormonal processes in order to define a reliable stress index, and 3) the importance of the points in time at which stress is measured owing to the potential transient effect once the stressful events have ceased. [ABSTRACT FROM AUTHOR]

In air traffic control (ATC), teamwork is a key component among air traffic control operators (ATCOs) to safely direct the aircraft through the sky and on the ground. To be able to design for future ATC systems, we must understand how ATCOs work together, their teamwork, and how they experience and perceive teamwork, in different ATC environments. We conducted interviews with 16 ATCOs working in four different ATC environments (en-route control, terminal area control, tower control for a small airport and tower control for a large airport in Sweden) and analysed the results in the light of the "Big Five" model of teamwork. The main contributions of this paper are to show: (1) how eight teamwork factors are differently manifested by the ATCOs in the different ATC environments, (2) that teamwork in ATC is important during routine operations, during stressful work, and during abnormal situations, and (3) that the design of the organisation, the environment, and the tools, affects teamwork and the importance of different teamwork factors. [ABSTRACT FROM AUTHOR]

Air traffic controllers are responsible for the safe, expeditious and orderly flow of the air traffic. Their training relies heavily on the use of simulators that can represent various normal and emergency situations. Accurate classification of air traffic scenarios can provide assistance towards a better understanding of how controllers respond to the complexity of a traffic scenario. To this end, we conducted a field study using qualified air traffic controllers, who participated in simulator sessions of terminal radar approach control in a variety of scenarios. The aim of the study was twofold, firstly to explore how decision trees and classification rules can be used for realistic classification of air traffic scenarios and secondly to explore which factors reflect better operational complexity. We applied machine learning methods to the data and developed decision trees and classification rules for these scenarios. Results indicated that decision trees and classification rules are useful tools in accurately categorizing scenarios and that complexity requires a larger set of predictors beyond simple aircraft counts. The derived decision trees and classification rules performed well in prediction, stability and interpretability. Practical benefits can be derived in the areas of operations and system design in the context of air traffic flow and capacity management systems. [ABSTRACT FROM AUTHOR]

The emission and contrail of flights at high altitude sector have a great impact on the environment. The paper establishes the multi-objective optimization method of high altitude sector operation based on environmental protection, which can reduce the impact of sector air traffic operation on the environment and enhance the operation efficiency of the high altitude sector. According to the operation characteristics of the sector, the aviation meteorological data of the sector are analyzed, and the forming conditions of the contrail are calculated. Considering the air traffic control rules and aircraft flight characteristics, a multi-objective optimization model of sector flight allocation strategy is established, and the influence of aviation meteorological conditions and different allocation strategy combinations on the optimal operation of sector is analyzed. To solve the multi-objective optimization model, the paper uses an evolutionary computation method in intelligence computing and takes non-dominated sorted genetic algorithm-II with elitist strategy (NSGA-II). Shanghai Area No.20 sector (ZSSSAR20) is selected as the case in this paper. Example verification reveals that taking number of contrails, fuel consumption and flight delays as the optimization targets, aircraft are more likely to rely on altitude deployment strategy so as to better reduce the impact of air traffic operation en route on environment, and improve sector operation efficiency en route. [ABSTRACT FROM AUTHOR]

Cloud computing distinguishes itself from other distributed computing paradigm through offering services on-demand basis without any geographical restrictions. This revolutionizes the computing by offering services to wide array of customers starting from casual user to highly business oriented Industries. In spite of its capabilities, cloud computing still struggle with handling wide array of faults, this causes loss of credibility to cloud computing. Among those faults Byzantine faults offers serious challenge to fault tolerance mechanism, because it often go undetected at the initial stage and it can easily propagate to other VMs before a detection is made. Consequently some of the mission critical application such as air traffic control, online baking etc. still staying away from the cloud for such reasons. However if a Byzantine faults is not detected and tolerated at initial stage then applications such as big data analytics can go completely wrong in spite of hours of computations performed by the entire cloud. Therefore in the previous work a fool-proof Byzantine fault detection has been proposed, as a continuation this work designs a scheduling algorithm (WSSS) and checkpoint optimization algorithm (TCC) to tolerate and eliminate the Byzantine faults before it makes any impact. The WSSS algorithm keeps track of server performance which is part of virtual clusters to help allocate best performing server to mission critical application. WSSS therefore ranks the servers based on a counter which monitors every virtual nodes (VN) for time and performance failures. The TCC algorithm works to generalize the possible Byzantine error prone region through monitoring delay variation to start new VNs with previous checkpointing. Moreover it can stretch the state interval for performing and error free VNs in an effect to minimize the space, time and cost overheads caused by checkpointing. The analysis is performed with plotting state transition and CloudSim based simulation. The result shows TCC reduces fault tolerance overhead exponentially and the WSSS allots virtual resources effectively. [ABSTRACT FROM AUTHOR]

The paper introduces the motivation for creating dedicated speech corpora of air traffic control communication, describes in detail the process of preparation of corpora for both automatic speech recognition and text-to-speech synthesis, presents an illustrative example of speech recognition system developed using the automatic speech recognition corpora and finally describes the technical aspects of the data and the distribution channel. [ABSTRACT FROM AUTHOR]

Interaction with automated systems and other types of technologies seems inevitable and almost a requirement of human work. The aviation sector, and in particular air traffic control, is devoting considerable efforts towards automation, to respond to the increased demand for capacity. Project AUTOPACE investigated the impacts of foreseeable automation over human performance and behaviour. The purpose was to identify new training requirements for air traffic controllers under foreseeable automation scenarios. In addition to the research carried out under the remit of AUTOPACE, the functional resonance analysis method was used to explore how the interactions between human operators and technology may change, as new automation features would be introduced into ATC operations. The FRAM model was developed based on AUTOPACE concept of operations, two levels of automation (E2 and E1) and was then used to instantiate three different non-nominal situations that were also investigated by the project. This paper presents the FRAM-based analysis carried out and discusses the potential impacts of automation, considering uncertainty and variability as two critical aspects that emerge from complex operation scenarios. The relation with AUTOPACE work is continuously established and the added value of FRAM for the pursuit of further AUTOPACE work is argued. [ABSTRACT FROM AUTHOR]

Aerial vehicle networks (AVNs) compose a large number of heterogeneous aerial nodes, such as unmanned aerial vehicles, aircrafts and helicopters. The main characteristics of these networks are the high mobility of aerial nodes and the dynamic network topology. AVNs represent attractive targets for attackers due to the fact that aerial nodes could be connected to an untrusted network and hence lead the attackers to launch lethal threats, e.g., aircraft crash. Therefore, the security of AVNs is mandatory. In this article, we examine the challenges of cyber detection methods to secure AVNs and review exiting security schemes proposed in the current literature. Furthermore, we propose a security framework to protect an aircraft (SFA) against malicious behaviors that target aircrafts communication systems. Numerical results show that SFA achieves a high accuracy detection and prediction rates as compared to the current intrusion detection for aircrafts communication system. [ABSTRACT FROM AUTHOR]

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management. [ABSTRACT FROM AUTHOR]

Air traffic management is organized into filters in order to prevent tactical controllers from dealing with complex conflicting situations. In this article, we describe an experiment showing that a dynamic conflict display could improve human performance on complex conflict situations. Specifically, we designed a display tool that represents the conflicting portions of aircraft trajectories and the evolution of the conflict zone when the user adds a maneuver to an aircraft. The tool allows the user to dynamically check the potential conflicting zones with the computer mouse before making a maneuver decision. We tested its utility on a population of forty students: twenty air traffic controller (ATC) students at the end of their initial training and twenty engineering students with the same background but no ATC training. They had to solve conflicts involving 2-5 aircraft with a basic display and with the dynamic visualization tool. Results show that in easy situations (2 aircraft), performance is similar with both displays. However, as the complexity of the situations grows (from 3 to 5 aircraft), the dynamic visualization tool enables users to solve the conflicts more efficiently. Using the tool leads to fewer unsolved conflicts and shorter delays. No significant differences are found between the two test groups except for delays: ATC students give maneuvers that generate less delays than engineering students. These results suggest that humans are better able to manage complex situations with the help of our conflict visualization tool. [ABSTRACT FROM AUTHOR]

The safe and efficient operation of air traffic is highly dependent on the performance of the Air Traffic Control Officer (ATCO). The ATCOs control the traffic within defined areas by monitoring the traffic and granting clearances. A key element in analyzing the ATCOs is their interaction with the environment through their workplace. Especially the influence of task load on their situation awareness (SA) and applied control strategy provides information on the quality of the workplace. As task load increases, controllers are able to maintain performance by using different management or compensation strategies. This article supports the evaluation of ATCO’s workplaces by focusing on whether probe techniques for assessing SA are applicable for tower control operation and for measuring the influences of increased task load on the control strategy. An experiment with nine ATCOs was conducted in a simulated real-time air traffic control environment. Different measurements for SA were applied and compared regarding their efficiency and validity. The manipulation of task load and visibility influenced the SA and control strategy at the same time. Performance metrics were selected in advance to evaluate the participant’s efficiency. SA was measured with a probe technique and an offline self-assessment method. Findings suggest that probe techniques increase the insight into the understanding of SA in comparison to self-assessment and that they are applicable to the air traffic control environment. Control strategies were derived from the information-gathering process via the eye-movement behavior and connected to task load. The results imply that SA is part of the individual performance and that increasing demand through task load is handled with an adaptation of the control strategy. [ABSTRACT FROM AUTHOR]

In complex cognitive domains such as air traffic control, professionals must be able to adapt to and act upon continuing changes in a highly advanced technological work environment. To function optimally in such an environment, the controllers must be able to regulate their learning. Although these regulation skills should be part of their training, this is not usually the case. This study evaluates a training program that integrates air traffic control skills with regulation skills. The participants were 29 air traffic control students who followed either the original training program (n = 12) or a new program (n = 17) in which the development of regulation skills was embedded in the training of domain specific skills. Compared to students in the original program, the students in the new program showed increased self-efficacy in the use of self-regulated learning skills with improved performance in domain specific competences. The implications of these findings are discussed with regard to the daily training practice of complex cognitive skills. [ABSTRACT FROM AUTHOR]

This article considers the theoretical basics of the flight safety risk management concept that are related to arranging and conducting aeromedical support activities for civil aviation. The characteristic of procedures of discovering flight safety risk factors and hazards is presented. The concept of medical risk is defined. The process of managing risk factors related to changes in the health state of aviation staff is considered. The practical significance of the flight safety risk management concept for the health assessment of aviation and airline traffic control staff is shown. [ABSTRACT FROM AUTHOR]

Aircraft arrival sequencing and scheduling is a classic problem in the air traffic control to ensure safety and order of the operations at the terminal area. Most of the related studies have formulated this problem as a static case and assume the information of all the flights is known in advance. However, the operation of the terminal area is actually a dynamic incremental process. Various kinds of uncertainties may exist during this process, which will make the scheduling decision obtained in the static environment inappropriate. In this paper, aircraft arrival sequencing and scheduling problem is tackled in the form of a dynamic optimization problem. An evolutionary approach, namely dynamic sequence searching and evaluation, is proposed. The proposed approach employs an estimation of distribution algorithm and a heuristic search method to seek the optimal landing sequence of flights. Compared with other related algorithms, the proposed method performs much better on several test instances including an instance obtained from the real data of the Beijing Capital International Airport. [ABSTRACT FROM AUTHOR]

Future air traffic control will have to rely on more advanced automation to support human controllers in their job of safely handling increased traffic volumes. A prerequisite for the success of such automation is that the data driving it are reliable. Current technology, however, still warrants human supervision in coping with (data) uncertainties and consequently in judging the quality and validity of machine decisions. In this study, ecological interface design was used to assist controllers in fault diagnosis of automated advice, using a prototype ecological interface (called the solution space diagram) for tactical conflict detection and resolution in the horizontal plane. Results from a human-in-the-loop simulation, in which sixteen participants were tasked with monitoring automation and intervening whenever required or desired, revealed a significant improvement in fault detection and diagnosis in a complex traffic scenario. Additionally, the experiment also exposed interesting interaction patterns between the participants and the advisory system, which seemed unrelated to the fault diagnosis task. Here, the explicit means-ends links appeared to have affected participants' control strategy, which was geared toward taking over control from automation, regardless of the fault condition. This result suggests that in realizing effective human-automation teamwork, finding the right balance between offering more insight (e.g., through ecological interfaces) and striving for compliance with single (machine) advice is an avenue worth exploring further. [ABSTRACT FROM AUTHOR]

Air traffic controllers must maintain a consistently high level of human performance in order to maintain flight safety and efficiency. In current control environments, performance-influencing factors such as workload, fatigue and situation awareness can co-occur and interact to affect performance. However, multifactor influences and the association with performance are under-researched. This study utilised a high-fidelity, human-in-the-loop, en route air traffic control simulation to investigate the relationship between workload, situation awareness and controller performance. The current study aimed to replicate Edwards et al.'s (in: Proceedings of the 4th AHFE international conference, 21-25th July, San Francisco, USA, 2012) previous finding that factors known to be associated with controller performance do co-vary and can interact, which is associated with a compound influence on performance. In addition, the current study aimed to extend Edwards et al.'s (2012) study by engaging retired controllers as participants and comparing multifactor relationships across four levels of automation. Results suggest that workload and situation awareness may interact to produce a compound (as opposed to cumulative) impact on controller performance. In addition, the effect of the interaction on performance may be dependent on the context and level of automation. Findings have implications for human-automation teaming in air traffic control, and the potential prediction of performance-influencing situations, supporting controller performance in the operational environment. [ABSTRACT FROM AUTHOR]

The next generation Air Transport Management (ATM) requires performance-based safety management that is fully integrated into seamless operational management. To realize this objective, a framework is proposed in this article that structures risk information on ATM consoles in terms of four design principles: (i) integrating operational and safety-related data, (ii) managing interactions and minimizing transfer of risks to other sectors, (iii) generating risk patterns from accumulated experience and (iv) managing resources and safety barriers. The framework is applied to an approach control unit of a regional airport for the design of a performance dashboard that is linked to a safety module for managing hazards and safety barriers. In this sense, safety management is not an additional task to normal operations since risk information is integrated with operational data on a daily basis. Furthermore, the safety module combines traditional elements of bow-ties with new safety indices such as complexity metrics and risk patterns. [ABSTRACT FROM AUTHOR]

The air traffic control system is designed so that controllers are able to accept aircraft into their airspace and handoff aircraft exiting their airspace with minimal need to collaborate with other controllers. This system is designed to manage safety through constraints that rely on prohibitions and protections that limit scope of action. We contend that the focus on constraints has led us to neglect how controllers are actively and collaboratively managing safety. A framework of collaboration in ATC is proposed that guides the research. To provide a preliminary evaluation of this framework, ten observations were conducted with eight controllers handling normal traffic in a terminal control unit serving a city in Australia. Participants were all experienced controllers with a minimum of 25-year ATC experience and at least 10-year experience in their current position. In contrast to what would be expected if controllers were working within the constrained system as designed, 28% of the interactions between controllers could be classified as involving modifications to the plans for aircraft, which shows that controllers are actively managing safety. Further, different types of plan modification (replanning, plan amendment and building plans) and different strategies to negotiate plan modification (deferential, preferential and generational) could be identified. ATC procedures and training programmes are typically based on constrained safety and are largely silent on how controllers actively manage safety. We therefore need to include a consideration of how controllers are actively managing safety if we are to ensure effective system management now and into the future. [ABSTRACT FROM AUTHOR]

Air Traffic Control (ATC) towers are among the most vital structures in each airport. Due to inadequate information regarding the seismic design and assessment of these types of structures, practicing engineers may refer to building codes. However, taking into account the special dynamic behavior of ATC towers, instructions and recommendations provided in building codes often do not comply with the required seismic performance levels of ATC towers. In this study, seismic behaviors of three in-service ATC towers with a dual concrete core lateral load resisting system were studied through pushover and incremental dynamic collapse analysis. Seismic design response factors of the reference towers were calculated. It was found that seismic design response factors adopted by the design code did not provide a uniform safety margin for all reference ATC towers. It was also observed that shorter towers have significantly higher response modification factors compared to taller towers. For the studied towers, a structural over-strength factor of 2.4 and a displacement amplification factor of 4 were obtained. [ABSTRACT FROM AUTHOR]

The paper is focused on one of the major air traffic management problem that consists in sequencing and scheduling airplanes landing and taking off on a runway. This difficult practical task is still carried out by flight controllers manually with little help from decision support systems. In this paper we propose an approach based on a time indexed integer programming formulation. The formulation is solved with a branch and cut method combined with some heuristic rules for dimension reduction. The effectiveness of the proposed approach is illustrated by computational experiments on real-life problem instances for the Milano Linate airport. [ABSTRACT FROM AUTHOR]

The management of uncertainty is a critical aspect of current as well as future air traffic control operations. This study investigated: (1) sources of uncertainty in enroute air traffic control, (2) strategies that air traffic controllers adopt to cope with uncertainty, (3) the trade-offs and contingencies that influences the adoption of these uncertainties, and (4) the requirements for system design that support controllers in following these strategies. The data were collected using a field study in two enroute air traffic control centres, involving 'over the shoulder' observation sessions, discussions with air traffic controllers, and document analysis. Three types of uncertainty coping strategies were identified: reducing uncertainty, acknowledging uncertainty, and increasing uncertainty. The RAWFS heuristic (Lipshitz and Strauss in Organ Behav Hum Decis Process 69:149-163, 1997) and anticipatory thinking (Klein et al. in Anticipatory thinking, Proceedings of the eighth international NDM conference, Pacific Grove, CA, 2007) were used to identify reduction and acknowledgement strategies. Recent suggestions by Grote (Saf Sci 71:71-79, 2015) were used to further explore strategies that increase uncertainty. The study presents a new framework for the classification of uncertainties in enroute air traffic control and identified the uncertainty management strategies and underlying tactics, in context of contingencies and trade-offs between operational goals. The results showed that controllers, in addition to reducing and acknowledging uncertainty, may deliberately increase uncertainty in order to increase flexibility for other actors in the system to meet their operational goals. The study describes new tactics for acknowledging and increasing uncertainty. The findings were summarized in the air traffic controller complexity and uncertainty management model. Additionally, the results bring to light system design recommendations that allow controllers to follow these different coping strategies, including (1) the design of alerts, (2) the transparency of prediction tools, and (3) system flexibility as a requirement for acknowledging and increasing uncertainty. The results are particularly important as uncertainty is likely to increase in future operations of enroute air traffic control, requiring automation support for controllers. Implications for future air traffic management scenarios as envisioned within the SESAR Joint Undertaking (SESAR JU in European ATM Master Plan, 2 eds, 2012) and NextGen (FAA in FAA's NextGen implementation plan, 2014) operational concepts are discussed. [ABSTRACT FROM AUTHOR]

This paper brings a social science perspective (from the ethnicity and diversity literature) to bear on a process that is regarded by many as essentially a technical one: the safe insertion of military unmanned aircraft systems (UAS) in the (inter)national European airspace. The aim of this qualitative study was to gain a more adequate scientific socio-technological understanding of the topic, so as to strengthen issue dialogue and discussion. Indeed, studying the 'integration' of these UASs (as this process is often referred to) through the lens of acculturation literature revealed some socio-technological processes that have been little noticed but which seem to underlie and inform this debate. For example, some voices seem to be favoured over others, a well-known phenomenon in the ethnicity and diversity literature. Safety, it could even be argued, is in this debate the pivot point around which social and other dynamics revolve. Belief and power may thus be more important factors here-'masked' of course-than technical aspects of safety. The results of this study are important not only for the military since the incorporation of military UAS occurs, partially at least, in civilian airspace. Civil actors thus formed a substantial subset of those interviewed here. [ABSTRACT FROM AUTHOR]

The study derives a framework for assessing airport efficiency through evaluating optimal arrival and departure delay thresholds. Assumptions of airport efficiency measurements, though based upon minimum numeric values such as 15 min of turnaround time, cannot be extrapolated to determine proportions of delay-days of an airport. This study explored the concept of delay threshold to determine the proportion of delay-days as an expansion of the theory of delay and our previous work. Data-driven approach using statistical modelling was employed to a limited set of determinants of daily delay at an airport. For the purpose of testing the efficacy of the threshold levels, operational data for Entebbe International Airport were used as a case study. Findings show differences in the proportions of delay at departure ( μ = 0.499; 95 % CI = 0.023) and arrival ( μ = 0.363; 95 % CI = 0.022). Multivariate logistic model confirmed an optimal daily departure and arrival delay threshold of 60 % for the airport given the four probable thresholds {50, 60, 70, 80}. The decision for the threshold value was based on the number of significant determinants, the goodness of fit statistics based on the Wald test and the area under the receiver operating curves. These findings propose a modelling framework to generate relevant information for the Air Traffic Management relevant in planning and measurement of airport operational efficiency. [ABSTRACT FROM AUTHOR]

Understanding speech in the presence of noise, plus remembering and acting upon the key information in speech, is critical for safe flight. Aviation headsets are used by crew in the cockpit of general aviation aeroplanes; however, the resultant noise within the headsets could still present a challenge in regard to the recall of key verbal information, especially if the pilot is a non-native English speaker. To assess the noise reduction provided by in-use aviation headsets, the performance of three headsets, two with the active noise cancellation feature, has been investigated. As expected, the active noise cancellation feature was shown to be effective at the low frequencies. For comparison the performance of three headphones (two with active noise control feature), which are widely promoted for use by aircraft passengers, were also investigated. [ABSTRACT FROM AUTHOR]

With increasing numbers of flights worldwide and a continuing rise in airport traffic, air-traffic management is faced with a number of challenges. These include monitoring, reporting, planning, and problem analysis of past and current air traffic, e.g., to identify hotspots, minimize delays, or to optimize sector assignments to air-traffic controllers. To cope with these challenges, cyber worlds can be used for interactive visual analysis and analytical reasoning based on aircraft trajectory data. However, with growing data size and complexity, visualization requires high computational efficiency to process that data within real-time constraints. This paper presents a technique for real-time animated visualization of massive trajectory data. It enables (1) interactive spatio-temporal filtering, (2) generic mapping of trajectory attributes to geometric representations and appearance, and (3) real-time rendering within 3D virtual environments such as virtual 3D airport or 3D city models. Different visualization metaphors can be efficiently built upon this technique such as temporal focus+context, density maps, or overview+detail methods. As a general-purpose visualization technique, it can be applied to general 3D and 3+1D trajectory data, e.g., traffic movement data, geo-referenced networks, or spatio-temporal data, and it supports related visual analytics and data mining tasks within cyber worlds. [ABSTRACT FROM AUTHOR]

Aircraft landing planning (ALP) is one of the most important challenging problems in the domain of air traffic control (ATC). Solving this NP-hard problem is a valuable aid in organizing air traffic in terminal control area (TCA), which itself leads to a decrease in aircraft fuel consumption, costs of airlines, and workload undertaken by air traffic controllers. In the present paper, the ALP problem is dealt with by applying effective rich knowledge to the optimization process (to remove obvious non-optimal solutions), and the first use of Gravitational Search Algorithm (GSA) in resolving such a case. In this regard, while the specific regulations for safe separation have been observed, the optimal landing time, the optimal runway, and the order of consecutive landings have been determined so that the main goal (minimizing total flight delays) would be best met. Results of simulations show that this approach, compared to previous ones, which are based on Genetic and Bionomic algorithms, GLS, and Scatter search method, considerably decreases total flight delays. Attaining zero in the total flight delays in three scenarios with real data shows that the suggested intelligent approach is more decisive than others in finding an optimal solution. [ABSTRACT FROM AUTHOR]

Methods for maintaining separation between aircraft in the current airspace system rely heavily on human operators. A conflict is an event in which two or more aircraft experience a loss of minimum allowable separation. Interest has grown in developing more advanced automation tools to predict when a traffic conflict is going to occur and to assist in its resolution. The term air space deconfliction is used to describe the resolution of conflicts after they have been predicted or detected. Due to the computationally intensive character of conflict detection and airspace deconfliction, as well as their data parallel nature, they are naturally amenable to parallel processing. This work discusses a parallel implementation of a conflict detection algorithm for application to airspace deconfliction. It uses the NVIDIA Quadro FX 5800 and the Tesla C1060 graphical processing units (GPUs) in conjunction with the Compute Unified Device Architecture (CUDA) hardware/software architecture. Details of the implementation are discussed, including the use of streams for asynchronous programming and the use of multiple GPUs. The performance of the parallel implementation is compared to that of an equivalent sequential version and shown to exhibit improvement in execution time. Recommendations are provided to further improve performance of the algorithm. [ABSTRACT FROM AUTHOR]

To study the relative merits of three training principles - difficulty of training, specificity of training, and variability of training - subjects were trained to follow navigation instructions to move in a grid on a computer screen. Subjects repeated and then followed the instructions by mouse clicking on the grid. They were trained, given a short distractor task, and then tested. There were three groups, each receiving different message lengths during training: easy (short lengths), hard (long lengths), and mixed (all lengths), with all subjects given all lengths at test. At test, the mixed group was best on most lengths, the easy group was better than the hard group on short lengths, and the hard group was better than the easy group on long lengths. The results support the advantages of both specificity and variability of training but do not support the hypothesis that difficult training of the form used here would lead to overall best performance at test. [ABSTRACT FROM AUTHOR]

Computing trajectories of a set of airplanes in their final descent is an important problem in air traffic control. It consists of deciding a trajectory, the runway, and the landing time for each airplane, such that several constraints are satisfied, while optimizing flying (fuel) costs, and minimizing waiting times. To solve this problem, we model it as a discrete game, the k-king puzzle, in which each airplane is represented (and it moves) as a king chess-piece on a chess-board. Although the model has already been introduced in the past, we propose several extensions, taking into account different aspects of the real problem, such as constrained airspaces, distinct airplane speeds, various separation distance among airplanes, and specific restrictions in the landing trajectories. Moreover, we model both static and dynamic cases for 2D and 3D airspaces. On these extensions, we describe an exact resolution method based on ideas and algorithms coming from the formal verification of correctness of hardware devices and software tools area. Furthermore, to improve the size and complexity of the models we are able to deal with, we propose a decomposition technique based on the divide-and-conquer paradigm. This solution, which we call Plane by Plane decomposition, trades-off between accuracy and efficiency, i.e., exact solutions degrade to non-optimal ones to maintain scalability. We finally propose an implementation of this algorithm based on (and taking advantages of) modern multi-core, multi-threaded, systems. We present a detailed description of the model and the algorithms, as well as our computational results for quite large static and dynamic 2D and 3D problems. [ABSTRACT FROM AUTHOR]