The normal operation of aircraft and flights can be affected by various unpredictable factors, such as severe weather, airport closure, and corrective maintenance, leading to disruption of the planned schedule. When a disruption occurs, the airline operation control center performs various operations to reassign resources (e.g., flights, aircraft, and crews) and redistribute passengers to restore the schedule while minimizing costs. We introduce different sources of disruption and corresponding operations. Then, basic models and recently proposed extensions for aircraft recovery, crew recovery, and integrated recovery are reviewed, with the aim of providing models and methods for different disruption scenarios in the practical implementation of airlines. In addition, we provide suggestions for future research directions in these topics.
The Single European Sky ATM Research (SESAR) project is the technological pillar of the European Commission's Single European Sky Initiative to modernize air traffic management (ATM). Here, we describe the process of establishing SESAR and the main parts of the project: the research and development (R&D) part, which is led by the SESAR Joint Undertaking; the deployment part, which is managed by the SESAR Deployment Manager; and the European ATM Master Plan, which collects and lays out both the R&D and deployment needs. The latest European ATM Master Plan was adopted just prior to the current pandemic. The huge loss in air traffic due to the pandemic, and the speed of the recovery of the aviation industry will require reprioritization, but the main elements that have been established—particularly those in support of the environment—remain valid.
Intractable delays occur in air traffic due to the imbalance between ever-increasing air traffic demand and limited airspace capacity. As air traffic is associated with complex air transport systems, delays can be magnified and propagated throughout these systems, resulting in the emergent behavior known as delay propagation. An understanding of delay propagation dynamics is pertinent to modern air traffic management. In this work, we present a complex network perspective of delay propagation dynamics. Specifically, we model air traffic scenarios using spatial-temporal networks with airports as the nodes. To establish the dynamic edges between the nodes, we develop a delay propagation method and apply it to a given set of air traffic schedules. Based on the constructed spatial–temporal networks, we suggest three metrics—magnitude, severity, and speed—to gauge delay propagation dynamics. To validate the effectiveness of the proposed method, we carry out case studies on domestic flights in the Southeastern Asia region (SAR) and the United States. Experiments demonstrate that the propagation magnitude in terms of the number of flights affected by delay propagation and the amount of propagated delays for the US traffic are respectively five and ten times those of the SAR. Experiments further reveal that the propagation speed for US traffic is eight times faster than that of the SAR. The delay propagation dynamics reveal that about six hub airports in the SAR have significant propagated delays, while the situation in the United States is considerably worse, with a corresponding number of around 16. This work provides a potent tool for tracing the evolution of air traffic delays.
In air traffic and airport management, experience gained from past operations is crucial in designing appropriate strategies when facing a new scenario. Therefore, this paper uses massive spatiotemporal flight data to identify similar traffic and delay patterns, which become critical for gaining a better understanding of the aviation system and relevant decision-making. However, as the datasets imply complex dependence and higher-order interactions between space and time, retrieving significant features and patterns can be very challenging. In this paper, we propose a probabilistic framework for high-dimensional historical flight data. We apply a latent class model and demonstrate the effectiveness of this framework using air traffic data from 224 airports in China during 2014–2017. We find that profiles of each dimension can be clearly divided into various patterns representing different regular operations. To prove the effectiveness of these patterns, we then create an estimation model that provides preliminary judgment on the airport delay level. The outcomes of this study can help airport operators and air traffic managers better understand air traffic and delay patterns according to the experience gained from historical scenarios.
Urban air mobility (UAM) is an emerging concept proposed in recent years that uses electric vertical take-off and landing vehicles (eVTOLs). UAM is expected to offer an alternative way of transporting passengers and goods in urban areas with significantly improved mobility by making use of low-altitude airspace. In addition to other essential elements, ground infrastructure of vertiports is needed to transition UAM from concept to operation. This study examines the network design of UAM on-demand service, with a particular focus on the use of integer programming and a solution algorithm to determine the optimal locations of vertiports, user allocation to vertiports, and vertiport access- and egress-mode choices while considering the interactions between vertiport locations and potential UAM travel demand. A case study based on simulated disaggregate travel demand data of the Tampa Bay area in Florida, USA was conducted to demonstrate the effectiveness of the proposed model. Candidate vertiport locations were obtained by analyzing a three-dimensional (3D) geographic information system (GIS) map developed from lidar data of Florida and physical and regulation constraints of eVTOL operations at vertiports. Optimal locations of vertiports were determined to achieve the minimal total generalized cost; however, the modeling structure allows each user to select a better mode between ground transportation and UAM in terms of generalized cost. The outcomes of the case study reveal that although the percentage of trips that switched from ground mode to multimodal UAM was small, users choosing the UAM service benefited from significant time saving. In addition, the impact of different parameter settings on the demand for UAM service was explored from the supply side, and different pricing strategies were tested that might influence potential demand and revenue generation for UAM operators. The combined effects of the number of vertiports and pricing strategies were also analyzed. The findings from this study offer in-depth planning and managerial insights for municipal decision-makers and UAM operators. The conclusion of this paper discusses caveats to the study, ongoing efforts by the authors, and future directions in UAM research.
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.
Aircraft ground movement plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This paper proposes a new approach for generating efficient four-dimensional trajectories (4DTs) on the basis of a high-fidelity aircraft model and gainscheduling control strategy. Working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines, the proposed approach generates fuel-efficient 4DTs in real time, while respecting operational constraints. The proposed approach can be used in two contexts: ① as a reactive decision support tool to generate new trajectories that can resolve unprecedented events; and ② as an autopilot system for both partial and fully autonomous taxiing. The proposed methodology is realistic and simple to implement. Moreover, simulation studies show that the proposed approach is capable of providing an up to 11% reduction in the fuel consumed during the taxiing of a large Boeing 747 jumbo jet.
Several studies have indicated that the oral and gut microbiota may exhibit differences in patients with cirrhosis. Less is known about the microbiota in the stomach, which is located between the oral cavity and the intestinal tract. In this study, the gastric mucosal microbiota of patients with liver cirrhosis and controls were analyzed with 16S ribosomal RNA (rRNA) pyrosequencing. Cirrhotic patients had significantly
lower Helicobacter pylori (H. pylori) infection rates, as confirmed by both the histological method and the pyrosequencing method. In H. pylori-negative subjects, gastric bacterial communities of healthy and cirrhosis cohorts were clustered into four clusters based on bacterial compositions: Cluster_1 and Cluster_2 (mostly cirrhosis), Cluster_3 (mostly healthy), and Cluster_4 (around half of each). Compositional and functional differences were observed among these different clusters. At the genus level, Cluster_1 and Cluster_2 showed enrichment of Neisseria and Streptococcus, respectively. Functionally, Cluster_2 was characterized as depleted of genetic information processing, as well as of modules related to glycan biosynthesis and metabolism. Patients in Cluster_2 had more severe gastrointestinal symptoms and a higher rate of previous endoscopic variceal ligation (EVL) therapy than patients in other clusters. Our findings suggest that the colonization of both H. pylori and non-H. pylori is influenced in liver cirrhosis. Although the H. pylori-negative gastric mucosal microbiota showed considerable heterogeneity, associations between specific gastric microbiota and clinical characteristics could be observed. Previous EVL therapy might lead to a distinct structure of the gastric mucosal microbiota, thus aggravating the gastrointestinal symptoms in H. pylori-negative cirrhotic patients.
Owing to its high heat storage capacity and fast heat transfer rate, packed bed latent heat storage (LHS) is considered as a promising method to store thermal energy. In a packed bed, the wall effect can impact the packing arrangement of phase change material (PCM) capsules, inducing radial porosity oscillation. In this study, an actual-arrangement-based three-dimensional packed bed LHS model was built to consider the radial porosity oscillation. Its fluid flow and heat transfer were analyzed. With different cylindrical sub-surfaces intercepted along the radial direction in the packed bed, the corresponding relationships between the arrangement of capsules and porosity oscillation were identified. The oscillating distribution of radial porosity led to a non-uniform distribution of heat transfer fluid (HTF) velocity. As a result, radial temperature distributions and liquid fraction distributions of PCMs were further affected. The effects of different dimensionless parameters (e.g., tube-to-capsule diameter ratio, Reynolds number, and Stefan number) on the radial characteristics of HTF and PCMs were discussed. The results showed that different diameter ratios correspond to different radial porosity distributions. Further, with an increase in diameter ratio, HTF velocity varies significantly in the near wall region while the non-uniformity of HTF velocity in the center region will decrease. The Reynolds and Stefan numbers slightly impact the relative velocity distribution of the HTF—while higher Reynolds numbers can lead to a proportional improvement of velocity, an increase in Stefan number can promote heat storage of the packed bed LHS system.
There is a lack of high-quality, large-scale, real-world evidence from patients with metastatic colorectal cancer (mCRC), especially in China. It remains unclear whether efforts to improve the quality of care for mCRC would improve patient survival outcomes in real-world practice. On the basis of an intelligent bigdata platform, we established a large-scale retrospective cohort of mCRC patients. We investigated the temporal changes in the systemic and local treatment (resection, ablation, or radiation to liver, lung, or extrahepatic and/or extrapulmonary metastases) patterns of mCRC, and whether these changes were associated with improved overall survival (OS) over time. Between July 2012 and December 2018, 3403 eligible patients were included in this research. The median OS was 42.8 months (95% confidence interval (CI), 40.7– 46.6) for the entire cohort, 25.6 months (95% CI, 24.7–26.9) for those treated with systemic therapy only, and not reached (95% CI, 78.6 months–not reached) for those receiving local therapy. The utility rate of local therapy increased continuously from 37.9% in 2012–2014 to 46.9% in 2017–2018. A dramatic increase in the utility rate of either cetuximab or bevacizumab was observed since 2017 (39.9%, 43.2%, and 60.3% in 2012–2014, 2015–2016, and 2017–2018, respectively). Compared with 2012–2014, the OS of the entire population significantly improved in 2015–2016 (hazard ratio (HR) = 0.87 (95% CI, 0.78–0.99); P = 0.034), but not for patients receiving systemic therapy only (HR = 0.99 (95% CI, 0.86–1.14); P = 0.889), whereas an improved OS was found in 2015–2018 for both the entire population (HR = 0.75 (95% CI, 0.70–0.81); P < 0.001) and for patients receiving systemic therapy only (HR = 0.83 (95% CI, 0.77–0.91); P < 0.001). In summary, the quality of care for mCRC, as indicated by the utility rate of targeted and local therapies, has been continuously improving over time in this study cohort, which is associated with continuously improving survival outcomes for these patients.
The Knudsen effusion cell is often used to grow high-quality Cu(In,Ga)Se2 (CIGS) thin film in coevaporation processes. However, the traditional single-heating Knudsen effusion cell cannot deliver complete metal selenides during the whole deposition process, particularly for a low-temperature deposition process, which is probably due to the condensation and droplet ejection at the nozzle of the crucible. In this study, thermodynamics analysis is conducted to decipher the reason for this phenomenon. Furthermore, a new single-heating Knudsen effusion is proposed to solve this difficult problem, which leads to an improvement in the quality of CIGS film and a relative increase in conversion efficiency of 29% at a growth rate of about 230 nm·min−1 , compared with the traditional efficiency in a lowtemperature rapid-deposition process.