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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>A two-stage stochastic optimization approach for mega-airport departure metering under data-driven taxi-time uncertainty predictions</title>
      <link>https://trid.trb.org/View/2655841</link>
      <description><![CDATA[Over the past decade, mega-airports have experienced a surge in air traffic demand, physical expansion, and increased complexity in apron layouts, leading to a high level of aircraft taxi-time uncertainty and shifting the airport surface management from integrated tower control to dedicated apron control. In this study, a two-stage stochastic optimization framework is developed for mega-airport departure metering (DM), which specializes apron-centric and tower-centric optimization in different stages. Moreover, a data-driven Mixture Density Network (MDN) is built to predict the aircraft taxi-time distribution and characterize the uncertainty levels. A large-scale trajectory dataset is collected from a representative mega-airport in China to illustrate the procedure. The results indicate that the developed two-stage stochastic optimization framework distinguishes tower control and apron control in the DM process, improving the overall flexibility of airport airside operations. The data-driven neural network could better predict the taxi-time uncertainty levels through multimodal probability distributions especially at mega-airport with volatile traffic situations. Furthermore, compared with state-of-the-art DM methods, the two-stage stochastic optimization framework could achieve more robust performance of airport departure management and better trade-off between gate-holding and runway throughput.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:15:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2655841</guid>
    </item>
    <item>
      <title>Estimation of key pollutant emission during the taxi-out phase using a novel hybrid forecasting algorithm(FoREC-HHO): Application to Istanbul Airport</title>
      <link>https://trid.trb.org/View/2640668</link>
      <description><![CDATA[Aircraft emissions during taxi-out operations constitute a significant portion of local air pollution at airports and are rarely modeled in conjunction with operational and meteorological variables. Although numerous studies focus on fuel burn or emission factors based on the ICAO LTO cycle, limited research integrates real-world airport conditions. In this article, CO₂, NOₓ, CO, and HC emissions during the taxi-out phase at Istanbul Airport for the period 2024–2030 were estimated by considering the technical specifications of aircraft, operational delays, and meteorological visibility indices. Detailed analyses were conducted under three categories (Best, Normal, Worst) and 12 scenarios, and the daily intensity of emissions per unit area was evaluated according to the IDLH health risk indicator. In the estimation study, a new hybrid method called the FoREC-HHO algorithm was developed and compared with machine learning, metaheuristic algorithms, and statistical techniques. As a result of the analysis, the FoREC-HHO algorithm showed the highest accuracy rate for all emission types and achieved the lowest MAE values, demonstrating superior prediction performance. According to the analysis findings, in the worst-case scenario, CO₂ emissions increased by 80 %, NOₓ by 76 %, HC by 78 %, and CO by 66 % between 2024 and 2030. In the normal scenario, the emission increases were observed as 57 % for CO₂, 52 % for NOₓ, 53 % for HC, and 46 % for CO. In contrast, under the best-case scenario, these increases were considerably more moderate, measured at 34 % for CO₂, 31 % for NOₓ, 30 % for HC, and 26 % for CO. In addition, by 2030, the risk density for CO₂ is projected to reach 2.33 kg/m²/day, while for CO, this value is 0.01293 kg/m²/day. The calculated densities for NOₓ and HC were determined to be 0.00221 and 0.00030 kg/m²/day, respectively. These values were found to potentially pose high acute toxicity risks for CO, chronic respiratory and nervous system risks for NOₓ and HC, and climate-related effects and physiological burdens on personnel working in enclosed spaces for CO₂. In this study, a comprehensive approach was presented for both temporal and seasonal estimation of emissions associated with the taxi-out process at airports and health-based risk assessment using the newly developed FoREC-HHO algorithm.]]></description>
      <pubDate>Tue, 03 Mar 2026 16:51:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2640668</guid>
    </item>
    <item>
      <title>A historical data approach to optimal gate assignment problem considering aircraft taxi delay and passenger walking time</title>
      <link>https://trid.trb.org/View/2627365</link>
      <description><![CDATA[Assigning appropriate gates to incoming flights, is a common challenge faced by airports and airlines. This study focuses on the gate assignment problem, considering taxi delays and passenger walking times with a historical data approach. Firstly, ten years of taxi time data from the Aviation Service Performance Metrics (ASPM) system and FlightAware were extracted. Then, using statistical analyses, key parameters for taxi time estimation were found, and by defining the Hour Modification Factor concluded from history-based data and Mixed-Integer Linear Programming, the new gate assignment scheme considering taxi delay was developed. As a case study, taxi delays and dedicated gate assignment for American and Southwest Airlines in Phoenix airport were performed. The results showed that the new gate assignment significantly reduced overall taxi times for both airlines with that 10% reduction in overall taxi times for American Airlines and a 16% reduction for Southwest Airlines, indicating a more efficient airport operation.]]></description>
      <pubDate>Thu, 05 Feb 2026 16:39:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2627365</guid>
    </item>
    <item>
      <title>Dynamic response analysis of the aircraft-snow runway coupling system during taxiing</title>
      <link>https://trid.trb.org/View/2643535</link>
      <description><![CDATA[Compared to traditional runways, compacted snow runways exhibit a reduced surface smoothness and modulus, leading to intensified dynamic responses during aircraft taxiing. This study establishes an aircraft-snow runway interaction model using ANSYS software to quantitatively analyze the effects of the runway wavelength, amplitude, modulus, and aircraft taxiing speed on system dynamics. The results of the study are largely in agreement with the results computed by the ADAMS dynamic analysis software. Specifically, as the wavelength-to-wheelbase ratio increases, the peak acceleration of the landing gear and runway surface decrease rapidly and then gradually stabilise, while the peak runway strain first increases rapidly and then stabilises. As the amplitude increases, the peak acceleration of the landing gear and runway surface continuously increase. Furthermore, the peak vertical strain of the runway decreases. As the runway modulus increases, the peak acceleration of both the landing gear and the runway, as well as the peak runway strain, continuously decrease. With increasing aircraft speed from 2 to 30 m/s, the peak landing gear acceleration rises sharply, while the peak runway acceleration increases correspondingly. The findings of this study offer valuable theoretical guidance for the design and construction of snow-covered runways.]]></description>
      <pubDate>Mon, 02 Feb 2026 10:12:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643535</guid>
    </item>
    <item>
      <title>Risk resolution of airport surface based on hybrid Petri nets and inverse reinforcement learning</title>
      <link>https://trid.trb.org/View/2623303</link>
      <description><![CDATA[Risk evaluation and conflict risk resolution of airport surface are two correlated challenge problems; however they are rarely studied jointly to reduce operation risk. Moreover, risk resolution performance of current methods also should be improved. Therefore, this paper proposes a novel risk resolution approach based on hybrid Petri nets and inverse reinforcement learning. Firstly, a hybrid Petri nets based on Bayesian fusion is presented to get taxiing risk evaluation of airport surface and its theorem of strict conservation and structure boundedness is proved. Secondly, a new risk model is proposed to minimize total operation cost of flights. Thirdly, a new risk evaluation method based on hybrid Petri nets is studied, and corresponding time complexity analysis is presented. Fourthly, a novel risk resolution algorithm based on inverse reinforcement learning strategy and game model is developed. Finally, datasets from Nanjing Lukou International Airport are used and experimental results show that: 1) our approach reduces delay time in multi-scale samples by 25% on average than state-of-the-arts methods; 2) taxiing conflicts with unacceptable risk are all resolved successfully; 3) it achieves stable convergence results while traditional methods oscillate uniformly, and even fail to reach a state of convergence; 4) sensitivity analysis and statistical result results verify the effectiveness of our method.]]></description>
      <pubDate>Mon, 26 Jan 2026 14:44:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623303</guid>
    </item>
    <item>
      <title>Lateral stability classification and active stabilisation of towing taxi-out system based on K-clustering classification</title>
      <link>https://trid.trb.org/View/2594066</link>
      <description><![CDATA[The towing taxi-out system composed of a tractor and a civil aircraft has the structural characteristics of driving in the front and mass in the back, which leads to accidents such as lateral instability and even system 'folding' when the system is steering. In this paper, a lateral stability evaluation method based on K-means cluster analysis is proposed to characterize the lateral stability state parameters of the system by four parameters of the lateral angular velocity of the tractor, the lateral angular velocity of the civil aircraft, the articulation angle and the articulation angular velocity. The lateral stability of the system is evaluated into four grades, and a fuzzy control-based control method is designed to control the additional lateral moment, and a differential braking method is used to achieve the lateral stability of the system. The simulation results show the evaluation and control method proposed in this paper can effectively ensure the driving safety of the system in the traction departure experiment with Beijing Daxing International Airport as the simulation scenario.]]></description>
      <pubDate>Wed, 19 Nov 2025 17:09:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2594066</guid>
    </item>
    <item>
      <title>Lateral stability control of heavy-towing taxi-out based on differential braking</title>
      <link>https://trid.trb.org/View/2573103</link>
      <description><![CDATA[The new mode of towing taxi-out consisting of rodless tractor and aircraft, is prone to extreme lateral instability accidents such as slipping and jack-knifing during high-speed turns. To address this issue, a ten-degree-of-freedom dynamic model of the aircraft towing system is established using the Lagrangian analysis method. This model does not consider constraints and limitations at hinge points. Based on this model, a high-speed turn differential braking controller uses yaw rate deviation as yaw moment controller inputs to determine their respective additional yaw moments, according to the braking strategy and braking moment allocation rules, the controller outputs the required braking moments for the target wheels. Finally, through Matlab/Simulink simulations, the steering angle input and initial speed are varied under J-turn and double lane change condition. This allows the delineation of safe and dangerous areas for steering angles and speeds in towing taxi-out mode, and a comparison of the control effectiveness of differential braking.]]></description>
      <pubDate>Wed, 19 Nov 2025 17:09:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2573103</guid>
    </item>
    <item>
      <title>Modeling aircraft emissions during the taxiing phase to assess air quality impacts: An Australian airport case study</title>
      <link>https://trid.trb.org/View/2617221</link>
      <description><![CDATA[As the aviation industry targets net-zero carbon emissions by 2050, the International Civil Aviation Organisation (ICAO) also encourages member states to adopt initiatives to improve air quality by addressing other pollutants. Some airport operators voluntarily evaluate their impact on air quality. Limited literature is available on air quality assessment methods around airports. This paper proposes a refined ICAO air quality model focused on the taxiing phase, customised for both aircraft and airport specifics, providing more accurate emission estimates than the standard model. This enhanced model enables airport operators, airlines, and general aviation operators to create more precise air pollutant emission inventories. Additionally, the results indicate that per-passenger emissions of CO, NOx, HC, and SOx during taxiing are higher in general aviation than for commercial flights at the selected Australian airport case study, with CO emissions around 71 times higher. Consequently, general aviation flights should be included in airport emission inventories due to their significant contribution. In the Australian airport case study, the A330 commercial aircraft and the BE76 general aviation aircraft show the highest CO emissions per passenger during taxiing. Therefore, airlines and general aviation operators should increasingly consider per-passenger emissions during the taxiing phase, along with other factors, in their fleet and operational decisions.]]></description>
      <pubDate>Wed, 12 Nov 2025 08:39:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2617221</guid>
    </item>
    <item>
      <title>Model predictive control with real-time variable weight for civil aircraft towing taxi-out control systems</title>
      <link>https://trid.trb.org/View/2596716</link>
      <description><![CDATA[Unlike the traditional method of taxiing, where civil aircraft reach the runway by relying on their engines, the new mode of towing taxi-out may become preferred because of its lower energy consumption, lower emissions, and higher efficiency. To improve the tracking accuracy and lateral stability of the towing system, this study used a two-level time-varying weight control method (RMPC) based on model predictive control (MPC) and fuzzy control. The tractor speed and front wheel angle were set as the control variables in the MPC controller, and the real-time lateral displacement error and yaw angle error of the tractor were set as the fuzzy control inputs. The influence of the weight matrix on the accuracy and stability of path tracking was coordinated by online optimization of the MPC objective function weight. The simulations of multiple working conditions were performed using TruckSim software in Simulink, which showed that the proposed control method can limit the lateral displacement error of the tractor and aircraft within 0.4 m, which can be reduced by 70.83% and 77.4% compared with the single MPC, respectively. Additionally, the maximum yaw angle errors of the tractor and aircraft are reduced by 76.11% and 51.31% compared with the single MPC, respectively. Furthermore, the yaw angle error of tractors can be limited to 4° and that of aircraft can be limited to 6.5°, which is an improvement of the lateral stability and driving safety for the civil aircraft towing system. The new controller may provide technical insights and support for the practical development and safe application of the towing taxi-out mode.]]></description>
      <pubDate>Wed, 24 Sep 2025 15:31:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2596716</guid>
    </item>
    <item>
      <title>Electric Taxiing System. Kinetic Energy Recovery System as an Electric Taxiing Solution: Economic and environmental analysis</title>
      <link>https://trid.trb.org/View/2548125</link>
      <description><![CDATA[The aviation industry has thrived in recent years, with a significant increase in passenger numbers, leading to the development of larger aircraft fleets and the expansion of airport infrastructure. In 2019, more than 4.5 billion passengers traveled by airplane, which represents an 80% increase compared to 2009. As a consequence, this growth has also resulted in longer taxiing times for aircraft, increasing fuel consumption and operational costs. The industry's CO₂ emissions have quadrupled since the 1960s, with over 1 billion tons released in 2019, contributing to global warming. Given that the improvements being made to current propulsion systems and the production of over 600 million liters of SAF (Sustainable Aviation Fuel) in 2023 do not seem sufficient to meet the ambitious goals set by regulators and operators, the logical solution would be to develop a system capable of moving the aircraft on the ground using alternative, cleaner, and cheaper energies, such as electric power. This paper explores the latest advancements in electric propulsion systems, specifically focusing on external and onboard systems. After the state of the art is established, this paper will cover a case study and propose a new alternative for an onboard system called ETS – Electric Taxiing System. The ETS is a 100% electric system for ground handling operations designed to use only the kinetic energy stored in a battery pack, which is recovered during aircraft landing and braking events. Although there are some studies on this topic, the author considers this paper to be more comprehensive as the results computed here take into account 96 variables, 60 simulations, several flight plans, and real-world data. The case study focuses on the implementation and dimensioning of the ETS, considering electric motor power and torque, and battery capacity for the Embraer Phenom 300. A brief economic and environmental analysis was also conducted, considering the operation of NetJets Europe, the world's leading and largest private jet company.]]></description>
      <pubDate>Wed, 09 Jul 2025 13:59:32 GMT</pubDate>
      <guid>https://trid.trb.org/View/2548125</guid>
    </item>
    <item>
      <title>Graph Neural Networks with Spatiotemporal Flow Features for Aircraft Taxi-Out-Time Prediction</title>
      <link>https://trid.trb.org/View/2558363</link>
      <description><![CDATA[This paper presents a framework for modeling and predicting impeded aircraft taxi-out times based on machine learning techniques. The presented framework can be integrated into departure management systems to support the pretactical/tactical planning of departure movements and the optimization of airport resources. The taxi-out time is modeled with two components: the time taken to travel from the gate to the departure queue and the time spent in the departure queue. The first component (termed the taxiing time) is mainly affected by surface traffic conditions, while the latter component (termed the queuing time) can be more accurately modeled using characteristics derived from the departure queue. To model the spatiotemporal dependencies on traffic flow, we represent the airport taxi system as a node-link model. Flow features are derived in the form of edge attributes based on route information and movement start times. Departure trajectories utilize the same node-link representation, in the form of a subgraph incorporating additional operationally available information. The taxi-out time of each trajectory is obtained by processing the subgraph using a graph neural network (GNN) with transformer layers. Predictions from the GNN model are compared against standard methodologies by the Federal Aviation Administration (FAA) and EUROCONTROL, as well as against predictions made by gradient boosting machines (GBM), a popular decision-tree-based machine learning technique. Results show that both GNN and GBM models outperform the standard FAA and EUROCONTROL methods (with the prediction errors of the former group lower by 40–60% relative to the latter), and the novel GNN model outperforms the GBM model by a considerable margin of approximately 8 s, translating to a 10% improvement in model performance of the GNN model relative to the GBM model.]]></description>
      <pubDate>Fri, 20 Jun 2025 11:58:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2558363</guid>
    </item>
    <item>
      <title>Research on Path Tracking Control of Electric Towbarless Aircraft Taxiing System under Crosswind Condition</title>
      <link>https://trid.trb.org/View/2547844</link>
      <description><![CDATA[The lateral dynamic and kinematic models of the electric towbarless towing vehicle (TLTV)–aircraft system, incorporating active front steering for the TLTV, are formulated to evaluate the impact of crosswind on the aircraft’s towing trajectory. This analysis considers scenarios with varying towing velocities and crosswind directions and intensities. To mitigate crosswind-induced disturbances affecting the aircraft’s motion, a high-speed and low-speed Model Predictive Control (MPC) strategy for the active front steering of a TLTV is proposed. This strategy is designed to optimize the TLTV’s steering performance under varying operational conditions, addressing the distinct dynamic characteristics of high-speed and low-speed towing scenarios. Simulation results demonstrate that the proposed control method achieves exceptional performance in both speed regulation and path tracking during towing operations.]]></description>
      <pubDate>Tue, 10 Jun 2025 16:02:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2547844</guid>
    </item>
    <item>
      <title>Trajectory Planning and Optimal Autonomous Steering Control Design for Towbarless Aircraft Taxiing System</title>
      <link>https://trid.trb.org/View/2547884</link>
      <description><![CDATA[An efficient and safe aircraft scheduling scheme is of great significance to the construction of smart airports. The towbarless aircraft taxiing system (TLATS) is a common dispatching method, which is composed of the towbarless towing vehicle (TLTV) and the aircraft. The system’s trajectory planning and autonomous steering control are being researched in order to improve steering accuracy, dispatching efficiency, and safety. In this article, the towbarless aircraft taxiing system is transformed into tractor-trailer system, the kinematic model and the dynamic model of the aircraft-tractor are established. Taking TLTV as a virtual subsystem of TLATS, and it is regarded as the controlled object of path planning and tracking. In response to the operational requirements of TLTV, an advanced A-star(A*) path planning algorithm is proposed to perform collision avoidance and turn radius restrictions during path planning resulting in a reference path for TLATS. Considering the estimation inaccuracy of the TLTV states and inertial parameters of the trailers, two force sensors are installed at the joint of the tractor and the aircraft wheel to obtain the force acting on the tractor, and the simplified tractor’s dynamic model is derived. With force measurements, TLTV can be a virtual subsystem, and the dynamic model is significantly simplified. The inaccurate parameters can be ignored, whereas their dynamic effects on the tractor are still precisely captured. Then, a trajectory tracking controller based on model predictive control (MPC) was designed according to the simplified dynamic model to compensate for the force of the tractor online and drive it to the desired trajectory, achieving autonomous steering of TLTV. Finally, simulation experiments are carried out to verify the autonomous steering control method. The experiments show that the optimization method has good performance of steering path tracking at a low speed of 5km/h conditions and achieves high precision tracking of standard trajectory in the presence of interference variables in the environment.]]></description>
      <pubDate>Thu, 05 Jun 2025 11:59:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/2547884</guid>
    </item>
    <item>
      <title>Toward Greener and Sustainable Airside Operations: A Deep Reinforcement Learning Approach to Pushback Rate Control for Mixed-Mode Runways</title>
      <link>https://trid.trb.org/View/2449301</link>
      <description><![CDATA[Airside taxi delays have adverse consequences for airports and airlines globally, leading to airside congestion, increased Air Traffic Controller/Pilot workloads, missed passenger connections, and adverse environmental impact due to excessive fuel consumption. Effectively addressing taxi delays necessitates the synchronization of stochastic and uncertain airside operations, encompassing aircraft pushbacks, taxiway movements, and runway take-offs. With the implementation of mixed-mode runway operations (arrivals-departures on the same runway) to accommodate projected traffic growth, complexity of airside operations is expected to increase significantly. To manage airside congestion under increased traffic demand, development of efficient pushback control, also known as Departure Metering (DM), policies is a challenging problem. DM is an airside congestion management procedure that controls departure pushback timings, aiming to reduce taxi delays by transferring taxiway waiting times to gates. Under mixed-mode runway operations, however, DM must additionally maintain sufficient runway pressure—departure queues near runway for take-offs—to utilize available departure slots within incoming arrival aircraft steams. While a high pushback rate may result in extended departure queues, leading to increased taxi-out delays, a low pushback rate can result in empty slots between incoming arrival streams, leading to reduced runway throughput. This study introduces a Deep Reinforcement Learning (DRL) based DM approach for mixed-mode runway operations. The authors cast the DM problem in a markov decision process framework and use Singapore Changi Airport surface movement data to simulate airside operations and evaluate different DM policies. Predictive airside hotspots are identified using a spatial-temporal event graph, serving as the observation to the DRL agent. Their DRL-based DM approach utilizes pushback rate as agent’s action and reward shaping to dynamically regulate pushback rates for improved runway utilization and taxi delay management under uncertainties. Benchmarking the learnt DRL-based DM policy against other baselines demonstrates the superior performance of their method, especially in high traffic density scenarios. During a typical day at Singapore Changi Airport, DRL-based DM reduces peak taxi times by 1-3 minutes on average, saves 26.6% in fuel consumption, and contributes to more environmentally friendly and sustainable airside operations.]]></description>
      <pubDate>Wed, 12 Mar 2025 09:23:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2449301</guid>
    </item>
    <item>
      <title>A dynamic control method for airport ground movement optimization considering adaptive traffic situation and data-driven conflict priority</title>
      <link>https://trid.trb.org/View/2506291</link>
      <description><![CDATA[The primary objective of this study is to address the taxiing uncertainty in airport ground movement optimization with a novel dynamic control method considering adaptive traffic situation and data-driven conflict priority. To better characterize the airport surface traffic situation, a refined link-level unimpeded taxi time (UTT) estimation method is designed to improve the UTT prediction accuracy, and a comprehensive airport operation evaluation method is developed to identify similar surface traffic situation scenarios. Then, a data-driven method is proposed to discover contributing factors to conflict priority assignment and obtain more realistic conflict resolution behaviors. Finally, the adaptive traffic situation and updated conflict priority approach are embedded into the proposed dynamic airport ground movement optimization framework. One month aircraft trajectory data is collected from a representative mega airport in China to illustrate the procedure. The results reveal that the dynamic control method could achieve better performance of airport ground movement than sequential approach through timely path-adjustment and effective collaboration with other aircraft. Moreover, the adaptive traffic situation approach could achieve better trade-off between computation efficiency and optimal solution, and the updated conflict priority approach tends to coordinate the taxiing aircraft to the route plan with lower additional taxi time and achieves a more balanced utilization of taxiway network. The study provides a promising approach to generate real-time conflict-free trajectories for airport operation in future surface trajectory-based operations (TBO) scenarios.]]></description>
      <pubDate>Fri, 07 Mar 2025 15:09:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2506291</guid>
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