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    <title>Transport Research International Documentation (TRID)</title>
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    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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    <language>en-us</language>
    <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>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>A novel Co-simulation method for the analysis of carrier-based aircraft ski-jump takeoff dynamics</title>
      <link>https://trid.trb.org/View/2611236</link>
      <description><![CDATA[Ski-jump takeoff is currently one of the primary takeoff methods for carrier-based aircraft, and many factors need to be considered. The loads on the aircraft’s landing gear exhibit high-frequency and large-amplitude characteristics during takeoff, posing higher requirements for the accuracy of landing gear system modeling and load calculation. To address this, a novel co-simulation method is proposed to analyze the dynamic response during ski-jump takeoff. A detailed rigid-flexible coupling finite element model at the component level for the carrier-based aircraft is established. The stability of the model and the accuracy of finite element method are verified through a drop test. Combining with the flight dynamic model, a co-simulation platform is built, realizing the connection of the finite element method and flight dynamics. Based on deck wind field test data, the influence of deck wind on the ski-jump takeoff performance of carrier-based aircraft is studied. The simulation results indicate that the co-simulation method proposed can effectively analyze the complex dynamics response of carrier-based aircraft during ski-jump takeoff. Deck wind field affects the dynamic characteristics of the ski-jump takeoff process of carrier-based aircraft, which is not only reflected in the flight trajectory, but also reflected in the load of the landing gears. This provides a valuable reference for the dynamic response analysis of carrier-based aircraft during takeoff and the design of carrier-based aircraft system.]]></description>
      <pubDate>Wed, 11 Mar 2026 14:44:03 GMT</pubDate>
      <guid>https://trid.trb.org/View/2611236</guid>
    </item>
    <item>
      <title>Time-Optimal Trajectory Generation Based on Quadrotor Dynamics Model Using Physics-Informed Neural Networks</title>
      <link>https://trid.trb.org/View/2612982</link>
      <description><![CDATA[Time-optimal trajectory generation is a critical challenge for quadrotor unmanned aerial vehicles (UAVs) in dynamic and constrained environments. This paper employs Physics-Informed Neural Networks (PINNs) to generate time-optimal trajectories for quadrotor UAVs using a comprehensive dynamic model. The proposed PINN framework embeds quadrotor dynamics as part of the learning process, ensuring physical consistency while enabling parallelized computations. The quadrotor’s dynamics are fully modeled with physical parameters incorporated into the loss function to enforce adherence to dynamics, initial and target conditions, and time minimization. Experimental evaluations demonstrate the PINN’s ability to generate smooth, physically accurate trajectories that satisfy time constraints. Comparative analysis with traditional solvers (e.g., ACADO) highlights the proposed method’s superior efficiency and solution quality. An ablation study further validates the contributions of individual loss components to the framework’s performance. This research advances trajectory optimization for quadrotor UAVs, offering a scalable, efficient, and physically grounded solution for real-world applications.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:20 GMT</pubDate>
      <guid>https://trid.trb.org/View/2612982</guid>
    </item>
    <item>
      <title>Impinging Shockwave/Boundary Layer Interaction–Induced Separation
                    Mitigation Using Passive Control</title>
      <link>https://trid.trb.org/View/2639320</link>
      <description><![CDATA[
                
                A passive control device to mitigate shock-induced separation in a generic
                    supersonic inlet model is computationally studied. The simulations were based on
                    the Favre-averaged Navier–Stokes equations with the Spalart–Allmaras (SA)
                    turbulence model. The shockwave was generated by an 8° turn supersonic inlet.
                    The Mach number in the inlet was varied between 2.1 and 2.46. The baseline
                    shockwave/boundary layer interaction (SBLI) simulation results compare favorably
                    with experimental data. The passive device, in the form of a splitter plate,
                    eliminates both the separation and flow unsteadiness. The splitter plate causes
                    reduction in the total pressure of the boundary layer at the exit of the inlet
                    due to increased skin friction on the floor and due to wake of the plate.
            ]]></description>
      <pubDate>Thu, 15 Jan 2026 14:31:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/2639320</guid>
    </item>
    <item>
      <title>Tactical demand and capacity balancing using incremental search in spatio-temporal graphs with flight uncertainty</title>
      <link>https://trid.trb.org/View/2608545</link>
      <description><![CDATA[Demand and Capacity Balancing (DCB) operations, typically implemented pre-flight, face limitations in effectiveness due to uncertainties during airspace operations. Therefore, executing DCB during the tactical phase (as close to the departure time as possible) holds promise for better addressing these uncertainties. This study proposes a tactical-phase DCB method that accounts for uncertainties to meet practical application scenarios and requirements: compatibility with dynamic environments, high-speed computation, fairness and transparency, and high customisability. The large-scale tactical DCB problem is transformed into a hotspot-free trajectory planning problem based on sequential planning to accommodate stakeholders’ diverse performance preferences. An adaptive directed spatio-temporal graph method is introduced, enabling the integration optimisation of multiple Air Traffic Flow Management (ATFM) measures (ground delay, rerouting, and speed control) while considering flight uncertainties and fuel consumption constraints. A Heterogeneous Multi-Objective Incremental A* (HMOIA*) path search method is also developed to significantly accelerate problem-solving and meet tactical operational demands, ensuring optimal solutions by designing an admissible heuristic function. Simulation experiments based on historical European data demonstrate that the proposed method can resolve all overloaded air traffic service units with acceptable arrival delay time and fuel consumption. Compared to the Computer-Assisted Slot Allocation (CASA) method currently used in European operations, the proposed approach reduces the number of delayed flights and average delay time by approximately 79.4 % and 92.1 %, respectively. The proposed method demonstrates its value for further development to explore its potential as an upgrade to the CASA method in real-world operations.]]></description>
      <pubDate>Mon, 05 Jan 2026 09:54:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2608545</guid>
    </item>
    <item>
      <title>Development of Anti-SideSlip Control for Motorcycles Using IMU</title>
      <link>https://trid.trb.org/View/2623861</link>
      <description><![CDATA[In motorcycle racing and other competitions, there is a technique to intentionally slide the rear wheel to make turns more quickly. While this technique is effective for high-speed riding, it is difficult to execute and carries risks such as falling. Therefore, an anti-sideslip control system that suppresses unintended or excessive sideslip is needed to ensure safe, natural, and smooth turning.In anti-sideslip control, the slip angle is usually used as a control parameter. However, for motorcycles, it is necessary to know the absolute direction of the vehicle's movement. To determine this, GPS or optical sensors are required, but using such sensors for driving is costly and may not provide accurate measurements due to contamination or other environmental factors, making it impractical. Therefore, an anti-sideslip control system was developed by calculating another parameter that indicates the characteristics of the slip angle, without measuring the slip angle itself, thus eliminating the need for impractical sensors.To detect sideslip, lean angles calculated using two different methods are used. The first lean angle calculates the true value even when side slip occurs, while the second lean angle shows a higher value than the true value when side slip occurs. The difference between these is defined as the slide amount, which can be detected as a parameter representing side slip.When a sideslip is detected, the drive force reduction control suppresses the sideslip to bring the slide amount closer to the target slide amount. To suppress sideslip, drive force reduction through ignition retardation is used.As an experiment, the slide amount obtained by the current method was compared with the values from a GPS device capable of calculating the slip angle. It was confirmed that the differential value of the slip angle obtained from the GPS and the slide amount had a very similar waveform. Furthermore, a test was conducted to verify whether the anti-sideslip control effectively suppressed sideslip during actual driving, and it was confirmed that applying this control allowed for more stable cornering.The effectiveness and validity of the anti-sideslip control were confirmed through the above experiment.]]></description>
      <pubDate>Thu, 13 Nov 2025 16:12:50 GMT</pubDate>
      <guid>https://trid.trb.org/View/2623861</guid>
    </item>
    <item>
      <title>A comprehensive framework for estimating aircraft fuel consumption based on flight trajectories</title>
      <link>https://trid.trb.org/View/2587304</link>
      <description><![CDATA[The calculation of aircraft fuel consumption is crucial for airline operations management, environmental protection, and other aspects. The use of Automatic Dependent Surveillance-Broadcast (ADS-B) data can significantly enhance the accuracy and resolution of fuel consumption calculations over flight routes. However, ADS-B data exhibits several issues, including inconsistent data point distribution, uneven interval widths, and variability in flight durations, making it difficult to align with fuel consumption data. This study developed a comprehensive mathematical framework and established a connection between flight dynamics in ADS-B data and fuel consumption, providing a set of high-precision, high-resolution fuel calculation methods. It also allows other practitioners to select data sources according to specific needs through this framework. The framework includes three main steps: (1) Using ADS-B data to determine the flight profile, which involves identifying aircraft behaviors such as acceleration, deceleration, climb, and descent, and establishing their theoretical relationships and parameters with fuel consumption. (2) Fitting the coefficients of the relationship between flight profiles and fuel consumption using available interval fuel consumption data. (3) Calculating instantaneous fuel consumption through monotonic and smooth interpolation. The authors' comprehensive analysis of ADS-B and Aircraft Communications Addressing and Reporting System (ACARS) datasets from April 2022 to December 2023, focusing on interval and instantaneous fuel consumption patterns among China’s 11 most prevalent aircraft types. The interval fuel consumption error demonstrates a reduction to 0.1% at the 10th percentile of the error distribution, while maintaining a mean error of 3.31%. Even at the 90th percentile, the error remains below 10%, demonstrating the model’s robustness across most scenarios. Regarding instantaneous fuel consumption model performance, the Boeing 737 MAX 800 demonstrates a cumulative instantaneous fuel consumption error of just 6.6% during cruise phase, while the Airbus A321-200 shows errors of 7.1% during climb and 8.4% during descent phases.]]></description>
      <pubDate>Tue, 02 Sep 2025 08:49:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2587304</guid>
    </item>
    <item>
      <title>Smoothing of Aircraft On-Board Measurements Based on the Use of Flight Dynamics Equations and Nonlinear Programming Methods</title>
      <link>https://trid.trb.org/View/2407923</link>
      <description><![CDATA[The paper discusses the use of nonlinear programming based on direct methods of optimal control for solving the problem of signal smoothing. The idea lies in the representation of the desired signal in a parametric finite-dimensional space, which reduces the original problem to the problem of parametric optimization. The efficiency of the proposed method is demonstrated by the example of smoothing signals of orientation angles and angular velocities of an aircraft in the presence of random errors. Comparison is conducted with polynomial smoothing and smoothing using the Kalman filter.]]></description>
      <pubDate>Mon, 14 Jul 2025 12:53:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2407923</guid>
    </item>
    <item>
      <title>Developing Alsim AL250 Based Evtol Flight Simulator</title>
      <link>https://trid.trb.org/View/2552321</link>
      <description><![CDATA[A novel eVTOL aircraft simulator was developed for research and teaching purposes. The simulator integrated MATLAB/Simulink flight dynamics model with Alsim AL250 FNPT II flight simulator. Simplified version of the Neoptera’s eOpter eVTOL aircraft was used as a test case to verify the flight simulator. It was shown that the aircraft responded as expected by the pilot and that the traditional handling qualities metrics and VTOL requirements (MIL-F-83300, MIL-F-8785C, EASA-SC-VTOL-02) could be used along the flight simulator to assess aircraft being tested. Take-off showed an increase in climb rate as well as overshoot of the target altitude with higher RPM setting. Qualitative assessment of transition showed suitable stability and control feel for the eVTOL to be operated by a single pilot. Quantitative assessment of the longitudinal manoeuvring characteristics showed Level 2 SPPO handling qualities for the tested eVTOL aircraft, qualitative definition of which agreed with the pilot’s opinion. It was also shown that increasing initial velocity for the SPPO mode test increased the mode’s natural frequency, but almost did not affect the damping ratio, which is within the expectations.]]></description>
      <pubDate>Tue, 27 May 2025 09:33:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2552321</guid>
    </item>
    <item>
      <title>A Comprehensive Review of Vehicle and Road Condition Estimation
                    Techniques</title>
      <link>https://trid.trb.org/View/2552084</link>
      <description><![CDATA[
                
                This article reviews the key physical parameters that need to be estimated and
                    identified during vehicle operation, focusing on two key areas: vehicle state
                    estimation and road condition identification. In the vehicle state estimation
                    section, parameters such as longitudinal vehicle speed, sideslip angle, and roll
                    angle are discussed, which are critical for accurately monitoring road
                    conditions and implementing advanced vehicle control systems. On the other hand,
                    the road condition identification section focuses on methods for estimating the
                    tire–road friction coefficient (TRFC), road roughness, and road gradient. The
                    article first reviews a variety of methods for estimating TRFC, ranging from
                    direct sensor measurements to complex models based on vehicle dynamics.
                    Regarding road roughness estimation, the article analyzes traditional methods
                    and emerging data-driven approaches, focusing on their impact on vehicle
                    performance and passenger comfort. In the section on road gradient estimation,
                    details are given on how to measure the grade and bank angles of a road, and
                    their role in enhancing vehicle stability under extreme driving conditions is
                    emphasized. The article also provides an in-depth overview of different vehicle
                    state estimation techniques, including model-based, observer-based, and
                    techniques using neural networks for estimation. Finally, the article summarizes
                    the challenges facing current research and suggests potential directions for
                    further research. The article emphasizes the importance of combining vehicle
                    state estimation with road condition recognition and suggests that this
                    combination has the potential to provide a more robust framework for adaptive
                    vehicle control systems in variable and complex driving environments.
            ]]></description>
      <pubDate>Tue, 13 May 2025 10:05:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2552084</guid>
    </item>
    <item>
      <title>Cost-Effective Sideslip Measurement: Beta for the People!</title>
      <link>https://trid.trb.org/View/2539713</link>
      <description><![CDATA[Vehicle sideslip is a valuable measurement for ground vehicles in both passenger vehicle and racing contexts. At relevant speeds, the total vehicle sideslip, beta, can help drivers and engineers know how close to the limits of yaw stability a vehicle is during the driving maneuver. For production vehicles or racing contexts, this measurement can trigger Electronic Stability Control (ESC). For racing contexts, the method can be used for driver training to compare driver techniques and vehicle cornering performance. In a fleet context with Connected and Autonomous Vehicles (CAVS) any vehicle telemetry reporting large vehicle sideslip can indicate an emergency scenario. Traditionally, sideslip estimation methods involve expensive and complex sensors, often including precise inertial measurement units (IMUs) and dead reckoning, plus complicated sensor fusion techniques. Standard GPS measurements can provide Course Over Ground (COG) with quite high accuracy and, surprisingly, the most challenging measurement is the vehicle orientation.This study presents a low- or moderate-cost method for real-time vehicle sideslip estimation using Real-Time Kinematic (RTK) Global Position System (GPS) receivers. The approach involves a pair of specialized GPS receivers with a moving base and moving rover RTK setup. RTK corrections are provided via an online wireless internet connection. The moving base is positioned at the vehicle's rear axle and the companion rover GPS device is located at the vehicle's center of gravity (CG). This arrangement provides both vehicle orientation and vehicle course over ground at 7Hz. RTK provides direct measurement of both quantities needed to compute vehicle sideslip in real time. The results demonstrate the feasibility of this approach and offers a practical solution for real-world automotive systems. A simple set of driving experiments demonstrate the method’s effectiveness. This approach is a cost-effective solution for sideslip estimation, with applications in ESC, CAVs, driver training and motorsports performance analysis.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539713</guid>
    </item>
    <item>
      <title>YRSRA: Yaw and Roll Stable Region Analytics for Ground Vehicle System with 5G-V2X</title>
      <link>https://trid.trb.org/View/2539711</link>
      <description><![CDATA[Since most of the existing studies focus on the identification of the yaw stable region, but ignore the identification of the roll stable region, this article presents a software tool YRSRA for calculating both the yaw and roll stable region for ground vehicle system with 5G-V2X. And the frequency of rollover instability of commercial vehicles such as trucks and buses is not low, and the cost of rollover accidents is often greater than the cost of yaw instability accidents. Therefore, it is necessary to identify the stability region of yaw and roll at the same time. Firstly, the iterative model of yaw rate and slip angle is constructed through deducing the two-degree-of-freedom vehicle dynamics. Secondly, the load transfer ratio (LTR) is coded with given yaw rate and slip angle. Thirdly, several Illustrative examples are depicted, such as variation of steer angle, road adhesion coefficient and vehicle speed. The software features an easy to generate yaw and roll stability region by on-demand configuring vehicle parameters, but can also be scripted and used as a library. The YRSRA software is written in a modular way using Matlab function script and the call case is also provided in Permanent link: https://gitee.com/smartcar502/yrsra.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:52 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539711</guid>
    </item>
    <item>
      <title>Stability Control for Distributed Drive Electric Vehicles Using Particle Filter and Fuzzy Integral Sliding Mode Control</title>
      <link>https://trid.trb.org/View/2539369</link>
      <description><![CDATA[The Distributed Drive Electric Vehicles (DDEVs) offer advantages such as independently controllable driving and braking forces at each wheel, rapid response, and precise control. These features enable effective electronic stability control (ESC) by appropriately distributing torque across each wheel. However, traditional ESC systems typically employ single-wheel hydraulic differential braking, failing to fully utilize the independent torque control capabilities of DDEVs. This study proposes a hierarchical control strategy for distributed driving and braking ESC based on particle filter (PF) and fuzzy integral sliding mode control (FISMC). First, the vehicle state estimation layer uses a three-degree-of-freedom vehicle model and the PF to estimate sideslip angle and vehicle speed. Next, the target torque decision layer includes a target speed tracking controller and a yaw moment decision controller. The yaw moment decision controller uses the FISMC to determine additional yaw moment by comparing the estimated yaw rate and sideslip angle with their ideal values, while dynamically adjusting the sliding mode surface parameters based on vehicle state and driving conditions. Finally, the dynamic torque distribution layer allocates the driving and regenerative braking torques to each wheel according to changes in vertical tire load. A co-simulation platform using MATLAB/Simulink and CarSim is established to validate the proposed control strategy under double lane change and J-turn maneuvers, comparing it with traditional ESC. The results show that the proposed ESC achieves high accuracy in estimating vehicle state and effectively adapts to varying driving conditions while maintaining stable vehicle speed, thereby enhancing driving stability.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539369</guid>
    </item>
    <item>
      <title>A Drive Anti-Slip and Lateral Stability Control Technique for Distributed Three-Axis Drive Vehicle</title>
      <link>https://trid.trb.org/View/2539301</link>
      <description><![CDATA[The research object of this project is the anti-slip and lateral stability control technique for a distributed three-axis drive vehicle. What differs from the traditional four-motor power system layout is that the third axle has two motors, while the second axle only has one motor. Compared with the traditional design, this layout can reduce dependence on battery performance and maintain motor operation in a high-efficiency range by switching between different operating modes. For example, when driving at high speeds, only the motor on the second axle works, which can improve motor efficiency. When accelerating or climbing, all motors work to provide a large power output. In the research, the vehicle model was first established in Simulink, and then co-simulated with TruckSim. The drive anti-slip control first identified the optimal slip rate for the road, and then used the sliding mode control to determine the driving torque for each wheel, achieving good control effects under various road conditions and driving modes. For example, it improves acceleration performance on muddy roads, bumpy roads, split-traction roads, and so on. The lateral control used a two-layer control strategy: the first layer is sliding mode control, and the second layer is a rule-based distribution layer, which outputs the driving torque for each wheel. Simulations were conducted under double-lane shift and snake test conditions with different adhesion coefficients to verify the effectiveness. The results showed that the control strategy can maintain lateral stability better than traditional systems. The control strategy of the distributed three-axle drive vehicle can achieve better performance than traditional systems.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539301</guid>
    </item>
    <item>
      <title>A Torque Distribution Strategy for the Six-Wheeled Lunar Rover</title>
      <link>https://trid.trb.org/View/2539290</link>
      <description><![CDATA[As a crucial tool for lunar exploration, lunar rovers are highly susceptible to instability due to the rugged lunar terrain, making control of driving stability essential during operation. This study focuses on a six-wheel lunar rover and develops a torque distribution strategy to improve the handling stability of the lunar rover. Based on a layered control structure, firstly, the approach establishes a two-degree-of-freedom single-track model with front and rear axle steering at the state reference layer to compute the desired yaw rate and mass center sideslip angle. Secondly, in the desired torque decision layer, a sliding mode control-based strategy is used to calculate the desired total driving torque. Thirdly, in the torque distribution layer, the optimal control distribution is adopted to carry out two initial distributions and redistribution of the drive torque planned by the upper layer, to improve the yaw stability of the six-wheeled lunar rover. Finally, a multi-body dynamics simulation platform for the six-wheel lunar rover is built using the open-source multi-physics simulation engine Chrono, exploring its dynamic behavior in soft ground conditions. Various operating scenarios are tested to verify the effectiveness, reliability, and safety of the designed coordinated control strategy. This research provides a reference for the design and control strategies of lunar rovers in future lunar exploration missions and offers guidance for the design and motion control of extraterrestrial planetary surface exploration vehicles.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539290</guid>
    </item>
    <item>
      <title>Estimation of Tire Longitudinal Force under Combined Slip Condition Using an Intelligent Tire System</title>
      <link>https://trid.trb.org/View/2539285</link>
      <description><![CDATA[Under extreme driving conditions, such as emergency braking, rapid acceleration, and high-speed cornering, the tire, as the vehicle’s only direct connection to the road, plays a critical role in influencing dynamic performance and driving stability. Accurately predicting and tire longitudinal force under such combined slip conditions is key to improving vehicle control precision and ensuring driving safety. This study proposes a tire longitudinal force estimation strategy based on an intelligent tire system. The core of this system consists of three integrated PVDF (Polyvinylidene Fluoride) sensors embedded in the tire, which, due to their exceptional sensitivity, can precisely capture dynamic deformation information of the tire under varying conditions. This provides real-time, detailed data to better understand the complex interaction forces between the tire and the road. To study and validate the longitudinal force estimation model, the research team employed a high-precision indoor tire test platform, simulating real tire conditions under different operating scenarios. By integrating strain data collected from the intelligent tire system with tire states and using advanced data analysis techniques to extract highly relevant features, a Gaussian Process Regression algorithm was used to develop a tire longitudinal force estimation model suitable for combined slip conditions. The model leverages the high-precision measurement capabilities of PVDF sensors to accurately predict tire longitudinal forces. Validation results indicate that the model demonstrates excellent accuracy and robustness under challenging combined slip conditions, laying a technical foundation for improving vehicle stability and safety.]]></description>
      <pubDate>Tue, 15 Apr 2025 13:56:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2539285</guid>
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