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    <title>Research in Progress (RIP)</title>
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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>
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      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>Roundabout Connected/Automated Vehicle Active Inference Control Strategies to Improve Safety for All Users</title>
      <link>https://rip.trb.org/View/2329136</link>
      <description><![CDATA[Many innovations have recently been adopted for new and existing urban roadways that improve safety for all users, with concomitant goals to also reduce congestion. Roundabout intersections, which have been adopted in many countries for decades, are becoming one such innovation adopted in the US. Voluminous studies confirm roundabouts reduce dangerous traffic conflicts between human operated vehicles, and their speeds, thereby potentially reducing serious crashes. One can appreciate the perceptual complexities presented to a human driver within these intersections when confronted with appropriate gap selection decisions simultaneously with vulnerable road user (VRU) interactions (varied behaviors of approaching bicyclists, public transit, and pedestrian crossings, for example). Automated vehicles in general will need to embed such perception-action behaviors at these intersections to correctly react to VRUs as well as likely interactions with human driven vehicles several years into the future.
The research team proposes to develop Active Inference Connected/Autonomous Vehicle (CAV) control strategies to reduce speed according to anticipated vehicle and pedestrian actions using real-time roadside sensor observation data. Originally grounded in neuropsychology and physiology, Active Inference is a probabilistic framework which contends perception, learning and decision making (and the resulting actions) are interdependent forms of inference. An agent (CAV) infers future actions most likely to generate preferred observations (states of all users and itself) concomitantly with sequences of actions that balance reducing uncertainty while encouraging learning. The mathematical framework will require significant observational data to formulate and validate the learned perception and decision models, as well as addressing computational challenges for the vehicle and edge processing. Accordingly, a two-phase study is proposed to address this problem. The first phase in year 1 deploys and evaluates roadside sensing (LiDAR and camera sensors) to accurately detect, edge-process, and package estimates of all user states in order to broadcast them through generated Basic Safety Messages (BSM). The roadside sensing challenge is to provide reliable ‘eyes’ to where the vehicle cannot adequately ‘see’ due to line of site limitations. The BSMs can alert human drivers to potential conflicts, such as far-side pedestrian crossing events for example, that may not be as visually evident to the human drivers. A second research phase will then focus on the automated vehicle control strategies to (i.e., reduce its speed, and invoke yield decisions accordingly), using the complete road user traffic states provided by the roadside detection. The artificial intelligence (AI) algorithm will be developed, tested, and demonstrated with the U of MN C/A research vehicle.]]></description>
      <pubDate>Sun, 28 Jan 2024 12:32:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/2329136</guid>
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    <item>
      <title>Computational Imaging for Improving Vehicle Safety</title>
      <link>https://rip.trb.org/View/2292650</link>
      <description><![CDATA[This project investigates the design and deployment of sensors and associated algorithms for handling harsh imaging conditions. The research team is particularly interested in depth perception in rain, snow and fog. By expanding the depth range at which objects can be reliably detected, especially in dense fog, the project will facilitate a higher level of safety for vulnerable road users. The project aims to improve perception in both autonomous as well as assisted settings; in the latter, the team will augment perception of  human drivers to better identify vulnerable road users in dense fog/rain.  Rain, snow and fog present challenging operating scenarios for camera and LIDAR-based depth perception. For passive cameras, imagery in such weather results in a loss of contrast. In turn, this makes it harder to match features across views, which reduces the effectiveness of stereo-based depth estimation. LIDAR, on the other hand, works on the principle of time of flight, measured by pulsing a laser and using a single-photon avalanche diode (SPAD) to measure the arrival time of the first returning photon. Here, fog and rain generate spurious photon arrivals that severely compromise the quality of depth measurements. The team approaches this problem as one of joint design of sensors and algorithms to overcome the challenges imposed by the physics of image formation. The main insight is that improved depth perception can be improved via careful imaging and algorithmic design that allows blocking of photons from the medium while preserving those from the scene of interest. This approach is central to very successful microscopy techniques such as confocal imaging, and diffuse optical tomography, for imaging in highly scattering media like biological tissue. The team will leverage this core intuition but expand it to macroscopic imaging in the real world. Improving perception in dense scattering media by blocking undesired photons requires imaging systems that can selectively choose between favorable light paths in the scene against unfavorable ones. The proposition is to build a structured light system with a high-speed projector and a ultra-high speed SPAD array. With this setup, the team will design patterns that will avoid single-bounce light paths off the medium. An example of this can be seen in prior work by the principal investigator (PI) where a laser line is used to illuminate a scene while sensing it with a line. This imaging configuration ensures light at the intersection of laser and the sensor planes (which is a line in the world) is preferred over other light paths induced by scattering. The team will leverage work by the PI in high-speed depth imaging using SPAD devices; the team will expand upon such a concept for the case of scattering media. This project will enhance the range of scenarios where a vehicle can safely operate in. Specifically, it will lead to increased range in depth perception in fog and rain, improving safety of vulnerable road users. In subsequent years, the team will look at imaging around visual occlusions (like cars) using non-light-of-sight techniques. This will lead to broader adoption of computational imaging with the eventual goal of increasing safety in transportation systems.]]></description>
      <pubDate>Mon, 20 Nov 2023 20:42:11 GMT</pubDate>
      <guid>https://rip.trb.org/View/2292650</guid>
    </item>
    <item>
      <title>Positioning and Localization Sensor Data Sets for Aerial Autonomy Research</title>
      <link>https://rip.trb.org/View/2010048</link>
      <description><![CDATA[This proposal deals with collecting and archiving a dataset that will be used to understand and solve the problems of perception and navigation in autonomous aerial vehicles envisioned for use in future transportation systems. The dataset will consist of sensor measurements from a multi-sensor suit developed specifically for this research by Honeywell International Inc. The sensor suite is hosted on an aerial vehicle that will be flown in representative operational scenarios. The resulting data will be archived and shared with the research community worldwide. The database developed will be an aerial analog to the automotive sensor database developed by the collaborative effort between Karlsruhe Institute of Technology and Toyota Technological Institute which is used worldwide (the so-called KTTI dataset which has been cited in over 2,000 publication). The deliverables of the proposed work are a preliminary database that can be used by researchers to understand the challenges of perception and navigation associated with autonomous aerial vehicle operations.]]></description>
      <pubDate>Fri, 19 Aug 2022 11:41:40 GMT</pubDate>
      <guid>https://rip.trb.org/View/2010048</guid>
    </item>
    <item>
      <title>Listening to Passengers: Data-Driven Investigations of Public Perceptions at Transportation Hubs to Improve Equity and Accessibility of Underrepresented Populations</title>
      <link>https://rip.trb.org/View/2008000</link>
      <description><![CDATA[In the built environment, accessibility of people with disabilities has been improved by adopting ADA standards. For the project life cycle aspect, current efforts focused rather on the design and construction stages, but with a lack of consideration for the operation of transportation infrastructure. After the COVID 19 pandemic, citizens have been asked (or required) to change their behavior at
public transportation hubs (e.g., airports). Consequently, new challenges for passengers with disabilities have arisen, demanding operational changes to ensure maintenance of equal access for all visitors. In the proposed research, the study team will identify such challenges by using a novel data mining
technique on a large public perception data (64 hub airports). Then, the team will apply their research findings to current guidelines as well as operation strategies at Dallas / Fort Worth International Airport (DFW). Specifically, the study team has developed a four-phase research method. First, they will collect location-based social media data from the Google Maps platform, which is distinct from other social media, where users share their experiences about specific locations. Then, they will apply a topic modeling technique to systemically identify public opinions of airport operations with the guidance of the Open Doors Organization, which is a non-profit focused on helping traveler with disabilities. The last two tasks will involve both professional aviation architects from Corgan and the customer satisfaction team at DFW to transfer the study team's research findings toward practical implementation. The team's innovative data-driven approach will help both designers and facility managers to make transportation hubs more equitable and accessible for passengers with disabilities.
]]></description>
      <pubDate>Tue, 16 Aug 2022 18:07:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2008000</guid>
    </item>
    <item>
      <title>Integration of Aerial Manipulation, Haptics-based Human-in-the-Loop Control, and Augmented Reality for Bridge Deck Hosing (AS-9)</title>
      <link>https://rip.trb.org/View/1969850</link>
      <description><![CDATA[A mobile-manipulating unmanned aerial system (MM-UAS) was designed, developed 
and deployed in Years 1 to 5. Called Avatar-Drone, this system serves as a robotic 
agent for an embodied operator. Thru Avatar-Drone, the user can remotely perform 
tasks like aerial drilling and hosing. This Final Year 6 proposal serves to both test-and evaluate (T&E) and verify-and-validate (V&V) this system. The net effect is a prove-on 
study with the objective of knowledge and technology transfer to the INSPIRE team and 
the US Department of Transportation (USDOT).]]></description>
      <pubDate>Tue, 31 May 2022 17:25:14 GMT</pubDate>
      <guid>https://rip.trb.org/View/1969850</guid>
    </item>
    <item>
      <title>Evaluation of Closed Crossing Tactile Indicators</title>
      <link>https://rip.trb.org/View/1906838</link>
      <description><![CDATA[There are currently no low maintenance, cost effective industry standard treatments for tactile closed crossing indicator markings for visually impaired people. Crossings are formally closed when an official sign prohibits such a crossing. Tactile pavement indicators are also needed as wayfinding cues for non-visual users.
Tactile paving surfaces can be used to convey important information to visually impaired pedestrians about their environment, for example, hazard warnings, directional guidance, or the presence of an amenity. Each type of tactile paving surface should be exclusively reserved for its intended use and consistently installed in accordance with guidelines. Visually impaired people are becoming increasingly mobile, both within their local area and more widely, and it is, therefore, very important that conflicting and confusing information is not conveyed. 
The development division has a proof-of-concept application applied in a partnership with WSDOT and The Lighthouse for the Blind. The new treatment application and project location provide a catalyst for evaluating, improving, and developing guidance, standards & specifications moving forward. 
The research will utilize the current WSDOT proof of concept tactile surface treatment pilot location to develop a set of guidelines that can be used to determine the frequency of the application, recommend the type of materials that should be used. Another desirable objective will be to establish a method for determining the maximum service life for different types of material markings placed on different types of pavement surfaces.
 
]]></description>
      <pubDate>Thu, 27 Jan 2022 18:36:34 GMT</pubDate>
      <guid>https://rip.trb.org/View/1906838</guid>
    </item>
    <item>
      <title>SPR-4640:  Geometric Constraints and Visual Field Related to Speed Management</title>
      <link>https://rip.trb.org/View/1898912</link>
      <description><![CDATA[The project deliverables will provide insights of how visual fields and optical cues affect perceived risks, speed perceptions, and driving speed, in different road locations. Based on the research outputs, cost-efficient road scene augmentation means that plants/shrubs, dividers, markings, overhead frames, or others will be designed. The strategy to implement certain levels of visual field restriction and types of optical cues at different locations will be discussed between the TASI research team and Indiana Department of Transportation (INDOT).]]></description>
      <pubDate>Mon, 20 Dec 2021 16:31:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/1898912</guid>
    </item>
    <item>
      <title> Creating a Situation-aware Sensing Environment for Cyclists: An Innovative and Cost-effective Smartphone-based Approach</title>
      <link>https://rip.trb.org/View/1756017</link>
      <description><![CDATA[This research will assess the feasibility and effectiveness of a Biker Assistance System (BAS) in different roadway contexts using a prototype mobile application. The application would make use of smartphones’ onboard speaker and microphones to monitor potential hazards and help bicyclists avoid crashes. The application will detect potential hazards by emitting an imperceptible sound and interpreting its reverberations, thus becoming a “mini-sonar system.” When certain potential hazards are detected, the smartphone will alert bicyclists of the hazard. This new approach to preventing bicycle crashes has yet to be developed or tested to the researchers’ knowledge. 
This project has four components. First, the project team proposes to analyze existing crash data sources to understand the types of crashes that can be prevented or mitigated with BAS. Second, the team proposes the development of the BAS for at least two hazardous scenarios – right turning vehicle detection and front/overtaking vehicle nearing. Additional scenarios may be added based on the crash data assessment. Third, a bike simulator study will be conducted to determine effective alerts for selected hazards. Based on the simulator study outcomes, a list of multi-modular alerts will be recommended which can be easily understood and interpreted by cyclists under both day and night lights. These alerts will be included in the BAS prototype. Finally, the project team proposes testing the efficacy of BAS in these scenarios via physical testing and naturalistic observation using an instrumented bicycle. This naturalistic database will be used to identify the critical cyclists-vehicle interaction regions and scenarios. Future research will expand the sensing capacity to function in different crash scenarios, investigate cyclists’ interactions with different road users, and provide cyclists with feedback to avoid different types of on-road hazards.
]]></description>
      <pubDate>Sat, 05 Dec 2020 18:16:08 GMT</pubDate>
      <guid>https://rip.trb.org/View/1756017</guid>
    </item>
    <item>
      <title>Utilizing Social Media Data for Estimating Transit Performance Metrics in a Pre- and Post-COVID-19 World</title>
      <link>https://rip.trb.org/View/1740570</link>
      <description><![CDATA[This project aims to assess the perception of public transit service from the standpoint of customer. Using publicly available social media posts and analyzing the sentiment over time, the service perception will be quantified. In addition, the perception of public transit can be determined by delay, station environment, etc. or perception of public health safety around the time of pandemics such as COVID-19. In this study, the research team will also study the public perception of transit service before and after the lockdown due to COVID-19 and draw insights on determining factors that drive customer perception of public transit.]]></description>
      <pubDate>Wed, 23 Sep 2020 21:59:41 GMT</pubDate>
      <guid>https://rip.trb.org/View/1740570</guid>
    </item>
    <item>
      <title>Augmented Bridge Inspection with Augmented Reality and Haptics-based Aerial Manipulation (AS-7)</title>
      <link>https://rip.trb.org/View/1739197</link>
      <description><![CDATA[Insight and leverage from prior project work identified the need for haptics; the inspector’s sense of touch is often used in bridge assessment. Haptics’ adoption in medical robotics shows potential for applications in remote inspection and maintenance. For example, surgeons employ haptic-based manipulators to both probe and operate minimally-invasive procedures. Such manipulators augment the surgeon by assessing areas that are difficult to see or reach with conventional tools. Likewise, envisioned is the augmentation of bridge personnel by providing a haptics-based aerial manipulator. Such a manipulator would reduce needs to ascend elevated structures like under bridge decks and cabling.
There is recent work that uses haptics in aerial manipulation. But these center on autonomous multi-drone flight formation for aerial shows or cooperative object delivery [1] [2]. The haptics research here is relatively straight-forward [3]. The sense of touch is used to provide spatial distance between drones. By contrast, this proposal’s novelty and merit are centered on human-in-the-loop aerial manipulation. The notion is that the expert bridge worker remotely configures the aerial manipulator to “probe” areas of interest and “operate” maintenance procedures.
This Year 4 proposal integrates haptics, augmented reality (AR), and drone-mounted arm. The specific objective is to characterize haptics and AR for their applicability to perform remote probing (i.e. inspection) and operation (i.e. maintenance).

Scope of Work in Year 4: The objectives of this study are to test, evaluate, verify and validate haptics-based aerial manipulation. Realizing such objectives would yield new capabilities for remote bridge inspection and maintenance. The tools and techniques would leverage bridge personnel expertise and introduce a new paradigm of human-in-the-loop aerial manipulation. This is akin to the advances and adoption found in medical robotics for minimally-invasive surgery.]]></description>
      <pubDate>Tue, 15 Sep 2020 18:40:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/1739197</guid>
    </item>
    <item>
      <title>Deflection Angle Effect on Continuous Driver Performance Along Horizontal Curves</title>
      <link>https://rip.trb.org/View/1705269</link>
      <description><![CDATA[Horizontal curves make up only a small share of mileage in the United States, yet accounted for 23% of all fatal crashes. Overall, research has shown that the average crash rate for horizontal curve locations is approximately three times the average crash rate for the tangent section. Thus, it is important to understand how drivers can more safely transverse these sections of roadways, such as slowing to a safe speed. Drivers’ speed at horizontal curves is due, in part, to their perception of the way in which curves look upon approach. This perception is linked to the curve radius and deflection angles of horizontal curves.
Previous research found that on the tangent section before a curve began, drivers cruised at their highest speeds before slowing most significantly right before the start of the curve (Point of Curvature). Following this, drivers increased their speed slightly until the midpoint of the curve, before exiting the curve at approximately the same speed as the midpoint. However, to date, research has not considered a continuous study of driver speed throughout the entire length of a curve in terms of the impact of the curve deflection angle alone. Thus, it is not known how the intensity of a curve impacts driver performance throughout the time a driver spends traversing the length of a curve, and what the relationship between speed, lane position, curve radii, and deflection angle is throughout each portion of a horizontal curve, from the tangent section prior to the curve, through the tangent section following the curve.
Phase 1: Virtual simulation scenarios will be developed using recorded video of representative horizontal curves from the instrumented vehicle (with camera and LiDAR) and with scenarios developed by GTA5 mod and/or CARLA. These scenarios do not require the physical presence of the participants, but instead can be completed remotely. The scenarios will record the actions of the participants (as if they are playing a video game or playback a video of the horizontal curve). Although this is a reduced version of the actual driving simulator, the recorded actions (e.g., key pressing for gas, brake, steering, etc.) will provide many insights without the need for the physical presence. In addition, this approach provides flexibility for different curves. Phase 2: When the human subject research resumes on campus, we will proceed with a series simulator tests to collect continuous lateral position, acceleration, and velocity data of each driver for each experimental drive on the simulator guided by the outcomes and configurations from Phase 1. Modeling techniques will be utilized to investigate the relationship between continuous speed (and speed differential), braking time, pedal movements, distance traveled along the curve, lateral position, curve radii, and deflection angle.]]></description>
      <pubDate>Thu, 07 May 2020 08:17:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/1705269</guid>
    </item>
    <item>
      <title>RES2019-06: Impacts and Adoption of Connected Autonomous Vehicles in Tennessee</title>
      <link>https://rip.trb.org/View/1632579</link>
      <description><![CDATA[Connected and autonomous vehicles (CAVs) can revolutionize the daily travel modes, personal, public, or
shared mobility because of technology-assisted driving. However, such a revolution will come at the cost of
numerous anticipated barriers like accident liabilities, data safety concerns, the addition of new infrastructure, and
increased emissions because of the induced travel demand. Adoption research from non-transportation related
innovation suggests that social network plays a pivotal role in deciding whether to adopt, defer, or not to adopt.
The existing literature in capturing the individuals’ intention to adopt autonomous vehicles based on their social
network is limited. Hence, this research aims to understand, model, and predict CAV market penetration in
Tennessee based on residents’ social network. Based on the statewide survey responses of 4,602 Tennesseans, a
hybrid choice model was modeled to capture the impact of attitudes and perception on their intention to adopt
CAVs. An agent-based model was also rendered to capture the impact of peer-to-peer interaction and the price of
CAVs on their future adoption. Key findings highlight the positive impact of residents’ perceptions towards their
social status, input received from their peers, tech-savvy lifestyle, and willingness to pay more towards
autonomous technology on their intention to adopt personally owned-CAVs. Finally, adoption forecasts showed
higher levels in four major counties of Tennessee, and an annual price reduction of 20% can increase the adoption
rate by 17 times. Based on these findings and COVID-19 impacts on CAVs, this research proposes some
recommendations to help planners boost the adoption of CAVs through policies focusing on infrastructure,
subsidies, and advertisement. 

]]></description>
      <pubDate>Tue, 02 Jul 2019 10:31:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1632579</guid>
    </item>
    <item>
      <title>Impacts of Emergency Traffic Patrol Lights on Human Perception</title>
      <link>https://rip.trb.org/View/1592817</link>
      <description><![CDATA[The overall goal of the project is to understand how color, intensity and other features of Emergency Traffic Patrol (ETP) lights affect human perception and thus to provide recommendations on improvement of warning light systems. The research team will perform empirical experiments to address the following specific aims based on the weather and traffic conditions in Maryland.
Aim 1. Characterize the relationships between color, intensity and flash pattern of warning lights and human perception-reaction time (PRT) under a variety of traffic environments.
Aim 2. Identify light patterns that minimize the interferences caused by bright warning light.
Aim 3. Optimize warning light system with an ambient light detector.]]></description>
      <pubDate>Fri, 15 Mar 2019 14:03:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1592817</guid>
    </item>
    <item>
      <title>Assessing and Improving the Cognitive and Visual Driving Fitness of CDL Drivers – Phase II</title>
      <link>https://rip.trb.org/View/1582364</link>
      <description><![CDATA[Driving is a highly dynamic task that requires intact cognitive and visual skills to perform safely. Driving commercial vehicles require even more careful planning and consideration to avoid unanticipated shifts in the center of gravity associated with sharp turns while speeding (slushing) or liquid surge with hazardous materials associated with sharp braking. Such planning and consideration are highly dependent on cognitive and visual skills for accuracy. In the first year of this proposal, the research team developed a driving fitness assessment battery that consisted of tests that have been shown in the geriatric literature to be reliable and valid measures of driving-related cognitive and visual skills. In year 2, the team began recruitment for CDL drivers over age 18 to: 1. Assess their cognitive and visual fitness, 2. Establish the usefulness and effectiveness of these tests to drivers before embarking on the journey, and 3. Identify potential risk factors that contribute to unsafe driving. The team anticipates that this part of the study will be helpful in identifying drivers who have cognitive and/or visual impairments that may make driving a commercial vehicle unsafe. A unique aspect of this part of the study is the possibility of improving driving fitness by offering drivers with demonstrated cognitive and visual deficits the opportunity to retrain and improve such skills in a technologically advanced high fidelity simulator. 

]]></description>
      <pubDate>Thu, 07 Feb 2019 16:37:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/1582364</guid>
    </item>
    <item>
      <title>Factors Influencing Visual Search in Complex Driving Environments</title>
      <link>https://rip.trb.org/View/1474329</link>
      <description><![CDATA[Research on distracted driving has primarily focused on in-vehicle distractions including texting and cell phone use, "infotainment" navigation and audio systems, and other in-vehicle devices. Human factors engineering, which attempts to account for the capabilities and limitations of drivers, promises to provide ways to improve safety by designing more forgiving systems and environments. Successful human factors engineering requires a multi-disciplinary understanding of human perception, cognition, and the associated response factors. By understanding the driver's perception of the environment, engineers can make informed design changes to operational environments (such as temporary workzone areas and approaches) and reduce the potential for driver confusion, thus improving safety for both workers and drivers. The central focus of this research is to identify changes in the visual search patterns of drivers as environments become more complex. Specifically, the project will look to evaluate response patterns for drivers as they approach a temporary workzone area in which traffic flow has been altered from the 'normal' pattern by the use of traffic control devices. The study results will allow engineering guidelines for the use of these traffic control devices to be developed, improved and refined and thereby enhance the safe passage of vehicles through these proven dangerous locations. The overarching objective of this project is to evaluate the impact of visual scene complexity on driver behavior and to recommend improved methods to convey appropriate information to the driver. The study will initially be restricted to a simulated freeway environment focusing on interchanges and ramps with and without work zones.]]></description>
      <pubDate>Thu, 13 Jul 2017 01:01:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1474329</guid>
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