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    <title>Research in Progress (RIP)</title>
    <link>https://rip.trb.org/</link>
    <atom:link href="https://rip.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>Research in Progress (RIP)</title>
      <url>https://rip.trb.org/Images/PageHeader-wTitle-RIP.jpg</url>
      <link>https://rip.trb.org/</link>
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    <item>
      <title>RailRisk Advisor: A Web-GIS Tool for Assessing Railroad Trespassing Risk and Recommending Countermeasures</title>
      <link>https://rip.trb.org/View/2772532</link>
      <description><![CDATA[Trespassing is the leading cause of rail-related deaths in the United States. According to the Federal Railroad Administration, more than 500 trespass fatalities occur nationally each year (Trespass Prevention, 2025). The number of trespassing occurrences on railroad property each year far exceeds the number of fatalities, which means that there is potential for more trespasser accidents. This proposal aims to develop a RailRisk Advisor, a web geographic information system (GIS)-based system, to enhance railroad safety by identifying and analyzing high-risk railroad segments for trespassing incidents. Using geospatial data, historical trespassing records, and contextual factors (such as train volume, train speed, land use, and population density), a data-driven model will be developed for calculating the risk score of each railroad segment. The calculated risk levels will be validated by comparing historical incident data. Advanced spatiotemporal analytics and predictive modeling will be employed to provide a user-friendly platform for detecting and visualizing risk hotspots, conducting interactive analyses, and developing targeted mitigation strategies. Finally, the platform will be made accessible through an interactive web-GIS based dashboard. The dashboard will feature tools for data querying, visualizing, analyzing, and reporting railroad risk data. The proposed RailRisk Advisor could potentially change the way railroad trespassing risks are managed by shifting from reactive to proactive strategies. By leveraging data-driven insights, transportation agencies can prioritize interventions, optimize resource allocation, and implement targeted countermeasures.


]]></description>
      <pubDate>Fri, 04 Sep 2026 08:29:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2772532</guid>
    </item>
    <item>
      <title>Risk and Resilience Geospatial Data Accessibility Tool</title>
      <link>https://rip.trb.org/View/2768414</link>
      <description><![CDATA[Researchers previously developed a data-driven geographic information system (GIS)-based framework that Kentucky Transportation Cabinet (KYTC) staff can use to quantify risk across the roadway network and prioritize resilience improvements. But it remains challenging for users to easily identify risk and resilience data that are relevant to proposed projects or corridors. The existing framework also needs to be updated using information on environmental stressors (e.g., flooding, landslides, rockfalls, seismic activity) derived from previous and ongoing research. One solution to this challenge is developing a geospatial tool that facilitates risk and resiliency evaluations.]]></description>
      <pubDate>Wed, 26 Aug 2026 17:04:23 GMT</pubDate>
      <guid>https://rip.trb.org/View/2768414</guid>
    </item>
    <item>
      <title>Feasibility Studies of the Conceptual Novel Elevated High-Speed Rail System with a Case Study in Texas</title>
      <link>https://rip.trb.org/View/2736754</link>
      <description><![CDATA[The high-speed rail system is a powerful rapid transportation tool with significant mobility, financial, and accessibility benefits. The United States is one of the first countries to develop high-speed trains, dating back to 1969. Recently, there has been another wave of inventions and studies of high-speed rail systems, including the California and Texas high-speed rail plans. Among them, the conceptual novel elevated high-speed rail (CNE-HSR) is unique due to its (1) fast construction speed (3 miles per day); (2) elevated design that affords minimal ground field land needs and minimal disruption to existing infrastructure and property, and no displacement of existing communities; (3) independent pods for exclusively nonstop travel and flexible allocation between passenger and cargo needs; and (4) deep last-mile reach. This seed grant project will conduct feasibility studies of CNE-HSR with a case study in Texas along the Houston-San Antonio corridor. Transportation, land use, socioeconomic, and other information will be collected with the assistance of local transportation agencies. The retrieved information will be fully employed to test the identified assessment tools for studies of impacts on the regional economy, housing and real estate development, job market growth, as well as socioeconomic progress. The research team will conduct engagement activities to incorporate advisory comments from policymakers, planners, engineers, community leaders, and others. Eventually, this project will develop recommendations and implementation strategies for the evaluated CNE-HSR system in Texas.]]></description>
      <pubDate>Wed, 29 Jul 2026 14:42:33 GMT</pubDate>
      <guid>https://rip.trb.org/View/2736754</guid>
    </item>
    <item>
      <title>Flood-resilient Transport System Through Integrated Modeling, ML &amp; Immersive AR/VR </title>
      <link>https://rip.trb.org/View/2732359</link>
      <description><![CDATA[Extreme rainfall events increasingly disrupt urban transportation systems by overwhelming drainage infrastructure and causing localized road flooding that impedes last-mile freight delivery, delays emergency response, and disrupts the broader multimodal supply chain. These disruptions limit access to essential services, delay emergency response, and threaten public safety. Building on the research team's previously developed framework (F25-26), this project advances a data-driven approach for high-resolution prediction of urban road flooding in Jackson, Mississippi, integrating geospatial databases, process-based H-H modeling (aligning with rigorous U.S. Army ERDC methodologies), and machine learning (ML) and artificial intelligence (AI) techniques to identify flood-prone road and railway segments. The ML-based surrogate models will maintain computational efficiency, enable timely identification of vulnerable transportation networks, and support emergency response by feeding into JSU Water Lab’s broader web-based-visualization interfaces. This project introduces an interactive K–12 STEM module, age-appropriate hands-on activities along with STEM curriculum module designed for upper-level Civil Engineering undergraduate and graduate students at JSU. This initiative transforms research outcomes into the classroom to modernize workforce training using integrated Augmented Reality (AR) and Virtual Reality (VR) and will be tested with summer student exchange programs. Students can explore and 3D print several transportation infrastructure components, such as culverts, bridges, and urban drainage systems, and evaluate their performance under simulated flood conditions, and experience AR/VR based immersive simulators. Using AR/VR tools, including the Meta Quest platform, available in the PI lab, future transportation engineers will visualize flood scenarios in immersive 3D environments built from existing topographical assets in Unity or Unreal Engine. By combining advanced predictive modeling with experiential learning, the project promotes advanced STEM engagement, and high-tech workforce development, aligning with broader goals of improving multimodal transportation system resilience. While the primary focus is on urban road and rail flooding, these transportation corridors serve as critical connectors to Mississippi's inland waterway freight network, including facilities linked to the Pearl River system and regional multimodal freight movements. Roadway disruptions during extreme rainfall events can delay freight access to ports, intermodal terminals, water-dependent industrial facilities, and affect supply-chain resilience. By identifying flood-vulnerable roadway and railway segments, the proposed framework will support more reliable connectivity between surface transportation infrastructure and maritime freight operations]]></description>
      <pubDate>Tue, 21 Jul 2026 16:34:57 GMT</pubDate>
      <guid>https://rip.trb.org/View/2732359</guid>
    </item>
    <item>
      <title>SPR-5133: Evaluation of In-CAB Virtual Sign Network on CMV Safety and Operations</title>
      <link>https://rip.trb.org/View/2727696</link>
      <description><![CDATA[Construction work zones often have reductions in lane widths, reductions in shoulder widths, soft shoulders, and uneven pavement profiles that can present challenges for Class 9 vehicles. Although roadside signs can be used, it is believed that targeted incab alerts can perhaps be more effective at alerting commercial drivers of these issues and improve safety.]]></description>
      <pubDate>Wed, 15 Jul 2026 11:42:05 GMT</pubDate>
      <guid>https://rip.trb.org/View/2727696</guid>
    </item>
    <item>
      <title>Intelligent Speed Assistance Guide for State Highway Safety Offices</title>
      <link>https://rip.trb.org/View/2720300</link>
      <description><![CDATA[Speeding remains one of the most persistent and deadly threats on U.S. roadways, accounting for more than 11,000 deaths in 2024, and 125,000 fatalities over the last decade. One promising countermeasure to help address speeding behavior is Intelligent Speed Assistance (ISA) technology, which uses real-time Global Positioning System (GPS) data to detect the speed limit and proactively alert (or limit) the driver if they are speeding, can reduce speeding and help promote long-term safe driving behaviors.
The Governors Highway Safety Association (GHSA) recently documented a growing number of examples of ISA’s effectiveness at the local level. In New York City, a pilot program involving 500 fleet vehicles saw a 64% reduction in speeds substantially above speed limits. A District of Columbia school bus pilot logged 10,000 miles with zero speeding events.
European research has shown that ISA can reduce crash risk and lessen the severity of injuries, particularly in areas with changing speed limits or heavy pedestrian activity. A 2019 policy report from the European Transport Safety Council estimated that ISA could cut road deaths across Europe by approximately 20%. Another study projected that equipping all vehicles with mandatory active ISA could reduce injury and fatal crashes by 20% and 37%, respectively.
Given the potential for wide adoption of ISA to substantially reduce speeding-related fatalities and serious injuries, research is needed to identify ways for state highway safety offices (SHSOs) to advance the use of this technology.

OBJECTIVE: The objective of this research is to develop a guide that supports efforts by SHSOs to: 1) Conduct comprehensive stakeholder assessment to identify which groups have the greatest need for education and which hold the most influence over its adoption. 2) Develop a core set of educational active ISA materials or leveraging materials available from other sources. 3) Ensure that SHSO staff have a strong foundational understanding of active ISA. Staff training should cover how active ISA works, its effectiveness as demonstrated in peer-reviewed research, relevant policy considerations and communication strategies tailored to different audiences. 4) Establish clear metrics and evaluation processes allows SHSOs to measure the effectiveness of educational initiatives and outreach efforts. 5) Engage with key stakeholders to advance pilot programs. 6.) Develop guidelines and implementation frameworks for pilot projects that help SHSOs evaluate and demonstrate effective strategies for modifying speeding behavior through ISA technologies, including  recommendations on target driver populations, stakeholder coordination, public communication, data collection, performance measures, privacy considerations, and evaluation methodologies to support broad adoption.
]]></description>
      <pubDate>Thu, 02 Jul 2026 20:16:13 GMT</pubDate>
      <guid>https://rip.trb.org/View/2720300</guid>
    </item>
    <item>
      <title>SPR 781 Optimizing SCDOT’s Permitting, Mitigation, and Compliance Systems to Improve Project Delivery</title>
      <link>https://rip.trb.org/View/2719301</link>
      <description><![CDATA[The overarching goal of Phase III is to optimize South Carolina Department of Transportation's (SCDOT’s) permitting, mitigation, and compliance
systems by leveraging emerging technologies. The objectives are to (1) maintain the existing
applications and enhance them through feedback from SCDOT users and consultants, (2) migrate
the geographic information system (GIS) applications to Experience Builder for improved functionality, (3) update the e-permitting
app to align with upcoming regulatory changes, (4) develop standardized templates for permit
drawings, and (5) explore emerging technologies to check the permit drawings and provide
feedback on errors and required changes, predict wetland impacts for future projects, and autogenerate National Environmental Policy Act (NEPA) applications classified as Categorial Exclusions.]]></description>
      <pubDate>Thu, 25 Jun 2026 08:53:55 GMT</pubDate>
      <guid>https://rip.trb.org/View/2719301</guid>
    </item>
    <item>
      <title>Developing an Automated Framework for Aggregating Right-of-Way Data through GIS and Computer Vision Methods</title>
      <link>https://rip.trb.org/View/2714448</link>
      <description><![CDATA[Ohio Revised Code 125.16 requires current and accurate records of tangible personal property and real property be maintained. The Ohio Department of Transportation (ODOT) can obtain records of state-owned right-of-way (ROW) by specific locations and/or project, however there is no simple repository or mechanism to obtain information in a wholistic, statewide manner. ODOT's Office of Real Estate has an online Arcis application, OhROW, that lets people click on a map and get direct access to ROW plans. However, OhROW does not have any data aggregation capabilities, data is not queryable, and not all areas are available in the application. ODOT's Office of Data Governance is working on an initiative to obtain ROW line data from new 3D plan sets. This process is tremendously slow and dependent on the availability of 3D plan sets that include some kind of ROW lines. It is estimated that it could take up to 50 years to cover all of ODOT's road network using this process. Other state DOTS, such as Texas and Nevada, have attempted to address this issue by manually reviewing every plan set of their road network and manually inputting the legal descriptions to make polygons of their ROW. This process is very labor intensive, time consuming, and is expected to take several years to complete. An innovative approach to obtain ROW data, in granular details (such as acreage, type, access parcels, nature of control, etc.), and create maps is needed. OBJECTIVE: Develop an innovative approach to identify ODOT owned property, propose methods for storing datasets, and utilize the data to pilot the approach by generating property maps of a specific county, city, township or State Route. The approach should be repeatable, reliable, and streamlined and findings should include recommendations for statewide implementation of the innovative approach.]]></description>
      <pubDate>Tue, 16 Jun 2026 15:19:31 GMT</pubDate>
      <guid>https://rip.trb.org/View/2714448</guid>
    </item>
    <item>
      <title>Developing a Standardized Framework for Real-Time Freight-Specific Traveler Information and Route Restrictions for Commercial Motor Vehicle Operators; Truck Parking Data Exchange Standards</title>
      <link>https://rip.trb.org/View/2709247</link>
      <description><![CDATA[Commercial motor vehicle (CMV) operations increasingly rely on maps and navigation systems that were not designed to address the unique needs of freight operations. This mismatch contributes to increased safety risks, including unplanned diversions, bridge strikes, congestion in freight corridors, lane geometry constraints, and other routing errors.

Today, the lack of a standard, consistent data structure or framework for sharing real-time freight-specific information remains a foundational challenge for public agencies and for the economy that depends heavily on the national roadway network. Public agencies currently lack a widely accepted standard or shared framework for communicating restrictions, alerts, and disruptions to CMV operators. Existing standards such as the Traffic Management Data Dictionary (TMDD) and SAE J2354 (Advanced Traveler Information Systems) support general traveler messaging but do not include freight-specific data elements.

In addition, the growing need for timely and reliable truck parking information, coupled with the rapid expansion of truck parking information systems, demonstrates the need for standardized methods to collect and disseminate truck parking data. As technologies used in these systems become increasingly ubiquitous, and as industry expectations and preferences continue to evolve, standardization of both information and dissemination tools becomes a critical next step.

OBJECTIVES: The objectives of this research are: (1) to develop a unified data framework for delivering time-sensitive, relevant, and actionable freight-specific traveler information messaging to CMV operators; and (2) to develop proposed data standards for real-time, public and private truck parking availability and attributes (including the number of spaces, size, hours of availability, and available amenities).

]]></description>
      <pubDate>Tue, 02 Jun 2026 14:33:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2709247</guid>
    </item>
    <item>
      <title>Infrastructure Data System (IDS)</title>
      <link>https://rip.trb.org/View/2696940</link>
      <description><![CDATA[Transportation data have been growing in volume, velocity, and variety, especially since the introduction of connected vehicle data, crowd-sourced data, and other recent technological advancements. While this is an enormous opportunity for transportation advancement, the challenge lies in translating these data into actionable information for analysts and decision-makers. Therefore, it is crucial to make these datasets easily accessible to industry professionals and decision-makers.
This project would establish the Infrastructure Data System (IDS), a robust data hub and information portal centered around various aspects of infrastructure and NCIT. IDS shall provide a one-stop shop solution for data and ad hoc- analysis needs by leveraging skills in building data systems, web-based tools, visualizations, and geographic information systems. This system could ultimately serve as a hub to host research results under NCIT and as a repository of data and information for researchers, analysts, and policymakers.
The IDS will ensure consistency by having one centralized location and will seamlessly integrate two key NCIT topical pillars: policy and technology. By leveraging advanced technology, the IDS will provide the tools and infrastructure needed to harness the power of data, driving innovation and enabling informed decision-making in the field of transportation. While the intent is to create an active data system, the lifespan of the application will be constrained due to limited funds. However, the results of this project will serve as a proof of concept, i.e. a stepping stone, for establishing a truly effective national-level system for NCIT.
]]></description>
      <pubDate>Sat, 30 May 2026 12:16:43 GMT</pubDate>
      <guid>https://rip.trb.org/View/2696940</guid>
    </item>
    <item>
      <title>Ensemble Radar Nowcasts for Probabilistic Road Disruption Prediction</title>
      <link>https://rip.trb.org/View/2706035</link>
      <description><![CDATA[Heavy precipitation and flash flooding can rapidly degrade roadway operating conditions, causing speed reductions, lane closures, detours, and secondary crashes. Current traffic management systems largely confirm disruptions after they have already developed, limiting the ability of transportation operators to act proactively. Deterministic weather products also provide limited information about forecast uncertainty, which is critical for risk-based operational decision-making.
This project develops a probabilistic road disruption nowcasting system that integrates ensemble radar precipitation forecasts with traffic observations and roadway attributes to produce segment-level disruption probabilities at lead times of 30 to 180 minutes. Using a multi-member ensemble framework applied to real-time radar precipitation data, the system will generate exceedance probabilities and persistence metrics that quantify near-term hazard likelihood. These probabilistic precipitation indicators will be fused with traffic state variables and roadway characteristics to estimate the likelihood of operational disruption. The result is a calibrated, segment-level decision-support tool that provides actionable lead time and quantified uncertainty to support safer and more reliable corridor operations.

]]></description>
      <pubDate>Sat, 23 May 2026 18:00:15 GMT</pubDate>
      <guid>https://rip.trb.org/View/2706035</guid>
    </item>
    <item>
      <title>Driver Takeover and Shared-Control Collaboration in Automated Driving Systems under Rural Conditions: Evaluating Real-Time Cognitive Responses in Field and Simulated Settings</title>
      <link>https://rip.trb.org/View/2703691</link>
      <description><![CDATA[This proposal outlines a multidisciplinary research initiative to assess drivers'
physiological and cognitive workload, stress levels, emotional states, and trust during takeover
performance in human-machine co-driving systems using an automated driving simulator. In specific,
three research objectives are to (1) Designing realistic and validated driving scenarios in simulators;
(2) Validating physiological sensors for measuring driver physiological responses; and (3)
Developing cognitive and situational awareness-based decision-making framework. To achieve these
objectives, simulator-based data will be collected, along with test drivers incorporating physiological
sensors while in the driving tests. Existing open-source data will be applied to expanding traffic
scenarios.
]]></description>
      <pubDate>Fri, 15 May 2026 14:19:20 GMT</pubDate>
      <guid>https://rip.trb.org/View/2703691</guid>
    </item>
    <item>
      <title>Traveler Information for Rural Maryland</title>
      <link>https://rip.trb.org/View/2701236</link>
      <description><![CDATA[Disseminating traveler information in rural Maryland has long been challenging due to the limited deployment of Intelligent Transportation System (ITS) devices, such as dynamic message signs (DMS) and highway advisory radios (HARs). This challenge becomes particularly acute during major events, such as hurricanes or large-scale evacuations, when clear and accessible communication is critical. HARs, which operate on AM radio frequencies, have been a key tool for disseminating detailed information, but their reliance on outdated technology has made maintenance costly and increasingly unfeasible as spare parts become unavailable. The Maryland Department of Transportation State Highway Administration (MDOT SHA) has already retired half of its HARs and faces difficulty maintaining the remaining units, which are still vital in certain areas. ]]></description>
      <pubDate>Wed, 13 May 2026 09:12:24 GMT</pubDate>
      <guid>https://rip.trb.org/View/2701236</guid>
    </item>
    <item>
      <title>Emergency Response and Access Mapping for Rural Navajo Communities </title>
      <link>https://rip.trb.org/View/2658056</link>
      <description><![CDATA[In the Navajo Nation Area, poorly maintained, unpaved, and seasonally hazardous road conditions in rural areas hinder timely response of emergency services. For example, in Crownpoint, heavy snowfall can make it difficult for ambulance services and firefighting vehicles to reach homes, as they must travel through unpaved or unmaintained roads to reach their destinations. Although current routing tools are able to locate the best route between two points, they do not contain pavement condition data or hazard data that would allow for the accurate determination of safe passage for emergency vehicles. Satellite images are also unable to show potholes, ruts, washouts, etc.; therefore, responders are forced to guess which is the best route based on their experience or try different routes until they find one that works. In many cases, this results in substantial delays, especially during severe weather when traditional navigation systems provide little guidance on actual road accessibility. A new platform is needed that has reliable and accessible data to help direct emergency responders to the safest route to the point of origin. Such a system would not only improve response time but also provide agencies with a standardized way to assess roadway risk during rapidly changing environmental conditions. This project will create a reliable, data driven, artificial intelligence (AI)-assisted Road Accessibility Index (RAI), and a geographic information services (GIS)-based routing dashboard utilizing Vialytics' smartphone-based road assessment capabilities, along with data on transportation, crashes, maintenance, and climate to provide real time accessibility ratings for each ten meter section of road within the Crownpoint area (150 miles total) and direct Emergency Medical Services, Fire and Law Enforcement departments towards the safest routes to travel to emergency locations. 
 ]]></description>
      <pubDate>Wed, 04 Feb 2026 19:20:18 GMT</pubDate>
      <guid>https://rip.trb.org/View/2658056</guid>
    </item>
    <item>
      <title>OpenRoad Link: A Public-Private Data Exchange for Safer, Smarter Trucking </title>
      <link>https://rip.trb.org/View/2646948</link>
      <description><![CDATA[Work zones, lane closures, and traffic incidents significantly impact roadway safety and efficiency. When lanes are blocked due to construction, crashes, or other disruptions, roadways no longer function as designed—leading unexpected congestion, increased crash risk, and reduced operational reliability. Many work zones are established to perform critical maintenance on aging infrastructure—essential to improving durability and extending the service life of roadways—but they also introduce temporary risks and delays that must be better managed.  Effects of lane blockages are particularly severe for commercial motor vehicles (CMVs), which require more time and space to slow or reroute and are subject to strict hours-of-service regulations that make delays especially costly. 

This project proposes to develop and evaluate a data exchange framework—OpenRoad Link—to integrate and share real-time lane closure, work zone, and incident data from the Oklahoma Department of Transportation (ODOT), the Oklahoma City and Tulsa Traffic Operations Centers (TOCs), and other key transportation and traffic enforcement partners. To build this framework, the project will first identify and assess the roadway data already collected and shared by these agencies, as well as the types of information currently accessible to the CMV industry through private telematics platforms. Building on national standards such as the Work Zone Data Exchange and SAE J2735 (the standard message set for vehicle-to-everything communications), the project will extend the data scope to include lane-blocking crashes, maintenance activities, and other short-term or unplanned restrictions not currently emphasized in existing feeds. Through collaboration with ODOT, city TOCs, and trucking industry partners—including a pilot with a major trucking company such as ABF—the project will demonstrate the delivery of curated, high-value information directly to in-cab devices or fleet management systems.  

Key tasks will include identifying and cataloging roadway and incident data currently collected by the Oklahoma Department of Transportation (ODOT) and the Traffic Operations Centers (TOCs) of Oklahoma City and Tulsa, as well as evaluating what information is already being shared with the commercial vehicle industry through private telematics platforms. The project will establish partnerships with ODOT, city transportation and public safety agencies, and private industry stakeholders to design and implement a unified, standards-compliant data exchange framework. Following the design phase, the team will develop and deploy the OpenRoad Link data feed, ensuring compliance with existing national standards and verifying data accuracy and reliability. A pilot deployment will be conducted in collaboration with a trucking company using a selected in-cab device to deliver actionable, real-time information directly to CMV drivers.  

Anticipated outcomes include improved safety for CMV drivers, a reduction in secondary crashes, enhanced freight reliability, and a validated proof-of-concept for scalable public-private data exchange. By producing a replicable model for collaboration between state DOTs and private-sector technology providers, the project aims to accelerate national adoption of interoperable safety data systems and promote safer, more efficient freight transportation. ]]></description>
      <pubDate>Tue, 06 Jan 2026 08:59:12 GMT</pubDate>
      <guid>https://rip.trb.org/View/2646948</guid>
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