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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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    <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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>Motorcyclists in Latin America: Current Situation, Lessons Learned and Recommended Best Practices for the Protection of its Users. 2nd Edition</title>
      <link>https://trid.trb.org/View/2730687</link>
      <description><![CDATA[Motorcyclists represent the primary challenge for road safety in Latin America and the Caribbean (LAC), where fatalities among this group have increased by 98% between 2010 and 2024, despite an overall 2% reduction in total road traffic deaths. The rapid growth of motorcycles as an affordable and efficient mode of transport, particularly for work-related purposes has amplified both mobility opportunities and exposure to risk. This publication, building upon the Inter-American Development Banks 2022 report Motorcycles in Latin America: Current and Recommended Best Practices for the Protection of Its Users, presents an updated and comprehensive assessment of motorcyclist safety across the region. The report integrates new data and original research conducted by the IDB, providing updated analysis on powered two-wheeler fleet growth, fatality trends, risk factors, and regulatory frameworks across countries and subregions. The publication adopts a holistic approach structured in four sections: (i) analysis of trends and patterns in motorcyclist fatalities, (ii) identification of key behavioral, infrastructural, and systemic risk factors, (iii) review of regulatory frameworks and best practices from governments, NGOs, and the private sector, and (iv) actionable recommendations to guide evidence-based policymaking. Aligned with the Decade of Action for Road Safety 2021-2030, the Sustainable Development Goals, and the Marrakech Declaration (2025), the publication underscores the urgency of coordinated, multisectoral efforts to reduce fatalities. Ultimately, this document aims to equip policymakers, practitioners, and stakeholders with the evidence and tools needed to improve motorcyclist safety and advance safer, more sustainable mobility systems in Latin America and the Caribbean.]]></description>
      <pubDate>Mon, 28 Sep 2026 08:36:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2730687</guid>
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    <item>
      <title>Road traffic crashes in Thailand 2017-2023: analysis and policy implications</title>
      <link>https://trid.trb.org/View/2727657</link>
      <description><![CDATA[Thailand continues to have one of the highest road traffic fatality rates globally, creating significant public health and economic challenges. This study examines road traffic crash trends in Thailand from 2017 to 2023, with a focus on the high involvement of motorcycles in fatal crashes, the demographics of victims, and the timing of incidents. The analysis highlights critical areas for intervention, including stricter enforcement of traffic laws, targeted measures for young motorcycle riders, and improvements in road infrastructure. Additionally, the study identifies shortcomings in current data collection methods that limit the effectiveness of safety policies. The findings underscore the need for a comprehensive approach that integrates technological advancements, legislative reforms, and public education to reduce crashes and enhance road safety in Thailand.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:56:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2727657</guid>
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    <item>
      <title>Alcohol consumption and the severity of lower limb and pelvic lesions resulting from motorcycle injuries</title>
      <link>https://trid.trb.org/View/2726607</link>
      <description><![CDATA[To analyze the association between alcohol consumption and the severity of injuries in the pelvic region and lower limbs among motorcyclists injured in traffic. This exploratory cross-sectional study was conducted with motorcyclists admitted to a public hospital in Recife (northeastern Brazil). Using questionnaires, the study collected sociodemographic data and information about accidents. Data were also sourced from medical records from February 1, 2023 to January 31, 2024. The severity of the injuries was assessed using the Abbreviated Injury Scale. The study involved 305 individuals, of which 292 (95.7%) were male, and 210 (70.9%) were aged 18–39 years. Severe injuries in the pelvic and lower limb areas occurred in 229 (74.6%) of motorcyclists. The analysis showed a statistically significant association between pelvic and lower limb injuries and alcohol consumption, even when adjusted for age, education level, income, type of income, and accident by impact and collision (adjusted odds ratio = 2.82; 95% confidence interval = 1.04–7.69) (p = 0.042). Motorcyclists who consume alcohol are at a higher risk of severe injury in the pelvic region and lower limbs. Despite the traffic regulations regarding alcohol consumption in Brazil and investments in educational campaigns, efforts have not been enough to reduce the incidence and severity of injuries in the studied population.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726607</guid>
    </item>
    <item>
      <title>Differential analysis of factors influencing motorcycle single-vehicle crash severity on roadway segments and intersections</title>
      <link>https://trid.trb.org/View/2726606</link>
      <description><![CDATA[Motorcycle single-vehicle crashes occurring on roadway segments and intersections exhibit significant differences. This study aims to explore the differences in the key factors influencing the severity of motorcycle single-vehicle crashes at roadway segments and intersections, based on historical accident data from Shandong Province, China. Additionally, it examines the temporal stability of these determinants and the unobserved heterogeneity over time. To account for heterogeneity in the data, Random parameters logit models with heterogeneity in means and variances are employed across different time periods and junction types. The analysis investigates the impact of various factors—driver, vehicle, road, and environmental—on the injury severity of motorcyclist. Additionally, the study assesses the temporal stability and unobserved heterogeneity of determinants in motorcycle crashes. Likelihood ratio tests are conducted to evaluate the transferability of model estimates across different years, and the marginal effects of explanatory variables are calculated. The results reveal significant variations in the effects of influencing factors between roadway segments and intersections. The driver age under 30 and during dawn or dusk are significantly associated with severe motorcycle single-vehicle crashes occurring exclusively at roadway segments. In contrast, visibility <100 m, visibility between 100 and 200 m, as well as the summer and winter seasons, are significant factors only for severe motorcycle single-vehicle crashes at intersections. Overall temporal instability is observed, while several indicators, including driver age over 50 and non-clear weather conditions, demonstrate relative spatial or temporal stability. The findings of the study provide valuable insights for decision-making regarding motorcycle crash prevention strategies at both roadway segments and intersections. The findings contribute to a deeper understanding of motorcycle crash dynamics and offer valuable guidance for policymakers and traffic safety experts in designing more effective safety measures.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726606</guid>
    </item>
    <item>
      <title>Injury risk curves for motorcycles in the United States</title>
      <link>https://trid.trb.org/View/2726604</link>
      <description><![CDATA[This study introduces a comprehensive injury risk model for motorcycle collisions tailored to the United States (U.S.). The proposed innovative approach enables the prediction of injury risk across the full range of crash speed and vehicle orientations, making it an essential tool for evaluating emerging safety measures for motorcycles. Data from the Motorcycle Crash Causation Study (MCCS) is used to train two multivariate regression models for predicting motorcycle injury risk. The data is weighted to align with national crash statistics and effectively represents the U.S. crash population. The models adopt motorcycle impact speed and vehicle-relative speed as key predictors while incorporating rider age, helmet use, and opponent vehicle type as covariates. The motorcycle’s principal direction of force (PDoF) is also employed as a surrogate metric for the vehicle orientation, enabling the models to account for the full range of two-vehicle crash configurations. The analysis clearly indicates that the motorcycle front-end crash—i.e., the motorcycle’s front collides with any side of the opposing vehicle—is the most dangerous crash configuration. Additionally, the results demonstrate significant variability in injury likelihood based on the PDoF, emphasizing the influence of vehicle alignment and orientation on riders’ injury outcomes. This study provides the first comprehensive injury risk models for motorcycle-involved two-vehicle collisions in the U.S., offering critical insights into the role of speed and vehicle orientation in injury outcomes. While further validation with larger datasets is required, the findings serve as a foundation for the evaluation of the safety benefits of emerging traffic safety systems.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2726604</guid>
    </item>
    <item>
      <title>Street view-based infrastructure assessment of motorcycle hotspots: A national road network analysis in Thailand</title>
      <link>https://trid.trb.org/View/2762670</link>
      <description><![CDATA[Motorcycle casualties in Thailand are strongly concentrated on a small proportion of the road network, yet road infrastructure assessment at the national scale remains constrained by incomplete road-inventory data and the cost of field audits. This study develops an integrated framework for identifying motorcycle crash hotspots and examining their visually observable road-environment correlates. Motorcycle crash records from the Thailand Road Accident Management System (2019–2024) were analyzed using Network Kernel Density Estimation (NKDE) on 100 m road segments. The NKDE output is interpreted as severity-weighted crash concentration, not exposure-normalized crash risk. For hotspot and matched control segments, representative Mapillary Street-View Imagery (SVI) was processed using a Vision–Language Model (VLM) pipeline to extract 27 iRAP-aligned attributes. Only manually validated, reliable VLM-derived attributes were retained and combined with route-level traffic-volume proxies in an XGBoost classifier, with SHAP used to interpret feature contributions. The selected NKDE configuration showed strong concentration: the top 1% of network length captured 52.2% of severity-weighted motorcycle crashes. Model interpretation indicates that hotspot classification is associated with commercial access activity, traffic-exposure proxies, constrained median and roadside environments, and rigid roadside objects. These associations are interpreted cautiously as hotspot indicators rather than causal effects. The framework provides a scalable approach for linking realized motorcycle casualty concentration with SVI-derived infrastructure hazard indicators in data-constrained settings.]]></description>
      <pubDate>Wed, 23 Sep 2026 15:55:43 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762670</guid>
    </item>
    <item>
      <title>From Young-to older-elderly: shifting casualty patterns in traffic accidents within a motorcycle-oriented transport environment</title>
      <link>https://trid.trb.org/View/2714280</link>
      <description><![CDATA[As Taiwan transitions into a super-aged society, elderly road users face increasing risks of traffic accidents due to age-related physiological and cognitive decline. This study compared traffic accident characteristics between young-elderly (65–74?years) and older-elderly (=75?years) road users in Taiwan, where motorcycles dominate the transportation environment. Chi-square and Mann–Whitney U tests were applied to examine differences in accident characteristics, injury and fatality proportions, and urbanization areas, while logistic regression were used to assess the age-related differences in injury and fatality outcomes. The results showed that motor accidents were prevalent among the elderly. Compared with the young-elderly, the older-elderly had higher injury and fatality proportions, with lower proportion of car accidents but higher proportions of bicycle and pedestrian accidents. In addition, drunk driving was more prevalent among the young-elderly across all vehicle types, whereas unlicensed motor vehicle driving was more common among the older-elderly. These findings reveal distinct risk patterns between elderly subgroups and underscore the need for targeted interventions to enhance traffic safety in both aging populations and motorcycle-dependent societies.]]></description>
      <pubDate>Wed, 16 Sep 2026 11:21:26 GMT</pubDate>
      <guid>https://trid.trb.org/View/2714280</guid>
    </item>
    <item>
      <title>Analysis and Risk Management of Motorcycle, Bicycle, and Pedestrian Crashes in Minnesota</title>
      <link>https://trid.trb.org/View/2762499</link>
      <description><![CDATA[In Minnesota, the reduction in overall roadway fatalities have plateaued, as motorcycle, bicycle, and pedestrian crashes and fatalities have plateaued relative to historical trends and pedestrian fatalities have increased in 2024. The causes of the increase in frequency and severity of these crashes have not been fully accounted for, resulting in little actionable information for local agencies to address the problem. The Minnesota Strategic Highway Safety Plan (SHSP) notes that the highest proportion of fatality and serious injury crash proportions are motorcyclists, pedestrians, and bicyclists. The research team intends to examine available data to examine this issue in detail on the basis of prior research and will lead a risk assessment using the information obtained in the previous analysis to better clarify the mechanisms behind the increases in vulnerable road user and motorcycle severe injury crashes and fatalities.]]></description>
      <pubDate>Wed, 19 Aug 2026 16:13:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2762499</guid>
    </item>
    <item>
      <title>Fifteen Instrumented Motorcycle Crash Tests into a Barrier and
                    Stiffness Comparison of Standard and Upside-Down Forks</title>
      <link>https://trid.trb.org/View/2742626</link>
      <description><![CDATA[Fifteen instrumented crash tests were performed using a 2005 Yamaha R6                     motorcycle. Seven tests were performed with upside-down (USD) forks and eight                     tests were performed with standard forks. The 2005 Yamaha R6 provided a platform                     where both types of front forks could be interchanged. For all tests, the                     motorcycle was delivered into a concrete block at speeds varying between 5 and                     23 mph. Seven tests were conducted at low speeds to determine the onset of                     permanent deformation. Eight tests were conducted at higher speeds to observe                     the wheelbase reduction of the motorcycle and its relationship to impact speed.                     This paper summarizes the data from these tests related to wheelbase reduction,                     impact dynamics, and post-impact movement, allowing comparison between two                     different suspension systems and historical datasets.]]></description>
      <pubDate>Thu, 13 Aug 2026 17:00:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2742626</guid>
    </item>
    <item>
      <title>Traffic Safety Facts 2024 Data: Young Drivers</title>
      <link>https://trid.trb.org/View/2724807</link>
      <description><![CDATA[In 2024 there were 2,162 young drivers who died in traffic crashes, a   slight increase from 2,156 in 2023.   In 2024 an estimated 184,129 young drivers injured in traffic crashes, a  2-percent increase from 180,218 in 2023.   Traffic fatalities in crashes involving young drivers increased 7 percent,  from 5,617 in 2023 to 5,247 in 2024.   The number of licensed young drivers increased 2.6 percent from 2023 to  2024.   Young drivers accounted for 9 percent of all drivers in fatal traffic  crashes in 2024. However, young drivers were only 5.2 percent of all  licensed drivers in 2024.   Young drivers involved in police-reported traffic crashes increased less  than 1 percent from 1,276,453 in 2023 to 1,279,893 in 2024. Young  drivers involved in fatal traffic crashes decreased 6 percent from 5,160 in  2023 to 4,873 in 2024.   The rate of drivers involved in fatal traffic crashes per 100,000 licensed  drivers for young female drivers was 20.27 in 2024. For young male  drivers in 2024 the involvement rate was 56.72, almost three times that  of young female drivers.   Of the young drivers of passenger vehicles killed with known restraint  use, 53 percent were unrestrained at the time of the traffic crashes in  2024, which is slightly higher than the percentage of all drivers of  passenger vehicles killed (49%).   Although people under 21 are legally prohibited from drinking alcohol,  29 percent of young drivers 15 to 20 years old who were killed in traffic  crashes in 2024 had blood alcohol concentrations (BACs) of .01 grams  per deciliter (g/dL) or higher; 25 percent had BACs of .08 g/dL or  higher.   During 2024 there were 583 motorcycle riders 15 to 20 years old killed  in traffic crashes, and an estimated 10,113 in that age group were injured.]]></description>
      <pubDate>Mon, 13 Jul 2026 13:51:35 GMT</pubDate>
      <guid>https://trid.trb.org/View/2724807</guid>
    </item>
    <item>
      <title>Traffic Safety Facts 2024 Data: Motorcycles</title>
      <link>https://trid.trb.org/View/2721751</link>
      <description><![CDATA[This fact sheet has motor vehicle traffic crash data from the Fatality Analysis Reporting System (FARS), National Automotive Sampling System (NASS) General Estimates System (GES), and Crash Report Sampling System (CRSS). Results from FARS such as fatal crashes and fatalities are actual counts, while results from NASS GES and CRSS such as non-fatal crashes and people injured are estimates. In 2024: (1) There were 6,228 motorcyclists killed in motor vehicle traffic crashes – a 2.2-percent decrease from the 6,366 motorcyclists killed in 2023. (2) Motorcyclists accounted for 16 percent of all traffic fatalities and 20 percent of all motor vehicle occupant (driver and passenger) fatalities. (3) Of the 6,228 motorcyclists killed in traffic crashes, 96 percent (5,950) were riders and 4 percent (278) were passengers. (4) An estimated 86,887 motorcyclists were injured in traffic crashes, an increase of 5.2 percent from 82,567 motorcyclists injured in 2023.]]></description>
      <pubDate>Mon, 13 Jul 2026 08:51:48 GMT</pubDate>
      <guid>https://trid.trb.org/View/2721751</guid>
    </item>
    <item>
      <title>Predicting motorcycle crash severity on Thailand's curved roadways: A deep learning approach</title>
      <link>https://trid.trb.org/View/2679110</link>
      <description><![CDATA[Motorcycle crashes on curved roadways present unique safety challenges due to complex vehicle dynamics and increased risk exposure. In Thailand, where motorcycles dominate transport, crash data from 2016 to 2022 reveals a significantly higher fatal/severe injury rate on curved segments (56.77%) compared to straight segments (44.41%), despite curves comprising a smaller portion of the road network. This study develops Convolutional Neural Network (CNN) models to predict binary crash severity outcomes on curved roadways using 2679 crash records from Thailand's Highway Accident Information Management System. The research systematically compared four CNN architectures with varying complexity (depths 3 to 10+) and found that simpler architectures (Layout I with depth 3) outperform deeper configurations (accuracy = 0.634, summary score = 53.61%). SHapley Additive exPlanations (SHAP) were applied to identify key risk factors and interaction effects. Results show that factors such as large trucks, head-on collisions, depressed medians, darkness conditions, speeding, two-lane roads, and work zones are associated with higher predicted crash severity on curves, while urban settings, side-swipe crashes, and barrier medians are associated with lower predicted severity. SHAP interaction analysis identified combinations associated with elevated severity predictions, notably large trucks operating in darkness and head-on collisions regardless of median type. These findings support targeted interventions including differential speed management for large vehicles, enhanced illumination strategies, barrier median installations, and modified work zone protocols for curved segments. This research advances both methodological approaches for crash severity prediction and practical applications for motorcycle safety in countries with similar transportation contexts.]]></description>
      <pubDate>Wed, 17 Jun 2026 12:23:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2679110</guid>
    </item>
    <item>
      <title>An explainable RF-CNN model for injury severity prediction in single-motorcycle crashes</title>
      <link>https://trid.trb.org/View/2670044</link>
      <description><![CDATA[Single-motorcycle crashes represent a critical transportation safety challenge, particularly in developing countries with high motorcycle usage. This study proposes a novel hybrid Random Forest-Convolutional Neural Network (RF-CNN) model for predicting injury severity in single-motorcycle crashes, addressing the persistent trade-off between predictive accuracy and model interpretability. Utilizing 5,975 single-motorcycle crashes from Thailand’s Highway Accident Information Management System (2016–2023), this study implemented a weighted ensemble framework combining Random Forest interpretability with CNN pattern recognition capabilities. The methodology included Random Forest-based feature selection identifying 25 critical variables across four domains (temporal, roadway, environmental, and crash characteristics), class balancing using SMOTE, and a weighted fusion strategy (85% CNN, 15% RF). Model performance was evaluated against standalone Random Forest, CNN, and Binary Logistic regression baselines using accuracy, precision, recall, and F1-Score metrics. The hybrid RF-CNN model achieved superior performance with 58.9% accuracy and a recall of 69.9%, outperforming all baseline models in severe-crash detection. SHAP explainability analysis revealed that nighttime conditions with electrical lighting emerged as the most influential severity factor, followed by lane configuration, curved roadway geometry, and behavioral factors including speeding and alcohol involvement. Complex interaction effects were identified, demonstrating the model’s capability to capture non-linear risk relationships. The findings provide actionable guidance for targeted interventions, including enhanced roadway lighting, curve-specific speed management, median upgrades, and improved work zone safety protocols. This research contributes to explainable AI applications in transportation safety, demonstrating that hybrid modeling approaches can balance predictive performance with interpretability while supporting evidence-based motorcycle safety policy in developing countries.]]></description>
      <pubDate>Fri, 15 May 2026 15:44:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2670044</guid>
    </item>
    <item>
      <title>Harnessing the Integrated Statistical Machine Learning for Traffic Crash Injury-Severity Modeling</title>
      <link>https://trid.trb.org/View/2664364</link>
      <description><![CDATA[Modeling the severity of traffic crash remains challenging due to the complexity, uncertainty, and heterogeneity inherent in crash datasets. Traditional statistical models often overlook interactions and structural dependencies, while machine learning methods, though effective with large datasets, struggle to capture spatial and temporal dynamics. To address these gaps, we propose the Latent Gaussian Process with Tree-Boosting Model (LGPBoost), which integrates tree-based machine learning with Gaussian process mixed effects models. This framework accounts for spatial, temporal, and grouped dependencies while capturing nonlinear feature–outcome relationships. To demonstrate the superiority of LGPBoost, we conducted a well-designed simulation experiment focused on datasets characterized by complex feature relationships and latent grouped random effects, as well as spatial and temporal variabilities. Applying the method to Florida motorcycle crashes (2014–2023) revealed that rural and less urbanized areas face significantly higher severe and fatal crash risks, underscoring the need for targeted enforcement and infrastructure improvements. Temporal instability analysis further showed evolving crash risks across regions, particularly in non-urban regions. By unifying spatial heterogeneity and temporal variability, LGPBoost provides a rigorous benchmark for reliability-oriented crash severity modeling, offering a comprehensive framework to identify risk factors, quantify non-linear effects, and capture intrinsic spatial-temporal dynamics.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664364</guid>
    </item>
    <item>
      <title>Exploring the motorcycle crash risks and riders' risk profiles: Evidence from the motorcycle crash causation study</title>
      <link>https://trid.trb.org/View/2659380</link>
      <description><![CDATA[Motorcyclists are among the most vulnerable road users in the United States, facing disproportionately high crash and fatality rates, while many states prioritize motorcycle safety through the Strategic Highway Safety Plan. Despite this alarming trend, crash reports often lack critical insights into rider behavior and contributing risk factors. National crash data highlights an alarming trend where motorcycle crashes and fatality rates significantly exceed those of passenger cars, with sharp increases in recent years. However, existing crash reports often lack critical details about motorcycle operator behavior and risk factors, limiting efforts to develop effective safety interventions. This study aims to bridge this gap by analyzing the Federal Highway Administration's Motorcycle Crash Causation Study dataset, incorporating both crash-involved motorcyclists and paired control groups who were not in the crashes. By applying a random parameter logit model to estimate crash likelihood and a random parameter Weibull model to assess hazard duration until crash occurrence, this research identifies key contributing factors. Findings reveal that rider age, annual mileage, prior crash experience, passenger presence, travel speed, licensing status, motorcycle maintenance practices, and riding tasks play pivotal roles in influencing motorcycle crash risks. These insights underscore the urgent need for targeted motorcycle rider training, policy enhancements, and proactive safety interventions. By collaborating with state and local stakeholders, decision-makers can implement strategies that reduce motorcycle-related crashes, ultimately improving roadway safety for all users.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659380</guid>
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