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    <title>Transport Research International Documentation (TRID)</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>Transport Research International Documentation (TRID)</title>
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      <link>https://trid.trb.org/</link>
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    <item>
      <title>An LLM-driven estimation framework for estimating regional travel structures based on spatiotemporal multi-modal travel data</title>
      <link>https://trid.trb.org/View/2664619</link>
      <description><![CDATA[Accurately characterizing regional travel structures (the distribution of travel modes) is essential for developing targeted interventions to promote green mobility and inform sustainable urban policies. However, despite considerable emphasis on optimizing regional travel structures to promote sustainable urban development, existing methods frequently overlook crucial aspects such as spatial heterogeneity and dynamic travel behaviors, limiting their effectiveness in guiding adaptive urban planning strategies. To address this gap, this study proposes a novel framework based on a fine-tuned large language model (LLM) to estimate regional travel structures. By transforming multimodal data, the task is reformulated as a text-to-text estimation problem using Llama3, enhanced through domain knowledge and chain-of-thought prompting. Applied to 335 street-level units in Beijing, the proposed model significantly improves estimation accuracy, reducing RMSE and MAE by 18%–25% compared to baseline methods. The results uncover distinct spatial patterns, with core districts heavily reliant on public transit, whereas suburban regions show greater dependence on private cars. The proposed framework shows advanced AI’s potential to offer interpretable and precise decision-support tools for optimizing urban transportation systems.]]></description>
      <pubDate>Fri, 01 May 2026 14:31:49 GMT</pubDate>
      <guid>https://trid.trb.org/View/2664619</guid>
    </item>
    <item>
      <title>Integrating convolutional neural networks and explainable AI for enhanced winter road surface conditions classification using stationary RWIS imagery</title>
      <link>https://trid.trb.org/View/2659635</link>
      <description><![CDATA[Adverse winter weather significantly compromises driving safety and mobility in regions such as Canada and the northern United States. This study addresses these challenges by utilizing stationary Road Weather Information Systems (RWIS) equipped with cameras. These images capture complex scenes, making automated road surface condition (RSC) classification systems particularly challenging. Unlike previous studies that required manual cropping of main road pavement, we applied convolutional neural networks (CNNs) directly to full stationary RWIS imagery to validate their effectiveness and generalizability for real-world winter road maintenance (WRM) applications. Our study focused on four key aspects: (1) rigorously validating CNN performance on stationary RWIS images without manual cropping, (2) systematically analyzing the influence of camera angles using explainable artificial intelligence (XAI) techniques, (3) evaluating the effect of image resolution on model accuracy, and (4) exploring data-quantity trade-offs, including the impact of adding or removing camera feeds, to develop robust and deployable CNN models. The developed CNN achieved excellent performance metrics, all exceeding 98%. Our findings indicate that optimizing camera orientation substantially enhances the model's focus on relevant features that align with human interpretation. Reducing background complexity and increasing road captures from different perspectives further enhanced model focus. Furthermore, increasing image resolution up to 224 × 224 improved performance, although gains were marginal beyond this point while computational costs rose substantially. This comprehensive evaluation demonstrates the high potential of using stationary RWIS imagery for RSC classification with CNNs, suggesting significant improvements in WRM efficiency and traffic safety during winter.]]></description>
      <pubDate>Wed, 29 Apr 2026 09:10:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2659635</guid>
    </item>
    <item>
      <title>Wheelchair users’ involvement in roadway crashes: Exploring injury severity with synthetic data augmentation and Bayesian analysis</title>
      <link>https://trid.trb.org/View/2692454</link>
      <description><![CDATA[Mobility-related disabilities, particularly among individuals who use wheelchairs, represent an important transport and public health concern. When crashes occur, wheelchair users may experience disproportionately severe injuries due to limited evasive capability, reduced visibility, and accessibility barriers in the built environment. However, evidence on the correlates of crash injury severity among wheelchair users remains limited. The analysis uses police-reported crash data from North Carolina (2020–2023), coded through the Pedestrian and Bicycle Crash Analysis Tool (PBCAT), which provides detailed crash-type descriptors and contextual information. Due to the limited sample size (N = 109) and severe class imbalance in injury outcomes, the dataset was enhanced using the Synthetic Minority Over-sampling Technique (SMOTE), resulting in a balanced sample (N = 220) across five injury severity categories. Bayesian ordered logit models with flat, weak, and strong prior specifications were estimated to examine the correlates of wheelchair user injury severity, given a crash. Results show that crashes occurring in rural areas (posterior mean = 1.112), at intersections (posterior mean = 1.103), and at locations with no traffic control (posterior mean = 1.683) are strongly associated with higher injury severity. The SMOTE-enhanced Bayesian models demonstrated improved numerical stability compared to models estimated using the original dataset; however, synthetic observations do not provide additional real-world information. The findings underscore the importance of improving lighting, curb cuts, accessible intersection infrastructure, and traffic control, particularly in rural and small-town settings, to reduce serious injuries among wheelchair users and promote safer, independent, and health-sustaining mobility for wheelchair users.]]></description>
      <pubDate>Mon, 27 Apr 2026 17:01:27 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692454</guid>
    </item>
    <item>
      <title>AI-based System for Road Surface Condition Forecasting Using Multi-Source Meteorological Data</title>
      <link>https://trid.trb.org/View/2683917</link>
      <description><![CDATA[Accurate and timely forecasts of road surface conditions are crucial for efficient winter maintenance, enhanced traffic safety, and the optimized use of de-icing agents. Road surface phenomena, in complex fields present challenges to traditional forecasting methods due to their nonlinear and localized nature. This study presents a machine learning framework predicting real-time road states (dry, wet, icy, snowy) across Bavaria, Germany. It integrates data from over 516 Road Weather Stations (RWS), thermal measurements from winter maintenance vehicles, and elevation data from the Open Elevation API. Data undergoes temporal alignment, spatial interpolation, and missing-value imputation. Decision Trees form the core model for interpretability and nonlinear pattern handling. Each RWS employs a localized model, while a generalized version covers unmonitored roads via spatial adjustments. With over 85% accuracy, the system facilitates dynamic winter maintenance and minimizes resource waste. Cyber-physical in smart mobility and transportation networks support improved real-time hazard responses. This approach shows how scalable infrastructure can be made resilient using machine learning.]]></description>
      <pubDate>Mon, 27 Apr 2026 15:01:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/2683917</guid>
    </item>
    <item>
      <title>Secure Journeys or Perceived Peril? Examining Gender Differences in Day-Night Public Transport Decision-Making in India Using PLS-SEM Multi-Group Analysis</title>
      <link>https://trid.trb.org/View/2687378</link>
      <description><![CDATA[Gender disparities in public transport safety perceptions create significant barriers to equitable urban mobility, particularly affecting women's access to economic and social opportunities in India. While existing studies document safety concerns in specific cities, quantitative research examining gender-temporal intersections across diverse urban contexts remains limited. This study examines gender differences in safety perceptions and public transport usage intentions during daytime and nighttime using PLS-SEM with multi-group analysis. A nationwide survey (December 2024-May 2025) yielded 726 responses from frequent users (474 males, 252 females) across Tier-1, Tier-2, and Tier-3 Indian cities, measuring service quality, safety assessments, social support, habitual usage, and intention to use public transport using standardized 5-point Likert scales. Safety perception emerged as the strongest predictor of behavioural intention, with women demonstrating significantly higher sensitivity than men. Substantial nighttime disparities were observed, with women reporting lower safety scores than men, representing a critical perception gap. Multi-group analysis revealed gender-moderated relationships: women showed stronger service quality sensitivity in forming habitual patterns, while men demonstrated greater social support reliance. Past experiences negatively influenced men's safety perceptions but not women's, suggesting persistent female vigilance. Infrastructure preferences revealed women's strong preference for female operators and surveillance cameras. Findings necessitate gender-differentiated approaches, including enhanced nighttime security, workforce diversification, comprehensive service quality improvements, and systemic policy reforms integrating gender considerations throughout transportation planning from design through operations.]]></description>
      <pubDate>Mon, 27 Apr 2026 14:58:57 GMT</pubDate>
      <guid>https://trid.trb.org/View/2687378</guid>
    </item>
    <item>
      <title>Modular Smart Corner Systems for Next-Generation Electric Vehicle Architecture</title>
      <link>https://trid.trb.org/View/2692031</link>
      <description><![CDATA[The transition to software-defined vehicles (SDVs) necessitates a paradigm shift in both control strategies and vehicle architecture. The EU-funded R&D project SmartCorners addresses this challenge by developing integrated, modular, and scalable smart corner systems (SCS) that combine in-wheel motor (IWM)-based propulsion, brake blending, active suspension system, and steer-by-wire functionality in one module. These SCS can be retrofit or smoothly integrated into the highly adaptable skateboard chassis architecture of modern electric vehicles (EVs), enabling scalable deployment across diverse vehicle types. The central approach of this paper is the utilization of artificial intelligence (AI) and machine learning (ML) to implement multi-layer, data-driven control strategies, facilitating real-time actuation, fault mitigation, and user-centric EV architecture. The SmartCorners project strives to demonstrate significant enhancements, including improved real-world driving range due to enhanced energy-efficiency, reduced component and system costs, and a cut-down in development time of EVs, enabled by digital-twin-based design methodologies. Beyond these performance gains, SmartCorners establishes the foundational principles of modularity, adaptability, and software integration that underpin the evolution toward SDVs. The role of thermal and cabin comfort control is completely different for EVs and internal combustion engine vehicles, with the latter using waste heat from the combustion of fossil fuels for cabin heating, ventilation, and cooling (HVAC). In EVs the required energy is directly taken from the traction battery and precise thermal and cabin comfort control affecting essential components of the vehicle but also the user-perceived driving experience. These project achievements highlight a critical bridge between innovation and electrification on component-level, and the holistic software-defined mobility systems of the future.]]></description>
      <pubDate>Tue, 14 Apr 2026 15:11:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2692031</guid>
    </item>
    <item>
      <title>Factors associated with transit fare savings through the integrated fare system: A spatiotemporal perspective</title>
      <link>https://trid.trb.org/View/2684565</link>
      <description><![CDATA[Implemented in Seoul, South Korea in 2004, the integrated public transit fare system allows passengers to make transfer trips up to four times without additional charges and with fare discounts. This study analyzes the spatiotemporal variability of the fare savings. To this end, one day's smart card data from a weekday in May 2023 were combined with demographic and transit infrastructure data at the administrative unit level. The results revealed that users could save approximately USD 0.9 per trip on average for transfer trips. Moreover, the Geographically and Temporally Weighted Regression modeling showed that key factors, such as travel distance, income level, and transit accessibility, had different effects on fare savings, depending on the time of the day and area. The fare savings due to the implementation of the integrated fare system were greater in areas with longer travel distances and lower income levels, suggesting enhanced socioeconomic equity. Moreover, they were greater in areas with low public transit accessibility, especially during the morning peak period. The research findings provide valuable insights for policymakers to optimize the public transit fare system, meet the needs of diverse populations within cities, and establish a more inclusive and sustainable public transit system.]]></description>
      <pubDate>Tue, 31 Mar 2026 10:15:39 GMT</pubDate>
      <guid>https://trid.trb.org/View/2684565</guid>
    </item>
    <item>
      <title>Governing mechanisms and practical implications of jamming transition in asphalt mixture packing</title>
      <link>https://trid.trb.org/View/2651672</link>
      <description><![CDATA[The stability of asphalt mixture packing is largely determined by the asphalt mixture design and compaction methods. This study investigates the jamming transition mechanisms during the asphalt mixtures packing behavior exposed to various compaction conditions by integrating laboratory experiments with discrete element method simulations. Confined, unconfined, and vibratory packing tests were performed to evaluate the effects of aggregate morphology, interparticle friction, binder adhesion, and vibration on the formation of jammed states. The results reveal that polygonal aggregates exhibit higher angles of repose than circular aggregates due to enhanced mechanical interlocking, while high temperatures weaken binder adhesion and reduce jammed-state stability. Increasing friction coefficients accelerates the jamming transition but limits particle rearrangement, resulting in looser packing structures. Compaction partially mitigates friction-induced variability through skeleton reorganization, yet optimal densification is achieved at the friction coefficient of 0.2, which balance particle mobility and load transfer resistance. The optimized vibratory loading significantly enhances initial packing, lowers final voids in mineral aggregate, and reduces the compaction impulse, achieving an equivalent temperature reduction of approximately 7.7 °C. These findings provide fundamental insights into the jamming transition of asphalt mixtures and offer practical guidance for improving construction protocols, reducing energy consumption, and enhancing pavement durability.]]></description>
      <pubDate>Mon, 30 Mar 2026 17:10:42 GMT</pubDate>
      <guid>https://trid.trb.org/View/2651672</guid>
    </item>
    <item>
      <title>A unified framework for evaluating urban ridesharing potential: Spatiotemporal patterns, scaling effects, and multi-city evidence from China</title>
      <link>https://trid.trb.org/View/2647535</link>
      <description><![CDATA[Ridesharing has gained global attention as a sustainable mobility strategy to reduce congestion, emissions, and vehicle use. However, most existing studies focus on single cities and define ridesharing potential narrowly through the sharing rate, limiting the ability to distinguish universal patterns from city-specific variations. This study redefines ridesharing potential as the theoretical upper bound of a city’s capacity to accommodate shared trips under idealized conditions, reflecting how demand intensity, travel efficiency, and passenger delay jointly determine structural feasibility. Using standardized trip density, we analyze large-scale GPS trajectory and road network data from four Chinese megacities (Beijing, Shanghai, Shenzhen, and Chengdu), representing diverse urban forms from monocentric to polycentric and corridor-based structures. The results reveal four stable theoretical patterns governing ridesharing systems: temporal regularity with daytime peaks (8:00 to 18:00), spatial concentration around business and transit centers, a Pareto distribution in which 80% of shared trips occur within 20% of grids, and scaling saturation where sharing rate increases logarithmically with trip density (R²>0.94). The multi-city findings also reveal how urban morphology systematically modulates their manifestation. Monocentric and transit-oriented cities exhibit higher and more scalable ridesharing potential, while polycentric and spatially dispersed structures experience early saturation. This morphological dependence underscores that ridesharing efficiency is inherently structural and can be strategically enhanced through fine-grained land use coordination and hub-based urban design. A subset of the dataset is publicly released to support further research: https://anonymous.4open.science/r/multi-city-GPS-trajectory-dataset-3F3D.]]></description>
      <pubDate>Fri, 27 Mar 2026 10:14:17 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647535</guid>
    </item>
    <item>
      <title>Designing age-friendly streetscapes: Assessing environmental risk factors for falls using street view images</title>
      <link>https://trid.trb.org/View/2636249</link>
      <description><![CDATA[As cities worldwide strive to become more age-friendly in response to rapidly aging populations, outdoor falls among older adults remain a persistent challenge that undermines these initiatives. The micro-scale environmental determinants of these falls remain inadequately explored, creating a significant gap in evidence-based urban design for inclusive urban environments. This study investigates the association between built environment features and outdoor fall risks among older adults using data from 6302 emergency dispatch cases in Jeonbuk Province, South Korea. By leveraging deep learning-based computer vision and a zero-inflated Poisson model, we analyze half a million street view images to identify micro-scale streetscape features associated with outdoor fall incidents. Our findings indicate that older adults are more likely to fall in areas with high population density and mixed-use land. While these environmental characteristics show higher fall incident rates, certain streetscape features, such as curbs and brick surfaces, can mitigate fall risks, while asphalt and concrete surfaces may exacerbate them. These findings demonstrate that environments promoting walking require enhanced fall risk management through appropriate design considerations for older adults. By recognizing older adults as ‘mobility minorities’ with specific needs, targeted micro-scale streetscape interventions, such as surface materials and curb designs, can enhance safety while preserving walkability benefits, supporting more inclusive urban environments.]]></description>
      <pubDate>Wed, 25 Feb 2026 16:28:51 GMT</pubDate>
      <guid>https://trid.trb.org/View/2636249</guid>
    </item>
    <item>
      <title>Integrative System Design and Implementation for Enhancing Public Transportation and Urban Mobility: A Case Study in Xi’an</title>
      <link>https://trid.trb.org/View/2613212</link>
      <description><![CDATA[The rapid advancement of technology and the urgent need for sustainable urban growth have driven the evolution of public transportation as a key component of smart city development. This paper explores an integrated city-level public transportation system designed for Xi’an, focusing on the seamless incorporation of advanced digital solutions to enhance urban mobility. The system comprises four core components: a mobile application, shuttle bus operations, an urban online bus-hailing service, and a 43-in. LCD electronic stop signs. These components are interconnected through a cloud-based data infrastructure that supports real-time data collection, analysis, and communication. By leveraging big data, IoT, and AI, the system facilitates adaptive service delivery, predictive route adjustments, and enhanced user experiences. The mobile application provides real-time navigation and trip planning, while the shuttle bus system incorporates GPS tracking, diagnostics, and passenger monitoring for safety and efficiency. The bus-hailing service uses data-driven algorithms to dynamically adjust routes based on passenger demand, improving coverage and reducing wait times. Additionally, the electronic stop signs ensure passengers receive timely updates at transit points. Despite the advancements showcased in this paper, challenges remain in scaling such comprehensive systems and integrating them within existing urban infrastructure. This study presents a scalable, adaptable model that demonstrates how intelligent, data-driven public transportation can be implemented to meet modern urban mobility needs, positioning Xi’an as a case study for other global cities.]]></description>
      <pubDate>Fri, 20 Feb 2026 15:28:23 GMT</pubDate>
      <guid>https://trid.trb.org/View/2613212</guid>
    </item>
    <item>
      <title>IoT-Enabled Cloud-Integrated Smart Parking System with Real-Time Monitoring and AI-Based Space Optimization for Next-Gen Mobility</title>
      <link>https://trid.trb.org/View/2669803</link>
      <description><![CDATA[This study presents the design and implementation of an advanced IoT-enabled, cloud-integrated smart parking system, engineered to address the critical challenges of urban parking management and next-generation mobility. The proposed architecture utilizes a distributed network of ultrasonic and infrared occupancy sensors, each interfaced with a NodeMCU ESP8266 microcontroller, to enable precise, real-time monitoring of individual parking spaces. Sensor data is transmitted via secure MQTT protocol to a centralized cloud platform (AWS IoT Core), where it is aggregated, timestamped, and stored in a NoSQL database for scalable, low-latency access. A key innovation of this system is the integration of artificial intelligence (AI)-based space optimization algorithms, leveraging historical occupancy patterns and predictive analytics (using LSTM neural networks) to dynamically allocate parking spaces and forecast demand. The cloud platform exposes RESTful APIs, facilitating seamless interoperability with user-facing mobile and web applications. These interfaces provide end-users with real-time visualization of parking availability, intelligent navigation to optimal spaces, and digital payment integration, thereby minimizing search time and enhancing user convenience. From an administrative perspective, the system delivers comprehensive analytics dashboards, including heatmaps of space utilization, anomaly detection for unauthorized parking, and predictive maintenance alerts for sensor nodes. Field trials conducted across a multi-level parking facility demonstrated a 32% reduction in average vehicle search time and a 21% improvement in space utilization efficiency compared to conventional systems. The end-to-end solution adheres to robust cybersecurity standards (TLS 1.2 encryption, role-based access control) and is designed for modular scalability, supporting integration with smart city infrastructure and electric vehicle charging stations. This research establishes a scalable, intelligent framework for urban parking management, contributing significantly to reduced congestion, optimized resource allocation, and enhanced urban mobility.]]></description>
      <pubDate>Tue, 17 Feb 2026 10:28:09 GMT</pubDate>
      <guid>https://trid.trb.org/View/2669803</guid>
    </item>
    <item>
      <title>Rethinking spatial community detection in human mobility: A random walk-based method</title>
      <link>https://trid.trb.org/View/2630708</link>
      <description><![CDATA[Spatial community detection plays a crucial role in human mobility analysis. However, most spatial community detection methods solely dependent on the topology of the network, which cannot uncover the characteristics of human mobility effectively. To solve this problem, a random walk-based spatial community detection method is developed in this study. Specifically, random walk -based community definition, rather than the most popular network topology-based community definition in GIScience, is adopted. First, a random walk-based modularity was adopted as objective function for spatial community detection in human mobility; Second, we enhanced an effective spatial community detection method, STOCS, by incorporating local movement, allowing it to explore a broader solution space. Experiments on synthetic datasets reflecting human mobility characteristics showed that the proposed method outperforms four widely-used spatial community detection methods. A case study conducted using the Shenzhen shared bike dataset demonstrated that spatial communities detected by the proposed method can better reveal the characteristic of human mobility.]]></description>
      <pubDate>Mon, 09 Feb 2026 16:18:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/2630708</guid>
    </item>
    <item>
      <title>Mapping micro-mobility risk: AI-powered geospatial analysis and predictive modelling</title>
      <link>https://trid.trb.org/View/2647876</link>
      <description><![CDATA[Micro-mobility vehicles have rapidly become widespread as a sustainable and practical alternative for urban transportation in recent years. In this study, micro-mobility vehicles refer to traditional bicycles, electric bicycles, and electric scooters, which represent the main categories of such modes involved in traffic crashes in Türkiye. Despite their growing popularity, the safety implications of these vehicles have not yet been fully understood, and comprehensive research addressing crash patterns and associated risk factors is required. To this end, this study employs an artificial intelligence-driven geospatial and statistical methodology. Crash reports involving micro-mobility vehicles in Türkiye between 2015 and 2023 were analyzed. Seventeen independent variables and 102 sub-variables were identified and integrated into a GIS environment for spatial analysis. The impact levels of risk factors were assessed using six different Large Language Models (DeepSeek, GEMINI, Perplexity, ChatGPT, Copilot, and Poe). Crash risk maps and corresponding weight values were combined to produce a crash suitability map indicating the potential risk of micro-mobility crashes. Furthermore, the significance of these factors across different collision types was tested using a multinomial logistic regression model. To the best of the authors’ knowledge, this is the first study to apply a macro-scale dataset and an AI-enhanced geospatial decision-making approach to analyze micro-mobility crashes. The findings highlight the need for local governments and urban planners to implement targeted safety measures in regions with high crash potential.]]></description>
      <pubDate>Fri, 06 Feb 2026 08:45:31 GMT</pubDate>
      <guid>https://trid.trb.org/View/2647876</guid>
    </item>
    <item>
      <title>Numerical simulation of periodic oscillatory Giesekus viscoelastic flow around a cylinder</title>
      <link>https://trid.trb.org/View/2649722</link>
      <description><![CDATA[This numerical study investigates the periodic oscillatory flow of a Giesekus fluid around a circular cylinder, focusing on the effects of the Weissenberg number (Wiϵ[0, 4]), mobility factor (αϵ[0.0001, 0.1]), viscosity ratio (β=0.5,0.9) and and Keulegan-Carpenter number (KCϵ[1, 8]) on the drag coefficient, inertia coefficient, surface stresses, and vortex shedding characteristics. The results indicate that both shear-thinning and elastic effects significantly modulate the flow: elastic effects can suppress instabilities and reduce the inertia coefficient, whereas under strong elasticity, they increase the drag coefficient and induce flow instabilities. Kinetic energy analysis reveals that polymer stretching and relaxation facilitate energy exchange between the polymers and flow structures, with enhanced elasticity promoting energy transfer to the flow and increased shear-thinning inhibiting it. Furthermore, for 1 < KC < 8, increasing KC intensifies vortex shedding and enhances flow instabilities.]]></description>
      <pubDate>Mon, 02 Feb 2026 09:31:33 GMT</pubDate>
      <guid>https://trid.trb.org/View/2649722</guid>
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