<?xml version="1.0" encoding="utf-8"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>TRB Publications Index</title><link>http://pubsindex.trb.org/</link><atom:link href="http://pubsindex.trb.org/common/TRIS Suite/feeds/rss.aspx?s=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJsb2NhdGlvbiIgdmFsdWU9IjIiIC8%2BPHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8%2BPC9wYXJhbXM%2BPGZpbHRlcnMgLz48cmFuZ2VzIC8%2BPHNvcnRzPjxzb3J0IGZpZWxkPSJyZWNvcmRjcmVhdGVkZGF0ZSIgb3JkZXI9ImRlc2MiIC8%2BPC9zb3J0cz48cGVyc2lzdHM%2BPHBlcnNpc3QgbmFtZT0icmFuZ2V0eXBlIiB2YWx1ZT0icHVibGlzaGVkZGF0ZSIgLz48L3BlcnNpc3RzPjwvc2VhcmNoPg%3D%3D" rel="self" type="application/rss+xml" /><description></description><language>en-us</language><copyright>Copyright © 2015. 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>TRB Publications Index</title><url>http://pubsindex.trb.org/Images/PageHeader-wTitle.png</url><link>http://pubsindex.trb.org/</link></image><item><title>Exploring the Potential of Remote Sensing and Machine Learning for Scalable Sidewalk Condition Assessment</title><link>http://pubsindex.trb.org/view/2726634</link><description><![CDATA[Sidewalk condition plays a critical role in ensuring pedestrian safety, accessibility, and compliance with regulatory standards. Conventional assessment methods typically involve manual inspections using categorical ratings, which are labor-intensive, subjective, and limited in spatial coverage. This study evaluates the use of satellite imagery and machine learning to support sidewalk condition assessments. A classification model was developed using synthetic aperture radar (SAR) imagery combined with sidewalk physical attributes, including width, slope, and material type. A random forest classifier was trained to predict four condition categories: good, fair, poor, and severe. To address the substantial imbalance in the distribution of classes, a binary formulation was also tested by grouping segments into defective and nondefective classes. Data resampling techniques combining under- and oversampling were applied to improve model performance. The results indicated that the binary model with combined sampling achieved the best performance, with a recall of 0.85 and G-mean of 0.81. Models trained on the original four classes showed lower performance owing to underrepresentation of the poor and severe categories. Feature-importance analysis highlighted SAR amplitude as the most influential predictor across all scenarios. The findings demonstrated the potential of SAR imagery to support scalable and data-driven evaluation of sidewalk conditions. This approach offers a viable complement to traditional inspection methods by enabling targeted resource allocation and broader spatial coverage in pedestrian infrastructure management.]]></description><pubDate>Mon, 13 Jul 2026 17:05:41 GMT</pubDate><guid>http://pubsindex.trb.org/view/2726634</guid></item><item><title>Optimization of Hot-Mix Asphalt Rolling Pattern Using Ground-Penetrating Radar and Markov Decision Processes</title><link>http://pubsindex.trb.org/view/2726620</link><description><![CDATA[Ground-penetrating radar (GPR) has been used for nondestructive evaluation of hot-mix asphalt (HMA) pavements including density prediction. HMA density is an acceptance quality characteristic (AQC) used across the U.S. AQCs are the basis for quality control and acceptance, and are used by agencies to determine contractors’ pay. Recently, roller-mounted GPR was introduced to monitor HMA density in real-time, enabling roller operators to make more informed decisions to avoid under- and over-compaction of HMA layers. In this study, a Markov decision process (MDP) is formulated to represent the rolling pattern optimization problem. This formulation accounts for GPR prediction error, density spatial variability, and uncertainty in density progression. The introduced MDP provides contractors and roller operators with a tool to minimize operational costs while achieving target density, thereby enhancing pavement service life and reducing maintenance activities. Data collected from several field projects were used in the MDP formulation. The benefits of using the developed MDP for compaction decisions were demonstrated using project data from Illinois, U.S. The analysis was conducted for an actual project scenario under Illinois’ quality control for performance (QCP) program and was extended to a hypothetical pay for performance (PFP) scenario to evaluate the generalizability of the approach under different risk levels. Compared with an experienced roller operator, MDP decisions reduced the construction time by 40.3% and 18.1% and increased the revenue by 9.7% and 50.2% for the QCP and PFP scenarios, respectively. Additional benefits in energy savings, reduced construction-related delays, and improved worker safety are expected.]]></description><pubDate>Mon, 13 Jul 2026 08:47:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2726620</guid></item><item><title>Deformation Analysis of Long-Deep Foundation Pit Excavation in Ningbo Metro Based Using Hardening Soil Small Strain Model</title><link>http://pubsindex.trb.org/view/2726619</link><description><![CDATA[Ningbo soft soil predominantly consists of silt, which is characterized by significant thickness, high natural moisture content, low strength, and slow consolidation. When this soft soil is disturbed, it leads to surface settlement. This study examines a subway deep foundation pit in Ningbo by simulating and analyzing the entire excavation process using the finite element software PLAXIS 3D. The study investigates the lateral displacement of the supporting structure and surface subsidence trends. In addition, it discusses the effects of the diaphragm wall and supporting stiffness on the surrounding ground settlement. The findings indicate that increasing the stiffness of the diaphragm wall or support structure effectively reduces ground settlement during subway excavation. Furthermore, the study confirms that the proposed method for determining the HSS model parameters is suitable and can offer valuable insights for similar projects in Ningbo and comparable regions.]]></description><pubDate>Mon, 13 Jul 2026 08:47:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2726619</guid></item><item><title>Development of a Permanent Deformation Model to Predict Rutting Performance in Substandard Airfield Pavements</title><link>http://pubsindex.trb.org/view/2724784</link><description><![CDATA[This paper discusses a permanent deformation model (PD model) developed with data collected from previous full-scale pavement testing experiments to improve the prediction of rutting development on airfield asphalt pavements. The data, including rut depths, pavement stiffness, and instrumentation, were collected from 34 different test items trafficked with a heavy vehicle simulator and deployable load-cart. The loading conditions of the test traffic corresponded to heavy aircraft including the C-17 (single wheel load of 45,000 lb), C-130 (single wheel load of 35,000 lb), and P-8 (total gear load of 89,000 lb). Pavement-Transportation Computer Assisted Structural Engineering (PCASE) version 7.0 was used to determine the predicted passes to failure based on measured pavement layer thickness and material properties and compare the predicted and measured passes to failure. It was observed that approximately 75% of the data fell below the line of equality, indicating that the current design methodology underpredicts passes to failure. A PD model was developed that computes a mechanistic response at predefined points within a theoretical unsaturated poroelastic multilayered structure caused by an aircraft load and then relates these responses to progressive rutting performance through an incremental-recursive rutting model. The performance of the PD model was verified with the data collected from full-scale test experiments. Results showed that the PD model consistently performed well over a range of different passes to failure.]]></description><pubDate>Mon, 13 Jul 2026 08:47:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724784</guid></item><item><title>Assessing Functional Performance of Asphalt Pavements under Data Sparsity: A Probabilistic-Deterministic Approach</title><link>http://pubsindex.trb.org/view/2724783</link><description><![CDATA[Asphalt pavements, subjected to continuous traffic loads and environmental stressors, undergo deterioration processes that gradually compromise their structural and functional integrity. Pavement management systems (PMS) have been implemented to forecast deterioration and plan maintenance, but empirical models commonly used in PMS encounter limitations in data-scarce environments. To address this, a probabilistic-deterministic approach is proposed to evaluate the functional condition of in-service asphalt pavements. The international roughness index (IRI) was selected as a functional condition indicator. IRI measurement data were used for two modeling approaches: (i) a Markov chain-based probabilistic model for short-term IRI condition states and (ii) a deterministic model for long-term IRI prediction and remaining service life estimation. The probabilistic model categorizes the IRI into five condition states, while the deterministic model uses exponential regression with estimated pavement age as input. Results show that the Markov chain model effectively represents functional deterioration and provides short-term predictions without historical data. Analysis revealed a gradual decline in pavement condition with a corresponding rise in lower condition states across all functional classes. State roads exhibited an accelerated transition to lower states, highlighting the need for early interventions. A faster degradation rate was also observed once pavements declined to “fair” or “poor.” Validation confirmed that the short-term predictions were consistent with field observations, with absolute state proportion differences ranging from 0.0002 to 0.1116. The deterministic model demonstrated accurate IRI predictions, with R² ranging from 0.84 to 0.86. Consequently, integrating both models enables reliable condition evaluation and maintenance planning, regardless of historical data availability.]]></description><pubDate>Mon, 13 Jul 2026 08:47:46 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724783</guid></item><item><title>Central Europe: Application of the Unified Theory of Acceptance and Use of Technology Framework</title><link>http://pubsindex.trb.org/view/2724781</link><description><![CDATA[This study investigates the behavioral intention and actual use of ride-hailing services among Generation Z (Gen Z) in Slovakia and the Czech Republic, applying the Unified Theory of Acceptance and Use of Technology (UTAUT), updated to its 2022 version. Data were collected from Slovak and Czech respondents between May and September 2024 and analyzed using PLS-SEM. The findings reveal notable cross-country similarities and differences. Behavioral intention was significantly influenced by performance expectancy, habit, and social influence in both countries. Effort expectancy was significant only in the Czech sample, while compatibility and personal innovativeness influenced behavioral intention only in Slovakia. Price value, hedonic motivation, and facilitating conditions showed no significant effects. Actual use was driven by behavioral intention in the Czech sample and by habit in both countries. This research fills a critical gap in understanding ride-hailing adoption within Gen Z in Central Europe, a demographic previously underexplored in the UTAUT context. The results provide actionable insights for service providers aiming to better align their offerings with the expectations and behaviors of young users. By addressing regional nuances, this study contributes to the global discourse on technology adoption and transport economics, offering a significant foundation for future research in this field.]]></description><pubDate>Mon, 13 Jul 2026 08:47:45 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724781</guid></item><item><title>Evaluation of Deep Learning Strategies Using Two Different Image Data Sets for Automated Pavement Distress Detection Methodology</title><link>http://pubsindex.trb.org/view/2724839</link><description><![CDATA[This paper investigates strategies to enhance the performance of deep learning models in pavement distress detection by utilizing two distinct datasets collected and annotated from different sources (vendors). The datasets consist of two- and three-dimensional images scanned from asphalt/concrete pavements and manually labeled for multiple surface distresses. Despite using the same base model to train individually, the performance of new models varies between the two datasets for the same pavement type. It remains a question whether transfer learning, knowledge distillation, or data merging will improve weaker models by leveraging stronger ones and increase the models’ robustness across different data sources. Experiments are conducted using these strategies to assess their applicability for single-source data (e.g., from a single vendor) and their generalization for multiple sources (e.g., from two or more vendors). This paper bridges a knowledge gap by qualitatively and quantitatively evaluating the effects of these strategies on current pavement distress detection practices using image data. Optimal approaches, such as fine-tuning or data merging, are recommended for various use cases aimed at real applications.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724839</guid></item><item><title>Resilient and Mechanistic Evaluation of Cement-Recycled Glass Treated Lateritic Soil for Sustainable Pavement Design</title><link>http://pubsindex.trb.org/view/2724837</link><description><![CDATA[Waste glass has shown promising results when used to stabilize soil, particularly in pavement applications. The elastic behavior of cement with recycled glass powder (RGP) in the lateritic soils has not been investigated yet. This study illustrates the potential use of RGP as a sustainable substitute for soil stabilization in urban pavement layers. The resilient behavior of clayey soils from southern Brazil, treated with high-early strength cement at contents of 3% and 6%, and RGP at contents of 3%, 6%, and 12%, was investigated. Resilient modulus (MR) tests were performed on untreated soil, soil–cement, and soil–cement-RGP specimens. Five MR prediction models were calibrated using data from repeated load triaxial (RLT) tests. Using Multiple Layer Elastic Analysis (AEMC) software, the useful life of an urban pavement was estimated, considering the properties of the subgrade, subbase, and base layers. Results showed that adding 3% RGP to cement-soil mixtures significantly improved the MR. Mechanistic analysis demonstrated that soil–cement-RGP mixtures C3RGP3 and C3RGP6 performed with a higher service life than cement-soil mixture C3, and soil–cement-RGP mixture C6RGP3 achieved a higher service life than cement-soil mixture C6. These findings underscore RGP’s effectiveness in enhancing pavement material properties. The results align with the integration of geotechnical engineering research and Brazilian national agencies, providing an effective alternative to traditional methods that benefits organizations such as the National Department of Transport Infrastructure, the National Land Transport Agency, the Road Research Institute, and highway contractors.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724837</guid></item><item><title>Streamlining Uniaxial Cyclic Fatigue Testing of Asphalt Mixtures Using a Mechanical Clamping System</title><link>http://pubsindex.trb.org/view/2724835</link><description><![CDATA[This study presents a patented collet–chuck-based clamping system for uniaxial cyclic fatigue testing of asphalt mixtures using small specimen geometry. Conventional specimen preparation is time- and resource-intensive, requiring precise cutting and the use of epoxy adhesives to mount specimens onto loading platens. These steps are critical for test success and typically require highly trained personnel, and they introduce delays because of epoxy curing. The proposed mechanical clamping system eliminates the need for cutting and epoxy adhesives, which allows rapid, repeatable specimen mounting, streamlining the workflow and reducing testing time. The system is compatible with a standard asphalt mixture performance tester and can be fabricated using commercially available, off-the-shelf components. To evaluate its applicability, eight asphalt mixtures from three different states were tested using the conventional glued-end-plate method and the collet–chuck clamping system. The mixtures covered a wide range of material characteristics, including different nominal maximum aggregate sizes (NMAS), binder types (unmodified, polymer-modified, and highly polymer-modified), binder contents, and reclaimed asphalt pavement percentages. The results showed strong agreement between both methods, with similar damage characteristic curves and comparable normalized variance index 𝙑𝘯𝘰𝘳𝘮 values. The collet–chuck system produced improved 𝙑𝘯𝘰𝘳𝘮  values in five of the eight mixtures. In addition, no statistically significant differences were observed in the failure criteria based on pseudo stiffness versus time curve, and apparent damage index parameter between the two systems, confirming that the proposed clamping approach is a viable alternative for uniaxial cyclic fatigue testing without compromising test integrity or results validity.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724835</guid></item><item><title>Investigating Vehicle Speed Interdependence in Work Zone Merging Scenarios</title><link>http://pubsindex.trb.org/view/2724833</link><description><![CDATA[Merging behavior in work zones with lane closures involves complex interactions, often among multiple vehicles. Although the merging maneuver of a vehicle is contingent on the behavior of surrounding vehicles, much of the existing research fails to consider the interdependence of these interactions. This oversight has resulted in a significant knowledge gap within the literature on work zone merging behavior. To address this gap, this study examines how the speeds of vehicles surrounding a merging vehicle, specifically the lead and following vehicles in both the open and closed lanes, collectively influence merging speed decisions. Video data from two work zones in New South Wales, Australia, were analyzed using a simultaneous equation modeling approach to capture the endogenous relationships among vehicle speeds. The results revealed significant interdependence, particularly between the merging vehicles and following vehicles in both closed and open lanes. The findings underscored the coordinated and interdependent nature of vehicle speed adjustments during lane merging, where the merging vehicle’s speed both influences and is influenced by surrounding traffic in their current lane (closed lane) and target lane (open lane). The use of a system-level modeling approach proved essential to capture these reciprocal dynamics and the temporal interplay among vehicle responses. These findings suggest that modeling merging decisions without accounting for these mutual influences may overlook critical safety concerns, especially in mixed-traffic environments involving heavy vehicles. The study provides new insights into vehicle interactions in work zones, with implications for improving traffic management and safety assessments in such settings.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724833</guid></item><item><title>Reliability-Centered Assessment of Misreporting-Related Accident Risks in Turkish Straits Passages</title><link>http://pubsindex.trb.org/view/2724829</link><description><![CDATA[Ship traffic through the Turkish Straits occurs in a highly constrained navigational environment, where inaccurate prepassage reports can significantly increase the risk of maritime accidents. Vessels are required to submit Sailing Plan declarations (SP-1 and SP-2) before entry, but these reports often fail to reflect the actual technical and operational condition of the ships. Commercial pressure, time constraints, and intentional misreporting can create discrepancies between declared and actual readiness, raising the likelihood of collisions, groundings, or loss of maneuverability in congested waters. This study develops a reliability-centered framework to assess accident risks associated with reporting, contributing to the literature on human reliability and uncertainty management in maritime traffic. Risk criteria were identified by reviewing regulatory requirements, accident records, and relevant literature, and refined through expert input from Vessel Traffic Services (VTS), pilotage, Port State Control (PSC), and ship operations. The framework combines the Fine–Kinney method with an Intuitionistic Fuzzy TODIM (an acronym in Portuguese for interactive and multicriteria decision-making). The results indicate that undisclosed propulsion deficiencies, steering problems, and nontransparent withdrawal from passage queues are the most critical accident precursors. These findings highlight the importance of reliable reporting and provide practical and transferable guidance for reducing the risks of maritime accidents and improving risk management for vessel traffic. In particular, the results support the prioritization of propulsion and steering system checks, as well as the closer scrutiny of vessels withdrawing from passage queues, offering actionable insights for VTS operators, PSC authorities and marine pilots.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724829</guid></item><item><title>Fabrication and Evaluation of a Superhydrophobic and Luminescent Emulsified Asphalt Coating for Road Applications</title><link>http://pubsindex.trb.org/view/2724828</link><description><![CDATA[Water damage and limited nighttime visibility are critical challenges for asphalt pavements, often leading to reduced durability and safety hazards. This study introduces a novel multifunctional coating combining superhydrophobic and luminescent properties, achieved through integrating innovative additive materials. The coating, designed to be applied over an emulsified asphalt, aims to enhance water repellency, minimize ice formation, and improve nighttime visibility without additional energy consumption. A factorial design approach was employed to evaluate the effects of additive dosage rates and spray duration on the coatings’ hydrophobicity, luminescence, and anti-icing performance. Results demonstrate significant improvements in hydrophobicity, with contact angles exceeding 150°, achieving superhydrophobicity in most cases. Work-of-adhesion values were substantially reduced, indicating superior ice repellency compared with the control. The luminescent properties of the coatings were optimized, with effective-luminescent-area ratios reaching 66.5%, enhancing visibility under low-light conditions. Anti-icing tests showed extended freezing times for coated surfaces, providing additional safety benefits during snowy or icy conditions. These findings indicate the potential of the modified coating as a sustainable, multifunctional solution for modern road infrastructure, particularly in regions prone to heavy rainfall, ice formation, and low visibility.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724828</guid></item><item><title>Using Connected Vehicle Data to Quantify Speeding Behavior on Large Networks</title><link>http://pubsindex.trb.org/view/2724774</link><description><![CDATA[This study demonstrates the use of connected vehicle data to conduct high-resolution speeding behavior analyses across a statewide highway network. Using over 3.5TB of Wejo trajectory data, we extract the maximum observed speeds for individual vehicles on defined road segments and aggregate them hourly to analyze temporal patterns. To assess excessive speeding, vehicle speeds are compared with posted speed limits across 13 thresholds, ranging from 35 to 90 mph, and sample sizes within each threshold are calculated. Based on this data, the paper presents a speeding index for identifying segments with potentially unlawful or unsafe speeding behavior. The analysis covers more than 120,000 road segments and considers differences by road type, day of the week, season, and holiday. Results reveal spatial and temporal variations in speeding intensity, including elevated rates during off-peak hours and certain holiday periods. When the Wejo sample size is sufficiently large, there is a good agreement with INRIX speed data. A web-based tool has been developed that enables users to dynamically explore excessive speeding patterns and download customized data sets. Findings highlight the value of connected car data as a supplement to traditional traffic data sets and offer insights for transportation agencies seeking to enhance safety and performance monitoring using emerging data sources.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724774</guid></item><item><title>Machine-Learning-Based Traffic State Prediction in Car–Bicycle Mixed Traffic Using Synthetic Data</title><link>http://pubsindex.trb.org/view/2724773</link><description><![CDATA[This study explores the use of machine learning models to predict traffic conditions in mixed car–bicycle traffic environments. A synthetic dataset was developed from numerical evaluations of traffic flow theory, capturing a wide range of multimodal traffic scenarios. Random forest (RF), multi-layer perceptron (MLP), and linear regression models were trained to estimate key traffic metrics, including output flow, delay, and density. The analysis focuses on model performance under different data splits, especially when sorting by variables such as initial car flow and bicycle flow. Results show that, while RF performs well for previously observed traffic conditions, MLP offers stronger generalization to unseen traffic conditions, particularly in high-flow and high-density regimes. However, prediction performance varies depending on the input variable used for sorting and the distribution of training data. These findings underscore the importance of balanced, diverse datasets and support the use of data-driven models for traffic state estimation in multimodal urban networks.]]></description><pubDate>Fri, 10 Jul 2026 12:12:17 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724773</guid></item><item><title>Residential Price Modeling Using Spatially Validated Machine Learning Methods: A Comparison Across Geographical Contexts</title><link>http://pubsindex.trb.org/view/2724620</link><description><![CDATA[Land use (i.e., buildings) and transportation infrastructure are tightly coupled systems, such that residential property prices play a critical role in transportation planning. In a similar manner, transportation infrastructure influences property prices, such that accurate forecasts of both systems are related research problems. The objective of this study is to examine these interactions and evaluate the effectiveness of machine learning methods in modeling residential real estate prices across different urban contexts. Specifically, we first examine the impact of land and transportation infrastructure on residential real estate prices using Extreme Gradient Boosting (XGBoost) and Random Forest (RF) machine learning methods, and compare results between two cities representing diverse geographic and socioeconomic contexts. Second, we investigate the application of machine learning methods on spatial data and provide a comparison of non-spatial and spatial cross-validation on the performance of machine learning methods. We use SHapley Additive exPlanations (SHAP) values to study the impact of land use and transportation infrastructure on real estate prices. The models are applied, and results are compared between the Rawalpindi and Islamabad Metropolitan Area in Pakistan and the City of Toronto in Canada. We find that, despite differences in demographics and economic development, the two cities exhibit similarities in the effect of transportation infrastructure and local amenities on dwelling prices. Proximity to the major central city cores (i.e., downtowns) increases sale price. The effect of transportation infrastructure is differentiated, with high quality transit (e.g., subway and BRT) increasing and conventional bus stop proximity decreasing sale price, respectively. We confirm the previous finding in other fields that non-spatial cross-validation over-estimates the prediction accuracy of machine learning algorithms on spatially referenced datasets. We find that the XGBoost model has slightly higher performance than the RF model. We recommend careful use of machine learning methods in the case of spatial data specifically in modeling of land prices.]]></description><pubDate>Thu, 09 Jul 2026 14:05:01 GMT</pubDate><guid>http://pubsindex.trb.org/view/2724620</guid></item></channel></rss>