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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=PHNlYXJjaD48cGFyYW1zPjxwYXJhbSBuYW1lPSJkYXRlaW4iIHZhbHVlPSJhbGwiIC8+PHBhcmFtIG5hbWU9InN1YmplY3Rsb2dpYyIgdmFsdWU9Im9yIiAvPjxwYXJhbSBuYW1lPSJ0ZXJtc2xvZ2ljIiB2YWx1ZT0ib3IiIC8+PHBhcmFtIG5hbWU9ImxvY2F0aW9uIiB2YWx1ZT0iMCIgLz48L3BhcmFtcz48ZmlsdGVycz48ZmlsdGVyIGZpZWxkPSJpbmRleHRlcm1zIiB2YWx1ZT0iJnF1b3Q7U3RhbmRhcmQgZGV2aWF0aW9uJnF1b3Q7IiBvcmlnaW5hbF92YWx1ZT0iJnF1b3Q7U3RhbmRhcmQgZGV2aWF0aW9uJnF1b3Q7IiAvPjwvZmlsdGVycz48cmFuZ2VzIC8+PHNvcnRzPjxzb3J0IGZpZWxkPSJwdWJsaXNoZWQiIG9yZGVyPSJkZXNjIiAvPjwvc29ydHM+PHBlcnNpc3RzPjxwZXJzaXN0IG5hbWU9InJhbmdldHlwZSIgdmFsdWU9InB1Ymxpc2hlZGRhdGUiIC8+PC9wZXJzaXN0cz48L3NlYXJjaD4=" 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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    <item>
      <title>Comparative analysis of different surrogate performance tests for evaluating the rutting potential of Marshall-designed bituminous mixtures</title>
      <link>https://trid.trb.org/View/2643792</link>
      <description><![CDATA[Rutting poses significant challenges to the transportation infrastructure and is a major cause of pavement deterioration. Among the several test methods available for evaluating rutting, the Hamburg Wheel-Tracking Test (HWTT) is popular for simulating traffic loads and demonstrates a strong correlation with field performance. This test, however, has several practical drawbacks. Therefore, there is a need for simpler performance tests that can be performed easily and quickly without compromising reliability, particularly for quality control and assurance during production. The primary objective of the study was to identify the most effective test method, along with a comparative analysis of different performance tests for assessing the rutting resistance of Marshall-designed bituminous mixtures. Further, the study also analyses the correlation between the different rutting tests. Additionally, the study investigates the correlation among these tests to understand their association in evaluating mixture performance. The results from the study showed that the dynamic creep test has the strongest correlation (R² = 0.92) with the HWTT and the other surrogate tests, highlighting its potential for inclusion in the Marshall mix design methodology. Moreover, the statistical tools such as Standard Deviation and Coefficient of Variation between the different tests reinforce the reliability and validity of these test methods.]]></description>
      <pubDate>Mon, 26 Jan 2026 08:41:44 GMT</pubDate>
      <guid>https://trid.trb.org/View/2643792</guid>
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    <item>
      <title>Measuring Travel Time Reliability for Urban Residents’ Commutes via the Integration of Information Entropy and Standard Deviation</title>
      <link>https://trid.trb.org/View/2434226</link>
      <description><![CDATA[Travel Time Reliability (TTR) plays a pivotal role in commuting. Nevertheless, existing measurement methods are not specifically designed for commuting scenarios, and their direct application to assess TTR for commuting may yield results incongruent with actual commuting conditions, as they overly rely on measures like mean and percentiles. Drawing on the cyclical characteristics of commuting, the study has established a TTR measurement model based on information entropy and standard deviation, tailored to individual commuters. By selecting commuting data from extensive travel datasets and applying both this model and conventional measurement methods, the focus is on quantitatively analyzing TTR for metro commuters and car commuters under various feature conditions, with a particular emphasis on commuting to work. The objective is to verify the feasibility and advantages of the proposed model. The research indicates that, compared to typical measurement methods, this model more accurately reflects TTR for commuting purposes. The results underscore a significantly superior TTR for metro commuters over car commuters. Distance and departure time exert a substantial impact on the TTR of car commuters, while distance and transfer times moderately influence the TTR of metro commuters. These findings serve as a crucial foundation for enhancing the quality of commuting experiences.]]></description>
      <pubDate>Mon, 30 Sep 2024 08:43:12 GMT</pubDate>
      <guid>https://trid.trb.org/View/2434226</guid>
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    <item>
      <title>The k-th order mean-deviation model for route choice under uncertainty</title>
      <link>https://trid.trb.org/View/2402499</link>
      <description><![CDATA[This study introduces the k-th order mean-deviation model for optimizing route choice within large, stochastic, and time-variant networks. This model addresses the limitations of the traditional mean-standard deviation approach by better handling extreme outcomes in travel times. It features an objective function called the “travel time budget”, which combines the average path travel time with a safety margin. This margin is defined by a trade-off coefficient and a selected deviation measure (total or semi) of the travel time. The model is divided into three variations: 1) The mean-total deviation (MTD) model for symmetric travel time distributions, 2) The mean-upper-semi-deviation (MUSD) model for asymmetric distributions prioritizing upper semi-deviations, suitable for risk-averse travelers, and 3) The mean-lower-semi-deviation (MLSD) model for asymmetric distributions focusing on lower semi-deviations, preferred by risk-prone individuals. The authors explore these models’ alignment with the stochastic dominance (SD) rule and develop a solution methodology based on SD principles. Numerical experiments in two real-world transportation networks demonstrate the models’ effectiveness and show how the choice of deviation affects route selection decisions.]]></description>
      <pubDate>Tue, 30 Jul 2024 16:26:59 GMT</pubDate>
      <guid>https://trid.trb.org/View/2402499</guid>
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    <item>
      <title>Improving Signalized Intersection's Capacity Through the Randomness of Saturation Flow Rate</title>
      <link>https://trid.trb.org/View/2335323</link>
      <description><![CDATA[The Highway Capacity Manual defines capacity at the intersection as the product of saturation flow rate (SFR) and effective green ratio. However, it is not totally correct in real-time control under the environment of intelligent transportation systems because imminent SFR (I-SFR) can be influenced by constantly changing (surrounding) traffic and environment and fluctuates around its mean value. Suppose the controller regards I-SFR as a positive coefficient in weight calculation when maximizing the weight. In that case, it could have higher capacity than the product of mean SFR (M-SFR) and effective green ratio (without changing the effective green ratio). This paper finds that I-SFR's standard deviation (SD) is an excellent index to reflect the potential for capacity improvement. In ideal conditions, the improvement could be about 0.23 times SD when the capacities of both streets are at the same. If one further sets the phase and cycle length fixed in real-time control, the improvement potential would be 30% off. The authors investigated two intersections in downtown Fuzhou, China, and the I-SFR’s SD is from 189 to 291 veh/h given a 20 s unit time interval. If one designs a virtual intersection of two single-lane one-way streets, its improvement potential would be from 40 veh/h to 55 veh/h, given an equal effective green ratio. The potential for capacity improvement is about 7% to 20% (5% to 14% when fixing the phase and cycle length).]]></description>
      <pubDate>Sun, 18 Feb 2024 16:02:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/2335323</guid>
    </item>
    <item>
      <title>Assessing the Representativeness of Survey Respondents for Micro-Level Network Resilience Modeling</title>
      <link>https://trid.trb.org/View/2071764</link>
      <description><![CDATA[Traditional modeling exercises in network robustness and related areas have primarily been done using aggregate data. Recent thinking suggests that a micro-level approach, using individual establishments as the unit of analysis, can improve the accuracy of models for characterizing existing conditions on the network. As the freight flows originate and terminate with individual firms, such an approach would allow for a more accurate depiction of freight flows in a regional context. However, the data requirements are more intensive than aggregate methods due to the need to collect detailed information about shippers. Even when such data is collected, the representativeness of the collected records must be examined to avoid any potential bias when performing statistical analysis. To this end, this study examines a dataset consisting of survey responses from Canadian business establishments that is considered for application in a micro-level modeling exercise. The survey collected data from over one thousand Canadian firms, including information about the respondent establishments’ characteristics, such as industry classification and employment size, and their shipping activities, such as goods classifications and origin and destination locations. For this data to be applied to a modeling exercise with any accuracy, it must be ensured to be representative of the entire population of Canadian shippers. The aim of the current paper is to determine the reliability of this dataset through exploring its representativeness. This will be done by analyzing the distribution of responses within the survey records, as well as comparisons with Canadian national averages for industry classifications, establishment locations, and other characteristics.]]></description>
      <pubDate>Tue, 29 Nov 2022 14:15:40 GMT</pubDate>
      <guid>https://trid.trb.org/View/2071764</guid>
    </item>
    <item>
      <title>Analysis of traffic noise distribution and influence factors in Chinese urban residential blocks</title>
      <link>https://trid.trb.org/View/1907577</link>
      <description><![CDATA[To improve the acoustic environment of residential blocks, noise mapping is employed in this study to analyze traffic noise distribution and the influence factors of four types of residential blocks in China. The study shows that high-rise small blocks have the highest average noise level (L<sub>avg</sub>) for ground and building facades, followed by small low-rise blocks while modern residential blocks yield the lowest value. An analysis of the standard deviation (STD) of spatial statistical noise level (Ln) shows that the STD of the ground and building façade of two types of small blocks is higher than that of other blocks. The analysis of influence factors indicates that the lot area of residential block has significant negative correlation with ground and building facade average noise level (L<sub>avg</sub>), and street coverage ratio (SCR) has significant positive correlation with ground and building facade average noise level (L<sub>avg</sub>). In low-rise and high-rise small blocks, ground space index (GSI) has significant negative correlation with ground and building facade average noise level (L<sub>avg</sub>); street interface density (SID) has significant positive correlation with the STDs of ground and building facade noise. Floor space index (FSI) shows significant positive correlation with the STDs of ground and building facade noise in low-rise small blocks.]]></description>
      <pubDate>Mon, 03 Oct 2022 14:18:30 GMT</pubDate>
      <guid>https://trid.trb.org/View/1907577</guid>
    </item>
    <item>
      <title>Effects of urban road environment on vehicular speed. Evidence from Brescia (Italy)</title>
      <link>https://trid.trb.org/View/1904536</link>
      <description><![CDATA[Inappropriate speeds are key factors that may affect the occurrence and the severity of road crashes. Although rural roads are influenced by more severe crashes than urban roads, perhaps due to the higher vehicular speeds, the latter suffer from a higher frequency of crashes. Therefore, exploring factors affecting the vehicular speed in the urban area is crucial. The literature provided several models to usually estimate the operating speed (i.e., V85) in rural roads. However, further investigations are needed to provide these estimations in the urban areas. In addition, these models often estimate the 85th percentile of the speed distribution, that cannot represent the entire distribution. Therefore, the problem of the speed prediction distribution is also a challenge in urban roads. This paper addresses this challenge by exploring the effects of some road factors on the vehicular speed along segments of urban roads. First, this speed is modelled as a random variable with a normal distribution. Next, by using 11,466 car spot speed data collected along a portion of the urban road network of city of Brescia (Italy), two multiple linear regression models were run for the estimation of the speed mean and the related standard deviation, respectively. Preliminary results showed that the presence of median, the bus stop density, the presence of curb and the type of adjacent land are significant predictors of the vehicular speed distribution on urban roads. These results may support road management agencies to set proper actions on speed management, especially for existing roads and/or critical section roads in urban areas.]]></description>
      <pubDate>Fri, 28 Jan 2022 09:15:18 GMT</pubDate>
      <guid>https://trid.trb.org/View/1904536</guid>
    </item>
    <item>
      <title>New Methodology of Designation the Precise Aircraft Position Based on the RTK GPS Solution</title>
      <link>https://trid.trb.org/View/1902804</link>
      <description><![CDATA[The paper presents the results of research on improving the accuracy of aircraft positioning using RTK-OTF (Real Time Kinematic–On The Fly) technique in air navigation. The paper shows a new solution of aircraft positioning for the application of the differential RTK-OTF technique in air navigation. In particular, a new mathematical model is presented which makes it possible to determine the resultant position of an aircraft based on the solution for the method of least squares in a stochastic process. The developed method combines in the process of alignment of GPS (Global Positioning System) observations, three independent solutions of the aircraft position in OTF mode for geocentric coordinates XYZ of the aircraft. Measurement weights as a function of the vector length and the mean vector length error, respectively, were used in the calculations. The applied calculation method makes it possible to determine the resultant position of the aircraft with high accuracy: better than 0.039 m with using the measurement weight as a function of the vector length and better than 0.009 m with the measurement weight as a function of the mean error of the vector length, respectively. In relation to the classical RTK-OTF solution as a model of the arithmetic mean, the proposed method makes it possible to increase the accuracy of determination of the aircraft position by 45–46% using the measurement weight as a function of the vector length, and 86–88% using the measurement weight as a function of the mean error of the vector length, respectively. The obtained test results show that the developed method improves to significantly improve the accuracy of the RTK-OTF solution as a method for determining the reference position in air navigation.]]></description>
      <pubDate>Mon, 24 Jan 2022 17:24:19 GMT</pubDate>
      <guid>https://trid.trb.org/View/1902804</guid>
    </item>
    <item>
      <title>Improvement of Knock Onset Determination Based on Supervised Deep Learning Using Data Filtering</title>
      <link>https://trid.trb.org/View/1847643</link>
      <description><![CDATA[Regulations regarding vehicles’ CO₂ emissions are continuing to become stricter due to global warming. The CO₂ regulations urge automobile manufacturers to develop gasoline engines with improved efficiency; however, the main obstacle to the improvement is the knock phenomenon in spark-ignition engines. If knock is predicted, the efficiency potential can be maximized in an engine by applying modest spark timing. Several research regarding knock prediction modeling have been conducted, and typically Livengood-Wu integral model is used to predict the knock occurrence. For the prediction, knock onset should be determined on a given pressure signal of given knock cycles for establishing the 0D ignition delay model. Several methodologies for knock onset determination have been developed because checking all the knock onset position by hand is impossible considering the breadth of data sets. Deep learning technique have been presented as a solution to the establishment of the knock onset methodology in the previous study. The model showed high accuracy and feasibility on knock onset determination; however, there was a limitation on robustness; the unstable robustness of knock onset determination could adversely affect the 0D ignition delay model. In this study, an adversarial attack was applied to the pervious deep learning model for verifying the robustness, and it was shown that the model had unstable robustness. This model was improved on the aspect of the robustness with data filtering. The model showed stable robustness even with the adversarial attack. The variance of the determination decreased by 64.1% from 0.136 to 0.082, and the standard deviation of the determination decreased by 40.0% from 0.019 to 0.007. Additionally, it was verified that the robustness of the base knock onset model could affect the robustness of the 0D ignition delay model; the variance and the standard deviation decreased by 18.2% and 9.6% respectively, when the improved deep learning knock onset model was used as base knock onset determination rather than using the previous model.]]></description>
      <pubDate>Tue, 26 Oct 2021 14:30:15 GMT</pubDate>
      <guid>https://trid.trb.org/View/1847643</guid>
    </item>
    <item>
      <title>New Approach to Determine Standard Deviation and Precision Estimates for ASTM D4867/D4867M and AASHTO T283</title>
      <link>https://trid.trb.org/View/1862528</link>
      <description><![CDATA[Moisture damage in asphalt pavements can be attributed to the incompatibility of the asphalt binder, aggregate, or the interaction of the asphalt binder and aggregate. Moisture damage can accelerate pavement distresses such as cracking and rutting. The most widely used test methods by state highway agencies are AASHTO T283-14, Standard Method of Test for Resistance of Compacted Asphalt Mixtures to Moisture-Induced Damage, and ASTM D4867/D4867M-09, Standard Test Method for Effect of Moisture on Asphalt Concrete Paving Mixtures. Some state highway agencies evaluate moisture resistance during production to ensure a quality asphalt mixture. Currently AASHTO T283-14 and ASTM D4867/D4867M-09 do not provide a method to calculate the standard deviation of the tensile strength ratio (TSR), nor does it provide precision estimates for the TSR. The objective of this paper is to show how one can calculate the variance (or standard deviation) of a variable when the value of interest (i.e., TSR) is calculated from two other random variables. The standard deviation of a random variable that is a quotient of two other variables can be calculated easily as long as the individual variables are random, independent, and the standard deviations are small relative to the mean. The novelty of this approach is in a statistics-based method for determining the standard deviation of the TSR based on using an approximation for the ratio variance using ASTM D4460-97, Standard Practice for Calculating Precision Limits Where Values Are Calculated from Other Test Methods. This new approach to calculate the standard deviation of the TSR will be used to develop a precision statement for AASHTO T283-14 and ASTM D4867/D4867M-09 following ASTM E691-19e1, Standard Practice for Conducting an Interlaboratory Study to Determine the Precision of a Test Method (Superseded).]]></description>
      <pubDate>Fri, 27 Aug 2021 14:58:25 GMT</pubDate>
      <guid>https://trid.trb.org/View/1862528</guid>
    </item>
    <item>
      <title>The Airline On-time Performance Impacts of the COVID-19 Pandemic</title>
      <link>https://trid.trb.org/View/1852996</link>
      <description><![CDATA[This paper examines the effects of the COVID-19 pandemic on flight delays in the U.S. airline industry. Using daily data on COVID-19 cases and flight on-time performance, and controlling for product, carrier and market characteristics, the author finds that increases in reported COVID-19 cases are associated with reductions in both departure and arrival delays. Specifically, a standard deviation increase in COVID-19 cases reduces arrival delay by 1 minute 42 seconds and departure delay by 2 minutes, on average. The author's results suggest that despite the economic fallout from the pandemic, a silver lining emerges—flights are departing and arriving with less delay amid the pandemic.]]></description>
      <pubDate>Fri, 30 Jul 2021 17:51:53 GMT</pubDate>
      <guid>https://trid.trb.org/View/1852996</guid>
    </item>
    <item>
      <title>Linking the Driver’s Driving Performance and Their Driving Safety: A Bridge via Naturalistic Driving Study</title>
      <link>https://trid.trb.org/View/1756776</link>
      <description><![CDATA[As traffic accident remains the leading causation of death in the world, this preventable tragedy could be prevented by understanding the driver’s behavior further. Specifically, which driving performance measure (DPM) should the research employ to evaluate the driving behavior and how the results should be interpreted requires more investigation. This is a twofold analysis to understand the DPM better. According to the result, in the one-to-one relationship, all the DPMs didn’t show a statistically significant relationship with the safety measure. In terms of the logistic models, five DPMs are selected as the predictor. Except for Standard Deviation of Only Deceleration, at the significance level of 0.05, the slope of the other four measures are significant in the regression formula. The model performs well in fitting the training data. This study extends our recognition about DPM and can be considered as the reference for DPMs.]]></description>
      <pubDate>Fri, 26 Mar 2021 17:47:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1756776</guid>
    </item>
    <item>
      <title>Concrete Pavement Reliability by Monte Carlo Simulation</title>
      <link>https://trid.trb.org/View/1759128</link>
      <description><![CDATA[Pavement reliability is a topic with which very few investigators are familiar enough to engage in a productive discussion about it, on a scientific, mathematical or philosophical level. Most engineers, however, are quite experienced in applying the concept of the safety factor, SF, in design. Monte Carlo Simulation is used in this paper to show that Reliability (R) is related to SF for any given level of assumed variability in material properties and traffic. A range of SF between 1 and 5 is found to be adequate in describing R from 50 to 99.9%, for typical concrete pavement sections. The relationship between R and SF is much more sensitive to changes in the variability of material properties, than to changes in traffic variability. The methodology developed in this study is simple to follow and leads to practical values of the overall standard deviation (So) required in American Association of State Highway and Transportation Officials (AASHTO) 86/93 designs. Findings corroborate the stipulation that for rigid pavements, So typically lies between 0.3 and 0.4, but this range depends on the assumed variability levels. The contributions of the Office of Engineering and of the Bureau of Statistics to the success or failure of a pavement are found to be essentially commensurate. The approach described herein addresses the debilitating weaknesses in the reliability methodology of AASHTO 86/93, which led to its abandonment in the National Cooperative Highway Research Program (NCHRP) Project 1-26 and 2008 Mechanistic-Empirical Pavement Design Guide (MEPDG) efforts. It is argued that the AASHTO 86/93 reliability approach is mathematically and philosophically superior to its successors and deserves to be reinstated.]]></description>
      <pubDate>Thu, 04 Feb 2021 16:48:11 GMT</pubDate>
      <guid>https://trid.trb.org/View/1759128</guid>
    </item>
    <item>
      <title>Effect of Nighttime Construction on Quality of Asphalt Paving</title>
      <link>https://trid.trb.org/View/1745747</link>
      <description><![CDATA[The need to meet increasing economic vitality has caused continued deterioration of road surfaces owing to the constant use fueled by high traffic volume. Nighttime paving operations are often seen as a viable solution to reduce traffic disruptions due to maintenance and construction activities. However, state highway agencies (SHAs) are uncertain as to whether the quality of nighttime paving is at the same level of daytime paving. Although percent within limits (PWL) has shown great promise as a quality-based pay factor tool, limited empirical research exists that focuses on using this process as a postconstruction pay tool. The goal of this study is to assess the impact of nighttime and daytime paving on pavement quality using PWL. To achieve the goal of this study, data extracted from a review of existing literature and archival studies were analyzed using multiple statistical tools. This study examines rideability quality for 86 different projects from both nighttime and daytime asphalt paving projects over a 2-year period in South Carolina. It was found that nighttime projects were awarded more rideability bonus payouts while daytime projects were awarded more 100% rideability payouts. This study fills a gap in knowledge and practices by providing relevant information required to use PWL as a tool for measuring, enhancing, and optimizing quality or asphalt pavement. SHAs should consider including PWL in highway contracts as a tool for assessing the quality of projects.]]></description>
      <pubDate>Thu, 19 Nov 2020 14:22:13 GMT</pubDate>
      <guid>https://trid.trb.org/View/1745747</guid>
    </item>
    <item>
      <title>Statistical Modeling of Quartiles, Standard Deviation, and Buffer Time Index of Optimal Tour in Traveling Salesman Problem and Implications for Travel Time Reliability</title>
      <link>https://trid.trb.org/View/1744784</link>
      <description><![CDATA[The traveling salesman problem (TSP) plays an important role in the field of transportation and logistics. While most studies focus on developing algorithms to find the shortest path and explore the average length of the shortest paths, the degree to which the shortest path deviates from its mean has not been studied. The study of deviation is important because it has implications for travel time reliability. Previous studies have used various indicators to measure this deviation, mainly including standard deviation, quantiles, and buffer time index (BTI). Therefore, this study aims to develop an empirical model to estimate the standard deviation, quantiles, and BTI for the optimal TSP tour. Experiments are performed to find the shortest path connecting N customers, which are generated randomly in a specified service area, using a genetic algorithm. The service area is a rectangle with ratio of length to width ranging from 1:1 to 8:1. Two types of lengths are considered: Euclidean and Manhattan. The number of customers considered ranges from 10 to 100 with intervals of 10. In the experimental design, the customers are generated randomly 500 times. The quartiles and standard deviations of the 500 shortest paths are recorded. The BTI is also calculated. Regression models are developed to estimate quartiles, standard deviation, and BTI using number of customers and parameters of service area as predictive variables. The models perform well on the testing data set. The constructed models can be used to estimate the standard deviation and reliability of travel time.]]></description>
      <pubDate>Thu, 15 Oct 2020 17:21:45 GMT</pubDate>
      <guid>https://trid.trb.org/View/1744784</guid>
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