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    <title>Transport Research International Documentation (TRID)</title>
    <link>https://trid.trb.org/</link>
    <atom:link href="https://trid.trb.org/Record/RSS?s=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" rel="self" type="application/rss+xml" />
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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>
    <image>
      <title>Transport Research International Documentation (TRID)</title>
      <url>https://trid.trb.org/Images/PageHeader-wTitle.jpg</url>
      <link>https://trid.trb.org/</link>
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    <item>
      <title>Field-Based Evaluation of Physiological Fatigue Reduction in Fuel Cell Refuse Collection Vehicle Using Heart Rate Variability</title>
      <link>https://trid.trb.org/View/2717319</link>
      <description><![CDATA[Understanding the physiological impact of vehicle electrification on operators remains an important but underexplored issue in commercial vehicle research. This study quantitatively evaluates the physiological fatigue of drivers and onboard crew members during real-world operation of commercial refuse-collection vehicles by comparing a diesel-powered vehicle with a fuel cell electric vehicle (FCEV). Both vehicles were operated on the same routes under comparable real-world operating conditions, including similar time periods and operational tasks, during municipal waste collection service. Heart Rate Variability (HRV) metrics were obtained from R-R interval (RRI) data recorded using a Polar heart rate sensor. The Root Mean Square of Successive Differences (RMSSD), a time-domain index reflecting short-term parasympathetic activity, and Poincaré (Lorenz) plot area (LP area), a nonlinear HRV index reflecting overall autonomic nervous system modulation, were calculated. In-cabin vibration and noise levels were also measured as supplementary context to support the interpretation of physiological responses.The results indicate that both RMSSD and LP area were higher during FCEV operation than during diesel vehicle operation. For the driver, RMSSD increased by approximately 61.65% and the LP area by approximately 49.91%. For the onboard crew member, RMSSD increased by approximately 18.79% and the LP area by approximately 46.02%.These findings suggest a consistent association between reduced vibration and noise characteristics in the FCEV and increased HRV indices, indicating reduced physiological fatigue during operation. This study provides quantitative evidence that fuel cell electric commercial vehicles are associated with improved occupational conditions, extending beyond conventional environmental benefits.]]></description>
      <pubDate>Tue, 23 Jun 2026 10:30:46 GMT</pubDate>
      <guid>https://trid.trb.org/View/2717319</guid>
    </item>
    <item>
      <title>Non-Intrusive Fatigue Detection for Pilots</title>
      <link>https://trid.trb.org/View/2712086</link>
      <description><![CDATA[Pilot fatigue represents a critical concern in aviation safety, as it can significantly impair cognitive functions, decision-making abilities, and reaction times. In addition to decreasing performance, in-flight chronic fatigue has negative long-term health effects. Possible causes of fatigue include sleep loss, extended time awake, circadian phase irregularities and workload. Conventionally, the risk due to fatigue in aerospace is reduced by flight time limits and controlled rest requirements. Despite regulations limiting flight time and enabling optimal rostering, fatigue cannot be prevented completely. Hence, there is need to detect pilot fatigue in real time.There is ongoing research to detect pilot fatigue using devices that can capture Electroencephalogram (EEG) and Electrocardiogram (ECG). Though these devices have high fidelity, they are intrusive and can limit pilot activity. This limitation could potentially be overcome by non-intrusive devices such as a smart watch/wrist band/goggles which can measure physiological parameters that provide insights into pilot’s mental health. Heart rate variability (HRV) is one such physiological marker of interest for detecting pilot fatigue in real time. HRV can be effectively derived by processing raw Photoplethysmography (PPG) signals to gain insights into the autonomic nervous system, enabling the assessment of physiological state. Wearable devices such as a wristwatch are used in the current study to measure PPG data. Time and frequency domain analysis were performed to evaluate the potential of HRV indices. The analysis of R-R intervals and the Low Frequency / High Frequency (LF/HF) ratio plots, derived from HRV signals, revealed distinct characteristics that differentiate between an alert and a fatigued pilot. This study demonstrates a reliable non-intrusive method for detecting pilot fatigue and enhancing flight safety.]]></description>
      <pubDate>Wed, 10 Jun 2026 13:18:08 GMT</pubDate>
      <guid>https://trid.trb.org/View/2712086</guid>
    </item>
    <item>
      <title>Analysis of Artificial Intelligence in Biofuel Industry: A Case Study</title>
      <link>https://trid.trb.org/View/2624055</link>
      <description><![CDATA[Increasing reservations about the mass consumption of fossil fuels because of their hazardous impact on ecosystem has led to an increased focus to look for renewable alternative. In the last decade, much research is made on production of biodiesel for blending with diesel to reduce diesel consumption in the transport sector. Studies suggest that biofuel do not provide any harm to environment because of their availability from natural resources. Biofuel production and its further utilization requires identifying unknown parameters having nonlinear relationships with each other. Accurate and better predictive tools are required at different stages during its usage. AI technique is one such tool that can provide support during production and utilization. The technique is utilized in designing, monitoring, predicting, decision making and optimizing systems. The present research investigates the areas of AI usage which makes use of models for designing better production strategies, accurate prediction of biofuel properties through machine learning tools. The concept used is similar with brain’s autolearning phenomena where system improves on its own and take better decisions for each problem it faces. The research will present a review of this AI application taking help from literature from year 2012 to 2024 to make decision for taking suitable action with the analyzed cases in literature. Findings of the study suggests AI will play a major role in the near future and a strong demand will be seen for AI application in this evolving area.]]></description>
      <pubDate>Thu, 13 Nov 2025 16:07:16 GMT</pubDate>
      <guid>https://trid.trb.org/View/2624055</guid>
    </item>
    <item>
      <title>Toward Designing Autonomous Shared Rides for People with Parkinson’s Disease: Barriers and User Needs Analysis</title>
      <link>https://trid.trb.org/View/2551112</link>
      <description><![CDATA[This study aims to support the inclusive design of autonomous shared rides (ASR) by identifying gaps related to efficient trips and human-machine interaction, specifically for people with Parkinson’s disease (PwPD). In-person interviews were conducted with 20 PwPD, aimed to understand PwPD’s travel experiences, potential user barriers, and needs with regard to an ASR service. During the interview, participants watched short video clips describing five trip segments (proposed by a U.S. Department of Transportation report) of an ASR trip (scenario animations) and responded to questions about these scenarios. Both qualitative (opinions) and quantitative (ranking/rating) data were collected. Results of the Friedman test indicated significant differences in PwPD’s rankings of various travel barriers. Safety and lack of customer service were among the top concerns for PwPD. Qualitative analysis of the interview data further suggested that PwPDs were mainly concerned with the following aspects of ASR: safety (ASR reliability and operation), availability and quality of real-person online customer service and human assistance, user-friendly technology with clear instructions, and accessibility for PwPD with varying levels of mobility, the capability of ASR to deal with emergency situations, and the assistance provided for finding seats and using seat belts. Overall, most PwPD participants ranked safety concern, lack of travel support/customer service, and technology issues as the top three travel barriers for ASR. Among the five trip segments (booking, identification, onboarding, traveling, and exiting), booking was perceived as the most anxiety-provoking segment. These unique data and findings have identified user barriers and needs for ASR, which can guide the design and implementation of future technical solutions to address a broader range of use groups.]]></description>
      <pubDate>Mon, 12 May 2025 17:08:56 GMT</pubDate>
      <guid>https://trid.trb.org/View/2551112</guid>
    </item>
    <item>
      <title>Effects of Aromas on Autonomic Nervous Activity of Car Drivers After the Alertness Impairment</title>
      <link>https://trid.trb.org/View/2486921</link>
      <description><![CDATA[The effects on autonomic nervous activity were investigated by supplying aromas to drivers whose alertness was impaired. α-pinene and limonene were compared with no-aroma control in a driving simulator. In the limonene-supply condition, autonomic indices suggested that mental tension might have been sustained, and the increase in the variance of respiratory frequency could be associated with maintained alertness. Physiological indices in the α-pinene condition showed the activation of sympathetic nervous activity, suggesting an enhancement in drivers’ efforts to maintain alertness. This study showed that supplying aroma might be effective in improving driver alertness.]]></description>
      <pubDate>Fri, 24 Jan 2025 11:13:22 GMT</pubDate>
      <guid>https://trid.trb.org/View/2486921</guid>
    </item>
    <item>
      <title>Summary of Poster Abstracts</title>
      <link>https://trid.trb.org/View/2483093</link>
      <description><![CDATA[Seventeen research posters were prepared and presented by student authors. The                     posters covered a wide breadth of works-in-progress and recently completed                     projects. Topics included a variety of body regions and injury scenarios:                                                      Biofidelity Corridors of Powered Two-Wheeler Rider Kinematics from                                 Full-Scale Crash Testing Using Postmortem Human Subjects, Meringolo                                 et al.                                                                               Cervical Vertebral and Spinal Cord Injuries Remain Overrepresented in                                 Rollover Occupants, Al-Salehi et al.                                                                               The Effect of Surfaces on Knee Biomechanics during a 90-Degree Cut,                                 Rhodes et al.                                                                               Investigating the Variabilities in the Spinal Cord Injury in Pig                                 Models Using Benchtop Test Model and Ultrasound Analyses, Borjali et                                 al.                                                                               Relationship between Tackle Form and Head Kinematics in Youth                                 Football, Holcomb et al.                                                                               Comparing Motor Vehicle Collision Injury Incidence between Pregnant                                 and Nonpregnant Individuals: A Case–Control Study, Levine et al.                                                                               Development of an Automated Pipeline to Characterize Full Rib Cage                                 Shape Variability, Robinson et al.                                                                               Soft Tissue Force Attenuation and Redistribution during Lateral Hip                                 Impacts, Pretty et al.                                                                               Hybrid III Small Female Neck Interaction with a Driver Airbag:                                 Preliminary Observations, Boyle et al.                                                                               Changes in Youth Football Athletes’ Oculomotor Task Metrics across                                 Three High School Seasons of Play, Pang et al.                                                                               Measurement of Shielding Stiffness in Ice Hockey, Vakili et al.                                                                               Investigating the Relationship between Vehicle-Based and Biomechanics                                 Injury Metrics in Car-to-End Terminal Crashes Using a Human Finite                                 Element Model, Buckland et al.                                                                               On-Field Instrumented Mouthguard Coupling, Luke et al.                                                                               Investigation of Rear-Seat Occupant Safety during High-Speed Frontal                                 Crashes Using GHBMC M50-O, Dahiya et al.                                                                               Deformable Headform Design Choices: An Evaluation of Brain Simulant                                 Stiffness Influence on Intracranial Displacements and Strain, Xu et                                 al.                                                                               Changes in Neurocognitive Outcomes among Youth Football Teams                                 Participating in an Intervention, Marks et al.                                                                               A Parametric Skeleton Model of Human Upper Extremities Accounting for                                 Morphological Variations among the Diverse Population, Neeluru et                                 al.]]></description>
      <pubDate>Mon, 30 Dec 2024 11:54:24 GMT</pubDate>
      <guid>https://trid.trb.org/View/2483093</guid>
    </item>
    <item>
      <title>Driver Fatigue Detection Using Measures of Heart Rate Variability and Electrodermal Activity</title>
      <link>https://trid.trb.org/View/2389691</link>
      <description><![CDATA[This paper investigated the feasibility and reliability of employing various physiological measures - for determining drivers’ fatigue levels, which may ultimately lead to a solution for real-time detection of driver fatigue state for improving driving and traffic safety. An experimental study was conducted to collect the data, including fatigue levels assessed via the Karolinska sleepiness scale and heart rate variability (HRV) and electrodermal activity (EDA) features. Based on an extensive statistical analysis of the collected data, significant differences in numerous HRV and EDA features were found across varying fatigue levels. Employing several machine learning techniques for classification purposes, the most favorable binary classification performance was achieved using the Light Gradient Boosting Machine classifier, with an accuracy rate of 88.7% when HRV and EDA features were utilized as inputs. Meanwhile, for three-class classification, the accuracy decreased slightly to 85.6% when employing the Random Forest classifier. These outcomes underscore the potential of HRV and EDA feature fusion in capturing diverse physiological responses to fatigue, thereby bolstering fatigue detection performance. Besides, subject-independent classification yielded an accuracy of 52.0% and 53.3%, reflecting the potential bias introduced by unobserved heterogeneity in classification models. Moreover, feature selection should be prioritized over dimensionality reduction in feature fusion endeavors to diminish feature redundancy and prevent information loss. The findings of this study could contribute to the development of reliable driver fatigue detection methodologies utilizing readily available measures of physiological response measures, such as HRV and EDA features.]]></description>
      <pubDate>Tue, 22 Oct 2024 09:07:34 GMT</pubDate>
      <guid>https://trid.trb.org/View/2389691</guid>
    </item>
    <item>
      <title>Summary of Poster Abstracts</title>
      <link>https://trid.trb.org/View/2341879</link>
      <description><![CDATA[Eighteen research posters were prepared and presented by student authors at the                     18th Annual Injury Biomechanics Symposium. The posters covered a wide breadth of                     works-in-progress and recently completed projects. Topics included a variety of                     body regions and injury scenarios such as:Head: Defining the mass, center of                                 mass, and anatomical coordinate system of the pig head and brain;                                 the influence of friction on oblique helmet testing; validation of                                 an in-ear sensor for measuring head impact exposure in American                                 footballNeck and spine:                                 Design of paramedic mannequin neck informed by adult passive neck                                 stiffness and range of motion data; identifying injury from                                 flexion-compression loading of porcine lumbar intervertebral                                 discThorax: Tensile                                 material properties of costal cartilage perichondrium; finite                                 element models of both an ovine thorax and adipose tissue for                                 high-rate non-penetrating blunt                                     impactPelvis:                                 Injurious pelvis deformation in high-speed rear-facing frontal                                 impactsLower extremities:                                 Generation of 3D pediatric femur models from 2D radiographs; plantar                                 thickness and stiffness using ultrasound; knee injuries in skiing                                 and snowboarding using artificial intelligence 3D modeling; jumping                                 kinematics, and kinetics in athletes with secondary task of heading                                 a soccer ballFull body, vehicle                                     occupants: Comparison of Hybrid III, THOR mid-size male,                                 and small female ATDs in frontal sled tests; effects of booster seat                                 on reclined small females during lateral oblique low-acceleration                                 impacts; airbag deployment for out-of-position 50th percentile male                                 human body modelFull body,                                     unique loading scenarios: Development of seat fixture and                                 restraints for FE human body model during vertical loading;                                 methodology for PMHS-occupied powered two wheeler and motor vehicle                                 crash scenario]]></description>
      <pubDate>Tue, 20 Feb 2024 10:03:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2341879</guid>
    </item>
    <item>
      <title>Developing an Ovine Model of Impact Traumatic Brain
          Injury</title>
      <link>https://trid.trb.org/View/2341874</link>
      <description><![CDATA[Traumatic brain injury is a leading cause of global death and disability.                     Clinically relevant large animal models are a vital tool for understanding the                     biomechanics of injury, providing validation data for computation models, and                     advancing clinical translation of laboratory findings. It is well-established                     that large angular accelerations of the head can cause TBI, but the effect of                     head impact on the extent and severity of brain pathology remains unclear.                     Clinically, most TBIs occur with direct head impact, as opposed to inertial                     injuries where the head is accelerated without direct impact. There are                     currently no active large animal models of impact TBI. Sheep may provide a                     valuable model for studying TBI biomechanics, with relatively large brains that                     are similar in structure to that of humans. The aim of this project is to                     develop an ovine model of impact TBI to study the relationships between impact                     mechanics and brain pathology. An elastic energy impact injury device has been                     developed to apply scalable head impacts to rapidly rotate the head without                     causing hard tissue damage. A motion constraint device has been developed to                     limit the head motion to a single plane of rotation. The apparatus has been                     tested using deceased animals to assess the controllability of impact                     velocities, the repeatability of head kinematics, and the dynamic response of                     the head to impact. Impact velocities are effectively controlled by modulating                     the elastic energy stored in the impact piston. The resulting head kinematics                     are somewhat variable, and are influenced by impact location, time-dependent                     postmortem tissue changes, and specimen head and neck physiology. Model                     development will continue, and in vivo testing will be conducted to assess the                     brain pathology following impacts of varying severity.]]></description>
      <pubDate>Tue, 20 Feb 2024 10:03:38 GMT</pubDate>
      <guid>https://trid.trb.org/View/2341874</guid>
    </item>
    <item>
      <title>Path Following of Autonomous Vehicles with an Optimized Brain
          Emotional Learning–Based Intelligent Controller</title>
      <link>https://trid.trb.org/View/2341736</link>
      <description><![CDATA[This article proposes a control framework which combines the longitudinal and                     lateral motion control of the path-following task for Autonomous Ground Vehicles                     (AGVs). In terms of lateral motion control, a modified kinematics model is                     introduced to improve the performance of path following, and Brain Emotional                     Learning–Based Intelligent Controller (BELBIC) is applied to control the heading                     direction. In terms of longitudinal motion control, a safe speed is derived from                     the road condition, and a Proportional-Integral (PI) controller is implemented                     to force the AGV to drive at the desired speed. In addition, for a better                     performance of path-following and driving stability, Particle Swarm Optimization                     (PSO) algorithm is used to tune the parameters of BELBIC. In this article, a                     Carsim and Simulink joint simulation is provided to verify the effectiveness of                     the modified model and the control framework. The simulation result indicates                     that, in the scenario of the modified kinematics model, the AGV could follow the                     desired path with a smalle lateral offset than the conventional model, except                     that the modified model is less sensitive to preview time. Compared with the                     Proportional-Integral-Derivative (PID) controller, the BELBIC allows the AGV to                     follow the desired path with a smaller lateral offset. Specifically, the maximum                     lateral offset with the BELBIC controller is 0.18 m, while it is up to 1.37 m                     with the PID controller.]]></description>
      <pubDate>Tue, 20 Feb 2024 10:03:02 GMT</pubDate>
      <guid>https://trid.trb.org/View/2341736</guid>
    </item>
    <item>
      <title>Automated Vehicles, the Driving Brain, and Artificial
     Intelligence</title>
      <link>https://trid.trb.org/View/2283545</link>
      <description><![CDATA[Automated driving is considered a key technology for                     reducing traffic accidents, improving road utilization, and enhancing                     transportation economy and thus has received extensive attention from academia                     and industry in recent years. Although recent improvements in artificial                     intelligence are beginning to be integrated into vehicles, current AD technology                     is still far from matching or exceeding the level of human driving ability. The                     key technologies that need to be developed include achieving a deep                     understanding and cognition of traffic scenarios and highly intelligent                     decision-making.Automated Vehicles, the Driving Brain, and                         Artificial Intelligenceaddresses brain-inspired driving and learning                     from the human brain's cognitive, thinking, reasoning, and memory abilities.                     This report presents a few unaddressed issues related to brain-inspired driving,                     including the cognitive mechanism, architecture implementation, scenario                     cognition, policy learning, testing, and validation.]]></description>
      <pubDate>Mon, 30 Oct 2023 15:37:55 GMT</pubDate>
      <guid>https://trid.trb.org/View/2283545</guid>
    </item>
    <item>
      <title>HORIZON Europe Project AeroSolfd: GPF-Retrofit for Cleaner Urban Mobility</title>
      <link>https://trid.trb.org/View/2239701</link>
      <description><![CDATA[Ultrafine particles, in particular solid sub-100 nm particles pose high risks to human health due to their high lung deposition efficiency, translocation to all organs including the brain and their harmful chemical composition; due to dense traffic, the population in urban environments is exposed to high concentrations of those toxic air contaminants, despite these facts, they are still widely neglected. Therefore, the EU-Commission set up a program for clean and competitive solutions for different problem areas which are regarded to be hotspots of such particles. HORIZON AeroSolfd is an EU project, co-funded by Switzerland that will deliver affordable, adaptable, and sustainable retrofit solutions to reduce exhaust tailpipe emissions from petrol engines, brake emissions and pollution in semi-closed environments. VERT, a Swiss based international industry organization, has a long research history in the field of nanoparticle filtration and it is in charge of reducing tailpipe emissions of gasoline vehicles by using the best available retrofit filtration technology (BAT). VERT will apply the newest high-efficient GPF technology in three high mileage fleets, in Germany, Switzerland and Israel. The project will also serve as a platform to continue research on PN emissions as well as on secondary emissions from GDI and PFI petrol engines. In addition, the “high emitter phenomena” will be further analysed with a NPTI testing campaign of 1000 gasoline vehicles, including GDI, PFI and GPF equipped vehicles.]]></description>
      <pubDate>Mon, 11 Sep 2023 13:40:28 GMT</pubDate>
      <guid>https://trid.trb.org/View/2239701</guid>
    </item>
    <item>
      <title>Heart Rate Variability of a Student Pilot During Flight Training</title>
      <link>https://trid.trb.org/View/2201933</link>
      <description><![CDATA[Heart rate (HR) indicates the number of beats per minute (bpm) of the heart, while heart rate variability (HRV) indicates the temporal fluctuation of the intervals between adjacent beats (NN). HRV expresses neuro-cardiac activity and is generated by heart-brain interactions and dynamics related to the function of the autonomic nervous system (ANS) and other components (e.g., body and ambient temperature, respiration, hormones, blood pressure). The authors are carrying out a series of experimental investigations with the aim of studying HRV in student pilots during training. For this purpose, the authors used a Holter electrocardiograph equipped with three channels and five electrodes positioned on the chest of the subject who participated in the investigation. The case report refers to a student pilot who, during a flight mission with the instructor, had to face a forced landing and a flap failure. The authors report data based on analysis of the time domain and frequency domain related to operations on the ground before the flight, during the flight, and on the ground after the flight. The initial conclusion is that the extent of HRV constitutes an “energy store” for better cardiac performance in eustress activities. During advanced tasks, the “Total Power” of the heart decreases because the RR intervals are forced toward low values, where the heart is less able to be modulated by its many controllers. Furthermore, this experimental protocol can be useful to flight instructors for the training process of student pilots.]]></description>
      <pubDate>Thu, 27 Jul 2023 16:55:29 GMT</pubDate>
      <guid>https://trid.trb.org/View/2201933</guid>
    </item>
    <item>
      <title>Tonality Perception Lessons Learned</title>
      <link>https://trid.trb.org/View/2173432</link>
      <description><![CDATA[The future is expected to bring Advanced Air Mobility (AAM) vehicles, including small unmanned aerial systems (sUAS), urban air mobility (UAM) vehicles and regional air mobility (RAM) vehicles. These manned and unmanned vehicles are propelled by rotors. Rotors tend to generate tonal sound as their blades interact periodically with airflow features. Since people are more sensitive to tonality, including tones, than broad band sound, AAM generated tonality is expected to be an important consideration for design. In this paper several tonality metrics are examined for their ability to explain perceived annoyance of AAM flyover noise as measured by NASA’s Rotorcraft Sound Quality Metric 1 (RoQM-1) test. The various investigated metrics use one-third octave band, narrow band, and autocorrelation analysis. It is observed that tonality influences but does not control perceived flyover noise annoyance due to other sound qualities like roughness, consistent with previous work. The metrics are also examined for their ability to explain perceived tonality of sounds generated by IT equipment. The metrics based on autocorrelation are observed to best explain perceived tonality while also being among the best for explaining flyover annoyance. The plausibility of the auditory nervous system as a physiological auto-correlator is discussed.]]></description>
      <pubDate>Tue, 16 May 2023 11:44:54 GMT</pubDate>
      <guid>https://trid.trb.org/View/2173432</guid>
    </item>
    <item>
      <title>Study on Influencing Factors of Hippocampal Injury in Closed Head Impact Experiments of Rats Using Orthogonal Experimental Design Method</title>
      <link>https://trid.trb.org/View/2155471</link>
      <description><![CDATA[The hippocampus plays a crucial role in brain function and is one of the important areas of concern in closed head injury. Hippocampal injury is related to a variety of factors including the strength of mechanical load, animal age, and helmet material. To investigate the order of these factors on hippocampal injury, a three-factor, three-level experimental protocol was established using the L9(34) orthogonal table. A closed head injury experiment regarding impact strength (0.3MPa, 0.5MPa, 0.7MPa), rat age (eight- week-old, ten-week-old, twelve-week-old), and helmet material (steel, plastic, rubber) were achieved by striking the rat's head with a pneumatic-driven impactor. The number of hippocampal CA3 cells was used as an evaluation indicator. The contribution of factors to the indicators and the confidence level were obtained by analysis of variance. The results showed that impact strength was the main factor affecting hippocampal injury (contribution of 89.2%, confidence level 0.01), rat age was a secondary factor (contribution of 8.9%, confidence level 0.05), and helmet material had no significant effect on hippocampal injury (contribution less than 1.9%). This paper provides a method to distinguish factors affecting hippocampal injury.]]></description>
      <pubDate>Wed, 19 Apr 2023 16:34:21 GMT</pubDate>
      <guid>https://trid.trb.org/View/2155471</guid>
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