Running Research Articles

The effect of forward postural lean on running economy, kinematics, and muscle activation.

Running economy, commonly defined as the metabolic energy demand for a given submaximal running speed, is strongly associated with distance running performance. It is commonly believed among running coaches and runners that running with increased forward postural lean either from the ankle or waist improves running economy. However, recent biomechanical research suggests using a large forward postural lean during running may impair running economy due to increased demand on the leg muscles. This study tests the effect of altering forward postural lean and lean strategy on running economy, kinematics, and muscle activity. 16 healthy young adult runners (23±5 years, 8M/8F) ran on a motorized treadmill at 3.58m/s using three postural lean angles [upright, moderate lean (50% of maximal lean angle), and maximal lean] and two strategies (lean from ankle and lean from waist [trunk lean]). Metabolic energy consumption, leg kinematics, and muscle activation data were recorded for all trials. Regardless of lean strategy, running with an increased forward postural lean (up to 8±2 degrees) increased metabolic cost (worsened economy) by 8% (p < .001), increased hip flexion (p < .001), and increased gluteus maximus (p = .016) and biceps femoris (p = .02) muscle activation during the stance phase. This relation between running economy and postural lean angle was similar between the ankle and trunk lean strategies (p = .743). Running with a large forward postural lean reduced running economy and increased reliance on less efficient extensor leg muscles. In contrast, running with a more upright or moderate forward postural lean may be more energetically optimal, and lead to improved running performance.

Long Jump Ability: Analyze of Leg Explosive Power and Running Speed for Junior High School Students

Long Jump Ability: Analyze of Leg Explosive Power and Running Speed for Junior High School Students

Modeling of Lane Changing Behavior of Connected Driving Based on Vehicle Interaction Potential

Aiming at the interactive behavior of vehicles changing lanes in the intelligent network traffic environment, the relative position of vehicle, speed, acceleration, and steering angle are introduced to correct the molecular interaction potential, and the vehicle interaction potential model is established. The spatial distribution map of the vehicle interaction potential field under different motion states was drawn, and the changing law of the vehicle interaction potential field was described. By analyzing the longitudinal critical distance of the lane changing safe critical time, the expression of acceptable clearance for lane changing is defined, and the relationship between acceptable clearance and the speed and acceleration of the interactive vehicle is verified by simulation. Combined with the acceptable clearance of lane changing, a lane changing behavior model based on the vehicle interaction potential is established, and the situation of lane changing vehicles subjected to the interaction potential and potential field forces generated by the surrounding vehicles is analyzed, and the driving decision of lane changing vehicles under the action of different potential field forces is given. Compared with the molecular dynamics lane changing model and LS2015 lane changing model, the results show that the vehicle action potential lane changing model can effectively improve the running speed and stability of traffic flow. The research has theoretical significance for the lane changing behavior decisions of networked autonomous vehicles in the future.

Flexible optimization of variables based on exponential and linear attenuation elimination-binary dragonfly algorithm in near infrared spectroscopic analysis

Single Binary Dragonfly Algorithm (Single-BDA) which is an intelligent optimization algorithm, normally needs more calculation time and often obtains unrobust variables. In this study, an Exponential and Linear Attenuation Elimination-Binary Dragonfly Algorithm (ELAE-BDA) is proposed to overcome the problems by combining exponential and linear attenuation functions. The algorithm can quickly eliminate a part of the variables with large RMSECV in the iterative process through the exponential attenuation function first, and use the linear attenuation function at the end of the iteration for meticulous elimination of invalid variables. It avoids the important variables being deleted unexpectedly, greatly improves the running speed of the algorithm, reduces the randomness of results, and significantly improves the analysis ability of the model. Three near-infrared spectral datasets are used as research objects to evaluate the performance of the ELAE-BDA algorithm, and 6 wavelength selection algorithms including Single-BDA, GA, CARS, MC-UVE, SCARS, and fiPLS are used for comparison. The results show that the ELAE-BDA algorithm can obtain results with almost the smallest number of wavelengths and the highest stability. The established PLSR models have the best prediction with the lowest RMSECV and RMSEP, indicating that the proposed algorithm can provide a more accurate and effective results of wavelength optimization for near-infrared spectral analysis.

Dynamic simultaneous localization and mapping based on object tracking in occluded environment

Abstract In practical applications, many robots equipped with embedded devices have limited computing capabilities. These limitations often hinder the performance of existing dynamic SLAM algorithms, especially when faced with occlusions or processor constraints. Such challenges lead to subpar positioning accuracy and efficiency. This paper introduces a novel lightweight dynamic SLAM algorithm designed primarily to mitigate the interference caused by moving object occlusions. Our proposed approach combines a deep learning object detection algorithm with a Kalman filter. This combination offers prior information about dynamic objects for each SLAM algorithm frame. Leveraging geometric techniques like RANSAC and the epipolar constraint, our method filters out dynamic feature points, focuses on static feature points for pose determination, and enhances the SLAM algorithm’s robustness in dynamic environments. We conducted experimental validations on the TUM public dataset, which demonstrated that our approach elevates positioning accuracy by approximately 54% and boosts the running speed by 75.47% in dynamic scenes.

Running performance of substitutes in the FIFA World Cup after the change in substitution rule: influence of match outcome, playing position and sex

ABSTRACT This study intended to investigate the impact of substitution rule change on the physical performance in World Cup tournaments. Running data from the 2022 and 2023 FIFA World Cups were used for analysis. The results show that substitutes have much more sprinting, high-speed running and moderate speed running per minute than starters (p < 0.05), with similar magnitudes of differences for the men’s (28.4–47.6%) and women’s World Cup (23.4–59.5%). Substitutes in winning vs losing teams had higher sprint distance (3.7 vs 2.8 m· min−1) and number of high-speed running (1.70 vs 1.58 times· min−1) in men’s World Cup and higher sprint distance (3.3 vs 2.5 m· min−1) in women’s World Cup. In the men’s World Cup, substituted midfielders had a higher frequency and greater distance covered with high-speed running (1.85 vs 1.57 and 1.44 times· min−1; 10.1 vs 8.9 and 7.8 m· min−1) and higher sprint frequency (0.74 vs 0.71 and 0.62 times· min−1) compared to forwards and defenders, which was not the case for the Women’s World Cup. Together, this study underlines the importance of substitutions for the team running performance in modern elite football under the new substitution rules, and practitioners need to pay attention to the use of substitute players under the new rules.

A soft resource optimization method based on autonomous coordination of unmanned swarms system driven by resilience

The resilience improvement of unmanned swarm systems endows them with enhanced survivability and mission execution capability in complex environments. However, the current methods for improving resilience in unmanned swarm systems face challenges such as high time costs, difficult implementation, and weak anti-interference ability. Therefore, a soft resource optimization method based on the autonomous collaboration of unmanned swarm systems is proposed in this paper. Firstly, a research framework illustrates the complete cycle of resilience improvement processes in distributed unmanned swarm systems. Secondly, a soft resource optimization model is developed based on autonomous collaboration among distributed unmanned swarm systems, providing detailed insights into autonomous collaboration in complex environments. Finally, simulation experiments are conducted to validate the advantages of the proposed soft resource optimization method. The experimental results show that compared with the centralized control, the soft resource optimization method through autonomous coordination not only improves the resilience, but also improves the running speed by 99.8%. In addition, a pre-analysis of resilience threats is also carried out to facilitate proactive implementation of resilience improvement measures for unmanned swarm systems by engineers in the simulation experiment.

Validation of Step Detection and Distance Calculation Algorithms for Soccer Performance Monitoring.

This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance.

ЖАЛПЫ БІЛІМ БЕРЕТІН МЕКТЕП ОҚУШЫЛАРЫНЫҢ ҚОЗҒАЛЫС БЕЛСЕНДІЛІГІН ЗЕРТТЕУ

Research by scientists shows that the modern way of life in the family and the organization of the educational process at school does not provide the biological need for the school body to move. The daily motor activity of elementary school students decreases by 50 percent compared to preschoolers, and high school students - by 75 percent. The lack of motor activity of schoolchildren leads to a decrease in the level of physical fitness. The reform of general education and vocational schools has set new tasks for the entire education system to improve the educational process and educate the younger generation.The article examines the motor mode of secondary school students. When conducting pedagogical observations and a questionnaire survey, the characteristic features of the manifestation of children's motor activity during the day, changes in the volume of activity against the background of daily activities, the proportion of dynamic and static loads coming to the body and background energy consumption of children were taken into account. The physical fitness of the students was determined during physical education lessons based on the results of the test tasks: running speed for various distances and strength exercises. The indicators of height, weight, dynamometry, motor and strength indicators were studied. Based on the results of the research, methodological recommendations have been developed for physical education teachers and parents. The purpose of these recommendations is to provide the most accessible, physiological substantiation material for the preparation of a training program.

Identifying physiological determinants of 800 m running performance using post-exercise blood lactate kinetics

PurposeThe aims of the present study were to investigate blood lactate kinetics following high intensity exercise and identify the physiological determinants of 800 m running performance.MethodsFourteen competitive 800 m runners performed two running tests. First, participants performed a multistage graded exercise test to determine physiological indicators related to endurance performance. Second, participants performed four to six 30-s high intensity running bouts to determine post-exercise blood lactate kinetics. Using a biexponential time function, lactate exchange ability (γ1), lactate removal ability (γ2), and the quantity of lactate accumulated (QLaA) were calculated from individual blood lactate recovery data.Results800 m running performance was significantly correlated with peak oxygen consumption (r = −0.794), γ1 and γ2 at 800 m race pace (r = −0.604 and −0.845, respectively), and QLaA at maximal running speed (r = −0.657). V˙\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\dot{\ ext{V}}}$$\\end{document}O2peak and γ2 at 800 m race pace explained 83% of the variance in 800 m running performance.ConclusionOur results indicate that (1) a high capacity to exchange and remove lactate, (2) a high capacity for short-term lactate accumulation and, (3) peak oxygen consumption, are critical elements of 800 m running performance. Accordingly, while lactate has primarily been utilized as a performance indicator for long-distance running, post-exercise lactate kinetics may also prove valuable as a performance determinant in middle-distance running.

A large-strain and ultrahigh energy density dielectric elastomer for fast moving soft robot.

Dielectric elastomer actuators (DEAs) with large actuation strain and high energy density are highly desirable for actuating soft robots. However, DEAs usually require high driving electric fields (>100 MV m-1) to achieve high performances due to the low dielectric constant and high stiffness of dielectric elastomers (DEs). Here, we introduce polar fluorinated groups and nanodomains aggregated by long alkyl side chains into DE design, simultaneously endowing DE with a high dielectric constant and desirable modulus. Our DE exhibits a maximum area strain of 253% at a low driving electric field of 46 MV m-1. Notably, it achieves an ultrahigh specific energy of 225 J kg-1 at only‍‌ ‌‌40‍‌ MV m-1, around 6 times higher than natural muscle and twice higher than the state-of-the-art DE. Using our DE, soft robots reach an ultrafast running speed of 20.6 BL s-1, 60 times higher than that of commercial VHB 4910, representing the fastest DEA-driven soft robots ever reported.

Effects of running-induced fatigue on joint kinematics and kinetics during overground running: a systematic review and meta-analysis.

This study evaluated the acute fatigue-inducing effect of distance running on kinematics and kinetics during overground running. Standardised mean differences (SMD) with 95% confidence intervals (95% CI) were used to pool data across 16 studies. Effects during consistent (pre- and post-fatigue running speed within ± 5%) versus varied speed running (difference of >5% between running speeds) were analysed separately. There was strong evidence that running-induced fatigue significantly increases ground contact times at consistent running speeds (SMD 0.52 [95% CI 0.22, 0.82]) and moderate evidence that step length shortens at varied running speeds (SMD -1.27 [95% CI -1.79, -0.75]). There was strong evidence that fatigue does not change peak: hip and knee flexion angles, hip adduction angle, hip and knee internal rotation angles, hip and knee extension moments, hip and knee abduction moments, knee abduction angle, knee flexion and extension moments, knee adduction moment, rearfoot eversion angle, and plantarflexion moments, or knee flexion and plantarflexion range of motion during stance. Running-induced fatigue increases contact times and reduces step length, whereas lower-body joint angles and moments are unchanged. Minimising changes in stride parameters could provide a mechanism for reducing the effects of fatigue on running performance.

Differences in walking and running parameters in elementary school students with and without backpacks.

Backpack syndrome (BS) is a term used to describe symptoms such as shoulder and back pain that are believed to be caused by carrying a backpack. Few studies have investigated the changes in walking and running parameters with and without backpacks. The present study aimed to investigate the differences in walking and running parameters with and without backpacks in primary school children with and without BS. This cross-sectional study included 51 children (average age 9.3 years, 6- 12 years). Two questions were asked beforehand: whether the respondents had shoulder or back pain when going to school and present; those who answered that they had pain in both cases were defined as the BS group, while the others were the control group. Walking and running at comfortable speeds and walking and running with a 6 kg backpack on their backs were measured. During walking, there were main effects for speed, cadence, and strike angle with and without the backpack, but no interactions were identified for any of the parameters. In running, however, there were main effects for speed, stride length, strike angle, and lift-off angle, and an interaction effect was observed for speed and stride length. During walking, carrying a 6 kg backpack increased cadence and walking speed. During running, carrying a backpack caused a decrease in stride length and running speed in the BS group, whereas there was no change in the control group, suggesting that the control and BS groups may respond differently.

Research on Defect Detection Method for Composite Materials Based on Deep Learning Networks

Compared to traditional industrial materials, composites have higher durability and compressive strength. However, some components may have flaws due to the manufacturing process. Traditional defect detection methods have low accuracy and cannot adapt to complex shooting environments. Aiming to address the issues of high computational requirements in traditional detection models and the lack of lightweight detection capabilities, the Ghost module is used instead of convolutional arithmetic to construct a lightweight model. To reduce the computational complexity of the feature extraction module, we have incorporated an improved Efficient Channel Attention mechanism to improve the model’s feature extraction capabilities. A rapid defect classification method is implemented to determine whether there are defects in the image or not by comparing the performance and running speed of models such as AlexNet, VGGNet, and ResNet. And ablation experiments are conducted for each model. The results show that the Ghost module model, which incorporates the improved Efficient Channel Attention mechanism, has a significant optimization effect on the convolutional neural network model. It can achieve a high accuracy rate when constructing lightweight models. It improves the running speed of the model, making it more efficient to use and deploy.

A wireless controlled robotic insect with ultrafast untethered running speeds

Running speed degradation of insect-scale (less than 5 cm) legged microrobots after carrying payloads has become a bottleneck for microrobots to achieve high untethered locomotion performance. In this work, we present a 2-cm legged microrobot (BHMbot, BeiHang Microrobot) with ultrafast untethered running speeds, which is facilitated by the complementary combination of bouncing length and bouncing frequency in the microrobot’s running gait. The untethered BHMbot (2-cm-long, 1760 mg) can achieve a running speed of 17.5 BL s−1 and a turning centripetal acceleration of 65.4 BL s−2 at a Cost of Transport of 303.7 and a power consumption of 1.77 W. By controlling its two front legs independently, the BHMbot demonstrates various locomotion trajectories including circles, rectangles, letters and irregular paths across obstacles through a wireless control module. Such advancements enable the BHMbot to carry out application attempts including sound signal detection, locomotion inside a turbofan engine and transportation via a quadrotor.

Research on application of multimodal data fusion in intelligent building environment perception

With the rapid development of the building industry, intelligent buildings benefit from its safety, energy saving, environmental protection and integration and other advantages have been widely loved by people, most operators also realize the importance of intelligent buildings to bring people humanized and customized services, and in order to realize the personalized service of the building, multi-modal data fusion is an effective method. On the other hand, in today’s Internet of Things society, many practical applications need to deploy a large number of sensing equipment for data collection and processing, so as to carry out high-quality monitoring of the physical world, but due to the inherent limitations of these hardware equipment and the influence of factors such as the environment, single mode data often cannot be completely and comprehensively monitored to the physical world’s changing characteristics. In this development context, multi-modal data fusion has become a research hotspot in the field of machine learning. Based on this, this paper proposes a one-stage fast object detection model with multi-level fusion of multi-modal features and end-to-end characteristics for building indoor environment perception, and conducts experimental analysis on the performance of the model. The verification results show that the accuracy of the proposed method is 50.7% and the running speed is 0.107 s, which has better performance than the existing detection methods.

Impact of treadmill running on distal femoral cartilage thickness: a cross-sectional study of professional athletes and healthy controls

PurposeThis present study aimed to assess the impact of treadmill running on distal femoral cartilage thickness.MethodsProfessional athletes aged 20 to 40 years with a history of treadmill running (minimum 75 min per week for the past three months or more) and age-, sex-, and body mass index (BMI)-matched healthy controls were recruited. Demographics and clinical features of participants were recorded. Athletes were divided into subgroup 1 with less than 12 months of treadmill running and subgroup 2 with 12 months or more of treadmill running. Distal femoral cartilage thicknesses were measured at the midpoints of the right medial condyle (RMC), right intercondylar area (RIA), right lateral condyle (RLC), left medial condyle (LMC), left intercondylar area (LIA), and left lateral condyle (LLC) via ultrasonography.ResultA total of 72 athletes (mean age: 29.6 ± 6.6 years) and 72 controls (mean age: 31.9 ± 6.7 years) were enrolled. Athletes had significantly thinner cartilages in the RLC (2.21 ± 0.38 vs. 2.39 ± 0.31 cm, p = 0.002), LLC (2.28 ± 0.37 vs. 2.46 ± 0.35 cm, p = 0.004), and LMC (2.28 ± 0.42 vs. 2.42 ± 0.36 cm, p = 0.039) compared with the control group. Furthermore, cartilage thickness was significantly thinner in subgroup 2 athletes compared with the control group in the RLC (2.13 ± 0.34 vs. 2.39 ± 0.31 cm, p = 0.001), LLC (2.22 ± 0.31 vs. 2.46 ± 0.35 cm, p = 0.005), and LMC (2.21 ± 0.46 vs. 2.42 ± 0.36 cm, p = 0.027); however, subgroup 1 athletes did not have such differences. There was a weak negative correlation between total months of treadmill running and cartilage thickness in the RLC (r = − 0.0236, p = 0.046) and LLC (r = − 0.0233, p = 0.049). No significant correlation was found between the distal femoral cartilage thickness at different sites and the patients’ demographic features, including age, BMI, speed and incline of treadmill running, and minutes of running per session and week (p > 0.05).ConclusionCompared with healthy controls, professional athletes with a history of long-term high-intensity treadmill running had thinner femoral cartilages. The duration (months) of treadmill running was weakly negatively correlated with distal femoral cartilage thickness. Longitudinal studies with prolonged follow-ups are needed to clarify how treadmill running affects femoral cartilage thickness in athletes.

The Cross-Correlation Between Spikes and Local Field Potentials at Different Running Speeds During Entorhinal Cortex Inactivation

Understanding neural spike dynamics and their interaction with local field potentials is crucial for deciphering neural connectivity. This study utilizes data generously shared by Zutshi et al. in 2022 (PMID: 34890566) to investigate spike characteristics and their cross-correlation with local field potentials. Our findings reveal significant shifts in the interspike interval (ISI) distributions during the medial entorhinal cortex (mEC) inactivation, with the instantaneous burst frequency exhibiting an overall decrease and preference for lower frequencies. These differences in the spike distributions were observed in individual cells, accompanied by changes in mean firing rates. Further exploration of the cross-correlation between spike multiunit activity (MUA) and the theta rhythm uncovers distinct coupling patterns. In the control cases, spikes closely align with theta peaks, whereas this alignment is disrupted during the mEC inactivation. Notably, we find that the strength of this cross-correlation is amplified at higher running speeds. This research enhances our understanding of neural dynamics, shedding light on the intricate relationship between spikes, local field potentials, and running speed, with potential implications for neural connectivity studies. R01MH126236 1R01MH109548-01A1. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Current State of Factors Influencing the Development of Running Speed in the 100m Sprint for Non-Specialized University Students at Tan Trao University

This study provides a comprehensive analysis of the current state of factors influencing the development of running speed in the 100m sprint for non-specialized university students at Tan Trao University. The primary objective of this research is to identify and examine the key elements that contribute to the improvement of short-distance running speed among this student population. Through an extensive review of both domestic and international literature, the study encompasses 20 relevant publications from 2012 to 2024, establishing a robust foundation for understanding the current state of research and best practices in this domain. The findings reveal that the development of running speed is influenced by a multifaceted interplay of biomechanical, physiological, psychological, and training-related factors. Specifically, the study delves into the significance of proper running technique, muscle strength and power, aerobic and anaerobic fitness, energy system utilization, athlete motivation, goal-setting, and mental preparation. Additionally, the paper highlights the importance of implementing effective training programs that incorporate periodization, volume and intensity management, and evidence-based injury prevention strategies. The implications of this study are paramount, as it offers valuable insights for coaches, physical education instructors, and sports science professionals working with non-specialized university students. The findings can inform the design and implementation of comprehensive training programs aimed at enhancing the speed capabilities of this population, while also prioritizing injury prevention and overall athletic development.

Prolonged Blue Light Exposure Does Not Elicit Adverse Changes to Physical Activity in Male Mice

Color plays a role in human and animal circadian rhythm patterns, behaviors, and emotional responses. Environments containing blue colors improve information storage in humans and reproductive success in animals; however, environments containing blue light influence behavioral and emotional responses in rodents and humans in adverse ways. Exposure to blue light alters sleeping patterns, induces the stress response, and elicits depressive behaviors. These initial responses then affect awake time functionality and productivity. Under normal physiological conditions, mice, when provided access to an in-cage running wheel, exhibit high levels of nocturnal physical activity behavior. It is unknown if this nocturnal behavior changes after prolonged blue light exposure. Therefore, this project's hypothesis is wheel running utilization decreases after exposure to blue light during the diurnal restful period in the normally nocturnal mouse. The purpose of this study was to expose mice to overhead blue LED lights during the daytime rest period and to examine nighttime physical activity patterns. Daily wheel running distance, duration, and speed monitoring in C57BL/6j male mice (n=12) occurred for 21 days. During the second week of the study, an experimental group (n=6) underwent blue light-emitting diode (LED) light exposure, and a control group (n=6) experienced white LED light exposure at the same intensity. The lights were on a 12-hour light/dark cycle at full brightness during the exposure period. Differences in wheel running distance, duration, and speed following exposure to blue LED light were assessed via individual two-way (group by phase of study) ANOVAs. Blue LED light exposure did not appear to affect average daily wheel running distance [F=0.42, p=0.57], duration [F=0.79, p=0.42], or speed [F=0.08, p=0.87]. Following exposure to blue LED light during the restful diurnal period, wheel running remained stable. This lack of effect indicates that mechanisms may exist to protect mice from the adverse effects associated with blue light exposure, especially as it pertains to participating in routine physical activity. These postulated mechanisms may allow robust participation in physical activity-related behaviors in these animals and represent a potential biological regulator that could aid in mitigating the adverse effects of blue light exposure experienced in humans. All funding for this research was provided by the Union University Department of Biology. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.