Acceleration Distance Research Articles

Prediction of Perceived Exertion Ratings in National Level Soccer Players Using Wearable Sensor Data and Machine Learning Techniques.

This study aimed to identify relationships between external and internal load parameters with subjective ratings of perceived exertion (RPE). Consecutively, these relationships shall be used to evaluate different machine learning models and design a deep learning architecture to predict RPE in highly trained/national level soccer players. From a dataset comprising 5402 training sessions and 732 match observations, we gathered data on 174 distinct parameters, encompassing heart rate, GPS, accelerometer data and RPE (Borg's 0-10 scale) of 26 professional male professional soccer players. Nine machine learning algorithms and one deep learning architecture was employed. Rigorous preprocessing protocols were employed to ensure dataset equilibrium and minimize bias. The efficacy and generalizability of these models were evaluated through a systematic 5-fold cross-validation approach. The deep learning model exhibited highest predictive power for RPE (Mean Absolute Error: 1.08 ± 0.07). Tree-based machine learning models demonstrated high-quality predictions (Mean Absolute Error: 1.15 ± 0.03) and a higher robustness against outliers. The strongest contribution to reducing the uncertainty of RPE with the tree-based machine learning models was maximal heart rate (determining 1.81% of RPE), followed by maximal acceleration (determining 1.48%) and total distance covered in speed zone 10-13 km/h (determining 1.44%). A multitude of external and internal parameters rather than a single variable are relevant for RPE prediction in highly trained/national level soccer players, with maximum heart rate having the strongest influence on RPE. The ExtraTree Machine Learning model exhibits the lowest error rates for RPE predictions, demonstrates applicability to players not specifically considered in this investigation, and can be run on nearly any modern computer platform.

Does the External Load Have an Impact on Winning Matches in Football?

The present study was conducted to examine the impact of external load on winning matches in football. The study group comprised players of the football team in the Spor Toto 1 st League. A total of 13 matches, including 6 won matches and 7 lost matches, were used in the analysis. The data on matches were taken from the 2022-2023 season of the Spor Toto 1st League. The following external load parameters were used in the analysis: total running distance (m), total high-speed running (HSR) distance, total sprint distance (m), acceleration distance (m), deceleration distance (m), ACD load, and maximum sprint reached (km/h). Data were collected with the wearable technology tracking system on football players during matches via GPS. The independent t-test was used in the program (SPSS 26.0) when analyzing normally distributed research data. The analysis of research data found a statistically significant difference in the total distance covered, total HSR distance, total sprint distance, and acceleration values among the external load results for the won and lost matches (p 0.05) but did not find a statistically significant difference in the deceleration, ACD load, and sprint speed values (p>0.05). Consequently, it can be concluded that differences in many values that are considered necessary for success in the football field have an effect on winning matches in football. For values with no statistical difference, it may be recommended to conduct studies in a higher league, with more matches and more players. Keywords: football, external load, gps

Shoal size as a key variable in fish behavioral ecotoxicology: an example using sertraline.

A significant limitation of behavioral ecotoxicology is the challenge of obtaining reproducible results due to a wide range of testing conditions. In particular, shoal size affects almost all aspects of fish behavior, but is rarely considered as a factor in ecotoxicological studies. In the present study, we compared the swimming and antipredator responses of different sized shoals of Arabian killifish (Aphaniops stoliczkanus) after exposure to environmentally realistic concentrations of the antidepressant medication sertraline. Groups of fish (1, 3 or 5 individuals) were exposed to either 5 or 50 ng/L sertraline. After 37 days, swimming behavior and responses to a predator alarm were measured. We found that the effects of group size were much stronger than the effects of sertraline on swimming. Group size was also the major factor influencing responses to the predator alarm, with single fish showing the strongest responses. Sertraline directly affected acceleration, turning speed and average distance to the arena wall. For all three parameters, there were significant interactions with shoal size, demonstrating that responses differed depending on the size of the group. We also found that effects of sertraline could still be observed 14 days after cessation of exposure. The study highlights the importance of considering social context and specifically shoal size when designing behavioral studies on chemicals. Failure to consider this may result in over- or under-estimation of risks.

Semi-Active Suspension Design for Truck Using Pneumatic Spring Joining MR Fluid Damper Based on Neural Networks Controller

<div>In order to modify both stiffness and damping rates according to various road conditions, this research introduces a pneumatic spring in conjunction with a magnetorheological (MR) fluid damper as a single suspension unit for each wheel in the truck. Preventing weight transfer and improving riding comfort during braking, acceleration, and trajectory prediction are the main objectives. A two-axle truck has been used, consisting of three degrees of freedom for the sprung mass, including vertical, pitch, and roll motions, and four degrees of freedom for the unsprung masses, which have been redesigned according to the different types of springs and dampers. Pneumatic-controlled springs, often referred to as dynamic or classic models, replace laminated leaf springs commonly found in vehicles. Additionally, an MR damper replaces a hydraulic double-acting telescopic shock absorber. These models are studied to evaluate the effect of pneumatic spring parameters on truck dynamics. Pneumatic stiffness and the intended damping force are monitored by a recurrent neural network in conjunction with leveling control. This process provides the recommended voltage for the MR damper based on the Signum function damper controller. The performance of the suspension is assessed in the time and frequency domains for both step and random road excitations using vehicle dynamic parameters. Six suspension system configurations are compared with the air spring dynamic model integrated with the MR damper (Model 6), which is recommended as a suspension system for trucks. According to simulation data, when compared to alternative suspension systems, Model 6 significantly enhances both ride comfort and vehicle stability. Model 6 offers improvements in tire workload, truck path, tire–ground contact point during acceleration, braking efficiency, and stopping distance. Compared to previous controlled models, Model 6 also demonstrates zero steady-state offset and zero steady-state error.</div>

Assessing pre-season workload variation in professional rugby union players by comparing three acute:Chronic workload ratio models based on playing positions

Quantifying the pre-season workload of professional Rugby Union players, in relation to their respective positions not only provides crucial insights into their physical demands and training needs but also underscores the significance of the acute:chronic workload ratio (ACWR) in assessing workload. However, given the diversity in ACWR calculation methods, their applicability requires further exploration. As a result, this study aims to analyze the workload depending on the player's positions and to compare three ACWR calculation methods. Fifty-seven players were categorized into five groups based on their playing positions: tight five (T5), third-row (3R), number nine (N9), center, and third line defense (3L). The coupled and uncoupled rolling averages (RA), as well as the exponentially weighted moving average ACWR method, were employed to compute measures derived from GPS data. Changes throughout the pre-season were assessed using the one-way and two-way analysis of variance. The results revealed that N9 covered significantly greater distances and exhibited higher player load compared to T5 and 3L [p < 0.05, effect size (ES) = 0.16–0.68]. Additionally, 3L players displayed the highest workload across various measures, including counts of accelerations and decelerations (>2.5 m s−2), accelerations (>2.5 m s−2), acceleration distance (>2 m s−2), high-speed running (>15 km h−1), very high-speed running (>21 km h−1, VSHR), sprint running (>25 km h−1, SR) distance. When using coupled RA ACWR method, centers exposed significantly greater values to T5 (p < 0.05, ES = 0.8) and 3R (p < 0.05, ES = 0.83). Moreover, centers exhibited greater (p < 0.05, ES = 0.67–0.91) uncoupled RA ACWR values for VHSR and SR than T5 and 3R. When comparing the three ACWR methods, although significant differences emerged in some specific cases, the ES were all small (0–0.56). In light of these findings, training should be customized to the characteristics of players in different playing positions and the three ACWR calculation methods can be considered as equally effective approaches.

Research on the traction characteristic evaluation of urban rail vehicles based on entropy weight TOPSIS

This paper aims to investigate the influence of different speed at the end of the constant power section of the traction characteristic curve (TCC) on the vehicle dynamic performance and evaluate the TCC. A dynamics co-simulation model is developed in SIMPACK and MATLAB for dynamic analysis of the urban rail vehicle (URV) under different traction characteristics. Then it is used to investigate how acceleration distance and vehicle dynamic performance varies with the speed at the end of the constant power section of the TCC under different starting torques. Moreover, the entropy weight TOPSIS method which is widely used to solve the multi-objective decision-making problem is chosen to evaluate the traction characteristics based on various dynamic performance indexes over 25 test sections and acceleration distance. The results show that under traction conditions, the starting torque has effects on the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces, derailment coefficient and wheel unloading factor. The higher the torque, the greater the value of each index, that means the poorer the vehicle ride characteristics and running safety. For a given starting torque, the dynamic performance indexes increase with the increase of the speed at the end of the constant power section from 50 km/h to 80 km/h during the vehicle speed-up process. The maximum growth rate of the lateral and vertical accelerations of car body, lateral and vertical wheel-rail contact forces are 17%, 8%, 8% and 2%, respectively, when the speed at the end of the constant power section increases from 50 km/h to 80 km/h. Last, the comprehensive evaluation of traction characteristics using entropy-weight TOPSIS method reveals when the starting torque is 800 [Formula: see text], 1000 [Formula: see text], 1200 [Formula: see text] and 1400 [Formula: see text], the corresponding optimal speed at the end of the constant power section is 80 km/h, 80 km/h, 70 km/h and 50 km/h, respectively. The result of the study can provide theoretical support for traction characteristic design and traction control for URVs.

Analysis of peak locomotor demands in women's football-the influence of different epoch lengths.

The quantification of peak locomotor demands has been gathering researchers' attention in the past years. Regardless of the different methodological approaches used, the most selected epochs are between 1-, 3-, 5- and 15-minutes time windows. However, the selection of these time frames is frequently arbitrary. The aim of this study was to analyse the peak locomotor demands of short time epochs (15, 30, 45, and 60 seconds) in women's football, with special emphasis over the high-speed metrics. During two seasons, the match physical performance of 100 female football players was collected with Global Positioning System units (STATSports Apex). Peak locomotor demands for the selected variables were calculated by using a 1-second moving average approach. For statistical procedures, linear mixed modelling was used, with total distance, high-speed running distance (>16 km∙h-1), sprint distance (>20 km∙h-1), and acceleration and deceleration distance (±2.26 m∙s-2) considered as the dependent variables and the epoch lengths (15, 30, 45, and 60 seconds) considered as the independent variables. A novel finding was the high ratio observed in the 15 seconds epochs of high-speed running distance and sprint distance (77.6% and 91.3%, respectively). The results show that most peak high-speed demands within 60 seconds are completed within just 15 seconds. Thus, intensity-related variables, such as high-speed metrics, would be better contextualised and adapted into training practices if analysed in shorter epoch lengths (15-30 seconds), while longer periods might be used for volume-related metrics (i.e., total distance), depending on the purpose of the analysis.

Impact of hindlimb length variation on jumping dynamics in the Longshanks mouse.

Distantly related mammals (e.g. jerboa, tarsiers, kangaroos) have convergently evolved elongated hindlimbs relative to body size. Limb elongation is hypothesized to make these species more effective jumpers by increasing their kinetic energy output (through greater forces or acceleration distances), thereby increasing take-off velocity and jump distance. This hypothesis, however, has rarely been tested at the population level, where natural selection operates. We examined the relationship between limb length, muscular traits and dynamics using Longshanks mice, which were selectively bred over 22 generations for longer tibiae. Longshanks mice have approximately 15% longer tibiae and 10% longer femora compared with random-bred Control mice from the same genetic background. We collected in vivo measures of locomotor kinematics and force production, in combination with behavioral data and muscle morphology, to examine how changes in bone and muscle structure observed in Longshanks mice affect their hindlimb dynamics during jumping and clambering. Longshanks mice achieved higher mean and maximum lunge-jump heights than Control mice. When jumping to a standardized height (14 cm), Longshanks mice had lower maximum ground reaction forces, prolonged contact times and greater impulses, without significant differences in average force, power or whole-body velocity. While Longshanks mice have longer plantarflexor muscle bodies and tendons than Control mice, there were no consistent differences in muscular cross-sectional area or overall muscle volume; improved lunge-jumping performance in Longshanks mice is not accomplished by simply possessing larger muscles. Independent of other morphological or behavioral changes, our results point to the benefit of longer hindlimbs for performing dynamic locomotion.

Effect of hydrogen ratio on leakage and explosion characteristics of hydrogen-blended natural gas in utility tunnels

Based on computational fluid dynamics technology, the effect of hydrogen blending ratio (HBR) on the concentration distribution of leaked gas diffusion in utility tunnels was investigated, and based on this, the explosion characteristics of hydrogen-blended natural gas (HBNG) were studied in an inhomogeneous concentration field. The results showed that the concentration field of HBNG gradually stabilized in the downstream of the leak site, and the peak concentration increased linearly with HBR. When HBR≤5%, a high concentration region above the upper explosive limit is formed inside the gas cloud. This characteristic of the distribution induces secondary combustion in utility tunnels. The maximum flame velocity and steady acceleration distance increase with the increase of HBR. The flame shape is significantly affected by the concentration field distribution. The explosion overpressure induced by the real concentration field created multiple peak structures that exacerbated the explosion disaster complexity. The first overpressure peak at the ends was formed by the superposition of the precursor shock wave and its reflected wave, and those at other locations are caused by the shock waves. However, all maximum overpressure peaks are formed by the superposition of multiple reflections of the explosion wave. The maximum peak overpressure at both ends of the tunnel is higher than that in the middle. The explosion overpressure induced by the inhomogeneous gas cloud is lower than that caused by homogeneous gas cloud under the same volume. The research results can provide an effective reference for the anti-explosion design and risk assessment of utility tunnel.

Simulating the secondary electron avalanche of MCP by Geant4

Nowadays, Microchannel Plate (MCP), as a kind of electron multipliers based on the secondary electron emission, is widely used in many high-sensitive experiments, such as neutrino detection, which require the noise to be as low as possible, while the conventional straight-channel MCP will inevitably have ion feedback, resulting in the sequential after-pulses being the major source of noise. Normally, the problem can be effectively avoided by coupling two straight-channel MCPs in cascade and combining the channels into a “V” shape known as chevron MCPs, but this method is limited by the manufacturing techniques due to the unavoidable gap between the two pieces that will worsen the resolution and peak-to-valley ratio. However, the ion feedback can be inhibited significantly for MCPs with curved channels. Based on Geant4, we investigate how the geometrical parameters of curved-channel MCP influence the gain and get the optimum pore diameter for an MCP to reach the maximum gain with fixed thickness and applied voltage. Additionally, the track-by-track simulation reveals that the average acceleration distance of a secondary electron inside the curved-channel is approximately 20μm when the applied voltage, length-to-diameter ratio and pore diameter are 950 V, 50:1 and 20μm, respectively.

Laser-machined two-stage nozzle optimised for laser wakefield acceleration

In this paper, the modelling and manufacturing of a two-stage supersonic gas jet nozzle enabling the formation of adaptive plasma concentration profiles for injection and acceleration of electrons using few-cycle laser beams are presented. The stages are modelled using the rhoSimpleFoam algorithm of the OpenFOAM computational fluid dynamics software. The first 200–300 ${\rm \mu}$ m diameter nozzle stage is dedicated to 1 % N2 + He gas jet formation and electron injection. By varying the pressure between the first and second stages of the injectors, the electron injection location could be adjusted, and the maximum acceleration distance could be ensured. By changing the concentration of the nitrogen in the gas mixture, the charge of the accelerated electrons could be controlled. The second nozzle stage is designed for acceleration in fully ionised He or hydrogen gas and forms the optimal plasma concentration for bubble formation depending on the laser pulse energy, duration and focused beam diameter. In order to reduce the diameter of the plasma profile formed by the first nozzle and the concentration drop gap between the two nozzles, a one-side straight section was introduced in the first nozzle. The shock wave reflected from the straight section of the wall propagates parallel to the shock wave of the intersecting supersonic jets and ensures a minimal gap between the jets. The second-stage longitudinal plasma concentration profile could have an increasing gas density gradient to compensate for dephasing between the electron bunch and the plasma wave due to wave shortening with increasing plasma concentration.

Analysis of dynamic determinants of vehicles involved in crash affecting severity based on in-depth crash data

Objective The dynamic characteristics of vehicles involved in crashes may be an important factor affecting the crash severity. This study investigates the relationship between the dynamic characteristics of vehicles involved in crashes in the five seconds before the occurrence and the crash severity. The findings aim to offer insights for preventing severe crashes and advancing autonomous vehicle technology. Methods This study aims to investigate the impact of dynamic features, such as speed, acceleration, and relative distance of vehicles involved in the crash in the five seconds before the crash, on the crash severity. Five hundred ninety-six crash samples from the China In-depth Mobility Safety Study-Traffic Accident database were selected for crash reconstruction. A random parameters logit model was used to extract and analyze the effect of dynamic features of the vehicles involved in the crash on the crash severity. Results The random parameters logit model demonstrated a satisfactory fit. Analysis of the parameter estimation results of the model showed that the variables of speed, acceleration, and relative distance between vehicles involved in the crash at some time points during the five seconds before the crash significantly affected the crash severity. Notably, the coefficient of variation of relative distance over 5 s emerged as the most influential positive determinant of the crash severity. Conclusions Certain dynamic characteristics of vehicles involved in a crash in the five seconds before a crash significantly impact the crash severity. The study’s findings can serve as a reference for preventing severe crashes and advancing the development of autonomous vehicles.

Exploring Upper Limb Kinematics in Limited Vision Conditions: Preliminary Insights from 3D Motion Analysis and IMU Data

This study examined upper limb kinematics under simulated visually impaired conditions. By investigating how upper limb movements adapt in these circumstances, we aimed to gain insights that may prove valuable for both healthy populations and those with clinical conditions. Data, including 3D marker-based motion capture and accelerometer readings, were collected from seven healthy volunteers walking on a treadmill at 1.5 m/s under two conditions: without glasses (NG) and with stroboscopic glasses (G). Each walking condition lasted 2 minutes, and two 10-second recordings were analyzed from the midpoint of each session. Contrary to our hypothesis, simulated visual impairment with stroboscopic eyewear at 6Hz did not statistically affect upper limb kinematics, specifically in terms of wrist acceleration and hand distance in the frontal plane. Future research should explore varied visual impairment conditions, extend task durations, and investigate the relationship between subjective gait difficulties and biomechanical data.

Top-class women's soccer performance: peak demands and distribution of the match activities relative to maximal intensities during official matches.

The aims of the current study were to determine the most demanding passages of match play (MDP) and the distribution of match activities relative to maximum intensities during official matches in top-class women soccer players. Twenty-eight women players competing in European championship and international UEFA competitions were monitored during 38 official matches (277 individual samples). Maximum relative (m · min-1) total distance (TD), high-speed running (HSRD), very high-speed running (VHSRD), sprint, acceleration and deceleration distances were calculated across different durations (1-5, 10, 15, 90 min) using a rolling average analysis. Maximum intensities (1-minpeak) were used as the reference value to determine the distribution of relative intensity across the whole-match demands (90-minavg). Time and distance higher than 90-minavg (> 90-minavg) were also calculated. MDP showed moderate to very large [effect size (ES): 0.63/5.20] differences between 1-minpeak vs all durations for each parameter. The relative (m · min-1) 1-minpeak was greater than 90-minavg of about +63% for TD, +358% for HSRD, +969% for VHSRD, +2785% for sprint, +1216% for acceleration, and +768% for deceleration. The total distance covered > 90-minavg was ~66.6% of the total distance covered during the 90-minavg for TD, ~84.8% for HSRD, ~97.4% for VHSRD, ~100% for sprint, ~99.1% for acceleration and ~98.2% for deceleration. The relative distance > 90-minavg was higher (P < 0.05) than the 90-minavg for each metric (ES: 2.22 to 7.58; very large). The present results may help coaches and sport scientists to replicate the peak demands during training routine in top-class women soccer players.

Assessing the locomotor demands of international men's and women's rugby sevens match-play according to passage of play.

This study aimed to evaluate the effect of discrete passages of play on locomotor demands of international men's and women's rugby sevens matches and their relationship with winning or losing. Thirteen men's and thirteen women's international rugby sevens players wore 10 Hz Global Positioning Systems during twelve Tokyo Olympic games matches (966 observations; 507 for men, 459 for women). Discrete ball-in-play periods were categorised as: 'Single-phase defence', 'single-phase attack', 'multi-phase defence', 'multi-phase attack', 'multi-phase defence to attack', or 'multi-phase attack to defence'. Relative total distance, alongside high-speed (>5.0 m∙s-1), acceleration (>3 m∙s-2), and deceleration (>3 m∙s-2) distances were recorded for each passage. Separately for men and women, linear mixed models examined the effect of passage type and match outcome (win or loss) on locomotor demands, whilst controlling for opposition ranking. In men, relative total distance ranged from 137 m∙min-1 to 174 m∙min-1 for 'multi-phase defence to attack' and 'multi-phase attack', respectively. In women, 'multi-phase attack' elicited the lowest relative total distance (118 m∙min-1), whereas the greatest values (186 m∙min-1) were recorded for 'single-phase defence'. For men, there were significant interactions between match outcome and passage type for relative total (p<0.001) and high-speed (p = 0.006) distance. During 'multi-phase attack', relative total distance was greater for wins versus losses (174 vs 138 m.min-1, p = 0.024). However, for 'single-phase defence', relative total distance was lower for wins (128 vs 164 m.min-1, p<0.001). For women, there were significant interactions between match outcome and passage type for relative total (p = 0.036), high-speed (p = 0.003), and deceleration (p = 0.015) distances. Locomotor responses were influenced by passage type and match result for men and women. Knowing the demands of each passage type may inform training drills targeted at developing match-play-specific physical, technical, and tactical adaptations. Understanding how passages differ between matches won and lost could also inform team technical/tactical preparation including selection.

A Scalable, High-Efficiency, Low-Energy-Spread Laser Wakefield Accelerator Using a Tri-Plateau Plasma Channel.

The emergence of multi-petawatt laser facilities is expected to push forward the maximum energy gain that can be achieved in a single stage of a laser wakefield acceleration (LWFA) to tens of giga-electron volts, which begs the question-is it likely to impact particle physics by providing a truly compact particle collider? Colliders have very stringent requirements on beam energy, acceleration efficiency, and beam quality. In this article, we propose an LWFA scheme that can for the first time simultaneously achieve hitherto unrealized acceleration efficiency from the laser to the electron beam of >20% and a sub-1% energy spread using a stepwise plasma structure and a nonlinearly chirped laser pulse. Three-dimensional high-fidelity simulations show that the nonlinear chirp can effectively mitigate the laser waveform distortion and lengthen the acceleration distance. This, combined with an interstage rephasing process in the stepwise plasma, can triple the beam energy gain compared to that in a uniform plasma for a fixed laser energy, thereby dramatically increasing the efficiency. A dynamic beam loading effect can almost perfectly cancel the energy chirp that arises during the acceleration, leading to the sub-percent energy spread. This scheme is highly scalable and can be applied to petawatt LWFA scenarios. Scaling laws are obtained, which suggest that electron beams with parameters relevant for a Higgs factory could be reached with the proposed high-efficiency, low-energy-spread scheme.

Ship Classification Based on AIS Data and Machine Learning Methods

AIS ship-type code categorizes ships into broad classes, such as fishing, passenger, and cargo, yet struggles with finer distinctions among cargo ships, such as bulk carriers and containers. Different ship types significantly impact acceleration, steering performance, and stopping distance, thus making precise identification of unfamiliar ship types crucial for maritime monitoring. This study introduces an original classification study based on AIS data for cargo ships, presenting a classifier tailored for bulk carriers, containers, general cargo, and vehicle carriers. The model’s efficacy was tested within the Changhua Wind Farm Channel using eight classification algorithms across tree-structure-based, proximity-based, and regression-based categories and employing standard metrics (Accuracy, Precision, Recall, F1-score) to assess the performance. The results show that tree-structure-based algorithms, particularly XGBoost and Random Forest, demonstrated superior performance. This study also implemented a feature selection strategy with five methods, revealing that a model trained with only four features (three ship-geometric features and one trajectory behavior feature) can achieve high accuracy. Conclusively, the classifier effectively overcame the challenges of limited AIS data labels, achieving a classification accuracy of 97% for ships in the Changhua Wind Farm Channel. These results are pivotal in identifying abnormal ship behavior, highlighting the classifier’s potential for maritime monitoring applications.

Weekly Training Load Differences between Starting and Non-Starting Soccer Players.

The aim of this study was to examine the differences in the weekly training load between starters and non-starters classified based on the match starting line-up, with respect to the playing position and a training day. Notably, 31 young adult soccer players (age: 18.79 ± 1.04 years) competing in the 3rd Czech division were monitored across the season. The weekly training load was measured using a GPS system as follows: total distance covered (TD), high-speed running distance (HSR), sprint running distance (SR), and acceleration and deceleration distance (ACDC). We found higher values in three out of four observed variables (HSR, SR, and ACDC, excluding TD) for starters compared to non-starters (p < 0.05), with small to moderate effect sizes (d = 0.40-0.49). Differences were observed especially in players who were fullbacks, offensive midfielders, and forwards. Moreover, the largest differences were found in training prior to a match day for HSR, SR, and ACDC (p < 0.05). Non-starters experienced lower weekly external loads in offensive player positions, predominantly in high-intensity variables, which are essential for their physical performance. It seems that non-starters may experience potential under-loading in the training process. Coaches and practitioners should be aware of this potential risk and find an appropriate method to compensate for load discrepancies, particularly in terms of high-intensity activities.

Characteristic period of response spectrum for surface and borehole ground motions

Determining the characteristic period of ground motions (Tg) is an essential step in the seismic design of structures, but a critical problem that needs to be solved is figuring out how to do so when bedrock ground motion is utilized as an input. In this paper, the relationship between surface and borehole temperature data is investigated, and the main factors affecting the Tg are analyzed. Then, the algorithm for predicting Tg is presented. It is found that two factors, i.e., peak ground acceleration (PGA) and fault distance have significant effects on the value of Tg. By comparing the values of Tg for borehole and surface motions, we find that, contrary to expectation, Tg for borehole motions is usually larger than that for surface motions in most cases. Finally, the conversion equation between the Tg for borehole and surface motions is given using regression analysis.

Vehicle Operation Status Monitoring Based on Distributed Acoustic Sensor

To develop implementation research on distributed optical fiber sensing technology, field tests were conducted on municipal roads and railways using a distributed acoustic sensor (DAS). Data were collected by the DAS during a field test for a long time period (more than 20 min), and we conducted short-term (&lt;10 s) and long-term (≥10 s) analyses on these data separately. In the short-term data analysis, the vehicle type, vehicle length, and working status of the vehicle engine or the compressor were identified. In the long-term data analysis, the traffic flow was monitored, and the running distance, acceleration, speed, and braking distance of the vehicle were obtained. The characteristics of the vehicle operation data obtained in these field tests are important in developing the data processing method of DASs, which will help to promote the implementation of DASs.