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Search Results (6,553)

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Keywords = kinematics

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31 pages, 25262 KiB  
Article
Optimal Design of a Bilateral Stand-Alone Robotic Motion-Assisted Finger Exoskeleton for Home Rehabilitation
by Tony Punnoose Valayil and Tanio K. Tanev
Machines 2024, 12(10), 685; https://doi.org/10.3390/machines12100685 (registering DOI) - 29 Sep 2024
Abstract
This paper presents a novel exoskeleton robot that can be used at home to rehabilitate the index fingers of stroke-affected patients. This exoskeleton is designed as a one-degree-of-freedom four-bar mechanism able to guide the human index finger to perform a finger curl exercise [...] Read more.
This paper presents a novel exoskeleton robot that can be used at home to rehabilitate the index fingers of stroke-affected patients. This exoskeleton is designed as a one-degree-of-freedom four-bar mechanism able to guide the human index finger to perform a finger curl exercise motion. The proposed device is the only lateral, stand-alone mechanism built to date that can carry the weight of the human hand, thus making the user free from wearing it. The design starts by tracing the trajectory of the index finger using ‘Angulus’ software. ‘SALAR Mechanism Synthesizer’ software is used for dimensional synthesis of the four-bar mechanism. Using additive manufacturing technology, a prototype of the proposed device is developed. Static force analysis is performed to select the most appropriate actuator for producing the required torque to manipulate the fingers effectively. The kinematics of the index finger while performing a finger curl exercise is obtained. The proposed linkage mechanism can drive the index fingers of both hands. Simulation and experimental results proved the feasibility and effectiveness of the proposed design to be used for index finger rehabilitation for a wide range of users and applications by making simple minor alterations in the design. Also, a scheme for when the device can be used for rehabilitating the middle finger together with the index finger when performing flexion and extension motions is discussed. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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Figure 1

Figure 1
<p>Anatomy of the human hand.</p>
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<p>Flexion/extension motions performed by the index finger.</p>
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<p>(<b>a</b>) Finger Curl exercise for stroke patients, (<b>b</b>) Four-bar mechanism attached to the lateral side of the finger.</p>
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<p>Joint angles measured during finger curl exercise (<b>a</b>–<b>c</b>) Joint angles measured at first position, (<b>d</b>–<b>f</b>) Joint angles measured at second position, (<b>g</b>–<b>i</b>) Joint angles measured at third position.</p>
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<p>Kinematic model of the human index finger.</p>
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<p>(<b>a</b>) Workspace of the index finger, (<b>b</b>) Fingertip positions.</p>
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<p>(<b>a</b>) Trajectory of the mechanism, (<b>b</b>) End-effector position.</p>
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<p>Results obtained using FMinCon optimization setup in SALAR.</p>
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<p>CAD model of the robot.</p>
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<p>Snapshots during the motion (<b>a</b>) developed prototype connected to a computer using Arduino board (<b>b</b>) coupler at position 1 (<b>c</b>) coupler at position 2 and (<b>d</b>) coupler at position 3.</p>
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<p>(<b>a</b>) Coupler for left-hand index finger and right-hand index finger when performing finger curl exercise. (<b>b</b>) Coupler for middle finger and index finger when performing flexion and extension motions.</p>
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<p>Dummy index finger.</p>
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<p>Design parameters for finger insert: (<b>a</b>) 5 mm width and no inclination, (<b>b</b>) 3 mm width and no inclination, (<b>c</b>) 3 mm width and 20° inclination, (<b>d</b>) 3 mm width and 45° inclinations.</p>
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<p>Robotic-fingered exoskeleton performing index finger rehabilitation.</p>
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<p>Schematic of four-bar mechanism for static force analysis.</p>
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<p>Torque variation during the motion of the mechanism.</p>
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<p>Torque variation versus the input angle <span class="html-italic">θ</span> and angle α.</p>
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<p>Change in threshold values.</p>
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20 pages, 16241 KiB  
Article
Seismic Performance of Pile Groups under Liquefaction-Induced Lateral Spreading: Insights from Advanced Numerical Modeling
by Rujiang Pan, Chengshun Xu, Romain Meite and Jilong Chen
Buildings 2024, 14(10), 3125; https://doi.org/10.3390/buildings14103125 (registering DOI) - 29 Sep 2024
Abstract
Post-earthquake investigations have shown that piles in liquefiable soils are highly susceptible to damage, especially in sloping sites. This study examines the seismic performance of pile groups with lateral spreading through advanced numerical modeling. A three-dimensional finite element model, validated against large-scale shaking [...] Read more.
Post-earthquake investigations have shown that piles in liquefiable soils are highly susceptible to damage, especially in sloping sites. This study examines the seismic performance of pile groups with lateral spreading through advanced numerical modeling. A three-dimensional finite element model, validated against large-scale shaking table test results, is implemented to capture the key mechanisms driving the dynamic response of pile groups under both inertial and kinematic loading conditions. Parametric seismic response analyses are conducted to compare the behavior of batter and vertical piles under varying ground motion intensities. The results indicate that batter piles experience increased axial compressive and tensile forces compared to vertical piles, up to 70% and 20%, respectively. However, batter piles provide enhanced lateral stiffness and shear resistance compared to vertical piles, reducing horizontal displacements by up to 20% and tilting the cap by 85% under strong ground motion. The results demonstrate that batter piles not only enhance the overall seismic stability of the structure but also mitigate the risk of liquefaction-induced lateral spreading in the near-field through pile-pinning effects. While vertical piles are more commonly used in practice, the distinct advantages of batter piles for seismic stability highlighted in this study may encourage using more advanced numerical modeling in engineering projects. Full article
(This article belongs to the Section Building Structures)
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<p>Schematic diagram of soil–structure system.</p>
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<p>Input ground motions: (<b>a</b>) normalized time–history acceleration and HUSID plots; (<b>b</b>) response spectra.</p>
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<p>Schematic and details of a three–dimensional numerical model of soil–structure system.</p>
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<p>Comparison between experimental and numerical simulations for Ottawa sand F65 with Dr (40%) and Dr (90%).</p>
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<p>Comparison of experiment and numerical simulation in a liquefiable horizontal site: (<b>a</b>) overall model view; (<b>b</b>) EPWP time history; (<b>c</b>) piles’ bending moment.</p>
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<p>Comparison of experiment and numerical simulation in a liquefiable sloping site: (<b>a</b>) overall model view; (<b>b</b>) near–field soil displacement; (<b>c</b>) piles’ curvature.</p>
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<p>Excess pore water pressure ratios (EPWPRs) predicted in numerical simulation using the Tabas earthquake record: (<b>a</b>) time-history far-field response; (<b>b</b>) near-field response for PVV configuration; (<b>c</b>) near-field response for PBB configuration.</p>
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<p>Peak soil displacements in the horizontal direction: (<b>a</b>) free–field and near–field; (<b>b</b>) weak earthquake (0.10 g); (<b>c</b>) strong earthquake (0.40 g).</p>
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<p>Contour map of vertical displacements developed in the soil–structure system.</p>
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<p>Horizontal pile displacements for different configurations: (<b>a</b>) downstream piles (0.10 g); (<b>b</b>) downstream piles (0.40 g); (<b>c</b>) upstream piles (0.40 g).</p>
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<p>Contour map of residual horizontal displacements for different pile–cap–structure configurations: (<b>a</b>) weak earthquake (0.10 g); (<b>b</b>) strong earthquake (0.40 g).</p>
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<p>Contour map of residual vertical displacements for different pile–cap–structure configurations: (<b>a</b>) weak earthquake (0.10 g); (<b>b</b>) strong earthquake (0.40 g).</p>
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<p>Dynamic earth pressures along the piles: (<b>a</b>) schematic diagram of friction pile forces; (<b>b</b>) weak earthquake (0.10 g); (<b>c</b>) strong earthquake (0.40 g).</p>
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<p>Dynamic frictional resistance along the piles: (<b>a</b>) weak earthquake (0.10 g); (<b>b</b>) strong earthquake (0.40 g).</p>
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<p>Axial forces along the piles: (<b>a</b>) downstream piles (0.10 g); (<b>b</b>) downstream piles (0.40 g); (<b>c</b>) upstream piles (0.40 g).</p>
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<p>Bending moments along the piles: (<b>a</b>) downstream piles (0.10 g); (<b>b</b>) upstream piles (0.10 g); (<b>c</b>) upstream piles (0.40 g).</p>
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<p>Shear forces along the piles: (<b>a</b>) downstream piles (0.10 g); (<b>b</b>) upstream piles (0.10 g); (<b>c</b>) upstream piles (0.40 g).</p>
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<p>Cap rotational deformation: (<b>a</b>) weak earthquake (0.10 g); (<b>b</b>) strong earthquake (0.40 g).</p>
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<p>Contour map of structural rotational deformation: (<b>a</b>) weak earthquake (0.10 g); (<b>b</b>) strong earthquake (0.40 g).</p>
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<p>Horizontal displacements of the cap and superstructure: (<b>a</b>) cap (0.10 g); (<b>b</b>) superstructure (0.10 g); (<b>c</b>) cap (0.40 g); (<b>d</b>) superstructure (0.40 g).</p>
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28 pages, 11014 KiB  
Article
Configuration and Parameter Optimization Design of a Novel RBR-2RRR Spherical Hybrid Bionic Shoulder Joint
by Shuyang Shi, Fengxin Wang and Yulin Zhou
Machines 2024, 12(10), 683; https://doi.org/10.3390/machines12100683 (registering DOI) - 29 Sep 2024
Abstract
To improve the workspace, linear displacement stiffness, and driving torque utilization of humanoid robot shoulder joint mechanisms, an offset-designed RBR-2RRR (R represents the revolute pair, and B represents the ball cage joint) spherical hybrid bionic shoulder joint configuration (SHBSJC) is proposed and its [...] Read more.
To improve the workspace, linear displacement stiffness, and driving torque utilization of humanoid robot shoulder joint mechanisms, an offset-designed RBR-2RRR (R represents the revolute pair, and B represents the ball cage joint) spherical hybrid bionic shoulder joint configuration (SHBSJC) is proposed and its structural parameters are optimized. Firstly, the shoulder joint’s physiological structure is biomimetically designed, a prototype mechanism of RBR-2RRR SHBSJC is proposed, and its kinematics are solved. The deformation response of RBR-2RRR and 3-RRR under the same load is compared to verify the obtained configuration can improve the linear displacement stiffness. Considering the workspace and singularity, using the GCI and GDCI as optimization functions, the recommended and adopted values of structural parameters are obtained. The distribution diagrams of the LCI and LDCI demonstrate that the configuration meets performance expectations. To further increase the prototype mechanism’s workspace and match the human shoulder joint’s motion range, an offset-designed RBR-2RRR SHBSJC is proposed, and the offset angle, installation posture angle, and spatial mapping relationship of the mechanism are determined. The results of workspace comparison and virtual model machine action simulation indicate that the final configuration meets the workspace expectations. This work enriches the shoulder joint configuration types and has engineering application value. Full article
(This article belongs to the Section Machine Design and Theory)
15 pages, 4123 KiB  
Article
Validity of Wearable Gait Analysis System for Measuring Lower-Limb Kinematics during Timed Up and Go Test
by Yoshiaki Kataoka, Tomoya Ishida, Satoshi Osuka, Ryo Takeda, Shigeru Tadano, Satoshi Yamada and Harukazu Tohyama
Sensors 2024, 24(19), 6296; https://doi.org/10.3390/s24196296 (registering DOI) - 29 Sep 2024
Abstract
Few studies have reported on the validity of a sensor-based lower-limb kinematics evaluation during the timed up and go (TUG) test. This study aimed to determine the validity of a wearable gait sensor system for measuring lower-limb kinematics during the TUG test. Ten [...] Read more.
Few studies have reported on the validity of a sensor-based lower-limb kinematics evaluation during the timed up and go (TUG) test. This study aimed to determine the validity of a wearable gait sensor system for measuring lower-limb kinematics during the TUG test. Ten young healthy participants were enrolled, and lower-limb kinematics during the TUG test were assessed using a wearable gait sensor system and a standard optical motion analysis system. The angular velocities of the hip, knee, and ankle joints in sit-to-stand and turn-to-sit phases were significantly correlated between the two motion analysis systems (R = 0.612–0.937). The peak angles and ranges of motion of hip, knee, and ankle joints in the walking-out and walking-in phases were also correlated in both systems (R = 0.528–0.924). These results indicate that the wearable gait sensor system is useful for evaluating lower-limb kinematics not only during gait, but also during the TUG test. Full article
(This article belongs to the Special Issue Advances in Mobile Sensing for Smart Healthcare)
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<p>Settings of the sensor and marker placement.</p>
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<p>Sensor calibration at the sitting (inclined) position to convert the sensor coordinate system to the body segment coordinate system.</p>
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<p>Phase classification of the TUG test using pelvis and thigh gyro sensors (the angular velocities of the pelvis and thigh). (<b>a</b>,<b>b</b>) Sit-to-stand phase, (<b>b</b>,<b>c</b>) walking-out phase, (<b>c</b>,<b>d</b>) turning phase, (<b>d</b>,<b>e</b>) walking-in phase, and (<b>e</b>,<b>f</b>) turn-to-sit phase. The roll direction represents sagittal plane motion, and the pitch direction represents horizontal plane motion.</p>
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<p>Definitions of variables (an example for hip joint). a: extension angular velocity. b: peak flexion. c: peak extension. d: range of motion. e: flexion angular velocity.</p>
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<p>Joint angle waveforms for the hip, knee, and ankle joints during the TUG test for the optical motion analysis system and the H-Gait system (an example). Black line: optical system. Red line: H-Gait system.</p>
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<p>Correlation and Bland–Altman plot of angular velocities during the sit-to-stand phase between the optical motion analysis system and the H-Gait system. <b>Left</b>: Scatter plot. <b>Right</b>: Bland–Altman plot, where X-axis is the average of the H-Gait system and the optical motion analysis system, and Y-axis is the difference between the H-Gait system and the optical motion analysis system. In both plots, the results for each subject were displayed in different colors. (<b>a</b>,<b>b</b>) hip extension. (<b>c</b>,<b>d</b>) knee extension. (<b>e</b>,<b>f</b>) ankle plantarflexion.</p>
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<p>Correlations and Bland–Altman plots of peak hip, knee, and ankle joint angles during the walking-out phase between the optical motion analysis system and the H-Gait system. In both plots, the results for each subject were displayed in different colors. (<b>a</b>,<b>b</b>) hip fleion. (<b>c</b>,<b>d</b>) hip extension. (<b>e</b>,<b>f</b>) knee flexion. (<b>g</b>,<b>h</b>) knee extension. (<b>i</b>,<b>j</b>) ankle dorsiflexion. (<b>k</b>,<b>l</b>) ankle plantarflexion.</p>
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<p>Correlations and Bland–Altman plots of the ROMs in the hip, knee, and ankle joints during the walking-in phase between the optical motion analysis system and the H-Gait system. <b>Left</b>: Scatter plot. <b>Right</b>: Bland–Altman plot, where X-axis is the average of the H-Gait system and the optical motion analysis system, and Y-axis is the difference between the H-Gait system and the optical motion analysis system. In both plots, the results for each subject were displayed in different colors. (<b>a</b>,<b>b</b>) hip ROM. (<b>c</b>,<b>d</b>) knee ROM. (<b>e</b>,<b>f</b>) ankle ROM. ROM: range of motion.</p>
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<p>Correlation and Bland–Altman plot of angular velocities during the turn-to-sit phase between the optical motion analysis system and the H-Gait system. <b>Left</b>: Scatter plot. <b>Right</b>: Bland–Altman plot, where X-axis is the average of the H-Gait system and the optical motion analysis system, and Y-axis is the difference between the H-Gait system and the optical motion analysis system. In both plots, the results for each subject were displayed in different colors. (<b>a</b>,<b>b</b>) hip flexion. (<b>c</b>,<b>d</b>) knee flexion. (<b>e</b>,<b>f</b>) ankle dorsiflexion.</p>
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16 pages, 5739 KiB  
Article
Comparison of IMU-Based Knee Kinematics with and without Harness Fixation against an Optical Marker-Based System
by Jana G. Weber, Ariana Ortigas-Vásquez, Adrian Sauer, Ingrid Dupraz, Michael Utz, Allan Maas and Thomas M. Grupp
Bioengineering 2024, 11(10), 976; https://doi.org/10.3390/bioengineering11100976 (registering DOI) - 28 Sep 2024
Abstract
The use of inertial measurement units (IMUs) as an alternative to optical marker-based systems has the potential to make gait analysis part of the clinical standard of care. Previously, an IMU-based system leveraging Rauch–Tung–Striebel smoothing to estimate knee angles was assessed using a [...] Read more.
The use of inertial measurement units (IMUs) as an alternative to optical marker-based systems has the potential to make gait analysis part of the clinical standard of care. Previously, an IMU-based system leveraging Rauch–Tung–Striebel smoothing to estimate knee angles was assessed using a six-degrees-of-freedom joint simulator. In a clinical setting, however, accurately measuring abduction/adduction and external/internal rotation of the knee joint is particularly challenging, especially in the presence of soft tissue artefacts. In this study, the in vivo IMU-based joint angles of 40 asymptomatic knees were assessed during level walking, under two distinct sensor placement configurations: (1) IMUs fixed to a rigid harness, and (2) IMUs mounted on the skin using elastic hook-and-loop bands (from here on referred to as “skin-mounted IMUs”). Estimates were compared against values obtained from a harness-mounted optical marker-based system. The comparison of these three sets of kinematic signals (IMUs on harness, IMUs on skin, and optical markers on harness) was performed before and after implementation of a REference FRame Alignment MEthod (REFRAME) to account for the effects of differences in coordinate system orientations. Prior to the implementation of REFRAME, in comparison to optical estimates, skin-mounted IMU-based angles displayed mean root-mean-square errors (RMSEs) up to 6.5°, while mean RMSEs for angles based on harness-mounted IMUs peaked at 5.1°. After REFRAME implementation, peak mean RMSEs were reduced to 4.1°, and 1.5°, respectively. The negligible differences between harness-mounted IMUs and the optical system after REFRAME revealed that the IMU-based system was capable of capturing the same underlying motion pattern as the optical reference. In contrast, obvious differences between the skin-mounted IMUs and the optical reference indicated that the use of a harness led to fundamentally different joint motion being measured, even after accounting for reference frame misalignments. Fluctuations in the kinematic signals associated with harness use suggested the rigid device oscillated upon heel strike, likely due to inertial effects from its additional mass. Our study proposes that optical systems can be successfully replaced by more cost-effective IMUs with similar accuracy, but further investigation (especially in vivo and upon heel strike) against moving videofluoroscopy is recommended. Full article
(This article belongs to the Special Issue Biomechanics of Human Movement and Its Clinical Applications)
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Figure 1
<p>The optical harness-based reference system, as well as two pairs of IMU sensors, were carefully positioned on each participant by a certified technician. One IMU pair was attached to the rigid harness of the reference system (“IMUs on harness”), and a second IMU pair was mounted on elastic hook-and-loop bands (“IMUs on skin”). As per the optical system manufacturer’s instructions, participants walked in socks on the treadmill.</p>
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<p>Mean tibiofemoral joint angles (solid lines) ± standard deviation (shaded areas), in degrees, as estimated by inertial measurement units (IMUs) on harness (purple), IMUs on skin (green), and optical motion capture (OMC) on harness (blue), averaged over all knees and cycles. Note that flexion angles have been illustrated as positive (despite representing a negative rotation around the laterally directed X-axis) for easier comparisons against other studies. Angles are shown as a percentage of the gait cycle under three conditions: (1) raw, i.e., in the absence of post-processing methods to correct reference frame orientation differences (<b>left</b>), (2) after implementation of REFRAME<sub><span class="html-italic">IMU</span>→<span class="html-italic">OMC</span></sub> (<b>middle</b>), and (3) after implementation of REFRAME<sub><span class="html-italic">RMS</span></sub> (<b>right</b>).</p>
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<p>Mean tibiofemoral joint angles (solid lines) ± standard deviation (shaded areas), in degrees, as estimated by inertial measurement units (IMUs) on harness (purple), IMUs on skin (green), and optical motion capture (OMC) on harness (blue), averaged over all cycles for knee 17. Note that flexion angles have been illustrated as positive (despite representing a negative rotation around the laterally directed X-axis) for easier comparisons against other studies. Angles are shown as a percentage of the gait cycle under three conditions: (1) raw, i.e., in the absence of post-processing methods to correct reference frame orientation differences (<b>left</b>), (2) after implementation of REFRAME<sub><span class="html-italic">IMU</span>→<span class="html-italic">OMC</span></sub> (<b>middle</b>), and (3) after implementation of REFRAME<sub><span class="html-italic">RMS</span></sub> (<b>right</b>).</p>
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<p>Mean ± standard deviation of root-mean-square errors (RMSEs, in degrees) between the optical reference system on a harness and the inertial measurement units on the harness (<b>left</b>), as well as between the optical reference system on a harness and the inertial measurement units on the skin (<b>right</b>). Shown for flexion/extension (<b>a</b>,<b>b</b>), abduction/adduction (<b>c</b>,<b>d</b>), and external/internal rotation (<b>e</b>,<b>f</b>). Significant changes in RMSEs after implementation of REFRAME<sub><span class="html-italic">IMU</span>→<span class="html-italic">OMC</span></sub> and of REFRAME<sub><span class="html-italic">RMS</span></sub>, as determined by paired <span class="html-italic">t</span>-tests, are shown (<span class="html-italic">p</span> &lt; 0.004 indicated by ***; full <span class="html-italic">p</span>-values are available in <a href="#app1-bioengineering-11-00976" class="html-app">Supplementary Materials Tables S121 and S122</a>).</p>
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<p>Mean ± standard deviation of root-mean-square errors (RMSEs, in degrees) between the optical reference system on a harness and the inertial measurement units on the harness (<b>left</b>), as well as between the optical reference system on a harness and the inertial measurement units on the skin (<b>right</b>). Shown for flexion/extension (<b>a</b>,<b>b</b>), abduction/adduction (<b>c</b>,<b>d</b>), and external/internal rotation (<b>e</b>,<b>f</b>). Significant changes in RMSEs after implementation of REFRAME<sub><span class="html-italic">IMU</span>→<span class="html-italic">OMC</span></sub> and of REFRAME<sub><span class="html-italic">RMS</span></sub>, as determined by paired <span class="html-italic">t</span>-tests, are shown (<span class="html-italic">p</span> &lt; 0.004 indicated by ***; full <span class="html-italic">p</span>-values are available in <a href="#app1-bioengineering-11-00976" class="html-app">Supplementary Materials Tables S121 and S122</a>).</p>
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17 pages, 11757 KiB  
Article
The Use of Waste Low-Density Polyethylene for the Modification of Asphalt Mixture
by Róbert Kovács, Adriana Czímerová, Adrián Fonód and Ján Mandula
Buildings 2024, 14(10), 3109; https://doi.org/10.3390/buildings14103109 (registering DOI) - 27 Sep 2024
Abstract
In this study, a critical evaluation and the benefits of using a waste and a virgin polymer in an asphalt mixture are presented. The present paper is the result of a three-year research effort to find a suitable recyclate compatible with asphalt binder [...] Read more.
In this study, a critical evaluation and the benefits of using a waste and a virgin polymer in an asphalt mixture are presented. The present paper is the result of a three-year research effort to find a suitable recyclate compatible with asphalt binder and setting reaction conditions in the preparation of asphalt mixtures with the mentioned recyclate. This suitable candidate was recycled low-density polyethylene (LDPE), which was produced by recycling old, worn-out bags and films. An amount of 6% of LDPE by the weight of the binder content was suggested as the best amount of the modifier. Physical tests, including penetration, softening point, and kinematic viscosity have been carried out to prove the effectiveness of the modification on the binder properties. The effectiveness of the blending process and the appropriate concentration of additives led to a homogeneous polymer-modified bitumen without any imperfections in the structure. After successful preparation under laboratory conditions, this paper describes the preparation of asphalt mixtures directly in an asphalt-mixing plant and the subsequent implementation of a verification section. The overall composition of prepared polymer-modified asphalt mixtures has been studied. An important result of this study is the preparation of the asphalt mixture with waste LDPE that meets all the technical requirements. Moreover, it has been proven that this type of waste PE is fully applicable in asphalt-mixing plants in Slovakia, with zero or minimal financial burden on construction companies to complete the construction of their production facilities. Using such a technology, we can reduce the amount of waste plastics that otherwise end up in landfill. Full article
(This article belongs to the Section Building Materials, and Repair & Renovation)
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<p>SEM microscopic analysis of CA 50/70 bitumen.</p>
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<p>Granules ready for mixing with asphalt and aggregate (white granulate is PE-LD, black is PE-LDrec).</p>
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<p>FTIR spectrum of PE-LDrec and results from comparison with spectral library.</p>
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<p>Rheological curves at 190 °C of samples PE-LD and PE-LDrec.</p>
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<p>Tankers with asphalt binder located in asphalt-mix plant.</p>
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<p>Photo documentation of the asphalt-mix preparation process in the asphalt-mixture plant.</p>
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<p>Photo documentation of the asphalt-mix laying process on the verification section.</p>
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<p>The verification section—scheme.</p>
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<p>Measurement of bearing capacity with the KUAB deflectometer.</p>
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<p>Equivalent modulus of elasticity for both lanes.</p>
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<p>Equivalent modulus of elasticity and individual qualification grades.</p>
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15 pages, 3473 KiB  
Article
Resveratrol and Exercise Produce Recovered Ankle and Metatarsus Joint Movements after Penetrating Lesion in Hippocampus in Male Rats
by Irene Guadalupe Aguilar-Garcia, Jonatan Alpirez, Rolando Castañeda-Arellano, Judith Marcela Dueñas-Jiménez, Carmen Toro Castillo, Lilia Carolina León-Moreno, Laura Paulina Osuna-Carrasco and Sergio Horacio Dueñas-Jiménez
Brain Sci. 2024, 14(10), 980; https://doi.org/10.3390/brainsci14100980 (registering DOI) - 27 Sep 2024
Abstract
Introduction: This study investigates how traumatic injuries alter joint movements in the ankle and foot. We used a brain injury model in rats, focusing on the hippocampus between the CA1 and dentate gyrus. Materials and Methods: We assessed the dissimilarity factor (DF) and [...] Read more.
Introduction: This study investigates how traumatic injuries alter joint movements in the ankle and foot. We used a brain injury model in rats, focusing on the hippocampus between the CA1 and dentate gyrus. Materials and Methods: We assessed the dissimilarity factor (DF) and vertical displacement (VD) of the ankle and metatarsus joints before and after the hippocampal lesion. We analyzed joint movements in rats after the injury or in rats treated with resveratrol, exercise, or a combination of both. Results: Resveratrol facilitated the recovery of DF in both legs, showing improvements in the ankle and metatarsus joints on the third and seventh days post-injury. The hippocampal lesion affected VD in both legs, observed on the third or seventh day after the injury. Both exercise and resveratrol partially recovered VD in the ankle and metatarsus joints on these days. These effects may be linked to increased hippocampal neurogenesis and reduced neuroinflammation. Conclusions: The study highlights the benefits of resveratrol and exercise in motor recovery following brain injury, suggesting their potential to enhance the quality of life for patients with neurological disorders affecting motor function and locomotion. These findings also suggest that resveratrol could offer a promising or complementary alternative in managing chronic pain and inflammation associated with orthopedic conditions, thus improving overall patient management. Full article
(This article belongs to the Section Neuromuscular and Movement Disorders)
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<p>(<b>A,B</b>) Left and right metatarsus. Bar graphs illustrate the dissimilarity factor (DF) among the control and experimental groups in metatarsus. Control is the blue bar; injury at three days (injury 3 dpi) is illustrated as green. (<b>C</b>,<b>D</b>). Left and right metatarsus at seven days (injury 7 dpi) post-injury, red bars. Exercise (Ex) at 3 and 7 days post-lesion is illustrated in pink and gray bars, respectively; resveratrol treatment (Resv) at 3 and 7 days post-lesion is in black and blue bars, respectively; and exercise with resveratrol treatment (Ex-Resv) at 3 and 7 days post-lesion is in orange and cyan bars, respectively. The values are expressed as median ± SE. The asterisks illustrate statistical differences between groups utilizing the Kruskal–Wallis test. <span class="html-italic">p</span> ≤ 0.05 (*), <span class="html-italic">p</span> ≤ 0.01 (**), <span class="html-italic">p</span> ≤ 0.001 (***), <span class="html-italic">p</span> ≤ 0.0001 (****).</p>
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<p>(<b>A</b>,<b>B</b>) Left and right ankle. Bar graphs illustrate the dissimilarity factor (DF) among the ankle’s control and experimental groups. Control, blue bar; injury at 3 days post-injury in green. (<b>C</b>,<b>D</b>), Blue bars illustrate control left and right ankle DF and red bars the DF at seven days post injury. Exercise (Ex) at 3 and 7 days post-injury, pink and gray bars, respectively; resveratrol treatment (Resv) at 3 and 7 days post-lesion, black and blue bars, respectively; and exercise with resveratrol treatment (Ex-Resv) at 3 and 7 days post-injury, orange and cyan bars, respectively. The values are expressed as median ± SE. The asterisks illustrate statistical differences between groups utilizing the Kruskal–Wallis test. <span class="html-italic">p</span> ≤ 0.05 (*), <span class="html-italic">p</span> ≤ 0.01 (**), <span class="html-italic">p</span> ≤ 0.001 (***), <span class="html-italic">p</span> ≤ 0.0001 (****).</p>
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<p>(<b>A</b>,<b>B</b>) The graphs illustrate the vertical displacement (VD) in the left and right metatarsus of the control (blue line), injury after three days (3 dpi) (green line), and exercise post-injury groups (Ex) (cyan line), respectively. (<b>C</b>,<b>D</b>) correspond to VD in the left and right metatarsus of control (blue line) injury after seven days (7 dpi) (red line) and exercise post-injury groups (Ex) (gray line), respectively. The asterisks illustrate the bins with a statistical difference (* <span class="html-italic">p</span> ≤ 0.05). The percent of change is expressed below the graphs.</p>
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<p>(<b>A</b>,<b>B</b>) The graphs illustrate the VD in the left and right ankle of the control (blue line), injury after three days (3 dpi) (green line), and exercise post-injury groups (Ex) (cyan line), respectively. (<b>C</b>,<b>D</b>) correspond to the VD in the left and right ankle of control (blue line) injury after seven days (7 dpi) (red line) and exercise post-injury groups (Ex) (gray line), respectively. The asterisks illustrate the bins with a statistical difference (* <span class="html-italic">p</span> ≤ 0.05). The percent of change is expressed below the graphs.</p>
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<p>(<b>A</b>,<b>B</b>) The graphs illustrate the VD in the left and right metatarsus of the control (blue line), injury after three days (3 dpi) (green line), and resveratrol post-injury groups (Res) (cyan line), respectively. (<b>C</b>,<b>D</b>) correspond to the VD in the left and right metatarsus of control (blue line), injury after seven days (7 dpi) (red line), and resveratrol post-injury groups (Resv) (gray line), respectively. The asterisks illustrate the bins with a statistical difference (* <span class="html-italic">p</span> ≤ 0.05). The percent of change is expressed below the graphs.</p>
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<p>(<b>A</b>,<b>B</b>) The graphs illustrate the vertical displacement (VD) in the left and right ankle of the control (blue line), injury after three days (3 dpi) (green line), and resveratrol post-injury groups (cyan line), respectively. (<b>C</b>,<b>D</b>) correspond to the VD in the left and right ankle of the control (blue line), injury after seven days (7 dpi) (red line), and resveratrol post-lesion groups (Resv) (gray line), respectively. The asterisks illustrate the bins with a statistical difference (* <span class="html-italic">p</span> ≤ 0.05). The percent of change is expressed below the graphs.</p>
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<p>(<b>A</b>,<b>B</b>) The graphs illustrate the vertical displacement (VD) in the left and right metatarsus of the control (blue line), injury after three days (3 dpi) (green line), and exercise plus resveratrol post-injury groups (Ex-Resv) (cyan line), respectively. (<b>C</b>,<b>D</b>) correspond to the VD in the left and right metatarsus of the control (blue line), injury after seven days (7 dpi) (red line), and exercise plus resveratrol post-injury groups (Ex-Resv) (gray line), respectively. The asterisks illustrate the bins with a statistical difference (* <span class="html-italic">p</span> ≤ 0.05). The percent of change is expressed below the graphs.</p>
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<p>(<b>A</b>,<b>B</b>) The graphs illustrate the vertical displacement (VD) in the left and right ankle of the control (blue line), injury after three days (3 dpi) (green line), and exercise plus resveratrol post-injury groups (Ex-Resv) (cyan line), respectively. (<b>C</b>,<b>D</b>) correspond to the VD in the left and right ankle of control (blue line), injury after seven days (7 dpi) (red line), and exercise plus resveratrolpost-injury groups (Ex-Resv) (gray line), respectively. The asterisks illustrate the bins with a statistical difference (* <span class="html-italic">p</span> ≤ 0.05). The percent of change is expressed below the graphs.</p>
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23 pages, 9211 KiB  
Article
Musculoskeletal Disorder Risk Assessment during the Tennis Serve: Performance and Prevention
by Philippe Gorce and Julien Jacquier-Bret
Bioengineering 2024, 11(10), 974; https://doi.org/10.3390/bioengineering11100974 - 27 Sep 2024
Abstract
Addressing the risk of musculoskeletal disorders (MSDs) during a tennis serve is a challenge for both protecting athletes and maintaining performance. The aim of this study was to investigate the risk of MSD occurrence using the rapid whole-body assessment (REBA) ergonomic tool at [...] Read more.
Addressing the risk of musculoskeletal disorders (MSDs) during a tennis serve is a challenge for both protecting athletes and maintaining performance. The aim of this study was to investigate the risk of MSD occurrence using the rapid whole-body assessment (REBA) ergonomic tool at each time step, using 3D kinematic analysis of joint angles for slow and fast serves. Two force platforms (750 Hz) and an optoelectronic system including 10 infrared cameras (150 Hz, 82 markers located on the whole body and on the racket) were used to capture the kinematics of the six REBA joint areas over five services in two young male and two young female ranked players. The mean REBA score was 9.66 ± 1.11 (ranging from 7.75 to 11.85) with the maximum value observed for the loading and cocking stage (REBA score > 11). The intermediate scores for each of the six joint areas ranged between 2 and 3 and the maximum value of their respective scales. The lowest scores were observed for the shoulder. Neck rotation and shoulder flexion are parameters that could be taken into account when analyzing performance in the context of MSD prevention. Full article
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<p>Three-dimensional visualization of markers positioned on players. (<b>A</b>) Position of markers at each key point of interest. (<b>B</b>) Marker trajectories during the serve. The illustration shows the first attempt by the first male player. The green and blue markers represent anatomical points on the right and left sides respectively. Yellow markers represent technical markers. The purple markers relate to the racket. The two blue squares represent the two force plates. The red vertical arrows represent ground reaction forces.</p>
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<p>Presentation of the experimental data processing used to carry out an ergonomic assessment of MSD risks during the tennis serve using REBA.</p>
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<p>Evolution of the REBA score (mean ± standard deviation) during the tennis serve. The background colors represent the REBA risk level (see last part of <a href="#bioengineering-11-00974-f002" class="html-fig">Figure 2</a>).</p>
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<p>Neck kinematic and ergonomic evaluations during the tennis serve. <b>Top panel</b>: Mean (±standard deviation) intermediate neck REBA score. <b>Bottom panel</b>: Mean (±standard deviation) neck flexion/extension, inclination, and axial rotation.</p>
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<p>Trunk kinematic and ergonomic evaluations during the tennis serve. <b>Top panel</b>: Mean (±standard deviation) intermediate trunk REBA score. <b>Bottom panel</b>: Mean (±standard deviation) trunk flexion/extension, inclination, and axial rotation.</p>
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<p>Knee kinematic and ergonomic evaluations during the tennis serve. <b>Top panels</b>: Mean (±standard deviation) intermediate leg REBA score. <b>Bottom panels</b>: Mean (±standard deviation) knee flexion.</p>
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<p>Shoulder kinematic and ergonomic evaluations during the tennis serve. <b>Top panel</b>: Mean (±standard deviation) intermediate shoulder REBA score. <b>Bottom panel</b>: Mean (±standard deviation) shoulder abduction/adduction, flexion/extension, and axial rotation.</p>
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<p>Elbow kinematic and ergonomic evaluations during the tennis serve. <b>Top panel</b>: Mean (±standard deviation) intermediate elbow REBA score. <b>Bottom panel</b>: Mean (±standard deviation) elbow flexion.</p>
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<p>Wrist kinematic and ergonomic evaluations during the tennis serve. <b>Top panel</b>: Mean (±standard deviation) intermediate wrist REBA score. <b>Bottom panel</b>: Mean (±standard deviation) wrist flexion/extension and radioulnar deviation.</p>
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<p>Evolution of the REBA score for slow (solid line) and fast (dotted line) serves. The background colors represent the REBA risk level (see last part of <a href="#bioengineering-11-00974-f002" class="html-fig">Figure 2</a>).</p>
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<p>Neck kinematic and ergonomic evaluations for the slow (solid line) and fast (dotted line) serves. <b>Top panel</b>: Neck REBA score. <b>Bottom panel</b>: Neck flexion/extension (blue), inclination (green), and axial rotation (red).</p>
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<p>Trunk kinematic and ergonomic evaluations for the slow (solid line) and fast (dotted line) serves. <b>Top panel</b>: Trunk REBA score. <b>Bottom panel</b>: Trunk flexion/extension (blue), inclination (green), and axial rotation (red).</p>
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<p>Back (left panels) and front (right panels) knee kinematic and ergonomic evaluations for the slow (solid line) and fast (dotted line) serves. <b>Top panels</b>: Knee REBA scores. <b>Bottom panels</b>: Knee flexion/extension (blue).</p>
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<p>Dominant shoulder kinematic and ergonomic evaluations for the slow (solid line) and fast (dotted line) serves. <b>Top panel</b>: Dominant shoulder REBA score. <b>Bottom panel</b>: Dominant shoulder abduction/adduction (blue), flexion/extension (green), and axial rotation (red).</p>
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<p>Dominant elbow kinematic and ergonomic evaluations for the slow (solid line) and fast (dotted line) serves. <b>Top panel</b>: Dominant elbow REBA score. <b>Bottom panel</b>: Dominant elbow flexion/extension (blue).</p>
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<p>Dominant wrist kinematic and ergonomic evaluations for the slow (solid line) and fast (dotted line) serve. <b>Top panel</b>: Dominant wrist REBA score. <b>Bottom panel</b>: Dominant wrist flexion/extension (blue), and radioulnar deviation (green).</p>
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<p>REBA method summary sheet. The left and right columns show the method for computing the intermediate scores, while the middle section contains the conversion charts for obtaining the final REBA score.</p>
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15 pages, 4185 KiB  
Article
Research on Vehicle-Driving-Trajectory Prediction Methods by Considering Driving Intention and Driving Style
by Liming Shao, Meining Ling, Ying Yan, Guangnian Xiao, Shiqi Luo and Qiang Luo
Sustainability 2024, 16(19), 8417; https://doi.org/10.3390/su16198417 - 27 Sep 2024
Abstract
With the rapid advancement of autonomous driving technology, the accurate prediction of vehicle trajectories has become a research hotspot. In order to accurately predict vehicles’ trajectory, this study comprehensively explores the impact of driving style and intention on trajectory prediction, proposing a novel [...] Read more.
With the rapid advancement of autonomous driving technology, the accurate prediction of vehicle trajectories has become a research hotspot. In order to accurately predict vehicles’ trajectory, this study comprehensively explores the impact of driving style and intention on trajectory prediction, proposing a novel prediction method. Firstly, the dataset AD4CHE was selected as the research data, from which the required trajectory data of vehicles were extracted, including 1202 lane-changing and 1137 car-following driving trajectories. Secondly, a long short-term memory (LSTM) network based on the Keras framework was constructed by using the TensorFlow deep-learning platform. The LSTM network integrates driving intention, driving style, and historical trajectory data as inputs to establish a vehicle-trajectory prediction model. Finally, the mean absolute error (MAE) and root-mean-square error (RMSE) were selected as the evaluation indicators for the models, and the prediction results of the models were compared under two conditions: not considering driving style and considering driving style. The results demonstrate that models incorporating driving style significantly outperformed those that did not, highlighting the critical influence of driving style on vehicle trajectories. Moreover, compared to traditional kinematic models, the LSTM-based approach exhibits notable advantages in long-term trajectory prediction. The prediction method that accounts for both driving intention and style effectively reduces RMSE, significantly enhancing prediction accuracy. The findings of this research provide valuable insights for vehicle-driving risk assessment and contribute positively to the advancement of autonomous driving technology and the sustainable development of road traffic. Full article
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<p>RNN network basic structure.</p>
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<p>LSTM network unit structure.</p>
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<p>Research flow chart.</p>
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<p>Left-lane-change trajectories cluster.</p>
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<p>Right-lane-change trajectories cluster.</p>
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<p>Car-following trajectories cluster.</p>
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<p>Prediction results of left lane-changing.</p>
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<p>Prediction results of right lane-changing.</p>
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<p>Prediction results of car following.</p>
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<p>Prediction results of left lane-changing.</p>
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<p>Prediction results of right lane-changing.</p>
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<p>Prediction results of car following.</p>
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15 pages, 2251 KiB  
Article
The Influence of Kinematics on Tennis Serve Speed: An In-Depth Analysis Using Xsens MVN Biomech Link Technology
by André V. Brito, Pedro Fonseca, Mário J. Costa, Ricardo Cardoso, Catarina C. Santos, Jaime Fernandez-Fernandez and Ricardo J. Fernandes
Bioengineering 2024, 11(10), 971; https://doi.org/10.3390/bioengineering11100971 - 27 Sep 2024
Abstract
An inertial measurement system, using a combination of accelerometers, gyroscopes and magnetometers, is of great interest to capture tennis movements. We have assessed the key biomechanical moments of the serve phases and events, as well as the kinematic metrics during the serve, to [...] Read more.
An inertial measurement system, using a combination of accelerometers, gyroscopes and magnetometers, is of great interest to capture tennis movements. We have assessed the key biomechanical moments of the serve phases and events, as well as the kinematic metrics during the serve, to analyze their influence on serve speed. Eighteen male competitive tennis players, equipped with the inertial measurement units, performed a prolonged serve game consisting of 12 simulated points. Participants were divided into groups A and B in accordance with their positioning above or below the sample average serve speed. Group A (compared with their counterparts) presented with lower back hip adduction and knee flexion, and a higher leftward thoracic tilt during the impact event (−14.9 ± 6.9 vs. 13.8 ± 6.4, 2.8 ± 5.9 vs. 14.3 ± 13.0 and −28.9 ± 6.3 vs. 28.0 ± 7.3°). In addition, group A exhibited higher maximal angular velocities in the wrist and thorax, as well as a lower maximal angular velocity in the back hip than group B (427.0 ± 99.8 vs. 205.4 ± 9.7, 162.4 ± 81.7 vs. 193.5 ± 43.8, 205.4 ± 9.7 vs. 308.3 ± 111.7, 193.5 ± 43.8 vs. 81.1 ± 49.7°/s). The relevant biomechanical differences during the serve were identified, highlighting the changes in joint angles and angular velocities between the groups, providing meaningful information for coaches and players to improve their serve proficiency. Full article
(This article belongs to the Section Biomechanics and Sports Medicine)
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<p>An illustration of the tennis court with the experimental material and the sequence of a simulated point (1, 2 and 3 corresponding to a flat serve, forehand and simulated backhand, respectively).</p>
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<p>A frontal and back view of the inertial measurement units’ distribution on the participants’ body segments.</p>
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<p>Characterization of tennis serve events and phases.</p>
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<p>The vertical center of mass displacement from the players’ initial position, during the tennis serve. Red, black and dashed gray lines represent group A, B, and overall, respectively.</p>
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<p>Statistical Parametric Mapping results of Group A and B (solid blue and red lines, respectively) between serve impact and finish event. Shoulder, elbow and wrist angular velocities (first, second and third panel, respectively). The period during which differences between groups with <span class="html-italic">p</span> ≤ 0.05 are statistically significant is highlighted by two solid black lines. Early and late cocked events are illustrated by red and blue dashed lines, respectively.</p>
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16 pages, 2454 KiB  
Article
Numerical Modeling of Plasticity in Metal Matrix Fiber Composites
by Gennadiy Lvov and Maria Tănase
Appl. Sci. 2024, 14(19), 8679; https://doi.org/10.3390/app14198679 - 26 Sep 2024
Abstract
This paper presents micromechanical analyses of an orthogonally reinforced composite with new constitutive equations of kinematic plastic hardening. The homogenization of plastic properties was performed through a numerical analysis of a representative volume using the finite element method. A modification of Prager’s theory [...] Read more.
This paper presents micromechanical analyses of an orthogonally reinforced composite with new constitutive equations of kinematic plastic hardening. The homogenization of plastic properties was performed through a numerical analysis of a representative volume using the finite element method. A modification of Prager’s theory was used to construct physical relations for an equivalent orthotropic material. In the proposed version of the theory, a special tensor for back stresses is introduced, which takes into account the difference in the rate of hardening for different types of plastic deformation. For boron–aluminum orthogonally reinforced composite with known mechanical properties of fibers and matrix, all material parameters of the theory were determined, deformation diagrams were constructed, and the equation for a plasticity surface in a six-dimensional stress space was obtained. The advantage of the developed method of numerical homogenization is that it only requires a minimal amount of experimental data. The efficiency of micromechanical analysis makes it possible to optimally design metal matrix composites with the required plastic properties. Full article
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<p>Representative volume element. (<b>a</b>) Geometrical model; (<b>b</b>) finite element model.</p>
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<p>Distributions of equivalent stresses: (<b>a</b>) uniaxial stretching along the 0X axis; (<b>b</b>) uniaxial stretching along the 0Z axis; (<b>c</b>) shear in the XY pane; (<b>d</b>) shear in the XZ plane.</p>
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<p>Distributions of equivalent plastic strains: (<b>a</b>) uniaxial stretching along the 0X axis; (<b>b</b>) uniaxial stretching along the 0Z axis; (<b>c</b>) shear in the XY pane; (<b>d</b>) shear in the XZ plane.</p>
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<p>Diagrams for (<b>a</b>) uniaxial stretching along the 0X and 0Z axes; (<b>b</b>) shear in the XY and XZ planes.</p>
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<p>Sections of plasticity surface in coordinates: (<b>a</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>x</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> </mrow> </semantics></math> under tension along x-axis; (<b>b</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>x</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>z</mi> </mrow> </msub> </mrow> </semantics></math> under tension along z-axis; (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>x</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math> under tension along x-axis; (<b>d</b>) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>σ</mi> </mrow> <mrow> <mi>x</mi> </mrow> </msub> <mo>−</mo> <msub> <mrow> <mi>τ</mi> </mrow> <mrow> <mi>x</mi> <mi>y</mi> </mrow> </msub> </mrow> </semantics></math> under shear in plane X0Y.</p>
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18 pages, 7864 KiB  
Article
Towards Simpler Approaches for Assessing Fuel Efficiency and CO2 Emissions of Vehicle Engines in Real Traffic Conditions Using On-Board Diagnostic Data
by Fredy Rosero, Carlos Xavier Rosero and Carlos Segovia
Energies 2024, 17(19), 4814; https://doi.org/10.3390/en17194814 - 26 Sep 2024
Abstract
Discrepancies between laboratory vehicle performance and real-world traffic conditions have been reported in numerous studies. In response, emission and fuel regulatory frameworks started incorporating real-world traffic evaluations and vehicle monitoring using portable emissions measurement systems (PEMS) and on-board diagnostic (OBD) data. However, in [...] Read more.
Discrepancies between laboratory vehicle performance and real-world traffic conditions have been reported in numerous studies. In response, emission and fuel regulatory frameworks started incorporating real-world traffic evaluations and vehicle monitoring using portable emissions measurement systems (PEMS) and on-board diagnostic (OBD) data. However, in regions with technical and economic constraints, such as Latin America, the use of PEMS is often limited, highlighting the need for low-cost methodologies to assess vehicle performance. OBD interfaces provide extensive vehicle and engine operational data in this context, offering a valuable alternative for analyzing vehicle performance in real-world conditions. This study proposes a straightforward methodology for assessing vehicle fuel efficiency and carbon dioxide (CO2) emissions under real-world traffic conditions using OBD data. An experimental campaign was conducted with three gasoline-powered passenger vehicles representative of the Ecuadorian fleet, operating as urban taxis in Ibarra, Ecuador. This methodology employs an OBD interface paired with a mobile phone data logging application to capture vehicle kinematics, engine parameters, and fuel consumption. These data were used to develop engine maps and assess vehicle performance using the vehicle-specific power (VSP) approach based on the energy required for vehicle propulsion. Additionally, VSP analysis combined with OBD data facilitated the development of an energy-emission model to characterize fuel consumption and CO2 emissions for the tested vehicles. The results demonstrate that OBD systems effectively monitor vehicle performance in real-world conditions, offering crucial insights for improving urban transportation sustainability. Consequently, OBD data serve as a critical resource for research supporting decarbonization efforts in Latin America. Full article
(This article belongs to the Special Issue CO2 Emissions from Vehicles (Volume II))
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<p>The methodology proposed in this study for the vehicle emissions analysis process.</p>
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<p>Schematic of the instrumentation.</p>
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<p>Typical engine operation patterns by load and speed for tested vehicles.</p>
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<p>Engine maps illustrating the CO2 emission rates for the Aveo (<b>a</b>), Sail (<b>b</b>) and Accent (<b>c</b>) vehicles.</p>
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<p>Correlation between fuel rate and VSP for tested vehicles.</p>
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<p>VSP frequency distribution for tested vehicles.</p>
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<p>Fuel emission rate (<b>a</b>) and CO<sub>2</sub> emission rate (<b>b</b>) by VSP bin for tested vehicles.</p>
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<p>Comparison of measured and simulated data using the VSP model for a typical trip of the Hyundai Accent vehicle: (<b>a</b>) Fuel consumption and (<b>b</b>) CO<sub>2</sub> emissions.</p>
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16 pages, 1374 KiB  
Article
Effect of Thawing Procedure and Thermo-Resistance Test on Sperm Motility and Kinematics Patterns in Two Bovine Breeds
by Juan M. Solís, Francisco Sevilla, Miguel A. Silvestre, Ignacio Araya-Zúñiga, Eduardo R. S. Roldan, Alejandro Saborío-Montero and Anthony Valverde
Animals 2024, 14(19), 2768; https://doi.org/10.3390/ani14192768 - 25 Sep 2024
Abstract
This investigation aimed to analyze the effect that thawing time and temperature in combination with a termo-resistance test had on straws from dairy bulls used for artificial insemination (AI) on semen motility and kinematic variables measured with CASA systems. Eight animals of Holstein [...] Read more.
This investigation aimed to analyze the effect that thawing time and temperature in combination with a termo-resistance test had on straws from dairy bulls used for artificial insemination (AI) on semen motility and kinematic variables measured with CASA systems. Eight animals of Holstein and Jersey breeds were used, and nine frozen-thawed semen doses per animal were analyzed for each breed. Three temperatures (35, 37, and 40 °C) and three thawing times (35, 40, and 45 s) were evaluated using a factorial design. Motility and kinematic patterns were analyzed using CASA-mot (Computer-Assisted Semen Analysis of motility) technology at different post-thawing times (0.5, 1, and 2 h). Sperm motility in Jersey bulls was higher (p < 0.05) than in Holstein ones (64.52 ± 1.45% and 53.10 ± 1.40%, respectively). The same effect was seen with progressive motility among the two breeds (Jersey: 45.29 ± 1.00%; Holstein: 36.30 ± 0.98%, p < 0.05). The Jersey breed presented higher values (p < 0.05) of curvilinear velocity (VCL), rectilinear velocity (VSL), average velocity (VAP), linearity on forward progression (LIN), and wobble (WOB). The Holstein breed showed a lower mean value (p < 0.05) of the beat-cross frequency (BCF) compared to the Jersey breed, thus suggesting an effect on VCL and VAP. During the post-thaw period, a gradual increase in VCL was observed at 2 h. VSL and VAP showed a decrease (p < 0.05) as the post-thaw period was prolonged. The study showed differences in sperm quality between Holstein and Jersey breeds, influenced by cryopreservation, thawing, and post-thawing incubation. Thawing at 37 °C for 30 s was considered optimal in relation to sperm motility. In addition, a decrease in sperm quality was observed as post-thawing time increased. Full article
(This article belongs to the Special Issue Technological Applications in Farm Animal Reproduction)
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<p>Interaction between breed, thawing temperature, and thawing time for the variable Total motility (TM,%). Area within the boxplot, indicates 50% of the observations between the 25th and 75th percentiles respectively. —: mean value; ┬ ┴: Minimum and maximum values within 3 standard deviation (SD) units.</p>
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<p>Interaction between breed, thawing temperature, and thawing time for the variable Progressive motility (PM,%). Area within the boxplot, indicates 50% of the observations between the 25th and 75th percentiles respectively. —: mean value; ┬ ┴: Minimum and maximum values within 3 standard deviation units (SD).</p>
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<p>Interaction between breed, thawing temperature, and thawing time for the variable Total motility (VCL, μm·s<sup>−1</sup>). Area within the boxplot indicates 50% of the observations between the 25th and 75th percentiles. —: mean value; ┬ ┴: Minimum and maximum values within 3 standard deviation units (SD).</p>
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<p>Interaction between breed and thawing temperature, thawing time, post-thawing period for the variable VCL (μm·s<sup>−1</sup>) using a confidence interval of 95% (IC 95%).</p>
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<p>Interaction between breed and thawing temperature, thawing time, and post-thawing period for the variable VSL (μm·s<sup>−1</sup>) using a confidence interval of 95% (IC 95%).</p>
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<p>Interaction between breed and thawing temperature, thawing time, post-thawing period for the variable VAP (μm·s<sup>−1</sup>) using a confidence interval of 95% (IC 95).</p>
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<p>Interaction between breed and thawing temperature, thawing time, and post-thawing period for the variable LIN (%) using a confidence interval of 95% (IC 95).</p>
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12 pages, 2902 KiB  
Article
Agreement and Sensitivity of the Acceleration–Velocity Profile Derived via Local Positioning System
by Mladen Jovanović, Adriano Arguedas-Soley, Dimitrije Cabarkapa, Håkan Andersson, Dóra Nagy, Nenad Trunić, Vladimir Banković, Répási Richárd, Sandor Safar and Laszlo Ratgeber
Sensors 2024, 24(19), 6192; https://doi.org/10.3390/s24196192 - 25 Sep 2024
Abstract
Sprint performance is commonly assessed via discrete sprint tests and analyzed through kinematic estimates modeled using a mono-exponential equation, including estimated maximal sprinting speed (MSS), relative acceleration (TAU), maximum acceleration (MAC), [...] Read more.
Sprint performance is commonly assessed via discrete sprint tests and analyzed through kinematic estimates modeled using a mono-exponential equation, including estimated maximal sprinting speed (MSS), relative acceleration (TAU), maximum acceleration (MAC), and relative propulsive maximal power (PMAX). The acceleration–velocity profile (AVP) provides a simple summary of short sprint performance using two parameters: MSS and MAC, which are useful for simplifying descriptions of sprint performance, comparison between athletes and groups of athletes, and estimating changes in performance over time or due to training intervention. However, discrete testing poses logistical challenges and defines an athlete’s AVP exclusively from the performance achieved in an isolated testing environment. Recently, an in situ AVP (velocity–acceleration method) was proposed to estimate kinematic parameters from velocity and acceleration data obtained via global or local positioning systems (GPS/LPS) over multiple training sessions, plausibly improving the time efficiency of sprint monitoring and increasing the sample size that defines the athlete’s AVP. However, the validity and sensitivity of estimates derived from the velocity–acceleration method in relation to changes in criterion scores remain elusive. To assess the concurrent validity and sensitivity of kinematic measures from the velocity–acceleration method, 31 elite youth basketball athletes (23 males and 8 females) completed two maximal effort 30 m sprint trials. Performance was simultaneously measured by a laser gun and an LPS (Kinexon), with kinematic parameters estimated using the time–velocity and velocity–acceleration methods. Agreement (%Bias) between laser gun and LPS-derived estimates was within the practically significant magnitude (±5%), while confidence intervals for the percentage mean absolute difference (%MAD) overlapped practical significance for TAU, MAC, and PMAX using the velocity–acceleration method. Only the MSS parameter showed a sensitivity (%MDC95) within practical significance (<5%), with all other parameters showing unsatisfactory sensitivity (>10%) for both the time–velocity and velocity–acceleration methods. Thus, sports practitioners may be confident in the concurrent validity and sensitivity of MSS estimates derived in situ using the velocity–acceleration method, while caution should be applied when using this method to infer an athlete’s maximal acceleration capabilities. Full article
(This article belongs to the Special Issue Sensor Techniques and Methods for Sports Science)
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Figure 1

Figure 1
<p>The grey rectangle represents time–velocity data that are used to train the model (Equation (1)), which involves velocity over 0.75 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> until the observed peak velocity is reached (indicated by a dotted horizontal line). (<b>a</b>) Laser Gun. The thin solid line indicates raw velocity (sampled at 1000 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">z</mi> </mrow> </semantics></math>). The thick solid line indicates smoothed velocity (the exact filtering/smoothing method is a proprietary secret of Ergotest Technology AS). The thick dashed line represents the mono-exponential model prediction. (<b>b</b>) Kinexon. The thick solid line indicates the reported device velocity (sampled at 20 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">z</mi> </mrow> </semantics></math>). The thick dashed line represents the mono-exponential model prediction.</p>
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<p>The grey rectangle represents time–velocity data that are used to train the model (Equation (1)), which involves velocity over 0.75 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> until the observed peak velocity is reached (indicated by a dotted horizontal line). (<b>a</b>) Laser Gun. The thin solid line indicates raw velocity (sampled at 1000 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">z</mi> </mrow> </semantics></math>). The thick solid line indicates smoothed velocity (the exact filtering/smoothing method is a proprietary secret of Ergotest Technology AS). The thick dashed line represents the mono-exponential model prediction. (<b>b</b>) Kinexon. The thick solid line indicates the reported device velocity (sampled at 20 <math display="inline"><semantics> <mrow> <mi mathvariant="normal">H</mi> <mi mathvariant="normal">z</mi> </mrow> </semantics></math>). The thick dashed line represents the mono-exponential model prediction.</p>
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<p>Velocity–acceleration method for estimating MSS and MAC parameters using the Kinexon data for a single individual across three sprint trials. (<b>a</b>) Instantaneous velocity and acceleration observations across three sprint trials for a single individual. Note: Trial 1—T01 (○ symbol); Trial 2—T02 (□ symbol); Trial 3—T03 (◇ symbol). (<b>b</b>) Using the velocity–acceleration method, only positive acceleration (i.e., over 0 ms<sup>−2</sup>) and velocity over 3 ms<sup>−1</sup> observations were used (grey rectangle) to estimate MSS and MAC parameters. A linear regression was then fitted (dashed line) using the two maximal acceleration observations that were collected for every 0.2 ms<sup>−1</sup> increment (filled points). The MAC parameter, which is equivalent to the estimated intercept of the linear regression model, can be visualized as the point where the regression line (dashed line) crosses the y-axis. The MSS parameter, which is equal to the estimated intercept divided by the negative estimated slope of the linear regression model, can be visualized as the point where the regression line (dashed line) crosses the x-axis.</p>
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<p>Velocity–acceleration method for estimating MSS and MAC parameters using the Kinexon data for a single individual across three sprint trials. (<b>a</b>) Instantaneous velocity and acceleration observations across three sprint trials for a single individual. Note: Trial 1—T01 (○ symbol); Trial 2—T02 (□ symbol); Trial 3—T03 (◇ symbol). (<b>b</b>) Using the velocity–acceleration method, only positive acceleration (i.e., over 0 ms<sup>−2</sup>) and velocity over 3 ms<sup>−1</sup> observations were used (grey rectangle) to estimate MSS and MAC parameters. A linear regression was then fitted (dashed line) using the two maximal acceleration observations that were collected for every 0.2 ms<sup>−1</sup> increment (filled points). The MAC parameter, which is equivalent to the estimated intercept of the linear regression model, can be visualized as the point where the regression line (dashed line) crosses the y-axis. The MSS parameter, which is equal to the estimated intercept divided by the negative estimated slope of the linear regression model, can be visualized as the point where the regression line (dashed line) crosses the x-axis.</p>
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<p>Distributions of the percent difference (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">%</mi> <mi mathvariant="normal">D</mi> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">f</mi> <mi mathvariant="normal">f</mi> </mrow> </semantics></math>) scores between the laser gun and Kinexon for (1) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> </mrow> </semantics></math>, (2) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">U</mi> </mrow> </semantics></math>, (3) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>, and (4) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> estimates using (1) time–velocity and (2) velocity–acceleration methods. Error bars represent <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">n</mi> </mrow> </semantics></math> and 2.5th and 97.5th percentiles (i.e., 95% range). Grey bars represent ±5% difference magnitude, used as a visual anchor. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> </mrow> </semantics></math>—maximum sprinting speed (expressed in <math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>); <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">U</mi> </mrow> </semantics></math>—relative acceleration (s); <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>—maximum acceleration (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>); <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math>—maximal relative power (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">W</mi> <mi mathvariant="normal">k</mi> <msup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>).</p>
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<p>Scatterplot between laser gun and Kinexon estimates for (1) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> </mrow> </semantics></math>, (2) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">U</mi> </mrow> </semantics></math>, (3) <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>, and (4) <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math> estimates using (1) time–velocity and (2) velocity–acceleration methods. Each circle represents a single athlete trial. The dotted diagonal line represents the identity line, at which all the observations would be positioned for perfect agreement. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> </mrow> </semantics></math>—maximum sprinting speed (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>); <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">U</mi> </mrow> </semantics></math>—relative acceleration (s); <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>—maximum acceleration (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>); <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math>—maximal relative power (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">W</mi> <mi mathvariant="normal">k</mi> <msup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>).</p>
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<p>Bootstrapped agreement estimators. Error bars represent 95% bias-corrected and accelerated (BCa) 5000 bootstrap resamples confidence intervals. Grey bars represent ±5% magnitude, used as a visual anchor. <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> </mrow> </semantics></math>—maximum sprinting speed (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>); <math display="inline"><semantics> <mrow> <mi mathvariant="normal">T</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">U</mi> </mrow> </semantics></math>—relative acceleration (s); <math display="inline"><semantics> <mrow> <mi mathvariant="normal">M</mi> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">C</mi> </mrow> </semantics></math>—maximum acceleration (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">m</mi> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mo>−</mo> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>); <math display="inline"><semantics> <mrow> <msub> <mrow> <mi mathvariant="normal">P</mi> </mrow> <mrow> <mi mathvariant="normal">m</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">x</mi> </mrow> </msub> </mrow> </semantics></math>—maximal relative power (<math display="inline"><semantics> <mrow> <mi mathvariant="normal">W</mi> <mi mathvariant="normal">k</mi> <msup> <mrow> <mi mathvariant="normal">g</mi> </mrow> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math>); Bias (%)—mean percent difference; MAD (%)—mean absolute percent difference; MDC (%)—minimum detectable percent difference.</p>
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12 pages, 9358 KiB  
Article
Constraints on the Geometry of Peripheral Faults above Mafic Sills in the Tarim Basin, China: Kinematic and Mechanical Approaches
by Zewei Yao
Appl. Sci. 2024, 14(19), 8621; https://doi.org/10.3390/app14198621 - 24 Sep 2024
Abstract
Host rock deformation associated with sill emplacement is used to constrain magma transfer and storage within the upper crust. In contrast to classic models suggesting that the host rock above mafic sills is dominated by elastic bending, recent studies show that bounding faults [...] Read more.
Host rock deformation associated with sill emplacement is used to constrain magma transfer and storage within the upper crust. In contrast to classic models suggesting that the host rock above mafic sills is dominated by elastic bending, recent studies show that bounding faults that limit the uplift area can occur at the peripheries of a mafic sill. However, the accurate dip of this type of fault, named peripheral faults here, is still not well constrained. Their origin is also controversial in some cases. In this study, kinematic modeling and limit analysis are performed to better constrain the structure and mechanical properties of the peripheral faults based on seismic interpretation of a mafic sill from the Tarim Basin, China. The trishear kinematic model successfully reproduces peripheral faulting and associated folding of the host rock by performing a displacement of 58 m on a vertical fault plane with a fault propagation (P) to fault slip (S) ratio of 2.5. The limit analysis also predicts vertical damage at the sill tip by sill inflation. These results suggest that the dip angle of the fault in the case study is 90°, which is more accurate than that from the seismic interpretation with an 88° inward dip. This value may vary in other cases as it depends on the sill geometry (such as diameter and inclination), thickness, depth, and mechanical properties of the host rock. The study supports that peripheral faulting and associated folding can occur at the tips of the mafic sill due to the vertical uplift of the host rock caused by sill inflation. It is also suggested that trishear kinematic modeling and limit analysis are effective methods for studying the geometry of peripheral faults. Full article
(This article belongs to the Special Issue Seafloor Magmatic and Hydrothermal Activity)
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Figure 1

Figure 1
<p>(<b>a</b>) Schematic diagram showing the uplifting and trapdoor faulting of the host rock above a mafic sill at Sierra Negra [<a href="#B14-applsci-14-08621" class="html-bibr">14</a>]. (<b>b</b>) Schematic sketch from an analog experiment illustrating the geometry of the saucer-shaped intrusion (simulated by vegetable polyglycerin) and host rock deformation (simulated by sand) [<a href="#B16-applsci-14-08621" class="html-bibr">16</a>].</p>
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<p>Location and structural units of the Tarim Basin after [<a href="#B18-applsci-14-08621" class="html-bibr">18</a>]. Bathymetric data of the inlet and the Tarim Basin are derived from GEBCO.</p>
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<p>(<b>a</b>) Semi-interpreted and (<b>b</b>) interpreted seismic profiles showing sill intrusions and peripheral faults in the central part of the Tarim Basin (for location, see <a href="#applsci-14-08621-f002" class="html-fig">Figure 2</a>) after [<a href="#B18-applsci-14-08621" class="html-bibr">18</a>,<a href="#B22-applsci-14-08621" class="html-bibr">22</a>]. T, P<sub>3</sub>, P<sub>2</sub>, P<sub>1</sub>, D<sub>3</sub>-C, S-D<sub>1–2</sub>, O<sub>3</sub>, Є-O<sub>1–2</sub> are the strata symbols of structural units. T—Triassic strata; P<sub>3</sub>—Upper Permian strata; P<sub>2</sub>—Middle Permian strata; P<sub>1</sub>—Lower Permian strata; D<sub>3</sub>-C—Upper Devonian to Carboniferous strata; S-D<sub>1–2</sub>—Silurian to Middle Devonian strata; O<sub>3</sub>—Upper Ordovician strata; Є-O<sub>1–2</sub>—Upper Cambrian to Middle Devonian strata. The seismic profile in the yellow box is used for time–depth conversion. The growth strata depicted here represent the strata deposited during the uplift. Pre-growth strata and post-growth strata refer to the layers formed before and after this stage, respectively (see text for details).</p>
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<p>Basic geometry of the trishear kinematic model redrawn from [<a href="#B32-applsci-14-08621" class="html-bibr">32</a>]. Velocity vectors within the hanging wall, shear zone, and sectors of equal velocity vector are illustrated schematically.</p>
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<p>(<b>a</b>) Structural pattern of the peripheral fault derived from seismic interpretation after time–depth conversion. Solid lines with color are the boundaries of the structural units and black concordant dashed lines are internal bedding planes. T, P<sub>3</sub>, P<sub>2</sub>, P<sub>1</sub>, D<sub>3</sub>-C, S-D<sub>1–2</sub> are strata symbols of structural units (see text or <a href="#applsci-14-08621-f003" class="html-fig">Figure 3</a> for details). (<b>b</b>) Initial trishear kinematic model of the peripheral fault based on seismic interpretation. Pre-growth, growth, and post-growth are defined based on the relative sequence of deposition and uplifting (see text or <a href="#applsci-14-08621-f003" class="html-fig">Figure 3</a> for details).</p>
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<p>(<b>a</b>) Structural pattern of the peripheral fault derived from the trishear kinematic model. (<b>b</b>) Structural pattern of the peripheral fault derived from seismic interpretation versus that from the trishear kinematic model. Pre-growth, growth, and post-growth are defined based on the relative sequence of deposition and uplifting (see text or <a href="#applsci-14-08621-f003" class="html-fig">Figure 3</a> for details).</p>
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<p>(<b>a</b>) Sketch of the model setup. The setup consists of a constant pressure cavity and homogeneous Mohr–Coulomb host rock. The model is 30 km wide, 10 km deep, and axisymmetric. The bottom surface is fixed, the top surface is free, and the right boundary allows vertical slip. (<b>b</b>) Distribution of adaptive triangular mesh. Note that the mesh is denser in areas of stress concentration.</p>
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<p>Shear dissipation map that predicts the damage distribution of the model. The white lines are the modeled sill. The energy dissipation by shear damage is represented by the color scale in kJ. V<sub>tip</sub> and V<sub>surf</sub> are measured at the sill tip and at the surface, respectively.</p>
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