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The Internet of Things (IoT) is a crucial technology widely utilized in various sectors in recent years. One significant application is in the healthcare system, particularly in mobile health and remote patient monitoring for individuals with different medical conditions such as kidney disease, heart disease, cancer, hypertension, diabetes, respiratory issues, and stroke. Integrating IoT with cloud computing can enhance the efficiency of healthcare systems and facilitate the creation of novel applications in the future. Load balancing is a significant issue in cloud computing systems that must be addressed. Solving the problem will decrease response time, power usage, and cost and improve server availability. This work consists of developing and executing a healthcare system utilizing IoT and addressing the load-balancing issue in cloud computing using an Improved version of the Grey Wolf Optimization (GWO) algorithm. The suggested method is named Improved GWO Virtual Machine Selector (IGWO-VMS). The proposed method chooses the Optimal Virtual Machine (VM) from a set of VMs based on its fitness value. Various tasks are prioritized and allocated to the most suitable VMs according to their Instruction Millions (IM), with tasks with high IM being assigned to VMs with high fitness values. The results showed that the suggested technique decreases latency and packet loss while maximizing throughput in healthcare systems. The efficiency and success of this technology surpass other state-of-the-art methods in decreasing makespan time and total processing time and ensuring load balancing across virtual machines. The GoCJ dataset used contain a number of jobs in terms of Million Instructions (MI) obtained from the workload behaviour witnessed in google cluster trace. The values of makespan time, throughput time and Standard Deviation (STD) where (23.05), (899.8979) and (177.7675), respectively, in case of applying 500, 600 and 900 tasks. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simultaneous localization and mapping, a critical technology for enabling the autonomous driving of vehicles and mobile robots, increasingly incorporates multi-sensor configurations. Inertial measurement units (IMUs), known for their ability to measure acceleration and angular velocity, are widely utilized for motion estimation due to their cost efficiency. However, the inherent noise in IMU measurements necessitates the integration of additional sensors to facilitate spatial understanding for mapping. Visual–inertial odometry (VIO) is a prominent approach that combines cameras with IMUs, offering high spatial resolution while maintaining cost-effectiveness. In this paper, we introduce our uncertainty-aware depth network (UD-Net), which is designed to estimate both depth and uncertainty maps. We propose a novel loss function for the training of UD-Net, and unreliable depth values are filtered out to improve VIO performance based on the uncertainty maps. Experiments were conducted on the KITTI dataset and our custom dataset acquired from various driving scenarios. Experimental results demonstrated that the proposed VIO algorithm based on UD-Net outperforms previous methods with a significant margin. [ABSTRACT FROM AUTHOR]

EMOKINE is a software package and dataset creation suite for emotional full-body movement research in experimental psychology, affective neuroscience, and computer vision. A computational framework, comprehensive instructions, a pilot dataset, observer ratings, and kinematic feature extraction code are provided to facilitate future dataset creations at scale. In addition, the EMOKINE framework outlines how complex sequences of movements may advance emotion research. Traditionally, often emotional-'action'-based stimuli are used in such research, like hand-waving or walking motions. Here instead, a pilot dataset is provided with short dance choreographies, repeated several times by a dancer who expressed different emotional intentions at each repetition: anger, contentment, fear, joy, neutrality, and sadness. The dataset was simultaneously filmed professionally, and recorded using XSENS® motion capture technology (17 sensors, 240 frames/second). Thirty-two statistics from 12 kinematic features were extracted offline, for the first time in one single dataset: speed, acceleration, angular speed, angular acceleration, limb contraction, distance to center of mass, quantity of motion, dimensionless jerk (integral), head angle (with regards to vertical axis and to back), and space (convex hull 2D and 3D). Average, median absolute deviation (MAD), and maximum value were computed as applicable. The EMOKINE software is appliable to other motion-capture systems and is openly available on the Zenodo Repository. Releases on GitHub include: (i) the code to extract the 32 statistics, (ii) a rigging plugin for Python for MVNX file-conversion to Blender format (MVNX=output file XSENS® system), and (iii) a Python-script-powered custom software to assist with blurring faces; latter two under GPLv3 licenses. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicles (AUVs) are widely used for hydrological monitoring, underwater exploration, and geological surveys. However, AUVs face limitations in underwater navigation due to the high costs associated with Strapdown Inertial Navigation System (SINS) and Doppler Velocity Log (DVL), hindering the development of low-cost vehicles. Micro Electro Mechanical System Inertial Measurement Units (MEMS IMUs) are widely used in industry due to their low cost and can output acceleration and angular velocity, making them suitable as an Attitude Heading Reference System (AHRS) for low-cost vehicles. However, poorly calibrated MEMS IMUs provide an inaccurate angular velocity, leading to rapid drift in orientation. In underwater environments where AUVs cannot use GPS for position correction, this drift can have severe consequences. To address this issue, this paper proposes Underwater Gyros Denoising Net (UGDN), a method based on dilated convolutions and LSTM that learns and extracts the spatiotemporal features of IMU sequences to dynamically compensate for the gyroscope's angular velocity measurements, reducing attitude and heading errors. In the experimental section of this paper, we deployed this method on a dataset collected from field trials and achieved significant results. The experimental results show that the accuracy of MEMS IMU data denoised by UGDN approaches that of fiber-optic SINS, and when integrated with DVL, it can serve as a low-cost underwater navigation solution. [ABSTRACT FROM AUTHOR]

Controlling the fluids in centrifugal microfluidic chips for precise sequential release is critical for multi-step reactions and immunoassays. Currently, the traditional methods of liquid sequential release mainly rely on various types of microvalves, which face the problems of complex operation and high costs. Here, this work presents a method for driving liquid release using the Euler force. Under continuous acceleration and deceleration, the centrifugal and Euler forces can transfer the liquid from the sample chamber to the collection chamber. The liquid sequential release mechanism based on the Euler force was analyzed, which showed that the angular acceleration is key to the liquid release. Then, the geometrical parameters affecting the angular acceleration of complete release were investigated and simulated. Finally, based on the relationship between the geometrical parameters of the connecting channels and the angular acceleration of complete release, a simple and precise sequential release structure was designed, which allowed for a sequential and stable transfer of the liquid into the reaction chamber. The results showed that the proposed method is capable of transferring liquid, and its simple structure, low manufacturing cost, and ease of operation enable precise sequential liquid release in centrifugal microfluidic platforms. [ABSTRACT FROM AUTHOR]

The present work discusses the numerical simulation of the tapping process to validate the earlier postulates related to the influence of BOF vessel shape on vortex formation. Numerical experiments were conducted by varying the initial filling flow rates (FR 40 and 20 lpm), dwell times (DT 90 and 30 seconds), nozzle diameters (ND 2.14 and 1.04 cm), and initial liquid height (LH 14 and 11 cm). It was earlier reported that the vortex formation is mainly dependent on the nozzle diameter and the stability of the vortex relay on the residual motion in the draining liquids. The present numerical study provides insight into the vortex stability and elucidates the role of residual motion in the draining liquids under different process conditions. The delay in vortex formation for the case of higher residual motion is due to a delay in acceleration and alignment of angular momentum at the nozzle axis vicinity. Further, it is also observed from the numerical experiments that the vertical velocity component's magnitude exceeds the curl velocity's horizontal velocity component to establish the stable vortex. The findings of simulated results are in good agreement with the experimental results reported earlier. It also supports the theory of controlling the vortex formation in BOF vessels (by tilting front/back) without using an external device, such as a dart, a device to arrest the slag entering the ladle at the tapping end. [ABSTRACT FROM AUTHOR]

Background: The automatic detection of activities of daily living (ADL) is necessary to improve long-term home-based monitoring of Parkinson's disease (PD) symptoms. While most body-worn sensor algorithms for ADL detection were developed using laboratory research systems covering full-body kinematics, it is now crucial to achieve ADL detection using a single body-worn sensor that remains commercially available and affordable for ecological use. Aim: to detect and segment Walking, Turning, Sitting-down, and Standing-up activities of patients with PD using a Smartwatch positioned at the ankle. Method: Twenty-two patients living with PD performed a Timed Up and Go (TUG) task three times before engaging in cleaning ADL in a simulated free-living environment during a 3 min trial. Accelerations and angular velocities of the right or left ankle were recorded in three dimensions using a Smartwatch. The TUG task was used to develop detection algorithms for Walking, Turning, Sitting-down, and Standing-up, while the 3 min trial in the free-living environment was used to test and validate these algorithms. Sensitivity, specificity, and F-scores were calculated based on a manual segmentation of ADL. Results: Sensitivity, specificity, and F-scores were 96.5%, 94.7%, and 96.0% for Walking; 90.0%, 93.6%, and 91.7% for Turning; 57.5%, 70.5%, and 52.3% for Sitting-down; and 57.5%, 72.9%, and 54.1% for Standing-up. The median of time difference between the manual and automatic segmentation was 1.31 s for Walking, 0.71 s for Turning, 2.75 s for Sitting-down, and 2.35 s for Standing-up. Conclusion: The results of this study demonstrate that segmenting ADL to characterize the mobility of people with PD based on a single Smartwatch can be comparable to manual segmentation while requiring significantly less time. While Walking and Turning were well detected, Sitting-down and Standing-up will require further investigation to develop better algorithms. Nonetheless, these achievements increase the odds of success in implementing wearable technologies for PD monitoring in ecological environments. [ABSTRACT FROM AUTHOR]

Interest in the exploration of other planets has led to the success of Ingenuity (Mars helicopter)'s first flight in the Martian atmosphere. Despite this achievement, the flight of aircraft under ultra-low air density, such as Mars, is still limited. A flapping-wing aircraft that mimics nature's flyers can provide an alternative solution to conventional aircraft. Successful flapping flight on Mars, where the air density is very thin, requires an understanding of aerodynamics, and prediction of the dynamic changes of the flapping wing under very-low air density. In this paper, we constructed a simple dynamic model that can predict the changes in aerodynamics and dynamic behavior under various low air densities. The predicted changes were compared with the measured ones in a chamber that could simulate the low air density with a modified flapping-wing system based on our flapping-wing micro-air-vehicle (FW−MAV) models, the KUBeetle. Both prediction and measurement showed that when the air density decreased, the flapping frequency tended to increase due to drag reduction, even without consuming additional power. The model reasonably predicted a large decrease in the lift for the 10 % air density, where the flapping frequency increased by 10 %. Under the 50 % and 25 % low air densities, the measured lift coefficients did not change much. However, under the 10 % air density, the lift coefficient decreased by about 16 % due to less wing rotation and wing twist. While the aerodynamic power was predicted to decrease significantly due to the large decrease in aerodynamic forces under low air density, an increase in the angular velocity and peak angular acceleration caused an increase in the inertial power. Therefore, the amount of reduction in the measured mechanical power consumption under the 10 % air density was relatively small, compared to that under the 100 % air density. This work may provide insights into the design of an FW−MAV for the ultra-thin air density, such as Mars. • The proposed dynamic model reasonably predicted the flapping motion under low air density. • The measured flapping frequency increased by about 15 % under the 10 % air density, consuming about 9 % less power. • The reduction in measured power consumption under low air density was small, due to the large inertial power. • The measured lift coefficient decreased by about 16 % under the 10 % air density, due to less wing Q4 rotation and wing twist. [ABSTRACT FROM AUTHOR]

Purpose. To carry out a kinematic analysis of a flat twelve-link hinged mechanism with three leading links, the basis of which is a structural group of links of the third class of the fourth order with rotational kinematic pairs, which is used in technological equipment. Methodology. The kinematic study of a flat complex mechanism of the third class with three leading links was performed using the mathematical modeling method in the Mathcad software environment. Findings. Using the method of mathematical modeling, the mechanism was presented in the form of free vectors built on its links, for which, taking into account the presence of three leading links, three vector contours were selected, which made it possible to draw up a system of vector equations of their closedness with further solving by a numerical method in the Mathcad software environment. The functions of the rotation angles of the mechanism links, their angular velocities and accelerations were obtained in analytical and graphical form depending on the rotation angle of the first driving crank of the mechanism, the calculation of the angular velocities and accelerations of all the driven links of the mechanism was performed. Originality. A sequence was developed and a kinematic study of a complex planar mechanism of the third class with three leading links was done, the basis of which is a structural group of links of the third class of the fourth order. Schematic modeling of the mechanism with three leading links was performed, a visualization schedule of its kinematic scheme was built, and a valid version of its assembly was obtained from many possible ones, for which the values of kinematic parameters of all its links were determined. Practical value. An analysis of the effect of the kinematic parameters of the three leading links of the third-class mechanism on the movement of the working body of the technological equipment was carried out. From the analysis of the obtained research results for the cycle of the mechanism the reason was found for the incomplete technological stoppage of the link, which should provide the working body of the machine for the required period of time. Recommendations of the elimination were made to identify the deficiency and proposals for the need to improve the drive of this equipment. [ABSTRACT FROM AUTHOR]

Repetitive movements and the speed of upper limbs increase the risk of musculoskeletal disorders. This study aimed to analyse the risk of shoulder injuries in repetitive tasks by evaluating the humerus angle, angular velocity, and angular acceleration during simulated chicken wing cutting. The study was conducted in a laboratory simulating a real environment. Thirty-six healthy right-handed volunteers were assessed using an electromagnetic tracking device, TrakSTAR, integrated with MotionMonitor™ (Innovative Sports Training, Inc. Chicago, IL) and software to collect 3-D kinematic data developed in the research centre. The equipment measured the angles performed by the upper limbs during the entire movement. The humerus angles were automatically transformed into angular velocity (°·s−1) and angular acceleration (°·s−2). Maximum angular velocities were 27.39°·s−1 (men, right humerus) and 22.39°·s−1 (women, left humerus), both below the safe limit. Maximum accelerations were 25.32°·s−2 (men, left side) and 28.94°·s−2 (women, left side); safety values for these accelerations are not established. Monotony is a risk factor, especially for the dominant side. Future studies should evaluate risk factors simultaneously in repetitive tasks. Repetitiveness exceeds the safe limit according to the OCRA method. [ABSTRACT FROM AUTHOR]

A method for measuring multi-joint finger trajectories is proposed using MediaPipe. In this method, a high-speed camera is used to record finger movements. Subsequently, the recorded finger movement data are input into MediaPipe, where the system automatically extracts the coordinate data of the key points in the finger movements. From this, we obtain data pertaining to the trajectory of the finger movements. In order to verify the accuracy and effectiveness of this experimental method, we compared it with the DH method and the Artificial keypoint alignment method in terms of metrics such as MAPE (Mean Absolute Percentage Error), maximum distance error, and the time taken to process 500 images. The results demonstrated that our method can detect multiple finger joints in a natural, efficient, and accurate manner. Then, we measured posture for three selected hand movements. We determined the position coordinates of the joints and calculated the angular acceleration of the joint rotation. We observed that the angular acceleration can fluctuate significantly over a very short period of time (less than 100 ms), in some cases increasing to more than ten times the initial acceleration. This finding underscores the complexity of finger joint movements. This study can provide support and reference for the design of finger rehabilitation robots and dexterous hands. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: The current study aimed to assess the protective performance of helmets equipped with multi-directional impact protection system (MIPS) under various oblique impact loads. Methods: Initially, a finite element model of a bicycle helmet with MIPS was developed based on the scanned geometric parameters of an actual bicycle helmet. Subsequently, the validity of model was confirmed using the KASK WG11 oblique impact test method. Three different impact angles (30°, 45°, and 60°) and 2 varying impact speeds (5 m/s and 8 m/s) were employed in oblique tests to evaluate protective performance of MIPS in helmets, focusing on injury assessment parameters such as peak linear acceleration (PLA) and peak angular acceleration (PAA) of the head. Results: The results demonstrated that in all impact simulations, both assessment parameters were lower during impact for helmets equipped with MIPS compared to those without. The PAA was consistently lower in the MIPS helmet group, whereas the difference in PLA was not significant in the no-MIPS helmet group. For instance, at an impact velocity of 8 m/s and a 30° inclined anvil, the MIPS helmet group exhibited a PAA of 3225 rad/s2 and a PLA of 281 g. In contrast, the no-MIPS helmet group displayed a PAA of 8243 rad/s2 and a PLA of 292 g. Generally, both PAA and PLA parameters decreased with the increase of anvil angles. At a 60° anvil angles, PAA and PLA values were 664 rad/s2 and 20.7 g, respectively, reaching their minimum. Conclusion: The findings indicated that helmets incorporating MIPS offer enhanced protection against various oblique impact loads. When assessing helmets for oblique impacts, the utilization of larger angle anvils and rear impacts might not adequately evaluate protective performance during an impact event. These findings will guide advancements in helmet design and the refinement of oblique impact test protocols.

This work reports a comprehensive multiphysics modeling and optimization of a MEMS-based piezoelectric inertial measurement unit (IMU) sensor using a single proof mass. The studied miniaturized sensor consists of a stick-shaped cylindrical proof mass with two concentric rings connected between them with joints where the stress is concentrated. The advantage of introducing this new piezoelectric IMU concept is the concurrent detection of all six degrees of freedom (DOFs), i.e., 3-axis linear accelerations and angular velocities, using a single seismic mass and only four electrodes. The piezoelectric IMU structure optimization was carried out using finite element analysis (FEA) in order to obtain the optimal dimensions and generate the maximum stress in the piezoelectric layer. In fact, the mechanical (displacement and stress) and electrical (potential and sensitivity) performance of the IMU structure was determined when 3-axis linear accelerations and 3-axis angular velocities were applied. The simulation results showed a sensitivity of 1 mV/g and 6.8 mV/g, respectively, for linear accelerations along the x-/y- and z-axes. Along these same axes, the angular velocity sensitivity was approximately 0.39 µV/rad/s and 1.7 mV/rad/s. Moreover, the simulation results claim that the proposed IMU has good performance in terms of cross-axis sensitivity of less than 0.88% for all six DOFs, which is due to its perfectly symmetric circular structure. Finally, a simple block diagram is presented to separate the outputs related to linear acceleration from those related to angular velocity from the four sensing electrodes. [ABSTRACT FROM AUTHOR]

Manual exploratory behaviors during object interaction that form the basis of tool use behavior, are mostly qualitatively characterized in terms of their frequency and duration of occurrence. To fully understand their functional and clinical significance, quantitative movement characterization is needed alongside their qualitative analysis. However, there are two challenges in quantifying them—(a) reliably classifying the type of movement and (b) performing this classification on a time series automatically. Here, we propose a machine learning-based classification method to address these challenges. We measured three common exploratory behaviors (object rotation, fingering, and throwing) in college-aged adults using "sensorized objects" that had wireless Inertial Measurement Units embedded in them. We then calculated several statistical features based on linear acceleration and angular velocity data to train machine learning classifiers to identify these behaviors. All classifiers identified the behaviors with a substantially higher accuracy (average accuracy = 84.95 ± 4.16%) than chance level (33.33%). Of all models tested, Support Vector Machine Quadratic, Support Vector Machine Medium Gaussian, and Narrow Neural Network were the best models in classifying the three behaviors (average accuracy = 89.34 ± 0.12%). This classification method shows potential for automating movement characterization of exploratory behaviors, thereby may aid early assessment of neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]

The present research proposes novel swept-curved bioinspired blades for Darrieus wind turbine. The design was influenced by the curved flippers of the bottle-nose dolphin. The proposed model was designed using the Solidworks software, empirically tested for power improvement, later fine-tuned adopting high fidelity numerical modeling. An in-depth analysis comparing the traditional H-bladed turbine with the new-bladed design was performed using OpenFOAM. Further, an elaborate experimental observations on the proposed design suggest that the model with 0.6 c forward sweep outperformed the rest. The efficiency of the curved blade was notably enhanced by 14.41%, in contrast to the conventional model, and the numerical analysis endorses this observation. The modified turbine blades have the maximum wind streamlines that converge toward the center of the blade's trailing edge due to the lateral shift, and the resulting convergence reduces blade tip losses. The modified blade models demonstrated improved angular acceleration at a given wind speed, indicating enhanced efficiency and superior self-starting capabilities. Notably, these improvements were evident even at a lower wind speed of 4.2 m / s compared to the traditional H-model. [ABSTRACT FROM AUTHOR]

Featured Application: The machine learning model predicts Minimum Foot Clearance heights to prevent tripping falls. Integrated into exoskeletons or other assistive devices, it offers real-time interventions for vulnerable populations, enhancing safety with quick and accurate foot clearance adjustments. Tripping is the largest cause of falls, and low swing foot ground clearance during the mid-swing phase, particularly at the critical gait event known as Minimum Foot Clearance (MFC), is the major risk factor for tripping-related falls. Intervention strategies to increase MFC height can be effective if applied in real-time based on feed-forward prediction. The current study investigated the capability of machine learning models to classify the MFC into various categories using toe-off kinematics data. Specifically, three MFC sub-categories (less than 1.5 cm, between 1.5 and 2.0 cm, and higher than 2.0 cm) were predicted to apply machine learning approaches. A total of 18,490 swing phase gait cycles' data were extracted from six healthy young adults, each walking for 5 min at a constant speed of 4 km/h on a motorized treadmill. K-Nearest Neighbor (KNN), Random Forest, and XGBoost were utilized for prediction based on the data from toe-off for five consecutive frames (0.025 s duration). Foot kinematics data were obtained from an inertial measurement unit attached to the mid-foot, recording tri-axial linear accelerations and angular velocities of the local coordinate. KNN, Random Forest, and XGBoost achieved 84%, 86%, and 75% accuracy, respectively, in classifying MFC into the three sub-categories with run times of 0.39 s, 13.98 s, and 170.98 s, respectively. The KNN-based model was found to be more effective if incorporated into an active exoskeleton as the intelligent system to control MFC based on the preceding gait event, i.e., toe-off, due to its quicker computation time. The machine learning-based prediction model shows promise for the prediction of critical MFC data, indicating higher tripping risk. [ABSTRACT FROM AUTHOR]

To provide the necessary theoretical models of sphere–soil interaction for the structural design, motion control, and simulation of spherical robots, this paper derives analytical expressions for traction force and driving torque when spherical robots slide and sink into sandy terrain, based on terramechanics and multibody dynamics. Furthermore, orthogonal experimental analysis identifies the load, joint angular acceleration, and maximum joint angular velocity of spherical robots as influencing factors, highlighting that the load significantly affects their longitudinal motion performance. Experimental results indicate that rolling friction and additional resistance on sandy terrain cannot be ignored. The corrected theoretical model effectively replicates the temporal variation of driving torque exerted by spherical robots on sandy terrain. Numerical computations and experimental analyses demonstrate that increasing the radius of the sphere shell, the load, and the slip ratio all lead to increased traction force and driving torque. However, traction force and driving torque begin to decrease once the slip ratio reaches approximately 0.5. Therefore, in the design of spherical robot structures and control laws, appropriate parameters such as load and slip ratio should be chosen based on the established sphere–soil interaction theoretical model to achieve high-quality longitudinal motion performance on sandy terrain. [ABSTRACT FROM AUTHOR]

Kinematic analysis is the first task that needs to be solved for further real-life problems such as machine dynamics and durability, machine balancing, and optimization, chemistry, etc. Currently, there are many methods to analyze the kinematic problem, for instance: vector graphics method, vector-analytic method, or Dennavit-Hartenberg-Craig matrix method. Although each method has its advantages, they have mostly encountered difficulties in establishing equations associated with complicated spatial structures. This research proposed a novel method, entitled "'perpendicular projection method" to solve the problem of spatial structural kinematic analysis. This is an approximate solution method that combines the use of generalized coordinates and direction cosine matrices. With this method, the problem of kinematic analysis of spatial structures will be solved simply and easily by establishing connection equations as well as simulating the motion of spatial structures. This method was then applied in conducting the kinematic analysis of a spatial mechanism with 1 rotating joint (R), 1 ball joint (S), 1 translational joint (P), and one cylindrical joint (C). The calculated results, including the velocity, acceleration of generalized coordinates, angular velocity, angular acceleration of the links, velocity, and acceleration of the center of mass of the links, showed the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Conventional angular acceleration estimation methods generate non-negligible errors when the control surface rapidly deflects; hence, the estimated angular acceleration cannot be used for aerodynamic parameter identification directly. This paper proposes an angular acceleration estimation method based on angular velocity equivalent model. The angular acceleration of the angular velocity equivalent model, whose angular velocity response is consistent with the flight test data, is used as the estimated angular acceleration. Firstly, the equivalent angular velocity model is established by combining the generic aerodynamic model with the rotational dynamic equations of aircraft. Secondly, the angular velocity data and the control surface deflection data are synchronized. Finally, the angular velocity response of the equivalent model is used as the predictor, and the estimated angular acceleration is obtained by extended Kalman filter. The aerodynamic parameter identification and validation are carried out using the estimated angular acceleration of a large civil aircraft flight test data. The results show that the angular acceleration obtained through the proposed angular acceleration estimation method meets the accuracy requirement of aerodynamic parameter identification. [ABSTRACT FROM AUTHOR]

The micro inertial measurement unit (MIMU) is widely used in various fields such as aerospace, automotive industry, smartphones, and wearable devices. Field calibration is the key to ensuring measurement accuracy and reliability. To address the complex problem of solving calibration parameters for MIMU system errors in nonlinear optimization calibration methods, an alternating minimization algorithm based on soft thresholding updates was proposed in this paper. An accelerometer error model was established regarding gravity, and the gyroscope error model was established on the basis of the calibrated acceleration and rotation angular velocity in this method. Finally, the error models were used in convex optimization to design a simplified solution process. Compared with the Gauss-Newton algorithm, the scale factor calibration accuracy was improved by one order of magnitude, and in the nonorthogonal error and bias error calibration, the accuracy was improved by one to two orders of magnitude. Compared with the calibration method using a high-precision turntable with an accuracy of 0.001 °/s, the proposed method achieved an accuracy of 10-3 through manual calibration. It can still maintain stability with initial values of different orders of magnitude, and ultimately, the global optimal solution for the error was obtained. [ABSTRACT FROM AUTHOR]

Storytelling is one of the most important learning activities for children since reading aloud from a picture book stimulates children's curiosity, emotional development, and imagination. For effective education, the procedures for storytelling activities need to be improved according to the children's level of curiosity. However, young children are not able to complete questionnaires, making it difficult to analyze their level of interest. This paper proposes a method to estimate children's curiosity in picture book reading activities at five levels by recognizing children's behavior using acceleration and angular velocity sensors placed on their heads. We investigated the relationship between children's behaviors and their levels of curiosity, listed all observed behaviors, and clarified the behavior for estimating curiosity. Furthermore, we conducted experiments using motion sensors to estimate these behaviors and confirmed that the accuracy of estimating curiosity from sensor data is approximately 72%. [ABSTRACT FROM AUTHOR]

• Why was this study conduted? • Some study investigated probe motion in simulated settings, but few in a clinical setting during second trimester scans. We hypothesized that probe trajectory might differ according to operator's level, fetal presentation (e.g., cephalic or breech) or patient body mass index (BMI). • What does this study add? • In a clinical setting, some differences in the path metrics (velocity, acceleration, jerk and working volume) were noticed according to participant level. However, angular metrics, i.e., the quantity of rotation applied to the probe, were not affected by operator's level, fetal presentations nor patient's BMI. This study aims to investigate probe motion during full mid-trimester anomaly scans. We undertook a prospective, observational study of obstetric sonographers at a UK University Teaching Hospital. We collected prospectively full-length video recordings of routine second-trimester anomaly scans synchronized with probe trajectory tracking data during the scan. Videos were reviewed and trajectories analyzed using duration, path metrics (path length, velocity, acceleration, jerk, and volume) and angular metrics (spectral arc, angular area, angular velocity, angular acceleration, and angular jerk). These trajectories were then compared according to the participant level of expertise, fetal presentation, and patient BMI. A total of 17 anomaly scans were recorded. The average velocity of the probe was 12.9 ± 3.4 mm/s for the consultants versus 24.6 ± 5.7 mm/s for the fellows (p = 0.02), the average acceleration 170.4 ± 26.3 mm/s2 versus 328.9 ± 62.7 mm/s2 (p = 0.02), and the average jerk 7491.7 ± 1056.1 mm/s3 versus 14944.1 ± 3146.3 mm/s3 (p = 0.02), the working volume 9.106 ± 4.106 mm3 versus 29.106 ± 11.106 mm3 (p = 0.03), respectively. The angular metrics were not significantly different according to the participant level of expertise, the fetal presentation, or to patients BMI. Some differences in the probe path metrics (velocity, acceleration, jerk and working volume) were noticed according to operator's level. [ABSTRACT FROM AUTHOR]

The current study aimed to assess the protective performance of helmets equipped with multi-directional impact protection system (MIPS) under various oblique impact loads. Initially, a finite element model of a bicycle helmet with MIPS was developed based on the scanned geometric parameters of an actual bicycle helmet. Subsequently, the validity of model was confirmed using the KASK WG11 oblique impact test method. Three different impact angles (30°, 45°, and 60°) and 2 varying impact speeds (5 m/s and 8 m/s) were employed in oblique tests to evaluate protective performance of MIPS in helmets, focusing on injury assessment parameters such as peak linear acceleration (PLA) and peak angular acceleration (PAA) of the head. The results demonstrated that in all impact simulations, both assessment parameters were lower during impact for helmets equipped with MIPS compared to those without. The PAA was consistently lower in the MIPS helmet group, whereas the difference in PLA was not significant in the no-MIPS helmet group. For instance, at an impact velocity of 8 m/s and a 30° inclined anvil, the MIPS helmet group exhibited a PAA of 3225 rad/s2 and a PLA of 281 g. In contrast, the no-MIPS helmet group displayed a PAA of 8243 rad/s2 and a PLA of 292 g. Generally, both PAA and PLA parameters decreased with the increase of anvil angles. At a 60° anvil angles, PAA and PLA values were 664 rad/s2 and 20.7 g, respectively, reaching their minimum. The findings indicated that helmets incorporating MIPS offer enhanced protection against various oblique impact loads. When assessing helmets for oblique impacts, the utilization of larger angle anvils and rear impacts might not adequately evaluate protective performance during an impact event. These findings will guide advancements in helmet design and the refinement of oblique impact test protocols. [ABSTRACT FROM AUTHOR]

INTRODUCTION: To detect early cognitive impairment in community‐dwelling older adults, this study explored the viability of artificial intelligence (AI)‐assisted linear acceleration and angular velocity analysis during walking. METHODS: This cross‐sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini‐Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides. RESULTS: The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets. DISCUSSION: AI‐enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention. Highlights: Artificial intelligence (AI)‐enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community‐dwelling cohort.AI‐assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia. [ABSTRACT FROM AUTHOR]

The hybrid system can extend the range of special vehicles and meet the electrical requirements of on-board equipment. In this paper, the driving force plummet problem of a new two-gear power-split hybrid system was studied during gear switches in a hybrid mode. The dynamic model of a hybrid electric system was established, and the effects of the engine angular acceleration and angular jerk on vehicle power and ride performance were obtained. The optimal ratio of the torque change rate of the motor and engine in the mode switch process was proposed. Considering the battery limitation and the external characteristics of the engine, the method of determining the target speed of the engine during shifting was proposed. Considering the response characteristics of each power source, the dynamic coordinated control strategy of multiple power sources in the mode switch process was proposed. The vehicle dynamics model was established based on the Matlab/Simulink 2020b and verified by simulation and a hardware-in-the-loop (HIL) test. The results show that the dynamic coordinated control strategy can reduce the peak impact by 80.33%, effectively improve the vehicle power and ride performance, and prevent the occurrence of high-current battery charging. [ABSTRACT FROM AUTHOR]

In large-scale satellite constellations, the efficiency of inter-satellite communication is paramount. Traditional topology control strategies, such as the Manhattan configuration, provide stable links but can result in indirect communication paths, affecting the efficiency of information transfer. This paper addresses this issue by proposing an innovative "3 + 1" dynamic topology control scheme. The scheme retains three static links determined by the relative angular velocity and acceleration while introducing a dynamic link based on distance and angular velocity constraints to optimize the link duration and overall network communication efficiency. To address the complexity of matching dynamic links, this paper introduces an elite strategy-based maximum weighted matching algorithm for general graphs. Compared to traditional greedy algorithms, our proposed algorithm significantly improves the link duration and topological stability. Through simulation experiments comparing communication delays between Xiamen and Los Angeles, our results show that the proposed dynamic link scheme substantially reduces the average delay, enhancing the efficiency and flexibility of inter-satellite communication. This research not only extends the duration of inter-satellite links but also provides new perspectives and methodologies for further studies on inter-satellite topology control strategies. [ABSTRACT FROM AUTHOR]

In addressing the challenges of high labor intensity, cost, and potential mechanical damage to banana fruit in orchards, this study presents the design of a banana bunch transport device featuring a lifting mechanism and an automatic fruit shaft bottom-fixing system. The device is tailored to the planting and morphological characteristics of banana bunches, aiming for efficient, low-loss, and labor-saving mechanized transport. Key design considerations included the anti-overturning mechanism and the lifting system based on transportation conditions and the physical dimensions of banana bunches. A dynamic simulation was conducted to analyze the angular velocity and acceleration during the initial conveying stages, forming the basis for the fruit shaft bottom-fixation mechanism. A novel horizontal multi-point scanning method was developed to accurately identify and secure the fruit shaft bottom, complemented by an automated control system. Experimental results showed a 95.83% success rate in identification and fixation, validated by field trials that confirmed the necessity and stability of the fixation mechanism. To enhance the durability of the fruit shaft bottom-fixation mechanism, a multi-factor test was conducted, optimizing the device's maximum travel speed and minimizing the banana bunch's oscillation angle. Field tests showed an oscillation angle of 8.961°, closely matching the simulated result of 9.526°, demonstrating the reliability of the response surface analysis model. This study offers a practical and efficient solution for banana bunch transport in orchards, showcasing significant practical value and potential for wider adoption. [ABSTRACT FROM AUTHOR]

The accurate analysis of human dynamic behavior is very important for overcoming the limitations of movement diversity and behavioral adaptability. In this paper, a wearable device-based human dynamic behavior recognition method is proposed. The method collects acceleration and angular velocity data through a six-axis sensor to identify information containing specific behavior characteristics in a time series. A human movement data acquisition platform, the DMP attitude solution algorithm, and the threshold algorithm are used for processing. In this experiment, ten volunteers wore wearable sensors on their bilateral forearms, upper arms, thighs, calves, and waist, and movement data for standing, walking, and jumping were collected in school corridors and laboratory environments to verify the effectiveness of this wearable human movement recognition method. The results show that the recognition accuracy for standing, walking, and jumping reaches 98.33%, 96.67%, and 94.60%, respectively, and the average recognition rate is 96.53%. Compared with similar methods, this method not only improves the recognition accuracy but also simplifies the recognition algorithm and effectively saves computing resources. This research is expected to provide a new perspective for the recognition of human dynamic behavior and promote the wider application of wearable technology in the field of daily living assistance and health management. [ABSTRACT FROM AUTHOR]

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AbstractThe inertial motion unit (IMU) is an effective tool for monitoring and assessing gait impairment in patients with lumbar disc herniation(LDH). However, the current clinical assessment methods for LDH gait focus on patients’ subjective scoring indicators and lack the assessment of kinematic ability; at the same time, individual differences in the motor function degradation of the healthy and affected lower limbs of LDH patients are also ignored. To solve this problem, we propose an LDH gait feature model based on multi-source adaptive Kalman data fusion of acceleration and angular velocity. The gait phase is segmented by using an adaptive Kalman data fusion algorithm to estimate the attitude angle, and obtaining gait events through a zero-velocity update technique and a peak detection algorithm. Two IMUs were used to analyze the gait characteristics of lumbar disc patients and healthy gait people, including 12 gait characteristics such as gait spatiotemporal parameters, kinematic parameters, gait variability and stability. Statistical methods were used to analyze the characteristic model and verify the biological differences between the healthy affected side of LDH and healthy subjects. Finally, feature engineering and machine learning technology were used to identify the gait pattern of inertial movement units in patients with lumbar intervertebral disc disease, and achieved a classification accuracy of 95.50%, providing an effective gait feature set and method for clinical evaluation of LDH. [ABSTRACT FROM AUTHOR]

Human trajectories can be tracked by the internal processing of a camera as an edge device. This work aims to match peoples' trajectories obtained from cameras to sensor data such as acceleration and angular velocity, obtained from wearable devices. Since human trajectory and sensor data differ in modality, the matching method is not straightforward. Furthermore, complete trajectory information is unavailable; it is difficult to determine which fragments belong to whom. To solve this problem, we newly proposed the SyncScore model to find the similarity between a unit period trajectory and the corresponding sensor data. We also propose a Likelihood Fusion algorithm that systematically updates the similarity data and integrates it over time while keeping other trajectories in mind. We confirmed that the proposed method can match human trajectories and sensor data with an accuracy, a sensitivity, and an F1 of 0.725. Our models achieved decent results on the UEA dataset. [ABSTRACT FROM AUTHOR]

This study proposes the slope of the engine speed trend line (SlopeESTL) as a new misfire detection index for a four-cylinder engine, not limited to four-cylinder engines. SlopeESTL is defined as angular acceleration between the first and the last teeth of each cylinder in the cycle. SlopeESTL is flat and near 0 when the cylinder is normally combusted. A misfire leads to an abrupt decrease under the threshold. While it is affected by post oscillation just after misfires, there is enough margin between SlopeESTL of the misfired cylinder and post-oscillated SlopeESTL. The mean misfire detection and detection fault rates of the SlopeESTL were over 97% and under 1% in this study, making it a good misfire detection index. However, most of the first misfired cylinder on dual cylinder misfire can seldom be detected as a misfire at the high engine speed over 5000 rpm in the single cylinder misfire pattern. This is caused by the inertia force of the crankshaft system and is a demerit for the misfire detection index. It is one of the two RPM slopes used to calculate the Gap and ΔGap slopes proposed by the author. SlopeESTL can be used with Gap and ΔGap slopes, because they have their specific characteristics as misfire detection indices and their combination logic should be studied more. Furthermore, SlopeESTL can be affected by the machining tolerance of the teeth in the sensor wheel, torsional vibration, and non-uniformity in the stroke operation of the piston. However, it uses the tooth time measured using the existing crankshaft position sensor; an additional sensor is not required, which makes it economical. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate walking independence in acute-care hospital patients using neural networks based on acceleration and angular velocity from two walking tests. Forty patients underwent the 10-m walk test and the Timed Up-and-Go test at normal speed, with or without a cane. Physiotherapists divided the patients into two groups: 24 patients who were monitored or independent while walking with a cane or without aids in the ward, and 16 patients who were not. To classify these groups, the Transformer model analyzes the left gait cycle data from eight inertial sensors. The accuracy using all the sensor data was 0.836. When sensor data from the right ankle, right wrist, and left wrist were excluded, the accuracy decreased the most. When analyzing the data from these three sensors alone, the accuracy was 0.795. Further reducing the number of sensors to only the right ankle and wrist resulted in an accuracy of 0.736. This study demonstrates the potential of a neural network-based analysis of inertial sensor data for clinically assessing a patient's level of walking independence. [ABSTRACT FROM AUTHOR]

The self-starting performance of vertical-axis wind turbines (VAWTs) is crucial for their widespread utilization. Conventional evaluation methods using the static torque coefficient (CTS) or self-starting time have limitations. "The minimum 1st derivative of angular acceleration in the lift acceleration state" is proposed to serve as a suitable indicator for the completion of self-starting. Understanding the behavior of the self-starting process in VAWTs is crucial for optimizing power output. A comprehensive methodology is used that integrates experiments and computational fluid dynamics (CFD). Wind tunnel experiments are conducted to evaluate the self-starting and power output performance of the turbines. CFD is employed utilizing the Fluent 6DOF module to investigate the torque and flow field characteristics during the self-starting process. Additionally, the objectives of our study are to investigate the effect of static evaluation methods on the dynamic start-up process and to explore the effects of airfoil type, pitch angle, and inlet wind speed on the self-starting behavior of turbines. The results indicate that a high CTS ensures initial rotation, but the subsequent self-starting time remains independent of this factor. Increasing the pitch angle enhances the self-starting performance. At an inlet speed of 5 m/s, for the NACA2418 airfoil turbine, the self-starting times for pitch angles of 10° and 5° are reduced by 20% and 12%, respectively, compared to that for 0°. The NACA0018 airfoil turbines with pitch angles of 0° and 5° fail to complete selfstarting. The airfoil type also plays a crucial role, with the NACA2418 airfoil demonstrating superior self-starting performance and power performance. Furthermore, the minimum self-starting wind speed of the NACA0018 airfoil turbine was explored and determined be between 5.5 m/s and 6 m/s. The utilization of this novel self-starting evaluation method addresses the limitations of traditional approaches, providing a more universally applicable interpretation of the characteristics of turbine self-starting behavior. [ABSTRACT FROM AUTHOR]

To solve the problems of excessive impact and low efficiency in conventional polynomial curve planning caused by frequent start and stop during the operation of palletizing robots, with the SP-120 series palletizing robots as the research object, the D-H coordinate system for the robot was established by using an improved D-H method, and the forward and inverse kinematic solution was analyzed. A trajectory planning method based on Fourier series expansion was used for point-to-point continuous trajectory planning from the starting point to the target point, and simulation analysis was carried out with MATLAB software. The simulation results show that the angular displacement, angular velocity and angular acceleration curves of each joint based on Fourier series expansion are continuous and stable, and the running trajectory of workspace is smooth, the problem of excessive impact under palletizing conditions. Compared with the quintic polynomial interpolation algorithm, a larger peak angular velocity and a constant peak angular acceleration are obtained, which solves the problem of low efficiency of conventional polynomial curve planning. [ABSTRACT FROM AUTHOR]

Industrial robot is a and multi‐output complex system with strong coupling and high nonlinearity. The motion control accuracy of the system is affected by many factors. To solve the difficulty in establishing the input and output characteristics of robot dynamics modeling, the robot motion model is established through the Lagrangian energy function. At the same time, the nonlinear relationship between angular velocity, angular acceleration, and robot torque is accurately expressed through improved cascaded neural network. In addition, the optimal time planning of the robot's trajectory in joint space is studied using multinomial interpolation method and the particle swarm optimization (PSO). In the simulation experiment, the effect of the proposed dynamic model fitting was outstanding. Under the mixed multinomial difference calculation planning, the angular position trajectories of the three joints changed very smoothly. In the data set application test, the average error of the PSO algorithm was 0.4061 mm and the average task time was 9.101 s, which were lower than other planning algorithms. Experiments showed that the Lagrangian dynamic model analysis based on genetic algorithm cascaded neural network and PSO trajectory scheduling method under mixed multinomial difference had better trajectory planning performance in handling tasks. [ABSTRACT FROM AUTHOR]

This contribution focuses on solving the inverse and forward kinematics in the kinematic analysis of a two-link manipulator model. It involves determining the trajectory of the end effector through fifth-degree polynomial interpolation. Furthermore, given initial and final arm angle values, it computes not only the end effector trajectory but also the angular parameters of individual actuators in both arms. Additionally, it calculates the variations of angular parameters such as rotation angle, angular velocity, and angular acceleration in each kinematic pair. The direct kinematics task is utilized to define the manipulator's workspace. The task is carried out using Matlab software, and the results are presented in the form of graphs and tables. [ABSTRACT FROM AUTHOR]

This study assessed the between-day reliability of change of direction (COD) biomechanics and speed in youth soccer players using inertial measurement units (IMUs) and high-speed video. A soccer-specific COD test including a 180° pivot turn was performed in both directions by 15 elite female (age: 15.3 ± 0.6 years; height 162.6 ± 5.5 cm; body mass: 56.7 ± 7.1 kg) and 22 elite male (age: 15.4 ± 0.5 years; height 169 ± 5.9 cm; body mass: 58.5 ± 8.5 kg) youth soccer players in two consecutive days. The reliability of the variables was quantified by using intra-class correlation coefficient (ICC) analysis with limits of agreement (LoA) and Bland–Altman plots. Based on the results, neither peak resultant acceleration (PRA) nor peak angular velocity (PAV) during final foot contact was sufficiently reliable for 180° pivot turn biomechanics (poor reliability, < 0.5), but the reliability of players' running time to turn and total time was between acceptable to good (0.9>; > 0.7). However, when analyzing females and males separately, the PRA and angular velocity ICC's for females had poor to acceptable reliability, when turning left, and were statistically different from males (Z -score > 1.96). Acceptable to good reliability with reasonable (max 15% difference) LoA implies that speed measures in different phases of COD could reliably reproduce individual differences in 180° pivot turn COD speed. Sex-related differences in repeatability of acceleration and angular velocity call for more comprehensive research in the future. PAV and PRA would not be recommended for the purpose of analyzing individual repeatability of specific steps of COD movement. [ABSTRACT FROM AUTHOR]

This article explores the significance of friction in mechanics textbooks and the various techniques used to measure coefficients of friction. It specifically focuses on the tilting-plane method, which is commonly employed in classroom teaching to measure the static friction coefficient. Additionally, the article introduces an enhanced version of the tilting-plane method that can measure static friction, kinetic friction, and rolling resistance coefficients in a single test. The setup for this method is simple to construct and suitable for classroom experiments. The article provides comprehensive instructions on how to set up and conduct this method. Furthermore, the text discusses a method for measuring friction coefficients between different surfaces using an experiment involving a metal coin and an inclined plane. By measuring the coin's acceleration as it rolls down the plane at various angles and analyzing the data through graphing, the coefficients of static friction (µs), kinetic friction (µk), and rolling resistance (CR) can be determined. This method can be applied to other objects like spheres, cylinders, or rings, and can aid students in comprehending the characteristics of friction and how to measure friction coefficients. [Extracted from the article]

Abstract Although global navigation satellite systems (GNSS) have been routinely applied to determine attitudes, there exists no literature on determining angular velocity and/or angular acceleration from GNSS. Motivated by the invention of computerized accelerometers of the correspondence author and following the success of accurately recovering translational velocity and acceleration waveforms from very high-rate GNSS precise positioning by Xu and his collaborators in 2021, we propose the concept of GNSS gyroscopes and reconstruct angular velocity and acceleration from very high-rate GNSS attitudes by applying regularization under the criterion of minimum mean squared errors. The major results from the experiments can be summarized in the following: (i) angular velocity and acceleration waveforms computed by applying the difference methods to high-rate GNSS attitudes are too noisy and can be physically not meaningful and numerically incorrect. The same can be said about inertial measurement unit (IMU) attitudes, if IMU gyros are not of very high accuracy; (ii) regularization is successfully applied to reconstruct the high-rate angular velocity and acceleration waveforms from 50 Hz GNSS attitudes and significantly outperforms the difference methods, validating the proposed concept of GNSS gyroscopes. By comparing the angular velocity and acceleration results by using the difference methods and regularization, we find that the peak values of angular velocity and acceleration by regularization are much smaller by a maximum factor of 1.57 in the angular velocity to a maximum factor of 8662.53 times in the angular acceleration in the case of high-rate GNSS, and by a maximum factor of 1.26 in the angular velocity to a maximum factor of 2819.85 times in the angular acceleration in the case of IMU, respectively; and (iii) the IMU attitudes apparently lead to better regularized angular velocity and acceleration waveforms than the high-rate GNSS attitudes, which can well be explained by the fact that the former is of better accuracy than the latter. As a result, to suppress the significant amplification of noise in GNSS attitudes, larger regularization parameters have to be chosen for the high-rate GNSS attitudes, resulting in smaller peak angular accelerations by a maximum factor of 37.55 percent in the angular velocity to a maximum factor of 6.20 times in the angular acceleration in comparison of the corresponding IMU results. Nevertheless, the regularized angular acceleration waveforms for both GNSS and IMU look more or less similar in pattern or waveform shape.

The study proposes a Kalman Filter Family-schemed algorithm to identify the vehicle's parameters and evaluate the road roughness simultaneously, based on the response of an adapted monitoring vehicle. The vehicle is simplified as a three-dimensional multi-body system consisting of the unsprung mass of wheels and the sprung mass of the car body, connected by springs and dampers that represent the suspension system. Accelerometers and a gyroscope measure the accelerations or angular velocity of different positions of the vehicle during vibrations. The Kalman filter (KF) with unknown inputs enables the prediction of the car body's accelerations from those of wheels. On the other hand, the Augmented KF can compute the accelerations of wheels based on the car body's accelerations. Thus, an inner-systemic objective function incorporating the vehicle's parameter is built between the predicted and measured accelerations and solved in an optimized manner with the Genetic Algorithm, so that an individual complicated impact test or bump test is avoided. The numerical examples carry out field tests on a 900 m long standardized test road under two scenarios of with and without a bump. The road roughness estimated by these two KF methods from different measurements is used to calculate the International Roughness Index (IRI) and further the Riding Quality Index, which are compared with those directly provided by a standardized laser IRI profilometer. Accordance in between confirms the accuracy of the proposed algorithm under different scenarios. The analysis underlines the measurements from unsprung wheels that contact directly with the road enable a more accurate estimation of road roughness than those from the sprung car body. In addition, a vehicle speed lower than 40 km/h can provide a better estimation of the road profiles. [ABSTRACT FROM AUTHOR]

Observations of wind distribution in the eye of tropical cyclones (TCs) are still limited. In this study, a method to derive atmospheric motion vectors (AMVs) for TCs is developed, where selection from multiple local rotation speeds is made by considering continuity among neighboring grid points. The method is applied to 2.5‐min interval image sequences of three TCs, Lan (2017), Haishen (2020), and Nanmadol (2022), observed by the Himawari‐8 satellite. The results are compared with AMVs derived from research‐based 30‐s Himawari‐8 special observations conducted for Haishen and Nanmadol, as well as with in‐situ dropsonde observations conducted for Lan and Nanmadol. In these storms, the AMVs obtained from the 2.5‐min interval images in the eye are found to be in good agreement with the dropsonde observations. Examinations of AMVs in the eye reveal transient azimuthal wavenumber‐1 features in all three TCs. These features are consistent with algebraically growing wavenumber‐1 disturbances, which transport angular momentum inward and accelerate the eye rotation. In the case of Lan, the angular velocity in the eye increased by approximately 1.5 times within 1 hr. This short‐term increase is further examined. Visualization of low‐level vorticity in the eye and angular momentum budget analysis suggest that angular momentum transport associated with mesovortices played an important role in the increase of tangential wind and the homogenization of angular velocity in the eye of Lan. Plain Language Summary: Observations of winds in the eye of tropical cyclones (TCs) are still limited. In this study, a new method is developed to derive the winds by objectively tracking the clouds in the eye of TCs using geostationary meteorological satellite imagery. The method is applied to 2.5‐min interval image sequences of three TCs observed by the Himawari‐8 satellite. The estimated winds in the eye are found to be in good agreement with in‐situ dropsonde observations. Examinations of asymmetric motions in the eye reveal transient azimuthal wavenumber‐1 features in all three TCs. These features contribute to the inward transport of angular momentum and acceleration of eye rotation. In the eye of Typhoon Lan (2017), the rotation speed increased by about 1.5 times within an hour. The study further examines this short‐term acceleration and suggests that angular momentum transport associated with mesoscale vortices played an important role in the measured increase in rotation speed and the homogenization of the rotation in the eye. Key Points: A novel cloud‐tracking method for deriving atmospheric motion vectors specifically for tropical cyclones has been developedThe low‐level winds in the eye of tropical cyclones were estimated from the Himawari‐8 satellite with an error of approximately 1–2 m/sA rapid increase in angular velocity was observed in an eye when the angular momentum transport associated with mesovortices was focused [ABSTRACT FROM AUTHOR]

This paper highlights the need for intelligent analysis of students' behavioral states in physical education tasks. The hand-ring inertial data is used to identify students' motion sequence states. First, statistical feature extraction is performed based on the acceleration and angular velocity data collected from the bracelet. After completing the filtering and noise reduction of the data, we perform feature extraction by Back Propagation Neural Network (BPNN) and use the sliding window method for analysis. Finally, the classification capability of the model sequence is enhanced by the Hidden Markov Model (HMM). The experimental results indicate that the classification accuracy of student action sequences in physical education exceeds 96% after optimization by the HMM method. This provides intelligent means and new ideas for future student state recognition in physical education and teaching reform. [ABSTRACT FROM AUTHOR]

This research purposes were (1) explained the difference in the effect of regular strength and core strength training programs on the results of football long pass based on biomechanics, (2) explain the difference in the results of football long pass between players who have high and low eye-foot coordination based on biomechanics, (3) the effect of the interaction between the training program and eye-foot coordination on the long passes football results. This research used an experimental method with a 2 x 2 factorial design. A total of 12 football players from the Faculty of Sports, Sebelas Maret University, Surakarta, aged 19.25±0.62 years, participated in this research. Each participant underwent an eye-foot coordination test and a long football pass. Data were analyzed using two-way ANOVA with a significance level of 0.05. Based on the research results, it can be concluded as follows: (1) there is a significant difference in effect of regular strength training program and core strength training program in improving football long pass results (Fcount = 6.323, p-value = 0.036). The effect of the core strength training program is better than the regular strength training program in improving the results of long pass football. Seen from the biomechanics, the improvement of football long pass results was supported by a lower angle of knee flexion of the backswing kick leg and a greater angle of knee extension of the frontswing kick leg, a lower angle of inclination of the kicking leg and supporting leg at impact, a higher angle of inclination of the hip and shoulder during impact. Then this movement produces frontswing angular speed and acceleration, although the result is longer in the frontswing movement, but it was done with greater force, effort, and power, this produces greater momentum and impulse and faster ball. The core strength training program group produced higher energy and ball height and the ball was closer to the best target with an accuracy closer to 0.09 m. This movement ends with a smaller follow-through of hip angle, faster angular follow-through speed and deceleration, (2) there is a significant difference in effect between high-foot coordination and low-foot coordination on long pass football long pass football (Fcount = 10.452, p-value = 0.012. The improvement of long pass results in football in players who have high eye-foot coordination is better than those who have low eye-foot coordination. Seen from the biomechanics, the improvement of football long pass results is supported by a lower angle of knee flexion of the backswing leg and the angle of extension of the knee of the frontswing leg, a lower angle of inclination of the kicking leg and supporting leg during impact, a higher angle of inclination of the hip and shoulder at the time of impact. Then this movement produces faster speed and angular acceleration in the frontswing movement, as well as greater force and power. However, the efforts made are not big so the momentum and impulse produced are smaller and the ball is also longer. With this support, players who have high eye-foot coordination produce lower energy and ball height, aiming to better control the fall of the ball, so that the fall of the ball is closer to the best target with a closer accuracy of 0.96 m. This movement, also ends with a smaller follow-through hip angle, as well as a faster angular follow-through speed and deceleration, (3) there is a significant interaction between the training program and eye-foot coordination for long pass football for long pass football (Fcount = 18.581, p-value = 0.003. Players who have high eye-foot coordination are more suitable if given a core strength training program. Players who have low eye-foot coordination are more suitable if given a regular strength training program. [ABSTRACT FROM AUTHOR]