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This preliminary study investigated whether direct measurement of head rotation improves prediction of mild traumatic brain injury (mTBI). Although many studies have implicated rotation as a primary cause of mTBI, regulatory safety standards use 3 degree-of-freedom (3DOF) translation-only kinematic criteria to predict injury. Direct 6DOF measurements of human head rotation (3DOF) and translation (3DOF) have not been previously available to examine whether additional DOFs improve injury prediction. We measured head impacts in American football, boxing, and mixed martial arts using 6DOF instrumented mouthguards, and predicted clinician-diagnosed injury using 12 existing kinematic criteria and 6 existing brain finite element (FE) criteria. Among 513 measured impacts were the first two 6DOF measurements of clinically diagnosed mTBI. For this dataset, 6DOF criteria were the most predictive of injury, more than 3DOF translation-only and 3DOF rotation-only criteria. Peak principal strain in the corpus callosum, a 6DOF FE criteria, was the strongest predictor, followed by two criteria that included rotation measurements, peak rotational acceleration magnitude and Head Impact Power (HIP). These results suggest head rotation measurements may improve injury prediction. However, more 6DOF data is needed to confirm this evaluation of existing injury criteria, and to develop new criteria that considers directional sensitivity to injury.

With the rapid development of the manufacturing industry, industrial automation equipment represented by computer numerical control (CNC) machine tools has put forward higher and higher requirements for the machining accuracy of parts. Compared with the multi-axis serial platform solution, the parallel platform solution is theoretically more suitable for high-precision machining equipment. There are many parallel platform solutions, but not one can provide a common physical platform to test the effectiveness of a variety of control algorithms. To achieve the goals, this paper is based on the Stewart six degrees of freedom parallel platform, and it mainly studies the platform construction. This study completed the mechanical structure design of the parallel platform. Based on the microprogrammed control unit (MCU) + pre-driver chip + three-phase full bridge solution, we have completed the circuit design of the motor driver. We wrote the program of MCU to drive six parallel robotic arms as well as the program of the parallel platform control center on the PC, and we completed the system joint debugging. The closed-loop control effect of the parallel platform workspace pose is realized.

We proposed a novel jaw movement tracking method that can measure in six degrees of freedom. The magnetic field generated by a permanent magnet paired with a small, low-power-consumption Hall effect magnetic sensor is used to estimate the relative distance between two objects—in this instance, the lower and upper jaws. By installing a microelectromechanical system (MEMS) orientation sensor in the device, we developed a mouthpiece-type sensing device that can measure voluntary mandibular movements in three-dimensional orientation and position. An evaluation of individuals wearing this device demonstrated its ability to measure mandibular movement with an accuracy of approximately 3 mm. Using the movement recording feature with six degrees of freedom also enabled the evaluation of an individual’s jaw movements over time in three dimensions. In this method, all sensors are built onto the mouthpiece and the sensing is completed in the oral cavity. It does not require the fixation of a large-scale device to the head or of a jig to the teeth, unlike existing mandibular movement tracking devices. These novel features are expected to increase the accessibility of routine measurements of natural jaw movement, unrestricted by an individual’s physiological movement and posture.

For real-time detection scenarios such as battlefield reconnaissance and surveillance, where high positioning accuracy is required and receiving station resources are limited, we propose an innovative distributed aerial target localization method with low degrees of freedom. This method is based on a hybrid measurement approach. First, a measurement model is established using the spatial geometric relationship between the distributed node network configuration and the target, with angle of arrival (AOA) and time difference of arrival (TDOA) measurements employed to estimate partial target parameters. Then, frequency difference of arrival (FDOA) measurements are utilized to enhance the accuracy of parameter estimation. Finally, using inter-node measurements, a pseudo-linear system of equations is constructed to complete the three-node aerial target localization. The method uses satellites as radiation sources to transmit signals, with unmanned aerial vehicles (UAVs) acting as receiving station nodes to capture the signals. It effectively utilizes hybrid measurement information, enabling aerial target localization with only three receiving stations. Simulation results validate the significant advantages of the proposed algorithm in enhancing localization accuracy, reducing system costs, and optimizing resource allocation. This technology not only provides an efficient and practical localization solution for battlefield reconnaissance and surveillance systems but also offers robust technical support and broad application prospects for the future development of unmanned systems, intelligent surveillance, and emergency rescue.

The seven-degree-of-freedom space manipulator, characterized by its redundant and aspheric wrist structure, is extensively used in space missions due to its exceptional dexterity and multi-joint capabilities. However, the non-spherical wrist structure presents challenges in solving inverse kinematics, as it cannot decouple joints using the Pieper criterion, unlike spherical wrist structures. To address this issue, this paper presents a closed-form analytical method for solving the inverse kinematics of seven-degree-of-freedom aspheric wrist space manipulators. The method begins by identifying the redundant joint through comparing the volumes of the workspace with different joints fixed. The redundant joint angle is then treated as a parametric joint angle, enabling the derivation of closed-form expressions for the non-parametric joint angles using screw theory. The optimal solution branch is identified through a comparative analysis of various self-motion manifold branches. Additionally, a hybrid approach, combining analytical and numerical methods, is proposed to optimize the parametric joint angle for a trajectory tracking task. Simulation results confirm the effectiveness of the proposed method.

The kinetostatic compliance method (KCM) models protein molecules as nanomechanisms consisting of numerous rigid peptide plane linkages. These linkages articulate with respect to each other through changes in the molecule dihedral angles, resulting in a kinematic mechanism with hyper degrees of freedom. Within the KCM framework, nonlinear interatomic forces drive protein folding by guiding the molecule’s dihedral angle vector towards its lowest energy state in a kinetostatic manner. This paper proposes a numerical integrator that is well suited to KCM-based protein folding and overcomes the limitations of traditional explicit Euler methods with fixed step size. Our proposed integration scheme is based on pseudo-transient continuation with an adaptive step size updating rule that can efficiently compute protein folding pathways, namely, the transient three-dimensional configurations of protein molecules during folding. Numerical simulations utilizing the KCM approach on protein backbones confirm the effectiveness of the proposed integrator.

Prime structures are link chains with 0 DoF (degrees of freedom), not including subchains with 0 or fewer DoF, which are expected to be used in systematic kinematic and dynamic analyses of link mechanisms. This paper describes the exhaustive enumeration of spatial prime structures with three–five links. There will be more types of spatial prime structures than planar prime structures due to the variety in the DoF of kinematic pairs and the existence of prime structures with idle DoF. In the enumeration, the graphs which represent the connection of links and pairs are used. The vertices of the graphs represent the links of structures, and the edges represent the kinematic pairs. To consider the pair DoF, weights are set on edges. First, the numbers of pairs are calculated using Grübler’s equation. Second, the graphs corresponding to structures are enumerated, without considering pair DoF, and the isomorphic graphs and other inappropriate graphs are eliminated. Then, the combinations of pair DoF arrangement are enumerated, and the isomorphic graphs and graphs which correspond to non-prime structures are eliminated. Finally, prime structures with idle DoF are considered. As a result, 3, 13, and 97 kinds of spatial prime structures for 3, 4, and 5 links, respectively, are obtained.

This paper proposes a unified reliability analysis framework for mechanical and structural systems equipped with Tuned Mass Dampers (TMDs), encompassing single-degree-of-freedom (1-DOF), two-degrees-of-freedom (2-DOF), and ten-degrees-of-freedom (10-DOF) configurations. The methodology integrates four main components: (i) probabilistic uncertainty modeling for mass, damping, and stiffness, (ii) Latin Hypercube Sampling (LHS) to efficiently explore parameter variations, (iii) Monte Carlo simulation (MCS) for estimating failure probabilities under stochastic excitations, and (iv) machine learning models, including Random Forest (RF), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Neural Networks (NNs), to predict structural responses and failure probabilities. The results demonstrate that ensemble methods, such as RF and XGBoost, provide high accuracy and can effectively identify important features. Neural Networks perform well for capturing nonlinear behavior, although careful tuning is required to prevent overfitting. The framework is further extended to a 10-DOF structure, and the simulation results confirm that machine learning-based models are highly effective for large-scale reliability analysis. These findings highlight the synergy between simulation methods and data-driven models in enhancing the reliability of TMD systems under uncertain inputs.

In recent years, the field of psychology has begun to conduct replication tests on a large scale. Here, we show that “replicator degrees of freedom” make it far too easy to obtain and publish false-negative replication results, even while appearing to adhere to strict methodological standards. Specifically, using data from an ongoing debate, we show that commonly exercised flexibility at the experimental design and data analysis stages of replication testing can make it appear that a finding was not replicated when, in fact, it was. The debate that we focus on is representative, on key dimensions, of a large number of other replication tests in psychology that have been published in recent years, suggesting that the lessons of this analysis may be far reaching. The problems with current practice in replication science that we uncover here are particularly worrisome because they are not adequately addressed by the field’s standard remedies, including preregistration. Implications for how the field could develop more effective methodological standards for replication are discussed.

A new dynamic mode decomposition (DMD) method is introduced for simultaneous system identification and denoising in conjunction with the adoption of an extended Kalman filter algorithm. The present paper explains the extended-Kalman-filter-based DMD (EKFDMD) algorithm which is an online algorithm for dataset for a small number of degree of freedom (DoF). It also illustrates that EKFDMD requires significant numerical resources for many-degree-of-freedom (many-DoF) problems and that the combination with truncated proper orthogonal decomposition (trPOD) helps us to apply the EKFDMD algorithm to many-DoF problems, though it prevents the algorithm from being fully online. The numerical experiments of a noisy dataset with a small number of DoFs illustrate that EKFDMD can estimate eigenvalues better than or as well as the existing algorithms, whereas EKFDMD can also denoise the original dataset online. In particular, EKFDMD performs better than existing algorithms for the case in which system noise is present. The EKFDMD with trPOD, which unfortunately is not fully online, can be successfully applied to many-DoF problems, including a fluid-problem example, and the results reveal the superior performance of system identification and denoising.