Force Matching Research Articles

Neural network-assisted model of interfacial fluids with explicit coarse-grained molecular structures.

Interfacial fluids are ubiquitous in systems ranging from biological membranes to chemical droplets and exhibit a complex behavior due to their nonlinear, multiphase, and multicomponent nature. The development of accurate coarse-grained (CG) models for such systems poses significant challenges, as these models must effectively capture the intricate many-body interactions, both inter- and intramolecular, arising from atomic-level phenomena, and account for the diverse density distributions and fluctuations at the interface. In this study, we use advanced machine learning techniques incorporating force matching and diffusion probabilistic models to construct a robust CG model of interfacial fluids. We evaluate our model through simulations in various settings, including the water-air interface, bulk decane, and dipalmitoylphosphatidylcholine monolayer membranes. Our results show that our CG model accurately reproduces the essential many-body and interfacial properties of interfacial fluids and proves effective across different CG mapping strategies. This work not only validates the utility of our model for multiscale simulations, but also lays the groundwork for future improvements in the simulation of complex interfacial systems.

Working memory load increases movement-related alpha and beta desynchronization

Working memory (WM) load has been well-documented to impair selective attention and inhibitory control. However, its effects on motor function remain insufficiently explored. To extend the existing literature, we investigated the impact of WM load on force control and movement-related brain activity. Sixteen healthy young participants performed a visual static force matching task using a pinch grip under varying WM loads. The task included low and high WM load conditions (memorizing one digit or six digits), and the precision level required to control force was adjusted by manipulating visual gain (low vs. high visual gains), with higher visual gain necessitating more precise force control. Peri-movement alpha and beta event-related desynchronization (ERD), along with force accuracy and steadiness, were measured using electroencephalography recorded over the central areas during the force control task. Results indicated that while force accuracy and steadiness significantly improved with higher visual gain, there was no significant effect of WM load on these measures. Alpha and beta ERD were greater under high than low visual gain, and also greater under high than low WM load. These findings suggest that in young adults, increased WM load leads to compensatory increases in sensorimotor cortical activity to mitigate potential declines in static force control performance that may result from the depletion of neural resources caused by WM load. Our findings extend current understanding of the interaction between WM and sensorimotor processes by offering new insights into how movement-related brain activity is influenced by heightened WM load.

Flocking analysis and control of a nonlinear collective migration model

Abstract In this paper, we study a nonlinear collective migration model with the Cucker–Smale type weight, the nonlinear velocity coupling and the distributed network. Finite-time flocking tracking can be achieved by the alignment force gathering agents and the tracking force matching target. A trade-off existing between the two forces is established by the tracking strategy that can be viewed as a control. When the strategy is time-invariant, finite-time flocking tracking would occur for any initial state under the long-range weight, only for partial initial state under the short-range weight. An invariant set of the system is proposed and proved to be an attractive domain of the flocking state. Then two time-varying strategies, the average strategy and the maximum strategy, are designed to overcome the constraint of initial state under the short-range weight. The average strategy has to mobilize all agents simultaneously, but only causes once switch. The maximum strategy only mobilizes the agent with the largest velocity deviation, but produces more switches. Several numerical simulations are provided to observe the effects of the time-invariant and time-varying strategies.

Unified Sampling and Ranking for Protein Docking with DFMDock.

Diffusion models have shown promise in addressing the protein docking problem. Traditionally, these models are used solely for sampling docked poses, with a separate confidence model for ranking. We introduce DFMDock (Denoising Force Matching Dock), a diffusion model that unifies sampling and ranking within a single framework. DFMDock features two output heads: one for predicting forces and the other for predicting energies. The forces are trained using a denoising force matching objective, while the energy gradients are trained to align with the forces. This design enables our model to sample using the predicted forces and rank poses using the predicted energies, thereby eliminating the need for an additional confidence model. Our approach outperforms the previous diffusion model for protein docking, DiffDock-PP, with a sampling success rate of 44% compared to its 8%, and a Top- 1 ranking success rate of 16% compared to 0% on the Docking Benchmark 5.5 test set. In successful decoy cases, the DFMDock Energy forms a binding funnel similar to the physics-based Rosetta Energy, suggesting that DFMDock can capture the underlying energy landscape.

The Functional Organization of Corticomotor Neurons Within the Motor Cortex Differs Among Basketball and Volleyball Athletes With Patellar Tendinopathy Compared to Asymptomatic Controls.

Patellar tendinopathy (PT) typically affects jumping-sport athletes with functional impairments frequently observed. Alterations to the functional organization of corticomotor neurons within the motor cortex that project to working muscles are evident in some musculoskeletal conditions and linked to functional impairments. We aimed to determine if functional organization of corticomotor neuron projections differs between athletes with PT and asymptomatic controls, and if organization is associated with neuromuscular control. We used a cross-sectional design, and the setting was Monash Biomedical Imaging. Basketball and volleyball athletes with (n = 8) and without PT (n = 8) completed knee extension and ankle dorsiflexion force matching tasks while undergoing fMRI. We determined functional organization via identification of the location of peak corticomotor neuron activation during respective tasks (expressed in X, Y, and Z coordinates) and calculated force matching accuracy for both tasks to quantify neuromuscular control. We observed significant interactions between group and coordinate plane for functional organization of corticomotor projections to knee extensors (p < 0.001) and ankle dorsiflexors (p = 0.016). Compared to controls, PT group peak corticomotor activation during the knee extension task was 9.6 mm medial (p < 0.001) and 5.2 mm posterior (p = 0.036), and during the ankle dorsiflexion task 8.2 mm inferior (p = 0.024). In the PT group, more posterior Y coordinate peak activation location during the knee extension task was associated with greater task accuracy (r = 0.749, p = 0.034). Functional organization of corticomotor neurons differed in jumping athletes with PT compared to controls. Links between functional organization and neuromuscular control in the PT group suggest organizational differences may be relevant to knee extension neuromuscular control preservation.

Reliability of cervicocephalic sense of force

Cervicocephalic proprioception (CCP) is an important assessment item for people with a range of clinical conditions, where reduced CCP is associated with neck pain and imbalance. Reliability has been established for a range of positional and movements tests, but there is limited data regarding sense of force, particularly across three planes of movement. The current test–retest study assessed reliability when evaluating sense of force in healthy adults (8 males, 6 females, mean age 31.50 years [SD 10.14]) over two sessions, 4–7 days apart. A force matching protocol was used to evaluate reliability of absolute error (AE), constant error (CE), and variable error (VE) for 10 % and 25 % maximal voluntary contraction (MVC) target forces for flexion, extension, lateral flexion, and rotation. Participants were strapped to a chair to limit trunk movement and data was captured using a compressive force transducer fixed to an adjustable wall mount. Six trials were performed for each contraction-type, totaling 72 submaximal MVCs per session. ICC estimates for AE (0.15–0.77), CE (0.01–0.85), and VE (0.00–0.83) were varied and confidence intervals were mostly wide. Considering lower limits of confidence intervals, CE had best reliability values generally, but more specifically the most reliable contraction type and movement was 25 % MVC flexion (ICC 0.85, confidence interval 0.54–0.95). This study found that reliability for sense of force testing was dependent upon contraction, type of error, and target force utilized. Further reliability analysis should be performed when applying this test to measure validity outcomes in clinical populations.

Real-time indoor tracking for augmented reality using computer vision technique

In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces

Coupled vibration analysis of the spacecraft with the flexible shaft and solar panels assembly

Dynamical characteristics of the spacecraft with flexible components are investigated in this paper. The component consists of a flexible shaft and solar panels, where solar panels are completely fixed on the shaft. A novel power series multiplier polynomial method is proposed to describe the connecting condition between the shaft and solar panels. The deformation and force matching conditions between the one dimensional shaft and two dimensional panels are established, which is the major contribution about the multidimensional combined structure of this study. According to constraining boundary conditions of the shaft and solar panels, the bending-torsion coupled deformation of the component is expressed. The attitude maneuvering of the central platform and the elastic vibration of the component cause the rigid-flexible coupled effect. Then the discrete dynamical equation of the spacecraft is obtained by using the rigid-flexible coupled modal shapes. The correctness of the proposed method is verified by comparing the natural characteristics with that of the finite element model. The relative error of the higher order frequency is not more than 4%. Numerical results show that the degree of the bending angle polynomial should be at least quadratic to ensure the accuracy of the calculation. The proposed power series multiplier polynomial breaks through the barriers that the traditional Lagrange multiplier cannot express the continuous constraint. It is an effective method to fit the constrained boundary by the polynomial rather than by the discrete multipoint method.

Force matching: motor effects that are not reported by the actor.

We explored unintentional drifts of finger forces during force production and matching task. Based on earlier studies, we predicted that force matching with the other hand would reduce or stop the force drift in instructed fingers while uninstructed (enslaved) fingers remain unaffected. Twelve young, healthy, right-handed participants performed two types of tasks with both hands (task hand and match hand). The task hand produced constant force at 20% of MVC level with the Index and Ring fingers pressing in parallel on strain gauge force sensors. The Middle finger force wasn't instructed, and its enslaved force was recorded. Visual feedback on the total force by the instructed fingers was either present throughout the trial or only during the first 5s (no-feedback condition). The other hand matched the perceived force level of the task hand starting at either 4, 8, or 15s from the trial initiation. No feedback was ever provided for the match hand force. After the visual feedback was removed, the task hand showed a consistent drift to lower magnitudes of total force. Contrary to our prediction, over all conditions, force matching caused a brief acceleration of force drift in the task hand, which then reached a plateau. There was no effect of matching on drifts in enslaved finger force. We interpret the force drifts within the theory of control with spatial referent coordinates as consequences of drifts in the command (referent coordinate) to the antagonist muscles. This command is not adequately incorporated into force perception.

Water Potential from Adaptive Force Matching for Ice and Liquid with Revised Dispersion Predicts Supercooled Liquid Anomalies in Good Agreement with Two Independent Experimental Fits.

A revised version of the Water potential from Adaptive force matching for Ice and Liquid (WAIL) was developed by using the previous data set for fitting the WAIL model but with a dispersion term calculated using symmetry adapted perturbation theory (SAPT). The model has no adjustable parameters and relies solely on fitting first-principles information. The new model, named revised WAIL (rWAIL), shows improved predictions of most properties of water when compared to the previously published WAIL model. The rWAIL model also compares favorably to other first-principles-derived water models, such as MB-Pol, at only a fraction of the computational cost. The rWAIL model is used to study the properties of supercooled water. The model shows evidence of a liquid-liquid phase transition (LLPT) in the supercooled regimes with the liquid-liquid critical point (LLCP) at 203 K and 90 MPa. This estimate is in good agreement with a recent polynomial fit to the experimental density of water. Also, the fit to the surface tension of supercooled water based on the rWAIL model shows excellent agreement with the corresponding fit to the experimental data. Consistent with previously published molecular dynamics and experimental data, the surface tension of water exhibits exponential growth in the supercooled regime, which is likely a result of the emergence of a low-density liquid form of water. The simulation thus unites two separate experimental fits with one first-principles-based model, lending strong evidence of an LLPT in real water.

Force matching and iterative Boltzmann inversion coarse grained force fields for ZIF-8.

Despite the intense activity at electronic and atomistic resolutions, coarse grained (CG) modeling of metal-organic frameworks remains largely unexplored. One of the main reasons for this is the lack of adequate CG force fields. In this work, we present iterative Boltzmann inversion and force matching (FM) force fields for modeling ZIF-8 at three different coarse grained resolutions. Their ability to reproduce structure, elastic tensor, and thermal expansion is evaluated and compared with that of MARTINI force fields considered in previous work [Alvares et al., J. Chem. Phys. 158, 194107 (2023)]. Moreover, MARTINI and FM are evaluated for their ability to depict the swing effect, a subtle phase transition ZIF-8 undergoes when loaded with guest molecules. Overall, we found that all our force fields reproduce structure reasonably well. Elastic constants and volume expansion results are analyzed, and the technical and conceptual challenges of reproducing them are explained. Force matching exhibits promising results for capturing the swing effect. This is the first time these CG methods, widely applied in polymer and biomolecule communities, are deployed to model porous solids. We highlight the challenges of fitting CG force fields for these materials.

How close are the classical two-body potentials to abinitio calculations? Insights from linear machine learning based force matching.

In this work, we propose a linear machine learning force matching approach that can directly extract pair atomic interactions from abinitio calculations in amorphous structures. The local feature representation is specifically chosen to make the linear weights a force field as a force/potential function of the atom pair distance. Consequently, this set of functions is the closest representation of the abinitio forces, given the two-body approximation and finite scanning in the configurational space. We validate this approach in amorphous silica. Potentials in the new force field (consisting of tabulated Si-Si, Si-O, and O-O potentials) are significantly different than existing potentials that are commonly used for silica, even though all of them produce the tetrahedral network structure and roughly similar glass properties. This suggests that the commonly used classical force fields do not offer fundamentally accurate representations of the atomic interaction in silica. The new force field furthermore produces a lower glass transition temperature (Tg ∼ 1800K) and a positive liquid thermal expansion coefficient, suggesting the extraordinarily high Tg and negative liquid thermal expansion of simulated silica could be artifacts of previously developed classical potentials. Overall, the proposed approach provides a fundamental yet intuitive way to evaluate two-body potentials against abinitio calculations, thereby offering an efficient way to guide the development of classical force fields.

Selective and Controlled Release Responsive Nanoparticles with Adsorption-Pairing Synergy for Anthocyanin Extraction.

Anthocyanins with different structures have different anti-inflammatory and anti-cancer properties. Precise structural use can improve the chemopreventive effects of anthocyanins and enhance treatment outcomes because the anthocyanin structure influences its functional sites and activities. However, owing to the available variety of anthocyanins and their complex structures, the low matching of intermolecular forces between existing adsorbents and anthocyanins limits the targeted separation of anthocyanin monomers. Short-range and efficient selective binding, which is difficult to achieve, is the current focus in the extraction field. We here developed self-assembled Fe3O4-based nano adsorbers with different surface modifications based on adsorption-pairing synergy. The electrostatic force, coordination bond, hydrogen bond, and π-π* bond together induced selective adsorption between Fe3O4 nanoparticles and anthocyanin molecules. An acid-release solution disrupted the polarity balance in the aforementioned association system, thereby promoting the controlled release of anthocyanins. Among the candidates, the effects of morphology, particle size, surface charge, and functional group on adsorption performance were analyzed. The polyacrylamide-modified magnetic Fe3O4 nanoparticles were found to be favorable for selectively extracting anthocyanin, with an adsorption capacity of 19.74 ± 0.07 mg g-1. The release percentage of cyanidin-3-O-glucoside reached up to 98.6% ± 1.4%. This study offers a scientific basis for developing feasible nanotechniques to extract anthocyanins and plant active substances.

Pinch force sense test–retest reliability evaluation using contralateral force matching task

A high test–retest reliability in measurement of pinch force sense is required to assess a clinical parameter accurately over a longitudinal study. Ipsilateral reproduction (IR) task and contralateral matching (CM) task have commonly been used for the assessment of force sense. To date, there has been little research on the test–retest reliability of pinch force sense utilizing the contralateral force matching task. This research aimed to explore this phenomenon across a spectrum of reference force levels (10, 30, and 50 percent maximum voluntary isometric contraction (MVIC)) using a contralateral matching task. Every participant in the study was tested twice by the same skilled experts, with each session separated by one week. Although normalized variable error indicated a poor level of reliability (intraclass correlation coefficient (ICC) = − 0.25 to 0.05) for these force sense tests, normalized constant error (ICC = 0.76–0.85) and normalized absolute error (ICC = 0.61–0.81) results indicated a fair to good of reliability. The lower bound of 95% CI of ICC for NAE and NCE indicated fair test–retest reliability (0.41–0.69). These findings suggest that investigators can reasonably obtain a fair to good test–retest reliability when investigating pinch force sense using the contralateral matching task. The Bland–Altman plots, SEM, and MDD95% were lower at these lower reference force level (10% MVIC) compared to the level of higher reference forces (30% and 50% MVIC). Therefore, when the reference force level increases, the participant needs a larger NAE or NCE decrease to show that their pinch force sense has indeed improved.

Modified Keypoint-Based Copy Move Area Detection

Detecting and identifying manipulated portions within images poses a formidable challenge in research. Manipulated images, often created using image editing software such as Picasa or Photoshop, serve to obscure information and intentionally mislead viewers. Consequently, ensuring the authenticity of images becomes imperative prior to extracting meaningful data. One prevalent form of tampering is copy-move forgery, where objects are intentionally duplicated within an image using region of the same image. This study introduces a method for detecting copy-move forgery areas based on locating Scale-Invariant Feature Transform (SIFT) keypoints in images. The SIFT technique is employed for feature extraction, while feature descriptors are matched using brute force matching. Subsequently, a clustering algorithm is applied to group spatially proximate keypoints, enabling the detection of cloned regions. Specifically, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is utilized to identify forged areas within the image. To mitigate erroneous forgery detection and reduce false positives, outlier detection techniques are employed. Experimental evaluations are conducted on the MICC-F220 and MICC-F600 datasets, with comparisons drawn against previously established methodologies.

Accuracy of Force Generation and Preparatory Prefrontal Oxygenation in Ballistic Hand Power and Precision Grips

It remains unclear whether accurate motor performance and cortical activation differ among grasping forms across several force levels. In the present study, a ballistic target force matching task (20%, 40%, 60%, and 80% of maximum voluntary force) with power grip, side pinch, and pulp pinch was utilized to explore the accuracy of the forces generated as well as the muscular activity of intrinsic and extrinsic hand muscles. By using near-infrared spectroscopy, we also examined bilateral dorsolateral prefrontal cortex (DLPFC) activation during the preparatory phase (initial 10 s) of the task. The accuracy of the power grip and pulp pinch was relatively higher than that of the side pinch, and the electromyographic activity of intrinsic hand muscles exhibited a similar trend for power grip and side pinch, while the opposite muscle recruitment pattern was observed for pulp pinch. The increment of DLPFC oxygenation across force levels differed among grasping forms, with greater activity at relatively higher levels in the power grip and side pinch, and at relatively lower levels in the pulp pinch. Taken together, the differential contribution of the DLPFC may be responsible for force generation depending on different grasping forms and force levels.

Effects of a 12-Week Low-Intensity Resistance Training Program on Force-Matching Task and Balance in Young Men

Background: The effects of low-intensity resistance training on health and muscular performance have been widely reported, but its effects on motor skills such as balance and force matching have been overlooked. Hence, the purpose of this study was to determine the effects of low-intensity resistance training on a force-matching task and balance. Methods: The subjects from the intervention group (EXP; n = 20) participated in a 12-week low-intensity resistance training program. The measurements of balance and force-matching ability were conducted before and after the intervention. To determine the accuracy and steadiness (variability) in the force matching task, we calculated the values of three errors: (1) absolute error (AE), (2) constant error (CE), and (3) variable error (VE). Results: In the force-matching task performed after the training, the values of two errors decreased: (1) AE (right leg, p = 0.0008; left leg, p = 0.0008), and (2) CE (right leg, p = 0.0064; left leg, p = 0.0440). Resistance training did not significantly affect VE and the parameters characterizing COP sway in the balance test. Conclusions: The 12-week low-intensity resistance training improved the accuracy of the force-matching task but did not change postural stability or postural strategies.

OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining.

The "bottom-up" approach to coarse-graining, for building accurate and efficient computational models to simulate large-scale and complex phenomena and processes, is an important approach in computational chemistry, biophysics, and materials science. As one example, the Multiscale Coarse-Graining (MS-CG) approach to developing CG models can be rigorously derived using statistical mechanics applied to fine-grained, i.e., all-atom simulation data for a given system. Under a number of circumstances, a systematic procedure, such as MS-CG modeling, is particularly valuable. Here, we present the development of the OpenMSCG software, a modularized open-source software that provides a collection of successful and widely applied bottom-up CG methods, including Boltzmann Inversion (BI), Force-Matching (FM), Ultra-Coarse-Graining (UCG), Relative Entropy Minimization (REM), Essential Dynamics Coarse-Graining (EDCG), and Heterogeneous Elastic Network Modeling (HeteroENM). OpenMSCG is a high-performance and comprehensive toolset that can be used to derive CG models from large-scale fine-grained simulation data in file formats from common molecular dynamics (MD) software packages, such as GROMACS, LAMMPS, and NAMD. OpenMSCG is modularized in the Python programming framework, which allows users to create and customize modeling "recipes" for reproducible results, thus greatly improving the reliability, reproducibility, and sharing of bottom-up CG models and their applications.

Neck Muscle Vibration Alters Cerebellar Processing Associated with Motor Skill Acquisition of a Proprioceptive-Based Task.

Experimentally induced neck fatigue and neck pain have been shown to impact cortico-cerebellar processing and sensorimotor integration, assessed using a motor learning paradigm. Vibration specifically impacts muscle spindle feedback, yet it is unknown whether transient alterations in neck sensory input from vibration impact these neural processing changes following the acquisition of a proprioceptive-based task. Twenty-five right-handed participants had electrical stimulation over the right median nerve to elicit short- and middle-latency somatosensory evoked potentials (SEPs) pre- and post-acquisition of a force matching tracking task. Following the pre-acquisition phase, controls (CONT, n = 13, 6 F) received 10 min of rest and the vibration group (VIB, n = 12, 6 F) received 10 min of 60 Hz vibration on the right sternocleidomastoid and left cervical extensors. Task performance was measured 24 h later to assess retention. Significant time by group interactions occurred for the N18 SEP peak, 21.77% decrease in VIB compared to 58.74% increase in CONT (F(1,23) = 6.475, p = 0.018, np2 = 0.220), and the N24 SEP peak, 16.31% increase in VIB compared to 14.05% decrease in CONT (F(1,23) = 5.787, p = 0.025, np2 = 0.201). Both groups demonstrated improvements in motor performance post-acquisition (F(1,23) = 52.812, p < 0.001, np2 = 0.697) and at retention (F(1,23) = 35.546, p < 0.001, np2 = 0.607). Group-dependent changes in the SEP peaks associated with cerebellar input (N18) and cerebellar processing (N24) suggests that an altered proprioceptive input from neck vibration impacts cerebellar pathways.

Encoding force modulation in two electrotactile feedback parameters strengthens sensory integration according to maximum likelihood estimation

Bidirectional human–machine interfaces involve commands from the central nervous system to an external device and feedback characterizing device state. Such feedback may be elicited by electrical stimulation of somatosensory nerves, where a task-relevant variable is encoded in stimulation amplitude or frequency. Recently, concurrent modulation in amplitude and frequency (multimodal encoding) was proposed. We hypothesized that feedback with multimodal encoding may effectively be processed by the central nervous system as two independent inputs encoded in amplitude and frequency, respectively, thereby increasing state estimate quality in accordance with maximum-likelihood estimation. Using an adaptation paradigm, we tested this hypothesis during a grasp force matching task where subjects received electrotactile feedback encoding instantaneous force in amplitude, frequency, or both, in addition to their natural force feedback. The results showed that adaptations in grasp force with multimodal encoding could be accurately predicted as the integration of three independent inputs according to maximum-likelihood estimation: amplitude modulated electrotactile feedback, frequency modulated electrotactile feedback, and natural force feedback (r2 = 0.73). These findings show that multimodal electrotactile feedback carries an intrinsic advantage for state estimation accuracy with respect to single-variable modulation and suggest that this scheme should be the preferred strategy for bidirectional human–machine interfaces with electrotactile feedback.