Force-based Methods Research Articles

Hidden Water's Influence on Rhodopsin Activation.

Structural biology relies on several powerful techniques, but these tend to be limited in their ability to characterize protein fluctuations and mobility. Over-reliance on structural approaches can lead to omission of critical information regarding biological function. Currently there is a need for complementary biophysical methods to visualize these mobile aspects of protein function. Here we review hydrostatic and osmotic pressure-based techniques to address this shortcoming for the paradigm of rhodopsin. Hydrostatic and osmotic pressure data contribute important examples which are interpreted in terms of an energy landscape for hydration-mediated protein dynamics. We find that perturbations of rhodopsin conformational equilibria by force-based methods are not unrelated phenomena; rather they probe various hydration states involving functional proton reactions. Hydrostatic pressure acts on small numbers of strongly interacting structural or solvent-shell water molecules with relatively high energies, while osmotic pressure acts on large numbers of weakly interacting bulk-like water molecules with low energies. Local solvent fluctuations due to the hydration shell and collective water interactions affect hydrogen-bonded networks and domain motions that are explained by a hierarchical energy landscape model for protein dynamics.

Scaling Properties of Liquid Dynamics Predicted from a Single Configuration: Pseudoisomorphs for Harmonic-Bonded Molecules.

Isomorphs are curves in the thermodynamic phase diagram of invariant excess entropy, structure, and dynamics, while pseudoisomorphs are curves of invariant structure and dynamics, but not of the excess entropy. The latter curves have been shown to exist in molecular models with flexible bonds [Olsen, A. E. J. Chem. Phys. 2016, 145, 241103]. We here present three force-based methods to trace out pseudoisomorphs based on a single configuration and test them on the asymmetric dumbbell and 10-bead Lennard-Jones chain models with bonds modeled as harmonic springs. The three methods are based on requiring that particle forces, center-of-mass forces, and torques, respectively, are invariant in reduced units. For each of the two investigated models we identify a method that works well for tracing out pseudoisomorphs, but these methods are not the same. Overall, we find that the more internal degrees of freedom there are in the molecule studied, the less they appear to affect the gross dynamical behavior. Moreover, the "internal" degrees of freedom (including rotation) do not significantly affect the scaling behavior of the dynamical/transport coefficients provided some 'quenching' is performed.

Generation of continuous and sparse space filling toolpath with tailored density for additive manufacturing of biomimetics

A method of generating a continuous toolpath that can be biased in a user-specified direction of travel is proposed for the fabrication of density-based functionally graded parts through Additive Manufacturing (AM). The methodology utilizes Lin Kernighan's (LK) Travelling Salesman Problem (TSP) solver over a digitized grid within the contour domain to generate a toolpath with minimal lifts and a common start and end point. Three force-based methods of digitization, namely rectangular, circular, and contour adaptive, are proposed in this work. Each of these methods initialize from a structured or an unstructured grid, where the grid points are assumed to be connected with either linear (rectangular digitization) or a combination of linear and torsional springs (circular and contour adaptive digitization). Enforcing an equilibrium amongst the spring forces and appropriately selecting the ideal spring length, the necessary configuration of grid points can be generated for a desired toolpath.The density of grid points (consequently, part density) can be varied through the user-defined input function or an image-based density map imposed on the ideal spring length over the contour domain. The proposed toolpath, as a case study, was implemented for printing a bone with density prescribed through a CT scan image stack. The CT scan of the printed part qualitatively establishes the conformity of the toolpath to the user-specified density gradient.

A review of perception sensors, techniques, and hardware architectures for autonomous low-altitude UAVs in non-cooperative local obstacle avoidance

Unmanned Aerial Vehicles (UAVs) can detect and communicate with cooperative obstacles through established protocols. However, non-cooperative obstacles pose a significant threat to UAVs during low-flight operations. These obstacles include static obstacles like buildings, trees, or communication towers and dynamic objects like other UAVs. The application of autonomous UAVs in low-altitude surveillance has motivated research into non-cooperative local obstacle avoidance. This paper provides an overview of such solutions that have been proposed within the last decade. Unlike most literature that limits obstacle avoidance to algorithms, this work provides an in-depth review of obstacle avoidance components, namely the perception sensor, techniques, and hardware architecture of the obstacle avoidance system. This review categorizes the non-cooperative obstacle avoidance techniques into four groups: gap-based methods, geometric methods, repulsive force-based methods, and Artificial Intelligence (AI) based methods. This paper provides a comprehensive resource for researchers working on collision-free surveillance by autonomous UAVs at low altitudes.

RL-LABEL: A Deep Reinforcement Learning Approach Intended for AR Label Placement in Dynamic Scenarios.

Labels are widely used in augmented reality (AR) to display digital information. Ensuring the readability of AR labels requires placing them in an occlusion-free manner while keeping visual links legible, especially when multiple labels exist in the scene. Although existing optimization-based methods, such as force-based methods, are effective in managing AR labels in static scenarios, they often struggle in dynamic scenarios with constantly moving objects. This is due to their focus on generating layouts optimal for the current moment, neglecting future moments and leading to sub-optimal or unstable layouts over time. In this work, we present RL-LABEL, a deep reinforcement learning-based method intended for managing the placement of AR labels in scenarios involving moving objects. RL-LABEL considers both the current and predicted future states of objects and labels, such as positions and velocities, as well as the user's viewpoint, to make informed decisions about label placement. It balances the trade-offs between immediate and long-term objectives. We tested RL-LABEL in simulated AR scenarios on two real-world datasets, showing that it effectively learns the decision-making process for long-term optimization, outperforming two baselines (i.e., no view management and a force-based method) by minimizing label occlusions, line intersections, and label movement distance. Additionally, a user study involving 18 participants indicates that, within our simulated environment, RL-LABEL excels over the baselines in aiding users to identify, compare, and summarize data on labels in dynamic scenes.

Human Posture Estimation: A Systematic Review on Force-Based Methods—Analyzing the Differences in Required Expertise and Result Benefits for Their Utilization

Force-based human posture estimation (FPE) provides a valuable alternative when camera-based human motion capturing is impractical. It offers new opportunities for sensor integration in smart products for patient monitoring, ergonomic optimization and sports science. Due to the interdisciplinary research on the topic, an overview of existing methods and the required expertise for their utilization is lacking. This paper presents a systematic review by the PRISMA 2020 review process. In total, 82 studies are selected (59 machine learning (ML)-based and 23 digital human model (DHM)-based posture estimation methods). The ML-based methods use input data from hardware sensors—mostly pressure mapping sensors—and trained ML models for estimating human posture. The ML-based human posture estimation algorithms mostly reach an accuracy above 90%. DHMs, which represent the structure and kinematics of the human body, adjust posture to minimize physical stress. The required expert knowledge for the utilization of these methods and their resulting benefits are analyzed and discussed. DHM-based methods have shown their general applicability without the need for application-specific training but require expertise in human physiology. ML-based methods can be used with less domain-specific expertise, but an application-specific training of these models is necessary.

Simulation of soft tissue deformation under physiological motion based on complementary dynamic method

Background and ObjectivePhysiological motions have a significant impact on soft tissue deformation and accuracy of surgical procedures, which is essential for realistic surgical simulation. While existing studies offer accurate simulation of soft tissue deformation, integrating physiological motions into deformation models of soft tissue remains a challenging task. MethodsThis paper introduces a novel deformation model, based on complementary dynamics, to animate soft tissue deformation under physiological motion. The finite element method is incorporated to accurately characterize the elastic behavior of the soft tissue. Mathematical models of physiological motion are utilized and the physiological effects are converted into displacements of a predefined set of handles within the soft tissue mesh. Complementary displacements derived from the inherent dynamics of the soft tissue are calculated, enabling the simulation of physiological motions and elastic behaviors in soft tissue deformation. ResultsExperiments were conducted to evaluate the performance and effectiveness of the proposed method in simulating soft tissue deformation under physiological motion. The simulation results show that the soft tissues exhibit physiological motion that corresponds to the rhythm of arterial pressure fluctuations, heartbeat or respiratory. Furthermore, the presented method exhibits stable performance compared with existing force-based methods. ConclusionsBoth elastic behaviors and physiological motions of soft tissue deformation can be governed by the proposed method. A high degree of realistic visualization is achieved for virtual surgery simulation.

On the simplified method for evaluating tunnel response due to overlying foundation pit excavation

Foundation pit excavation may cause excessive uplift displacement and structural damage to the underlying shield tunnel. In this paper, the rationality of the excavation-induced inputs in the current force-based and displacement-based methods is analyzed theoretically and verified by comparing the elastic finite element method (FEM) and the elastic continuum solution. Then, the force input is modified by the load amplification factor to obtain a reasonable tunnel response, and the calculation method of the load amplification factor is given. Based on the mechanism of tunnel-soil interaction, the difference between Winkler subgrade moduli under active and passive conditions is analyzed. It is recommended that the active and passive Winkler subgrade moduli should be adopted under the force and displacement inputs, respectively. By comparing the elastic continuum solution and the Winkler solution, the validity of the Winkler subgrade moduli under active and passive conditions is verified. Thus, a force-based Winkler solution with modified force input is proposed. Finally, the proposed method is applied to a case study, and its applicability is verified by comparing the measured results and the FEM analysis with the HS-Small model.

Probabilistic detection of impacts using the PFEEL algorithm with a Gaussian Process Regression Model

Methods for identifying human activity have a wide range of potential applications, including security, event time detection, intelligent building environments, and human health. Current methodologies typically rely on either wave propagation or structural dynamics principles. However, force-based methods, such as the probabilistic force estimation and event localization algorithm (PFEEL), offer advantages over wave propagation methods by avoiding challenges such as multi-path fading. PFEEL utilizes a probabilistic framework to estimate the force of impacts and the event locations in the calibration space, providing a measure of uncertainty in the estimations.This paper presents a new implementation of PFEEL using a data-driven model based on Gaussian process regression (GPR). The new approach was evaluated using experimental data collected on an aluminum plate impacted at eighty-one points, with a separation of five centimeters. The results are presented as an area of localization relative to the actual impact location at different probability levels. These results can aid analysts in determining the required precision for various implementations of PFEEL.

Comparative Assessment of Performance-Based Design Methodologies Applied to a R.C. Shear-Wall Building

Performance-based design has been increasingly used in practice due to computational improvements, the sophistication and dissemination of nonlinear analysis methods, and the development of commercial programs that facilitate its use. We can evaluate the nonlinear effects of seismic events of great magnitude on the structural behavior of a building, verify preliminary designs based on force-based methods, validate standard design regulations, determine deformations, and calculate accelerations that can be translated into parameters of structural damage and economic losses, among other functions. Guiding documents have presented methodologies to establish requirements, evaluation criteria, analysis methods, etc., each with different objectives, revealing the lack of a consensus method. In this paper, the state of the art of performance-based design is studied, and some of the most relevant methods, such as ASCE 41-17, ASCE 7-16, and the alternative procedure of ACHISINA, are applied to a structure with shear walls designed according to current Chilean regulations. Additionally, modal-response spectrum analysis is used. The modeling of the earthquake-resistant structure of the building, the preparation of seismic records, and the consideration of aspects that limit the rigorous application of the method are addressed in a nonelastic analysis framework. Results obtained in the respective analyses that are used to evaluate the structural performance are compared with the corresponding performance criteria for each standard, considering the characteristics of each methodology. Moreover, the main complications that can occur during the application of the methods are discussed.

Simultaneous Online Registration-Independent Stiffness Identification and Tip Localization of Surgical Instruments in Robot-assisted Eye Surgery.

Notable challenges during retinal surgery lend themselves to robotic assistance which has proven beneficial in providing a safe steady-hand manipulation. Efficient assistance from the robots heavily relies on accurate sensing of surgery states (e.g. instrument tip localization and tool-to-tissue interaction forces). Many of the existing tool tip localization methods require preoperative frame registrations or instrument calibrations. In this study using an iterative approach and by combining vision and force-based methods, we develop calibration- and registration-independent (RI) algorithms to provide online estimates of instrument stiffness (least squares and adaptive). The estimations are then combined with a state-space model based on the forward kinematics (FWK) of the Steady-Hand Eye Robot (SHER) and Fiber Brag Grating (FBG) sensor measurements. This is accomplished using a Kalman Filtering (KF) approach to improve the deflected instrument tip position estimations during robot-assisted eye surgery. The conducted experiments demonstrate that when the online RI stiffness estimations are used, the instrument tip localization results surpass those obtained from pre-operative offline calibrations for stiffness.

T-SNE, forceful colorings, and mean field limits

t-SNE is one of the most commonly used force-based nonlinear dimensionality reduction methods. This paper has two contributions: the first is forceful colorings, an idea that is also applicable to other force-based methods (UMAP, ForceAtlas2,...). In every equilibrium, the attractive and repulsive forces acting on a particle cancel out: however, both the size and the direction of the attractive (or repulsive) forces acting on a particle are related to its properties: the force vector can serve as an additional feature. Secondly, we analyze the case of t-SNE acting on a single homogeneous cluster (modeled by affinities coming from the adjacency matrix of a random k-regular graph); we derive a mean-field model that leads to interesting questions in classical calculus of variations. The model predicts that, in the limit, the t-SNE embedding of a single perfectly homogeneous cluster is not a point but a thin annulus of diameter $\sim k^{-1/4} n^{-1/4}$. This is supported by numerical results. The mean field ansatz extends to other force-based dimensionality reduction methods.

Investigating the behaviour factor and seismic response of structural wall systems in low- to medium-rise buildings when soil-structure interaction is considered

Practicing engineers typically follow linear methods for seismic design and assessment, confining their approach to the requirements of SANS 10160-4 (SANS 2017). This generally leads to a conservative design, leaving little space to apply additional tools for design refinement. Soil-structure interaction has beneficial effects for most building structures under seismic action. However, incorporating soil-structure interaction in the analysis influences the fundamental period, damping and ductility, and will therefore influence the behaviour factor prescribed by design codes. The behaviour factor is necessary for linear methods (force-based methods) to predict the nonlinear behaviour of the structure. This investigation assessed the current behaviour factor for reinforced concrete walls in low- to medium-rise buildings, as prescribed by SANS 10160-4 (SANS 2017), when soil-structure interaction is incorporated in the analysis. The buildings were initially designed and detailed using linear methods, with the prescribed behaviour factor, and then tested using nonlinear methods that do not require the use of a behaviour factor.

A Critical Review on the Sensing, Control, and Manipulation of Single Molecules on Optofluidic Devices.

Single-molecule techniques have shifted the paradigm of biological measurements from ensemble measurements to probing individual molecules and propelled a rapid revolution in related fields. Compared to ensemble measurements of biomolecules, single-molecule techniques provide a breadth of information with a high spatial and temporal resolution at the molecular level. Usually, optical and electrical methods are two commonly employed methods for probing single molecules, and some platforms even offer the integration of these two methods such as optofluidics. The recent spark in technological advancement and the tremendous leap in fabrication techniques, microfluidics, and integrated optofluidics are paving the way toward low cost, chip-scale, portable, and point-of-care diagnostic and single-molecule analysis tools. This review provides the fundamentals and overview of commonly employed single-molecule methods including optical methods, electrical methods, force-based methods, combinatorial integrated methods, etc. In most single-molecule experiments, the ability to manipulate and exercise precise control over individual molecules plays a vital role, which sometimes defines the capabilities and limits of the operation. This review discusses different manipulation techniques including sorting and trapping individual particles. An insight into the control of single molecules is provided that mainly discusses the recent development of electrical control over single molecules. Overall, this review is designed to provide the fundamentals and recent advancements in different single-molecule techniques and their applications, with a special focus on the detection, manipulation, and control of single molecules on chip-scale devices.

Coupling Approaches for Classical Linear Elasticity and Bond-Based Peridynamic Models

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models and the accuracy of nonlocal models. This paper studies the continuous and discrete formulations of three existing approaches for the coupling of classical linear elasticity and bond-based peridynamic models, namely (1) a method that enforces matching displacements in an overlap region, (2) a variant that enforces a constraint on the stresses instead, and (3) a method that considers a variable horizon in the vicinity of the interfaces. The performance of the three coupling approaches is compared on a series of one-dimensional numerical examples that involve cubic and quartic manufactured solutions. Accuracy of the presented methods is measured in terms of the difference between the solution to the coupling approach and the solution to the classical linear elasticity model, which can be viewed as a modeling error. The objective of the paper is to assess the quality and performance of the discrete formulation for this class of force-based coupling methods.

Quasistatic cohesive fracture with an alternating direction method of multipliers

A method for quasistatic cohesive fracture is introduced that uses an alternating direction method of multipliers (ADMM) to implement an energy approach to cohesive fracture. The ADMM algorithm minimizes a non-smooth, non-convex potential functional at each strain increment to predict the evolution of a cohesive-elastic system. The optimization problem bypasses the explicit stress criterion of force-based (Newtonian) methods, which interferes with Newton iterations impeding convergence. The model is extended with an extrapolation method that significantly reduces the computation time of the sequence of optimizations. The ADMM algorithm is experimentally shown to have nearly linear time complexity and fast iteration times, allowing it to simulate much larger problems than were previously feasible. The effectiveness, as well as the insensitivity of the algorithm to its numerical parameters is demonstrated through examples. It is shown that the Lagrange multiplier method of ADMM is more effective than earlier Nitsche and continuation methods for quasistatic problems. Close spaced minima are identified in complicated microstructures and their effect discussed.

CIRCULARVIZ: AN ENHANCED RADVIZ VISUALIZING FOR MULTIDIMENSIONAL DATA

Visualizing for multi-dimensional data points in data space is a central topic with many supplications in many branches of science. RadViz visualizes multi-dimensional data points in the 2D visual space based on force-based methods. Radviz visualizes information about relationships of multi-dimensional data points of data space. The traditional Radviz method finds the best order of anchor points on the circumference of the circle with radius one to enhance the relationships of multi-dimensional data sets. In our paper, we modify the Radviz method named CircularViz method that improves the Radviz layout for the group’s preservation the relationships of multi-dimensional data sets. The idea of the CircularViz method presents data dimensions by circular segments on the circle with radius 1 and finds the most suitable to gaining insights into class structures of multi-dimensional data sets. Our approach make provision an augmentation in visualizing group structures of multi-dimensional data on the 2D visual space. The effectiveness of the CircularViz method evidences by quality visualization measurements and several supervised high-dimensional data sets.

Force-Based Wetting Characterization of Stochastic Superhydrophobic Coatings at Nanonewton Sensitivity.

Superhydrophobic coatings have extraordinary properties like self-cleaning and staying dry, and have recently appeared on industrial and consumer markets. The stochastic nature of the coating components and coating processes (e.g., spraying, painting) affects the uniformity of the water repellency across the coated substrate. The wetting properties of those coatings are typically quantified on macroscale using contact angle goniometry (CAG). Here, highly sensitive force-based methods, scanning droplet adhesion microscopy (SDAM), and micropipette force sensor (MFS), are used, to quantify the microscale heterogeneity in the wetting properties of stochastic superhydrophobic coatings with irregular surface topography that cannot be investigated by CAG. By mapping the wetting adhesion forces with SDAM and friction forces with MFS, it is demonstrated that even the best coatings on the market are prone to heterogeneities that induce stick-slip motion of droplets. Thus, owing to their high spatial and force resolution, the advantages of these techniques over CAG are demonstrated.

Hybrid War as a Part of the Post-global World

The ongoing process of militarization of the informational environment leads to the evolution of approaches to the force-based methods of transformation of the geopolitical balance. It appears that the methods based upon the capabilities to limit the escalation appear to be the most acceptable. However, they open the door for chaotization of viral regions. That increases sharply the interest of the key players in the world politics towards this model of interstate competition that includes military means but is still below the level of classic conventional conflict. Earlier the use of such methods based upon the methods of interrelated informational, psychological and cyber-informational influence were addressed with caution since even 5–7 years ago these methods were not properly elaborated yet and did not give guaranteed results, as well as were related to the high risks of disclosure of the basic information about the organizers of actions and mostly were regarded as supplementary element to the more robust and tested methods of direct military aggression. Nowadays the leading role in the spectrum of military instruments is occupied by the hybrid wars that are a complex phenomenon that includes diverse instruments of political, informational and military (force) nature. The new potential of digital information society serves as an integrating basis for hybrid wars. With their emergence and practical approbation of the new model of war as well as their structural sophistication integration with the force instruments world enters a drastically new political era in which hybrid wars and especially the methods of informational manipulations take the leading roles and become the major instruments of the implementation of the state politics. For Russia that means a substantial change of the environment for competition with other countries as well as for an ultimate necessity to supplement to the foreign policy inventory with the new capabilities that go beyond classic diplomacy and also beyond the soft power potential that is rather underdeveloped in Russia.

Microneedle Array Electrode-Based Wearable EMG System for Detection of Driver Drowsiness through Steering Wheel Grip.

Driver drowsiness is a major cause of fatal accidents throughout the world. Recently, some studies have investigated steering wheel grip force-based alternative methods for detecting driver drowsiness. In this study, a driver drowsiness detection system was developed by investigating the electromyography (EMG) signal of the muscles involved in steering wheel grip during driving. The EMG signal was measured from the forearm position of the driver during a one-hour interactive driving task. Additionally, the participant’s drowsiness level was also measured to investigate the relationship between muscle activity and driver’s drowsiness level. Frequency domain analysis was performed using the short-time Fourier transform (STFT) and spectrogram to assess the frequency response of the resultant signal. An EMG signal magnitude-based driver drowsiness detection and alertness algorithm is also proposed. The algorithm detects weak muscle activity by detecting the fall in EMG signal magnitude due to an increase in driver drowsiness. The previously presented microneedle electrode (MNE) was used to acquire the EMG signal and compared with the signal obtained using silver-silver chloride (Ag/AgCl) wet electrodes. The results indicated that during the driving task, participants’ drowsiness level increased while the activity of the muscles involved in steering wheel grip decreased concurrently over time. Frequency domain analysis showed that the frequency components shifted from the high to low-frequency spectrum during the one-hour driving task. The proposed algorithm showed good performance for the detection of low muscle activity in real time. MNE showed highly comparable results with dry Ag/AgCl electrodes, which confirm its use for EMG signal monitoring. The overall results indicate that the presented method has good potential to be used as a driver’s drowsiness detection and alertness system.