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Choosing the best acrobatic technique for each athlete remains a challenge for coaches. Predictive simulations may support coaches, but only a few athlete morphologies have been simulated yet. It is assumed that the optimal acrobatic techniques are somehow generalisable across athletes. However, anthropometry characteristics can influence the twist rotation outcome of an acrobatic technique. Our objective was to assess the differences in optimal techniques caused by the anthropometric differences between athletes. Anthropometry-specific techniques of double pike forward somersaults ending with $$1{1 \over 2}$$112 or $$2{1 \over 2}$$212 twists were generated using predictive simulations and the measurements of 18 acrobatic athletes presenting a wide range of anthropometry. We found that anthropometry had an impact on the optimal acrobatic techniques by modifying the amplitude of the strategies used or, more drastically, by modifying the strategies used. Some athletes had a morphological advantage for twist creation, which was measured using the combined twist potential, a metric introduced in the current study. This metric was very strongly correlated with the complexity of the techniques; models with an advantage for twist creation needed fewer/shorter limb movements to generate twists. This research shows that coaches should consider their athletes’ anthropometry to offer them better guidance. [ABSTRACT FROM AUTHOR]

AbstractOpenSim Moco enables solving for an optimal motion using Predictive and Tracking simulations. However, Predictive simulations are computationally prohibitive, and the efficacy of Tracking in deviating from its reference is unclear. This study compares Tracking and Predictive approaches applied to the generation of morphology-specific motion in statistically-derived musculoskeletal shoulder models. The signal analysis software, CORA, determined mean correlation ratings between Tracking and Predictive solutions of 0.91 ± 0.06 and 0.91 ± 0.07 for lateral and forward-reaching tasks. Additionally, Tracking provided computational speed-up of 6–8 times. Therefore, Tracking is an efficient approach that yields results equivalent to Predictive, facilitating future large-scale modelling studies. [ABSTRACT FROM AUTHOR]

Context: Predicting security and trust vulnerabilities and issues is crucial for IoT interconnected systems and ecosystems, especially when integrating new, third-party or open-source components. Objective: One way to ensure timely predictions is by using a smart monitoring framework to continuously verify functional and non-functional property violations during the executions of the systems and their components. Method: This paper presents a set of guidelines for the Smart Monitoring Framework definition and its application process. Results and Conclusion: The paper provides the reference architecture of the Smart Monitoring Framework and its possible implementation to promptly detect suspicious behavior or property violations. The paper also illustrates how the provided implementation satisfies the defined guidelines by design. [ABSTRACT FROM AUTHOR]

The article presents the design concept of a surface acoustic wave (SAW)-based lab-on-a-chip sensor with multifrequency and multidirectional sensitivity. The conventional SAW sensors use delay lines that suffer from multiple signal losses such as insertion, reflection, transmission losses, etc. Most delay lines are designed to transmit and receive continuous signal at a fixed frequency. Thus, the delay lines are limited to only a few features, like frequency shift and change in wave velocity, during the signal analysis. These facts lead to limited sensitivity and a lack of opportunity to utilize the multi-directional variability of the sensing platform at different frequencies. Motivated by these facts, a guided wave sensing platform that utilizes simultaneous tone burst-based excitation in multiple directions is proposed in this article. The design incorporates a five-count tone burst signal for the omnidirectional actuation. This helps the acquisition of sensitive long part of the coda wave (CW) signals from multiple directions, which is hypothesized to enhance sensitivity through improved signal analysis. In this article, the design methodology and implementation of unique tone burst interdigitated electrodes (TB-IDT) are presented. Sensing using TB-IDT enables accessing multiple frequencies simultaneously. This results in a wider frequency spectrum and allows better scope for the detection of different target analytes. The novel design process utilized guided wave analysis of the substrate, and selective directional focused interdigitated electrodes (F-IDT) were implemented. The article demonstrates computational simulation along with experimental results with validation of multifrequency and multidirectional sensing capability. [ABSTRACT FROM AUTHOR]

Emergency and crisis simulations play a pivotal role in equipping authorities worldwide with the necessary tools to minimize the impact of catastrophic events. Various studies have explored the integration of intelligence into Multi-Agent Systems (MAS) for crisis simulation. This involves incorporating psychological behaviours from the social sciences and utilizing data-driven machine learning models with predictive capabilities. A recent advancement in behavioural modelling is the Conscious Movement Model (CMM), designed to modulate an agent's movement patterns dynamically as the situation unfolds. Complementing this, the model incorporates a Conscious Movement Memory-Attention (CMMA) mechanism, enabling learnability through training on pedestrian trajectories extracted from video data. The CMMA facilitates mapping a pedestrian's attention to their surroundings and understanding how their past decisions influence their subsequent actions. This study proposes an efficient framework that integrates the trained CMM into a simulation model specifically tailored for emergency evacuations, ensuring realistic outcomes. The resulting simulation framework automates strategy management and planning for diverse emergency evacuation scenarios. A single-objective method is presented for generating prescriptive analytics, offering effective strategy options based on predefined operational rules. To validate the framework's efficacy, a case study of a theatre evacuation is conducted. In essence, this research establishes a robust simulation framework for crisis management, with a particular emphasis on modelling pedestrians during emergency evacuations. The framework generates prescriptive analytics to aid authorities in executing rescue and evacuation operations effectively. [ABSTRACT FROM AUTHOR]

Neuromuscular disorders often lead to ankle plantar flexor muscle weakness, which impairs ankle push-off power and forward propulsion during gait. To improve walking speed and reduce metabolic cost of transport (mCoT), patients with plantar flexor weakness are provided dorsal-leaf spring ankle-foot orthoses (AFOs). It is widely believed that mCoT during gait depends on the AFO stiffness and an optimal AFO stiffness that minimizes mCoT exists. The biomechanics behind why and how an optimal stiffness exists and benefits individuals with plantar flexor weakness are not well understood. We hypothesized that the AFO would reduce the required support moment and, hence, metabolic cost contributions of the ankle plantar flexor and knee extensor muscles during stance, and reduce hip flexor metabolic cost to initiate swing. To test these hypotheses, we generated neuromusculoskeletal simulations to represent gait of an individual with bilateral plantar flexor weakness wearing an AFO with varying stiffness. Predictions were based on the objective of minimizing mCoT, loading rates at impact and head accelerations at each stiffness level, and the motor patterns were determined via dynamic optimization. The predictive gait simulation results were compared to experimental data from subjects with bilateral plantar flexor weakness walking with varying AFO stiffness. Our simulations demonstrated that reductions in mCoT with increasing stiffness were attributed to reductions in quadriceps metabolic cost during midstance. Increases in mCoT above optimum stiffness were attributed to the increasing metabolic cost of both hip flexor and hamstrings muscles. The insights gained from our predictive gait simulations could inform clinicians on the prescription of personalized AFOs. With further model individualization, simulations based on mCoT minimization may sufficiently predict adaptations to an AFO in individuals with plantar flexor weakness.

The ‘Timed Up and Go’ test (TUG) is a widely used clinical tool for assessing gait and balance, relying primarily on timing as a measure. However, there are more observable biomechanical compensation strategies within TUG that are indicative of underlying neuromuscular issues and movement priorities. In individuals with unilateral knee osteoarthritis, an increased trunk flexion during TUG is a common phenomenon, often attributed to muscle weakness and/or pain avoidance. Unfortunately, it is difficult to differentiate between these underlying causes using experimental studies alone. This study aimed to distinguish between muscle weakness and pain avoidance as contributing factors, using predictive neuromuscular simulations of the sit-to-walk movement. Muscle weakness was simulated by reducing the maximum isometric force of the vasti muscles (ranging from 20% to 60%), while pain avoidance was integrated as a movement objective, ensuring that peak knee load did not exceed predefined thresholds (2–4 times body weight). The simulations demonstrate that a decrease in muscular capacity led to greater trunk flexion, while pain avoidance led to slower movement speeds and altered muscle recruitments, but not to greater trunk flexion. Our predictive simulations thus indicate that increased trunk flexion is more likely the result of lack of muscular reserve rather than pain avoidance. These findings align with reported differences in kinematics and muscle activations between moderate and severe knee osteoarthritis patients, emphasizing the impact of severe muscle weakness in those with advanced knee osteoarthritis. The simulations offer valuable insights into the mechanisms behind altered movement strategies, potentially guiding more targeted treatment.

Scoliosis is a medical condition marked by an abnormal lateral curvature of the spine, typically forming a sideways “S” or “C” shape. Mechanically, it manifests as a three-dimensional deformation of the spine, potentially leading to diverse clinical issues such as pain, diminished lung capacity, and postural abnormalities. This research specifically concentrates on the Adolescent Idiopathic Scoliosis (AIS) population, as existing literature indicates a tendency for this type of scoliosis to deteriorate over time. The principal aim of this investigation is to pinpoint the biomechanical factors contributing to the progression of scoliosis by employing Finite Element Analysis (FEA) on computed tomography (CT) data collected from adolescent patients. By accurately modeling the spinal curvature and related deformities, the stresses and strains experienced by vertebral and intervertebral structures under diverse loading conditions can be simulated and quantified. The transient simulation incorporated damping and inertial terms, along with the static stiffness matrix, to enhance comprehension of the response. The findings of this study indicate a significant reduction in the Cobb angle, halving from its initial value, decreasing from 35° to 17°. In degenerative scoliosis, failure was predicted at 109 cycles, with the Polypropylene brace deforming by 10.34 mm, while the Nitinol brace exhibited significantly less deformation at 7.734 mm. This analysis contributes to a better understanding of the biomechanical mechanisms involved in scoliosis development and can assist in the formulation of more effective treatment strategies. The FEA simulation emerges as a valuable supplementary tool for exploring various hypothetical scenarios by applying diverse loads at different locations to enhance comprehension of the effectiveness of proposed interventions.

An emerging option for internal hemipelvectomy surgery is custom prosthesis reconstruction. This option typically recapitulates the resected pelvic bony anatomy with the goal of maximizing post-surgery walking function while minimizing recovery time. However, the current custom prosthesis design process does not account for the patient's post-surgery prosthesis and bone loading patterns, nor can it predict how different surgical or rehabilitation decisions (e.g., retention or removal of the psoas muscle, strengthening the psoas) will affect prosthesis durability and post-surgery walking function. These factors may contribute to the high observed failure rate for custom pelvic prostheses, discouraging orthopedic oncologists from pursuing this valuable treatment option. One possibility for addressing this problem is to simulate the complex interaction between surgical and rehabilitation decisions, post-surgery walking function, and custom pelvic prosthesis design using patient-specific neuromusculoskeletal models. As a first step toward developing this capability, this study used a personalized neuromusculoskeletal model and direct collocation optimal control to predict the impact of ipsilateral psoas muscle strength on walking function following internal hemipelvectomy with custom prosthesis reconstruction. The influence of the psoas muscle was targeted since retention of this important muscle can be surgically demanding for certain tumors, requiring additional time in the operating room. The post-surgery walking predictions emulated the most common surgical scenario encountered at MD Anderson Cancer Center in Houston. Simulated post-surgery psoas strengths included 0% (removed), 50% (weakened), 100% (maintained), and 150% (strengthened) of the pre-surgery value. However, only the 100% and 150% cases successfully converged to a complete gait cycle. When post-surgery psoas strength was maintained, clinical gait features were predicted, including increased stance width, decreased stride length, and increased lumbar bending towards the operated side. Furthermore, when post-surgery psoas strength was increased, stance width and stride length returned to pre-surgery values. These results suggest that retention and strengthening of the psoas muscle on the operated side may be important for maximizing post-surgery walking function. If future studies can validate this computational approach using post-surgery experimental walking data, the approach may eventually influence surgical, rehabilitation, and custom prosthesis design decisions to meet the unique clinical needs of pelvic sarcoma patients.

Since the pioneering work of Oren et al. (SPE J 3(04):324–336, 1998), several attempts have been made to predict relative permeability curves with digital rock physics (DRP) technique. However, the problem has proved more complex than what researchers have expected, and these attempts failed. One of the main issues was the high number of uncertain parameters, especially for the wettability input, and this gets worst in mixed-wet scenario as the number of parameters is higher than in water-wet and oil-wet cases. In fact, Sorbie and Skauge (Petrophysics 53(06):401–409, 2012) stated that wettability assignment is the most complex and least validated stage in the DRP simulation workflow. Similarly, Bondino et al. (54(6):538–546, 2013) concluded that "genuine prediction" of multiphase flow properties will remain not credible until important progress is achieved in the area of wettability characterization at the pore scale. In this work, we propose a pragmatic approach to tackle these problems. First, we parallelize our pore network simulator in order to achieve large-scale PNM simulations. Then, we develop an innovative and fast anchoring experiment imaged by micro-CT scanner that helps to determine several wettability parameters needed for the DRP simulation (including the fraction of oil-wet/water-wet pores, any spatial or radius correlation of oil-wet pores, etc.). This experiment also provides an estimation of macroscopic parameters that help to anchor our pore-scale simulations and further reduce the uncertainty. In addition to help reducing the uncertainty of the simulation, this experiment provides a fast estimation of the wettability of the system. Images representing large volumes with low resolution are, first, improved with Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to obtain a large image with high resolution. Then, a pore network is extracted, and TotalEnergies' parallel pore network simulator is used for multiphase flow simulations considering the constraints from the anchoring experiment to reduce the uncertainty. Finally, we compare our simulations against high-quality SCAL experiment performed in-house and we assess the predictive power of our DRP workflow. Article Highlights: A new methodology to determine wettability input for pore-scale simulation was developed. Direct and clear observations that wettability is correlated with the pore radii were made and mixed-wet small wettability model was observed. Wettability spatial correlation was observed, and correlation length was measured. Layer models in the PNM simulator were changed, and a historical modeling artifact was corrected. As a direct consequence, expected relative permeability trends after a change of wettability have been observed. PNM simulator was parallelized for faster PNM simulations. The pore-scale simulation coupled with the wettability anchoring experiment was found to be able to predict the results of a mixed-wet SCAL experiment performed on the same rock with the same fluids. [ABSTRACT FROM AUTHOR]

The objective of this study is twofold: firstly, to demonstrate the consistency between the anomalous transport results produced by updated Multi-Mode Model (MMM) version 9.0.4 and those obtained through gyrokinetic simulations; and secondly, to showcase MMM’s ability to predict electron and ion temperature profiles in low aspect ratio, high beta NSTX discharges. MMM encompasses a range of transport mechanisms driven by electron and ion temperature gradients, trapped electrons, kinetic ballooning, peeling, microtearing, and drift resistive inertial ballooning modes. These modes within MMM are being verified through corresponding gyrokinetic results. The modes that potentially contribute to ion thermal transport are stable in MMM, aligning with both experimental data and findings from linear CGYRO simulations. The isotope effects on these modes are also studied and higher mass is found to be stabilizing, consistent with the experimental trend. The electron thermal power across the flux surface is computed within MMM and compared to experimental measurements and nonlinear CGYRO simulation results. Specifically, the electron temperature gradient modes (ETGM) within MMM account for 2.0 MW of thermal power, consistent with experimental findings. It is noteworthy that the ETGM model requires approximately 5.0 ms of computation time on a standard desktop, while nonlinear CGYRO simulations necessitate 8.0 h on 8 K cores. MMM proves to be highly computationally efficient, a crucial attribute for various applications, including real-time control, tokamak scenario optimization, and uncertainty quantification of experimental data.

Software packages that use optimization to predict the motion of dynamic systems are powerful tools for studying human movement. These "predictive simulations" are gaining popularity in parameter optimization studies for designing assistive devices such as exoskeletons. The cost function is a critical component of the optimization problem and can dramatically affect the solution. Many cost functions have been proposed that are biologically inspired and that produce reasonable solutions, but which may lead to different conclusions in some contexts. We used OpenSim Moco to generate predictive simulations of human walking using several cost functions, each of which produced a reasonable trajectory of the human model. We then augmented the model with motors that generated hip flexion, knee flexion, or ankle plantarflexion torques, and repeated the predictive simulations to determine the optimal motor torques. The model was assumed to be planar and bilaterally symmetric to reduce computation time. Peak torques varied from 41.3 to 79.0 N · m for the hip flexion motors, from 48.0 to 94.2 N · m for the knee flexion motors, and from 42.6 to 79.8 N · m for the ankle plantarflexion motors, which could have important design consequences. This study highlights the importance of evaluating the robustness of results from predictive simulations. [ABSTRACT FROM AUTHOR]

An efficient inverse optimal control method named Adaptive Reference IOC is introduced to study natural walking with musculoskeletal models. Adaptive Reference IOC utilizes efficient inner-loop direct collocation for optimal trajectory prediction along with a gradient-based weight update inspired by structured classification in the outer-loop to achieve about 7 times faster convergence than existing derivative-free methods while maintaining similar outcomes in terms of gait trajectory matching. The proposed method adequately reconstructed the reference data when applied to experimental walking data from ten participants walking at various speeds and stride lengths. The proposed framework can facilitate efficient personalized cost function optimization for specific walking tasks, and provide guidance to personalized reference trajectory design for assistive robotic systems such as lower-limb exoskeletons.

Surface deformation poses a great threat to the safety of Jinchuan mining area production activities. At present, the spatio-temporal evolution law and mechanism of surface deformation in the Jinchuan mining area are unclear, and it is difficult to obtain reliable prediction results using the existing spatio-temporal prediction methods due to the lack of model parameters or relevant data. To solve these problems, this study proposes a new unified convolutional neural network with peephole long short-term memory (CNN-PhLSTM). Small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technology was used to obtain the spatio-temporal evolution characteristics of surface deformation in the period of 2014–2021. Time series InSAR deformation data are merged into a unified network model in series with a time-distributed CNN segmentation and stacked PhLSTM. The InSAR measurement results are shown to be reliable by comparison and verification with the benchmark and InSAR results of different orbits. The proposed CNN-PhLSTM model was evaluated by mean absolute error and structural similarity (SSIM) evaluation indexes, and was compared with support vector regression (SVR), multilayer perceptron (MLP) and CNN-LSTM models. The results show three continuous subsidence areas, namely the Longshou, second western and third eastern mining areas. The cumulative surface deformation continued to increase from 2014 to 2021. Faults and lithology control the spatial distribution of surface deformation in the Jinchuan mining area. The prediction results demonstrate that the surface deformation range will continue to expand and that time-series surface deformation will show a slow deceleration trend in the next two years.

The role of the biomechanical stimulation generated from soft tissue has not been well quantified or separated from the self-regulated hard tissue remodeling governed by Wolff's Law. Prosthodontic overdentures, commonly used to restore masticatory functions, can cause localized ischemia and inflammation as they often compress patients' oral mucosa and impede local circulation. This biomechanical stimulus in mucosa is found to accelerate the self-regulated residual ridge resorption (RRR), posing ongoing clinical challenges. Based on the dedicated long-term clinical datasets, this work develops an in-silico framework with a combination of techniques, including advanced image post-processing, patient-specific finite element models and unsupervised machine learning Self-Organizing map algorithm, to identify the soft tissue induced RRR and quantitatively elucidate the governing relationship between the RRR and hydrostatic pressure in mucosa. The proposed governing equation has not only enabled a predictive simulation for RRR as showcased in this study, providing a biomechanical basis for optimizing prosthodontic treatments, but also extended the understanding of the mechanobiological responses in the soft-hard tissue interfaces and the role in bone remodeling., (© 2024 The Author(s). Advanced Healthcare Materials published by Wiley‐VCH GmbH.)

An emerging option for internal hemipelvectomy surgery is custom prosthesis reconstruction. This option typically recapitulates the resected pelvic bony anatomy with the goal of maximizing post-surgery walking function while minimizing recovery time. However, the current custom prosthesis design process does not account for the patient’s post-surgery prosthesis and bone loading patterns, nor can it predict how different surgical or rehabilitation decisions (e.g., retention or removal of the psoas muscle, strengthening the psoas) will affect prosthesis durability and post-surgery walking function. These factors may contribute to the high observed failure rate for custom pelvic prostheses, discouraging orthopedic oncologists from pursuing this valuable treatment option. One possibility for addressing this problem is to simulate the complex interaction between surgical and rehabilitation decisions, post-surgery walking function, and custom pelvic prosthesis design using patient-specific neuromusculoskeletal models. As a first step toward developing this capability, this study used a personalized neuromusculoskeletal model and direct collocation optimal control to predict the impact of ipsilateral psoas muscle strength on walking function following internal hemipelvectomy with custom prosthesis reconstruction. The influence of the psoas muscle was targeted since retention of this important muscle can be surgically demanding for certain tumors, requiring additional time in the operating room. The post-surgery walking predictions emulated the most common surgical scenario encountered at MD Anderson Cancer Center in Houston. Simulated post-surgery psoas strengths included 0% (removed), 50% (weakened), 100% (maintained), and 150% (strengthened) of the pre-surgery value. However, only the 100% and 150% cases successfully converged to a complete gait cycle. When post-surgery psoas strength was maintained, clinical gait features were predicted, including increased stance width, decreased stride length, and increased lumbar bending towards the operated side. Furthermore, when post-surgery psoas strength was increased, stance width and stride length returned to pre-surgery values. These results suggest that retention and strengthening of the psoas muscle on the operated side may be important for maximizing post-surgery walking function. If future studies can validate this computational approach using post-surgery experimental walking data, the approach may eventually influence surgical, rehabilitation, and custom prosthesis design decisions to meet the unique clinical needs of pelvic sarcoma patients.

An efficient inverse optimal control method named Adaptive Reference IOC is introduced to study natural walking with musculoskeletal models. Adaptive Reference IOC utilizes efficient inner-loop direct collocation for optimal trajectory prediction along with a gradient-based weight update inspired by structured classification in the outer-loop to achieve about 7 times faster convergence than existing derivative-free methods while maintaining similar outcomes in terms of gait trajectory matching. The proposed method adequately reconstructed the reference data when applied to experimental walking data from ten participants walking at various speeds and stride lengths. The proposed framework can facilitate efficient personalized cost function optimization for specific walking tasks, and provide guidance to personalized reference trajectory design for assistive robotic systems such as lower-limb exoskeletons. [ABSTRACT FROM AUTHOR]

This work determines the relationships between the intrinsic properties of the soils on the one hand and the parameters of the mathematical models and the geotechnical quantities of 9 clays and 3 clayey sands, used in the manufacture of mud bricks, on the other hand. The modeling of the particle size curves and the processing of the geotechnical data was done with the software "Origin.Pro.2019b". The correlations retained are those whose coefficient of determination is greater than or equal to 0.9. The results obtained show that the models used describe the experimental curves well. The parameters of the models are correlated with the different granulometric fractions and geotechnical quantities. For the evolution of the points expressing the model parameters as a function of the geotechnical quantities, the three models follow the polynomial law. The specific surface and the cation exchange capacity, two fundamental properties that dominate the behavior of fine soils, are compared with the various mineralogical indicator parameters. This study is decisive for the prediction of a geotechnical quantity from a modeled grading curve, by solving the mathematical expressions of the models used. [ABSTRACT FROM AUTHOR]

Maintaining substantial mobility is essential for those who suffer from reduced mobility to regain their independence in daily motion tasks. In recent years, robotic solutions to human mobility aid have been functionally verified by various applications. Moreover, with the emergence of new robots and systems, the robot design theory is also under rapid evolution. This paper proposes a methodology to enhance the design of robotic exoskeleton. The aim was to help the designer to select adequate dynamical behaviors to the development of control scheme for the human motions assisted by a robotic assistance device. The main contribution of this work resides in the proposition of optimized impedance parameters for a particular human movement via neuromusculoskeletal (NMS) modelization and predictive simulation. The technique of NMS modeling that represents the motions of human upper limb was applied to study the underlying mechanisms of human movements. Predictive simulation integrated with the NMS model was formulated and solved for generating a series of optimized human dynamic parameters. In this paper, a case study of human-robot interface has been proposed to exemplify our methodology. The modeling and simulation processes were validated with experimental tools. According to the simulated human dynamics, the optimized stiffness and damping coefficients of one degree of freedom were calculated. Results show that our methods are promising and allowed to specify the human movement for a given task, and can provide the design parameters to control scheme of a robotic exoskeleton. [ABSTRACT FROM AUTHOR]

Fossil trackways provide a glimpse into the behavior of extinct animals. However, while providing information of the trackmaker size, stride, and even speed, the actual gait of the organism can be ambiguous. This is especially true of quadrupedal animals, where disparate gaits can have similar trackway patterns. Here, predictive simulation using trajectory optimization can help distinguish gaits used by trackmakers. First, we demonstrated that a planar, five-link quadrupedal biomechanical model can generate the qualitative trackway patterns made by domestic dogs, although a systematic error emerges in the track phase (relative distance between ipsilateral pes and manus prints). Next, we used trackway dimensions as inputs to a model of Batrachotomus kupferzellensis, a long-limbed, crocodile-line archosaur (clade Pseudosuchia) from the Middle Triassic of Germany. We found energetically optimal gaits and compared their predicted track phases to those of fossil trackways of Isochirotherium and Brachychirotherium. The optimal results agree with trackways at slow speeds but differ at faster speeds. However, all simulations point to a gait transition around a non-dimensional speed of 0.4 and another at 1.0. The trackways likewise exhibit stark differences in the track phase at these speeds. In all cases, including when simulations are constrained to the fossil track phase, the optimal simulations after the first gait transition do not correspond to a trot, as often used by living crocodiles. Instead, they are a diagonal sequence gait similar to the slow tölt of Icelandic horses. This is the first evidence that extinct pseudosuchians may have exhibited different gaits than their modern relatives and of a gait transition in an extinct pseudosuchian. The results of this analysis highlight areas where the models can be improved to generate more reliable predictions for fossil data while also showcasing how simple models can generate insights about the behavior of extinct animals.

Building end-use functional metal parts by metal fused filament fabrication (MF3) is an emerging topic in additive manufacturing. MF3 involves extrusion of polymer filaments that are highly filled with metal powder to print three-dimensional parts, followed by debinding and sintering to eliminate the polymer binder and get a fully dense metal part, respectively. Material properties, part design and processing conditions have a significant influence on the quality of MF3 printed parts. Part distortion and dimensional variations are significant quality challenges that hinder the acceptance of printed parts in potential functional applications. Trial-and-error experiments to find the best conditions are commonly used for defect avoidance, though they are time-consuming and expensive. Hence, computational simulation and design solutions are required for MF3 to enable a virtual analysis of the process outcome and reduce dependency on experimental methods. This paper investigates the applicability of a thermo-mechanical model for finite element simulation of the MF3 printing process. The quantitative influence of material properties on MF3 printed part quality was estimated using a simulation platform. The simulation results of two materials, a Ti-6Al-4V filled polymer and an unfilled ABS copolymer, were compared to experiments. It was determined that the unfilled polymer showed greater shrinkage and warpage than the Ti-6Al-4V filled polymer in simulations and experiments. Further, the trend in the distribution of warpage was consistent between experiments and simulation results for both materials. Finally, warpage compensation algorithms showed improvement in dimensional control for both materials in simulations and were consistent with experimental results. [ABSTRACT FROM AUTHOR]

Predictive simulation of human motion could provide insight into optimal techniques. In repetitive or long-duration tasks, these simulations must predict fatigue-induced adaptation. However, most studies minimize cost function terms related to actuator activations, assuming it minimizes fatigue. An additional modeling layer is needed to consider the previous use of muscles to reveal adaptive strategies to the decreased force production capability. Here, we propose interfacing Xia's three-compartment fatigue dynamics model with rigid-body dynamics. A stabilization invariant was added to Xia's model. We simulated the maximum repetition of dumbbell biceps curls as an optimal control problem (OCP) using direct multiple shooting. We explored three cost functions (minimizing torque, fatigue, or both) and two OCP formulations (full-horizon and sliding-horizon approaches). We adapted Xia's model by adding a stabilization invariant coefficients S = 10 5 for direct multiple shooting. Sliding-horizon OCPs achieved 20 to 21 repetitions. The kinematic strategy slowly deviated from a plausible dumbbell lifting task to a swinging strategy as fatigue onset increasingly compromised the humerus to remain vertical. In full-horizon OCPs, the latter kinematic strategy was used over the whole motion, resulting in 32 repetitions. We showed that sliding-horizon OCPs revealed a reactive strategy to fatigue when only torque was included in the cost function, whereas an anticipatory strategy was revealed when the fatigue term was included in the cost function. Overall, the proposed approach has the potential to be a valuable tool in optimizing performance and helping reduce fatigue-related injuries in a variety of fields. [ABSTRACT FROM AUTHOR]

We present a model and simulation for predicting the detected signal of a fluorescence-based optical biosensor built from optofluidic waveguides. Typical applications include flow experiments to determine pathogen concentrations in a biological sample after tagging relevant DNA or RNA sequences. An overview of the biosensor geometry and fabrication processes is presented. The basis for the predictive model is also outlined. The model is then compared to experimental results for three different biosensor designs. The model is shown to have similar signal statistics as physical tests, illustrating utility as a pre-fabrication design tool and as a predictor of detection sensitivity. [ABSTRACT FROM AUTHOR]

The ultimate goal of most neuromusculoskeletal modeling research is to improve the treatment of movement impairments. However, even though neuromusculoskeletal models have become more realistic anatomically, physiologically, and neurologically over the past 25 years, they have yet to make a positive impact on the design of clinical treatments for movement impairments. Such impairments are caused by common conditions such as stroke, osteoarthritis, Parkinson's disease, spinal cord injury, cerebral palsy, limb amputation, and even cancer. The lack of clinical impact is somewhat surprising given that comparable computational technology has transformed the design of airplanes, automobiles, and other commercial products over the same time period. This paper provides the author's personal perspective for how neuromusculoskeletal models can become clinically useful. First, the paper motivates the potential value of neuromusculoskeletal models for clinical treatment design. Next, it highlights five challenges to achieving clinical utility and provides suggestions for how to overcome them. After that, it describes clinical, technical, collaboration, and practical needs that must be addressed for neuromusculoskeletal models to fulfill their clinical potential, along with recommendations for meeting them. Finally, it discusses how more complex modeling and experimental methods could enhance neuromusculoskeletal model fidelity, personalization, and utilization. The author hopes that these ideas will provide a conceptual blueprint that will help the neuromusculoskeletal modeling research community work toward clinical utility. [ABSTRACT FROM AUTHOR]

Postural responses to similar perturbations of standing balance vary widely within and across subjects. Here, we identified two sources of variability and their interactions by combining experimental observations with computational modeling: differences in posture at perturbation onset across trials and differences in task-level goals across subjects. We first collected postural responses to unpredictable backward support-surface translations during standing in 10 young adults. We found that maximal trunk lean in postural responses to backward translations were highly variable both within subjects (mean of ranges = 28.3°) and across subjects (range of means = 39.9°). Initial center of mass (COM) position was correlated with maximal trunk lean during the response, but this relation was subject specific (R2 = 0.29–0.82). We then used predictive simulations to assess causal relations and interactions with task-level goal. Our simulations showed that initial posture explains the experimentally observed intrasubject variability with a more anterior initial COM position increasing the use of the hip strategy. Differences in task-level goal explain observed intersubject variability with prioritizing effort minimization leading to ankle strategies and prioritizing stability leading to hip strategies. Interactions between initial posture and task-level goal explain observed differences in intrasubject variability across subjects. Our findings suggest that variability in initial posture due to increased sway as observed in older adults might increase the occurrence of less stable postural responses to perturbations. Insight in factors causing movement variability will advance our ability to study the origin of differences between groups and conditions. [ABSTRACT FROM AUTHOR]

Neuromuscular disorders often lead to ankle plantar flexor muscle weakness, which impairs ankle push-off power and forward propulsion during gait. To improve walking speed and reduce metabolic cost of transport (mCoT), patients with plantar flexor weakness are provided dorsal-leaf spring ankle-foot orthoses (AFOs). It is widely believed that mCoT during gait depends on the AFO stiffness and an optimal AFO stiffness that minimizes mCoT exists. The biomechanics behind why and how an optimal stiffness exists and benefits individuals with plantar flexor weakness are not well understood. We hypothesized that the AFO would reduce the required support moment and, hence, metabolic cost contributions of the ankle plantar flexor and knee extensor muscles during stance, and reduce hip flexor metabolic cost to initiate swing. To test these hypotheses, we generated neuromusculoskeletal simulations to represent gait of an individual with bilateral plantar flexor weakness wearing an AFO with varying stiffness. Predictions were based on the objective of minimizing mCoT, loading rates at impact and head accelerations at each stiffness level, and the motor patterns were determined via dynamic optimization. The predictive gait simulation results were compared to experimental data from subjects with bilateral plantar flexor weakness walking with varying AFO stiffness. Our simulations demonstrated that reductions in mCoT with increasing stiffness were attributed to reductions in quadriceps metabolic cost during midstance. Increases in mCoT above optimum stiffness were attributed to the increasing metabolic cost of both hip flexor and hamstrings muscles. The insights gained from our predictive gait simulations could inform clinicians on the prescription of personalized AFOs. With further model individualization, simulations based on mCoT minimization may sufficiently predict adaptations to an AFO in individuals with plantar flexor weakness., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Kiss, Waterval, van der Krogt, Brehm, Geijtenbeek, Harlaar and Seth.)

The 'Timed Up and Go' test (TUG) is a widely used clinical tool for assessing gait and balance, relying primarily on timing as a measure. However, there are more observable biomechanical compensation strategies within TUG that are indicative of underlying neuromuscular issues and movement priorities. In individuals with unilateral knee osteoarthritis, an increased trunk flexion during TUG is a common phenomenon, often attributed to muscle weakness and/or pain avoidance. Unfortunately, it is difficult to differentiate between these underlying causes using experimental studies alone. This study aimed to distinguish between muscle weakness and pain avoidance as contributing factors, using predictive neuromuscular simulations of the sit-to-walk movement. Muscle weakness was simulated by reducing the maximum isometric force of the vasti muscles (ranging from 20% to 60%), while pain avoidance was integrated as a movement objective, ensuring that peak knee load did not exceed predefined thresholds (2-4 times body weight). The simulations demonstrate that a decrease in muscular capacity led to greater trunk flexion, while pain avoidance led to slower movement speeds and altered muscle recruitments, but not to greater trunk flexion. Our predictive simulations thus indicate that increased trunk flexion is more likely the result of lack of muscular reserve rather than pain avoidance. These findings align with reported differences in kinematics and muscle activations between moderate and severe knee osteoarthritis patients, emphasizing the impact of severe muscle weakness in those with advanced knee osteoarthritis. The simulations offer valuable insights into the mechanisms behind altered movement strategies, potentially guiding more targeted treatment., Competing Interests: Author TG was employed by Goatstream. The remaining author declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Van Der Kruk and Geijtenbeek.)

Simulation of musculoskeletal systems using dynamic optimization is a powerful approach for studying the biomechanics of human movements and can be applied to human‐robot interactions. The simulation results of human movements augmented by robotic devices may be used to evaluate and optimize the device design and controller. However, simulations are limited by the accuracy of the models which are usually simplified for computation efficiency. Typically, the powered robotic devices are often modeled as massless, ideal torque actuators that is without mass and internal dynamics, which may have significant impacts on the simulation results. This article investigates the effects of including the mass and internal dynamics of the device in simulations of assisted human movement. The device actuator was modeled in various ways with different detail levels. Dynamic optimization was used to find the muscle activations and actuator commands in motion tracking and predictive simulations. The results showed that while the effects of device mass and inertia can be small, the electrical dynamics of the motor can significantly impact the results. This outcome suggests the importance of using an accurate actuator model in simulations of human movement augmented by assistive devices. Novelty Demonstrating the effects of including mass and internal dynamics of the actuator in simulations of assisted human movementA new OpenSim electric motor actuator class to capture the electromechanical dynamics for use in simulation of human movement assisted by powered robotic devices [ABSTRACT FROM AUTHOR]

Much of the previous research on modeling and simulation of cycling has focused on seated pedaling, modeling the crank load with an effective resistive torque and inertia. This study focuses on modeling standing starts, a component of certain track cycling events in which the cyclist starts from rest and attempts to accelerate to top speed as quickly as possible. A ten degree-of-freedom, two-legged cyclist and bicycle model was developed and utilized for predictive dynamic simulations of standing starts. Experimental data including crank torque, cadence, and joint kinematics were collected for a member of the Canadian Olympic team performing standing starts on the track. Using direct collocation optimal control to maximize the simulated distance traveled, the predictive simulations aligned well with the experiments and replicated key aspects of the standing start technique such as the drive and reset. The model's use in "What if?" scenarios presents interesting possibilities for investigating optimal techniques and equipment in cycling. [ABSTRACT FROM AUTHOR]

Fluctuations in groundwater levels along the coast have a significant impact on the extent of saltwater intrusion into freshwater aquifers. This study aims to simulate the groundwater flow and solute transport in the region by using the mSim toolbox in the MATLAB and COMSOL Multiphysics. The investigation is focussed on a micro-basin of Pavanje river located along the west coast of India. The model results are calibrated and validated against the field observations. The results show that the variation of the water table over the year is significant and range from about 3–14 m. There exists a reasonable correlation between the simulated and observed values of groundwater level and salinity. The wells that are most vulnerable to seawater intrusion in the region are identified. The COMSOL model estimated a salinity range of 0–20 mol/m3. Additionally, the model is used to understand the response of coastal aquifer to various stress scenarios. The study reveals that reduced recharge rate with increased pumping has a serious impact on aquifer system. [ABSTRACT FROM AUTHOR]

Effective conductivity and permeability of a versatile, graph-based model of random structures are investigated numerically. This model, originally introduced in Gaiselmann et al. (2014) allows one to simulate a wide class of realistic materials. In the present work, an extensive dataset of two-phase microstructures with wide-ranging morphological features is used to assess the relationship between microstructure and effective transport properties, which are computed using Fourier-based methods on digital images. Our main morphological descriptors are phase volume fractions, mean geodesic tortuosity, two "hydraulic radii" for characterizing the length scales of heterogeneities, and a "constrictivity" parameter that describes bottleneck effects. This additional parameter, usually not considered in homogenization theories, is an essential ingredient for predicting transport properties, as observed in Gaiselmann et al. (2014). We modify the formula originally developed in Stenzel et al. (2016) for predicting the effective conductivity and propose a formula for permeability. For the latter one, different geometrical definitions of the hydraulic radius are compared. Our predictions are validated using tomographic image data of fuel cells. [ABSTRACT FROM AUTHOR]

This paper deals with the well known bi-variate Weibull distribution developed by Marshall and Olkin. In the light of prior information, this paper derives the posterior distribution and performs Markov chain Monte Carlo methods to obtain posterior based inferences. This paper also checks the sensitivity of posterior estimates by changing the prior variances followed by Bayesian prediction using sample-based approaches. Numerical illustrations are provided for real as well as simulated data sets. [ABSTRACT FROM AUTHOR]