Dynamics Trajectories Research Articles

Vehicle Interactive Dynamic Graph Neural Network-Based Trajectory Prediction for Internet of Vehicles

Vehicle Interactive Dynamic Graph Neural Network-Based Trajectory Prediction for Internet of Vehicles

Fluorination Affects the Force Sensitivity and Nonequilibrium Dynamics of the Mechanochemical Unzipping of Ladderanes.

When multiple reaction steps occur before thermal equilibration, kinetic energy from one reaction step can influence overall product distributions in ways that are not well predicted by transition state theory. An understanding of how the structural features of mechanophores, such as substitutions, affect reactivity, product distribution, and the extent of dynamic effects in the mechanochemical manifolds is necessary for designing chemical reactions and responsive materials. We synthesized two tetrafluorinated [4]-ladderanes with fluorination on different rungs and found that the fluorination pattern influenced the force sensitivity and stereochemical distribution of products in the mechanochemistry of these fluorinated ladderanes. The threshold forces for mechanochemical unzipping of ladderane were decreased by α-fluorination and increased by γ-fluorination; these changes correlated to the different stabilizing or destabilizing effects of fluorination patterns on the first transition state. Using ab initio steered molecular dynamics (AISMD), we compared the product distributions of synthesized and hypothetical ladderanes with different substitution patterns. These calculations suggest that fluorination on the first two bonds of ladderane gives rise to a larger fraction of dynamic trajectories and a larger fraction of E-alkene product through a mechanism resulting from larger momentum because of the greater atomic mass of fluorine. Fluorination on the third and fourth rungs instead gives a larger fraction of E-alkene product primarily due to electronic effects. These combined experimental and computational studies of the mechanochemical unzipping of fluorinated ladderanes provide an example of how relatively simple substituents can affect the extent of nonstatistical dynamics and, thus, mechanochemical outcomes.

Visual Piezoresistive Dual‐Response Sensor Based on CaZnOS: Mn Mechanoluminescence Materials

AbstractThe ability to achieve real‐time movement visualization in piezoresistive sensors remains a challenge. A visual piezoresistive sensor to perceive the intensity of mechanical stimuli and visible spatial location information is designed based on the mechanoluminescence material CaZnOS: Mn. The linearity, detection range, sensitivity, and stability of the sensor are tested, and the sensing mechanism of the sensor is discussed, and the relationship between force, resistance, and light intensity is established. A 5 × 5 sensor array is prepared to realize the visual detection of dynamic force trajectory, and combined with a convolutional neural network and random forest algorithm, the human writing number and pressure characteristics are recognized, and the writing path and handwriting of the robot arm are controlled by multi‐feature input. The experimental results show that the machine learning algorithm is very reliable with an accuracy of 98.33% for digit recognition and 97.21% for identity recognition. The visual piezoresistive pressure sensor provides a new idea for the visualization of flexible pressure and promotes the development of flexible sensors towards integration and intelligence.

Physics-Informed Neural Networks-Based Online Excavation Trajectory Planning for Unmanned Excavator

As a large-scale mining excavator, the electric shovel (ES) has been extensively employed in open-pit mines for overburden removal and mineral loading. In the development of unmanned operations for ES, dynamic excavation trajectory planning is essential, as it directly influences operational efficiency and energy consumption by guiding the dipper during excavation. However, conventional optimization-based methods for excavation trajectory planning typically start from scratch, resulting in a time-consuming process that fails to meet real-time requirements. To address this challenge, we propose an innovative online trajectory planning framework based on physics-informed neural networks (PINNOTP) that utilizes advanced data-driven techniques. The input to PINNOTP consists of on-site working conditions, including the initial state of the ES and the material surface being excavated. The output is a smooth, polynomial-based curve that serves as the reference trajectory for the dipper. To ensure smooth execution of the generated trajectory, prior domain knowledge—such as physics-based target-oriented constraints, essential system dynamics, and mechanical constraints—is explicitly incorporated into the loss function during training. A case study is presented to validate the proposed method, demonstrating that PINNOTP effectively addresses the challenges of online excavation trajectory planning.

Neuroblastoma plasticity during metastatic progression stems from the dynamics of an early sympathetic transcriptomic trajectory.

Despite their indisputable importance in neuroblastoma (NB) pathology, knowledge of the bases of NB plasticity and heterogeneity remains incomplete. They may be rooted in developmental trajectories of their lineage of origin, the sympatho-adrenal neural crest. We find that implanting human NB cells in the neural crest of the avian embryo allows recapitulating the metastatic sequence until bone marrow involvement. Using deep single cell RNA sequencing, we characterize transcriptome states of NB cells and their dynamics over time and space, and compare them to those of fetal sympatho-adrenal tissues and patient tumors and bone marrow samples. Here we report remarkable transcriptomic proximities restricted to an early sympathetic neuroblast branch that co-exist with phenotypical adaptations over disease progression and recapitulate intratumor and interpatient heterogeneity. Combining avian and patient datasets, we identify a list of genes upregulated during bone marrow involvement and associated with growth dependency, validating the relevance of our multimodal approach.

Sharp Curve Trajectory Tracking of a Universal Omni-Wheeled Mobile Robot Using a Sliding Mode Controller

Abstract In our previous works, Amarasiri et al. (2022, “Robust Dynamic Modeling and Trajectory Tracking Controller of a Universal Omni-Wheeled Mobile Robot,” ASME Lett. Dyn. Sys. Control 2(4), p. 040902) and Amarasiri et al. (2024, “Investigating Suitable Combinations of Dynamic Models and Control Techniques for Offline Reinforcement Learning Based Navigation: Application of Universal Omni-Wheeled Robots,” ASME Lett. Dyn. Sys. Control 4(2), p. 021007), two dynamic models of a universal omni-wheeled mobile robot (UOWMR), and trajectory-tracking controllers (three linear and one nonlinear) were developed and presented. The purpose of that work was to identify suitable combinations of dynamic models and trajectory-tracking controllers, choosing the combination with the highest tracking accuracy and the lowest solver execution time. The ultimate purpose is to utilize these physics-based tools in a reinforcement learning (RL) agent developed for path planning and navigation in an unstructured environment. Three trajectories were investigated including a smooth path, a sharp curved path, and a smooth path with disturbance forces. The sliding mode controller (SMC) following a trajectory with sharp (right-angled) curves was not investigated in that work. The sharp corners caused indeterminacy in the path derivatives required for the SMC algorithm. In this short article, we complete our studies of the SMC on sharp corner paths utilizing slight trajectory corner smoothing.

Biologically inspired adaptive crack network reconstruction based on slime mould algorithm.

The dynamic crack propagation trajectories play a crucial role in enhancing our understanding of spatial mechanisms involved in crack expansion. However, visualization of internal cracks under complex crack conditions has always been a challenge. Biological networks have been honed by many cycles of evolutionary selection pressure and are likely to yield reasonable solutions to such combinatorial optimization problems. This study applied the slime mould algorithm to improve the accuracy of internal crack localization in rocks and employed Minimum spanning tree and Gaussian mixture model to construct the crack propagation trajectories. By introducing the concept of bond length, the evolution characteristics of crack levels were effectively characterized. Research results showed that this approach effectively preserves essential crack localization information while mitigating the influence of interfering parameters, providing crack characterization results that exhibit high consistency with actual fracture patterns. The curves of cumulative bond length and relative bond length over time conform to the trend of a Growth/Sigmoidal curve. The strength of the bond was correlated with the temporal process of crack propagation. This result could be helpful for analyzing crack trajectories and predicting rock stability.

Inferring the existence of hydrogen bonds directly from statistical analysis of molecular dynamics trajectories.

As a demonstration of how fundamental chemical concepts can be gleaned from data using machine learning methods, we demonstrate the automated detection of hydrogen bonds by statistical analysis of molecular dynamics trajectories. In particular, we infer the existence and nature of electrostatically driven noncovalent interactions by examining the relative probability of supramolecular configurations with and without electrostatic interactions. Then, using Laplacian eigenmaps clustering, we identify hydrogen bonding motifs in hydrogen fluoride, water, and methanol. The hydrogen bonding motifs that we identify support traditional geometric criteria.

StreaMD: the toolkit for high-throughput molecular dynamics simulations.

Molecular dynamics simulations serve as a prevalent approach for investigating the dynamic behaviour of proteins and protein-ligand complexes. Due to its versatility and speed, GROMACS stands out as a commonly utilized software platform for executing molecular dynamics simulations. However, its effective utilization requires substantial expertise in configuring, executing, and interpreting molecular dynamics trajectories. Existing automation tools are constrained in their capability to conduct simulations for large sets of compounds with minimal user intervention, or in their ability to distribute simulations across multiple servers. To address these challenges, we developed a Python-based tool that streamlines all phases of molecular dynamics simulations, encompassing preparation, execution, and analysis. This tool minimizes the required knowledge for users engaging in molecular dynamics simulations and can efficiently operate across multiple servers within a network or a cluster. Notably, the tool not only automates trajectory simulation but also facilitates the computation of free binding energies for protein-ligand complexes and generates interaction fingerprints across the trajectory. Our study demonstrated the applicability of this tool on several benchmark datasets. Additionally, we provided recommendations for end-users to effectively utilize the tool.Scientific contributionThe developed tool, StreaMD, is applicable to different systems (proteins, ligands and their complexes including co-factors) and requires a little user knowledge to setup and run molecular dynamics simulations. Other features of StreaMD are seamless integration with calculation of MM-GBSA/PBSA binding free energies and protein-ligand interaction fingerprints, and running of simulations within distributed environments. All these will facilitate routine and massive molecular dynamics simulations.

Explicit Configurational Entropy of Mixing in Molecular Dynamics Simulations.

The entropy of mixing of a multicomponent system of particles is a simple expression of the molar fractions for the equilibrium state, but its intermediate values for transient (nonequilibrium) states can not be calculated directly from the particle coordinates so far. We propose a simple expression for the configurational entropy of mixing based solely on the set of instantaneous coordinates, which is suitable for the on-the-fly determination of the degree of mixing along a molecular dynamics trajectory. We illustrate the applicability of our scheme with the example of several molecular mixtures that exhibit fast and slow mixing and demixing processes within a molecular dynamics simulation.

Dynamic trajectories of left ventricular ejection fraction in heart failure with improved ejection fraction.

Heart failure with improved ejection fraction (HFimpEF) has been regarded as a new heart failure (HF) type in 2022. However, studies on the impact of left ventricular ejection fraction (LVEF) trajectories on the prognosis of patients with HFimpEF are scarce. In this study, we investigated dynamic trajectories of LVEF and different clinical outcomes in HFimpEF. This was a multi-center study included patients diagnosed with HF with reduced ejection fraction (HFrEF) between January 1, 2015, and October 31, 2022. Enrolled patients were divided into HFimpEF and persistent HFrEF groups. To further investigate different LVEF trajectories in HFimpEF patients, they were classified into persistent HFimpEF and transient HFimpEF subgroups. Adverse clinical outcomes encompassed all-cause death, cardiovascular death, and HF-related rehospitalization. A total of 734 patients were included (HFimpEF: n = 162; persistent HFrEF: n = 572). Cox regression analysis revealed that compared with persistent HFrEF, patients with HFimpEF experienced a lower risk of all-cause and cardiovascular death. Subgroup analysis determined that only 113 (69.75%) patients maintained an LVEF exceeding 40%. Cox regression analysis revealed that persistent HFimpEF patients had a lower risk of all-cause and cardiovascular death compared to those with transient HFimpEF. Finally, multivariate logistic analysis showed that gender and high-density lipoprotein cholesterol levels were independent predictors of persistent HFimpEF. HFimpEF does not accurately represent HF recovery, given that there are different trajectories of LVEF in HFimpEF. Patients with persistent HFimpEF experience better clinical outcomes, highlighting clinicians should identify clinical modifiable factors to maintain a stable HF stage for better prognosis. ChiCTR2400086622, 08/07/2024.

Monitoring C–C coupling in catalytic reactions via machine-learned infrared spectroscopy

Abstract Tracking atomic structural evolution along chemical transformation pathways is essential for optimizing chemical transitions and enhancing control. However, molecule-level knowledge of structural rearrangements during chemical processes remains a great challenge. Here, we couple infrared spectroscopy as a non-invasive method to probe molecular transformations, with a machine-learned protocol to immediately map the spectroscopic fingerprints to atomistic structures. From the theoretical perspective, we demonstrate it here with the example of C–C coupling in catalytic reactions, elucidating various structural conformations along dynamic trajectories. Within the transferable application to the specific CO–CO dimerization reaction, the structural and energetic variations of the critical chemical species could be identified via infrared spectroscopy. This approach extends the power of spectroscopy from fingerprinting chemical configurations to using them for assigning dynamic structural information.

Dynamics trajectory of patient-reported quality of life and its associated risk factors among hepatocellular carcinoma patients receiving immune checkpoint inhibitors: a prospective cohort study

ObjectiveWe aimed to characterize quality of life (QOL) trajectories among patients with intermediate and advanced hepatocellular carcinoma patients treated with immunotherapy.MethodsBarcelona Clinic Liver Cancer (BCLC) stage B-C HCC patients receiving immunotherapy at Guangxi Medical University Cancer Hospital were included. Trajectories of QOL, assessed using the Functional Assessment of Cancer Therapy-Hepatobiliary (FACT-Hep) questionnaire, were identified through iterative estimations of group-based trajectory models. Associations with trajectory group membership were analyzed using multivariable multinomial logistic regression.ResultsThree trajectory groups were identified (n=156): excellent (35.3%), poor (43.6%), and deteriorating (21.1%) QOL. The deteriorating trajectory group reported a mean QOL score of 124.79 (95% CI, 116.58–133.00), but then declined significantly at month-2 (estimated QOL score 98.67 [95% CI, 84.33–113.00]), and the lowest mean score is reached at month-6 (estimated QOL score 16.58 [95% CI, 0–46.07]). Factors associated with membership to the deteriorating group included no drinking (odds ratio [OR] vs yes [95% CI], 3.70 [1.28–11.11]), no received radiotherapy (OR vs yes [95% CI], 8.33 [1.41–50.00]), diabetes (OR vs no [95% CI], 6.83 [1.57–29.73]), and extrahepatic metastasis (OR vs no [95% CI], 3.08 [1.07–8.87]). Factors associated with membership to the poor group also included body mass index ≤24.0 kg/m2 (OR vs no [95% CI], 4.49 [1.65–12.22]).ConclusionsThis latent-class analysis identified a high-risk cluster of patients with severe, persistent post-immunotherapy QOL deterioration. Screening relevant patient-level characteristics may inform tailored interventions to mitigate the detrimental impact of immunotherapy and preserve QOL.

Vibration characteristics of piezoelectric pipe conveying fluid subjected to fluid-structure-electrical interaction

This paper mainly investigates the vibration characteristics of piezoelectric pipe conveying fluid subjected to fluid-structure-electrical interaction. Based on the Hamilton’s principle, the dynamic equations of the cantilever piezoelectric pipe subjected to the weight and fluid-structure-electrical interaction are established and solved by using the Galerkin method. Ultimately, complex frequency and critical flow velocity are obtained. The main discussion focused on the influence of electromechanical coupling, resistive load, and dimensionless capacitance on the critical flow velocity under different lengths of piezoelectric materials. It also examined the dynamic trajectories of the real and imaginary parts of the complex frequency under various mass ratios and resistive loads. Furthermore, it explored the impact of parameters representing voltage on the stability of the system. The results indicate various stability evolutions under different lengths of piezoelectric materials, flow velocities, and parameters representing voltage. The stability of the system pipe is influenced by factors such as the length of the piezoelectric material and resistive load.

Regulating of Lithium‐Ion Dynamic Trajectory by Ferromagnetic CoF2 to Achieve Ultra‐Stable Deep Lithium Deposition Over 10000 h

AbstractCurrently, the realization of controllable Li electrodeposits to further extend the cycling life of Li metal anode remains challenging. Herein, it is reported that carbon nanosheet array‐loaded ferromagnetic CoF2 nanoparticles on carbon cloth (CC@CoF2/C) as an internal micro‐magnetic field source to manipulate the dynamic trajectory of Li+ deposition via the magnetohydrodynamic effect. This approach ensures uniform lithium‐ion distribution and improves deep plating capacity, achieving a prolonged cycle life of the dendrite‐free Li anode. Finite element simulations, in situ characterizations, and electrochemical tests confirm that magnetic CoF2 not only guides Li+ migration through Lorentz force to prevent dendritic growth but also improves uniform Li deposition due to the in situ conversion of LiF‐rich solid electrolyte interphase during electroplating. Meanwhile, a CC@CoF2/C‐based half‐cell operates stably over 10 000 h at 1 mA cm−2 with a low 7.8 mV overpotential. When matched with a commercial LiFePO4 cathode, the full cell reveals a high capacity of 122.96 mAh g−1 at a 2 C rate after 1000 cycles, retaining 91.95% capacity. The proposed strategy can be effectively expanded and adapted to investigate the deposition behavior of a wide range of metal anodes, offering a versatile and robust analytical framework for addressing diverse metal‐based electrochemical systems.

Virtual screening of plant-derived molecules against zinc-dependent imipenemases in class B metallo-β-lactamases of Acinetobacter baumannii.

Metallo-β-lactamases (MBLs), enzymes of class B, employ zinc ions to degrade β-lactam antibiotics such as penicillins, cephalosporins, carbapenems, and cephamycins. Carbapenem-resistant Acinetobacter baumannii (CRAB) is linked to the existence of carbapenemase enzymes such as oxacillinase and MBL. The most prevalent resistance mechanisms include imipenemases (IMP), verona integron-encoded MBL, and New Delhi MBL-1. The effectiveness of current antibiotics against the MBL enzyme is limited due to the presence of metal ions, underscoring the need for new antimicrobial agents. Recent research has demonstrated that natural compounds can effectively inhibit MBL. This study aims to screen natural phytochemicals against IMP-2 MBL using in silico virtual screening techniques via AutoDock Vina and molecular dynamic simulations with GROMACS for 200ns, followed by molecular mechanics/Poisson‒Boltzmann surface area analysis. This procedure identified new lead molecules against A. baumannii that produce IMP. A total of 588 natural compounds were screened against IMP, along with the imipenem substrate and known inhibitors of L-captopril. The top four compounds, N025-0038 (NC1), N062-0008 (NC2), eupalitin, and Rosmorinic acid, demonstrated binding affinities of ‒8.5, ‒8.4, ‒7.5, and ‒7.2kcal/mol, respectively. The structural stability of these complexes was observed to be maintained throughout the simulation in a dynamic environment, as determined by molecular dynamics trajectory analysis, and all these compounds met the SWISS-ADME (adsorption, distribution, metabolism, and excretion) properties. NC1 and NC2 compounds are considered potential drug molecules against IMP. However, while these selected compounds showed superior binding energy in computational analysis, further in vitro analysis is required to establish an effective drug regimen against A. baumannii that produces IMP.

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

Autonomous navigation is critical to the development of next-generation shipping systems. The proposal of intelligent ships enables innovation in the shipping and shipbuilding industry and increases the safety and efficiency of ship operations. Autonomous control of surface ships to follow a prescribed trajectory is critical in a variety of maritime applications. This paper proposes a trajectory tracking control strategy for autonomous surface ships that combines nonparametric modelling using Gaussian Process Regression (GPR) with the Model Predictive Control (MPC) framework. A Bézier curve-based Virtual Ship(BVS) guidance strategy is proposed to convert dynamic trajectory points into reference heading angles and speeds, such that the trajectory tracking problem can be decomposed into heading control and speed control problems. Gaussian process regression is utilised to identify the correlation between propeller revolution speed and ship speed, as well as the correlation between rudder angle and heading angle based on experimental data. Two GPR models are therefore constructed as the prediction models for designing MPC controllers for heading control and speed control, respectively. Nonlinear optimisation algorithms are utilised to search for optimal control commands in each sampling interval to solve the optimisation problem in MPC with GPR models and input constraints. Simulations are carried out to evaluate the effectiveness of the proposed method.

Nonlinear dynamic of flexible rotor supported by turbulent bearings with quadratic damping and temperature dependent viscosity

A nonlinear dynamic model of a rotor supported by the turbulent journal bearing with quadratic damping under nonlinear suspension is presented in this study, and the lubricating oil is assumed to be temperature-dependent. The bifurcation diagrams, dynamic trajectories, power spectra, Poincaré map, Lyapunov exponent, and fractal dimension are used to verify the dynamic characteristics of the rotor-bearing system, and abundant subharmonic, and even chaotic motions are found. Temperature is one of the key parameters affecting the dynamic responses of the system, and the dynamic responses will perform non-periodic or even chaotic motions with higher working temperatures of lubricant fluid, as proved in this study. Nonlinear dynamic simulation analysis results will help engineers avoid unnecessary trial and error when designing and applying rotating machinery systems.

DFCNet +: Cross-modal dynamic feature contrast net for continuous sign language recognition

In sign language communication, the combination of hand signs and facial expressions is used to convey messages in a fluid manner. Accurate interpretation relies heavily on understanding the context of these signs. Current methods, however, often focus on static images, missing the continuous flow and the story that unfolds through successive movements in sign language. To address this constraint, our research introduces the Dynamic Feature Contrast Net Plus (DFCNet+), a novel model that incorporates both dynamic feature extraction and cross-modal learning. The dynamic feature extraction module of DFCNet+ uses dynamic trajectory capture to monitor and record motion across frames and apply key features as an enhancement tool that highlights pixels that are critical for recognizing important sign language movements, allowing the model to follow the temporal variation of the signs. In the cross-modal learning module, we depart from the conventional approach of aligning video frames with textual descriptions. Instead, we adopt a gloss-level alignment, which provides a more detailed match between the visual signals and their corresponding text glosses, capturing the intricate relationship between what is seen and the associated text. The enhanced proficiency of DFCNet+ in discerning inter-frame details translates to heightened precision on benchmarks such as PHOENIX14, PHOENIX14-T and CSL-Daily. Such performance underscores its advantage in dynamic feature capture and inter-modal learning compared to conventional approaches to sign language interpretation. Our code is available at https://github.com/fyzjut/DFCNet_Plus.

A fast calculation method for three-impulse contingency return trajectory during the near-moon phase based on differential algebra

Aimed at the demand of contingency return at any time during the near-moon phase in the manned lunar landing missions, a fast calculation method for three-impulse contingency return trajectories is proposed. Firstly, a three-impulse contingency return trajectory scheme is presented by combining the Lambert transfer and maneuver at the special point. Secondly, a calculation model of three-impulse contingency return trajectories is established. Then, fast calculation methods are proposed by adopting the high-order Taylor expansion of differential algebra in the two-body trajectory dynamics model and perturbed trajectory dynamics model. Finally, the performance of the proposed methods is verified by numerical simulation. The results indicate that the fast calculation method of two-body trajectory has higher calculation efficiency compared to the semi-analytical calculation method under a certain accuracy condition. Due to its high efficiency, the characteristics of the three-impulse contingency return trajectories under different contingency scenarios are further analyzed expeditiously. These findings can be used for the design of contingency return trajectories in future manned lunar landing missions.