Dynamics Trajectories Research Articles

Craving changes in first 14 days of addiction treatment: an outcome predictor of 5 years substance use status?

Addiction is considered a chronic disorder that requires long-term treatment. Early identification of predictors of outcome may enable better and early adjustment of treatment. Daily fluctuations of craving have been shown to predict substance use within hours, making it a major target for treatment. The objective of this study was to examine whether trajectory and temporal dynamics of craving, at the initiation of outpatient addiction treatment, were associated to long-term substance use outcome. An Ecological Momentary Assessment study collected craving intensity changes and substance use during the first 14-days of treatment, followed by prospective regular follow-ups for 5 years or more to assess long-term outcome. Analysis investigated whether individual differences in craving trajectory (linear trend) and dynamics (inertia, variability and instability) predicted 5+ years follow-up outcome: substance use (1 day or more of primary substance use/past 30 days) versus abstinence. Thirty-nine participants were enrolled in addiction clinic in Bordeaux, France. Results showed that substance use at 5+ years was significantly associated with slower decrease of craving intensity (p < 0.001), and a lower craving inertia (p = 0.038), i.e. tendency to persist from one moment to the other, compared to abstinence status. Conversely, craving intensity was not found associated with substance use/abstinence at follow-up. Results suggest that a slower decrease in craving at treatment initiation could express a greater resistance to treatment. This resistance may have many mechanisms, among which a persistent reactivity to cues – as suggested by lower inertia – that could constitute a vulnerability to use and a valuable indicator of long-term outcomes.

Vibronic Conical Intersection Trajectory Signatures in Wave Packet Coherences.

Conical intersections are ubiquitous in the energy landscape of chemical systems, drive photochemical reactivity, and are extremely challenging to observe spectroscopically. Using two-dimensional electronic spectroscopy, we observe the nonadiabatic dynamics in Wurster's Blue after excitation to the lowest two vibronic excited states. The excited populations relax ballistically through a conical intersection in 55 fs to the electronic ground state potential energy surface as the molecule undergoes an intramolecular electron transfer. While the kinetics are identical on both vibronic energy surfaces, we observe different patterns of coherent oscillations after traversing the conical intersection indicating distinct nonadiabatic relaxation pathways through the conical energetic funnel. These coherences are not created directly by the excitation pulses but are the result of the dynamical trajectories projecting differently on the conical intersection vibrational space. Our spectroscopic data offers a fresh perspective into the complex conical intersection topology and dynamics that emphasizes the critical involvement of the intersection space in dictating the dynamics.

Experimentation Evaluation of Dynamics of Steel Wire Rope

Wire ropes represent a distinct category of ropes synthesized through the intertwining or braiding of individual steel wires. This unique construction confers notable attributes such as strength, flexibility, and durability to the resultant rope. The pervasive wire ropes across diverse industries underscores their capacity to adeptly manage substantial loads and endure adverse environmental conditions finding its application in mechanical, civil, mining, and marine engineering. This paper presents usage of image processing method to detect the deflection of a steel wire rope. The system comprises of dividing the wire rope into different sections, spatial referencing, frame separation, color-based detection, morphological operations, data collection and visualization. The steel wire rope deflection program will allow designers to conveniently process the transverse deflection trajectories of a steel wire rope in real time. One may further introduce any control actions when the deflection distance reaches a threshold value. The image-based algorithm enabled a robust detection of the deformed shape as a function of time, thus obtaining its dynamic trajectories. The deflection shapes and trajectories are compared with numerical predictions made using Cosserat Rod theory, which considers the geometric nonlinearities introduced due to large deflection. The numerical solution gave a rough estimate of the static deformation state of the wire, which agrees with the experimental results. This study will be useful for Structural Health Monitoring, Safety Assurance, Fatigue Analysis and Performance optimization. Moreover, the Continuum Mechanics employs Cosserat rod theory to model continuum robots which will provide enhanced computational efficiency and better dynamic simulation capabilities[18]. The deflection shapes and trajectories are compared with analytical predictions made using Cosserat Rod theory, which considers the geometric nonlinearities introduced due to large deflection.

Consumption Dynamics in Mixed-Income Neighborhoods with Connected Households

AbstractWe investigate the dynamics of household consumption in a setting in which households are connected across income classes. Low- and high-income households form preferences endogenously, conditional on their own and their neighbor’s past consumption. The modeling effort relies on a stochastic dynamic model of interdependent consumer choice in which the demand for commodities evolves according to a non-linear difference equation with stochastic initial states. The analysis targets a region of the parameter space that corresponds to salient features of a mixed-income neighborhood in which households are connected. Standard methods of asymptotic analysis of dynamic systems (e.g. bifurcation analysis) are combined with numerical simulation, statistical modelling of extreme events and statistical estimation techniques to investigate the dynamics. From the mathematical point of view, our analysis reveals the existence of intricate bifurcation pattern, coexistence of multiple attractors, complex basins and long transients. The essential economic finding states that key features of household consumption vary significantly in the influence the high-income households exert on the preference formation of the low-income households. In particular, we find that the prevalence of long transients, i.e. long waiting times before convergence to asymptotic states occur, is inversely related to the type of connectedness considered. We demonstrate that the dynamics of the consumption trajectory evolving over an extended time period before it settles on long-run simple consumption pattern, may not at all be captured by an asymptotic state. Thus, policies targeting the economies in mixed-income neighborhoods that are solely based on information about long-run consumption states, might trigger unwanted, unanticipated effects.

SARS-CoV-2 seroprevalence and antibody trajectories after easing of COVID-19 restrictions: a longitudinal study in China.

We aimed to evaluate the seroprevalence of SARS-CoV-2 and investigate the trajectories of protective immunity and associated risk factors in eastern China between March and November 2023 after the easing of COVID-19 restrictions. We conducted repeated population-based seroepidemiologic studies using a multistage, population-stratified, cluster random sampling method. We measured neutralizing antibodies (nAbs) using a fluorescence immunoassay. We calculated both overall and stratified seroprevalence. The latent class growth mixed model (LCGMM) was used to analyze the dynamic trajectories of antibodies, and a multinomial logistic regression model was used to identify factors associated with different antibody trajectory patterns. A total of 6,147 participants were included at baseline, with a median age of 53.61 years. Both observed and adjusted seroprevalence remained high and stable throughout the study period. The LCGMM identified four distinct antibody trajectories: 75.22% of participants had a high and stable antibody trajectory, while nearly 8% of them exhibited an increase, decline, or low-stable antibody trajectory. Younger participants, women, those fully vaccinated, and individuals with a history of previous infection were more likely to have high and stable antibody trajectories. The majority of the population maintained sustained protective immunity after the outbreak, following the easing of COVID-19 restrictions across the country.

Training improvement methods of ANN trajectory predictors in power systems

AbstractThis paper proposes training improvement methods of artificial neural networks (ANN) trajectory predictors. First, a dynamic power system time‐series trajectory is split into several different segments to simplify the original ANN training problem. Moreover, the time‐derivative of the trajectory is included to obtain an augmented loss function. Compared to previous studies which mainly focused on increasing the prediction accuracy, the aim of these novel techniques is to reduce the computational burden where the ANN output performance is still acceptable. The effectiveness of the developed methods is validated based on the WSCC three‐machine nine‐bus and IEEE 39‐bus system models. The mean absolute error (MAE) and trajectory prediction results are analysed, in which the numbers of neurons, hidden layers, and training epochs are constrained during the ANN training process. Rotor‐angle difference between generators and the system frequency are investigated as the dynamic trajectories of the power system models. The approaches are revealed to be effective when the ANN architecture and epochs are constrained. The MAE results can be reduced by up to 65% in the power system models depending on the ANN hyperparameters and training epochs. The ANN training results can better reflect the original trajectory as well.

Digital twin dynamic force-thermal physics sub-cell for CFRP drilling process

The digital transformation of cutting processes is anticipated to advance digital twin (DT) automated machining, particularly for difficult-to-cut materials. This paper develops a dynamic physical sub-cell for the CFRP drilling process, facilitating real-time force-thermal analysis during tool degradation and enhancing DT machining technology. A comprehensive dynamic model was developed to account for force-thermal effects as the tool wears. This model included five correction functions that consider the wear characteristics of the drill bit, allowing the improved cutting force model to track the dynamic force trajectory accurately. The finite difference method was employed to analyze drilling temperature and heat accumulation, with the heat source generated by the cutting force. The heat source intensity was determined by the heat generation efficiency function. Furthermore, the cutting force at the exit was calculated, as the exit temperature alters the material's thermal properties at the entrance. Taking experimental data from 100 holes at each feed rate as a baseline, optimization algorithms determined the values of each correction coefficient within the comprehensive model. The model's validity was verified by comparing experimental results, showing average errors of 5.14N for thrust, 0.0165N·m for torque, and 7.04°C for temperature. This model was packaged as a dynamic physical sub-cell demo, which can be integrated into a DT machining system for rapid response to multifaceted force-thermal attributes.

Serum Albumin and Its Trajectory Are Associated with Therapeutic Outcomes in Ulcerative Colitis.

The relationship between serum albumin levels and therapeutic outcomes in ulcerative colitis (UC) has been debated. Additionally, the dynamic changes in albumin levels remain understudied. We conducted a pooled analysis of five clinical trials involving 3,268 patients with UC. Short- and long-term therapeutic outcomes were assessed at the end of the induction and maintenance phases. Multivariate logistic regression and random effects models were used to pool the predictive effects of albumin levels. The dynamic trajectory of albumin was fitted using latent class growth mixed models. Baseline and week-2 albumin were independent predictors of short-term outcomes, with pooled adjusted odds ratios (aORs) [95% confidence intervals (CIs)] of 1.07 [1.05-1.09] and 1.11 [1.08-1.15] per 1 g/L increase for clinical response, respectively. Higher post-induction albumin levels predicted better long-term outcomes, including clinical (aOR [95% CI]: 1.16 [1.12-1.21]), endoscopic (1.13 [1.10-1.16]) and histological remission (1.11 [1.03-1.18]). Furthermore, there are three classes of albumin trajectories: sustained medium-to-high, rapidly ascending and poor response. Compared to the sustained medium-to-high class, patients in the poor response class had a lower probability of long-term endoscopic remission (aOR 0.35 [95% CI: 0.23-0.50]; P<0.001), while no significant difference was observed between the rapidly ascending class and the sustained medium-to-high class. Higher albumin levels were associated with better therapeutic outcomes in patients with UC. However, patients with low but rapidly ascending albumin levels would achieve outcomes comparable to those with medium-to-high levels of albumin.

Intelligent Dynamic Trajectory Planning of UAVs: Addressing Unknown Environments and Intermittent Target Loss

Precise trajectory planning is crucial in terms of enabling unmanned aerial vehicles (UAVs) to execute interference avoidance and target capture actions in unknown environments and when facing intermittent target loss. To address the trajectory planning problem of UAVs in such conditions, this paper proposes a UAV trajectory planning system that includes a predictor and a planner. Specifically, the system employs a bidirectional Temporal Convolutional Network (TCN) and Gated Recurrent Unit (GRU) network algorithm with an adaptive attention mechanism (BITCN-BIGRU-AAM) to train a model that incorporates the historical motion trajectory features of the target and motion intention the inferred by a Dynamic Bayesian Network (DBN). The resulting predictions of the maneuvering target are used as terminal inputs for the planner. An improved Radial Basis Function (RBF) network is utilized in combination with an offline–online trajectory planning method for real-time obstacle avoidance trajectory planning. Additionally, considering future practical applications, the predictor and planner adopt a parallel optimization and correction algorithm structure to ensure planning accuracy and computational efficiency. Simulation results indicate that the proposed method can accurately avoid dynamic interference and precisely capture the target during tasks involving dynamic interference in unknown environments and when facing intermittent target loss, while also meeting system computational capacity requirements.

Screening Fast-Mode Motion in Collective Variable Discovery for Biochemical Processes.

Collective variables (CVs) describing slow degrees of freedom (DOFs) in biomolecular assemblies are crucial for analyzing molecular dynamics trajectories, creating Markov models and performing CV-based enhanced sampling simulations. While time-lagged independent component analysis (tICA) and its nonlinear successor, time-lagged autoencoder (tAE), are widely used, they often struggle to capture protein dynamics due to interference from random fluctuations along fast DOFs. To address this issue, we propose a novel approach integrating discrete wavelet transform (DWT) with dimensionality reduction techniques. DWT effectively separates fast and slow motion in protein simulation trajectories by decoupling high- and low-frequency signals. Based on the trajectory after filtering out high-frequency signals, which corresponds to fast motion, tICA and tAE can accurately extract CVs representing slow DOFs, providing reliable insights into protein dynamics. Our method demonstrates superior performance in identifying CVs that distinguish metastable states compared to standard tICA and tAE, as validated through analyses of conformational changes of alanine dipeptide and tripeptide and folding of CLN025. Moreover, we show that DWT can be used to improve the performance of a variety of CV-finding algorithms by combining it with Deep-tICA, a cutting-edge CV-finding algorithm, to extract CVs for enhanced-sampling calculations. Given its negligible computational cost and remarkable ability to screen fast motion, we propose DWT as a "free lunch" for CV extraction, applicable to a wide range of CV-finding algorithms.

Deep Reinforcement Learning-Assisted Teaching Strategy for Industrial Robot Manipulator

This paper introduces an innovative algorithm aimed at enhancing robot learning using dynamic trajectory modeling and time-dependent state analysis. By integrating reinforcement learning (RL) and trajectory planning, the proposed approach enhances the robot’s adaptability in diverse environments and tasks. The framework begins with a comprehensive analysis of the robot’s operational space, focusing on Cartesian coordinates and configuration systems. By modeling trajectories and states within these systems, the robot achieves sequential tracking of arbitrary states, facilitating efficient task execution in various scenarios. Experimental results demonstrate the algorithm’s efficacy in manipulation tasks and path planning in dynamic environments. By integrating dynamic trajectory modeling and time-dependent state analysis, the robot’s adaptability and performance improve significantly, enabling precise task execution in complex environments. This research contributes to advancing robot learning methodologies, particularly in human–robot interaction scenarios, promising applications in manufacturing, healthcare, and logistics.

3T-VASP: fast ab-initio electrochemical reactor via multi-scale gradient energy minimization

Ab-initio methods such as density functional theory (DFT) is useful for fundamental atomistic-level study and is widely used across many scientific fields, including for the discovery of electrochemical reaction byproducts. However, many DFT steps may be needed to discover rare electrochemical reaction byproducts, which limits DFT’s scalability. In this work, we demonstrate that it is possible to generate many elementary electrochemical reaction byproducts in-silico using just a small number of ab-initio energy minimization steps if it is done in a multi-scale manner, such as via previously reported tiered tensor transform (3T) method. We first demonstrate the algorithm through a simple example of a complex floppy organic molecule passivator binding onto perovskite solar cell surface defect site. We then demonstrate more complex examples by generating hundreds of electrochemical reaction byproducts in lithium-ion battery liquid electrolyte (many are verified in previous experimental studies), with most trajectories completed within 50–100 DFT steps as opposed to more than 10,000 steps typically utilized in an ab-initio molecular dynamics trajectory. This approach requires no machine learning training data generation and can be directly applied on any new chemistries, making it suitable for ab-initio elementary chemical reaction byproduct investigation when temperature dependence is not required.

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.

BaNDyT: Bayesian Network modeling of molecular Dynamics Trajectories.

Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical datasets. Concurrently, our ability to perform long-timescale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially. However, the analysis of MD simulation trajectories has not been data-driven but rather dependent on the user's prior knowledge of the systems, thus limiting the scope and utility of the MD simulations. Recently, we pioneered using BNM for analyzing the MD trajectories of protein complexes. The resulting BN models yield novel fully data-driven insights into the functional importance of the amino acid residues that modulate proteins' function. In this report, we describe the BaNDyT software package that implements the BNM specifically attuned to the MD simulation trajectories data. We believe that BaNDyT is the first software package to include specialized and advanced features for analyzing MD simulation trajectories using a probabilistic graphical network model. We describe here the software's uses, the methods associated with it, and a comprehensive Python interface to the underlying generalist BNM code. This provides a powerful and versatile mechanism for users to control the workflow. As an application example, we have utilized this methodology and associated software to study how membrane proteins, specifically the G protein-coupled receptors, selectively couple to G proteins. The software can be used for analyzing MD trajectories of any protein as well as polymeric materials.

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.