Dynamics Trajectories Research Articles

Dynamical Origin of Rebound versus Dissociation Selectivity during Fe-Oxo-Mediated C-H Functionalization Reactions.

The mechanism of catalytic C-H functionalization of alkanes by Fe-oxo complexes is often suggested to involve a hydrogen atom transfer (HAT) step with the formation of a radical-pair intermediate followed by diverging pathways for radical rebound, dissociation, or desaturation. Recently, we showed that in some Fe-oxo reactions, the radical pair is a nonstatistical-type intermediate and dynamic effects control rebound versus dissociation pathway selectivity. However, the effect of the solvent cage on the stability and lifetime of the radical-pair intermediate has never been analyzed. Moreover, because of the extreme complexity of motion that occurs during dynamics trajectories, the underlying physical origin of pathway selectivity has not yet been determined. For the reaction between [(TQA_Cl)FeIVO]+ and cyclohexane, here, we report explicit solvent trajectories and machine learning analysis on transition-state sampled features (e.g., vibrational, velocity, and geometric) that identified the transferring hydrogen atom kinetic energy as the most important factor controlling rebound versus nonrebound dynamics trajectories, which provides an explanation for our previously proposed dynamic matching effect in fast rebound trajectories that bypass the radical-pair intermediate. Manual control of the reaction trajectories confirmed the importance of this feature and provides a mechanism to enhance or diminish selectivity for the rebound pathway. This led to a general catalyst design principle and proof-of-principle catalyst design that showcases how to control rebound versus dissociation reaction pathway selectivity.

Respiratory Complex III: A Bioengine with a Ligand-Triggered Electron-Tunneling Gating Mechanism.

Respiratory complex III (a.k.a., the bc1 complex) plays a key role in the electron transport chain in aerobic cells. The bc1 complex exhibits multiple unique electron tunneling (ET) processes, such as ET-bifurcation at the Qo site and movement of the Rieske domain. Moreover, we previously discovered that electron tunneling in the low potential arm of the bc1 complex is regulated by a key phenylalanine residue (Phe90). The main goal of the current work is to study the dynamics of the key Phe90 residue in the electron tunneling reaction between heme bL and heme bH as a function of the occupancy of the Qo and Qi binding sites in the bc1 complex. We simulated the molecular dynamics of four model systems of respiratory complex III with different ligands bound at the Qo and Qi binding sites. In addition, we calculated the electron tunneling rate constants between heme bL and heme bH along the simulated molecular dynamics trajectories. The binding of aromatic ligands at the Qo site induces a conformational cascade that properly positions the Phe90 residue, reducing the through-space ET distance from ∼7 to ∼5.5 Å and thus enhancing the electron transfer rate between the heme bL and the heme bH redox pair. Also, the binding of aromatic ligands at the Qi site induces conformational changes that stabilize the Phe90 conformational variation from ∼1.5 to ∼0.5 Å. Hence, our molecular dynamics simulation results show an on-demand two-step conformational connection between the occupancy of the Qo and Qi binding sites and the conformational dynamics of the Phe90 residue. Additionally, our dynamic electron tunneling results confirm our previously reported findings that the Phe90 residue acts as an electron-tunneling gate or switch, controlling the electron transfer rate between the heme bL and heme bH redox systems.

Time-Resolved Graphs of Polymorphic Cycles for H-Bonded Network Identification in Flexible Biomolecules.

A novel approach based on a coarse-grained representation of topological graphs is proposed for the automatic analysis of molecular dynamics (MD) trajectories of hydrogen-bonded (H-Bonded) flexible biomolecules. Herein, our approach models an H-Bonded biomolecule by its H-Bonded cycles and its graph of cycles in which the vertices and links represent the intersections between these cycles. We propose a methodology in which each identified conformer/isomer from the MD is represented by a well-chosen set of H-Bonded cycles called a minimum cycle basis. The key component is the "polycycles" that distinguish the cycles that play the same polymorphic role in the molecule from the ones that lead to an actual conformational change of the molecule. The relevance of our proposed method is evaluated on MD trajectories of gas-phase biomolecules, for which the covalent bonds are unchanged over time and only the hydrogen bonds change over time. The polygraphs and their time evolution are shown to reveal the dynamicity of the metastructure(s) of the H-Bonded biomolecules while providing polymorphic information on the cycles. Such information on the dynamics and changes in the H-bond network, as some cycles change identity while retaining the same role in the overall structure, is not easily captured at the atomic level of representation. Such information can instead be captured by polymorphic cycles.

Person group detection with global trajectory extraction in a disjoint camera network

Person group detection refers to the grouping of people with similar spatio-temporal trajectories. In this work, we address the problem of automatically detecting groups of people from disjoint camera views, which has an essential application in public safety but has not been seriously studied. The main challenge of this task is that the sparse distribution of cameras in a large surveillance area makes it difficult to infer and match people’s trajectories across cameras. To address this challenge, we propose a CCRF (Cyclic Conditional Random Fields) based model for cross-camera trajectory extraction, which takes both visual appearance and heterogeneous spatio-temporal information (including camera locations, video capture times, and the map of the surveillance area) as input and infers multiple candidate cross-camera trajectories for each person. Then, for each pair of people, we propose to use a dynamic trajectory warping (DTW) method to measure the similarity of their trajectories. DTW uses visual features to optimize the selection of trajectories and addresses the problem of trajectory length matching. Since there is no existing dataset that can directly support our research, we enrich our previously built Person Trajectory Dataset by adding the person group annotation and then verify the effectiveness of the proposed method on this dataset. The dataset and code are released at https://github.com/zhangxin1995/PTD_GROUP.

MACNS: A generic graph neural network integrated deep reinforcement learning based multi-agent collaborative navigation system for dynamic trajectory planning

Multi-agent collaborative navigation is prevalent in modern transportation systems, including delivery logistics, warehouse automation, and personalised tourism, where multiple moving agents (e.g., robots and people) must meet at a common destination from different starting points. However, traditional methods face challenges in efficiently and accurately optimising routes for multiple moving agents (e.g., robots). These include dynamically adjusting the common destination in response to changing traffic conditions, encoding the real-world map, and the training speed. Therefore, we propose a generic Multi-Agent Collaborative Navigation System (MACNS) to address those challenges. First, we formalise the solution of the problem and challenges into a Markov Decision Process (MDP), which is further developed as a training environment where a Deep Reinforcement Learning (DRL) agent can learn patterns efficiently. Second, the proposed framework integrates a Graph Neural Network (GNN) into the policy network of Proximal Policy Optimisation for the homogeneous decision-making of each individual agent, showing excellent generalisation, extensibility and convergence speed. Finally, we demonstrate how MACNS can be applied and implemented in a real-world use case: tour groups gathering and picking up. Extensive simulations and real-world tests validate the effectiveness of the MACNS-based use case, showcasing its superiority over other state-of-the-art PPO-related methods.

Characterizing conformational states in GPCR structures using machine learning

G protein-coupled receptors (GPCRs) play a pivotal role in signal transduction and represent attractive targets for drug development. Recent advances in structural biology have provided insights into GPCR conformational states, which are critical for understanding their signaling pathways and facilitating structure-based drug discovery. In this study, we introduce a machine learning approach for conformational state annotation of GPCRs. We represent GPCR conformations as high-dimensional feature vectors, incorporating information about amino acid residue pairs involved in the activation pathway. Using a dataset of GPCR conformations in inactive and active states obtained through molecular dynamics simulations, we trained machine learning models to distinguish between inactive-like and active-like conformations. The developed model provides interpretable predictions and can be used for the large-scale analysis of molecular dynamics trajectories of GPCRs.

The dynamics and mediating factors of students’ emotional and social engagement in a project-based writing course

Studies of student engagement in language learning have burgeoned in the last three decades. However, its dynamic trajectory and the attendant mediating factors leading to various outcomes in project-based language learning (PBLL) have been underexplored. PBLL emphasizes social communication and engagement as students complete complex tasks over an extended period of time, making it distinct from traditional teacher-centered instruction. With this focus in mind, our study draws on Complexity of Dynamic Systems Theory (CDST) to examine the longitudinal development of emotional and social engagement among Chinese EFL students in an English for Academic Purposes (EAP) writing course, and to explore factors accounting for possible changes in those two types of engagement. Using three sets of questionnaires to track student engagement over the course of a learning project, we found an overall increase of emotional and social engagement during students’ learning process, despite a slight dip at the midway point. A follow-up interview showed that engagement changes were mainly driven by peer influence, task completion in PBLL, leadership, and preference for PBLL. These findings provide insight into the dynamics of student engagement in PBLL settings in China and have implications for EAP writing pedagogy.

Analyzing Molecular Dynamics Trajectories Thermodynamically through Artificial Intelligence.

Molecular dynamics simulations produce trajectories that correspond to vast amounts of structure when exploring biochemical processes. Extracting valuable information, e.g., important intermediate states and collective variables (CVs) that describe the major movement modes, from molecular trajectories to understand the underlying mechanisms of biological processes presents a significant challenge. To achieve this goal, we introduce a deep learning approach, coined DIKI (deep identification of key intermediates), to determine low-dimensional CVs distinguishing key intermediate conformations without a-priori assumptions. DIKI dynamically plans the distribution of latent space and groups together similar conformations within the same cluster. Moreover, by incorporating two user-defined parameters, namely, coarse focus knob and fine focus knob, to help identify conformations with low free energy and differentiate the subtle distinctions among these conformations, resolution-tunable clustering was achieved. Furthermore, the integration of DIKI with a path-finding algorithm contributes to the identification of crucial intermediates along the lowest free-energy pathway. We postulate that DIKI is a robust and flexible tool that can find widespread applications in the analysis of complex biochemical processes.

Design of a New Glass Cleaning Robot

Improve the various abilities of glass cleaning robots, such as climbing ability and obstacle crossing ability at any angle, change the traditional movement mode of robots, and enable them to perform obstacle free movement in three-dimensional space. Taking inspiration from the quadruped movement of geckos, the UG software in mechanical CAD/CAM is used to simulate the structure of a glass cleaning robot for gait planning and structural design. By studying the force situation, the dimensions of the four bar mechanism and the size of the suction cup of the vacuum suction cup quadruped glass cleaning robot were derived. The structural design diagram was drawn, and its operating trajectory and mode were explained. Create post-processing through Postbuilder, conduct comprehensive testing and simulation to ensure accuracy before processing its components. The calculation shows that the thigh step distance within one cycle is 89.83 mm, and the calf step distance is 3.11 mm. So, the total step distance of a single leg in one cycle is 92.94 mm. Based on this, a top-down modeling approach was adopted, and assembly methods, connection methods, and driving methods were set in the simulation. The cosine function was used to control the motion trajectory of the legs. This design effectively simulates the motion of geckos. In future research, we will continue to focus on the dynamic trajectory of the robot's tail, in order to enable the robot to adjust its posture through its tail under microgravity and make the motion of the gecko like robot more flexible.

Phylogenetic-informed graph deep learning to classify dynamic transmission clusters in infectious disease epidemics.

In the midst of an outbreak, identification of groups of individuals that represent risk for transmission of the pathogen under investigation is critical to public health efforts. Dynamic transmission patterns within these clusters, whether it be the result of changes at the level of the virus (e.g. infectivity) or host (e.g. vaccination), are critical in strategizing public health interventions, particularly when resources are limited. Phylogenetic trees are widely used not only in the detection of transmission clusters, but the topological shape of the branches within can be useful sources of information regarding the dynamics of the represented population. We evaluated the limitation of existing tree shape metrics when dealing with dynamic transmission clusters and propose instead a phylogeny-based deep learning system -DeepDynaTree- for dynamic classification. Comprehensive experiments carried out on a variety of simulated epidemic growth models and HIV epidemic data indicate that this graph deep learning approach is effective, robust, and informative for cluster dynamic prediction. Our results confirm that DeepDynaTree is a promising tool for transmission cluster characterization that can be modified to address the existing limitations and deficiencies in knowledge regarding the dynamics of transmission trajectories for groups at risk of pathogen infection. DeepDynaTree is available under an MIT Licence in https://github.com/salemilab/DeepDynaTree.

Theoretical Prediction of Thermal Expansion Anisotropy for Y2Si2O7 Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment.

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs. In this work, we use machine learning to train a deep neural network potential (DNP) for Y2Si2O7, which is then applied to calculate the thermodynamic and thermo-mechanical properties at near-DFT accuracy much faster and using less computational resources than DFT. We use this DNP to predict the phonon-based thermodynamic properties of Y2Si2O7 with good agreement to DFT and experiments. We also utilize the DNP to calculate the anisotropic, lattice direction-dependent coefficients of thermal expansion (CTEs) for Y2Si2O7. Molecular dynamics trajectories using the DNP correctly demonstrate the accurate prediction of the anisotropy of the CTE in good agreement with the diffraction experiments. In the future, this DNP could be applied to accelerate additional property calculations for Y2Si2O7 compared to DFT or experiments.

Women's neuroplasticity during gestation, childbirth and postpartum.

Pregnancy is a unique neuroplastic period in adult life. This longitudinal study tracked brain cortical changes during the peripartum period and explored how the type of childbirth affects these changes. We collected neuroanatomic, obstetric and neuropsychological data from 110 first-time mothers during late pregnancy and early postpartum, as well as from 34 nulliparous women evaluated at similar time points. During late pregnancy, mothers showed lower cortical volume than controls across all functional networks. These cortical differences attenuated in the early postpartum session. Default mode and frontoparietal networks showed below-expected volume increases during peripartum, suggesting that their reductions may persist longer. Results also pointed to different cortical trajectories in mothers who delivered by scheduled C-section. The main findings were replicated in an independent sample of 29 mothers and 24 nulliparous women. These data suggest a dynamic trajectory of cortical decreases during pregnancy that attenuates in the postpartum period, at a different rate depending on the brain network and childbirth type.

Optimal tracking control for underactuated airship

A non-linear mathematical model of underactuated airship is derived in this paper based on Euler-Newton approach. The model is linearized with small disturbance theory, producing a linear time varying (LTV) model. The LTV model is verified by comparing its output response with the result of the nonlinear model for a given input signal. The verified LTV model is used in designing the LQT controller. The controller is designed to minimize the error between the output and required states response with acceptable control signals using a weighted cost function. Two LQT controllers are presented in this work based on two different costates transformations used in solving the differential Riccati equation (DRE). The first proposed assumption of costates transformation has a good tracking performance, but it is sensitive to the change of trajectory profile, whereas the second one overcomes this problem due to considering the trajectory dynamics. Therefore, the first assumption is performed across the whole trajectory tracking except for parts of trajectory profile changes where the second assumption is applied. The hybrid LQT controller is used and tested on circular, helical, and bowed trajectories. The simulation assured that the introduced hybrid controller results in improving airship performance.

Spatio-temporal variability of fishing habitat suitability to tuna purse seine fleet in the Western and Central Pacific Ocean

Understanding the spatial distribution of fishing activity is important for ecosystem-based fisheries management.The dynamic vessel trajectory information provided by Automatic Identification System (AIS) together with local remote ocean environment data, provide an opportunity for new technologies to enable improvements in fisheries management.The spatio-temporal and variability to tuna purse seine fleet suitable fishing were evaluated based on fishing effort from AIS by constructing an integrated habitat suitability index (HSI), which weight of variables were determined with boosted regression trees (BRT) model. The results show that the fishing activity tuna purse-seine fleet and catch data from the Western and Central Pacific Fisheries Commission (WCPFC) have the same spatial distribution.The tuna purse seine fleet operation concentrate in EEZ area of the Pacifc Island countries and has seasonal environmental preferences. The fishing habitat suitability can be characterized by a few environmental factors. The HSI model had good capacity to predict habitat suitability to tuna purse seine fleet for mostly catch and fishing effort located in the suitable area. The suitable fishing areas showed significant seasonal changes and low utilization of the available environmentally suitable fishing habitat in each month. The most stable suitable area located in the New Guinea Offshore latitudinal region, may be experiencing high tuna purse-seine fishing pressure throughout the year. The research could help to improve the scientific and effective management of distant water fisheries under limited regulatory capacity.

An experimental study of the sensorized strain wave gear RT1-T and its capabilities for torque control in robotic joints.

The idea of sensorizing a strain wave gear to measure the transmitted torque has been reported since the 1980s. The strain in the elastic flex spline is typically measured by strain gages attached to it. The resulting voltages relate to the transmitted torque in the gear. However, periodic inaccuracies in the measured torque signal (sensing ripple), resulting from positioning inaccuracies of strain gages on the flex spline, prevented this technology from being used outside a lab environment. Regardless of these difficulties, measuring the torque directly in the strain wave gear would bring many advantages, especially in robotic applications, where design space is highly limited. Traditionally, robotic joints are equipped with link-sided torque sensors, which reduce the available design volume, lower the joint stiffness, and require complex cable routing. This paper presents an experimental study of a novel sensorized strain wave gear named RT1-T, which was developed by Schaeffler Technologies. The study was implemented on a joint testbed, including a high-resolution reference torque sensor at the link side. In addition to the measurement accuracy and linearity, a torque ripple analysis is performed. The joint torque control capabilities are determined along dynamic trajectories and compared to the performance achieved with a link-sided reference sensor. The sensor employed in the testbed has a static torque error of 0.42 Nm and an average closed-loop torque control error of 0.65 Nm above the reference sensor.

Spectral analysis of soot dynamics in an aero-engine model combustor

The dynamics of soot evolution in a swirl-stabilized model aero-engine combustor are studied using Large-Eddy Simulation (LES) and state-of-the-art combustion and soot models. The simulated combustor is a dual-swirl combustor with and without secondary oxidation air at a pressure of 3 bar. The comparison with experimental data shows that the simulation accurately captures the gas-phase statistics. Overall, soot statistics are well captured, particularly in the shear layers, although underpredicted in other locations. Three locations in the combustor: Shear Layer (SL), Inner Recirculation Zone (IRZ), and Outer Recirculation Zone (ORZ) are probed to obtain velocity, temperature, and soot volume fraction signals. Lomb–Scargle spectral analysis on the probed signals reveals that soot evolution in the SL is characterized by high-frequency dynamics, whereas in the IRZ and ORZ, it is characterized by low-frequency dynamics. Within the SL, couplings between the soot and flow dynamics are observed, with the soot volume fraction and velocity sharing a common dominant frequency. However, in the IRZ and ORZ, such couplings are not evident. Additionally, the wavelet transform is applied to the probed signals to study the temporal distribution of the frequencies. The analysis indicates that, in the SL, the dominant frequency of the velocity occurs continuously throughout the entire time series, while higher frequencies occur in short bursts. Conversely, for the soot volume fraction, both the dominant frequency and higher frequencies appear in short bursts. In the ORZ and IRZ, the soot volume fraction scalogram shows that low frequencies dominate and occur continuously throughout the time series. Finally, the phase space reconstructions show that the trajectory of the soot dynamics shows a circular pattern, indicative of periodic behavior. The center of attraction remains stationary over a relatively large time scale, suggesting stability in the dynamics as they evolve.

US-Russia Rivalry in the 21st Century: New Cold War and Russian Resurgence in the Changing Global Power Dynamics

Introduction: The Russia-Ukraine conflict, involving major powers, has profoundly impacted global peace and security, sparking initial fears of nuclear escalation. Rooted in various factors, Russia's claim to Ukrainian territory stems from linguistic and familial ties, compounded by perceived American betrayal of promises made during the Cold War's end. NATO's expansion into Eastern Europe further strained relations, culminating in the Euro-Maidan Revolution. The alignment of Russian and Chinese interests against the United States signals a new Cold War dynamic. However, scholarly discourse debates whether this rivalry is a new phenomenon or a continuation of past tensions. Methodology: This paper employs qualitative research methodology to examine the authenticity of American promises to the Soviet Union regarding NATO's non-expansion. Through qualitative analysis, it seeks to unravel the complexities of geopolitical negotiations and ascertain the extent to which these promises were upheld. Results/Findings: The study sheds light on the historical antagonism between Russia and the United States, contextualizing the current conflict within this broader narrative. It highlights the ramifications of broken promises and geopolitical maneuvering, revealing the intricate dynamics of global power struggles. Future Direction: Further research is needed to explore the implications of the Russia-Ukraine conflict on international relations and global security. Investigating the evolving dynamics of the Sino-Russian partnership and its impact on the geopolitical landscape will provide valuable insights into the trajectory of contemporary power dynamics. Additionally, examining the role of regional actors and the potential for diplomatic resolutions to mitigate tensions is essential for fostering lasting peace in the region.

A Rendezvous Mission to the Second Earth Trojan Asteroid 2020 XL5 with Low-Thrust Multi-Gravity Assist Techniques

As the second of Earth’s Trojan asteroids, 2020 XL5 is worthy of rendezvous and even sample return missions in many aspects. In this paper, a rendezvous mission to Earth’s second Trojan asteroid 2020 XL5 is proposed. However, due to its high inclination and large eccentricity, direct impulsive transfer requires large amounts of fuel consumption. To address this challenge, we explore the benefits of electric propulsion and multi-gravity assist techniques for interplanetary missions. These two techniques are integrated in this mission design. The design of a low-thrust gravity-assist (LTGA) trajectory in multi-body dynamics is thoroughly investigated, which is a complex process. A comprehensive framework including three steps is presented here for optimization of LTGA trajectories in multi-body dynamics. The rendezvous mission to 2020 XL5 is designed with this three-step approach. The most effective transfer sequence among the outcomes involves Earth–Venus–Earth–Venus-2020 XL5. Numerical results indicate that the combination of electric propulsion and multi-gravity assists can greatly reduce the fuel consumption, with fuel consumption of 9.03%, making it a highly favorable choice for this rendezvous mission.

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

Can digital economy foster synergistic increases in green innovation and corporate value? Evidence from China.

The rapid evolution of the digital economy has significantly accelerated progress towards achieving green and sustainable processes, particularly in the field of green production. While existing research has delved into the effects of the digital economy on Green Innovation (GI) and the consequences of digital transformation on Corporate Value (CV), there remains a notable gap in the literature regarding the potential for synergistic enhancements in firms' GI&CV through the ongoing digital revolution. This study utilizes an evolutionary game model and employs system dynamics methods to simulate the dynamic evolution trajectory of the influence of the digital economy on the synergy between GI&CV. Subsequently, it empirically assesses the interconnected synergies between GI&CV using a dataset comprising information from Chinese listed firms spanning from 2011 to 2020, examining the impact of the digital economy on these synergies. Moreover, the study delves into the analysis of the transmission mechanism and conducts an extended investigation to further explore this phenomenon. The findings of this paper including: (1) The digital economy acts as a driving force behind the synergistic enhancement of firm GI&CV. Moreover, this effect is further augmented by governmental environmental regulation and green subsidy policies. (2) Drawing upon the information asymmetry theory and the resource-based theory, the regional marketization level and firms' digital transformation play intermediary roles. (3) The heterogeneity test indicate that firms situated in eastern regions and those classified as non-heavily polluted benefitted to a greater extent. This study sheds light on the incentive implications of digital economy for the synergistic upgrading of GI&CV, thereby extending the breadth of study on the consequences of digital economy. Moreover, it offers actionable suggestions for enterprises to leverage digital economy development towards achieving a synergistic improvement of GI&CV.