Dynamic Transition Research Articles

Exactly solvable floquet dynamics for conformal field theories in dimensions greater than two

We find classes of driven conformal field theories (CFT) in d + 1 dimensions with d > 1, whose quench and floquet dynamics can be computed exactly. The setup is suitable for studying periodic drives, consisting of square pulse protocols for which Hamiltonian evolution takes place with different deformations of the original CFT Hamiltonian in successive time intervals. These deformations are realized by specific combinations of conformal generators with a deformation parameter β; the β < 1 (β > 1) Hamiltonians can be unitarily related to the standard (Lüscher-Mack) CFT Hamiltonians. The resulting time evolution can be then calculated by performing appropriate conformal transformations. For d ≤ 3 we show that the transformations can be easily obtained in a quaternion formalism. Evolution with such a single Hamiltonian yields qualitatively different time dependences of observables depending on the value of β, with exponential decays characteristic of heating for β > 1, oscillations for β < 1 and power law decays for β = 1. This manifests itself in the behavior of the fidelity, unequal-time correlator, and the energy density at the end of a single cycle of a square pulse protocol with different hamiltonians in successive time intervals. When the Hamiltonians in a cycle involve generators of a single SU(1, 1) subalgebra we calculate the Floquet Hamiltonian. We show that one can get dynamical phase transitions for any β by varying the time period of a cycle, where the system can go from a non-heating phase which is oscillatory as a function of the time period to a heating phase with an exponentially damped behavior. Our methods can be generalized to other discrete and continuous protocols. We also point out that our results are expected to hold for a broader class of QFTs that possesses an SL(2, C) symmetry with fields that transform as quasi-primaries under this. As an example, we briefly comment on celestial CFTs in this context.

Hydrogen Bond‐Induced Flexible and Twisted Self‐Assembly of Functionalized Carbon Dots with Customized‐Color Circularly Polarized Luminescence

Circularly polarized luminescence (CPL) is widely applied in optical data storage, quantum computing and backlights in three‐dimensional (3D) displays. Carbon dots (CDs) exhibit competitive optical properties, in addition to excellent resistance to photo‐ and chemical‐bleaching after carbonization. Combining the superior optical performance with polarization peculiarities through hierarchical structure engineering is imperative for the development of CDs. Here, oriented assembly was driven by hydrophobic interactions of aromatic ligands, which participated in the surface‐ligand post‐modification process on ground‐state chiral carbon core. Furthermore, the residual chiral amides on CDs formed multi‐hydrogen bonds during gradual aggregation, causing the assembled materials to form asymmetric bending structure. Superficial ligands interfered with optical dynamics of exciton radiation transition and promoted the excited state of the assembled materials to achieve a circularly polarized signal. The linkage ligands successfully overcame the frequent phenomenon of aggregation‐induced quenching and contributed further to the formation of self‐supporting films by assembly and facilitated chiral optical expression. The full‐color and white CPL were manipulated by simply regulating the functional groups on the ligands. Finally, based on the stable chiral powder phosphors, large chiral flexible films and multicolor chiral light‐emitting diodes were constructed which provide feasible materials and technical support for flexible 3D displays.

Mapping Full Conformational Transition Dynamics of Intrinsically Disordered Proteins Using a Single-Molecule Nanocircuit.

Intrinsically disordered proteins (IDPs) are emerging therapeutic targets for human diseases. However, probing their transient conformations remains challenging because of conformational heterogeneity. To address this problem, we developed a biosensor using a point-functionalized silicon nanowire (SiNW) that allows for real-time sampling of single-molecule dynamics. A single IDP, N-terminal transactivation domain of tumor suppressor protein p53 (p53TAD1), was covalently conjugated to the SiNW through chemical engineering, and its conformational transition dynamics was characterized as current fluctuations. Furthermore, when a globular protein ligand in solution bound to the targeted p53TAD1, protein-protein interactions could be unambiguously distinguished from large-amplitude current signals. These proof-of-concept experiments enable semiquantitative, realistic characterization of the structural properties of IDPs and constitute the basis for developing a valuable tool for protein profiling and drug discovery in the future.

Hybrid Artificial Protozoa-Based JADE for Attack Detection

This paper presents a novel hybrid optimization algorithm that combines JADE Adaptive Differential Evolution with Artificial Protozoa Optimizer (APO) to solve complex optimization problems and detect attacks. The proposed Hybrid APO-JADE Algorithm leverages JADE’s adaptive exploration capabilities and APO’s intensive exploitation strategies, ensuring a robust search process that balances global and local optimization. Initially, the algorithm employs JADE’s mutation and crossover operations, guided by adaptive control parameters, to explore the search space and prevent premature convergence. As the optimization progresses, a dynamic transition to the APO mechanism is implemented, where Levy flights and adaptive change factors are utilized to refine the best solutions identified during the exploration phase. This integration of exploration and exploitation phases enhances the algorithm’s ability to converge to high-quality solutions efficiently. The performance of the APO-JADE was verified via experimental simulations and compared with state-of-the-art algorithms using the 2022 IEEE Congress on Evolutionary Computation benchmark (CEC) 2022 and 2021. Results indicate that APO-JADE achieved outperforming results compared with the other algorithms. Considering practicality, the proposed APO-JADE was used to solve a real-world application in attack detection and tested on DS2OS, UNSW-NB15, and ToNIoT datasets, demonstrating its robust performance.

Effect of Different Welding Modes on Morphology and Property of SS316L Stainless Steel Deposition by Robotic Metal-Inert Gas Welding.

The widespread adoption of arc additive manufacturing techniques across various industries has advanced the field of SS316L stainless steel manufacturing. It is crucial to acknowledge that different welding modes exert distinct influences on the forming and mechanical performance. This study analyzed the thermal input associated with four specific welding modes in LORCH MIG welding, clarifying the transition dynamics of molten droplets through waveform analysis and examining the resultant effects on microstructure and performance characteristics. The Pulse, Speed-Pulse-XT, and Twin-Pulse modes were found to induce spatter during the manufacturing process, consequently reducing molding efficiency in comparison to the SA-XT mode. Notably, the Twin-Pulse mode, characterized by double-pulse agitation, generated fish scale patterns along the lateral surfaces of the fabricated parts, promoting anisotropic grain growth. This microstructural refinement, compared to single-pulse samples with equivalent thermal input, resulted in enhanced mechanical properties. Nevertheless, the horizontal tensile strength of the three pulse modes was lower than the industrial standard for SA-XT mode and forging. In contrast, the SA-XT mode with an average hardness of 168.1 ± 6.9 HV and a tensile strength of 443.58 ± 5.7 MPa. Therefore, while three pulse modes offer certain microstructural advantages, the SA-XT mode demonstrates superior overall performance.

Beyond labs: unveiling dynamics of dental students' transition from pre-clinical to clinical training in a Saudi dental school.

To assess the factors affecting the transition of dental students from pre-clinical to clinical courses in an outcome-based curriculum. This cross-sectional study surveyed dental students in the third and fourth academic years of the Bachelor of Dental and Oral Surgery (BDS) program at the College of Dentistry, Jouf University. Ethically approved and powered by the G Power software, the study employed a modified questionnaire validated through a pilot test to assess five domains. Likert scale responses were analyzed using SPSS v.25, revealing insights into clinical workload, patient interaction, and learning experiences. Multiple regression analysis was used to assess the impact of clinical skill application, workload, transition to clinics, and patient interaction on learning experience as well as CGPA. The Mann-Whitney U test compared the ranks of two independent samples, making it less sensitive to outliers and more suitable for data with non-normal distributions. In this study, the response rate of the participants was 70%. A total of 44 dental students in their third and fourth years of the program completed the survey. The multiple regression analysis showed that the predictors collectively explained 36.1% of the variance in the learning experience (Adjusted R2 = 0.361). "Transition to Clinics" had a significant positive effect on learning experience (β = 0.292, p = 0.012), "Workload" (β = -0.203, p = 0.393) and "Patient Interaction" (β = 0.443, p = 0.168) were not significant predictors. The Mann-Whitney U test revealed no significant gender differences in transition to clinics, workload, patient interaction, application of clinical skills, and learning experience (U = 33.09 to -40.33, p > 0.05), but a significant difference in transition to clinics between third- and fourth-year students (U = 31.56 to -43.24, p < 0.05). The results of this study demonstrate that the transition to clinical training can be intricate, and that multiple elements have an impact on this process. It is crucial to have support systems that facilitate the transition into the clinical learning environment.

Markovian to non-Markovian phase transition in the operator dynamics of a mobile impurity

We study a random unitary circuit model of an impurity moving through a chaotic medium. The exchange of information between the medium and impurity is controlled by varying the velocity of the impurity, v_dvd, relative to the speed of information propagation within the medium, v_BvB. Above supersonic velocities, v_d&gt; v_Bvd&gt;vB, information cannot flow back to the impurity after it has moved into the medium, and the resulting dynamics are Markovian. Below supersonic velocities, v_d&lt; v_Bvd&lt;vB, the dynamics of the impurity and medium are non-Markovian, and information is able to flow back onto the impurity. We show the two regimes are separated by a continuous phase transition with exponents directly related to the diffusive spreading of operators in the medium. This is demonstrated by monitoring an out-of-time-order correlator (OTOC) in a scenario where the impurity is substituted at an intermediate time. During the Markovian phase, information from the medium cannot transfer onto the replaced impurity, manifesting in no significant operator development. Conversely, in the non-Markovian phase, we observe that operators acquire support on the newly introduced impurity. We also characterize the dynamics using the coherent information and provide two decoders which can efficiently probe the transition between Markovian and non-Markovian information flow. Our work demonstrates that Markovian and non-Markovian dynamics can be separated by a phase transition, and we propose an efficient protocol for observing this transition.

Adaptive phase lock loop system for global navigation satellite systems signals

Formulation of the problem. Classical optimal Bayesian phase lock loop (PLL) algorithms require a priori knowledge of the phase dynamics parameters and signal-to-noiz ratios of the received signals. In practice, these parameters vary significantly and, as a rule, are unknown. Taking into account the fact that the operation of such algorithms under conditions different from those a priori specified is not reliable according to the criterion of minimum variance error. Purpose of the study. Develop a phase-locked loop system for the Global Navigation Satellite System (GNSS) signal that adapts to phase dynamics and signal-to-noise ratio to maintain phase tracking over the widest possible range of operating conditions. Results. A multi-channel adaptive phase-locked loop system (MAPLL system) has been developed. Practical significance. The developed system is capable of processing a jump in the signal-to-noise ratio from 50 to 9 dBHz and back without losing phase tracking (under conditions of low dynamics), maintaining phase tracking during abrupt transitions of dynamics between low (due only to the dynamics of the reference oscillator) and high (sinusoidal acceleration 10g and sinusoidal jerk 10 g/s) at a signal-to-noise ratio of 24 dBHz. Thus, in real-world conditions where object dynamics and S/N ratios of received signals change in unpredictable ways, MAPLL system maintains phase tracking over a much wider range of conditions than non-adaptive PLL.

Septin Organization and Dynamics for Budding Yeast Cytokinesis.

Cytokinesis, the process by which the cytoplasm divides to generate two daughter cells after mitosis, is a crucial stage of the cell cycle. Successful cytokinesis must be coordinated with chromosome segregation and requires the fine orchestration of several processes, such as constriction of the actomyosin ring, membrane reorganization, and, in fungi, cell wall deposition. In Saccharomyces cerevisiae, commonly known as budding yeast, septins play a pivotal role in the control of cytokinesis by assisting the assembly of the cytokinetic machinery at the division site and controlling its activity. Yeast septins form a collar at the division site that undergoes major dynamic transitions during the cell cycle. This review discusses the functions of septins in yeast cytokinesis, their regulation and the implications of their dynamic remodelling for cell division.

Striding towards a greener future: Unlocking the potential of natural resources and employment dynamics in green energy transition in sub‐Saharan Africa

AbstractThe adoption and utilization of renewable energy offer potential benefits such as enhanced energy efficiency, cost savings, and ecological advantages. However, a key research question addressed in this analysis is whether natural resource rent and employment dynamics influence renewable energy consumption in Africa. Previous research has predominantly focused on the aggregate employment rate, overlooking the nuances of labor diversity across sectors and employment types. Hence, this study evaluates the importance of natural resource rent and employment in driving the transition to green energy in sub‐Saharan Africa from 1991 to 2022. It employs the innovative method of moments quantile regression (MMQR) model for this purpose. The findings reveal a positive connection between natural resource rent and the adoption of green energy. When considering employment types, the study observes that self‐employment and wages/salaried workers undermine clean energy utilization. Moreover, the study highlights that employment across key economic sectors also plays a role. While employment in the agriculture and service sectors fosters green energy utilization, employment in the industrial sector impedes renewable energy consumption. To advance the development of green energy in Africa, this study underscores a range of policy options.

Mechano-osmotic signals control chromatin state and fate transitions in pluripotent stem cells.

Acquisition of specific cell shapes and morphologies is a central component of cell fate transitions. Although signaling circuits and gene regulatory networks that regulate pluripotent stem cell differentiation have been intensely studied, how these networks are integrated in space and time with morphological transitions and mechanical deformations to control state transitions remains a fundamental open question. Here, we focus on two distinct models of pluripotency, primed pluripotent stem cells and pre-implantation inner cell mass cells of human embryos to discover that cell fate transitions associate with rapid changes in nuclear shape and volume which collectively alter the nuclear mechanophenotype. Mechanistic studies in human induced pluripotent stem cells further reveal that these phenotypical changes and the associated active fluctuations of the nuclear envelope arise from growth factor signaling-controlled changes in chromatin mechanics and cytoskeletal confinement. These collective mechano-osmotic changes trigger global transcriptional repression and a condensation-prone environment that primes chromatin for a cell fate transition by attenuating repression of differentiation genes. However, while this mechano-osmotic chromatin priming has the potential to accelerate fate transitions and differentiation, sustained biochemical signals are required for robust induction of specific lineages. Our findings uncover a critical mechanochemical feedback mechanism that integrates nuclear mechanics, shape and volume with biochemical signaling and chromatin state to control cell fate transition dynamics.

Characterization of Chronotypes Using the Symbolic Aggregate apprXimation (SAX) on Actigraphy Data.

In this study, we discuss an efficient approach to characterizing chronotypes using Symbolic Aggregate approXimation (SAX) on actigraphy data. Actigraphy, a non-invasive monitoring of human rest/activity cycles, provides valuable insights into sleep-wake behaviors and circadian rhythms. However, the high dimensionality of actigraphy data poses significant challenges in storage, processing, and analysis. To address these challenges, we applied the SAX algorithm to transform continuous time-series actigraphy data into a symbolic representation, enabling dimensionality reduction while preserving essential patterns. We analyzed actigraphy data from the National Health and Nutrition Examination Survey (NHANES) database, covering over 10,000 individuals, and used unsupervised clustering to identify distinct chronotype patterns. The SAX transformation facilitated the application of machine learning techniques, revealing five chronotype clusters characterized by differences in activity onset, resolution, and intensity. Age distribution analysis showed biases towards specific age groups within the clusters, highlighting the relationship between age and chronotype. Key findings include age-related Chronotype variations with younger individuals exhibiting delayed chronotypes with significant differences in sleep onset (SOT) and wake time (WT) compared to older adults, suggesting a phase delay in sleep patterns as age decreases and activity transition dynamics where clusters showed distinct patterns in winding up and winding down periods, providing insights into the dynamics of activity transitions. This study demonstrates the efficiency and effectiveness of SAX in processing large-scale actigraphy data, enabling robust chronotype characterization that can inform personalized healthcare and public health initiatives. Further exploration of SAX integration with other biometric measures could deepen our understanding of human circadian biology and its impact on health and behavior.

KPZ scaling from the Krylov space

Recently, a superdiffusion exhibiting the Kardar-Parisi-Zhang (KPZ) scaling in late-time correlators and autocorrelators of certain interacting many-body systems, such as Heisenberg spin chains, has been reported. Inspired by these results, we explore the KPZ scaling in correlation functions using their realization in the Krylov operator basis. We focus on the Heisenberg time scale, which approximately corresponds to the ramp–plateau transition for the Krylov complexity in systems with a large but finite number degrees of freedom. Two frameworks are under consideration: i) the system with growing Lanczos coefficients and an artificial cut-off, and ii) the system with the finite Hilbert space. In both cases via numerical analysis, we observe the transition from Gaussian to KPZ-like scaling, or equivalently, the diffusion-superdiffusion transition at the critical Euclidean time tE∗\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ {t}_E^{\\ast } $$\\end{document} = ccrK, for the Krylov chain of finite length K, and ccr = O(1). In particular, we find a scaling ~ K1/3 for fluctuations in the one-point correlation function and a dynamical scaling ~ K−2/3 associated with the return probability (Loschmidt echo) corresponding to autocorrelators in physical space. In the first case, the transition is of the 3rd order and can be considered as an example of dynamical quantum phase transition (DQPT), while in the second, it is a crossover. For case ii), utilizing the relationship between the spectrum of tridiagonal matrices at the spectral edge and the spectrum of the stochastic Airy operator, we demonstrate analytically the origin of the KPZ scaling for the particular Krylov chain using the results of the probability theory. We argue that there is interesting outcome of our study for the double scaling limit of matrix models. For the case of topological gravity, the white noise term of order O (1/N) is identified, which should be taken into account in the controversial issue of ensemble averaging in 2D/1D holography.

Robustly encoding certainty in a metastable neural circuit model.

Localized persistent neural activity can encode delayed estimates of continuous variables. Common experiments require that subjects store and report the feature value (e.g., orientation) of a particular cue (e.g., oriented bar on a screen) after a delay. Visualizing recorded activity of neurons along their feature tuning reveals activity bumps whose centers wander stochastically, degrading the estimate over time. Bump position therefore represents the remembered estimate. Recent work suggests bump amplitude may represent estimate certainty reflecting a probabilistic population code for a Bayesian posterior. Idealized models of this type are fragile due to the fine tuning common to constructed continuum attractors in dynamical systems. Here we propose an alternative metastable model for robustly supporting multiple bump amplitudes by extending neural circuit models to include quantized nonlinearities. Asymptotic projections of circuit activity produce low-dimensional evolution equationsfor the amplitude and position of bump solutions in response to external stimuli and noise perturbations. Analysis of reduced equationsaccurately characterizes phase variance and the dynamics of amplitude transitions between stable discrete values. More salient cues generate bumps of higher amplitude which wander less, consistent with experiments showing certainty correlates with more accurate memories.

Neuron configuration enhances the synchronization dynamics in ring networks with heterogeneous firing patterns

Neuron synchronization is fundamental for brain dynamics. While several efforts have been made to understand neuron coupling between tonic and bursting neurons, the position of neurons in a neural network and its relationship with synchronization is not fully understood. This work investigates the impact of neuronal heterogeneity on firing pattern transitions and synchronization in networks comprising tonic and bursting neurons. Using the Huber-Braun model, we explore two configurations: one with neurons grouped closely and another with maximal separation. Our findings reveal that while increased coupling strength generally promotes synchronization, the specific mix of neuron types and their spatial configuration crucially modulate both synchronization and firing pattern transitions, leading to phenomena such as cluster synchronization and global phase synchrony. The results indicate that the configuration with maximal separation and with mostly bursting neurons synchronizes more quickly. Firing pattern transitions are also configuration-dependent. For example, in the network with half tonic and half bursting, the configuration grouped closely undergoes diverse complex dynamics transition in the synchronized state, while the other configuration synchronize with chaotic bursting dynamics. Behaviors like cluster synchronization are observed in this network. The study underscores the significance of considering neuronal heterogeneity in understanding network dynamics.

Dynamics of spontaneous mutations: implications of guanine–cytosine tautomers on chromatin integrity

This study investigates the origin and implications of spontaneous mutations within DNA, focusing on the dynamics of guanine–cytosine (GC) base pairs. We propose inter – and intramolecular hydrogen transfer as potential mechanisms for the formation of rare tautomers, which can significantly impact the structural conformity and operational integrity of chromatin. The nitrogenous bases in DNA are pivotal for genetic coding and protein synthesis, while chromatin plays a fundamental role in DNA compaction, organisation, gene expression control and genome integrity preservation. Our research identifies 56 previously unrecorded guanine–cytosine tautomers and explores diverse potential energy levels, leading to the discovery of 67 transition states across pathways. These transitions involve five distinct types of hydrogen transfer: N-H→N, N-H→O, N-H→C, C–H→N and O-H→N. Utilising an innovative approach, we establish a Markov chain to assess the probability of molecular production from the same source, unveiling insights into transitional dynamics. Furthermore, our study facilitates the identification of tautomers capable of swift conversion to the GC1 base pair, which is dependent on transition states and molecular pathways. Our findings contribute to a deeper understanding of spontaneous mutations and their impact on chromatin integrity, offering valuable insights for further research in this field.

Thickness-Dependent Segmental Dynamics in Supported Thin Films: Insights from a Dynamically Correlated Network Model.

A large body of experimental studies shows that the local dynamics in supercooled liquids are significantly altered by spatial nanoconfinement. In a previous study, we proposed a concept of a dynamically correlated network (DCN) model, which assumes that segments in a supercooled liquid undergo cooperative rearrangements within a network-like cluster. We further demonstrated that a model modified for freestanding thin films can predict for the glass transition dynamics in atactic polystyrene (PS) films consistent with experimental results. In this study, we adapted the model to apply it to supported thin films by introducing a layer of virtual vacant segments at the free surface and virtual anchoring segments at the liquid/substrate interface. The latter segments, carrying a finite number of virtual segments, reduce mobility at the interface. We evaluated the cooperative cluster size and distribution with respect to temperature and film thickness, along with the average relaxation time and glass transition temperature Tg for supported thin films of PS. The model predicted that the thickness dependence of Tg for PS becomes stronger with increasing time scale, and this result agreed well with experimental data across different timescales from pseudothermodynamic and dynamic measurements. The results provide insights into the origin of the dynamical decoupling between pseudothermodynamic and dynamic glass transition behaviors.

Hydrodynamic behavior near dynamical criticality of a facilitated conservative lattice gas.

We investigate a 2d-conservative lattice gas exhibiting a dynamical active-absorbing phase transition with critical density ρ_{c}. We derive the hydrodynamic equationfor this model, showing that all critical exponents governing the large scale behavior near criticality can be obtained from two independent ones. We show that as the supercritical density approaches criticality, distinct length scales naturally appear. Remarkably, this behavior is different from the subcritical one. Numerical simulations support the critical relations and the scale separation.

Data-driven insights into cavitation phenomena: From spatiotemporal features to physical state transitions

The application of data-driven methods to study cavitation flow provides insights into the underlying mechanisms and richer physical details of cavitation phenomena. This paper aims to analyze the physically interpretable multi-state cavitation behavior. Initially, the spatiotemporal features of the cavitation flow are represented as network trajectories using principal component analysis. The k-means++ algorithm is then employed to obtain coarse-grained flow field states, and the centroid of each cluster served as a representative for the attributes of that state. Subsequently, the Markov state model is constructed to capture the dynamic transitions in the cavitation flow field. Through a detailed analysis of the dynamic transition model, the cavitation flow field states with genuine physical mechanisms are refined. Finally, proper orthogonal decomposition (POD) is utilized to extract the flow patterns corresponding to different states. The distribution characteristics of the flow field modes in different states correspond to their physical properties. These data-driven algorithm enables a detailed analysis of the typical states in periodic cavitation processes, such as cavity growth, development, shedding, and collapse, providing a deeper understanding of the cavitation flow characteristics in different typical states.

Enhancing spontaneous and continuous liquid directional transport on peristome-mimetic surface with hierarchical microgrooves

Directional liquid transport function discovered on the peristome of Nepenthes alata has attracted considerable attention for its diverse potential applications. Despite the extensive efforts made for the peristome-mimetic surface fabrication and the anisotropic liquid spreading regulation, it remains a daunting challenge to reveal the synergistic effect of hierarchical structures on the liquid spreading and pinning dynamics. Here, we demonstrate the first-tier microgroove morphology, as well as the presence of second-tier microgrooves, play an important role in homogenous film formation and the directional liquid transport control. Through experimental investigation and theoretical analysis, the enhanced spreading and pinning effect is validated. Moreover, the preferential directional liquid spreading will collapse on the peristome-mimetic surface without a rational parameter design, and the threshold value for the transition of liquid propagation dynamics is determined. Spontaneous directional liquid transport from the cold region to the hot region and smart liquid transport regulation was also realized on the peristome-mimetic surface. This work will provide guidance to the design of effective open microfluidic systems, and open a new way for thermal management and lab-on-chip applications.