Dynamic Scaling Research Articles

WEDAP: A Python Package for Streamlined Plotting of Molecular Simulation Data.

Given the growing interest in path sampling methods for extending the time scales of molecular dynamics (MD) simulations, there has been great interest in software tools that streamline the generation of plots for monitoring the progress of large-scale simulations. Here, we present the WEDAP Python package for simplifying the analysis of data generated from either conventional MD simulations or the weighted ensemble (WE) path sampling method, as implemented in the widely used WESTPA software package. WEDAP facilitates (i) the parsing of WE simulation data stored in highly compressed, hierarchical HDF5 files and (ii) incorporates trajectory weights from WE simulations into all generated plots. Our Python package consists of multiple user-friendly interfaces: a command-line interface, a graphical user interface, and a Python application programming interface. We demonstrate the plotting features of WEDAP through a series of examples using data from WE and conventional MD simulations that focus on the HIV-1 capsid protein's C-terminal domain dimer as a showcase system. The source code for WEDAP is freely available on GitHub at https://github.com/chonglab-pitt/wedap.

A Dynamic Spatiotemporal Understanding of Changes in Social Vulnerability to Wildfires at Local Scale

Research on wildfires and social vulnerability has gained significant importance due to the increasing frequency and severity of wildfires around the world. This study investigates the dynamic changes in social vulnerability to wildfires over a decade in Idaho, USA, utilizing GIS-based tools and a quasi-experimental design. We assess the evolving nature of social vulnerability at a local scale, emphasizing both spatial and temporal dynamics. Initially, we identified social vulnerability trends in relation to varying levels of wildfire risk. The research then employs propensity score matching to contrast areas affected by wildfires in 2012 with similar non-affected regions, thereby quantifying the short-term shifts in social vulnerability post-wildfires. The results indicate that regions with a high wildfire risk may display elevated vulnerability, characterized by an increase in unemployment rates and a reduction in high-income households. These findings tentatively demonstrate the compounded effect of wildfires on already vulnerable populations, highlighting the critical need for targeted interventions. Ultimately, this study underscores the importance of integrating dynamic social vulnerability assessments into wildfire management and planning, aiming to enhance community resilience and equitable resource distribution in the face of escalating wildfire threats.

Measurement of the depth-dependent local dynamics in thin polymer films through rejuvenation of ultrastable glasses

The depth dependence of structural relaxation dynamics is a key part of understanding thin glassy films. Despite this importance and decades of research, a method to provide this information has proved elusive. We measure the isothermal rejuvenation of stable glass films of poly(styrene), and demonstrate that the propagation of the front responsible for the transformation to a supercooled-liquid state serves as a highly localized probe of the local dynamics of the supercooled liquid. We use this connection to probe the depth-dependent relaxation rate with nanometric precision for a series of polystyrene films over a range of temperatures near the bulk glass transition temperature. The analysis shows the spatial extent of enhanced surface mobility and reveals the existence of an unexpected large dynamical length scale in the system. The results are compared with the cooperative-string model for glassy dynamics. The data reveals that the film-thickness dependence of whole film properties arises mainly from the volume fraction of the near-surface region. While the dynamics farthest from the free surface shows the expected bulk-like temperature dependence, the dynamics in the near-surface region shows very little dependence on temperature. This technique can be used in a broad range of thin film materials to gain previously unattainable information about localized structural relaxation.

Transient energy transfer and cascade analysis for stratified turbulent channel flows

A comprehensive and fine-grained transient analysis of the kinetic budget equation in the spectral and spatial domains for channel flow subjected to stable stratification is performed, through a series of large eddy simulations (LES) at a bulk Reynolds number of 2200 and for Froude numbers of 0.5, 2.0, 4.5 and 6.8, thereby covering a range of stratification strengths from high to low. The dynamic Smagorinsky eddy viscosity-based sub-grid scale (SGS) model is used for closure. Validation of the methodology is carried out by comparing the turbulent flow LES results to previously published channel flow results. The analysis is focussed on the effect of stratification on the energy cascade process of an initially turbulent flow in a channel configuration. The terms in the kinetic budget equation, in the homogeneous and inhomogeneous directions, are compared for different Froude numbers to assess the effects of weak and strong stratification on turbulent statistics. Transient results are plotted to highlight the role played by different terms in the budget equation and to understand the formation of the gravity waves in the core or outer regions of the wall-bounded flow in the presence of stratification. The energy cascade reveals some interesting observations about the functions of viscous diffusion, pressure diffusion, and inhomogeneous energy transfer in the initial phase of the development of flow, which in turn helps to explain the stationary waves observed in the presence of strong stratification.

Dynamic scaling relation in quantum many-body systems

Dynamic scaling relation in quantum many-body systems

Wigner Analysis of Particle Dynamics and Decoherence in Wide Nonharmonic Potentials

We derive an analytical expression of a Wigner function that approximately describes the time evolution of the one-dimensional motion of a particle in a nonharmonic potential. Our method involves two exact frame transformations, accounting for both the classical dynamics of the centroid of the initial state and the rotation and squeezing about that trajectory. Subsequently, we employ two crucial approximations, namely the constant-angle and linearized-decoherence approximations, upon which our results rely. These approximations are effective in the regime of wide potentials and small fluctuations, namely potentials that enable spatial expansions orders of magnitude larger than the one of the initial state but that remain smaller compared to the relevant dynamical length scale (e.g., the distance between turning points). Our analytical result elucidates the interplay between classical and quantum physics and the impact of decoherence during nonlinear dynamics. This analytical result is instrumental to designing, optimizing, and understanding proposals using nonlinear dynamics to generate macroscopic quantum states of massive particles.

An Implementation on Energy Efficient Task Scheduling in Cloud Environment

Abstract: Cloud computing has revolutionized the way computing resources are utilized by providing scalable, on-demand access to a shared pool of resources over the internet. It offers significant advantages such as cost savings, flexibility, and accessibility, making it an essential technology for businesses and individuals alike. However, the increasing energy consumption associated with cloud data centres has become a critical concern, necessitating the development of energy-efficient task scheduling algorithms. This paper explores various algorithms designed to optimize task scheduling in cloud environments to reduce energy consumption. Among these, heuristic algorithms like Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are popular for their ability to find near-optimal solutions in large search spaces. Additionally, machine learning-based approaches, such as Reinforcement Learning (RL), have shown promise in dynamically adapting to workload variations and improving energy efficiency. Other notable algorithms include Ant Colony Optimization (ACO) and Dynamic Voltage and Frequency Scaling (DVFS), each offering unique mechanisms to balance performance and energy usage. The focus of this paper is on the implementation and comparative analysis of these task scheduling algorithms in a cloud environment. We present a comprehensive evaluation of their performance, highlighting their strengths and limitations in achieving energy efficiency. The results demonstrate that while no single algorithm is universally optimal, tailored combinations of these approaches can significantly enhance energy savings in cloud data centres.

Dynamic Component of the Asthenosphere: Lateral Viscosity Variations Due To Dislocation Creep at the Base of Oceanic Plates

AbstractThe asthenosphere is commonly defined as an upper mantle zone with low velocities and high attenuation of seismic waves, and high electrical conductivity. These observations are usually explained by the presence of partial melt, or by a sharp contrast in the water content of the upper mantle. Low viscosity asthenosphere is an essential ingredient of functioning plate tectonics. We argue that a substantial component of asthenospheric weakening is dynamic, caused by dislocation creep at the base of tectonic plates. Numerical simulations of subduction show that dynamic weakening scales with the surface velocity both below the subducting and the overriding plate, and that the viscosity decrease reaches up to two orders of magnitude. The resulting scaling law is employed in an apriori estimate of the lateral viscosity variations (LVV) below Earth's oceans. The obtained LVV help in explaining some of the long‐standing as well as recent problems in mantle viscosity inversions.

Dynamic Scaling of Two-Dimensional Polar Flocks.

We propose a hydrodynamic description of the homogeneous ordered phase of polar flocks. Starting from symmetry principles, we construct the appropriate equation for the dynamics of the Goldstone mode associated with the broken rotational symmetry. We then focus on the two-dimensional case considering both "Malthusian flocks" for which the density field is a fast variable that does not enter the hydrodynamic description and "Vicsek flocks" for which it does. In both cases, we argue in favor of scaling relations that allow one to compute exactly the scaling exponents, which are found in excellent agreement with previous simulations of the Vicsek model and with the numerical integration of our hydrodynamic equations.

A novel UV-curable amphiphobic coating with dynamic air-layer copolymer brush offering stable dual-functional anti-scaling and anti-corrosion properties

Scale deposition and metal corrosion, especially in the petroleum industry, have always been serious issues that seriously affect economic efficiency and safety. However, conventional anti-scaling strategies including mechanical descaling and chemical detergents are energy-consuming and environmentally damaging, which can't achieve efficient and long-term protection. Based on mechanical descaling and chemical detergents, and anti-corrosion strategies based on filler are inadequate to realize efficient protection. Herein, this work reports a novel UV-curable polymer coating with long-term anti-scaling and anti-corrosion properties. The excellent anti-scaling capability was achieved by inhibiting interfacial nucleation and reducing interfacial adhesion work of scaling products. Significantly, the anti-scaling capability of the designed coating mainly originates from two aspects: inhibiting interfacial nucleation and reducing interfacial adhesion work of scaling. Compared with pure polyurethane acrylic coating (0 % FGSS), the 20 % FGSS coating shows significantly improved scaling repellence even after a 240 h dynamic scaling test while maintaining a fairly low theoretical adhesion work (≈0.1 mN/m). This outstanding anti-corrosion capability of the FGSS coating mainly originates from two aspects: the physical barrier effect of the air layer and low surface energy resistance barriers with dynamic FGSS brushes. The physical barrier effect of the air layer and the low surface energy resistance barriers also lead to outstanding anti-corrosion property (with the |Z|0.01 Hz value maintained at 108 Ω·cm2). This study presents a simple and effective design and fabricated strategy, paving a new pathway for developing UV-cured coatings with unique anti-corrosion and anti-scaling properties. This facile and effective UV-curable polymer coating shows great potential in metal protection and paves a new pathway for the development of long-term functional protection coatings.

Negative storm surges in the Río de la Plata Estuary: mechanisms, variability, trends and linkage with the Continental Shelf dynamics

Negative storm surge (NSS) events frequently affect the Río de la Plata Estuary (RdP), limiting the access to major ports and waterways and emptying the freshwater intakes of one of the most socially developed basins of the Southwestern Atlantic Continental Shelf (SWACS). It is important to re-examine this issue, as the available studies are outdated, there are some contradictions in the literature and a regional framework of the adjacent shelf dynamics is not yet available. In this work (i) nonlinear statistical analyses were applied to review and update the climatological description of NSS in the RdP; and, then, (ii) an ocean numerical model was employed to describe and understand the surge dynamics framed into the larger scale dynamics of the atmospherically forced SWACS. An analysis of 87 years of hourly residual sea level observations (RSL) reveals that the number of yearly NSS events time series presents (i) a statistically significant multidecadal non-linear trend or envelope and (ii) two significant pseudo-cycles centred at 3.5 and 11.5 years that explain a large fraction of the interannual variability embedded in the multidecadal modulation.Numerical simulations results show that the surges (both negative and positive) that are generated in the SWACS and reach the RdP result from the passage of atmospheric planetary waves over Patagonia which produce winds with a significant alongshore (N-S) component. These winds force an Ekman transport either offshore or onshore at the coast, generating a negative or positive sea level anomaly, which will be larger as the atmospheric system is slower and more persistent. When synoptic atmospheric systems migrate away from the coast on their northeastward propagation, the forced sea level anomalies propagate along the coast to the north as free Kelvin waves, eventually reaching the estuary; thus the forcing for surges at the RdP can be remote. In this sense, despite the large gulfs and bays, the response of the Patagonian coast is similar to that of a linear coastline oriented in a north–south direction. In addition to remotely forced surges, locally forced events occur in the RdP as a result of the estuarine geometry and its resonant response to winds with a component longitudinal to the estuary axis (NW-SE), which could be forced by other atmospheric configurations besides Rossby waves, such as cyclogenesis of the Argentine Littoral.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits.

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

Scaling and universality at ramped quench dynamical quantum phase transitions

The nonequilibrium dynamics of a periodically driven extended XY model, in the presence of linear time dependent magnetic field, is investigated using the notion of dynamical quantum phase transitions (DQPTs). Along the similar lines to the equilibrium phase transition, the main purpose of this work is to search fundamental concepts such as scaling and universality at the ramped quench DQPTs. We have shown that the critical points of the model, where the gap closing occurs, can be moved by tuning the driven frequency and consequently the presence of or absence of DQPTs can be flexibly controlled by adjusting the driven frequency. We have uncovered that, for a ramp across the single quantum critical point, the critical mode at which DQPTs occur is classified into three regions: the Kibble–Zurek (KZ) region, where the critical mode scales linearly with the square root of the sweep velocity, the pre-saturated (PS) region, and the saturated (S) region where the critical mode makes a plateau versus the sweep velocity. While for a ramp that crosses two critical points, the critical modes disclose just the KZ and PS regions. On the basis of numerical simulations, we find that the dynamical free energy scales linearly with time, as approaches to DQPT time, with the exponent ν=1±0.01 for all sweep velocities and driven frequencies.

Capturing Hydrated Vanadium Ion Dynamics in Ionomer Nanocomposites Used for Redox Flow Batteries.

Herein, we employed high-flux backscattering spectroscopy to capture for the first time the motions of hydrated vanadyl ions in ionomer nanocomposites prepared by both solution-cast and in situ sol-gel condensation methods. Both local and jump diffusion coefficients of the hydrated vanadyl (VO2+) ions as well as the dynamic length scales of ion motions and the fraction of immobile hydrogen atoms were extracted from the scattering spectra. Notably, for solution-cast membranes, the jump and local diffusion coefficients of hydrated VO2+ ions were seen to decrease by over 10- and 4-fold, respectively, with the introduction of 10 mass % silica nanoparticles (SiNPs) compared to their neat counterparts. Further, the VO2+ diffusion coefficients were observed to decrease with thermal annealing, though the impact of annealing was less significant than that seen with the introduction of SiNPs. Finally, in general, thermal annealing and the introduction of SiNPs had no measurable impact on the fraction of immobile hydrogen atoms in both solution-cast and sol-gel ionomer nanocomposites. The data observed in this work, in conjunction with previous structural and chain dynamics studies on hydrated Nafion-SiNP nanocomposites, suggest that a combination of stiffening of the segmental dynamics as well as a decrease in available sulfonic acid groups facilitating transport leads to an overall decrease in mobility of vanadium ions in these ionomer nanocomposites.

Thermal Safe Power Constrained Dynamic Mapping for Heterogeneous Multicore System

Heterogeneous multicore system could provide high flexibility for applications through integrating different types of cores. As the number of cores increases, more and more transistors are integrated which could lead to the rise of chip temperature, thus have a negative impact on the system performance. At present, the typical method to tackle this problem is dynamic voltage and frequency scaling (DVFS) which reduces the voltage and frequency to achieve cooling. However, DVFS will make the heterogeneous multicore system unable to take full advantage of its performance. This paper proposes a dynamic mapping method called TspDM which maximizes the system performance target under the premise of satisfying the temperature safety power. TspDM exploits the runtime performance counters of running threads to dynamically adjust thread mapping. Specifically, a lightweight Artificial Neural Network (ANN) model is proposed to obtain the thread-to-core type mapping when a remapping process is activated. Subsequently, TspDM determines the specific core locations of threads according to the number of threads mapped to a certain core type and the thermal safety power computation. The experimental results show that the proposed method is 21% better than that of PCMig while ensuring the thermal safety of heterogeneous platform. In addition, TspDM outperforms recent performance constrained and power constrained literature in terms of system throughput.

Vehicle detection in varied weather conditions using enhanced deep YOLO with complex wavelet

Traffic congestion is prevalent in many major and medium-sized cities throughout different countries in contemporary society. In traffic images, various multi-sized vehicles are tightly clustered together and obstructed from one another. Identifying vehicles in such instances is crucial for urban traffic surveillance, safety monitoring, and legal concerns but it also presents major challenges. The remarkable detection accuracy and efficiency of deep learning-based systems have led to their recent and extensive use in vehicle identification. There are significant advanced YOLO models with different backbone architectures and frameworks developed for vehicle detection. Yet, the performance of YOLO variants are facing the challenges of handling false detection against occluded and densely sophisticated scenarios. The proposed model is developed to address such types of limitations, for example; dynamic illumination, noisy images, and scale sensitivity to improve the vehicle detection rate in different traffic scenarios and varying weather conditions. The proposed study employs an improved YOLOv4 to identify moving vehicles in different lighting conditions including daylight, cloudy, rainy, and night. For hybridization, three techniques are utilized such as the Multiscale Retinex, Dual tree complex wavelet transform (DTCWT), and Pulse Coupled Neural Networks (PCNN). The DTCWT is employed for multiscale decomposition and to denoise the complex high frequency subband information, then the denoised subbands are reconstructed into a denoised image. The Multiscale retinex is utilized to reduce the halo artifacts on high-contrast edges and maintain the balance with dynamic range compression and color reproduction. The synchronizing pulse burst property of PCNN is used to detect the isolated noisy pixels and modify the detected noisy pixels. From the results it is worth noting that the developed model surpasses state-of-the-art methods in sunny, night, cloudy, and rainy modes. The proposed method using the DTCWT technique can detect the vehicles with mAP of 91.09% and 35FPS.

Kinetics of the one-dimensional voter model with long-range interactions

The voter model is an extremely simple yet nontrivial prototypical model of ordering dynamics, which has been studied in great detail. Recently, a great deal of activity has focused on long-range statistical physics models, where interactions take place among faraway sites, with a probability slowly decaying with distance. In this paper, we study analytically the one-dimensional long-range voter model, where an agent takes the opinion of another at distance r with probability ∝r−α . The model displays rich and diverse features as α is changed. For α > 3 the behavior is similar to the one of the nearest-neighbor version, with the formation of ordered domains whose typical size grows as R(t)∝t1/2 until consensus (a fully ordered configuration) is reached. The correlation function C(r,t) between two agents at distance r obeys dynamical scaling with sizeable corrections at large distances r>r∗(t) , slowly fading away in time. For 2<α⩽3 violations of scaling appear, due to the simultaneous presence of two lengh-scales, the size of domains growing as t(α−2)/(α−1) , and the distance L(t)∝t1/(α−1) over which correlations extend. For α⩽2 the system reaches a partially ordered stationary state, characterised by an algebraic correlator, whose lifetime diverges in the thermodynamic limit of infinitely many agents, so that consensus is not reached. For a finite system escape towards the fully ordered configuration is finally promoted by development of large distance correlations. In a system of N sites, global consensus is achieved after a time T∝N2 for α > 3, T∝Nα−1 for 2<α⩽3 , and T∝N for α⩽2 .

Scaling Energy Transfer in Ball Mills: A Scale-Agnostic Approach through a Universal Scaling Constant

Ball mills are widely used for size reduction in mineral processing, but effective scaling from laboratory to industrial scale remains challenging. This study introduces a novel scaling constant approach to replicate energy transfer to ore during milling across different scales by adjusting rotational speed and grinding medium size distribution. The scaling constant encapsulates parameters like the number of balls per working area, rotational speed, and an average ball’s maximum potential and kinetic energies. Experiments were conducted using a laboratory ball mill with interchangeable drum sizes (300, 400, and 500 mm) and a Design of Experiments methodology. Statistical analysis revealed that the scaling constant was more effective at maintaining consistent specific energy and energy per rotation across scales than size reduction, especially in dry milling. Wet milling results showed no significant differences in all metrics across scales. The dominant charge motion shifted from centrifuging to cascading as the mill diameter increased, highlighting the complex scaling dynamics. While the scaling constant shows promise for maintaining energy utilization, additional factors like charge motion and particle breakage mechanisms should be considered. The findings provide insights for improving ball mill design and optimization in mineral processing.

Learning about structural errors in models of complex dynamical systems

Complex dynamical systems are notoriously difficult to model because some degrees of freedom (e.g., small scales) may be computationally unresolvable or are incompletely understood, yet they are dynamically important. For example, the small scales of cloud dynamics and droplet formation are crucial for controlling climate, yet are unresolvable in global climate models. Semi-empirical closure models for the effects of unresolved degrees of freedom often exist and encode important domain-specific knowledge. Building on such closure models and correcting them through learning the structural errors can be an effective way of fusing data with domain knowledge. Here we describe a general approach, principles, and algorithms for learning about structural errors. Key to our approach is to include structural error models inside the models of complex systems, for example, in closure models for unresolved scales. The structural errors then map, usually nonlinearly, to observable data. As a result, however, mismatches between model output and data are only indirectly informative about structural errors, due to a lack of labeled pairs of inputs and outputs of structural error models. Additionally, derivatives of the model may not exist or be readily available. We discuss how structural error models can be learned from indirect data with derivative-free Kalman inversion algorithms and variants, how sparsity constraints enforce a “do no harm” principle, and various ways of modeling structural errors. We also discuss the merits of using non-local and/or stochastic error models. In addition, we demonstrate how data assimilation techniques can assist the learning about structural errors in non-ergodic systems. The concepts and algorithms are illustrated in two numerical examples based on the Lorenz-96 system and a human glucose-insulin model.

Energy-aware task scheduling for streaming applications on NoC-based MPSoCs

Streaming applications are being extensively run on portable embedded systems, which are battery-operated and with limited memory. Thus, minimizing the total energy consumption of such a system is important. We investigate the problem of offline scheduling for streaming applications composed of non-preemptible periodic dependent tasks on homogeneous Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoCs) such that their total energy consumption is minimized under memory constraints. We propose a novel unified approach that integrates task-level software pipelining with Dynamic Voltage and Frequency Scaling (DVFS) to solve the problem. Our approach is supported by a set of novel techniques, which include constructing an initial schedule based on a list scheduling where the priority of each task is its approximate successor-tree-consistent deadline such that the workload across all the processors is balanced, a retiming heuristic to transform intra-period dependencies into inter-period dependencies for enhancing parallelism, assigning an optimal discrete frequency for each task and each message using a Non-Linear Programming (NLP)-based algorithm and an Integer-Linear Programming (ILP)-based algorithm, and an incremental approach to reduce the memory usage of the retimed schedule in case of memory size violations. Using a set of real and synthetic benchmarks, we have implemented and compared our unified approach with two state-of-the-art approaches, RDAG+GeneS (Wang et al., 2011) , and JCCTS (Wang et al., 2013a). Experimental results show that our approach’s maximum, average, and minimum improvements over RDAG+GeneS (Wang et al., 2011) are 31.72%, 14.05%, and 7.00%, respectively. Our approach’s maximum, average, and minimum improvement over JCCTS (Wang et al., 2013a) are 35.58%, 17.04%, and 8.21%, respectively.