Dynamic Scaling Research Articles

The Effects of the Pericapsular Nerve Group Block on Postoperative Pain in Patients with Hip Fracture: A Multicenter Study.

An adequate early mobilization followed by an effective and pain-free rehabilitation are critical for clinical and functional recovery after hip and proximal femur fracture. A multimodal approach is always recommended so as to reduce the administered dose of analgesics, drug interactions, and possible side effects. Peripheral nerve blocks should always be considered in addition to spinal or general anesthesia to prolong postoperative analgesia. The pericapsular nerve group (PENG) block appears to be a less invasive and more effective analgesia technique compared to other methods. We conducted multicenter retrospective clinical research, including 98 patients with proximal femur fracture undergoing osteosynthesis surgery within 48 h of occurrence of the fracture. Thirty minutes before performing spinal anesthesia, 49 patients underwent a femoral nerve (FN) block plus a lateral femoral cutaneous nerve (LCFN) block, and the other 49 patients received a PENG block. A non-parametric Wilcoxon-Mann-Whitney (α = 0.05) test was performed to evaluate the difference in resting and dynamic numerical rating scale (NRS) at 30 min, 6 h, 12 h, and 24 h. the PENG block administration was more effective in reducing pain intensity compared to the FN block in association with the LFCN block, as seen in the resting and dynamic NRS at thirty minutes and 12 h follow-up. the PENG block was more effective in reducing pain intensity than the femoral nerve block associated with the lateral femoral cutaneous nerve block in patients with proximal femur fracture undergoing to osteosynthesis.

Energy efficient Security Driven Scheduling for Real-Time Tasks through DVS-enabled Fog Networks

The issue of task scheduling for a multi-core processor in Fog networks, with a focus on security and energy efficiency is of great importance in real-time systems. Currently, scheduling algorithms designed for cluster computing environments utilize dynamic voltage scaling (DVS) to decrease CPU power consumption, albeit at the expense of performance. This problem becomes more pronounced when a real-time task requires robust security, resulting in heavily overloaded nodes (CPUs or computing systems) in a cluster computing environment. To address such challenges, a solution called “Energy efficient Security Driven Scheduling of Real-Time Tasks using DVS-enabled Fog Networks (ESDS)” has been proposed. The primary goal of ESDS is to dynamically adjust CPU voltages or frequencies based on the workload conditions of nodes in Fog networks, thereby achieving optimal trade-offs between security, scheduling, and energy consumption for real-time tasks. By dynamically reducing voltage or frequency levels, ESDS conserves energy while still meeting deadlines for both running and new tasks, especially during periods of high system workload. Comprehensive experiments have been carried out to compare the ESDS algorithm with established baseline algorithms, including MEG, MELV, MEHV, and AEES. These experiments affirm the originality and effectiveness of the ESDS algorithm.

Crossover effects and dynamic scaling properties from Eden growth to diffusion-limited aggregation

The diffusion-limited aggregation (DLA) model is a classical fractal aggregation model that describes numerous fractal phenomena, and the Eden model is a typical discrete growth model for investigating cell clusters, such as bacteria or tissue cultures. There is a significant difference between these two discrete models in their growth rules and dynamic scaling properties. In this paper, we introduce a generalized growth theory to describe effectively these two distinct growth classes based on the competitive growth rules. By controlling the adhesion coefficient, a continuous transition exists from dense polymerization to finger growth, i.e., the evolution from Eden growth to DLA in both two-dimensional and three-dimensional cases. To simulate effectively the generalized growth system in the most physically relevant case of three-dimensions, we employ a proper technique named the Marsaglia method for correctly obtaining distributed points to avoid the inappropriate simulation schemes commonly used. Furthermore, the crossover of these competitive growth exhibits non-trivial scaling behavior, and the quantitative relation between the corresponding critical exponents and the adhesion parameter is also investigated. Concurrently, the porosities of the generalized growth system are calculated, which exhibit obviously nontrivial dynamic properties depending on the growth dimensions.

Scaling of stochastic growth and division dynamics: A comparative study of individual rod-shaped cells in the Mother Machine and SChemostat platforms.

Microfluidic platforms enable long-term quantification of stochastic behaviors of individual bacterial cells under precisely controlled growth conditions. Yet, quantitative comparisons of physiological parameters and cell behaviors of different microorganisms in different experimental and device modalities is not available due to experiment-specific details affecting cell physiology. To rigorously assess the effects of mechanical confinement, we designed, engineered, and performed side-by-side experiments under otherwise identical conditions in the Mother Machine (with confinement) and the SChemostat (without confinement), using the latter as the ideal comparator. We established a protocol to cultivate a suitably engineered rod-shaped mutant of Caulobacter crescentus in the Mother Machine and benchmarked the differences in stochastic growth and division dynamics with respect to the SChemostat. While the single-cell growth rate distributions are remarkably similar, the mechanically confined cells in the Mother Machine experience a substantial increase in interdivision times. However, we find that the division ratio distribution precisely compensates for this increase, which in turn reflects identical emergent simplicities governing stochastic intergenerational homeostasis of cell sizes across device and experimental configurations, provided the cell sizes are appropriately mean-rescaled in each condition. Our results provide insights into the nature of the robustness of the bacterial growth and division machinery.

Departure from the statistical equilibrium of large scales in forced three-dimensional homogeneous isotropic turbulence

We study the statistically steady states of the forced dissipative three-dimensional homogeneous isotropic turbulence at scales larger than the forcing scale in real separation space. The probability density functions (p.d.f.s) of longitudinal velocity difference at large separations are close to, but deviate from, Gaussian, measured by their non-zero odd parts. The analytical expressions of the third-order longitudinal structure functions derived from the Kármán–Howarth–Monin equation prove that the odd-part p.d.f.s of velocity differences at large separations are small but non-zero. Specifically, when the forcing effect in the displacement space decays exponentially as the displacement tends to infinity, the odd-order longitudinal structure functions have a power-law decay with an exponent of $-$ 2, implying a significant coupling between large and small scales. Under the assumption that forcing controls the large-scale dynamics, we propose a conjugate regime to Kolmogorov's inertial range, independent of the forcing scale, to capture the odd parts of p.d.f.s. Thus, dynamics of large scales departs from the absolute equilibrium, and we can partially recover small-scale information without explicitly resolving small-scale dynamics. The departure from the statistical equilibrium is quantified and found to be viscosity-independent. Even though this departure is small, it is significant and should be considered when studying the large scales of the forced three-dimensional homogeneous isotropic turbulence.

Turbulence induced by a swarm of rising bubbles from coarse-grained simulations

We performed numerical simulations of a homogeneous swarm of bubbles rising at large Reynolds number, $Re=760$ , with volume fractions ranging from 1 % to 10 %. We consider a simplified model in which the interfaces are not resolved, but which allows us to simulate flows with a large number of bubbles and to emphasize the interactions between bubble wakes. The liquid phase is described by solving, on an Eulerian grid, the Navier–Stokes equations, including sources of momentum which model the effect of the bubbles. The dynamics of each bubble is determined within the Lagrangian framework by solving an equation of motion involving the hydrodynamic forces exerted by the fluid accounting for the correction of the fictitious self-interaction of a bubble with its own wake. The comparison with experiments shows that this coarse-grained simulations approach can reliably describe the dynamics of the resolved flow scales. We use conditional averaging to characterize the mean bubble wakes and obtain in particular the typical shear imposed by the rising bubbles. On the basis of the spectral decomposition of the energy budget, we observe that the flow is dominated by production at large scales and by dissipation at small scales and we rule out the presence of an intermediate range in which the production and dissipation are locally in balance. We propose that the $k^{-3}$ subrange of the energy spectra results from the mean shear rate imposed by the bubbles, which controls the rate of return to isotropy.

Computational dielectric spectroscopy on solid-solution interface by time-dependent voltage applied molecular dynamics simulation.

A frequency-dependent dielectric constant characterizes the dielectric response of a medium and also represents the time scale of system's collective dynamics. Although it is valuable not only academically but also practically for developing advanced devices, getting the value of a solution at the interface with a solid or electrode surface is challenging both experimentally and computationally. Here, we propose a computational method that imitates the dielectric spectroscopy and AC impedance measurement. It combines a time-dependent voltage applied molecular dynamics simulation with an equivalent circuit representation of a system composed of a solution confined between two identical electrodes. It gives the frequency-dependent dielectric constants of the bulk solution and the interface simultaneously. Unlike the conventional method, it does not require computation of a dipole autocorrelation function and its Fourier transformation. Application of the method on a system of water confined between polarizable Pt electrodes gives the static dielectric constant and the relaxation time of the bulk water in good agreement with previous simulation results and experimental values. In addition, it gives a much smaller static dielectric constant at the interface, consistent with previous observations. The outline of the dielectric dispersion curve of the interface seems similar to that of the bulk, but the relaxation time is several times faster.

Examining Trajectories of Change on the Dynamic Risk Assessment for Offender Re-Entry (DRAOR).

Dynamic risk scales have largely been evaluated using singular assessment scores, including those obtained at the start of supervision. While this approach includes assessment of dynamic factors, it ignores changes with reassessment, failing to examine whether an instrument is truly dynamic in nature. This is problematic, as proximal risk assessments have consistently outperformed baseline assessments in the prediction of recidivism. In the current study, we examined the dynamic properties of the Dynamic Risk Assessment for Offender Reentry (DRAOR) in 4,736 adults on community supervision in Iowa, United States (N = 33,965 assessments). As expected, while clients demonstrated statistically significant changes on the DRAOR domains over time, changes were small in magnitude. We also examined the predictive validity of baseline and proximal DRAOR total and domain scores on criminal recidivism and revocation in a larger sample of 11,421 adults in the same jurisdiction. While DRAOR baseline scores did predict both outcomes, prediction did not improve with proximal scores. This conflicted with expected findings from previous research on the DRAOR in New Zealand. The results of both of these research questions indicate there was an overall lack of change reflected in this sample. Potential issues regarding implementation fidelity are discussed. Additional research is needed to examine the dynamic properties of the DRAOR in Iowa given the importance of reassessment data in community corrections.

Individual differences in short-term social dynamics: Theoretical perspective and empirical development of the social dynamics scale

People have a need to form and maintain fulfilling social contact, yet they differ with respect to with whom they satisfy the need and how quickly this need is deprived or overly satiated. These social dynamics across relationships and across time are theoretically delineated in the current article. Furthermore, we developed a questionnaire to measure individual differences in three aspects of such social dynamics: (a) Family-friends interdependence, (b) Social deprivation, and (c) Social oversatiation. In a longitudinal study spanning 9 weeks in spring 2020, in total 471 participants (18–75 years, 47% women) answered the newly developed items on social dynamics, questionnaires on social dispositions (e.g., affiliation motive, need to be alone, social anxiety), and questions on personal and indirect contact with family and friends during nationwide contact restrictions related to COVID-19. The results showed that individual differences in Family-friends interdependence, Social deprivation, and Social oversatiation can be measured reliably, validly, and with predictive value for changes in daily contact as contact restrictions were loosened. We discuss potential applications of the Social Dynamics Scale (SDS) for studying social relationships in healthy and clinical populations, and conclude that the brief self-report questionnaire of social dynamics can be useful for situations and samples where direct behavioral observations are not feasible.

Dynamic Data Scaling Techniques for Streaming Machine Learning

This research delves into innovative dynamic data scaling techniques designed for streaming machine learning environments. In the realm of real-time data streams, conventional static scaling methods may encounter challenges in adapting to evolving data distributions. To overcome this hurdle, our study explores dynamic scaling approaches capable of adjusting and optimizing scaling parameters dynamically as the characteristics of incoming data shift over time. The objective is to augment the performance and adaptability of machine learning models in streaming scenarios by ensuring that the scaling process remains responsive to changing patterns in the data. Through empirical evaluations and comparative analyses, the study aims to showcase the efficacy of the proposed dynamic data scaling techniques in enhancing predictive accuracy and sustaining model relevance in dynamic and fast-paced streaming environments. This research contributes to the advancement of scalable and adaptive machine learning methodologies, particularly in applications where timely and accurate insights from streaming data are crucial.

Deconfinement Dynamics of Fractons in Tilted Bose-Hubbard Chains.

Fractonic constraints can lead to exotic properties of quantum many-body systems. Here, we investigate the dynamics of fracton excitations on top of the ground states of a one-dimensional, dipole-conserving Bose-Hubbard model. We show that nearby fractons undergo a collective motion mediated by exchanging virtual dipole excitations, which provides a powerful dynamical tool to characterize the underlying ground-state phases. We find that, in the gapped Mott insulating phase, fractons are confined to each other as motion requires the exchange of massive dipoles. When crossing the phase transition into a gapless Luttinger liquid of dipoles, fractons deconfine. Their transient deconfinement dynamics scales diffusively and exhibits strong but subleading contributions described by a quantum Lifshitz model. We examine prospects for the experimental realization in tilted Bose-Hubbard chains by numerically simulating the adiabatic state preparation and subsequent time evolution and find clear signatures of the low-energy fracton dynamics.

De novo variants in GABRA4 are associated with a neurological phenotype including developmental delay, behavioral abnormalities and epilepsy

Nine out of 19 genes encoding GABAA receptor subunits have been linked to monogenic syndromes characterized by seizures and developmental disorders. Previously, we reported the de novo variant p.(Thr300Ile) in GABRA4 in a patient with epilepsy and neurodevelopmental abnormalities. However, no new cases have been reported since then. Through an international collaboration, we collected molecular and phenotype data of individuals carrying de novo variants in GABRA4. Patients and their parents were investigated either by exome or genome sequencing, followed by targeted Sanger sequencing in some cases. All variants within the transmembrane domain, including the previously reported p.(Thr300Ile) variant, were characterized in silico and analyzed by molecular dynamics (MD) simulation studies. We identified three novel de novo missense variants in GABRA4 (NM_000809.4): c.797 C > T, p.(Pro266Leu), c.899 C > A, p.(Thr300Asn), and c.634 G > A, p.(Val212Ile). The p.(Thr300Asn) variant impacts the same codon as the previously reported variant p.(Thr300Ile) and likely arose post-zygotically as evidenced by sequencing oral mucosal cells. Overlapping phenotypes among affected individuals included developmental delay (4/4), epileptiform EEG abnormalities (3/4), attention deficits (3/4), seizures (2/4), autistic features (2/4) and structural brain abnormalities (2/4). MD simulations of the three variants within the transmembrane domain of the receptor indicate that sub-microsecond scale dynamics differ between wild-type and mutated subunits. Taken together, our findings further corroborate an association between GABRA4 and a neurological phenotype including variable neurodevelopmental, behavioral and epileptic abnormalities.

Surfaces properties correlation with optical parameters of thickness dependent self-affine nanostructured SnS thin films: A study based on scaling law

Fractal concepts are employed to investigate the surface properties of tin sulfide (SnS) thin films, which are deposited on Fluorine-doped tin oxide (FTO)- coated glass substrates by the thermal evaporation (TE) technique. The surface topography of each deposited film is captured by AFM. The fractal dimensions of the AFM image were extracted through the Higuchi algorithm, and it is found that the thinner sample surface exhibits the maximum fractal dimension. The autocorrelation function and height-height correlation function are used to investigate the self-affinity behavior of surfaces. Dynamic surface roughening is characterized by different scaling exponents, such as the roughness exponent, the growth exponent, the dynamic scaling exponent, and the steepening exponent. It was observed that the values of average roughness, interface width, lateral correlation length, and mean square local slope are strongly influenced by film thickness. The self-affine fractal nature of thin film is estimated by the roughness exponent; however, island- or mound-type growth with quick surface roughening behavior is predicted by the growth exponent and the dynamic scaling exponent. Further, we have tried to correlate the optical properties such as transmission, reflection, and refractive index with the fractal dimension of SnS thin film. The increase in optical reflection with decreasing roughness indicates that absorber surfaces of the best crystalline films have the lowest reflectivity. The present results suggest that such surfaces, having a maximum fractal dimension and minimum optical reflectance, can be used as photon absorber layers for advanced solar cell devices.

Ordering kinetics of the two-dimensional voter model with long-range interactions.

We study analytically the ordering kinetics of the two-dimensional long-range voter model on a two-dimensional lattice, where agents on each vertex take the opinion of others at distance r with probability P(r)∝r^{-α}. The model is characterized by different regimes, as α is varied. For α>4, the behavior is similar to that of the nearest-neighbor model, with the formation of ordered domains of a typical size growing as L(t)∝sqrt[t], until consensus is reached in a time of the order of NlnN, with N being the number of agents. Dynamical scaling is violated due to an excess of interfacial sites whose density decays as slowly as ρ(t)∝1/lnt. Sizable finite-time corrections are also present, which are absent in the case of nearest-neighbor interactions. For 0<α≤4, standard scaling is reinstated and the correlation length increases algebraically as L(t)∝t^{1/z}, with 1/z=2/α for 3<α<4 and 1/z=2/3 for 0<α<3. In addition, for α≤3,L(t) depends on N at any time t>0. Such coarsening, however, only leads the system to a partially ordered metastable state where correlations decay algebraically with distance, and whose lifetime diverges in the N→∞ limit. In finite systems, consensus is reached in a time of the order of N for any α<4.

Calculations of Absolute Free Energies, Enthalpies, and Entropies for Drug Binding.

Computer simulation methods can aid in the rational design of drugs aimed at a specific target, typically a protein. The affinity of a drug for its target is given by the free energy of binding. Binding can be further characterized by the enthalpy and entropy changes in the process. Methods exist to determine exact free energies, enthalpies, and entropies that are dependent only on the quality of the potential model and adequate sampling of conformational degrees of freedom. Entropy and enthalpy are roughly an order of magnitude more difficult to calculate than the free energy. This project combines a replica exchange method for enhanced sampling, designed to be efficient for protein-sized systems, with free energy calculations. This approach, replica exchange with dynamical scaling (REDS), uses two conventional simulations at different temperatures so that the entropy can be found from the temperature dependence of the free energy. A third replica is placed between them, with a modified Hamiltonian that allows it to span the temperature range of the conventional replicas. REDS provides temperature-dependent data and aids in sampling. It is applied to the bromodomain-containing protein 4 (BRD4) system. We find that for the force fields used, the free energies are accurate but the entropies and enthalpies are not, with the entropic contribution being too positive. Reproducing the entropy and enthalpy of binding appears to be a more stringent test of the force fields than reproducing the free energy.

LEC-MiCs: Low-Energy Checkpointing in Mixed-Criticality Multi-Core Systems

With the advent of multicore platforms in designing Mixed-Criticality Systems (MCSs), simultaneous management of reliability and energy while guaranteeing an acceptable service level for low-criticality tasks is a crucial challenge. To ensure the reliability of the MCSs against transient faults, fault-tolerant techniques are employed which will increase energy consumption. To mitigate the energy overhead, the Dynamic Voltage and Frequency Scaling (DVFS) technique will be exploited. However, this technique might lead to violating the timing constraints of high-criticality tasks. Therefore, this paper presents, for the first time, the low-energy checkpointing technique to guarantee the reliability of multiple preemptive periodic mixed-criticality tasks in a multicore platform. In contrast to the previous works in checkpointing technique which consider a specific number of faults that all the tasks in the system should tolerate, in this paper, the number of tolerable faults for each execution section of a task, and in each voltage and frequency level is determined through proposed formulas to meet the reliability target based on safety standards. Then, our proposed method determines the number of checkpoints and their non-uniform intervals for the normal and overrun sections of each task to reduce energy consumption, respectively. Moreover, the unified demand bound function (DBF) analysis is proposed for analyzing the schedulability of the task set, where each high-criticality task meets its timing and reliability constraints, and low-criticality tasks execute based on their derived guaranteed periods in each operational mode of the system. Experimental results show that our proposed scheme meets the timing and reliability constraints while at the same time, improving the QoS of low-criticality tasks, and managing energy consumption with an average of 29.49%, and 32.78%, respectively.

Cheaper and Faster: Distributed Deep Reinforcement Learning with Serverless Computing

Deep reinforcement learning (DRL) has gained immense success in many applications, including gaming AI, robotics, and system scheduling. Distributed algorithms and architectures have been vastly proposed (e.g., actor-learner architecture) to accelerate DRL training with large-scale server-based clusters. However, training on-policy algorithms with the actor-learner architecture unavoidably induces resource wasting due to synchronization between learners and actors, thus resulting in significantly extra billing. As a promising alternative, serverless computing naturally fits on-policy synchronization and alleviates resource wasting in distributed DRL training with pay-as-you-go pricing. Yet, none has leveraged serverless computing to facilitate DRL training. This paper proposes MinionsRL, the first serverless distributed DRL training framework that aims to accelerate DRL training- and cost-efficiency with dynamic actor scaling. We prototype MinionsRL on top of Microsoft Azure Container Instances and evaluate it with popular DRL tasks from OpenAI Gym. Extensive experiments show that MinionsRL reduces total training time by up to 52% and training cost by 86% compared to latest solutions.

Non-equilibrium Dynamics of Vortices in Two-Dimensional Quantum Gases: Determining the Dynamical Scaling Region Using the Mahalanobis Distance

When a system undergoes a rapid quench from a disordered to an ordered phase, it does not order instantly but instead relaxes towards equilibrium over time. During this relaxation, the dynamical scaling hypothesis predicts that the length scale of ordered regions increases, with later patterns statistically similar to earlier ones except for a change in global scale. Quantum gases are one of many systems in which such out of equilibrium behaviour is predicted to occur. Here, we present a method for systematically testing when dynamical scaling is taking place, by quantifying the similarity of the rescaled two-point correlation function over time using the Mahalanobis distance. Data on the velocity field of a two-dimensional quantum fluid, generated from point vortex simulations, are used to illustrate the application of this method.

Architectural and Technological Approaches for Efficient Energy Management in Multicore Processors

Benchmarks play an essential role in the performance evaluation of novel research concepts. Their effectiveness diminishes if they fail to exploit the available hardware of the evaluated microprocessor or, more broadly, if they are not consistent in comparing various systems. An empirical analysis of the consecrated Splash-2 benchmarks suite vs. the latest version Splash-4 was performed. It was shown that on a 64-core configuration, half of the simulated benchmarks reach temperatures well beyond the critical threshold of 105 °C, emphasizing the necessity of a multi-objective evaluation from at least the following perspectives: energy consumption, performance, chip temperature, and integration area. During the analysis, it was observed that the cores spend a large amount of time in the idle state, around 45% on average in some configurations. This can be exploited by implementing a predictive dynamic voltage and frequency scaling (DVFS) technique called the Simple Core State Predictor (SCSP) to enhance the Intel Nehalem architecture and to simulate it using Sniper. The aim was to decrease the overall energy consumption by reducing power consumption at core level while maintaining the same performance. More than that, the SCSP technique, which operates with core-level abstract information, was applied in parallel with a Value Predictor (VP) or a Dynamic Instruction Reuse (DIR) technique, which rely on instruction-level information. Using the SCSP alone, a 9.95% reduction in power consumption and an energy reduction of 10.54% were achieved, maintaining the performance. By combining the SCSP with the VP technique, a performance increase of 8.87% was obtained while reducing power and energy consumption by 3.13% and 8.48%, respectively.

Risk-based hydrologic design under climate change using stochastic weather and watershed modeling

Water resources planning and management requires the estimation of extreme design events. Anticipated climate change is playing an increasingly prominent role in the planning and design of long-lived infrastructure, as changes to climate forcings are expected to alter the distribution of extremes in ways and to extents that are difficult to predict. One approach is to use climate projections to force hydrologic models, but this raises two challenges. First, global climate models generally focus on much larger scales than are relevant to hydrologic design, and regional climate models that better capture small scale dynamics are too computationally expensive for large ensemble analyses. Second, hydrologic models systematically misrepresent the variance and higher moments of streamflow response to climate, resulting in a mischaracterization of the extreme flows of most interest. To address both issues, we propose a new framework for non-stationary risk-based hydrologic design that combines a stochastic weather generator (SWG) that accurately replicates basin-scale weather and a stochastic watershed model (SWM) that accurately represents the distribution of extreme flows. The joint SWG-SWM framework can generate large ensembles of future hydrologic simulations under varying climate conditions, from which design statistics and their uncertainties can be estimated. The SWG-SWM framework is demonstrated for the Squannacook River in the Northeast United States. Standard approaches to design flows, like the T-year flood, are difficult to interpret under non-stationarity, but the SWG-SWM simulations can readily be adapted to risk and reliability metrics which bare the same interpretation under stationary and non-stationary conditions. As an example, we provide an analysis comparing the use of risk and more traditional T-year design events, and conclude that risk-based metrics have the potential to reduce regret of over- and under-design compared to traditional return-period based analyses.