Dynamic Scaling Research Articles

High-resolution ghost imaging through dynamic and complex scattering media with adaptive moving average correction

Optical imaging through dynamic and complex scattering media has attracted various applications, e.g., ranging from scene imaging to cell imaging. Nevertheless, imaging through complex media is full of challenges attributed to the inhomogeneous scattering, leading to nonlinear effects. Although ghost imaging (GI) has proven effective in solving some scattering problems, dynamic and complex scattering still requires an efficient solution. In this Letter, we report a model based on adaptive moving average (AMA) to correct the influence of dynamic scattering media from a statistical perspective for high-resolution GI. The developed AMA correction method selects an appropriate time window based on the changing trend of measured single-pixel light intensities to accurately correct a series of dynamic scaling factors. Then, the corrected single-pixel light intensities are used for ghost reconstruction using a second-order correlation algorithm. A series of optical experiments are conducted to verify superiority of the proposed method. Moreover, the proposed method can be applied with other algorithms to enhance the quality of the reconstructed ghost images. By leveraging a statistical model based on the measured data, the proposed scheme offers an enhanced solution to solving dynamic and complex scattering problems in GI.

A novel dynamic scale factor designed for recovering global TWS changes

For time-variable satellite gravity solutions of GRACE and GRACE-FO in terms of spherical harmonics coefficients, the Scale Factor (SF) is often used to recover the close to “true” signal of Terrestrial Water Storage (TWS) anomalies. However, the conventional SF method has some limitations that may hinder its effectiveness, including: (1) their dependency on input hydrological models that may lead to divergent estimations of SFs; (2) the arbitrary choice of filter strength, which may not be representative in different regions; (3) limited consideration of SF in the temporal dynamics (the conventional SF was fixed value) for monthly varied TWS. Here, we propose a new Dynamic SF method to overcome these limitations and increase accuracy of the restored global TWS changes. This method involves: (1) the Bayesian Three-Cornered Hat (BTCH) method is applied to merge three sets of hydrological products into an optimal hydrological dataset to be used for estimating a unique SF, (2) the anisotropic DDK3 filter (found to be numerically optimal) is applied to suppress the correlated noise, and (3) an iterative Kalman filter process is formulated and implemented to estimate monthly Dynamic SF corresponding to monthly global TWS fields. The Dynamic SF outperformed an ordinary SF method in terms of the Root Mean Squared Error (RMSE), the Mean Absolute Error (MAE), and the Signal to Noise Ratio (SNR), which were found to be improved by 30.8%, 32.2%, and 41.3%, respectively. Moreover, the recovered TWS using the Dynamic SF method showed a good agreement with the GRACE/GRACE-FO RL06 mascon solutions of the CSR and that of JPL regarding the long-term trend, seasonal, and interannual variations.

Water hyacinths retain river plastics

Rivers represent one of the main conduits for the delivery of plastics to the sea, while also functioning as reservoirs for plastic retention. In tropical regions, rivers are exposed to both high levels of plastic pollution and invasion of water hyacinths. This aquatic plant forms dense patches at the river surface that drift due to winds and currents. Recent work suggests that water hyacinths play a crucial role in influencing plastic transport, by efficiently trapping the majority of surface plastic within their patches. However, a comprehensive understanding of the interaction between water hyacinths and plastics is still lacking. We hypothesize that the properties relevant to plastic transport change due to their trapping in water hyacinth patches. In particular, the length scale, defined as the characteristic size of the transported material, is a key property in understanding how materials move within rivers. Here, we show that water hyacinth patches trap on average 54%–77% of all observed surface plastics at the measurement site (Saigon river, Vietnam). Both temporally and spatially, we found that plastic and water hyacinth presence co-occur. The formation of plastic-plant aggregates carries significant implications for both clean-up and monitoring purposes, as these aggregates can be detected from space and need to be jointly removed. In addition, the length scale of trapped plastics (∼4.0 m) was found to be forty times larger than that of open water plastics (∼0.1 m). The implications of this increased length scale for plastic transport dynamics are yet to be fully understood, calling for further investigation into travel distances and trajectories. The effects of plastic trapping likely extend to other key properties of plastic-plant aggregates, such as effective buoyancy and mass. Given the prevalence of plant invasion and plastic pollution in rivers worldwide, this research offers valuable insights into the complex environmental challenges faced by numerous rivers.

A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments.

The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%-96% higher accuracy than the existing approaches.

Percolation phase transition, self-organization processes and thermal conductivity in PbSe1-xTex semiconductor solid solutions at small Te concentration

ABSTRACT The isotherms of the lattice thermal conductivity λ L for the PbSe1-xTex semiconductor solid solutions (х = 0–0.045) were obtained in the temperature range T = (200–600) K. The λ L(x) dependences exhibit a general trend to a decrease in λ L with increasing x, but the peaks are observed near x = 0.007, 0.015 and 0.0025 indicating the presence of phase transformations. We associated the first peak with a percolation-type phase transition to the impurity continuum and estimated the critical index for λ L within the framework of dynamic scaling theory. The second and third peaks were attributed to the self-organization processes in the PbSe1-xTex after the percolation threshold reaching. The effective cross-sections σ s for phonon scattering by impurity (Te) atoms were determined experimentally and calculated theoretically using the Klemens theory. The data obtained are another confirmation of our assumption about the presence of such percolation-type phase transition in any solid solution.

Dynamic Personality of Proteins and Effect of the Molecular Environment.

Protein dynamics display distinct traits that are linked to their specific biological function. However, the interplay between intrinsic dynamics and the molecular environment on protein stability remains poorly understood. In this study, we investigate, by incoherent neutron scattering, the subnanosecond time scale dynamics of three model proteins: the mesophilic lysozyme, the thermophilic thermolysin, and the intrinsically disordered β-casein. Moreover, we address the influence of water, glycerol, and glucose, which create progressively more viscous matrices around the protein surface. By comparing the protein thermal fluctuations, we find that the internal dynamics of thermolysin are less affected by the environment compared to lysozyme and β-casein. We ascribe this behavior to the protein dynamic personality, i.e., to the stiffer dynamics of the thermophilic protein that contrasts the influence of the environment. Remarkably, lysozyme and thermolysin in all molecular environments reach a critical common flexibility when approaching the calorimetric melting temperature.

Goat Q: A distributed message broker

goat Q, a distributed message broker explores the design and implementation of a robust message broker system. Utilizing advanced distributed concepts of network and synchronization the project aims to enhance efficiency and reliability of message delivery which fits in an event driven system. It also ensures fault tolerance and scalability. It also considers the integration with containerization and on premise and cloud deployment models to use goatQ as a message broker solution in production. The project seeks to unravel the intricate design of message brokers, illuminating the functionality of publishers, subscribers, and message queues. A key focus lies in a rigorous performance analysis, evaluating metrics such as throughput, latency, and resource use across wide variety message broker implementations. Scalability and fault tolerance, critical considerations in the realm of distributed systems, are addressed through an exploration of how message brokers adapt to dynamic scaling requirements and recover from failures. By synthesizing findings from diverse dimensions of message broker technology, this research project aims to equip technology professionals with valuable insights, aiding informed decision-making in the selection, optimization, and integration of message brokers within their ecosystems. Keyword: About four key words or phrases in alphabetical order, separated by commas. Keywords are used to retrieve documents in an information system such as an online journal or a search engine. (Mention 4-5 keywords)

Structural Change in the 21st Century: The Role of the Modern Services Sector in the Economic Development Strategy

ABSTRACT Traditional literature on Economic Growth highlights the role of the manufacturing sector in fostering economic growth. This sector, characterized by increasing returns, plays a fundamental role in both static and dynamic scale gains within the economy. Recently, an increasing number of studies have suggested that the increased share of the modern services sector also significantly contributes to innovation, productivity, and increased output. The aim of this paper is to reevaluate growth theories in light of the emerging prominence of the services sector and to outline key components of a research agenda that encompasses this sector as a crucial area of investigation. To substantiate this argument, we apply a GMM (Generalized Method of Moments) model to a dataset spanning from 1990 to 2018, encompassing fifty-one (51) countries, in order to evaluate the impact of modern services on manufacturing performance. An interaction variable is employed to test whether a symbiotic relationship between industry and services is the driving force behind economic growth. The results demonstrate the relevance of the services sector. Finally, we suggest possible research lines to be developed.

Deciphering multi-temporal scale dynamics in the concentration, sources and processes of near surface ozone over different climatic regions of India.

This study aims to investigate the factors influencing seasonal and long-term (2003-2021) changes in the near surface ozone (850 hpa) concentrations over different climatic sub-regions of India. Detailed comparison of daily (2019-2021) near surface ozone values of ERA-5 and CAAQMS (Continuous Ambient Air Quality Monitoring Stations) ground-based measurements revealed that ERA-5 is temporally in phase with CAAQMS measurements falling indifferent climatic sub-regions of India. ERA-5 near surface ozone shows statistically significant long-term (2003-2021) positive trends [2-4 percent per decade (ppd)] over most of the climatic sub-regions, over Indo-Gangetic Planes (IGPs), Southern and Central India trends are particularly strong. Trends were also estimated for each season separately, which were largely positive (2-6 ppd) over Central and Southern India in the Autumn and Winter seasons. Extensive climatological analysis reveals that the reversal of winds in the Indian monsoonal system plays a vital role in such trend patterns across the Indian subcontinent. South-westerly winds from June through September presumably bring ozone deficit air of marine origin, thus causing a dilution effect while the North-easterly winds during late Autumn and early Winters plausibly bring ozone-rich air from the stratospheric-tropospheric efflux dominated Himalayan region. It allows near surface ozone enhancement over Central and Southern India. Seasonal Principal component analysis (PCA) revealed that precursor gases (CH4 and NO2) and climatic variables especially specific humidity (SH) are the primary drivers of near surface ozone variability in the Winter season, while in Spring, climatic variables like boundary layer height (BLH), temperature (T) and SH have a significant role. Principal component regression (PCR) reveals a long-term increase in near surface ozone levels mostly dominated by precursor concentration over IGPs and Southern sub-regions. Whereas, BLH, T and SH significantly explain near surface ozone trends over North-eastern and Coastal India.

Energy-efficient scheduling for parallel applications with reliability and time constraints on heterogeneous distributed systems

Reliability is a crucial index of the system, and many safety-critical applications have reliability requirements and deadline constraints. In addition, in order to protect the environment and reduce system operating costs, it is necessary to minimize energy consumption as much as possible. This paper considers parallel applications on heterogeneous distributed systems and proposes two algorithms to minimize energy consumption for meeting the deadline and satisfying the reliability requirement of the applications. The first algorithm is called minimizing scheduling length while satisfying the reliability requirement (MSLSRR). It first transforms the reliability requirement of the application into the reliability requirement of the task and then assigns the task to the processor with the earliest finish time. Since the reliability generated by MSLSRR is often higher than the reliability requirement of the application, and the scheduling length is also less than the deadline, an algorithm called improving energy efficiency (IEE) is designed, which redefined the minimum reliability requirement for the task and applied dynamic voltage and frequency scaling (DVFS) technique for energy conservation. The proposed algorithms are compared with existing algorithms by using real parallel applications. Experimental results demonstrate that the proposed algorithms consume the least energy.

Quasi-equilibrium chemical evolution in starless cores

ABSTRACT The chemistry of H2O, CO, and other small molecular species in an isolated pre-stellar core, L1544, has been assessed in the context of a comprehensive gas-grain chemical model, coupled to an empirically constrained physical/dynamical model. Our main findings are (i) that the chemical network remains in near equilibrium as the core evolves towards star formation and the molecular abundances change in response to the evolving physical conditions. The gas-phase abundances at any time can be calculated accurately with equilibrium chemistry, and the concept of chemical clocks is meaningless in molecular clouds with similar conditions and dynamical time-scales, and (ii) A comparison of the results of complex and simple chemical networks indicates that the abundances of the dominant oxygen and carbon species, H2O, CO, C, and C+ are reasonably approximated by simple networks. In chemical equilibrium, the time-dependent differential terms vanish, and a simple network reduces to a few algebraic equations. This allows rapid calculation of the abundances most responsible for spectral line radiative cooling in molecular clouds with long dynamical time-scales. The dust ice mantles are highly structured and the ice layers retain a memory of the gas-phase abundances at the time of their deposition. A complex (gas-phase and gas-grain) chemical structure therefore exists, with cosmic-ray induced processes dominating in the inner regions. The inferred H2O abundance profiles for L1544 require that the outer parts of the core and also any medium exterior to the core are essentially transparent to the interstellar radiation field.

Unified control of temporal and spatial scales of sensorimotor behavior through neuromodulation of short-term synaptic plasticity.

Neuromodulators have been shown to alter the temporal profile of short-term synaptic plasticity (STP); however, the computational function of this neuromodulation remains unexplored. Here, we propose that the neuromodulation of STP provides a general mechanism to scale neural dynamics and motor outputs in time and space. We trained recurrent neural networks that incorporated STP to produce complex motor trajectories-handwritten digits-with different temporal (speed) and spatial (size) scales. Neuromodulation of STP produced temporal and spatial scaling of the learned dynamics and enhanced temporal or spatial generalization compared to standard training of the synaptic weights in the absence of STP. The model also accounted for the results of two experimental studies involving flexible sensorimotor timing. Neuromodulation of STP provides a unified and biologically plausible mechanism to control the temporal and spatial scales of neural dynamics and sensorimotor behaviors.

Online Energy-Aware Scheduling for Deadline-Constrained Applications in Distributed Heterogeneous Systems

In the current computing environment, the significance of distributed heterogeneous systems has gained prominence. The research on scheduling problems in distributed systems that consider energy consumption has garnered substantial attention due to its potential to enhance system stability, achieve energy savings, and contribute to environmental preservation. However, efficient scheduling in such systems necessitates not only the consideration of energy consumption but also the ability to adapt to the dynamic nature of the system. To tackle these challenges, we propose an online energy-aware scheduling algorithm for deadline-constrained applications in distributed heterogeneous systems, leveraging dynamic voltage and frequency scaling (DVFS) techniques. First, the algorithm models the continuously arriving applications and heterogeneous processors and proposes a novel task-sorting method to prioritize tasks, ensuring that more applications are completed within their respective deadlines. Second, the algorithm controls the selection range of processors based on the task’s subdeadline and assigns the task to the processor with the minimum energy consumption. Through experiments conducted with randomly generated applications, our approach consistently exhibits superior performance when compared to similar scheduling algorithms.

Revolutionizing vascular health through the temporal convolutional transformer for drug screening and model evolution

Vascular illnesses include a spectrum of disorders affecting the blood vessels and continue to be major global health challenges. Vascular disorders are major worldwide health concerns that require ongoing development of drug screening models in order to find efficient treatment approaches. The ever-changing field of vascular health demands novel ways to medication screening due to the enduring difficulties presented by a variety of illnesses. This abstract explores a paradigm shift, highlighting the innovative role of the Temporal Convolutional Transformer (TCT) in redefining drug screening techniques and promoting model evolution. With its seamless integration of transformer mechanisms and temporal convolutional layers, the TCT is a revolutionary hybrid algorithm. Because of this special combination, the model can simultaneously identify spatial subtleties and capture temporal dynamics in complicated biological information. The temporal convolutional layers of the TCT are particularly good at revealing the sequential dependencies that are present in the development of vascular disorders. The model ensures a detailed knowledge of the developing nature of these diseases by analyzing temporal dynamics with skill. Simultaneously, the TCT’s transformer mechanisms enable a thorough examination of long-range interdependence and global interconnections. The TCT’s dual capability establishes it as a comprehensive solution by offering a nuanced perspective on spatial patterns that are essential for identifying the complexity of vascular health. The TCT is hybrid in the sense that it can learn features at several scales, supporting different temporal and spatial resolutions. This flexibility guarantees that the model may extract pertinent information at various scales, enhancing the characterization of the complex nature of vascular illnesses. The TCT is positioned as a holistic solution since its transformer mechanisms allow for a thorough investigation of long-range dependencies and global interconnections at the same time. This combined capacity offers a sophisticated viewpoint on spatial patterns that is essential for understanding the intricacies of vascular health. Moreover, the remarkable flexibility of the TCT to dynamic temporal scales is consistent with the dynamic character of biological processes, providing a strong and adaptable framework for drug screening. The TCT is used in many different fields of vascular disease research. The model performs well in image-based screening, collecting sequential dependencies over time and processing spatial information from medical imaging datasets. The TCT’s capacity to identify long-range dependencies is essential for molecular interaction research since it helps to clarify the complex pathways associated with vascular disorders. Its adaptability also extends to personalized treatment approaches, which allow for the efficient modeling of patient-specific data, including genetic information. The TCT is a revolutionary tool that will continue to evolve as drug screening models do. By integrating temporal convolutional and transformer mechanisms, its all-encompassing methodology provides a sophisticated and comprehensive approach to deciphering the intricacies of vascular disorders. Across all evaluated metrics, the proposed method achieves near-perfect accuracy of 98.45%, sensitivity of 93.45%, specificity of 98.43%, precision of 84.25, AUC of 99 % and F1 score values 96.01%, respectively. This abstract sheds light on how the TCT has significantly changed drug development approaches, opening the door for more focused and effective therapies in the field of vascular health.

PuffChain: A Dynamic Scaling Blockchain System With Optimal Effective Throughput

PuffChain: A Dynamic Scaling Blockchain System With Optimal Effective Throughput

Deep learning-enhanced ghost imaging through dynamic and complex scattering media with supervised corrections of dynamic scaling factors

Ghost imaging (GI) through dynamic and complex scattering media remains challenging. The existence of dynamic scattering gives rise to a failure of GI schemes. Here, we report a deep learning-enhanced GI scheme with supervised corrections (SCGI) of dynamic scaling factors to realize high-resolution ghost reconstruction through dynamic and complex scattering media. The SCGI scheme is developed to approximate the variation of dynamic scaling factors in an optical channel and correct the recorded light intensities with a Gaussian prior. An untrained neural network powered by regularization by denoising for the SCGI scheme (SCGI-URED) is developed to further recover high-visibility ghost images. Experimental results demonstrate that high-resolution and high-visibility GI can be realized in dynamic and complex scattering media. The proposed method provides a reliable tool for implementing high-resolution and high-visibility GI through dynamic and complex scattering media and could give an impetus to developing dynamic scattering imaging in real-world scenarios.

Global adaptive practical tracking control for high‐order uncertain nonstrict feedback nonlinear systems with unknown control coefficients

SummaryIn this article, the problem of global adaptive practical tracking for high‐order uncertain nonstrict feedback nonlinear systems with unknown control coefficients is studied. To avoid the algebraic loop problem associated with the nonstrict feedback condition and guarantee the controllability of the tracking error, a novel dual dynamic gain scaling method is introduced to compensate nonlinearities and the tracking error simultaneously. Besides, by incorporating the sign functions into the design of adding a power integrator, a general approach for the handing of unknown control coefficients and the direction design of the controller is developed. The presented control scheme can ensure that all system states are globally bounded without constraints on state variables while the reference signal is tracked with expected precision. Three simulation examples, including a practical application, are provided to illustrate the validity of the control scheme.

Large eddy simulation of a turbulent channel flow using dynamic Smagorinsky subgrid scale model and differential equation wall model

A large eddy simulation (LES) is conducted in a plane channel flow which is turbulent. For understanding the mechanism of the turbulent motion more efficiently, the present study is an attempt to conduct the LES using a Dynamic Smagorinsky Model (DSM) and a Differential Equation Wall Model (DEWM). DSM is a subgrid-scale model and DEWM is for approximating the near wall layer. The simulation is performed in a domain of 2 π δ × 2 δ × π δwhich is discretized by 32 × 20 × 32grid points where δ is the channel half width. The discretization techniques have been used in the simulation, are the 3rd order low storage R-K method in time and 2nd order finite difference formulation in space. To assess the performance of the LES with DSM and DEWM, the simulation results are compared with direct numerical simulation (DNS) data and LES data using Standard Smagorinsky Model (SSM). Comparing the results in the essential turbulence statistical field it has been found that DSM performs better than SSM in most of the cases and shows better agreement with the DNS results. Flow structures in the turbulent flow field are compared in isosurfaces and contour plots, and found that tube-like vortical structures are randomly distributed over the entire flow field, and the number of the vortical structures are more for the DSM case than that of SSM.

Aging following a zero-temperature quench in the d=3 Ising model.

Aging in phase-ordering kinetics of the d=3 Ising model following a quench from infinite to zero temperature is studied by means of Monte Carlo simulations. In this model the two-time spin-spin autocorrelator C_{ag} is expected to obey dynamical scaling and to follow asymptotically a power-law decay with the autocorrelation exponent λ. Previous work indicated that the lower Fisher-Huse bound of λ≥d/2=1.5 is violated in this model. Using much larger systems than previously studied, the instantaneous exponent for λ we obtain at late times does not disagree with this bound. By conducting systematic fits to the data of C_{ag} using different Ansätze for the leading correction term, we find λ=1.58(14), with most of the error attributed to the systematic uncertainty regarding the Ansätze. This result is in contrast to the recent report that below the roughening transition universality might be violated.

Pattern dynamics of density and velocity fields in segregation of fluid mixtures.

We present comprehensive numerical results from a study of model H, which describes phase separation kinetics in binary fluid mixtures. We study the pattern dynamics of both density and velocity fields in d = 2, 3. The density length scales show three distinct regimes, in accordance with analytical arguments. The velocity length scale shows a diffusive behavior. We also study the scaling behavior of the morphologies for density and velocity fields and observe dynamical scaling in the relevant correlation functions and structure factors. Finally, we study the effect of quenched random field disorder on spinodal decomposition in model H.