Multiscale Applications Research Articles

Multiscale multivariate transfer entropy and application to functional corticocortical coupling

Objective. Complex biological systems consist of multi-level mechanism in terms of within- and cross-subsystems correlations, and they are primarily manifested in terms of connectivity, multiscale properties, and nonlinearity. Existing studies have each only explored one aspect of the functional corticocortical coupling (FCCC), which has some limitations in portraying the complexity of multivariable systems. The present study investigated the direct interactions of brain networks at multiple time scales. Approach. We extended the multivariate transfer entropy (MuTE) method and proposed a novel method, named multiscale multivariate transfer entropy (MSMVTE), to explore the direct interactions of brain networks across multiple time scale. To verify this aim, we introduced three simulation models and compared them with multiscale transfer entropy (MSTE) and MuTE methods. We then applied MSMVTE method to analyze FCCC during a unilateral right-hand steady-state force task. Main results. Simulation results showed that the MSMVTE method, compared with MSTE and MuTE methods, better detected direct interactions and avoid the spurious effects of indirect relationships. Further analysis of experimental data showed that the connectivity from left premotor/sensorimotor cortex to right premotor/sensorimotor cortex was significantly higher than that of opposite directionality. Furthermore, the connectivities from central motor areas to both sides of premotor/sensorimotor areas were higher than those of opposite directionalities. Additionally, the maximum coupling strength was found to occur at a specific scale (3–10). Significance. Simulation results confirmed the effectiveness of the MSMVTE method to describe direct relationships and multiscale characteristics in complex systems. The enhancement of FCCC reflects the interaction of more extended activation in cortical motor regions. Additionally, the neurodynamic process of brain depends not only on emergent behavior at small scales, but also on the constraining effects of the activity at large scales. Taken together, our findings provide a basis for better understanding dynamics in brain networks.

Least Afflicted Load Balancing Algorithm (LALBA) for Performance Improvement of Multi-Scale Applications in Cloud Environment

Cloud computing is new image of Information Technology and the way applications are available to end-users. Also, the applications available are gaining popularity as being able to demonstrate the flexibility of use of resources as in Multi-Scale Cloud-Based Applications. These applications on the cloud are hosted on remote virtual machines with multi-tenancy architecture. Multi-tenant cloud computing architecture helps cloud users and customers to share computing resources and still the individual tenant's data is stored separately so as to make it is invisible to other tenants. With this multi-tenancy architecture, the application is being used by multiple people at the same time. Also, the application resides in various copies across various servers situated at different geographical locations. Virtual Machines that are housed in multiple Data Centers are coupled by way of load balancers. These load balancers are responsible for the optimal distribution of load/demand/tenant requests and the size of the data held within the request. So in short, two areas need to be focused on when architecting such systems. Firstly, the load must be balanced optimally across data centers and virtual machines for seamless operation at the back end. Secondly, at the front end, it is imperative that every tenant enjoys optimal service speeds without any degradation of service request speed. In case the back-end virtual machines are unbalanced in their load then there is bound to be degradation of services. For this very purpose, various researchers/academicians, and practitioners in the cloud space have been working tirelessly towards developing optimal algorithms to get the best load balancing architecture to ensure that the objective of seamless back end and front end quality of service in terms of average response times. Multi-Scale applications consist of variable payload for time and often unpredictable i.e. load can be increased/decreased suddenly or gradually. Hence to balance these loads equally at any time, a new algorithm is proposed. This algorithm considers the characteristics of multiscale applications in terms of data size and runtime characteristics of VM in terms of capacity and availability. Finally, the performance of this method is analyzed qualitatively as well as quantitatively by performing simulation experiments of multiscale applications.

Uncertainty quantification patterns for multiscale models.

Uncertainty quantification (UQ) is a key component when using computational models that involve uncertainties, e.g. in decision-making scenarios. In this work, we present uncertainty quantification patterns (UQPs) that are designed to support the analysis of uncertainty in coupled multi-scale and multi-domain applications. UQPs provide the basic building blocks to create tailored UQ for multiscale models. The UQPs are implemented as generic templates, which can then be customized and aggregated to create a dedicated UQ procedure for multiscale applications. We present the implementation of the UQPs with multiscale coupling toolkit Multiscale Coupling Library and Environment 3. Potential speed-up for UQPs has been derived as well. As a proof of concept, two examples of multiscale applications using UQPs are presented.This article is part of the theme issue ‘Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico’.

Interaction and kinetic analysis of co-gasification of bituminous coal with walnut shell under CO2 atmosphere: Effect of inorganics and carbon structures

In this study, co-gasification behaviors and synergistic interaction of bituminous coal and walnut shell under CO2 atmosphere were investigated by using a thermogravimetric analyzer. The evolution of physicochemical properties of samples was characterized with an inductively coupled plasma emission spectrometer, an X-ray fluorescence spectrometer, and an X-ray diffractometer. The results indicated that the addition of walnut shell significantly enhanced the gasification reactivity of coal, and coal/biomass blend ratio of 7:3 showed an intense gasification. Moreover, a significant co-gasification interaction was observed in the char gasification stage and demonstrated that a high coal/biomass blend ratio induced the formation of amorphous carbons and further accelerated the co-gasification reaction. Meanwhile, the active inorganics were severely released as 30 wt% bituminous coal added to walnut shell, causing a deteriorative gasification reactivity. Furthermore, three kinetic models illustrated that the coal/biomass blend ratio of 7:3 required a minimum activation energy to initialize gasification. The findings in this work would be beneficial for revealing the co-gasification mechanism of biomass and coal, and provide a useful basis on their multi-scale applications.

Adaptively Iterative Multiscale Switching Simulation Strategy and Applications to Protein Folding and Structure Prediction.

Structure prediction is an important means to quickly understand new protein functions. However, the prediction of effects of proteins that have no detectable templates is still to be improved. Molecular dynamics simulation is supposed to be the primary research tool for structure predictions, but it still has limitations of huge computational cost in all-atom (AA) models and rough accuracy in coarse-grained (CG) models. We propose a universal multiscale simulation strategy named AIMS in which simulations can iteratively switch among multiple resolutions in order to adaptively trade off AA accuracy and CG high-efficiency. AIMS follows the idea of CG-guided enhanced sampling so that final results always keep AA accuracy. We successfully achieve four ab initio and four data-assisted protein structure predictions using AIMS. The prediction result is an ensemble rather than a structure and provides special insights on folding metastable states. AIMS is estimated to achieve a computational speed about 40 times faster than that of conventional AA simulations.

A data-driven approach to estimate leaf area index for Landsat images over the contiguous US

Leaf Area Index (LAI) is a fundamental vegetation biophysical variable serving as an essential input to many land surface and atmospheric models. Long-term LAI maps are typically generated with satellite images at moderate spatial resolution (0.25 to 1 km), such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS). While useful for regional-scale land surface modeling, these moderate resolution products often cannot resolve spatial heterogeneity important for many agricultural and hydrological applications. This paper proposes an approach to map LAI at 30-m resolution based on Landsat images for the Contiguous US (CONUS) consistent with the MODIS product, aimed at multi-scale modeling applications. The algorithm was driven by 1.6 million spatially homogeneous samples derived from MODIS LAI and Landsat surface reflectance products from 2006 to 2018. Based on these samples, we trained separate random forest models to estimate LAI from Landsat surface reflectance for eight biomes of the National Land Cover Database (NLCD). A balanced sample design regarding the saturation status of MODIS LAI and a machine-learning-based noise detection technique were introduced to mitigate the trade-off in estimation accuracy between medium LAI (e.g., 3 to 4, unsaturated) and high LAI (e.g., 4–6, saturated).This approach was evaluated using ground measurements from 19 National Ecological Observatory Network (NEON) sites and eight independent sites from other sources. These sites comprise a representative sample of forests, grasslands, shrublands, and croplands across the US. For NEON sites, the LAI estimates show an overall Root Mean Squared Error (RMSE) of 0.8 with r2 of 0.88. For the eight independent sites, the Landsat LAI algorithm achieves RMSE between 0.52 and 0.91. The uncertainty in Landsat estimated LAI varies across biomes and locations. The proposed algorithm was implemented on the Google Earth Engine platform, allowing for the rapid generation of long-term high-resolution LAI records from the 1980s using Landsat images (code is available at https://github.com/yanghuikang/Landsat-LAI). Our findings also highlight the importance of sample balance on regression-based modeling in remote sensing applications.

Intever Public Database for Arcing Event Detection: Feature Analysis, Benchmark Test, and Multi-Scale CNN Application

In this article, we introduce the Intever public database for arcing event detection (IAED) along with novel feature extraction methods and an end-to-end deep neural network. First, we describe the details of arc simulation process and database specifications. Second, we handcraft a set of 29 efficient arcing features, including novel nonlinear dynamic features extracted from reconstructed phase space. Third, we propose a customized multiscale convolutional neural network (Multi-Scale CNN) model. Finally, we present the benchmark testing results on six IAED cohorts and 25 load settings and analyze the time complexity of each algorithm. Experimental results show that the highest F1 scores on high-resolution cohort and low-resolution cohort are achieved by Multi-Scale CNN at 94.16% and 87.59%, respectively. Also, Multi-Scale CNN gives superior results on various challenging load settings, such as induction cooker connected with air purifier in high resolution and microwave connected with refrigerator in low resolution, and is proved run faster than traditional arcing detection methods.

Efficacy of the Cell Perturbation Method in Large-Eddy Simulations of Boundary Layer Flow over Complex Terrain

A challenge to simulating turbulent flow in multiscale atmospheric applications is the efficient generation of resolved turbulence motions over an area of interest. One approach is to apply small perturbations to flow variables near the inflow planes of turbulence-resolving simulation domains nested within larger mesoscale domains. While this approach has been examined in numerous idealized and simple terrain cases, its efficacy in complex terrain environments has not yet been fully explored. Here, we examine the benefits of the stochastic cell perturbation method (CPM) over real complex terrain using data from the 2017 Perdigão field campaign, conducted in an approximately 2-km wide valley situated between two nearly parallel ridges. Following a typical configuration for multiscale simulation using nested domains within the Weather Research and Forecasting (WRF) model to downscale from the mesoscale to a large-eddy simulation (LES), we apply the CPM on a domain with horizontal grid spacing of 150 m. At this resolution, spurious coherent structures are often observed under unstable atmospheric conditions with moderate mean wind speeds. Results from such an intermediate resolution grid are often nested down for finer, more detailed LES, where these spurious structures adversely affect the development of turbulence on the subsequent finer grid nest. We therefore examine the impacts of the CPM on the representation of turbulence within the nested LES domain under moderate mean flow conditions in three different stability regimes: weakly convective, strongly convective, and weakly stable. In addition, two different resolutions of the underlying terrain are used to explore the role of the complex topography itself in generating turbulent structures. We demonstrate that the CPM improves the representation of turbulence within the LES domain, relative to the use of high-resolution complex terrain alone. During the convective conditions, the CPM improves the rate at which smaller-scales of turbulence form, while also accelerating the attenuation of the spurious numerically generated roll structures near the inflow boundary. During stable conditions, the coarse mesh spacing of the intermediate LES domain used herein was insufficient to maintain resolved turbulence using CPM as the flow develops downstream, highlighting the need for yet higher resolution under even weakly stable conditions, and the importance of accurate representation of flow on intermediate LES grids.

Thermal field manipulation via a two-phase thermal metamaterial

Conventional methods for manipulating heat flow are restricted by a trade-off between tunable capability and efficient thermal conductivity. Herein, we report a novel method to manipulate thermal fields by using a reconfigurable two-phase thermal metamaterial composed of a translucent liquid and controllable micro magnetic particles. The thermal behavior as a function of the components of two-phase media is predicted on the basis of coupled radiative and conductive theory. The response of the thermal field can be efficiently tuned by the radial distribution of discrete microparticles. Precise and reconfigurable thermal coding/decoding information transmission is demonstrated by using multiple thermal particles regulating units. Our work should lead to the development of platforms for manipulating physical fields with multi-scale and multi-domain applications, which can be analogized to optics and acoustics. This work pioneers physical manipulations and should aid in the development of smart functional devices with micromanipulators for multiple physical environments.

Analytical solution for the stress field of hierarchical defects: multiscale framework and applications

Hierarchical defects are defined as adjacent defects at different length scales. Involved are the two scales where the stress field distribution is interrelated. Based on the complex variable method and conformal mapping, a multiscale framework for solving the problems of hierarchical defects is formulated. The separated representations of mapping function, the governing equations of potentials, and the stress field are subsequently obtained. The proposed multiscale framework can be used to solve a variety of simplified engineering problems. The case in point is the analytical solution of a macroscopic elliptic hole with a microscopic circular edge defect. The results indicate that the microscopic defect aggregates the stress concentration on the macroscopic defect and likely leads to global propagation and rupture. Multiple micro-defects have interactive effects on the distribution of the stress field. The level of stress concentration may be reduced by the coalescence of micro-defects. This work provides a unified method to analytically investigate the influence of edge micro-defects within the scope of multiscale hierarchy. The formulated multiscale approach can also be potentially applied to materials with hierarchical defects, such as additive manufacturing and bio-inspired materials.

Multiscale frictional behaviors of sp2 nanocrystallited carbon films with different ion irradiation densities

sp2 nanocrystallited carbon films with large nanocrystallite sizes, smooth surfaces, and relative high hardness were prepared with different ion irradiation densities regulated with the substrate magnetic coil current in an electron cyclotron resonance plasma sputtering system. Their multiscale frictional behaviors were investigated with macro pin-on-disk tribo-tests and micro nanoscratch tests. The results revealed that, at an ion irradiation density of 16 mA/cm2, sp2 nanocrystallited carbon film exhibits the lowest friction coefficient and good wear resistant properties at both the macroscale and microscale. The film sliding against a Si3N4 ball under a contact pressure of 0.57 GPa exhibited a low friction coefficient of 0.09 and a long wear life at the macroscale. Furthermore, the film sliding against a diamond tip under a contact pressure of 4.9 GPa exhibited a stable low friction coefficient of 0.08 with a shallow scratch depth at the microscale. It is suggested that sp2 nanocrystallites affect the frictional behaviors in the cases described differently. At the macroscale, the contact interface via the small real contact area and the sp2 nanocrystallited transfer layer dominated the frictional behavior, while the sp2 nanocrystallited structure in the film with low shear strength and high plastic resistivity, as well as the smooth surface morphology, decided the steady low nanoscratch properties at the microscale. These findings expand multiscale tribological applications of sp2 nanocrystallited carbon films.

Groundwater modeling in data scarce aquifers: The case of Gilgel-Abay, Upper Blue Nile, Ethiopia

Groundwater (GW) is the main source of domestic water supply in Ethiopia (85%), however, despite widespread acknowledgement of its potential for resource-based development and climate change adaptation, the sector is still quite under-investigated. This is mainly due to the scarcity of in situ data, which are essential to building robust impact models. To address this, we developed a fine-resolution (500 m) GW model using MODFLOW-NWT, focusing on the Gilgel-Abay Catchment located in the Upper Blue Nile basin, fed with daily distributed input forcings of recharge and streamflow simulated by the Coupled Routing and Excess Storage (CREST) hydrological model. The model was calibrated against instantaneous observation records of GW table for 38 historical wells, and validated at selected sites using time series data collected from the Citizen Science Initiative (PIRE CSI), and the Innovation Lab for Small Scale Irrigation (ILSSI) project. An RMSE of 14.4 m (1.8% of range) was achieved for calibration and same for validation was 18.21 m and 15.76 m at the PIRE CSI and ILSSI sites, respectively. The findings of this research indicate substantial physical GW resource availability in the Gilgel-Abay region. Moreover, we expect the model to have multiscale future applications. These include obtaining dynamically downscaled boundary conditions for a local-scale GW model, to be developed in the next phase of our research. Further, an upscaled version of this model to encompass the entire Tana Basin would be developed to simulate lake-aquifer interactions. Finally, the approach of this research combining different types of datasets (e.g., reanalysis products, satellite data, citizen science data, etc.) is adaptable to other global data-scarce regions. Moreover, the method overcomes specific challenges associated to in situ data scarcity, limited knowledge on GW resources availability in the area, interaction with complex boundary conditions, and sensitivity under meteorological boundary forcings.

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper, we discuss recent advances in deep convolutional neural networks (CNNs) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F1-score of ∼91% on individual time-slices using only the ECEi data. This indicates that the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales for not only mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including the impact of recent upgrades to the ECEi diagnostic.

Spatiotemporal multiscale molecular cavity visualization and visual analysis

The analysis of molecular cavities, which are transport pathways in protein structures, is critical to the understanding of molecular phenomena. However, this work is challenging due to the high complexity and diversity of the macromolecular shapes in dynamic processes. In this paper, we propose a novel multiscale visualization method for visualizing the interaction of protein cavities. We design a series of scales and visualizations of cavities based on both temporal and spatial perspectives to allow domain experts to process their work at any scale of semantic abstraction. These scales demonstrate the chemical and structural properties of cavities and span from a complete protein to a cavity at a specific moment in temporal and spatial dimensions. We also create a continuous interaction space for multiscale applications. Finally, the applicability of our approach is proven through experimental use cases, with cavities in proteins being visualized and analyzed in a focus-and-context manner. Our collaborating domain experts confirmed that our approach is an efficient and reliable method of analyzing cavities with great potential for large dynamic cavity data analysis.

A new perspective to model subsurface stratigraphy in alluvial hydrogeological basins, introducing geological hierarchy and relative chronology

This paper presents a novel perspective for modelling alluvial stratigraphy. It integrates the spatial geological information, geological maps and well-log descriptions, with the rules describing the hierarchy and relative chronology of the geological entities. As geological modelling tools are moving fast forward, the urgent need for expert geological input, codified as modelling rules, persists. Concerning subsurface alluvial architectures, the concepts of “stratigraphic hierarchy” and “relative chronology” provide the most relevant rules which permit to link the modelling procedure to the geo-history of a region.The paper shows how to formalize this knowledge into modelling rules. This is illustrated and implemented in a Python™ module named HIEGEO which is applied on a 2-D cross-section from the Po Basin (N-Italy). The stratigraphic correlation yields 2-D pictures of the hierarchic stratigraphy and relative chronology of the units. The input are: an attribute table of stratigraphic boundaries expressing their hierarchy and chronology; contact points where these boundaries cross the control logs. Since the aim of HIEGEO is to illustrate the principle of the method but not to replace existing 3-D geological modelling tools, it implements a linear interpolation algorithm which creates joins between contact points. It plots linear joins framing polygons based on their hierarchy, at any user's desired detail. HIEGEO highlights potential inconsistencies of the input dataset, helping to re-evaluate the geological interpretation.The proposed workflow allows to: i) translate geological knowledge into modelling rules; ii) compute stratigraphic models constrained by the hierarchy of stratigraphic entities and the relative chronology of geological events; iii) represent internal geometries of the stratigraphic units, accounting for their composite nature; iv) reduce uncertainty in modelling alluvial architectures. It represents a starting point for multi-scale applications and could be easily integrated into 3-D modelling packages, to couple the hierarchical concept proposed here with existing advanced interpolation methods.

An Exponential-Based DGTD Method for Modeling 3-D Plasma-Surrounded Hypersonic Vehicles

Plasma surrounding a hypersonic vehicle can lead to the absorption of incoming radar radiation or the interruption of communication. Therefore, the development of hypersonic vehicle relies on a clear understanding of the strong interaction between the surrounding plasma and the incident electromagnetic wave. However, because of the presence of complex geometrical features and heterogeneous media, these applications are often multiscale. The modeling of such applications in 3-D is extremely challenging for traditional numerical methods. In this work, an efficient discontinuous Galerkin time-domain (DGTD) method for the 3-D modeling of plasma-surrounded hypersonic vehicles is presented. To overcome the grid-induced stiffness problem arising from the modeling of such multiscale applications with dispersive media, and to improve the modeling efficiency, a high-order exponential-based time integration for the time-domain Maxwell’s equations discretized by DG schemes with a general flux formulation is proposed. This time integration has excellent stability properties in the locally refined part of the mesh and hence enables the usage of larger time steps compared with the explicit time marching scheme. Additionally, the exponential-based time integration is developed to adapt an anisotropic perfectly matched layer for the modeling of open-domain problems. Finally, numerical experiments are presented to investigate the accuracy and convergence properties and to demonstrate the well-modeling capability of the proposed method.

Mechanistic Modeling and Multiscale Applications for Precision Medicine: Theory and Practice

Drug research, therapy development, and other areas of pharmacology and medicine can benefit from simulations and optimization of mathematical models that contain a mathematical description of inte...

All-Dielectric Metasurface-Enabled Near-Infrared Switching Based on Ge2Sb2Te5 Phase-Change Material

A metasurface for use in multiscale applications in the near-infrared (NIR) range is proposed herein. A detailed study is carried out on a polarization-insensitive, single-layered, reconfigurable swastika-like metasurface for NIR switching. Using germanium antimony telluride (Ge2Sb2Te5) as a phase-change material, the metasurface works as a NIR switch in the wavelength range from 1.8 μm and 2.2 μm. The metasurface consists of a patterned Ge2Sb2Te5 thin film on a glass substrate, making it an all-dielectric metastructure. A high switching contrast ratio, i.e., ratio of maximum to minimum transmission, of 150 was obtained. Light with different polarizations had no effect on the switching behavior of the metasurface, making it a potential candidate for the development of advanced, tunable, and integrated photonic devices.

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

BackgroundObesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR).MethodThis paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study.ResultsThough the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori. In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts.ConclusionThe results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

Comparison and Application of Four Multi-scale Decomposition Methods for the Price of Different Carbon Markets

In this paper, EMD, EEMD, CEEMD and MEEMD methods are used to analyze the multi-scale decomposition and application effect of carbon price time series. Through the comparison of indexes, such as orghogonality, completeness and so on, the MEEMD is proved to be the most effective method. MEEMD is used to conduct an empirical analysis of Hubei and EU carbon market. The results show that the biggest impact on the price of Hubei carbon market is the low frequency component, indicating that it is more easily to be interfered by external factors. The most influential factor on the EU carbon market price is the trend component which confirms that the EU carbon market has a strong price discovery function, that is, it has better effectiveness than Hubei carbon market.