Multiscale Integration Research Articles

A uniformly accurate method for the Klein-Gordon-Dirac system in the nonrelativistic regime

A multiscale time integrator Fourier pseudospectral (MTI-FP) method is presented for discretizing the massive Klein-Gordon-Dirac (KGD) system which involves a small dimensionless parameter 0<ε≤1. In the nonrelativistic limit regime, the KGD system admits rapid oscillations in time as ε→0+. In addition, the nonlinear Yukawa interaction and the indefinite Dirac operator bring other significant difficulties. The main idea of the MTI-FP method is to construct a precise multiscale decomposition by the frequency (MDF) to the solution of the KGD system at each time step and then employ the Fourier pseudospectral discretization for the spatial derivatives followed with the exponential wave integrator (EWI) for the time marching. This approach is explicit, easy to implement and preforms significantly better than the classical methods in the literature. More specifically, we rigorously establish the uniform error bounds at O(τ+hm0−1) for all ε∈(0,1] and optimal quadratic temporal error bounds at O(τ2) in the ε=O(1) regime, where τ is the time step size, h is mesh size and m0 depends on the regularity of the solution. Extensive numerical results demonstrate that our error bounds are optimal and sharp. Finally, we apply the MTI-FP method to numerically study the nonrelativistic limit behaviors of the KGD system when ε→0+.

Prostate cancer segmentation from MRI by a multistream fusion encoder.

Targeted prostate biopsy guided by multiparametric magnetic resonance imaging (mpMRI) detects more clinically significant lesions than conventional systemic biopsy. Lesion segmentation is required for planning MRI-targeted biopsies. The requirement for integrating image features available in T2-weighted and diffusion-weighted images poses a challenge in prostate lesion segmentation frommpMRI. A flexible and efficient multistream fusion encoder is proposed in this work to facilitate the multiscale fusion of features from multiple imaging streams. A patch-based loss function is introduced to improve the accuracy in segmenting smalllesions. The proposed multistream encoder fuses features extracted in the three imaging streams at each layer of the network, thereby allowing improved feature maps to propagate downstream and benefit segmentation performance. The fusion is achieved through a spatial attention map generated by optimally weighting the contribution of the convolution outputs from each stream. This design provides flexibility for the network to highlight image modalities according to their relative influence on the segmentation performance. The encoder also performs multiscale integration by highlighting the input feature maps (low-level features) with the spatial attention maps generated from convolution outputs (high-level features). The Dice similarity coefficient (DSC), serving as a cost function, is less sensitive to incorrect segmentation for small lesions. We address this issue by introducing a patch-based loss function that provides an average of the DSCs obtained from local image patches. This local average DSC is equally sensitive to large and small lesions, as the patch-based DSCs associated with small and large lesions have equal weights in this average DSC. The framework was evaluated in 931 sets of images acquired in several clinical studies at two centers in Hong Kong and the United Kingdom. In particular, the training, validation, and test sets contain 615, 144, and 172 sets of images, respectively. The proposed framework outperformed single-stream networks and three recently proposed multistream networks, attaining F1 scores of 82.2 and 87.6% in the lesion and patient levels, respectively. The average inference time for an axial image was 11.8 ms. The accuracy and efficiency afforded by the proposed framework would accelerate the MRI interpretation workflow of MRI-targeted biopsy and focaltherapies.

Multi-Scale Integration and Distribution of Soil Organic Matter Spatial Variation in a Coal–Grain Compound Area

Soil organic matter (SOM) scale effects are critical for crop growth and food security, especially in coal–grain complexes. However, few studies describe the spatial variation in SOM and its influencing factors at different sampling scales. Here, geostatistical theory and mathematical statistical methods were adopted to analyze the spatial variation characteristics of and structural differences in SOM in the coal mining subsidence area at Zhaogu No. 2 Mine at different sampling scales. The results showed that SOM varied spatially at large, medium, and small scales, and the coefficients of variation were 28.07%, 14.93%, and 14.31%, respectively, which are moderate values. The characteristic functions of the SOM content at different sampling scales differed, and the spatial structure scale effect was obvious. The spatial distribution of the SOM content fitted by the multiscale fitting model method was generally the same as the spatial distribution law of the SOM content fitted by the single scale kriging interpolation method; however, in terms of the detailed expression and spatial distribution of small-scale SOM content, the fitting model method was more accurate, and the accuracy increased by 36%. At the different sampling scales, sample size and soil type had specific effects on the SOM spatial distribution. These results provide research concepts and technical countermeasures for improving food security and the ecological environment in the coal–grain complex and help ensure sustainable agricultural lands.

CAVER: Cross-Modal View-Mixed Transformer for Bi-Modal Salient Object Detection.

Most of the existing bi-modal (RGB-D and RGB-T) salient object detection methods utilize the convolution operation and construct complex interweave fusion structures to achieve cross-modal information integration. The inherent local connectivity of the convolution operation constrains the performance of the convolution-based methods to a ceiling. In this work, we rethink these tasks from the perspective of global information alignment and transformation. Specifically, the proposed cross-modal view-mixed transformer (CAVER) cascades several cross-modal integration units to construct a top-down transformer-based information propagation path. CAVER treats the multi-scale and multi-modal feature integration as a sequence-to-sequence context propagation and update process built on a novel view-mixed attention mechanism. Besides, considering the quadratic complexity w.r.t. the number of input tokens, we design a parameter-free patch-wise token re-embedding strategy to simplify operations. Extensive experimental results on RGB-D and RGB-T SOD datasets demonstrate that such a simple two-stream encoder-decoder framework can surpass recent state-of-the-art methods when it is equipped with the proposed components.

THE THEORY OF COMMUNICATION DYNAMICS: Application to Modelling the Valence Shell Orbitals of Periodic Table Elements

Atoms are considered basic building blocks of the material world. Computational modelling is a useful technique for studying and predicting natural events. Due to the complexity and wide scale range of particle systems, current computational modelling approaches, including Classical Mechanics, General Relativity, and Quantum Mechanics are separately designed to describe systems at different sizes and precisions. While these disparate models have practical value for discrete, domain-specific problems, lack of consistency between models results in challenges when multi-scale integration and computational scalability is required. In this paper, we proposed a novel theoretical framework, inspired by the communication theory of Shannon, to describe physical reality from a new perspective. We call this approach Communication Dynamics. As an initial demonstration of the relevancy of this model, we represent electron orbital structures of atoms. Our model aims to use a uniformly applicable mathematical formula to describe natural structures at different scales. We believe this information theoretical approach represents a new way to investigate particle-wave duality and opens a pathway to multi-scale model integration between physics and other fields of science. The appendix containing actual calculations is 141-page Wolfram Mathematica file, which can be obtained from SDPS web page.

What neural oscillations can and cannot do for syntactic structure building.

Understanding what someone says requires relating words in a sentence to one another as instructed by the grammatical rules of a language. In recent years, the neurophysiological basis for this process has become a prominent topic of discussion in cognitive neuroscience. Current proposals about the neural mechanisms of syntactic structure building converge on a key role for neural oscillations in this process, but they differ in terms of the exact function that is assigned to them. In this Perspective, we discuss two proposed functions for neural oscillations - chunking and multiscale information integration - and evaluate their merits and limitations taking into account a fundamentally hierarchical nature of syntactic representations in natural languages. We highlight insights that provide a tangible starting point for a neurocognitive model of syntactic structure building.

FlowSRNet: A multi-scale integration network for super-resolution reconstruction of fluid flows

A wide range of research problems in physics and engineering involve the acquisition of high-resolution data. Recently, deep learning has proved to be a prospective technique for super-resolution (SR) reconstruction of fluid flows. General deep learning methods develop temporal multi-branch networks to improve SR accuracy while ignoring computational efficiency. Further, the generalization ability of the deep learning model in different fluid flow scenarios is still an unstudied issue. In this article, we propose an efficient multi-scale integration network called FlowSRNet to reconstruct the high-resolution flow fields. Specifically, we elaborately design a lightweight multi-scale aggregation block (LMAB) to capture multi-scale features of fluid data, which contains a parallel cascading architecture and feature aggregation module. The residual backbone of FlowSRNet is built by cascading the LMABs (cascaded blocks number N = 8) in a serial manner. Also, we present a small architecture LiteFlowSRNet (cascaded blocks number N = 2) for comparison. In addition, a corresponding SR dataset is constructed to train and test the proposed model, which contains different kinds of fluid flows. Finally, extensive experiments are performed on different fluid data to evaluate the performance of the proposed model. The results demonstrate that our approach achieves state-of-the-art SR performance on various fluid flow fields. Notably, our method enjoys merit of lightweight, which facilitates the development of the complicated calculation in computational fluid dynamics.

A multi-scale threshold integration encoding strategy for texture classification

A multi-scale threshold integration encoding strategy for texture classification

Biological-based and remote sensing techniques to link vegetative and reproductive development and assess pollen emission in Mediterranean grasses

Connecting the signals of the vegetative and reproductive cycles of plants using large-scale phenological techniques is not always an easy task, and this complexity increases considerably when analysing the plant life cycle in grasses, due to the ubiquity and diversity of this taxonomic family. This work integrates remote sensing techniques (NDVI from satellite remote sensing data and greenness from near-surface imagery) and biological-based techniques (airborne pollen monitoring and field observations and sampling) to analyse phenological patterns and productivity in grass-dominated vegetation types. We aim to answer two main applied and unanswered questions; i) how are the specific phases of vegetative and reproductive cycles in grasses linked at the species and plant community level? and ii) which grass-dominant habitats are the major contributors of grass pollen emission to the atmosphere at the plant community level? The multi-scale integration and validation of large-scale methods such as satellite remote sensing data and aerobiological monitoring using high-resolution or field phenological techniques is recommended. The results clearly support the hypothesis that the highest rates of grass pollen emission are successively produced when the major grass-dominated vegetation types go through the final phases of vegetative development during their biological senescence or equivalent phases. At the plant community level, natural and semi-natural grass-dominated vegetation types, rather than grass cropland habitats, constitute the major sources of pollen emission. The major contributors to the grass pollen emission at the species level are also identified. Finally, a positive relationship between year-to-year primary productivity (measured as annual sum or maximum NDVI) and pollen production (measured as airborne pollen intensity) was observed at the community level. This is a very timely study, as the availability of remote sensing data is increasing interest in generating enhanced forecasting model of allergenic airborne pollen.

The geometry of the Western Boundary Fault (WBF) of Amer oil field and its influence on hydrocarbon trapping, western central Gulf of Suez, Egypt

Amer ridge holds one of the most prolific oil fields in the western central Gulf of Suez rift basin and was discovered in 1965. The brownfield produces oil from the Miocene-Eocene carbonates and Late Cretaceous sandstone reservoirs with oil water contact 1150 m. The present work provides a structural model expressing the role of inherited structures and the tectonic evolution of the western fault bounding the field. This model resulted from the multi-scale integration of subsurface data from 90 wells, various borehole images, and 3-D seismic volumes attributes. The interpretation demonstrated the vertical profile of the western boundary fault of Amer field (WBF) to be a ramp-flat-ramp geometry pattern cutting through lithological units of different rheology. The developed fault surface of the WBF divided the penetrated sedimentary succession into two distinct strain intervals. The inherited structural fabrics of the lower interval of pre-rift units are documented with 1) an anomalous southwest-stratal dip, and 2) severe deformation of the WBF footwall with extensional faults of gulf parallel and gulf-orthogonal trends. Simultaneously, the roll-over anticline-syncline structure that developed within the upper, less-strained Miocene interval, coincided with the flat of the WBF geometry.By applying this model, it was possible to i) distinguish the three-footwall structurally controlled plays of Amer area into lower ramp-1, flat, and upper ramp-2 and ii) enlarge the size of the brownfield. Recent development activity in Amer area verified the new structural model and expanded the drilling activity to target the Paleozoic clastic reservoir of Nubia C as a new exploration objective. The Amer structural model can serve as a useful analogue for oil field development and production enhancement in other complex rift basin settings.

A spatiotemporal two-level method for high-fidelity thermal analysis of laser powder bed fusion

Numerical simulation of the laser powder bed fusion (LPBF) procedure for additive manufacturing (AM) is difficult due to the presence of multiple scales in both time and space, ranging from the part scale (order of centimeters/seconds) to the powder scale (order of microns/milliseconds). This difficulty is compounded by the fact that the regions of small-scale behavior are not fixed, but change in time as the geometry is produced. While much work in recent years has been focused on resolving the problem of multiple scales in space, there has been less work done on multiscale approaches for the temporal discretization of LPBF problems. In the present contribution, we extend on a previously introduced two-level method in space by combining it with a multiscale time integration method. The unique transfer of information through the transmission conditions allows for interaction between the space and time scales while reducing computational costs. At the same time, all of the advantages of the two-level method in space (namely its geometrical flexibility and the ease in which one may deploy structured, uniform meshes) remain intact. Adopting the proposed multiscale time integration scheme, we observe a computational speed-up by a factor × 2 . 44 compared to the same two-level approach with uniform time integration, when simulating a laser source traveling on a bare plate of nickel-based superalloy material following an alternating scan path of fifty laser tracks. • A Two-level method is extended to a spatiotemporal formulation without continuous mesh update. • The two-level formulation allows for full scale separation, such that different spatial and temporal approximations are used on each scale. • An overall speed-up factor of approximately 2.5 is observed while the temperature profile closely captures reference solutions.

An Attention-Based 3D CNN With Multi-Scale Integration Block for Alzheimer's Disease Classification.

Convolutional Neural Networks (CNNs) have recently been introduced to Alzheimer's Disease (AD) diagnosis. Despite their encouraging prospects, most of the existing models only process AD-related brain atrophy on a single spatial scale, and have high computational complexity. Here, we propose a novel Attention-based 3D Multi-scale CNN model (AMSNet), which can better capture and integrate multiple spatial-scale features of AD, with a concise structure. For the binary classification between 384 AD patients and 389 Cognitively Normal (CN) controls using sMRI scannings, AMSNet achieves remarkable overall performance (91.3% accuracy, 88.3% sensitivity, and 94.2% specificity) with fewer parameters and lower computational load, generally surpassing seven comparative models. Furthermore, AMSNet generalizes well in other AD-related classification tasks, such as the three-way classification (AD-MCI-CN). Our results manifest the feasibility and efficiency of the proposed multi-scale spatial feature integration and attention mechanism used in AMSNet for AD classification, and provide potential biomarkers to explore the neuropathological causes of AD.

Synthesis of a Multiperiod C–H–O Symbiosis Network for the Potential Valorization of Glycerol to PHA Production

The synthesis of the carbon–hydrogen–oxygen symbiosis network (CHOSYN) provides a structured framework for dealing with multiscale integration of hydrocarbon processing industries to improve resource efficiency and reduce waste generation. However, the earlier CHOSYN design approaches presumed that the networks operate in a single mode throughout the year. This may not be the case in real-world situations where the production of certain plants such as polyhydroxyalkanoates (PHAs) from bio-based glycerol may require multiple operational modes to accommodate the seasonal variability and availability of raw material. PHAs are biodegradable polymers produced by bacterial fermentation of carbon sources, which are the potential substitutions for petrochemical-based plastics. Hence, this work aims to propose a systematic approach to optimize multiperiod mass integration in the development of a CHOSYN. The central principle of the proposed approach is the incorporation of a storage and dispatch system into the conventional CHOSYN design for the supply of chemical species needed in the network throughout different operational periods. A case study considering glycerol valorization and other chemical plants using the devised CHOSYN model was conducted to evaluate the performance of the proposed design against the conventional CHOSYN in terms of sustainability metrics. The results demonstrate the advantages of the CHOSYN model with a storage and dispatch system by exhibiting a better profitability outcome than the conventional multiperiod CHOSYN through operating cost savings.

Generative adversarial networks-based image steganography with multiscale features integration

As an effective method of information hiding, steganography embeds secret information into images in a way that is not perceived by humans. Recent interest in the combination of image steganography and generative adversarial networks (GANs) has yielded rapid progress. However, existing steganography frameworks still suffer from the low quality of the steganographic images and weak resistance to detection by steganalysis algorithms. To overcome these limitations, we propose an effective GAN-based image steganography framework with multiscale features integration. Specifically, we construct the secret image feature extraction network (SfeNet), which is driven by the spatial attention mechanism to extract multiscale features of secret images. And the encoder combined with the efficient channel attention mechanism is presented to embed multiscale features of secret images into the cover image. Subsequently, a steganalyzer is incorporated as the discriminator with the encoder in GAN to strengthen the ability of the model to resist steganalysis. Besides, a mixed loss function is proposed by combining perceptual loss, MS-SSIM, and L1 loss to preserve the structural similarity of images. Experimental results on ImageNet, Pascal VOC2012, and LFW show that the proposed method achieves better quality steganographic images and better resistance to steganalysis compared to some of the state-of-the-art steganography algorithms.

Multi-scale integration simulation of microbially induced carbonate precipitation using reaction–diffusion and homogenization models

Multi-scale integration simulation of microbially induced carbonate precipitation using reaction–diffusion and homogenization models

Rookognise: Acoustic detection and identification of individual rooks in field recordings using multi-task neural networks

Individual-level monitoring is essential in many behavioural and bioacoustics studies. Collecting and annotating those data is costly in terms of human effort, but necessary prior to conducting analysis. In particular, many studies on bird vocalisations also involve manipulating the animals or human presence during observations, which may bias vocal production. Autonomous recording units can be used to collect large amounts of data without human supervision, largely removing those sources of bias. Deep learning can further facilitate the annotation of large amounts of data, for instance to detect vocalisations, identify the species, or recognise the vocalisation types in recordings. Acoustic individual identification, however, has so far largely remained limited to a single vocalisation type for a given species. This has limited the use of those techniques for automated data collection on raw recordings, where many individuals can produce vocalisations of varying complexity, potentially overlapping one another, with the additional presence of unknown and varying background noise. This paper aims at bridging this gap by developing a system to identify individual animals in those difficult conditions. Our system leverages a combination of multi-scale information integration, multi-channel audio and multi-task learning. The multi-task learning paradigm is based the overall task into four sub-tasks, three of which are auxiliary tasks: the detection and segmentation of vocalisations against other noises, the classification of individuals vocalising at any point during a sample, and the sexing of detected vocalisations. The fourth task is the overall identification of individuals. To test our approach, we recorded a captive group of rooks, a Eurasian social corvid with a diverse vocal repertoire. We used a multi-microphone array and collected a large scale dataset of time-stamped and identified vocalisations recorded, and found the system to work reliably for the defined tasks. To our knowledge, the system is the first to acoustically identify individuals regardless of the vocalisation produced. Our system can readily assist data collection and individual monitoring of groups of animals in both outdoor and indoor settings, even across long periods of time, and regardless of a species’ vocal complexity. All data and code used in this article is available online.

A Novel Deep-Learning-Based Enhanced Texture Transformer Network for Reference Image Super-Resolution

The study explored a deep learning image super-resolution approach which is commonly used in face recognition, video perception and other fields. These generative adversarial networks usually have high-frequency texture details. The relevant textures of high-resolution images could be transferred as reference images to low-resolution images. The latest existing methods use transformer ideas to transfer related textures to low-resolution images, but there are still some problems with channel learning and detailed textures. Therefore, the study proposed an enhanced texture transformer network (ETTN) to improve the channel learning ability and details of the texture. It could learn the corresponding structural information of high-resolution texture images and convert it into low-resolution texture images. Through this, finding the feature map can change the exact feature of images and improve the learning ability between channels. We then used multi-scale feature integration (MSFI) to further enhance the effect of fusion and achieved different degrees of texture restoration. The experimental results show that the model has a good resolution enhancement effect on texture transformers. In different datasets, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) were improved by 0.1–0.5 dB and 0.02, respectively.

A generalised multi-scale Peridynamics–DEM framework and its application to rigid–soft particle mixtures

The discrete element method (DEM) is the most dominant method for the numerical prediction of dynamic behaviour at grain or particle scale. Nevertheless, due to its discontinuous nature, the DEM is inherently unable to describe microscopic features of individual bodies which can be considered as continuous bodies. To incorporate microscopic features, efficient numerical coupling of the DEM with a continuous method is generally necessary. Thus, a generalised multi-scale PD–DEM framework is developed in this work. In the developed framework, meshfree discretised Peridynamics (PD) is used to describe intra-particle forces within bodies to capture microscopic features. The inter-particle forces of rigid bodies are defined by the DEM whereas a hybrid approach is applied at the PD–DEM interface. In addition, a staggered multi-scale time integration scheme is formulated to allow for an efficient numerical treatment of both methods. Validation examples are presented and the applicability of the developed framework to capture the characteristics mixtures with rigid and deformable bodies is shown.

Multi-source and multi-scale data integration for the assessment of the marine environmental status of the Basque Coast (SE Bay of Biscay)

In the context of the European Marine Strategy Framework Directive (MSFD, 2008/56/EC), Member States are required to assess the environmental status of marine waters. With the aim to support practitioners in this effort, and guide them in the identification of management solutions, a team of researchers has brought together available information for the Basque coastal zone (SE Bay of Biscay), and assessed the marine environmental status of these waters. To undertake the study, the area was divided into Marine Reporting Units (MRUs), following the MSFD terminology, which had specific areas assigned. The information used for the assessment was obtained from a variety of open sources (i.e., in-situ long-term monitoring data, biological data from annual/triannual surveys, multibeam echosounder data, satellite data, and had been collected at different spatial scales, from the coastline to offshore, covering the whole Exclusive Economic Zone. A total of 67 indicators were used, covering some ecosystem components: physico-chemistry of sediments and waters, phytoplankton, benthic fauna and flora, fish, birds, mammals, and alien species. The associated data, for 2010 to 2019, were then collated and integrated to assess the environmental status in a holistic way, using NEAT (Nested Environmental status Assessment Tool). The analyses included spatial scenarios (coastline to offshore), as well as filtering by different MSFD descriptors (i.e., biodiversity, alien species, eutrophication, seafloor integrity and contamination). Overall, the Basque coast is in good status, with no clear spatial gradient. But when focusing on specific ecosystem components (e.g., seabirds, mammals, some commercial fish species), it is possible to identify some components in bad status, with scope for improvement through the implementation of management measures. The integrated assessment approach presented and the outputs that are obtained can be used to guide European practitioners in their efforts to prioritize management measures. Furthermore, results are comparable to those reported in official MSFD assessments, confirming the power of the NEAT software.

Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities.

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.