Multiscale Systems Research Articles

Development of an intelligent multi-scale defect detection system for aspheric lenses with high accuracy and efficiency

Existing methods struggle to fulfill the demand for fast and precise detection of multi-scale faults in aspheric lenses. A high accuracy and efficiency intelligent detection system for aspheric lenses is developed, which utilizes four-step phase-shifted grating optical imaging and Fourier low-pass filtering as image acquisition and preprocessing systems. Accurate and efficient defect feature extraction methods are designed based on regression loss and lightweight feature selection networks, and the model's ability to learn multi-scale defect information is improved. Finally, experimental tests were conducted based on the self-constructed multi-scale defect datasets of aspheric lenses. The results show that the detection system achieves a global accuracy of 96.3 %, an accuracy of 63.8 % for tiny defects detection, as well as a single-image detection time of 24.9 ms, which is a significant improvement in terms of accuracy and efficiency compared with existing methods. Therefore, the intelligent detection system can be widely applied to defect detection in aspheric lenses.

A preference group consensus method with three-way decisions and regret theory under multi-scale information systems

The foundation of intelligent computing and expert systems relies on the data processing conducted within the realm of multi-scale information systems (MSISs). Data processing within MSISs caters to diverse analytical needs, where the preference hierarchy among various factors plays a vital role in the consensus reaching process (CRP). Additionally, the three-way decisions (TWD) theory serves as an efficient tool, while regret theory (RT) quantifies decision-makers' risk inclinations associated with various psychological behaviors. Thus, the paper aims to introduce an innovative approach known as the CRP-TWD-RT-MSIS method. The study draws inspiration from spatial geometry, incorporating concepts like points, lines, surfaces, and bodies to create MSISs. This process generates a matrix of fuzzy preference relations (FPRs) among distinct decision-makers through data preprocessing. In addition, the paper explores feedback mechanisms based on identification and directional rules, as well as those rooted in minimal adjustments or cost considerations. Simultaneously, it takes negotiation and discussion time into account, culminating in the development of a personalized adjustment optimization model that focuses on minimizing time costs. In the end, the method's effectiveness and superiority are validated through a case study and a comparative analysis of real data from the Chinese Weather and Meteorological Bureau's website.

The Multiscale Principle in Nature (Principium luxuriæ): Linking Multiscale Thermodynamics to Living and Non-Living Complex Systems

Why do fractals appear in so many domains of science? What is the physical principle that generates them? While it is true that fractals naturally appear in many physical systems, it has so far been impossible to derive them from first physical principles. However, a proposed interpretation could shed light on the inherent principle behind the creation of fractals. This is the multiscale thermodynamic perspective, which states that an increase in external energy could initiate energy transport mechanisms that facilitate the dissipation or release of excess energy at different scales. Within this framework, it is revealed that power law patterns, and to a lesser extent, fractals, can emerge as a geometric manifestation to dissipate energy in response to external forces. In this context, the exponent of these power law patterns (thermodynamic fractal dimension D) serves as an indicator of the balance between entropy production at small and large scales. Thus, when a system is more efficient at releasing excess energy at the microscopic (macroscopic) level, D tends to increase (decrease). While this principle, known as Principium luxuriæ, may sound promising for describing both multiscale and complex systems, there is still uncertainty about its true applicability. Thus, this work explores different physical, astrophysical, sociological, and biological systems to attempt to describe and interpret them through the lens of the Principium luxuriæ. The analyzed physical systems correspond to emergent behaviors, chaos theory, and turbulence. To a lesser extent, the cosmic evolution of the universe and geomorphology are examined. Biological systems such as the geometry of human organs, aging, human brain development and cognition, moral evolution, Natural Selection, and biological death are also analyzed. It is found that these systems can be reinterpreted and described through the thermodynamic fractal dimension. Therefore, it is proposed that the physical principle that could be behind the creation of fractals is the Principium luxuriæ, which can be defined as “Systems that interact with each other can trigger responses at multiple scales as a manner to dissipate the excess energy that comes from this interaction”. That is why this framework has the potential to uncover new discoveries in various fields. For example, it is suggested that the reduction in D in the universe could generate emergent behavior and the proliferation of complexity in numerous fields or the reinterpretation of Natural Selection.

A multiscale magma system beneath the Tengchong volcano in western Yunnan revealed by ambient noise tomography

SUMMARY The western Yunnan is located in the SE Tibetan Plateau, and is characterized by the active Tengchong volcano (TCV), complex crust–mantle coupling and intense earthquakes. To elucidate tectonism in the western Yunnan, we construct a 3-D S-wave velocity model to 80 km depth via ambient noise tomography using dense seismic stations. Our model shows significant low-velocity anomalies at different depths in the crust and uppermost mantle. Compared with the results of previous regional tomography, we image low-velocity anomalies consistent with a large-scale source of partial melts in the uppermost mantle beneath the Tengchong and Baoshan blocks, rather than just below the Tengchong block. Our results also reveal a magma chamber extending from the shallow subsurface to the lower crust beneath the TCV, which is fed by the mantle source. Based on these findings, we propose that the mantle source and crustal magma chamber form a multiscale magma system. Moreover, the mantle source is potentially resulted from asthenospheric upwelling, which is related to the subduction of the Indian slab. In addition, our model shows that the 1976 M7.4 and M7.3 Longling earthquakes occurred near a magma chamber. Thus, fluids from the magma chamber likely reduced the frictional coefficient on the seismogenic fault and caused the Longling earthquakes.

Integrating multiscale data for digital twin-enhanced manufacturing systems: A conceptual and case demonstration

This paper explores the integration of data within digital twin-driven multiscale manufacturing systems, examining the core characteristics of data flows in digital twin (DT) environments tailored to manufacturing. In DT frameworks, data are inherently bidirectional, automated, and real-time, which are critical for maintaining the operational integrity of interconnected manufacturing subsystems. Different scales (from machine tool focus to supply chains) and timelines (from operations events to long term planning) make manufacturing system operation complex. This in turn means data integration across these scales and timelines is inherently complex. This study focuses on the dynamics of data flows in DT-driven manufacturing systems that support both forward and backward communication across DT modules and the central DT platform. The overall DT data scheme manages the supervision and cataloging of actions, which includes technical data related to machinery, processes, resources, and rules, alongside performance metrics across various scales and levels over time. A case study of a DT-driven manufacturing unit illustrates the data flow and interdependency in DT data framework. It highlights how data integration across multiple manufacturing stages can improve operational efficiency, fault diagnosis, and resource optimisation. This study lays the groundwork for the implementation of DT in complex, multiscale manufacturing systems by providing an extendible structured framework.

INTELLIGENT EXTRACTION OF COMPLEXITY TYPES IN FRACTAL RESERVOIR AND ITS SIGNIFICANCE TO ESTIMATE TRANSPORT PROPERTY

Fractal pore structure exists widely in natural reservoir and dominates its transport property. For that, more and more effort is devoted to investigate the control mechanism on mass transfer in such a complex and multi-scale system. Apparently, effective characterization of the fractal structure is of fundamental importance. Although the newly emerged concept of complexity assembly clarified the complexity types and their assembly mechanism in a fractal system, equivalent extraction of the complexity types is the key for effective characterization. For these, we proposed a deep learning-based method to extract the original and behavioral complexity assembled in bed-packing fractal porous media for simplification and without loss of generality. In detail, the UNeXt network model was trained to obtain the independent connected regions of scaling objects with different scales, the edge detection and clustering analysis algorithms were employed to extract the number-size relationship between two successive scaling objects, and the unique inversion of fractal behavior was realized by taking the number-size model and fractal topography together. Consequently, an equivalent characterization method for fractal complex pore structure was developed based on the concept of complexity assembly. Our investigation provides a theoretical guidance and method reference for the quantitative characterization of fractal porous media that will guarantee the fundamental requirement for the accurate evaluation of the transport properties of natural reservoir.

Feature subset selection for multi-scale neighborhood decision information system via mutual information

Feature subset selection for multi-scale neighborhood decision information system via mutual information

A FRACTAL-BASED OIL TRANSPORT MODEL WITH UNCERTAINTY REDUCTION FOR A MULTI-SCALE SHALE PORE SYSTEM

The challenges of modeling shale oil transport are numerous and include strong solid-fluid interactions, fluid rheology, the multi-scale nature of the pore structure problem, and the different pore types involved. Until now, theoretical studies have not fully considered shale oil transport mechanisms and multi-scale pore structure properties. In this study, we propose a fractal-based oil transport model with uncertainty reduction for a multi-scale shale pore system. The fractal properties of the shale pore system are obtained using high-resolution scanning electron microscope (SEM) imaging combined with laboratory core sample gas permeability measurements to reduce the model uncertainty. This fractal-based oil transport model accounts for boundary slippage, fluid rheology, the adsorption layer, and different pore types. We further pinpoint the effects of the fractal properties (pore fractal dimension, tortuosity fractal dimension), the shale pore properties (pore type, pore size, total organic carbon in volume), and the fluid properties (yield stress, liquid slippage, adsorption layer) on the shale oil permeability and mobile oil saturation using the proposed model. The results reveal that the size of the inorganic pores has the largest influence on the shale oil transport properties, followed by the yield stress, tortuosity fractal dimension, and the fractal dimension of the inorganic pores.

Strategies and Practices of Intelligent Imputation in Data Mining Based on Contact Number Evaluation

Abstract This paper proposes the general architecture of a multi-scale classification system based on the intelligent imputation method of multi-scale classification. SVM is used to transform the storage structure model of the object, obtain the trained support vector set with corresponding weights, measure the similarity between two data points based on the Hausdorff distance, realize the construction of a similarity matrix, adopt the idea of mean value, blur the data information, and improve the mechanism of on-scale imputation. The bicubic difference method is used as the theoretical basis of scale-down extrapolation, and the scale-down extrapolation algorithm DAMSC is established. Based on the quaternionic linkage number, the dataset data are preprocessed, and the data eigenvalue weights are calculated at the same time. The multi-scale classification validity index evaluates the model’s accuracy in classification and its performance in both upward and downward scale extrapolation. When the categorized data is 7, the MSCVI metric of the model attains a peak of 0.92254 in the segmentation dataset run results, which is more advantageous than other metrics. In the regression test of the model’s imputation results, the correlation coefficient of bus passengers is 0.99, the adjusted R-square is 0.97043, and the coefficient of the regression equation is 1.042, and the model in this paper is valid and reliable for the imputation of passenger drop-off points.

Numerical investigation of energy loss distribution in the cavitating wake flow around a cylinder using entropy production method

The wake flow of a circular cylinder is numerically investigated by Large Eddy Simulation (LES) combined with the Schnerr–Sauer cavitation model. By comparing entropy production in the presence or absence of cavitation, the energy loss distribution in the wake flow field of a cylinder is explored, shedding light on the interactions between multiscale vortex systems and cavitation. The comparative results reveal that, under non-cavitating conditions, the energy loss region in the near-wake area is more concentrated and relatively larger. Energy dissipation in the wake flow field occurs in regions characterized by very high velocity gradients, primarily near the upper and lower surfaces of the cylinder near the leading edge. The influence of cavitation bubbles on entropy production is predominantly observed in the trailing-edge region (W1) and the near-wake region (W2). The distribution trends of wall entropy production on the cylinder’s surface are generally consistent in both conditions, with wall entropy production primarily concentrated in regions exhibiting high velocity gradients.

A single-sensor multi-scale quality monitoring methodology for laser-directed energy deposition: Example with height instability and porosity monitoring in additive manufacturing of ceramic thin-walled parts

Laser Additive Manufacturing (LAM) faces various technical challenges, encompassing issues with dimensional accuracy, mechanical properties, and processing-related defects, such as surface roughness, cracking, and residual porosity. Currently, the quality of safety-critical parts produced by LAM processes, which is still improved through trial-and-error adjustments of multiple process variables, have limited real-time monitoring capabilities. This study introduces an innovative method for real-time quality assessment of laser-directed energy deposition (LDED) procedures across multiple scales (macroscale, mesoscale, and microscale) utilizing a single sensor. This approach is founded on two fundamental components: the single-sensor system for data acquisition and the algorithm for simultaneous multi-scale quality monitoring. For the data acquisition system aspect, a single-sensor, high dynamic range (HDR), multi-scale information acquisition system has been developed to mitigate intense radiation, eliminate plume disturbances, and rectifies backlight shadows, facilitating clearer images of deposition contours and the molten pool region. In the realm of data monitoring algorithms, a physics model-driven supervisory algorithm is employed to enable monitoring of contour height instability, while a convolutional neural network (CNN), driven by image data, is utilized for porosity prediction. Then, this algorithm adeptly tackles the challenge of simultaneously monitoring macroscopic contour and mesoscale porosity. Regarding the validation of the method, the outcomes of single-sensor multi-scale quality monitoring in additive manufacturing of ceramic thin-wall parts indicate a 100% recognition rate for deposition contour and molten pools, contour feature recognition with a relative error of under 0.05%, porosity prediction accuracy surpassing 99%, and a monitoring time of 80.4 ms per frame. Single-sensor multi-scale real-time quality monitoring can serve as a methodological support for future real-time quality control in LDED.

Personalized ECG monitoring and adaptive machine learning

This non-technical review introduces key concepts in personalized ECG monitoring (pECG), which aims to optimize the detection of clinical events and their warning signs as well as the selection of alarm thresholds. We review several pECG methods, including anomaly detection and adaptive machine learning (ML), in which learning is performed sequentially as new data are collected.We describe a distributed-network multiscale pECG system to show how the computational load and time associated with adaptive ML could be optimized. In this architecture, the limited analysis of ECG waveforms is performed locally (e.g., on a smart phone) to determine a small number of clinically important ECG elements, and an adaptive ML engine is located on a remote server (Internet cloud) to determine an individual's “fingerprint” basis patterns and to detect anomalies in those patterns.

Seismogram Synthesis of Multi-Scale Layered Transversely Isotropic Saturated Half-Space Using a Revised Stiffness Matrix Method

Seismogram synthesis for a multi-scale transversely isotropic (TI) saturated layered half-space subjected to seismic dislocation source is studied by a revised stiffness matrix method in this paper. First, based on Biot’s theory of wave propagation in a fluid-saturated porous solid, the governing equations of motion in TI saturated medium are transformed by Fourier–Hankel integral transform. Next, the contribution of the seismic dislocation source can be imposed in the form of body forces. Then, the global stiffness matrix with well-conditioned properties is obtained by assembling each layer’s exact stiffness matrix, which can avoid accumulating errors in the matrix transmission. By extracting the divergence exponent terms of the stiffness matrix concerning thickness and wavenumber, the bottleneck of simulating the wave propagation process in multi-scale half-space, where the layer thickness and velocity vary sharply from the near-surface region to the crustal zone, could be overcome, and a fast and accurate seismogram synthesis could be achieved. The accuracy of the proposed method is verified by comparing the calculated results with those of published literature. Finally, the method is applied to the multi-scale system with realistic superficial fine layers. Numerical examples regarding thickness, TI properties and porosity are presented to illustrate the effects of superficial layers on ground motion. The numerical results show that the surface properties affect the wave propagation and energy distribution, leading to variable impacts on the surface response in different directions; this phenomenon is more pronounced in the case of high-frequency ground motions.

Molecular simulation of the adsorption and diffusion properties of CH4 and CO2 in the microporous system of coal

Micropores are the main adsorption sites for gases in coal. The research of the adsorption and diffusion pattern of gases in micropores is important for CO2-ECBM engineering and other measures to improve production. In this study, a multiscale microporous system with intramolecular pores, intermolecular pores, and slit pores is proposed. Adsorption and diffusion characteristics of CH4 and CO2 in different microporous systems were investigated. The results show that the adsorption of different microporous systems is basically consistent at low pressures (<2 MPa). Upon further increase in pressure, adsorption increases with increasing slit pore size. Gas molecules in coal are preferentially adsorbed in the intramolecular and intermolecular pores, and gas molecules in the slit pores are preferentially adsorbed in the slit pore walls. The increase in gas pressure and slit pore size decreases the proportion of gases adsorbed in the intramolecular and intermolecular pores and also increases the proportion adsorbed in the slit pores. Increase in size of the slit pores and decrease in adsorption will result in an increase in self-diffusion coefficients of gases. The results of self-diffusion coefficients of pores with different scales indicate that slit pores have the largest self-diffusion coefficients, followed by the intermolecular pores, and the smallest for the intramolecular pores. The isosteric heat of gases tends to decrease and then increase with increasing adsorption. The increase of slit pore size will decrease interaction energy. This study gives a reference for adsorption and diffusion mechanisms of gases in microporous systems, as well as a basis for gas injection displacement studies.

Data-Driven Design for Metamaterials and Multiscale Systems: A Review.

Metamaterials are artificial materials designed to exhibit effective material parameters that go beyond those found in nature. Composed of unit cells with rich designability that are assembled into multiscale systems, they hold great promise for realizing next-generation devices with exceptional, often exotic, functionalities. However, the vast design space and intricate structure-property relationships pose significant challenges in their design. A compelling paradigm that could bring the full potential of metamaterials to fruition is emerging: data-driven design. This review provides a holistic overview of this rapidly evolving field, emphasizing the general methodology instead of specific domains and deployment contexts. Existing research is organized into data-driven modules, encompassing data acquisition, machine learning-based unit cell design, and data-driven multiscale optimization. The approaches are further categorized within each module based on shared principles, analyze and compare strengths and applicability, explore connections between different modules, and identify open research questions andopportunities.

MULTI-SCALE DYNAMIC PARTITIONING SYSTEM OF URBAN SPATIAL UNITS

Abstract. As the spatial structure of cities becomes increasingly complex and sustainable development goals are promoted, society places higher demands on the management and planning of cities. As the basic unit of urban analysis, the combination pattern and scale shape of urban spatial units are crucial for rational management and planning of cities. However, existing urban analysis management systems often adopt a prefabricated fixed cell division method, which is difficult to meet the needs of high precision and multi-scale analysis of urban information. Therefore, this paper proposes an interactive dynamic partitioning technology, and designs a multi-scale dynamic partitioning system for urban spatial units (SUPS), in order to meet the diverse needs of urban management and planning. The system consists of a data management module, a spatial unit module, an integration module and a visualization module. The system not only realises the multi-scale dynamic partitioning of spatial units in the form of interactive operation, but also obtains more detailed identification results by applying the multi-scale spatial units to the identification of urban functional areas, verifying the effectiveness and feasibility of the interactive multi-scale dynamic partitioning of spatial units, and providing a new technical support for fine urban management.

On the nonlinear dynamics of a multi-scale flexoelectric cylindrical shell

The flexoelectric property shows the relationship between the strain gradient and the electric polarization and has a significant effect on static and dynamic responses of structures at small scales such as micro and nano. Nowadays, experimental studies for intelligent/composite ultra-small mechanical systems are complex, challenging, and in most cases still impossible, especially for nonlinear dynamic behavior analysis. For the first time, this study presents an advanced and complex generalized model for the nonlinear vibrations of a piezo-flexoelectric mechanical system based on a cylindrical shell. It should be noted, that the formulated boundary value problem is generalized, well-posed, and can be applied to the system at micro and nano scales. Hamilton's variational principle as well as assumptions of the first-order shear deformation theory (FSDT) and reformulated flexoelectric theory were used to derive equations of motion of the multi-scale intelligent shell system. To introduce nonlinear effects, von-Kármán strains are applied. Appropriate classical and non-classical mechanical/electric boundary conditions as well as higher-order forces and moments are determined to fully close the formulated problem. The Galerkin method combined with the GDQM, supported by the displacement control strategy and the weighted residual method, have been used to discretize and solve the nonlinear governing equations of the shell with different boundary conditions. Good convergence between the current results and the results from previous studies for simpler cases of the shell was proved. Furthermore, the results showed that geometric ratios of the structure, gradation of material properties, thickness of the flexoelectric layers, and the size effect parameter significantly influence the nonlinear frequency of the structure under the direct flexoelectric effect.

A physics-informed data-driven algorithm for ensemble forecast of complex turbulent systems

A new ensemble forecast algorithm, named the physics-informed data-driven algorithm with conditional Gaussian statistics (PIDD-CG), is developed to predict the probability density functions (PDFs) of complex turbulent systems with partial observations. The PIDD-CG algorithm integrates a unique multiscale statistical closure modeling strategy with a highly efficient nonlinear data assimilation scheme to create a mixture of conditional statistics. An effective data-driven modeling method is integrated with the dominant conditional statistics to serve as the forecast ensemble members that can significantly reduce the high computational cost of recovering high-dimensional PDFs. The multiscale features in the time evolution of these conditional statistics ensembles are effectively predicted by an appropriate combination of physics-informed analytic formulae and recurrent neural networks. An information metric is adopted as the loss function for the latter to more accurately capture the desirable turbulent features. The proposed algorithm displays effective forecasting performance in both the transient and statistical equilibrium non-Gaussian PDFs of strongly turbulent systems with intermittency, regime switching, and extreme events. It also facilitates the development of efficient statistical reduced-order models in recovering and predicting the large-scale coherent structures of a large group of multiscale complex systems.

Spontaneous Imbibition in Nanomatrix-Fracture of Low Permeability Using Multiscale Nanofluidic Chips.

Spontaneous imbibition has garnered increasing attention as an attractive mechanism for developing tight reservoirs. Despite valuable insights from previous experiments, there remains a lack of understanding regarding the imbibition process within multiscale nanopore-fracture networks. In this work, we devised an innovative multiscale model incorporating over 105 nanochannels and integrating a microfracture network to explore the complex imbibition behavior in tight formations. Additionally, fracture-free nanomatrix models with low permeability were developed for comparative discussions. The results show that the Lucas-Washburn equation remains valid at the tremendous fracture-free nanopore networks under the confinement of 500 nm, with a relative deviation of ±6%. The nanomatrix's heterogeneity hinders the imbibition rate, resulting in a reduction of 4.6 to 10.8% in the imbibition slope. The viscosity plays a dominant role in the change of imbibition slope as temperature varies. Our experiments also found that the interactions between the nanomatrix and bulk fracture complicate the imbibition process. A single wetting front no longer applies in the nanomatrix-fracture networks. Differing fracture/microchannel connectivity leads to disparities in macroscopic patterns, saturation rates, and flow directions. The spatial arrangement of fractures significantly impacts the imbibition time. Overall, this work based on nanofluidic techniques systematically explores the effects of matrix heterogeneity, temperature, and fractures on the imbibition process. The real-time in situ visualization of fluid distribution in multiscale matrix-fracture systems has been achieved, which offers theoretical guidance for practical engineering applications.

Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California’s Sierra Nevada mountains

Robust carbon monitoring systems are needed for land managers to assess and mitigate the changing effects of ecosystem stress on western United States forests, where most aboveground carbon is stored in mountainous areas. Atmospheric carbon uptake via gross primary productivity (GPP) is an important indicator of ecosystem function and is particularly relevant to carbon monitoring systems. However, limited ground-based observations in remote areas with complex topography represent a significant challenge for tracking regional-scale GPP. Satellite observations can help bridge these monitoring gaps, but the accuracy of remote sensing methods for inferring GPP is still limited in montane evergreen needleleaf biomes, where (a) photosynthetic activity is largely decoupled from canopy structure and chlorophyll content, and (b) strong heterogeneity in phenology and atmospheric conditions is difficult to resolve in space and time. Using monthly solar-induced chlorophyll fluorescence (SIF) sampled at ∼4 km from the TROPOspheric Monitoring Instrument (TROPOMI), we show that high-resolution satellite-observed SIF followed ecological expectations of seasonal and elevational patterns of GPP across a 3000 m elevation gradient in the Sierra Nevada mountains of California. After accounting for the effects of high reflected radiance in TROPOMI SIF due to snow cover, the seasonal and elevational patterns of SIF were well correlated with GPP estimates from a machine-learning model (FLUXCOM) and a land surface model (CLM5.0-SP), outperforming other spectral vegetation indices. Differences in the seasonality of TROPOMI SIF and GPP estimates were likely attributed to misrepresentation of moisture limitation and winter photosynthetic activity in FLUXCOM and CLM5.0 respectively, as indicated by discrepancies with GPP derived from eddy covariance observations in the southern Sierra Nevada. These results suggest that satellite-observed SIF can serve as a useful diagnostic and constraint to improve upon estimates of GPP toward multiscale carbon monitoring systems in montane, evergreen conifer biomes at regional scales.