Structures Of Different Scales Research Articles

Research on fractional wavelet transform combined with parameter adaptive PCNN for infrared and visible image fusion algorithm

Infrared and visible image fusion holds significant value across various fields due to its ability to provide complementary information. However, existing fusion algorithms often suffer from susceptibility to external physical conditions of source images, leading to inconsistent fusion quality and limited adaptive capability. In this paper, we propose a fractional wavelet transform fusion algorithm for infrared and visible images. This algorithm uses fractional wavelet domain image decomposition to more accurately locate the image structures of different scales. Initially, we perform image decomposition using a non-subsampled shearlet transform (NSST) with multi-scale and multi-directional decomposition. Subsequently, to effectively fuse detailed information, we employ discrete fractional wavelet transform (DFRWT) to decompose low-frequency subbands while selecting an appropriate p-value. High-frequency subbands are fused using a parameterized adaptive pulse-coupled neural network (PCNN) model. To validate the superiority of our algorithm, we conduct visual and quantitative comparative analyses against several existing algorithms. The results confirm that our proposed algorithm exhibits robustness against external physical conditions and surpasses these existing algorithms, making it highly applicable for infrared–visible fusion tasks.

Cause-and-effect chain analysis of combustion cyclic variability in a spark-ignition engine using large-eddy simulation, Part I: From tumble compression to flame initiation

Understanding, modeling, and reducing the cycle-to-cycle variability (CCV) of combustion in internal combustion engines (ICE) is a critical challenge to design engines of high efficiency and low emissions. A high level of CCV may contribute to partial burn, misfire, and knock in extreme engine cycles, which affects engine performance and eventually damages the engine. The origins of CCV have been studied both experimentally and numerically, and the variability of in-cylinder aerodynamics is recognized as one of the most important sources of CCV. However, a detailed and quantitative explanation of how in-cylinder flow CCV is generated is not yet clear. The objective of the present study is to develop a methodology to localize inside the chamber of a spark-ignition engine (SIE) the origins of flow variabilities and to identify some driving mechanisms leading to combustion variabilities. Multi-cycle wall-modeled large-eddy simulations (LES) for the TU Darmstadt optical engine under fired conditions are performed using the CFD solver Converge 3.0. The evolution of organized large-scale structures and the small-scale turbulence of the in-cylinder flow are analyzed using a developed methodology that includes the empirical mode decomposition (EMD) method adapted for 2D and 3D flow fields, and a vortex identification tool Γ3p. The contributions of different parts of the flow to CCV are quantified. In Part I of this work, the LES framework is validated against experimental data, and CCV of large-scale structures is characterized at spark timing. In Part II, the overall flow development during compression and intake strokes are quantitatively analyzed, and links are built between different engine phases to establish the cause-and-effect chain. Other CCV factors, such as spray injection and exhaust gas recirculation, are not included in the current study. However, the developed methodology for in-cylinder flow analysis could be used in studies on other engine configurations to improve the development of engine designs.Novelty and significance statementIn this work, the cycle-to-cycle variability (CCV) of combustion in a spark ignition engine is investigated to give a deeper understanding of CCV generation. The present study focuses on CCV caused by the stochastic nature of internal turbulent flow structures. LES approach is chosen due to its ability to capture CCV. The LES methodology was validated in a motored case in Ding et al. (2023). In the present study, it is validated in a reactive case against experimental in-cylinder pressures and velocity fields.A first novelty is the application of EMD methods combined with topology-based techniques to reactive LES results to characterize flow structures of different scales in the three-dimensional domain and to quantify separately their impacts on combustion.A second novelty and important finding is that a link is established between the combustion speed and the tumble formation and destabilization near BDC.Throughout our analyses in Part I and II, starting from the spark-ignition timing and going back to the early intake phase, a cause-and-effect chain is finally established between the development of in-cylinder flow and the combustion variability.

The role of Ti in mitigating thermal expansion of silica from molecular dynamics simulations

AbstractMolecular dynamics simulations are performed to investigate the structural response of titania silicate glass to temperature. The coefficient of thermal expansion is computed for two titania silicate glasses with 0 and 10 mol% titania content, the structures of which are presented in terms of radial and angular distributions. Revealed by the different changing rates of intertetrahedra bond angles and bond lengths with respect to the Ti and Si atoms, the glass structures tend to exhibit a nonvectorized expansion process at elevated temperatures, leading to inconsistent expansion rates of the structures in different scales. While the average length of TiO and Si‐O bonds both increases with temperature, the decrease in the coefficient of thermal expansion by the addition of Ti atoms is associated with the different expansion rate of tetrahedra. Arising from the gradual decrease in atomic overlapping, decrease in free volume inside the glass with temperature is also identified.

A Spatial Correlation Identification Model for Coherent Structure Extraction and Three-Dimensional Visualization

Multi-scale coherent structures have been observed in ocean currents, which are induced by the interaction of shear flows with different velocities. Understanding the spatial configuration and scale characteristics of coherent structures will promote the explanation of physical ocean phenomena. Considering the self-similarity, we propose a spatial correlation identification model for coherent structure extraction and three-dimensional visualization based on the wavelet transform and time-dependent intrinsic correlation method. The spatial and scale distributions of coherent structures are related to the dissipation rate variation. Most large-scale coherent structures, with the largest length scale of 13 m, are found to exist in stable fluid, such as the water column below 50 m. However, small-scale structures are found in chaotic fluids, such as the upper layer. Furthermore, we found that coherent structures of different scales coexist simultaneously in the same depth range, indicating a simultaneous multi-scale structure pattern for turbulent flow investigations.

Abdominal multi-organ segmentation using multi-scale and context-aware neural networks

Recent advancements in AI have significantly enhanced smart diagnostic methods, bringing us closer to achieving end-to-end diagnosis. Ultrasound image segmentation plays a crucial role in this diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we consider two inherent features of ultrasound images: (1) different organs and tissues vary in spatial sizes, and (2) the anatomical structures inside the human body form a relatively constant spatial relationship. Based on those two ideas, we proposed two segmentation models combining multi-scale convolution neural network backbones and a spatial context feature extractor. We discuss two backbone structures to extract anatomical structures of different scales: the Feature Pyramid Network (FPN) backbone and the Trident Network backbone. Moreover, we show how Spatial Recurrent Neural Network (SRNN) is implemented to extract the spatial context features in abdominal ultrasound images. Our proposed model has achieved dice coefficient score of 0.919 and 0.931, respectively.

Grafting–inspired bionic hierarchical structure for efficiently enhancing cemented carbide tool performance in green turning

Constructing bionic hierarchical structure on tool surface is an effective method for enhancing tool performance in vegetable oil-based minimum quantity lubrication (VMQL) assisted turning. Taking the features of VMQL assisted turning into account, the bionic hierarchical structure is expected to possess high durability and superoleophobicity. In this way, the structure function can be efficiently maintained and the vegetable oil droplets can be fully utilized. This work explored a novel grafting–inspired bionic hierarchical structure (GBHS) on cemented carbide tool surface. The agriculture technique named grafting provided inspiration for fusing the bionic structures obtained by examining dung beetle and lotus leaf. Two kinds of GBHS, namely VDGS and VAGS, were acquired due to the changes of the diameter Dc of the bionic circular recessed microstructure and the laser processing angle Al. Morphology, wetting behavior, durable superoleophobicity, mechanism and green turning performance were analyzed for GBHS. It was verified that GBHS integrated the advantages of the bionic structures of different scales and weakened the shortcomings of them. VDGS and VAGS separately exhibited highly durable low-adhesive superoleophobicity and highly durable high-adhesive superoleophobicity when they were subjected to mechanical load and high temperature. The use of VAGS prepared at the laser angle Al of 60° caused the best machining results in short-time turning. VDGS with a diameter Dc of 360 µm can be employed to acquire the optimum machining results in long-time turning. The construction strategy of GBHS can be extended to other tool materials and green machining conditions for obtaining enhanced tool performance.

Turbulence intensity effect on axial-flow-induced vibration of a two-cylinder cluster

This numerical study aims to investigate the effect of inlet turbulence intensity Tu (0.05 – 5.0%) on flow-induced vibration of an elastic cylinder with fixed ends placed next to a rigid cylinder of the same diameter and length in axial flow. Turbulent flow structure and fluid-structure interaction are captured using large eddy simulation and two-way coupling calculation, respectively. It is found that Tu produces a pronounced effect on vibration characteristics of the elastic cylinder. Given a dimensionless velocity U̅ (i.e., 3.49, less than critical U̅ for onset of buckling), root-mean-square vibration amplitude Arms⁎ increases rapidly with increasing Tu from 0.7% to 5.0% for any given center-to-center cylinder spacing P⁎ (1.20 – ∞). This change in Arms* is attributed to the interactions between turbulent structures of different scales, where interactions between large-scale eddies result in the formation of small-scale eddies in the interstitial flow between the cylinders, thus enhancing greatly the flow instability and hence Arms*. Another interesting phenomenon takes place at U̅ = 7.62 (beyond the critical U̅ for onset of buckling) and P⁎ = 1.80, where the elastic cylinder buckles into a new dynamic equilibrium state. As Tu increases from 0.3% to 1.5%, the flutter instability occurs merely along the y direction that is normal to the plane of two cylinders axes and also normal to main stream direction. It has been found that the high-Tu flow triggers two vortices in this direction, giving rise to two regions of different pressures around the cylinder. The two regions reposition quasi-periodically themselves, thus producing lateral forces that account for this flutter instability in the y direction. Scaling analysis of the dependence of Arms⁎ on Tu and U̅ reveals that the relationship Arms⁎ = g1(Tu, U̅) may be reduced to Arms⁎ = g2(U̅eff); g1 and g2 are different functions and the scaling factor U̅eff = U̅ TF, where TF is the turbulence factor that accounts for the effect of Tu on Arms⁎. The scaling law is discussed in detail, along with the physical meanings of U̅eff.

Anatomical Prior-Based Automatic Segmentation for Cardiac Substructures from Computed Tomography Images.

Cardiac substructure segmentation is a prerequisite for cardiac diagnosis and treatment, providing a basis for accurate calculation, modeling, and analysis of the entire cardiac structure. CT (computed tomography) imaging can be used for a noninvasive qualitative and quantitative evaluation of the cardiac anatomy and function. Cardiac substructures have diverse grayscales, fuzzy boundaries, irregular shapes, and variable locations. We designed a deep learning-based framework to improve the accuracy of the automatic segmentation of cardiac substructures. This framework integrates cardiac anatomical knowledge; it uses prior knowledge of the location, shape, and scale of cardiac substructures and separately processes the structures of different scales. Through two successive segmentation steps with a coarse-to-fine cascaded network, the more easily segmented substructures were coarsely segmented first; then, the more difficult substructures were finely segmented. The coarse segmentation result was used as prior information and combined with the original image as the input for the model. Anatomical knowledge of the large-scale substructures was embedded into the fine segmentation network to guide and train the small-scale substructures, achieving efficient and accurate segmentation of ten cardiac substructures. Sixty cardiac CT images and ten substructures manually delineated by experienced radiologists were retrospectively collected; the model was evaluated using the DSC (Dice similarity coefficient), Recall, Precision, and the Hausdorff distance. Compared with current mainstream segmentation models, our approach demonstrated significantly higher segmentation accuracy, with accurate segmentation of ten substructures of different shapes and sizes, indicating that the segmentation framework fused with prior anatomical knowledge has superior segmentation performance and can better segment small targets in multi-target segmentation tasks.

Three-dimensional Turbulent Reconnection within the Solar Flare Current Sheet

Solar flares can release coronal magnetic energy explosively and may impact the safety of near-Earth space environments. Their structures and properties on the macroscale have been interpreted successfully by the generally accepted 2D standard model, invoking magnetic reconnection theory as the key energy conversion mechanism. Nevertheless, some momentous dynamical features as discovered by recent high-resolution observations remain elusive. Here, we report a self-consistent high-resolution 3D magnetohydrodynamical simulation of turbulent magnetic reconnection within a flare current sheet. It is found that fragmented current patches of different scales are spontaneously generated with a well-developed turbulence spectrum at the current sheet, as well as at the flare loop-top region. The close coupling of tearing mode and Kelvin–Helmholtz instabilities plays a critical role in developing turbulent reconnection and in forming dynamical structures with synthetic observables in good agreement with realistic observations. The sophisticated modeling makes a paradigm shift from the traditional to a 3D turbulent reconnection model unifying flare dynamical structures of different scales.

Carboxymethyl cellulose/polyvinyl alcohol composite aerogel supported beta molecular sieve for CH4 adsorption and storage

Biomass aerogel is attractive in various applications due to their renewable, biodegradable and eco-friendly advantages. Herein, a novel beta molecular sieve/carboxymethyl cellulose/polyvinyl alcohol composite aerogel (beta/CP) is prepared by direct mixing and directional freeze-drying as an efficient gas adsorbent with hierarchical porosity. The beta molecular sieve is uniformly dispersed in the three-dimensional skeleton of the aerogel. By adjusting the loading mass of the beta molecular sieve to constitute a reasonable porosity and pore size distribution, the synergistic effect between pore structures of different scales improves the adsorption performance. The experiment results of beta/CP-4 show that the CH4 adsorption capacity can reach 60.33 cm3/g at 298 K and 100 bar, which is almost the same as that of the pure beta molecular sieve (62.09 cm3/g). The strong interaction between the aerogel and it prevents the molecular sieve agglomeration, improves the pore utilization, and also reduces the cost of using molecular sieve adsorbent. The above results indicate that the composite has good potential for application in the field of CH4 storage.

Proper orthogonal decomposition energy analysis in a nuclear reactor coolant pump based on PIV at the best efficiency point

The nuclear reactor coolant pump (RCP) is the core equipment of a nuclear power plant (NPP), and its stable and efficient operation is one of the keys to the development of the RCP. The RCP's large-scale and high energy flow structure can be revealed through effective experimental and post-processing methods, which will help fully understand the flow mechanism. Thus, the present work aims to conduct a comprehensive study on the RCP model pump based on Particle Image Velocimetry (PIV) flow field measurement, internal flow structure analysis, and POD (Proper Orthogonal Decomposition) at the best efficiency point. From the POD overall energy analysis, it is found that even if the diffuser is installed in the spherical casing, the energy distribution of each diffuser flow channel has great difference. Through the analysis and comparison of the original flow and the flow field after POD, the main flow structure in the diffuser flow channel 2 is the wake flow vortex shedding γ area. Still, the primary energy area is the low-speed area close to the discharge nozzle after POD. Hence, the main flow structure in the diffuser flow channel is inconsistent with the primary energy area, which proves that it is not comprehensive to analyze the flow loss only from the perspective of flow field analysis. POD has apparent advantages in decomposing flow coherent structures of different scales with different energies. The high energy flow structure can be more clearly clarified through the mode decomposition of POD.

Insights into boosting SiC membrane superhydrophobic-superoleophilic property and oil purification performance by hierarchical structure control

Engineering of superhydrophobic-superoleophilic (SHB-SOL) ceramic membrane surface with hierarchical structures remains challenging and the relation between physicochemical structure-dependent wettability and separation performance lacks investigation. Herein, SHB-SOL SiC ceramic membranes with different hierarchical micro-nano structures were fabricated by ZnO nanoflower (NF) deposition and silane grafting. Three SiC ceramic membranes with different pore sizes were purposefully used as the pristine, these membranes displayed similar geometric structures in different scales. The modified SiC membrane surface becomes stronger in SHB-SOL property as the pore size decreases and the optimal membrane possessing the lowest pore size presented a water contact angle of 172.46° and a water sliding angle of 5°. When used for pure oil or oily mixture separation, all the SHB-SOL SiC membranes exhibited enhanced oil fluxes compared with their pristine forms. At a pressure of 0.5 bar, the optimal membrane could completely reject water droplets and graphite solids (D50 =4.8 µm) for the oil-water-solid mixture containing 0.05 wt% water and 0.05 wt% graphite, showing a stable oil (hexane) flux of 1383.8 L·m−2·h−1. This work also reveals the effects of the hierarchical structures on the SHB-SOL property formation and the resulting antifouling mechanism, which offers novel insights into boosting ceramic membranes with desired surface wettability for enhanced separation performance.

Quantitative characterization of pore-fracture structure of medium and high-rank coal based on micro-CT technology

ABSTRACT Coal is a natural and complex porous medium, which contains pore-fracture structures of different scales. The pore-fracture structures of coal directly affect the characteristics of gas storage and migration. In this paper, high-resolution X-ray CT scanning technology is used to quantitatively characterize the pore-fracture structure of medium and high-rank coals. First, the porosity inversion method is used to determine the optimal threshold segmentation of pores and fracture in medium and high-rank coals. Then, the pore and fracture model of representative element volume (REV) was extracted and established to characterize the area porosity, pore surface area, and shape factor of the two coals, and the relationship between pore surface area and shape factor was investigated. Finally, an equivalent pore network model (PNM) of connected pore topology was established, and pore-throat parameters are statistically analyzed, including pore volume, pore radius, throat radius, throat length, and coordination number. The results show that the average pore diameter and average surface area of macropores in medium-rank coals are larger than those of high-rank coals, and high-rank coals have fewer isolated pores. With the increase of coal rank, the length and radius of the throat gradually decrease, which indicates that the throat of the medium-rank coal has a higher degree of development, and the gas seepage ability in the throat is stronger. The outcome of this study is of great significance for comprehensively understanding the pore-fracture structure of coal in micro-scale.

Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation

Transformer models have achieved great results in the field of computer vision over the past 2 years, drawing attention from within the field of remote sensing. However, there are still relatively few studies on this model in the field of remote sensing. Which method is more suitable for remote-sensing segmentation? In particular, how do different transformer models perform in the face of high-spatial resolution and the multispectral resolution of remote-sensing images? To explore these questions, this paper presents a comprehensive comparative analysis of three mainstream transformer models, including the segmentation transformer (SETRnet), SwinUnet, and TransUnet, by evaluating three aspects: a visual analysis of feature-segmentation results, accuracy, and training time. The experimental results show that the transformer structure has obvious advantages for the feature-extraction ability of large-scale remote-sensing data sets and ground objects, but the segmentation performance of different transfer structures in different scales of remote-sensing data sets is also very different. SwinUnet exhibits better global semantic interaction and pixel-level segmentation prediction on the large-scale Potsdam data set, and the SwinUnet model has the highest accuracy metrics for KAPPA, MIoU, and OA in the Potsdam data set, at 76.47%, 63.62%, and 85.01%, respectively. TransUnet has better segmentation results in the small-scale Vaihingen data set, and the three accuracy metrics of KAPPA, MIoU, and OA are the highest, at 80.54%, 56.25%, and 85.55%, respectively. TransUnet is better able to handle the edges and details of feature segmentation thanks to the network structure together built by its transformer and convolutional neural networks (CNNs). Therefore, TransUnet segmentation accuracy is higher when using a small-scale Vaihingen data set. Compared with SwinUnet and TransUnet, the segmentation performance of SETRnet in different scales of remote-sensing data sets is not ideal, so SETRnet is not suitable for the research task of remote-sensing image segmentation. In addition, this paper discusses the reasons for the performance differences between transformer models and discusses the differences between transformer models and CNN. This study further promotes the application of transformer models in remote-sensing image segmentation, improves the understanding of transformer models, and helps relevant researchers to select a more appropriate transformer model or model improvement method for remote-sensing image segmentation.

Multiscale and Cross-Level Attention Learning for Hyperspectral Image Classification

Transformer-based networks, which can well model the global characteristics of inputted data using the attention mechanism, have been widely applied to hyperspectral image (HSI) classification and achieved promising results. However, the existing networks fail to explore complex local land cover structures in different scales of shapes in hyperspectral remote sensing images. Therefore, a novel network named multiscale and cross-level attention learning (MCAL) network is proposed to fully explore both the global and local multiscale features of pixels for classification. To encounter local spatial context of pixels in the transformer, a multiscale feature extraction (MSFE) module is constructed and implemented into the transformer-based networks. Moreover, a cross-level feature fusion (CLFF) module is proposed to adaptively fuse features from the hierarchical structure of MSFEs using the attention mechanism. Finally, the spectral attention module (SAM) is implemented prior to the hierarchical structure of MSFEs, by which both the spatial context and spectral information are jointly emphasized for hyperspectral classification. Experiments over several benchmark datasets demonstrate that the proposed MCAL obviously outperforms both the convolutional neural network (CNN)-based and transformer-based state-of-the-art networks for hyperspectral classification.

Abdominal Multi-Organ Segmentation Based on Feature Pyramid Network and Spatial Recurrent Neural Network

As recent advances in AI are causing the decline of conventional diagnostic methods, the realization of end-to-end diagnosis is fast approaching. Ultrasound image segmentation is an important step in the diagnostic process. An accurate and robust segmentation model accelerates the process and reduces the burden of sonographers. In contrast to previous research, we take two inherent features of ultrasound images into consideration: (1) different organs and tissues vary in spatial sizes, (2) the anatomical structures inside human body form a relatively constant spatial relationship. Based on those two ideas, we propose a new image segmentation model combining Feature Pyramid Network (FPN) and Spatial Recurrent Neural Network (SRNN). We discuss why we use FPN to extract anatomical structures of different scales and how SRNN is implemented to extract the spatial context features in abdominal ultrasound images.

Flow boiling heat transfer enhancement via micro-pin-fins/ZnO nanorods hierarchical surface

In this study, ZnO nanorods were synthesized on micro-pin-fins by a simple and inexpensive aqueous solution method for flow boiling heat transfer enhancement. The morphology, chemical composition and wickability of the micro/nano hierarchical surface (PF+NR) were characterized. The heat transfer performance of PF+NR was investigated under various flow velocities together with a micro-pin-finned surface (PF), a ZnO nanorod-coated surface (NR) and a smooth surface (S). The results indicated that the boiling performance is improved significantly by the hierarchical surface and demonstrated a synergism between the microstructure and the nanostructure. The maximum boiling heat transfer coefficient of PF+NR is increased by 63%, 76%, and 158% compared with PF, NR and S, respectively. The critical heat flux is also increased by 31%, 33%, and 74%, respectively. The onset of nucleate boiling temperature of PF+NR is decreased compared with that of the other surfaces. The comprehensive improvement of boiling heat transfer performance is attributed to the cooperation of structures in different scales: the nanorods provide abundant nucleation sites, and the micro-pin-fins improve the liquid replenishment capacity and suppress the lateral coalescence of large bubbles at high heat fluxes.

A perceptual framework for infrared–visible image fusion based on multiscale structure decomposition and biological vision

Infrared–visible image fusion is of great value in many applications due to their highly complementary information. However, it is hard to obtain high-quality fused image for current fusion algorithms. In this paper, we reveal an underlying deficiency in current fusion framework limiting the quality of fusion, i.e., the visual features used in the fusion can be easily affected by external physical conditions (e.g., the characteristics of different sensors and environmental illumination), indicating that those features from different sources have not been ensured to be fused on a consistent basis during the fusion. Inspired by biological vision, we derive a framework that transforms the image intensities into the visual response space of human visual system (HVS), within which all features are fused in the same perceptual state, eliminating the external physical factors that may influence the fusion process. The proposed framework incorporates some key characteristics of HVS that facilitate the simulation of human visual response in complex scenes, and is built on a new variant of multiscale decomposition, which can accurately localize image structures of different scales in visual-response simulation and feature fusion. A bidirectional saliency aggregation is proposed to fuse the perceived contrast features within the visual response space of HVS, along with an adaptive suppression of noise and intensity-saturation in this space prior to the fusion. The final fused image is obtained by transforming the fusion results in human visual response space back to the physical domain. Experiments demonstrate the significant improvement of fusion quality brought about by the proposed method.

Fractals and Forecasting of Economic Cycles

For many centuries, scientists have used classical methods of calculation in the process of exploring and modeling the universe. And such functions that did not carry the proper smoothness or regularity were often considered as pathologies and were not given much attention. The fractal geometry of Benoit Mandelbrot deals with the study of such irregular sets. The basic concept of fractal geometry is a fractal, the origin of which is associated with computer modeling. With the help of fractals, it was possible to describe the glow of the sky during a thunderstorm, the dynamics of the growth of tree branches, the fractal structure of the internal organs of a person, the psyche and organizational systems, etc. Recently, fractals have been widely used to study economic cycles. In particular, such a characteristic of a time series as fractal dimension makes it possible to determine the moment when the system becomes unstable and is ready to move to a new state. Modern science widely uses the theory of fractals to study time series in order to increase the reliability of forecasting economic dynamics. The main feature of a fractal is the infinite repetition of a self-similar structure in different scales. Analogous nature characterizes economic cycles. Samuelson and Hicks created an appropriate mathematical model for modeling economic cycles, later this approach was developed in the works of Goodwin. Recently, fractals have been widely used to study economic processes. To date, there are many different mathematical models of fractals, each of which differs from the others in a certain recursive function. With the help of methods of fractal analysis and their modifications, it is possible to determine moments of qualitative change in the behavior of the system and, accordingly, to predict their subsequent behavior in a relatively long-term perspective. The most urgent task, solved by the methods developed within this approach, is the identification of the moment of the onset of major economic crises, that is, forecasting them in the near future. Keywords: Fractals. Economic cycles. Prognosis.

Remote Magnetic Microengineering and Alignment of Spheroids into 3D Cellular Fibers

AbstractDeveloping in vitro models that recapitulate the in vivo organization of living cells in a 3D microenvironment is one of the current challenges in the field of tissue engineering. In particular for anisotropic tissues where alignment of precursor cells is required for them to create functional structures. Herein, a new method is proposed that allows aligning in the direction of a uniform magnetic field both individual cells (muscle, stromal, and stem cells) or spheroids in a thermoresponsive collagen hydrogel. In an all‐in‐one approach, spheroids are generated at high throughput by magnetic engineering using microfabricated micromagnets and are used as building blocks to create 3D anisotropic tissue structures of different scales. The magnetic cells and spheroids alignment process is optimized in terms of magnetic cell labeling, concentration, and size. Anisotropic structures are induced to form fibers in the direction of the magnetic alignment, with the respective roles of the magnetic field, the mechanical stretching of hydrogel or co‐culture of the aligned cells with non‐magnetic stromal cells, being investigated. Over days, spheroids fuse into 3D tubular structures, oriented in the direction of the magnetic alignment. Moreover, in the case of the muscle cells model, multinucleated cells can be observed within the fibers.