Multiscale Approach Research Articles

When Tautomers Matter: UV-Vis Absorption Spectra of Hypoxanthine in Aqueous Solution from Fully Atomistic Simulations.

The UV-Vis spectrum of the solvated purine derivative Hypoxanthine (HYX) is investigated using the Quantum Mechanics/Fluctuating Charges (QM/FQ) multiscale approach combined with a sampling of configurations through atomistic Molecular Dynamics (MD) simulations. Keto 1H7H and 1H9H tautomeric forms of HYX are the most stable in aqueous solution and form different stable complexes with the surrounding water molecules, ultimately affecting the electronic absorption spectra. The final simulated spectrum resulting from the combination of the individual spectra of tautomers agrees very well with most of the characteristics in the measured spectrum. The importance of considering the effect of the solute tautomers and, in parallel, the contribution of the different solvent arrangements around the solute when modeling spectral properties, is highlighted. In addition, the high quality of the computed spectra leads to suggesting an alternative way for acquiring tautomeric populations from combined computational/experimental spectra.

A Study on the Performance, Structure, Composition, and Release Behavior Changes of Polybutylene Adipate Terephthalic Acid (PBAT) Film during Food Contact

Polybutylene adipate terephthalic acid (PBAT) is an emerging biodegradable material in food packaging. However, concerns have been raised regarding the potential hazards it could pose to food safety. In this study, the changes of PBAT films during food contact and the release of small molecules were inestigated by a multiscale approach. On a macro-scale, the surface roughness of the films increased with the reduction in the concentration of food simulants and the increase in contact temperatures, especially after immersion in acidic food environments. On a micro-scale, the crystallinity (Xc) and degradation indexes (DI) of the films increased by 5.7% - 61.2% and 7.8% - 48.6%, respectively, which led to a decrease in thermal stability. On a scale approaching the molecular level, 2,4-di-tert-butylphenol (2,4-DTBP) was detected by gas chromatography-mass spectrometry (GC-MS/MS) with the highest migration content, and the release behavior of 2,4-DTBP was further investigated by migration kinetics. In addition, terephthalic acid (TPA), a hydrolysis product of PBAT, was detected in acidic food environments by liquid chromatography-mass spectrometry (LC-MS/MS). The results of this study could provide practical guidance and assistance to promote sustainable development in the field of food packaging. Environment ImplicationPBAT film is a new biodegradable material in food packaging that may release small molecular during its contact with food, and pose potential risks to human health. In this study, PBAT films were immersed in a variety of food simulants, and it was found there released a variety of small molecules including TPA, resulting from the hydrolysis of PBAT. The study also revealed that some of these small molecules inhibited cell growth. These findings could promote the safe application of PBAT films in food packaging.

Computer vision models enable mixed linear modeling to predict arbuscular mycorrhizal fungal colonization using fungal morphology

The presence of Arbuscular Mycorrhizal Fungi (AMF) in vascular land plant roots is one of the most ancient of symbioses supporting nitrogen and phosphorus exchange for photosynthetically derived carbon. Here we provide a multi-scale modeling approach to predict AMF colonization of a worldwide crop from a Recombinant Inbred Line (RIL) population derived from Sorghum bicolor and S. propinquum. The high-throughput phenotyping methods of fungal structures here rely on a Mask Region-based Convolutional Neural Network (Mask R-CNN) in computer vision for pixel-wise fungal structure segmentations and mixed linear models to explore the relations of AMF colonization, root niche, and fungal structure allocation. Models proposed capture over 95% of the variation in AMF colonization as a function of root niche and relative abundance of fungal structures in each plant. Arbuscule allocation is a significant predictor of AMF colonization among sibling plants. Arbuscules and extraradical hyphae implicated in nutrient exchange predict highest AMF colonization in the top root section. Our work demonstrates that deep learning can be used by the community for the high-throughput phenotyping of AMF in plant roots. Mixed linear modeling provides a framework for testing hypotheses about AMF colonization phenotypes as a function of root niche and fungal structure allocations.

Elastic behaviour of bamboo at nano- and microscale

This paper presents an anatomy-based numerical multiscale approach for modelling the mechanical behaviour of Moso bamboo, addressing limitations in previous research where the intricate microstructure was often simplified. The mechanical response of bamboo culms is predicted at the nano and microscale considering the orthotropic behaviour of cellulose microfibrils in different layers of tissues, deviating from conventional isotropic assumptions. Through nano and microscale finite element models, the impact of chemical composition and microfibril angle on elastic properties of different layers of the fibre and parenchyma cells are explored at two different levels of moisture content. At low moisture content, fibres within vascular bundles exhibit significantly higher longitudinal and transverse elastic moduli compared to parenchyma cells. When the moisture content is high, the properties of the fibre and parenchyma cells are less different. Anisotropic behaviour at the nanoscale contributes to a more accurate prediction of the mechanical properties of bamboo culms at the microscale, particularly in the transverse direction in moist conditions. This study lays the groundwork for forthcoming research in modelling the mechanical behaviour of bamboo culm at meso and macroscales.

Multi-scale deep learning ensemble for segmentation of endometriotic lesions

AbstractUltrasound is a readily available, non-invasive and low-cost screening for the identification of endometriosis lesions, but its diagnostic specificity strongly depends on the experience of the operator. For this reason, computer-aided diagnosis tools based on Artificial Intelligence techniques can provide significant help to the clinical staff, both in terms of workload reduction and in increasing the overall accuracy of this type of examination and its outcome. However, although these techniques are spreading rapidly in a variety of domains, their application to endometriosis is still very limited. To fill this gap, we propose and evaluate a novel multi-scale ensemble approach for the automatic segmentation of endometriosis lesions from transvaginal ultrasounds. The peculiarity of the method lies in its high discrimination capability, obtained by combining, in a fusion fashion, multiple Convolutional Neural Networks trained on data at different granularity. The experimental validation carried out shows that: (i) the proposed method allows to significantly improve the performance of the individual neural networks, even in the presence of a limited training set; (ii) with a Dice coefficient of 82%, it represents a valid solution to increase the diagnostic efficacy of the ultrasound examination against such a pathology.

Multi-scale lumped modeling of micro-channels cooling structure for W7-X divertor unit target module

A novel concept for the cooling system of the W7-X divertor unit target module has been proposed, which involves tiles equipped with parallel arrays connected by hundreds of sub-millimeter rectangular micro-channels (MCs), obtained using Additive Manufacturing techniques. The structural material proposed for the heat sink substrate is galvanized copper, while the plasma facing material is tungsten. To reduce the high computational cost of thermal-hydraulic simulations of the tiles, a multi-scale lumped modeling approach has been built in previous studies. This involves replacing a group of hydraulic parallel MCs with a porous strip (PS), suitably calibrated to reproduce similar thermal-hydraulic behavior of the MCs. The aim of the current work is to verify that the PS-model is also suitable for the thermo-mechanical stress evaluation, comparing the results from arrays with MCs and PS. Since the heat sink and plasma-facing tiles are bonded, the interfacial delamination and shear stresses are analyzed, being crucial for structural integrity. The analyses, carried out in the elastic regime, show that the PS model returns conservative results when compared to the MCs model, with an overestimation of the delamination and shear stresses at the free edge. Results from both models reveal nearly identical interfacial stress predictions at the free surface edge. Moreover, it is found that both MCs and PS blocks, located near the bond interface, contribute to high-stress fluctuations that could lead to the delamination of the bond interface, suggesting that the distance of the microchannel from the interface should be increased. The PS model can reliably be used to design and verify the most convenient series/parallel connection of the tiles in the divertor unit target module, taking operational constraints into consideration.

Optimizing Insulator Defect Detection with Improved DETR Models

With the increasing demand for electricity, the power grid is undergoing significant advancements. Insulators, which serve as protective devices for transmission lines in outdoor high-altitude power systems, are widely employed. However, the detection of defects in insulators captured under challenging conditions, such as rain, snow, fog, sunlight, and fast-moving drones during long-distance photography, remains a major challenge. To address this issue and improve the accuracy of defect detection, this paper presents a novel approach: the Multi-Scale Insulator Defect Detection Approach using Detection Transformer (DETR). In this study, we propose a multi-scale backbone network that effectively captures the features of small objects, enhancing the detection performance. Additionally, we introduce a self-attention upsampling (SAU) module to replace the conventional attention module, enhancing contextual information extraction and facilitating the detection of small objects. Furthermore, we introduce the insulator defect (IDIoU) loss, which mitigates the instability in the matching process caused by small defects. Extensive experiments were conducted on an insulator defect dataset to evaluate the performance of our proposed method. The results demonstrate that our approach achieves outstanding performance, particularly in detecting small defects. Compared to existing methods, our approach exhibits a remarkable 7.47% increase in the average precision, emphasizing its efficacy in insulator defect detection. The proposed method not only enhances the accuracy of defect detection, which is crucial for maintaining the reliability and safety of power transmission systems but also has broader implications for the maintenance and inspection of high-voltage power infrastructure.

Revealing the grain boundary effect on interfacial adhesion in polycrystal Fe/Ni interfaces by using a multi-scale CZM-CPFEM approach

In layered structure, interface plays a significant role in property improvement, whereas its multiple factors at various length scales restricted further understanding. Previous research found a novel grain boundary (GB)-interface interaction: crack initiation locally occurred at the junctions between pre-existing GBs and interface. This leads to decreased interfacial strength. To clarify the mechanisms of such GB-interface interaction, the present study proposed a multi-scale cohesive zone model-crystal plasticity finite element method (CZM-CPFEM) model to investigate the effect of pre-existing GB on intrinsic bonding strength at atomic scale and interfacial stress distribution at micro scale, respectively. At atomic scale, interfacial fracture criteria were modelled according to Molecular Dynamics (MD)-based traction-separation relationships. The accuracy of CZMs was improved by considering both isothermal holding and tilting configuration effects. At micro scale, a practical interfacial microstructure was used to construct CPFEM models. Tensile simulations showed that the local stress concentration occurred at junctions, attributed to the anisotropic plastic deformation behaviours across the GB. On the other hand, weak bonding adhesion at the junction only affects the local crack initiation strength, showing insignificant impacts on the overall interfacial fracture behaviour.

A multiscale examination of heat health risk inequality and its drivers in mega-urban agglomeration: A case study in the Yangtze River Delta, China

Increased frequency of extreme urban heat and its exposure to urban populations is one of the challenges presented by climate change, especially in urban clusters. Due to the rapid but unequal development, heat exposure disproportionately increased in the underdeveloped regions compared to the developed regions in urban agglomeration. To address this issue, it is crucial to clarify the spatial pattern of heat health risk (HHR) inequality for urban heat resilience. However, analyses for the disparity of HHR inequality often used a single scale, neglecting important spatial context effects at other scales. Moreover, the rationale of HHR inequality remains unclear. Here, we took the well-developed and highly urbanized Yangtze River Delta (YRD) region as a case study and employed multiscale approaches to examine how and why the HHR inequality varied at and within the regional scale. We first assessed HHR using a comprehensive assessment framework at a 1 km grid level. Then, we quantified the inequality between regions using local Moran's I and KS distance. Therefore, we utilized the Gini coefficient and Bayes quantile regression to quantify inequality and identify its drivers within the regional scale. Finally, we proposed a conceptual framework to inform policymaking in regions with different patterns of multiscale equality. Our results found that the HHR in YRD exhibited significant spatial inequality at the regional scale (Moran's I = 0.562, P < 0.001) and within the regional scale (Gini coefficient: 0.27–0.54). Higher population concentrations and building densities often led to higher HHR. In high HHR areas, intra-regional inequality was often lower due to high and coordinated socioeconomic levels (Gini coefficient: 0.27–0.34). Additionally, in areas with low and medium levels of risk, healthcare resource availability and local temperatures had a greater impact on intra-regional inequities, which varied at different levels of inequality. This study contributes to a better understanding of multiscale HHR inequality, which helps optimize heat risk management strategies and regional sustainable development.

Meshfree multiscale method for the infiltration problem in permafrost

This study introduces a multiscale simulation approach, employing the Meshfree Generalized Multiscale Finite Element Method (GMsFEM), to numerically model fluid seepage in permafrost soil conditions. Combining Meshfree and Finite Element Methods, the coarse-grid system is addressed, while the standard Finite Element Method operates on the fine grid. For representing fractures at the fine grid level, the Discrete Fracture Model (DFM) is utilized. The findings demonstrate the method’s efficacy in accurately depicting soil heterogeneity and the impact of fractures on soil temperature.

A Multiscale Method for Two-Component, Two-Phase Flow with a Neural Network Surrogate

AbstractUnderstanding the dynamics of phase boundaries in fluids requires quantitative knowledge about the microscale processes at the interface. We consider the sharp-interface motion of the compressible two-component flow and propose a heterogeneous multiscale method (HMM) to describe the flow fields accurately. The multiscale approach combines a hyperbolic system of balance laws on the continuum scale with molecular-dynamics (MD) simulations on the microscale level. Notably, the multiscale approach is necessary to compute the interface dynamics because there is—at present—no closed continuum-scale model. The basic HMM relies on a moving-mesh finite-volume method and has been introduced recently for the compressible one-component flow with phase transitions by Magiera and Rohde in (J Comput Phys 469: 111551, 2022). To overcome the numerical complexity of the MD microscale model, a deep neural network is employed as an efficient surrogate model. The entire approach is finally applied to simulate droplet dynamics for argon-methane mixtures in several space dimensions. To our knowledge, such compressible two-phase dynamics accounting for microscale phase-change transfer rates have not yet been computed.

Tailoring porosity and acoustic properties in bi-layered diatomite-based foams through multiscale structural approach

A tailored multifunctional diatomite-based foam with porosity and density gradients has been developed through a new technology that enables an unprecedented integration of acoustic, mechanical and thermal properties. The production process consists of a custom-made pilot plant for foaming capable of mixing solid phases, such as diatomite, catalyst and silicon chemical blowing agent, with a liquid solution, made of sodium silicate cross-linking agent and vegetable foaming agent. The resulting foam exhibits a coin-like performance, with a side A showing an open cell morphology, high interconnectivity and low density, and a side B characterized by a partially open cells morphology, low interconnectivity and high density. This approach enables morphology and density to be layered along thickness. A transition zone with intermediate density and morphology separates the two sides, thus rendering the structure a gradient meta-material with hierarchical porosity. Macro- and micro-porosity are generated by physical and chemical foaming agents, while diatomite is responsible for the presence of nanoporosity. The gradient structure has a huge influence on functional properties, such as acoustic (α equal to 0.95 at 800 Hz for side A), thermal (thermal conductivity less than 0.061 W/(m·K) for side A and less than 0.12 W/(m·K) for side B) and mechanical properties.

Assessment of Embedding Schemes in a Hybrid Machine Learning/Classical Potentials (ML/MM) Approach.

Machine learning (ML) methods have reached high accuracy levels for the prediction of in vacuo molecular properties. However, the simulation of large systems solely through ML methods (such as those based on neural network potentials) is still a challenge. In this context, one of the most promising frameworks for integrating ML schemes in the simulation of complex molecular systems are the so-called ML/MM methods. These multiscale approaches combine ML methods with classical force fields (MM), in the same spirit as the successful hybrid quantum mechanics-molecular mechanics methods (QM/MM). The key issue for such ML/MM methods is an adequate description of the coupling between the region of the system described by ML and the region described at the MM level. In the context of QM/MM schemes, the main ingredient of the interaction is electrostatic, and the state of the art is the so-called electrostatic-embedding. In this study, we analyze the quality of simpler mechanical embedding-based approaches, specifically focusing on their application within a ML/MM framework utilizing atomic partial charges derived in vacuo. Taking as reference electrostatic embedding calculations performed at a QM(DFT)/MM level, we explore different atomic charges schemes, as well as a polarization correction computed using atomic polarizabilites. Our benchmark data set comprises a set of about 80k small organic structures from the ANI-1x and ANI-2x databases, solvated in water. The results suggest that the minimal basis iterative stockholder (MBIS) atomic charges yield the best agreement with the reference coupling energy. Remarkable enhancements are achieved by including a simple polarization correction.

Study of helium diffusion in yttria: A multiscale approach based on the density functional theory and kinetic Monte Carlo, with transmission electron microscopy and thermo-desorption spectroscopy

A known issue for future nuclear reactors is helium accumulation inside the steel structure materials, responsible for structural issues such as embrittlement and cracking. One possible solution is using new types of reinforced steel, such as oxide dispersion strengthened (ODS) steel. It consists of adding oxide nanoparticles to the Fe-based material, especially yttrium oxide (yttria, Y2O3), improving its properties. Therefore, one first step is understanding the helium diffusion inside this system. Very little is known about helium inside yttria, with most studies being theoretical ones. Based on this context, this work proposes a combined theoretical and experimental multiscale approach to investigate helium diffusion inside yttria.The theoretical approach starts with the density functional theory, used to model the atomic yttria cell and determine helium insertion sites. The transitions between the sites were described using the Nudged Elastic Bond (NEB) method. Kinetic Monte Carlo was then employed to obtain the interstitial diffusion coefficient expression for the first time for this material. It showed a limited diffusion at temperatures below 600 K, which may indicate a tendency for He to be blocked in the oxide. Then, the charged vacancies were explored. It showed that the vacancy further reduces helium diffusion. Finally, it was demonstrated that helium spreads across different vacancies and interstitial sites.The experimental part involved implanting helium ions at 50 keV in samples with nanometric or micrometric grains. Then, the specimens were characterised with transmission electron microscopy (TEM) and thermo-desorption spectroscopy (TDS) techniques. TEM did not evidence detectable bubbles even at the highest studied fluence (1 × 1016 cm−2). The TDS highlighted different mechanisms for helium diffusion and the grain size's role, providing a novel model for diffusion coefficient calculation based on interstitial diffusion.

Innovative Financial Management in Higher Education: A Multi-Scale Deep Learning Approach for Risk Reduction and Quality Enhancement

This paper presents a novel university financial management system leveraging multi-scale deep learning. With rising college enrollment and teaching complexities, traditional financial models require adaptation to mitigate risks and improve management quality. The system integrates hardware and software innovations: multiple sensors enhance data scanning, coordinated by a central coordinator, ensuring comprehensive financial database coverage. Software-wise, a structured database establishes attribute-based financial connections, crucial for weight assignment. Employing a multilayer perceptual network topology, a full interconnection model based on multi-scale deep learning facilitates profound data extraction. Experimental evaluations demonstrate the system's superior financial risk assessment capabilities compared to traditional approaches, extracting a broader spectrum of financial parameters for comprehensive risk warnings. By embracing multi-scale deep learning, this system promises significant advancements in university financial management, enhancing adaptability and risk mitigation in college finance departments.

Revealing the percolation–agglomeration transition in polymer nanocomposites via MD-informed continuum RVEs with elastoplastic interphases

This contribution builds the concluding step of a multiscale approach to effectively capture the mechanical behavior of polymer nanocomposites (PNCs), in this case, silica-modified polystyrene. By introducing continuum-based representative volume elements (RVEs) that employ previously identified elastoplastic property gradients for the interphases surrounding the fillers, the effects of particle size, particle volume fraction, and agglomeration on the mechanical performance are investigated. Uniaxial tension tests are simulated with the respective finite-element RVEs, and stress–strain curves are derived. The elastic and plastic material properties of the RVE can then be extracted and analyzed quantitatively by fitting the stress–strain curves with a Voce-type elastoplasticity formulation.At small degrees of agglomeration, i.e., good particle dispersion, in combination with sufficiently large particle volume fraction, percolation bands form, leading to improved elastic and plastic properties. Higher degrees of agglomeration or particle clusters behave like large single particles, which has an adverse effect, i.e., the nanoscale size effect is thereby neutralized. Therefore, the precise MD-informed elastoplastic interphase representation of our RVEs enables the investigation of the transition from beneficial percolation to unfavorable agglomeration. Ultimately, this contribution establishes a link between the effects of particle size, particle volume fraction, agglomeration, and percolation, which have so far only been discussed separately in the literature.Our methodology offers new insights into the structure–property relations of PNCs and their resulting mechanical behavior. The underlying multiscale approach with a systematic transition from molecular to microscopic scales is required to complement experimental observations and exploit the full potential of PNCs.

Quantitative Predictive Theories through Integrating Quantum, Statistical, Equilibrium, and Nonequilibrium Thermodynamics.

Today's thermodynamics is largely based on the combined law for equilibrium systems and statistical mechanics derived by Gibbs in 1873 and 1901, respectively, while irreversible thermodynamics for nonequilibrium systems resides essentially on the Onsager Theorem as a separate branch of thermodynamics developed in 1930s. Between them, quantum mechanics was invented and was quantitatively solved in terms of density functional theory (DFT) in 1960s. These three scientific domains operate based on different principles and are very much separated from each other. In analogy to the parable of the blind men and the elephant articulated by Perdew, they individually represent different portions of a complex system and thus are incomplete by themselves alone, resulting in the lack of quantitative agreement between their predictions and experimental observations. Over the last two decades, the author's group has developed a multiscale entropy approach (recently termed as zentropy theory) that integrates DFT-based quantum mechanics and Gibbs statistical mechanics and is capable of accurately predicting entropy and free energy of complex systems. Furthermore, in combination with the combined law for nonequilibrium systems developed by Hillert, the author developed the theory of cross phenomena beyond the phenomenological Onsager Theorem. The zentropy theory and theory of cross phenomena jointly provide quantitative predictive theories for systems from electronic to any observable scales as reviewed in the present work.

Multiscale approach to the characterization of the interfacial properties of micellar casein and whey protein blends and their effects on recombined dairy creams

In this study, whipped cream with blends of micellar casein (MCN) and whey protein (WPI) in different ratios were prepared to investigate the role of protein interfacial behavior in determining foam properties at multiple scales, using theoretical modeling, and microscopic and macroscopic analysis. Fluid force microscopy has been used for the first time as a more realistic and direct means of analyzing interfaces properties in multiphase systems. The adsorption kinetics showed that the interfacial permeability constant of WPI (4.24 × 10-4 s−1) was significantly higher than that of the MCN (2.97 × 10-4 s−1), and the WPI interfacial layer had a higher modulus of elasticity (71.38 mN/m) than that of the MCN (47.89 mN/m). This model was validated via the mechanical analysis of the fat globules in real emulsions. The WPI-stabilized fat globule was found to have a higher Young's modulus (219.67 Pa), which contributes to the integrity of its fat globule morphology. As the ratio of MCN was increased in the sample, however, both the interfacial modulus and Young's modulus decreased. Moreover, the rate of partial coalescence was found to increase, a phenomenon that decreased the stability of the emulsion and increased the rate of aeration. The mechanical analysis also revealed a higher level of adhesion between MCN-stabilized fat globule (25.16 nN), which increased fat globule aggregation and emulsion viscosity, while improving thixotropic recovery. The synergistic effect of the blended MCN and WPI provided the highest overrun, at 194.53 %. These studies elucidate the role of the interfacial behavior of proteins in determining the quality of whipped cream and provide ideas for the application of proteins in multiphase systems.

Porous structures inspired by porcupine quill: multiscale design optimization approach

This paper presents a novel approach for designing a freeform bending-resistant structure from the combination of explicit discrete component-based topology optimization (TO) and the porcupine quill-inspired features. To embed the porcupine quill’s features into the TO formulations, the method involves constructing discrete components at various scales to imitate features including solid shell, stochastically distributed pores, and graded stiffeners. The components are iteratively updated, and the optimization process allows for the grading of quill-inspired features while achieving optimal structural compliance under bending loads. The proposed approach is demonstrated to be effective through the resolution of Messershmitt–Bolkow–Blohm (MBB) beam designs, parameterized studies of geometric parameters, and numerical validation of long-span and short-span quill-inspired beam designs. By examining the von Mises stress distribution, the study highlights the mitigation of material yielding at the shell region brought by the geometric features of porcupine quills, leading to the potential theory support for the bending resistance. The optimized MBB beams are manufactured using the material extrusion technique, and three-point bending tests are conducted to explore the failure mitigation capability of the quill-inspired beam under large deformation. Consequently, the study concludes that the proposed quill-inspired component-based TO approach can design a structure with excellent bending resistance according to the improved energy absorption as well as increased deformation after reaching 75% peak load.

A multiscale symbolic approach to decoding delta and ripple oscillation bands as biomarkers for epileptiform discharges.

We use a multiscale symbolic approach to study the complex dynamics of temporal lobe refractory epilepsy employing high-resolution intracranial electroencephalogram (iEEG). We consider the basal and preictal phases and meticulously analyze the dynamics across frequency bands, focusing on high-frequency oscillations up to 240 Hz. Our results reveal significant periodicities and critical time scales within neural dynamics across frequency bands. By bandpass filtering neural signals into delta, theta, alpha, beta, gamma, and ripple high-frequency bands (HFO), each associated with specific neural processes, we examine the distinct nonlinear dynamics. Our method introduces a reliable approach to pinpoint intrinsic time lag scales τ within frequency bands of the basal and preictal signals, which are crucial for the study of refractory epilepsy. Using metrics such as permutation entropy (H), Fisher information (F), and complexity (C), we explore nonlinear patterns within iEEG signals. We reveal the intrinsic τmax that maximize complexity within each frequency band, unveiling the nonlinear subtle patterns of the temporal structures within the basal and preictal signal. Examining the H×F and C×F values allows us to identify differences in the delta band and a band between 200 and 220 Hz (HFO 6) when comparing basal and preictal signals. Differences in Fisher information in the delta and HFO 6 bands before seizures highlight their role in capturing important system dynamics. This offers new perspectives on the intricate relationship between delta oscillations and HFO waves in patients with focal epilepsy, highlighting the importance of these patterns and their potential as biomarkers.