Search Results (353)

Background/Objectives: Chest radiographs contain hierarchical relationships between global thoracic configuration and organ-specific findings, together with scale-dependent anatomical boundary geometry and heterogeneous texture distributions. Coarse image–report alignment may overlook regional evidence, whereas isolated local alignment can fragment complete-examination semantics. We propose Anatomy-Guided Image–Report Alignment (AGIRA) for fine-grained chest X-ray (CXR) representation learning and investigate its behavior through post hoc finite-scale fractal and multifractal analysis. Methods: AGIRA constructs whole-image, left-lung, right-lung, and heart image–text pairs, adapts frozen vision and language backbones using lightweight Anatomy Sensors, and retains masked image and language reconstruction objectives to preserve global–local semantic continuity. Temporal–multiview fusion and hybrid soft labels reduce false-negative supervision. Post hoc analysis estimates effective box-counting dimensions, generalized dimensions, multifractal spectrum width, and a finite-scale attention concentration index. We additionally compare these descriptors with conventional intensity, texture, and uncertainty features using nested cross-validated incremental-value analyses. Results: AGIRA achieves 92.0% AUC, 84.2% accuracy, and 79.0% F1-score on RSNA Pneumonia, together with 57.5% zero-shot accuracy and 53.7% image-to-text P@5 on CheXpert 5 × 200, while updating only 7.54% of the complete model. On an independently annotated 600-unit report-routing set, the deterministic parser obtains macro-F1 0.953 and exact multi-label routing accuracy 0.918. Under the identical 34,080-record cohort, AGIRA remains above GLoRIA-ViT, MLIP, and BCC on all reported downstream metrics. Adding finite-scale descriptors to conventional intensity/texture/uncertainty features increases cross-validated AUROC for identifying anatomy-alignment-sensitive cases from 0.671 to 0.731 for zero-shot classification and from 0.651 to 0.704 for retrieval. Mild anatomical crop perturbations (±5%) change CheXpert P@5 and zero-shot accuracy by less than one percentage point, whereas severe 10% perturbations and explicit anatomy swaps cause progressively larger degradation. Conclusions: AGIRA improves cross-dataset benchmark transfer and retrieval while preserving parameter efficiency. The post hoc fractal/multifractal analysis contributes measurable incremental explanatory information about anatomy-alignment-sensitive model behavior beyond conventional descriptors without entering model training, and the added controlled experiments support parser reliability, cohort fairness, parameter robustness, and tolerance to moderate anatomical-localization noise. Full article

To accurately and rapidly assess the damage level of slabs with openings in subway stations, a graded earth pressure loading device was developed. Model experiments revealed the crack evolution characteristics on the concrete surface of slabs with openings under graded earth pressure. A damage assessment method based on fractal theory was proposed, and empirical equations were established linking the fractal dimension with slab deflection, static stiffness, and the damage index. The results demonstrate that the damage process of opening slab structures can be divided into four stages: initial damage accumulation, damage manifestation, damage intensification, and damage saturation. The fractal dimension effectively characterizes the crack evolution features and damage states of the concrete surface in opening slabs. Under varying earth pressure levels, regions with the same opening area exhibit a linear increase in fractal dimension as earth pressure levels rise. During the graded earth pressure loading process, the fractal dimension of cracks in the slab opening region ranges from 1.45 to 1.88. Exponential relationships were identified between the fractal dimension of opening slabs and their mid-span deflection, static stiffness, and damage indices. This approach provides a novel method for rapidly evaluating the damage level of opening slabs based on fractal dimension analysis. Full article

Fractures exhibit complex spatial structures and multiscale geometric characteristics, and their accurate characterization is fundamental to reservoir evaluation, fluid migration analysis, and rock mechanics. To address the limitations of single-scale local fractal methods in simultaneously capturing fracture details and global structures, as well as the dependence of supervised learning on labeled data, this study proposes an unsupervised fracture identification method integrating Complexity-Adaptive Box-Height Differential Box-Counting (CABH-DBC) with a self-organizing map (SOM). Local fractal features are extracted using fixed multiscale windows, while the box height along the gray-level dimension is adaptively refined according to the local grayscale standard deviation. The multiscale features are then fed into the SOM for clustering, with grayscale information assisting in fracture-cluster determination. Experiments on borehole image logs from ten depth intervals of the CCSD main borehole yield mean F1 and IoU values of 0.659 and 0.493, respectively. Compared with DBC-Kmeans, the proposed method improves F1 and IoU by 39.0% and 58.0%, respectively; compared with DBC-SOM, the strongest baseline in this study, the improvements are 16.6% and 24.8%. Ablation experiments further demonstrate the complementary contributions of complexity-adaptive box-height refinement, fixed multiscale fractal features, and SOM clustering. Full article

Scaffold architecture shapes tissue regeneration through the mechanical, topographical, and biochemical cues it presents to cells, yet geometrically elaborate scaffolds do not reliably produce more organized tissues, while comparatively simple architectures can exert strong organizational effects. We argue that scaffold performance is better understood by distinguishing geometric complexity from effective informational dimensionality: a relational property of the scaffold–cell system, defined as the number of independently manipulated architectural directions that produce distinguishable, above-noise changes in a jointly measured mechanotransductive response. Unlike structural entropy, fractal dimension, or feature-counting metrics, this construct depends on cellular accessibility, cue persistence, and non-redundancy. Mechanotransduction supplies its biological basis, integrating scaffold-derived cues through focal adhesions, cytoskeletal organization, nuclear deformation, and YAP/TAZ signaling, and we distinguish early resolvability from later organizational stabilization. We outline an operational strategy for estimating both from factorial scaffold libraries, common readout panels, and rank-based analysis of the response mapping, illustrated with selected experimental precedents rather than a systematic evidence sample. Positioned relative to biomimetic, mechanobiology-guided, and morphospace approaches, it yields testable predictions on dimensional compression, redundancy, and the resolvability–stability dissociation. Scaffold design is thus reframed from maximizing complexity or native resemblance toward engineering stable, cell-readable dimensions of organization. Full article

The variation features of metallic element grades can reflect the enrichment level of mineral deposits. Thus, quantitative characterization of the spatial patterns of metallogenic elements is fundamental to studying ore-forming processes and implementing mineral exploration. This study applies fractal theory and self-developed MATLAB computational scripts to process Cu grade datasets from 28 drillholes within the Pulang porphyry copper deposit, Yunnan Province. Both rescaled range (R/S) analysis and correlation integral methods were applied to clarify the spatial patterns of Cu grades in drill-cores. The calculated Hurst exponents ranged from 0.510 to 0.636, which demonstrated the persistent variation of Cu grades along the vertical direction of drillholes. This work further explored the correlation between Cu mineralization and fluctuations in correlation dimension (D_C), with D_C values spanning 0.011–2.873. Results indicate steep fractal gradient zones host high-grade copper ore bodies, and fractal dimension is a robust indicator to trace the migration of hydrothermal fluids. The Hurst exponents of Cu grade sequences correlate strongly with mineralization intensity, and ore-bearing veins extend continuously throughout all sampled drillholes. Accordingly, fractal gradients can be utilized to depict prospective zones for favorable mineralization in uncharted regions. This methodology may be applicable to other structurally controlled mineral deposits where similar fracture-controlled mineralization occurs, though further testing on different deposit types is needed. Full article

Natural aggregate concrete is prone to crack propagation and overall crushing under impact load, making it difficult to satisfy the service requirements for collapse resistance in building structures. In order to improve the impact resistance of concrete, this study investigated the impact of the mechanical behavior of steel–polypropylene hybrid fiber-reinforced concrete (SPFRC) using a split Hopkinson pressure bar apparatus. The effects of steel fiber content, polypropylene fiber content, and strain rate on the fractal dimension were examined, and the relationship between total energy dissipation and fractal dimension was established. The results show that the mean fragment size of the crushed specimens decreases linearly with increasing driving voltage, whereas it increases with fiber content. The fractal dimension monotonically increases with an increasing strain rate and decreases as the fiber content increases. Under a driving voltage of 1200 V and steel fiber content of 0.5%, increasing the polypropylene fiber content from 0% to 0.1% yields the largest reduction in the fractal dimension (15.00%) for specimen S0.5P0.1, exceeding the reductions from increments of 0.1%–0.25% and 0.25%–0.5%. Comparative results demonstrate that SPFRC exhibits superior impact failure resistance compared with concrete reinforced by a mono type of fiber. Exploring the correlation between fractal features and total energy dissipation can realize a more systematic and comprehensive performance assessment of concrete materials. The research results can provide quantitative theoretical support for the impact resistance evaluation and ratio optimization of SPFRC. Full article

Rolling bearing fault features are often weak and easily affected by noise and interference. To improve fault feature extraction performance, this paper proposes an AHBKA-VME-SMHD method. First, the black-winged kite algorithm is improved by opposition-based learning, a Gompertz-based adaptive step size strategy, and an NGO-inspired random displacement strategy. Then, the improved algorithm is used to optimize the penalty factor and desired mode center frequency of VME, guided by a composite fitness function combining Higuchi fractal dimension and energy concentration index. Finally, SMHD is applied to enhance periodic impulsive components, and envelope spectrum analysis is used to identify fault characteristic frequencies. The proposed method is validated using simulated signals and two real-world bearing datasets, namely the CWRU and XJTU-SY datasets. The results show that the proposed method extracts clearer fault-related harmonics than the comparison methods. In addition, it obtains higher kurtosis and Gini index values and lower envelope spectrum entropy values, demonstrating its effectiveness for rolling bearing fault feature extraction. Full article

The mechanical behavior of calcareous sand differs significantly from that of conventional quartz sands, leading to challenges in offshore geotechnical engineering applications. This distinctive response is closely associated with the complex three-dimensional morphology of calcareous sand particles. However, existing characterization methods are often limited to specific morphological scales and cannot fully describe the multiscale complexity of particle shape. To address this issue, this study performs a comparative morphological analysis of calcareous sand (CS) and Fujian quartz sand (FS) across three particle-size ranges by integrating X-ray micro-computed tomography with spherical harmonic (SH) analysis. Individual particles are reconstructed using SH representation, and a multiscale morphology characterization framework is developed by decomposing particle morphology into three distinct scale levels: large-scale form represented by sphericity, medium-scale angular features represented by roundness, and small-scale surface texture represented by roughness. The results demonstrate that CS and FS exhibit distinct morphological characteristics across different scales, while particle-size effects remain less pronounced within the investigated range. Furthermore, the SH amplitude spectra reveal statistically self-similar characteristics of particle surfaces, allowing the fractal dimension to be correlated with multiscale morphological descriptors. The proposed framework provides a quantitative description of complex particle morphology across multiple scales and may facilitate further investigations of particle-scale mechanical behavior in granular materials. Full article

Wearable and intelligent bioelectronic systems are emerging as a key enabling technology for continuous, non-invasive health monitoring, coupling physiological sensing with data-driven inference. Within this paradigm, electroencephalography (EEG) provides a wearable-compatible biosensing modality for capturing the pre-sleep neurophysiological dynamics linked to emotional regulation and sleep onset. Insomnia affects approximately 10–15% of adults worldwide and is often associated with dysregulated emotions and pre-sleep hyperarousal. Existing EEG-based affective and sleep-onset processing pipelines often rely either on deep-learning architectures with limited interpretability or on hand-crafted spectral descriptors with weak theoretical motivation. This study presents an exploratory proof-of-principle bioelectronic processing framework in which EEG sensing features are organized according to ANT-7 (artificial neurotransmitter seven-dimensional model), a neurotransmitter-inspired computational taxonomy introduced as a heuristic feature-design prior rather than as a validated neurochemical theory. The proposed feature set includes the alpha/theta power ratio, sample entropy, Higuchi fractal dimension, and phase-locking value extracted from the public DREAMER and DEAP datasets (23 and 32 subjects, respectively). SVM, Random Forest, and 1D-CNN classifiers are trained under subject-independent leave-one-subject-out cross-validation with strict within-fold normalization to prevent data leakage, and interpretability is assessed through SHAP values and permutation importance (PI). To stress-test whether this feature organization transfers beyond the affective benchmarks on which it is trained, classifier outputs are then related to sleep-onset latency in Sleep-EDF Expanded through a deliberately cautious cross-dataset transfer analysis. Within this protocol, the best model reaches 88.4% accuracy in three-class affective-state recognition (stress/neutral/relaxed; AUC-ROC = 0.93). As an exploratory secondary analysis, classifier-derived relaxation estimates show a statistically significant negative association with polysomnographic sleep-onset latency and improve over a single alpha/theta-ratio baseline; this cross-dataset result is reported as a proof of concept, not as a validated sleep-onset predictor. Interpretability analyses (SHAP and permutation importance) indicate that the learned feature rankings are internally consistent with the neurotransmitter-inspired feature design, a property we interpret as internal coherence rather than as independent confirmation of the taxonomy. Together, these elements outline a complete sensor-to-AI processing chain—from EEG biosensing, through neurotransmitter-inspired signal-feature extraction, to interpretable and computationally lightweight inference—designed for compatibility with low-density wearable EEG devices and edge deployment. However, EEG does not measure neurotransmitter concentrations, the study does not benchmark ANT-7 directly against competing taxonomies such as valence-arousal/circumplex or RDoC-inspired feature organizations, and the Sleep-EDF analysis should not be interpreted as evidence that the model measures a validated latent construct of sleep readiness. Accordingly, the manuscript should be read as a framework-validation study of one interpretable feature taxonomy, not as a theory-validation study of ANT-7 or as a clinical validation study. Full article

The prominent multi-scale pore heterogeneity widely developed in deep-buried coal reservoirs, which seriously restricts accurate reservoir characterization and precise resource evaluation for deep coalbed methane exploitation. Taking deep-buried coal reservoirs of the Benxi Formation in the Ordos Basin as the research target, this study integrates data from low-temperature CO₂/N₂ adsorption and high-pressure mercury intrusion experiments. Segmented monofractal quantification and multi-fractal singularity analysis are further adopted. Fractal differentiation characteristics, scale effects of pores at different scales, and the synergistic control mechanism of multiple geological factors were indicated. Eight segmented single-fractal dimensions (D1~D8) are defined. The results indicate an obvious scale-dependent zonal distribution of pore heterogeneity in deep-buried coal. Micropores smaller than 1.2 nm possess the largest fractal dimension and the strongest heterogeneity. They act as the primary adsorption and storage space for coalbed methane. Mesopores ranging from 1.1 nm to 8 nm have the lowest fractal dimension with the most homogeneous structure, serving as major gas migration pathways. The heterogeneity of macropores gradually increases with pore size. A continuous full-scale pore size distribution curve is reconstructed. Coal reservoirs exhibit a typical bimodal pore structure dominated by micropores and macropores, with the micropore-dominated storage and macropore-dominated seepage. Key multi-fractal indicators including Δα, Δf and the Hurst index are used for quantitative comparison. Micropores display strong aggregation and weak interpore connectivity. Mesopores own superior connectivity, while their heterogeneity differs greatly between individual samples. Macropores feature moderate aggregation and connectivity. Coalification degree, organic macerals, clay minerals and industrial parameters are associated with the formation and differentiation of pore heterogeneity. Each factor differentially regulates the fractal evolution of pores across various scales. Full article

Tight dolomite gas reservoirs are promising exploration targets, yet their evaluation is complicated by multiscale pore-throat heterogeneity and poor seepage connectivity. Here, high-pressure mercury intrusion (HPMI), nuclear magnetic resonance (NMR), scanning electron microscopy (SEM), and deep-learning-assisted pore extraction were integrated to characterize the pore-throat structure and fractal features of the Middle Ordovician Majiagou Formation in the Ordos Basin. The reservoir is dominated by diagenetic-origin pores, mainly intercrystalline and intragranular dissolution pores, together with microfractures, and can be classified into three types with progressively poorer connectivity and flow capacity. Type I reservoirs contain more regular pores, larger pore-throat systems, and better storage and seepage capacity; Type II reservoirs are intermediate, whereas Type III reservoirs exhibit complex pore morphology, isolated pore networks, poor petrophysical properties, and limited gas-flow potential. The corresponding fractal dimensions are weakly correlated but complementary: D_SEM captures pore-boundary complexity, D_HPMI reflects pore-throat architecture and capillary-pressure-controlled seepage pathways, and D_NMR reflects multiscale movable-fluid distribution. Clay minerals, especially illite-rich mixed layers, further intensify pore-throat heterogeneity. Increasing fractal dimension is generally associated with higher displacement and median pressures, but poorer connectivity, porosity, permeability, movable-fluid content, and gas deliverability. These results provide a basis for the quantitative evaluation of multiscale pore systems and reservoir quality in tight dolomite gas reservoirs. Full article

Conducting urbanization monitoring at key nodes of the Mongolian Plateau holds significant importance for evaluating the economic and social development of cities within the China–Mongolia–Russia transportation corridor. These cities are sparsely distributed in the vast grassland areas, posing challenges for detecting urban changes in the transboundary regions. This study proposes a method for characterizing urban development supported by multi-source remote sensing data, including nighttime light (NTL) data with superior resolution, focusing on 11 major cities along the China-Mongolia-Russia Economic Corridor within the Mongolian Plateau. A City Development Index (CDI) is introduced, utilizing “information entropy” with the entropy weight method. The development status of 11 cities in the study area was analyzed using fractal dimensions, compactness, index of economy, index of social development, and the CDI. Results show Baotou has the highest CDI (0.82), followed by Hohhot (0.77), Ordos (0.73), and Ulanqab (0.66). In contrast, Mongolian cities exhibit significantly lower CDIs, relatively. The capital, Ulaanbaatar, has a CDI of 0.63, while no other Mongolian city exceeds 0.60, correlating with scattered populations. The results indicate that Inner Mongolia and Mongolia exhibit different geographical features in urban development. This approach provides a quantitative method for detecting and assessing urban development of the arid and semi-arid regions using NTL satellite data. Full article

Alternative methodologies to analyze and quantify surface morphology using techniques such as Scanning Electron Microscopy (SEM) and Atomic Force Microscopy (AFM) are of great interest to researchers. This study presents an approach using fractal dimension (D) analysis to characterize FeCrAl alloy plates oxidized in an air atmosphere at 750, 800, 850, and 900 °C for 24 h. SEM and AFM surface traces were used to determine the fractal dimensions via Rescaled Range (R\S) analysis using the BENOIT program; with this, it is expected that the fractal dimension results are between 1 ≤ D ≤ 2. The fractal dimension analysis describes with precision the surface morphology, given that local topographic features are represented by the analyzed traces. The experimental results demonstrate that D increases with the oxidation temperature for both techniques, which correlated with an increase in surface roughness. Specifically, SEM results in D values of 1.402, 1.545, 1.557 and 1.583, while AFM results in values of 1.494, 1.561, 1.573 and 1.593 at respective temperatures. Fractal dimension analysis is a robust tool for quantifying micro- and nanostructured roughness. This approach allows researchers to track surface changes induced by thermal, mechanical, or environmental processes, thus transforming SEM into a quantitative complementary technique. Full article

The quantitative characterization of microscopic pore structure has long been a challenge in reservoir evaluation for igneous reservoirs, owing to their pronounced heterogeneity and complex pore geometry. In this study, thin-section casting, X-ray diffraction, high-pressure mercury intrusion, nuclear magnetic resonance, and fractal theory were employed to investigate the reservoir-space types, pore-structure characteristics, and fractal features of the igneous rocks both quantitatively and qualitatively. The relationships among reservoir petrophysical properties, pore structure, movable-fluid saturation, and fractal dimension were examined. The results indicate that the reservoirs in the study area are characterized by medium-to-low porosity and medium-to-low permeability, with mean values of 6.57% and 2.06 mD, respectively; the storage performance of andesite was found to exceed that of tuff. Based on the morphology of the mercury intrusion curves and the petrophysical parameters, the reservoirs were classified into three categories. From Class I to Class III, the displacement pressure increased progressively, the movable-fluid saturation declined from 9.65% to 8.54%, and the heterogeneity was markedly enhanced. The fractal analysis revealed that the reservoirs exhibit distinct piecewise fractal behavior with a well-defined inflection point, allowing two fractal intervals to be distinguished: large pore-throats (D₁) and small pore-throats (D₂). The mean total fractal dimension was 2.8996, and the large pore-throat fractal dimension (mean = 2.9607) exceeded that of the small pore-throats (mean = 2.3863), indicating that large pore-throats serve not only as the principal contributor to reservoir space but also as the dominant control on heterogeneity. Correlation analysis demonstrated that D₁ is significantly negatively correlated with both porosity and permeability, making it a key indicator for evaluating reservoir flow capacity, whereas D₂ is positively correlated with petrophysical properties, reflecting the role of fine throats in improving the connectivity of isolated pores. Notably, the large-pore-throat fractal dimension (D₁) of these igneous reservoirs generally exceeds that of tight sandstone, whereas the small-pore-throat fractal dimension (D₂) is positively correlated with petrophysical properties rather than negatively, in contrast to sandstone reservoirs; this indicates that the pore-structure behavior of igneous reservoirs is distinct from that of conventional clastic reservoirs. This study offers a new perspective on the quantitative characterization of pore structure in igneous reservoirs and provides a scientific basis for reservoir evaluation and exploration-and-development efforts in the study area. Full article

In this paper, we propose the Fractal Architecture Neural Network (FANN), a recursive neural framework inspired by self-similar fractal geometry. The architecture is governed by a fractal dimension parameter $\alpha$, which controls the branching structure and connectivity density of the network, enabling multiscale feature representation through parameter sharing across recursive paths. We evaluate FANN on synthetic nonlinear regression tasks and compare it with a standard artificial neural network (ANN) and FractalNet in terms of accuracy, training behavior, and model complexity. Experimental results show that FANN achieves competitive or improved predictive performance under comparable computational budgets, demonstrating effective accuracy-to-parameter efficiency. These results suggest that fractal-inspired recursive connectivity can provide a compact mechanism for hierarchical representation learning in neural networks. Full article