Search Results (414)

Background: This study aimed to evaluate the impact of varying durations of ultrasonic condensation on the formation of internal and external voids and the microhardness of apical plugs created with premixed bioceramic putty Well-Root^TM PT in standardized 3D-printed immature permanent teeth using micro-CT imaging and Vickers microhardness testing. Methods: Forty-eight 3D-printed upper incisors with simulated open apices (2 mm canal diameter) were divided into four groups (n = 12 each) based on apical plug condensation technique as follows: Group 1 (control, manual condensation), Group 2 (3-s Ultrasonic at 25 kHz), Group 3 (9-s Ultrasonic at 25 kHz), and Group 4 (15-s Ultrasonic at 25 kHz). Well-Root^TM PT was used to form 5 mm apical plugs under a microscope. Samples were stored at 37 °C and 100% humidity for one week. Micro-CT imaging was used to quantify internal, external, and total void volumes (% of total material volume), while microhardness was measured using a Vickers tester (1 kgf load, 10 s) on polished apical plug sections. Statistical analysis was performed using ANOVA and Tukey post hoc tests. Results: Group 4 (15-s Ultrasonic) exhibited significantly higher external and total void volumes compared to Groups 1–3 (p < 0.001), with no significant differences in internal voids across groups (p > 0.05). Microhardness was highest in Group 1 (mean VHN: 76.95 ± 3.73), followed by Group 2 (73.11 ± 4.82), Group 3 (55.11 ± 5.28), and Group 4 (51.25 ± 7.73) (p < 0.05). Shorter ultrasonic durations (3-s Ultrasonic) resulted in fewer voids and higher microhardness compared to longer durations (15-s Ultrasonic). There was no statistically significant difference in void size among the groups compared (p > 0.05). Fractal dimension analysis showed that prolonged ultrasonic condensation results in less complex voids compared to shorter activation. Conclusion: Manual condensation of premixed bioceramic putty, by promoting denser particle packing without ultrasonic-induced disruptions, leads to higher microhardness. Brief ultrasonic activation (3-s Ultrasonic) optimizes the quality of Well-Root^TM PT apical plugs by minimizing voids and maintaining higher microhardness, thus enhancing the apical seal. Prolonged ultrasonic activation (15-s Ultrasonic) increases void formation and reduces microhardness, potentially compromising the long-term integrity of the apical barrier. Full article

This study aims to compare different classifiers in the context of distinguishing two classes of signals: nonlinear electrocardiography (ECG) signals and stochastic artifacts occurring in ECG signals. The ECG signals from a single-lead wearable Movesense device were analyzed with a set of eight features: variance (VAR), three fractal dimension measures (Higuchi fractal dimension (HFD), Katz fractal dimension (KFD), and Detrended Fluctuation Analysis (DFA)), and four entropy measures (approximate entropy (ApEn), sample entropy (SampEn), and multiscale entropy (MSE) for scales 1 and 2). The minimum-redundancy maximum-relevance algorithm was applied for evaluation of feature importance. A broad spectrum of machine learning models was considered for classification. The proposed approach allowed for comparison of classifier features, as well as providing a broader insight into the characteristics of the signals themselves. The most important features for classification were VAR, DFA, ApEn, and HFD. The best performance among 34 classifiers was obtained using an optimized RUSBoosted Trees ensemble classifier (sensitivity, specificity, and positive and negative predictive values were 99.8, 73.7%, 99.8, and 74.3, respectively). The accuracy of the Movesense device was very high (99.6%). Moreover, the multifractality of ECG during sleep was observed in the relationship between SampEn (or ApEn) and MSE. Full article

To overcome the limitations imposed by the anisotropy and heterogeneity of natural loess, this study establishes a novel quantitative macro–micro correlation framework for investigating the deformation mechanisms of artificial structural loess (ASL). ASL samples were prepared by mixing remolded loess with cement (0–4%) and NaCl (0–16%), followed by static compaction (95% degree) and 28-day curing (20 ± 2 °C, >90% RH) to replicate the structural properties of natural loess under controlled conditions. An integrated experimental methodology was employed, incorporating consolidation/collapsibility tests, particle size analysis, X-ray diffraction (XRD), and mercury intrusion porosimetry (MIP). A three-dimensional nonlinear model was proposed. The findings show that intergranular cementation, particle size distribution, and pore architecture are the main factors influencing loess’s compressibility and collapsibility. A critical transition from medium to low compressibility was observed at cement content ≥1% and moisture content ≤16%. A strong correlation (Pearson |r| > 0.96) was identified between the mesopore volume ratio and the collapsibility coefficient. The innovation of this study lies in the establishment of a three-dimensional nonlinear model that quantitatively correlates key microstructural parameters (fractal dimension value (D), clay mineral ratio (C), and large and medium porosity (n)) with macroscopic deformation indicators (porosity ratio (e) and collapsibility coefficient (δ_s)). The measured data and the model’s output agree quite well, with a determination coefficient (R²) of 0.893 for porosity and 0.746 for collapsibility, verifying the reliability of the model. This study provides a novel quantitative tool for loess deformation prediction, offering significant value for engineering settlement assessment in controlled cementation and moisture conditions, though its application to natural loess requires further validation. Full article

Objectives: This study evaluated the utility of complex morphometric analyses for predicting radiation pneumonitis (RP) and proposed a quantitative prognostic framework for patients with non-small cell lung cancer (NSCLC) undergoing curative-intent radiotherapy (RT). Imaging biomarkers, including box-counting fractal dimension (BoxFD), lacunarity, and minimum spanning tree fractal dimension (MSTFD), were assessed for their prognostic significance. Materials and Methods: We retrospectively analyzed 166 NSCLC patients who received curative-intent RT and had both pre-treatment and follow-up chest CT scans. Among them, 85 received RT alone and 81 underwent concurrent chemoradiotherapy (CCRT). Fractal features were measured to build a Random Forest model (RFM) predicting RP of grade ≥ 2, and the most important features were used to construct a decision tree model. Results: RP of grade ≥ 2 occurred in 19 patients (22.3%) in the RT alone group and 44 patients (54.3%) in the CCRT group. Lacunarity increased significantly post-RT in both groups, while BoxFD and MSTFD showed no significant changes. In the RFM, pre-RT MSTFD and lung dose parameters (V10 in RT alone; V5–V20 in CCRT) were identified as key predictors. Decision tree models based on these features achieved high predictive performance, with AUROC of 0.83 and 0.85, and F1 scores of 0.92 and 0.76 for RT alone and CCRT groups, respectively. Conclusions: Fractal imaging biomarkers demonstrated promising prognostic value for predicting grade ≥ 2 RP in NSCLC patients. The proposed decision tree model may serve as a practical tool for early identification of high-risk patients, facilitating personalized treatment strategies and informing future research. Full article

Exoskeleton-assisted bodyweight support training (BWST) has demonstrated enhanced neurorehabilitation outcomes in which joint motion prediction serves as the critical foundation for adaptive human–machine interactive control. However, joint angle prediction under dynamic unloading conditions remains unexplored. This study introduces an adaptive wavelet-denoising fusion (AWDF) model to predict lower-limb joint angles during BWST. Utilizing a custom human-tracking bodyweight support system, time series data of surface electromyography (sEMG), and inertial measurement unit (IMU) from ten adults were collected across graded bodyweight support levels (BWSLs) ranging from 0% to 40%. Systematic comparative experiments evaluated joint angle prediction performance among five models: the sEMG-based model, kinematic fusion model, wavelet-enhanced fusion model, late fusion model, and the proposed AWDF model, tested across prediction time horizons of 30–150 ms and BWSL gradients. Experimental results demonstrate that increasing BWSLs prolonged gait cycle duration and modified muscle activation patterns, with a concomitant decrease in the fractal dimension of sEMG signals. Extended prediction time degraded joint angle estimation accuracy, with 90 ms identified as the optimal tradeoff between system latency and prediction advancement. Crucially, this study reveals an enhancement in prediction performance with increased BWSLs. The proposed AWDF model demonstrated robust cross-condition adaptability for hip and knee angle prediction, achieving average root mean square errors (RMSE) of 1.468° and 2.626°, Pearson correlation coefficients (CC) of 0.983 and 0.973, and adjusted R² values of 0.992 and 0.986, respectively. This work establishes the first computational framework for BWSL-adaptive joint prediction, advancing human–machine interaction in exoskeleton-assisted neurorehabilitation. Full article

Due to the diverse origins of shale reservoirs, the coal measure shales of the Wuxiang block, Qinshui Basin typically exhibit fractal pore structures, which significantly influence shale gas occurrence and migration. Clarifying the fractal nature of pore structures is significant for the efficient development and utilization of shale gas. In this study, mercury intrusion porosimetry and liquid nitrogen adsorption experiments were conducted to develop a method that integrates pore compressibility correction and nitrogen adsorption for pore structure characterization. On this basis, this study analyzed the fractal characteristics of coal measure shale pore structures across multiple scales. The results reveal that coal measure shale pores exhibit a three-stage fractal pattern, consisting of three regions with pore diameters >65 nm (seepage pores), 6–65 nm (transition pores), and <6 nm (micropores). Samples with fractal dimensions of seepage pores ($D_{\mathrm{a}}$) exceeding 2.9 and transition pores ($D_1$) exceeding 2.5 tend to have larger specific surface areas and more complex pore structures; this is indicated by the increased surface roughness of large-scale pores, which hinders gas seepage. Samples with lower fractal dimension of micropores ($D_2$)—in the range of 2.2–2.8—exhibit higher micropore development, larger specific surface area, and simpler pore structures, as demonstrated by a greater number of micropores and a more uniform pore distribution, which promotes gas adsorption. Full article

This work analyzes the behavior of the boundary layer subjected to stresses by obstacles using hourly measurements, in the form of time series, of meteorological variables (temperature (T), relative humidity (RH), and magnitude of the wind speed (WS)) in a given period. The study region is Santiago, the capital of Chile. The measurement location is in a rugged basin geography with a nearly pristine atmospheric environment. The time series are analyzed through chaos theory, demonstrating that they are chaotic through the calculation of the parameters Lyapunov exponent (λ > 0), correlation dimension (D_C < 5), Kolmogorov entropy (S_K > 0), Hurst exponent (0.5 < H < 1), and Lempel–Ziv complexity (LZ > 0). These series are simultaneous measurements of the variables of interest, before and after, of three different volumetric geometries arranged as obstacles: a parallelepiped, a cylinder, and a miniature mountain. The three geometries are subject to the influence of the wind and present the same cross-sectional area facing the measuring instruments oriented in the same way. The entropies calculated for each variable in each geometry are compared. It is demonstrated, in a first approximation, that volumetric geometry impacts the magnitude of the entropic fluxes associated with the measured variables, which can affect micrometeorology and, by extension, the climate in general. Furthermore, the study examines which geometry favors greater information loss or greater fractality in the measured variables. Full article

This paper presents a systematic methodology for identifying optimal scaling regions in segment-based box-counting fractal dimension calculations through a three-phase algorithmic framework combining grid offset optimization, boundary artifact detection, and sliding window optimization. Unlike traditional pixelated approaches that suffer from rasterization artifacts, the method used directly analyzes geometric line segments, providing superior accuracy for mathematical fractals and other computational applications. The three-phase optimization algorithm automatically determines optimal scaling regions and minimizes discretization bias without manual parameter tuning, achieving significant error reduction compared to traditional methods. Validation across the Koch curve, Sierpinski triangle, Minkowski sausage, Hilbert curve, and Dragon curve demonstrates substantial improvements: excellent accuracy for the Koch curve (0.11% error) and significant error reduction for the Hilbert curve. All optimized results achieve $R^2\ge 0.9988$. Iteration analysis establishes minimum requirements for reliable measurement, with convergence by level 6+ for the Koch curve and level 3+ for the Sierpinski triangle. Each fractal type exhibits optimal iteration ranges where authentic scaling behavior emerges before discretization artifacts dominate, challenging the assumption that higher iteration levels imply more accurate results. Application to a Rayleigh–Taylor instability interface (D = 1.835 ± 0.0037) demonstrates effectiveness for physical fractal systems where theoretical dimensions are unknown. This work provides objective, automated fractal dimension measurement with comprehensive validation establishing practical guidelines for mathematical and real-world fractal analysis. The sliding window approach eliminates subjective scaling region selection through systematic evaluation of all possible linear regression windows, enabling measurements suitable for automated analysis workflows. Full article

Physical activity has been shown to influence ocular health, yet the acute effects of exercise on retinal and choroidal structures remain underexplored. This prospective pre-post study evaluated 30 low-fit adults without diagnosed cardiovascular disease who underwent comprehensive ophthalmologic assessments, including OCT and OCTA imaging, before and after a submaximal aerobic capacity test. Statistically significant thinning was observed in specific retinal sectors, affecting both inner and outer layers, including the retinal pigment epithelium (RPE). Vascular analysis using the OCTAVA toolbox revealed a significant post-exercise reduction in vessel length density, total vessel length, branchpoint density and fractal dimension in the peripapillary plexus; and mean tortuosity in the macular superficial vascular complex (SVC). Choroidal thickness also showed a significant reduction in several regions. No significant changes were found in the foveal avascular zone (FAZ). These findings suggest that acute submaximal physical activity induces transient yet measurable changes in retinal and choroidal microvasculature. The results have potential implications for understanding ocular vascular dynamics and for evaluating ocular health in clinical and sports medicine contexts. Full article

Ultra-deep tight sandstone gas reservoirs are key targets for natural gas exploration, yet their pore structures under high temperature, pressure, and stress greatly affect gas occurrence and flow. This study investigates representative reservoirs in the Kelasu structural belt, Tarim Basin. Porosity–permeability were measured under in situ conditions, and multi-scale pore structures were analyzed using thin sections, a SEM, mercury intrusion, and nitrogen adsorption. The results show that (1) the median permeability of cores at an ambient temperature and a confining stress of 3 MPa is 13.33–29.63 times that under the in situ temperature and pressure conditions. When the core permeability is lower than 0.1 mD, the stress sensitivity effect is significantly enhanced; (2) nanopores and micron-fractures are well developed yet exhibit poor connectivity. The majority of a core’s porosity is derived from the intergranular pores in clay minerals; (3) the volume of nano-sized pores within the 100 nm diameter range is mainly composed of mesopores, with an average proportion of 73.37%, while the average proportions of macropores and micropores are 22.29% and 4.34%, respectively; (4) full-scale pore sizes show bimodal peaks at 100–1000 nm and >100 μm, which are poorly connected; (5) the pore structure exhibits distinct fractal characteristics. The fractal dimension D_f1 (2.65 on average) corresponds to the larger pore diameters of the primary intergranular pores, residual intergranular pores, and intragranular dissolution pores. The fractal dimension D_f2 (2.10 on average) corresponds to the grain margin fractures, micron-fractures and partial throats. The pore types corresponding to the fractal dimensions D_f3 (2.36 on average) and D_f4 (2.58 on average) are mainly intercrystalline pores of clay minerals and a small number of intraparticle dissolution pores. These findings clarify the pore structure of ultra-deep tight sandstones and provide insights into their gas occurrence and flow mechanisms. Full article

The root causes forcing the differential pore-throat performances and crude oil recoverability in heterogeneous shale lithofacies of saline-lacustrine fine-grained mixed sedimentary sequences are still debated. Especially application cases of fractal theory in characterizing pore-throat heterogeneity are still lacking and the significance of differential multifractal distribution patterns on reservoir assessment remains controversial. This present study focuses on the shale-oil reservoirs in saline-lacustrine fine-grained mixed depositional sequences of the Middle Permian Lucaogou Formation (southern Junggar Basin, NW China), and presents a set of new results from petrographical investigation, field-emission scanning electron microscopy (FE-SEM) imaging, fluid injection experiments (low-pressure N₂ adsorption and high-pressure mercury intrusion porosimetry (HMIP)), nuclear magnetic resonance (NMR) spectroscopy and T₁-T₂ mapping, directional spontaneous imbibition, as well as contact angle measurements. Our results demonstrated that the investigated lithofacies are mainly divided into a total of five lithofacies categories: felsic siltstones, sandy dolomitic sandstones, dolarenites, micritic dolomites, and dolomitic mudstones, respectively. More importantly, the felsic siltstone and sandy dolomitic siltstones can be identified as the most advantageous lithofacies categories exhibiting the strongest movable oil-bearing capacity owing to an acceptable complexity and heterogeneity of mesopore-throat structures, as evidenced by the corresponding moderate fractal dimension of mesopores (D₂) from HMIP and apparently lower fractal dimension of movable fluids’ pores (D₂) from NMR results. Particularly noteworthy is the relatively poor shale-oil movability recognized in the dolarenites, micritic dolomites, and dolomitic mudstones due to heterogeneous and unfavorable pore-throat systems, even though an acceptable micro-connectivity and a more oleophilic interfacial wettability prevails in crucial dolomitic components. Finally, a comprehensive and conceptual model is established for an effective and characteristic parameter system for assessing differential reservoir petrophysical properties, interfacial wettability, and shale-oil movability concerning heterogeneous lithofacies categories. Our achievements can serve as an analog for investigating saline-lacustrine mixed shale-oil reservoirs to gain a more comprehensive understanding of differential recoverability of dessert reservoir intervals, and to guide the assessment of “sweet spots” distribution and optimization of engineering technique schemes for commercial exploitation. Full article

Fractal dimensions for the daily discharge data series of several karstic springs in northeast Hungary have recently been computed and analyzed. We model four of those series with similar fractal dimensions using a superposition of a fractional Ornstein–Uhlenbeck process and a jump process of renewal–reward type. Beyond some usual goodness-of-fit measures, simulations of the model show an visually appealing good fit. When the fractal dimension is not taken into account in the modeling, the simulated accumulated discharges tend to significantly exceed realistic values. Full article

The anti-slip performance of road surfaces directly affects traffic safety, yet existing evaluation methods based on texture features often suffer from limited interpretability and low accuracy. To overcome these limitations, a portable 3D laser surface analyzer was used to acquire road texture data, while a dynamic friction coefficient tester provided friction measurements. A multi-view fractal dimension index was developed to comprehensively describe the complexity of texture across spatial, cross-sectional, and depth dimensions. Combined with road surface temperature, this index was integrated into an XGBoost-based prediction model to evaluate friction at driving speeds of 10 km/h and 70 km/h. Comparative analysis with linear regression, decision tree, support vector machine, random forest, and backpropagation (BP) neural network models confirmed the superior predictive performance of the proposed approach. The model achieved backpropagation (R²) values of 0.80 and 0.82, with root mean square errors (RMSEs) of 0.05 and 0.04, respectively. Feature importance analysis indicated that fractal characteristics from multiple texture perspectives, together with temperature, significantly influence anti-slip performance. The results demonstrate the feasibility of using non-contact texture-based methods to replace traditional contact-based friction testing. Compared with traditional statistical indices and alternative machine learning algorithms, the proposed model achieved improvements in R² (up to 0.82) and reduced RMSE (as low as 0.04). This study provides a robust indicator system and predictive model to advance road surface safety assessment technologies. Full article

Cell–cell communication and extracellular matrix (ECM) organization in a bone microenvironment are essential to replicate the bone microenvironment accurately. In this study, the extracellular matrix (ECM) was emulated by incorporating M13 phages, selected through phage display for displaying engineered peptides that mimic bone matrix proteins, into human osteoblast cultures to develop a three-dimensional bone model (3D BMP-Phage). Comprehensive analysis was performed to investigate: (i) the morphological development of spheroids, assessed by optical microscopy and quantified via fractal dimension analysis using box-counting algorithms; (ii) the biochemical composition of the extracellular matrix, evaluated by Raman spectroscopy; (iii) ECM protein deposition, analyzed through immunofluorescence staining; (iv) matrix mineralization, assessed by Alizarin Red staining and alkaline phosphatase (ALP) activity assay; and (v) osteogenic gene expression, measured by quantitative RT-PCR. The findings demonstrate that the 3D BMP-Phage model, facilitated by a cocktail of bone-mimicking peptides, enhances structural integrity, ECM complexity, mineralization, and osteogenic pathways compared to the control. This novel approach replicates key aspects of the bone microenvironment, providing a valuable platform for advanced physiological and regenerative medicine research under controlled conditions. Full article

Background/Objectives: Bruxism is a prevalent parafunctional activity that induces masticatory muscle hypertrophy and physiological mandibular bone alterations. While botulinum toxin type A (BTX-A) injections effectively reduce muscle hyperactivity, objective radiological assessment of their skeletal effects remains limited. This study aimed to quantitatively evaluate the impact of BTX-A on mandibular trabecular bone structure by assessing potential reductions in fractal dimension (FD) in bruxism patients compared to controls. Methods: This retrospective matched-pair study included 42 bruxism patients (39 females, 3 males) receiving 50 U of BTX-A in masseter muscles and 32 age-matched non-bruxism controls (30 females, 2 males). Pre- and 6-month post-treatment panoramic radiographs were analyzed. FD values were calculated from bilateral condylar neck, ramus, and angulus regions. Results: Statistically significant reductions in FD values were observed in the angulus regions post-treatment (Right: 1.3856 to 1.2995; Left: 1.3718 to 1.2529, p < 0.001). Postoperative FD values in the BTX-A group showed no significant differences when compared to the control group (p > 0.05). Conclusions: BTX-A administration leads to measurable, region-specific reductions in mandibular trabecular bone fractal dimension, particularly in the angulus. The postoperative similarity to the control group suggests a potential normalization of bone structure following treatment. These findings highlight the clinical utility of fractal analysis for monitoring osseous adaptations in neuromuscular interventions. Full article