Search Results (49)

This systematic review explores recent advancements in antenna and rectifier systems for radio frequency (RF) energy harvesting within the gigahertz frequency range, aiming to support the development of sustainable and efficient low-power electronic applications. Conducted under the PRISMA methodology, our review filtered 2465 initial records down to 80 relevant studies, addressing three research questions focused on antenna design, operating frequency bands, and rectifier configurations. Key variables such as antenna type, resonant frequency, gain, efficiency, bandwidth, and physical dimensions were examined. Antenna designs including fractal, spiral, bow-tie, slot, and rectangular structures were analyzed, with fractal antennas showing the highest efficiency, while array antennas exhibited lower performance despite their compact dimensions. Frequency band analysis indicated a predominance of 2.4 GHz and 5.8 GHz applications. Evaluation of substrate materials such as FR4, Rogers, RT Duroid, textiles, and unconventional composites highlighted their impact on performance optimization. Rectifier systems including Schottky, full-wave, half-wave, microwave, multi-step, and single-step designs were assessed, with Schottky rectifiers demonstrating the highest energy conversion efficiency. Additionally, correlation analyses using boxplots explored the relationships among antenna area, efficiency, operating frequency, and gain across design variables. The findings identify current trends and design considerations crucial for enhancing RF energy harvesting technologies. Full article

Bipolar disorder (BD) is a complex psychiatric condition characterized by alternating episodes of mania and depression, posing significant challenges for accurate and timely diagnosis. This study explores the use of the Random Forest (RF) algorithm as a machine learning approach to classify patients with BD and healthy controls based on electroencephalogram (EEG) data. A total of 330 participants, including euthymic BD patients and healthy controls, were analyzed. EEG recordings were processed to extract key features, including power in frequency bands and complexity metrics such as the Hurst Exponent, which measures the persistence or randomness of a time series, and the Higuchi’s Fractal Dimension, which is used to quantify the irregularity of brain signals. The RF model demonstrated robust performance, achieving an average accuracy of 93.41%, with recall and specificity exceeding 93%. These results highlight the algorithm’s capacity to handle complex, noisy datasets while identifying key features relevant for classification. Importantly, the model provided interpretable insights into the physiological markers associated with BD, reinforcing the clinical value of EEG as a diagnostic tool. The findings suggest that RF is a reliable and accessible method for supporting the diagnosis of BD, complementing traditional clinical practices. Its ability to reduce diagnostic delays, improve classification accuracy, and optimize resource allocation make it a promising tool for integrating artificial intelligence into psychiatric care. This study represents a significant step toward precision psychiatry, leveraging technology to improve the understanding and management of complex mental health disorders. Full article

Calcium carbonate scaling causes significant damage and financial losses to various industries, particularly in deep-water oil exploration. It is affected by factors like pressure, temperature, pH, solution chemistry, and surface finish. Surface finish is critical, as it interacts with the fluid and serves as a substrate for the anchoring of calcium carbonate crystals. However, many studies investigate this phenomenon under conditions that differ from those encountered in deep-water oil exploration. Tests are commonly performed under atmospheric pressure and lacking fluid flow or CO₂ influence, which limits their relevance to industrial conditions. This study aims to evaluate the influence of surface finish on the formation of calcium carbonate scaling under conditions that more closely resemble actual operating environments. 304 stainless steel was selected to replicate industrial conditions, owing to its chemical stability and common use in industrial settings. The tests were conducted in a plant with high-pressure capabilities, operating under continuous flow conditions with CO₂ injection. Controlled surfaces were prepared through metallographic polishing, machining, sandblasting, and laser texturing techniques. Surface characterization was performed using a 3D optical profilometer and scratch testing to measure the average adhesion force. The polymorphs formed were characterized by Raman spectroscopy. Fractal dimension analysis was applied to quantify the complexity of the analyzed surfaces. The results indicate that surfaces with higher fractal dimensions exhibit greater scaling mass and higher adhesion force. The main polymorph observed was calcite. Additionally, it was noted that the texture orientation relative to the flow affects scaling, with higher scaling values observed on surfaces oriented perpendicular to the flow. These findings are crucial for optimizing material selection and surface treatments in deep-water oil exploration, enhancing operational efficiency and reducing costs. Full article

The effective removal of impurities from natural quartz is a very challenging subject, but there is no relevant study on the mesoscopic structure of quartz sand particles, and there is still a lack of direct evidence on the structure-activity relationship between mesoscopic structure and purification effect. In this paper, the effects of calcination temperature, calcination time, quenching frequency and grinding frequency on the formation of mesoscopic fractures in natural quartz sand were studied, and a linear regression model was established by fractal and differential methods. The results show that the cracked structure of quartz sand and its variation law have remarkable fractal characteristics, and that thermal expansion and phase transformation are the main factors affecting the cracked structure and specific surface area of quartz sand. The non-phase change thermal expansion results in the formation of semi-closed wedge-shaped fractures in the open fractures of quartz sand, resulting in a significant decrease in the specific surface area of the cracked sand. On the contrary, the phase change expansion is conducive to the generation of more Me10 mesoporous fractures and the increase of the specific surface area of cracked sand. In addition, thermal stress and mechanical force are more likely to form Me50 and Me10 mesoporous cracks, where the average proportion of Me50 is higher than 75%. Based on this, the linear regression model between the fractal dimension and the pore volume distribution, S_BET, is further established, and the correlation coefficient R² is mostly above 96%. In addition to offering insightful findings for the investigation of the structure-activity relationship between the purification effect and the mesoscopic structure of quartz sand, this paper also establishes the groundwork for the advancement of high purification technologies for natural quartz sand. Full article

Surface roughness significantly affects the performance of microelectromechanical systems (MEMS) and piezoelectric films. This study investigates the impact of surface roughness on the mechanical properties of thin piezoelectric films using nanoindentation and scanning probe microscopy (SPM). Four piezoelectric films with different thicknesses (220, 350, and 450 nm) and substrate configurations (LNO/SiO₂/Si or LNO/Si) were analyzed. A discriminant analysis revealed that the fractal dimension is more effective than the arithmetic mean height (Sa) for distinguishing surfaces, with only 2% misclassification versus 25% for Sa. A multiscale analysis identified the Smr2 parameter with low-pass filtering at 140 nm as highly effective for surface discrimination, achieving only 0.1% misclassification. The analysis of the roughness parameter Sa at various scales showed that band-pass filtering at 500 nm yielded a 0.7% misclassification rate, indicating its relevance for fractal roughness characterization. Most relevant roughness parameters for mechanical property correlation were found: Smr2 with low-pass filtering at 500 nm correlated best with hardness (R² = 0.82), and Vvc with low-pass filtering at 2 nm correlated best with reduced elastic modulus (R² = 0.84). These results demonstrate that surface roughness features like valley volume and voids significantly impact the apparent mechanical properties of piezoelectric films. Full article

Background/Objectives: Although there has been extensive research on the orofacial morphologic effects of cleft lip and palate (CLP), the effects of CLP on mandibular structures remain largely unknown. The aim of this study was to investigate the trabeculation differences in the mandibular osseous architecture of patients with bilateral CLP (BCLP) and left-sided unilateral CLP (UCLP) using fractal dimension (FD) analysis and to compare these findings with healthy controls without CLP. Methods: A total of 63 patients (27 females, 36 males) with a mean age of 9.69 ± 1.5 years in the pre-peak growth stage were divided into three groups (n = 21 per group): the control group (CG), the BCLP group, and the UCLP group. The FD analysis was conducted on selected regions of interest (ROIs) from the mandibular condyle, angulus, corpus, and coronoid areas in TIFF-formatted panoramic radiographs. Statistical analyses were performed using the paired t-test and ANOVA for parametric data, and the Wilcoxon and Kruskal–Wallis tests for nonparametric data. Statistical significance was set at p < 0.05. Results: The FD values obtained from the ROIs of the right condyle were found to be significantly lower in the BCLP group compared to the CG and UCLP groups (p < 0.05). Conversely, the FD values for the left condyle were significantly higher in the CG group (p < 0.05), while no significant differences were observed between the BCLP and UCLP groups (p > 0.05). The FD value of the left condyle in the UCLP group was found to be significantly lower than that of the right condyle (p < 0.05). In the CG group, the FD values for both the right and left mandibular condyle and corpus were significantly higher than those for the angulus and coronoid regions; in the UCLP group, only the FD values of the right mandibular condyle and corpus were significantly higher than those for the same regions (p < 0.05). Conclusions: The reduced FD values in the mandibular condyle of CLP patients during the pre-peak growth stage suggest a loss of trabeculation and lower metabolic activity, while similarly, reduced FD values in the corpus region contribute to delayed tooth eruption timing, likely due to decreased masticatory forces during the intercuspal position and altered occlusal relationships. Clinical Relevance: In treating CLP patients, particularly with orthopedic face masks, the reduction in metabolic activities in these areas should be considered to achieve the optimal mandibular growth and development, and dental eruptions during the distribution of force from the chin to the corpus and condyle. Full article

Despite the huge number of studies of the three-body problem in physics and mathematics, the study of this problem remains relevant due to both its wide practical application and taking into account its fundamental importance for the theory of dynamical systems. In addition, one often has to answer the cognitive question: is irreversibility fundamental for the description of the classical world? To answer this question, we considered a reference classical dynamical system, the general three-body problem, formulating it in conformal Euclidean space and rigorously proving its equivalence to the Newtonian three-body problem. It has been proven that a curved configuration space with a local coordinate system reveals new hidden symmetries of the internal motion of a dynamical system, which makes it possible to reduce the problem to a sixth-order system instead of the eighth order. An important consequence of the developed representation is that the chronologizing parameter of the motion of a system of bodies, which we call internal time, differs significantly from ordinary time in its properties. In particular, it more accurately describes the irreversible nature of multichannel scattering in a three-body system and other chaotic properties of a dynamical system. The paper derives an equation describing the evolution of the flow of geodesic trajectories, with the help of which the entropy of the system is constructed. New criteria for assessing the complexity of a low-dimensional dynamical system and the dimension of stochastic fractal structures arising in three-dimensional space are obtained. An effective mathematical algorithm is developed for the numerical simulation of the general three-body problem, which is traditionally a difficult-to-solve system of stiff ordinary differential equations. Full article

In response to the challenges posed by long-term cyclic loading and unloading in underground rock engineering, this study systematically investigates the macro- and meso-mechanical response mechanisms of fractured rock masses under cyclic loading conditions. We performed graded cyclic loading–unloading tests on parallel double-fractured sandstone samples with varying spatial distribution configurations. These tests were integrated with digital image correlation (DIC) technology, fractal dimension analysis, and discrete element method (DEM) numerical simulations to analyze the mechanical properties, deformation characteristics, crack propagation features, and meso-fracture mechanisms of the fractured rock masses. The findings indicate that the diverse spatial distribution characteristics of the double fractures exert a significant influence on the loading–unloading processes, surface deformation fields, and fracture states of the rock. Cyclic loading leads to an increase in the fractal dimension of the fractured samples, resulting in more intricate and chaotic crack propagation patterns. Furthermore, DEM simulations reveal the impact of fracture spatial configurations on the force chain distribution within the rock bridges. The equivalent stress nephogram effectively represents the stress field distribution. This offers valuable insights for predicting meso-fracture trends in rocks. This paper comprehensively integrates both experimental and numerical simulation methodologies to deliver a thorough analysis of the complex mechanical behavior of fractured rock masses under cyclic loading conditions, with direct relevance to engineering applications such as mine excavation and slope stabilization. Full article

Hydrogen permeation resistance in the welded region of 06Cr18Ni11Ti steel is relatively weak due to surface defects, which need high integrity surface machining. The parameters of the welding material for 06Cr18Ni11Ti steel are currently unavailable, which causes some inconvenience for simulation studies. To fill the lack of 06Cr18Ni11Ti steel weld material parameters in the relevant literature at the present stage, the quasi-static tensile test at different strain rates and notch specimen tensile tests were conducted in this paper and determined the Johnson–Cook (J-C) constitutive model parameters and Johnson–Cook failure model parameters. Subsequently, a multi-grain grinding simulation model was built based on W-M fractal dimension theory by using the determined material parameters. The influence of processing parameters on grinding heat was analyzed. Grinding experiments were conducted to analyze the influence of processing parameters on grinding heat and grinding force. By comparing the simulation and experimental results, it is revealed that the average error is 9.37%, indicating relatively small discrepancy. It is demonstrated that the grinding simulation model built in this paper could efficiently simulate the grinding process, and the determined weld material parameters of 06Cr18Ni11Ti steel have been verified to possess high accuracy and reliability. Full article

A new instrument using reflectance transformation imaging (RTI), named MorphoLight, has been developed for surface characterization. This instrument is designed to be adjustable to surfaces, ergonomic, and uses a combination of high-resolution imaging functions, i.e., focus stacking (FS) and high dynamic range (HDR), to improve the image quality. A topographical analysis method is proposed with the instrument. This method is an improvement of the surface gradient characterization by light reflectance (SGCLR) method. This aims to analyze slope/curvature maps, traditionally studied in RTI, but also to find the most relevant lighting position and 3D surface parameter which highlight morphological signatures on surfaces and/or discriminate surfaces. RTI measurements and analyses are performed on two zones, sky and sea, of a naval painting which have the same color palette but different painting strokes. From the statistical analysis using bootstrapping and analysis of variance (ANOVA), it is highlighted that the high-resolution images (stacked and tonemapped from HDR images) improve the image quality and make it possible to better see a difference between both painting zones. This difference is highlighted by the fractal dimension for a lighting position (θ, φ) = (30°, 225°); the fractal dimension of the sea part is higher because of the presence of larger brushstrokes and painting heaps. Full article

In this work, a computational scheme is proposed to identify the main combinations of handcrafted descriptors and deep-learned features capable of classifying histological images stained with hematoxylin and eosin. The handcrafted descriptors were those representatives of multiscale and multidimensional fractal techniques (fractal dimension, lacunarity and percolation) applied to quantify the histological images with the corresponding representations via explainable artificial intelligence (xAI) approaches. The deep-learned features were obtained from different convolutional neural networks (DenseNet-121, EfficientNet-b2, Inception-V3, ResNet-50 and VGG-19). The descriptors were investigated through different associations. The most relevant combinations, defined through a ranking algorithm, were analyzed via a heterogeneous ensemble of classifiers with the support vector machine, naive Bayes, random forest and K-nearest neighbors algorithms. The proposed scheme was applied to histological samples representative of breast cancer, colorectal cancer, oral dysplasia and liver tissue. The best results were accuracy rates of $94.83\%$ to $100\%$, with the identification of pattern ensembles for classifying multiple histological images. The computational scheme indicated solutions exploring a reduced number of features (a maximum of 25 descriptors) and with better performance values than those observed in the literature. The presented information in this study is useful to complement and improve the development of computer-aided diagnosis focused on histological images. Full article

Fractional and high-order PDEs have become prominent in theory and in the modeling of many phenomena. In this article, we study the temporal fractal nature for fourth-order time-fractional stochastic partial integro-differential equations (TFSPIDEs) and their gradients, which are driven in one-to-three dimensional spaces by space–time white noise. By using the underlying explicit kernels, we prove the exact global temporal continuity moduli and temporal laws of the iterated logarithm for the TFSPIDEs and their gradients, as well as prove that the sets of temporal fast points (where the remarkable oscillation of the TFSPIDEs and their gradients happen infinitely often) are random fractals. In addition, we evaluate their Hausdorff dimensions and their hitting probabilities. It has been confirmed that these points of the TFSPIDEs and their gradients, in time, are most likely one everywhere, and are dense with the power of the continuum. Moreover, their hitting probabilities are determined by the target set B’s packing dimension ${dim}_{{}_p}\left(B\right)$. On the one hand, this work reinforces the temporal moduli of the continuity and temporal LILs obtained in relevant literature, which were achieved by obtaining the exact values of their normalized constants; on the other hand, this work obtains the size of the set of fast points, as well as a potential theory of TFSPIDEs and their gradients. Full article

The fractal dimension is widely used in many fields as a parameter to characterize the geometric complexity and geometric distribution relationship of research objects. To study the surface characteristics of sulfide ore particles, the fractal theory was applied to quantitatively characterize the surface fractal dimension Ds of sulfide ore microparticles in three particle size ranges, 60–100 mesh, 100–140 mesh and >200 mesh, based on the area–perimeter method. Using an optical microscope, grain projection images of the particles were obtained. The grain shape and characteristics of sulfide ore particles were studied by means of an image processing system. The results demonstrate that the grain shape of sulfide ore particles can be expressed by fractal dimension, and the particle surface fractal dimension ranges from 2.4392 to 2.5492. It was found that the fractal properties begin to decrease due to the increasing of the particle size. The larger the fractal dimension, the finer the particles are. The fractal dimension of sulfide ore particles can be used as an important indicator of their particle shape distribution characteristics, which can provide important information for further study of the relevant physical and chemical properties of sulfide ore particles and provide a new theoretical method and basis for the adhesion and removal of sulfide ore dust. With the quantitative description of the fractal distribution of sulfide ore particles, a new way to study the adhesive force between particles is offered for further research. Full article

Tree species classification is a ubiquitous task in the forest inventory field. Only directly measured feature vectors have been applied to most existing methods that use LiDAR technology for tree species classification. As a result, it is difficult to obtain a satisfactory tree species classification performance. To solve this challenge, the authors of this paper developed two new kinds of feature vectors, including fractal geometry-based feature vectors and quantitative structural model (QSM)-based feature vectors. In terms of fractal geometry, both two fractal parameters were extracted as feature vectors for reflecting how tree architecture is distributed in three-dimensional space. In terms of QSM, the ratio of length change and the ratio of radius change of different branches were extracted as feature vectors. To reduce the feature vector dimensionality and explore valuable feature vectors, feature vector dimension reduction was conducted using the classification and regression tree (CART). Five hundred and sixty-eight individual trees with five tree species were selected for evaluating the performance of the developed feature vectors. The experimental results indicate that the tree species of Fagus sylvatica achieved the highest overall accuracy, which is 98.06%, while Quercus petraea obtained the lowest overall accuracy, which is 96.65%. Four other classical supervised learning methods were adopted for comparison. The comparison result indicates that the proposed method outperformed the other four supervised learning methods no matter which accuracy indicator was adopted. In comparison with the relevant method, the eight feature vectors developed in this paper also performed much better. This indicates that the fractal geometry-based feature vectors and QSM-based feature vectors developed in this paper can effectively improve the performance of tree species classification. Full article

The Brazil–Malvinas Confluence (BMC) is one of the most complex oceanic areas in the Earth’s oceans and the prediction of high frequency structures tends to fail. The authors studied the BMC using Multiscale Ultrahigh Resolution (MUR) imagery for the Sea Surface Temperature (SST) to address why the predictions are not as good as expected. The studies were carried out by means of two approaches. The first approach is the non-linear fitting of a harmonic model keeping the frequencies as parameters pixel by pixel. The second approach is from fractal geometry. The three first q-order Rényi dimensions were computed. At the same time, an inverse fractal interpolation was carried out to compute the contraction factor. Both of them are related to the chaotic behavior of nature. This work has three relevant contributions. The correlation between the harmonic models and the SST data is quite poor in general, implying the low harmonicity, and low harmonic predictability, of the pixel-by-pixel time series. It is verified that the quasi-annual and quasi-semiannual waves have periods of about 420 and 210 days, respectively. The second one is the confirmation of the high complexity of the BMC area because the three Rényi dimensions are equal. This has the strong finding of the monofractality of the dynamic of the SST in the BMC. Finally, the contraction factor, one of the parameters of the fractal interpolation, is relatively high, implying the presence of highly complex internal structures in the SST temporal evolution. Full article