Search Results (2,801)

The ATLAZUL project is an Interreg effort among 18 partners from Spain and Portugal along the Atlantic Iberian coasts. One of its objectives is the development of new methods and data processing for oceanic information to produce useful products for private and public stakeholders. This study proposes a new insight on the sea surface dynamic of the ATLAZUL area based on almost two years of multiscale high resolution sea surface temperature imagery. The use of techniques such as the Karhunen–Loève transform (Empirical Orthogonal Function) and the Maximum Entropy Spectral Analysis were applied to study long- and short-term features in the sea surface temperature imagery. Mathematical Morphology and the Geometrical Theory of Measure are utilized to compute the Medial Axis Transform and the Hausdorff dimension. The results can be summarized as follows: (i) the tow upwelling areas are identified along the Galician–Portugal coast as indicated in the second and third modes of KLT/EOF analysis, and they are partially affected by wind; (ii) the tow warm water outflows from the Bay of Cádiz to the Gulf of Cádiz are identified as the second and third modes of KLT/EOF analysis, which are also influenced by wind; (iii) the skeletons of the surface signature of the upwelling and of the warmer water outflow, along with their fractal dimensions, indicate a chaotic pattern of spatial distribution and (iv) the harmonic prediction model should be combined with the wind prediction. Full article

Two novel crossover models for breast cancer that incorporate $\Psi$-Caputo fractal variable-order fractional derivatives, fractal fractional-order derivatives, and variable-order fractional stochastic derivatives driven by variable-order fractional Brownian motion and the crossover model for breast cancer that incorporates Atangana–Baleanu Caputo fractal variable-order fractional derivatives, fractal fractional-order derivatives, and variable-order fractional stochastic derivatives driven by variable-order fractional Brownian motion are presented here, where we used a simple nonstandard kernel function $\Psi \left(t\right)$ in the first model and a non-singular kernel in the second model. Moreover, we evaluated our models using actual statistics from Saudi Arabia. To ensure consistency with the physical model problem, the symmetry parameter $\zeta$ is introduced. We can obtain the fractal variable-order fractional Caputo and Caputo–Katugampola derivatives as special cases from the proposed $\Psi$-Caputo derivative. The crossover dynamics models define three alternative models: fractal variable-order fractional model, fractal fractional-order model, and variable-order fractional stochastic model over three-time intervals. The stability of the proposed model is analyzed. The $\Psi$-nonstandard finite-difference method is designed to solve fractal variable-order fractional and fractal fractional models, and the Toufik–Atangana method is used to solve the second crossover model with the non-singular kernel. Also, the nonstandard modified Euler–Maruyama method is used to study the variable-order fractional stochastic model. Numerous numerical tests and comparisons with real data were conducted to validate the methods’ efficacy and support the theoretical conclusions. Full article

Soil aggregates are important indicators of soil structure stability and quality. The black soil region of northeast China, known for its high agricultural productivity, faces significant challenges due to soil erosion. This study investigates the impact of terracing on the stability and erodibility characteristics of soil aggregates in sloped farmlands, which is crucial for this important agricultural area. Three research sites with the same basic management modes were selected along a latitudinal gradient, from the mid-temperate zone to the cold temperate zone, in the black soil region of northeast China. The Savinov method was used to analyze the differences in soil aggregate size distribution, stability characteristics, and soil erodibility between terraced and non-terraced slopes at each research site. The results showed that terracing increased the content of large soil aggregates (>0.25 mm) by 5.38–6.35%, with the increase becoming more pronounced from north to south. The improvement in soil structure varied by location and slope position, with the most significant improvement at the middle slope position. Terracing enhanced soil aggregate stability, reduced soil erodibility, and improved soil structure by increasing clay and soil organic matter (SOM) content and reducing soil bulk density (BD), promoting the conversion of small aggregates to large aggregates. Soil stability indicators such as water-stable aggregates (WSAs), mean weight diameter (MWD), and geometric mean diameter (GMD) were dominated by aggregates > 5 mm, while erodibility indicators such as fractal dimensions (Ds) and the soil erodibility factor (K values) were mainly influenced by aggregates < 0.25 mm. Terraces can improve the soil structure and stability of sloping farmland by increasing the content of large soil aggregates and enhancing overall soil quality. The benefits of these improvements increase with latitude. These findings provide critical insights for determining effective management practices for sloped farmlands in the black soil region under various site conditions. They offer scientific evidence for preventing soil erosion and improving soil quality, thus supporting the sustainable development strategy for protecting black soil and ensuring long-term agricultural productivity. Full article

The occurrence of landslide hazards significantly induces changes in slope surface displacement. This study conducts an in-depth analysis of the multifractal characteristics and displacement prediction of highway slope surface displacement sequences. Utilizing automated monitoring devices, data are collected to analyze the deformation patterns of the slope surface layer. Specifically, the multifractal detrended fluctuation analysis (MF-DFA) method is employed to examine the multifractal features of the monitoring data for slope surface displacement. Additionally, the Mann–Kendall (M-K) method is combined to construct the α indicator and $f\left(\alpha \right)$ indicator criteria, which provide early warnings for slope stability. Furthermore, the long short-term memory (LSTM) model is optimized using the particle swarm optimization (PSO) algorithm to enhance the prediction of slope surface displacement. The results indicate that the slope displacement monitoring data exhibit a distinct fractal sequence characterized by $h\left(q\right)$, with values decreasing as the fluctuation function q decreases. Through this study, the slope landslide warning classification has been determined to be Level III. Moreover, the PSO-LSTM model demonstrates superior prediction accuracy and stability in slope displacement forecasting, achieving a root mean square error (RMSE) of 0.72 and a coefficient of determination (R²) of 91%. Finally, a joint response synthesis of the slope landslide warning levels and slope displacement predictions resulted in conclusions. Subsequent surface displacements of the slope are likely to stabilize, indicating the need for routine monitoring and inspection of the site. Full article

Microwave continuous-flow heating has been proven to reduce the time of chemical reaction, increase the conversion rate, and improve product purity effectively. However, there are still problems such as relatively low heating efficiency, unideal heating homogeneity, and poor compactness, which brings further drawbacks like difficulty in fabrication and integration. In this study, a compact microwave continuous-flow heater based on six fractal antennas is proposed to address the problems above. First, a multi-physics simulation model is built, while heating efficiency and the volumetric coefficient of variance (COV) are improved through adjusting the geometric structure of this heater and the phase assignment of each radiator. Second, an experiment is conducted to verify the simulation model, which is consistent with the simulation. Third, a method of fast varying phases to achieve greater heating efficiency and heating homogeneity is adopted. The results show that the single-phase radiator improved efficiency by 31.1%, and COV was significantly optimized, reaching 64%. Furthermore, 0–100% ethanol–water solutions are processed by the heater, demonstrating its strong adaptability of vastly changing relative permittivity of liquid load. Moreover, an advance of this microwave continuous-flow heater is observed, compared with conventional multi-mode resonant cavity. Last, the performance of this microwave continuous-flow heater as the chemical reactor for biodiesel production is simulated. This design enables massive chemical production in fields like food industry and biodiesel production, with enhanced compactness, heating efficiency, and heating homogeneity. Full article

In order to cope with climate change and achieve the goal of carbon neutrality, the use of carbonization technology to enhance the performance of cement-based materials and achieve the purpose of carbon sequestration has become a very promising research direction. This paper considers the use of CO₂NBW as mixing water for cement-based materials, aiming to improve the carbonization efficiency of materials to achieve the goal of carbon neutrality. This time, the effect of CO₂NBW on cementitious filling materials under different aggregate fractal dimensions was studied through uniaxial compression tests and acoustic emission technology. The effect of CO₂NBW on the mechanical properties and crack evolution of the material was discussed. The results showed that CO₂ nanobubbles significantly improved the strength of cemented filling materials under different fractal dimensions, and the uniaxial compressive strength was most significantly improved by 23.04% when the fractal dimension was 2.7824. In addition, the characteristics of acoustic emission ring counts and energy parameters indicate that CO₂ nanobubbles help improve the overall pore structure of the sample, affecting the macroscopic strength. However, the addition of CO₂ nanobubbles reduces the limit energy storage ratio of elastic strain energy, which indicates that excessive CO₂ concentration may affect the hydration reaction of the cementing material. Full article

Estimating the impact of volatility in financial markets is challenging due to complex dynamics, including random fluctuations involving white noise and trend components involving brown noise. In this study, we explore the potential of leveraging the chaotic properties of time series data for improved accuracy. Specifically, we introduce a novel trading strategy based on a technical indicator, Moving Hurst (MH). MH utilizes the Hurst exponent which characterizes the chaotic properties of time series. We hypothesize and then prove empirically that MH outperforms traditional indicators like Moving Averages (MA) in analyzing Indian equity indices and capturing profitable trading opportunities while mitigating the impact of volatility. Full article

Wagyu beef is gaining worldwide popularity, primarily due to the fineness of its marbling. Currently, the evaluation of this marbling is performed visually by graders. This method has several issues: varying evaluation standards among graders, reduced accuracy due to long working hours and external factors causing fatigue, and fluctuations in grading standards due to the grader’s mood at the time. This paper proposes the use of fractal analysis for the grading evaluation of beef marbling to achieve automatic grading without the inconsistencies caused by human factors. In the experiments, cross-sectional images of the parts used for visual judgment were taken, and fractal analysis was performed on these images to evaluate them using fractal dimensions. The results confirmed a correlation between the marbling evaluation and the fractal dimensions, demonstrating that quantitative evaluation can be achieved, moving away from qualitative visual assessments. Full article

Currently, the focus of production is shifting towards the use of innovative manufacturing techniques and away from traditional methods. Additive manufacturing technologies hold great promise for creating industrial products. The industry aims to enhance the reliability of individual components and structural elements, as well as the ability to accurately anticipate component failure, particularly due to fatigue. This paper explores the possibility of predicting component failure in parts produced using the WAAM (wire arc additive manufacturing) method by employing fractal dimension analysis. Additionally, the impact of manufacturing imperfections and various heat treatment processes on the fatigue resistance of 30CrMnSi steel has been investigated. Fatigue testing of samples and actual components fabricated via the WAAM process was conducted in this study. The destruction of the examined specimens and products was predicted by evaluating the fractal dimensions of micrographs acquired at different stages of fatigue testing. It has been established that technological defects are more dangerous in terms of fatigue failure than microstructural ones. The correctly selected mode of heat treatment for metal after electric arc welding allows for a more homogeneous microstructure with a near-complete absence of microstructural defects. A comparison of the fractal dimension method with other damage assessment methods shows that it has high accuracy in predicting part failure and is less labor-intensive than other methods. Full article

In the field of nonlinear mathematical physics, Ablowitz et al.’s algorithm has recently made significant progress in the inverse scattering transform (IST) of fractional-order nonlinear evolution equations (fNLEEs). However, the solved fNLEEs are all constant-coefficient models. In this study, we establish a fractional-order KdV (fKdV)-type equation with variable coefficients and show that the IST is capable of solving the variable-coefficient fKdV (vcfKdV)-type equation. Firstly, according to Ablowitz et al.’s fractional-order algorithm and the anomalous dispersion relation, we derive the vcfKdV-type equation contained in a new class of integrable fNLEEs, which can be used to describe the dispersion transport in fractal media. Secondly, we reconstruct the potential function based on the time-dependent scattering data, and rewrite the explicit form of the vcfKdV-type equation using the completeness of eigenfunctions. Thirdly, under the assumption of reflectionless potential, we obtain an explicit expression for the fractional n-soliton solution of the vcfKdV-type equation. Finally, as specific examples, we study the spatial structures of the obtained fractional one- and two-soliton solutions. We find that the fractional soliton solutions and their linear, X-shaped, parabolic, sine/cosine, and semi-sine/semi-cosine trajectories formed on the coordinate plane have power–law dependence on discrete spectral parameters and are also affected by variable coefficients, which may have research value for the related hyperdispersion transport in fractional-order nonlinear media. Full article

Hybrid-carrageenan hydrogels are characterized using novel techniques based on high-resolution speckle imaging, namely image dynamic light scattering (IDLS) and ultra-small-angle light scattering (USALS). These techniques, used to probe the microscopic structure of the system in sol–gel phase separation and at different concentrations in the gel phase, give access to a better understanding of the network’s topology on the basis of fractals in the dense phase. Observations of the architecture and the spatial and the size distributions of gel phase and fractal dimension were performed by USALS. The pair-distance distribution function, P(r), extracted from USALS patterns, is a new methodology of calculus for determining the network’s internal size with precision. All structural features are systematically compared with a linear and non-linear rheological characterization of the gels and structure–elasticity relationships are identified in the framework of fractal colloid gels in the diffusion limit. Full article

Experiments to measure a single molecule/particle, i.e., an individual molecule/particle, at room temperature or under physiological conditions without immobilization—for example, on a surface or without significant hydrodynamic flow—have so far failed. This failure has given impetus to the underlying theory of Brownian molecular motion towards its stochastics due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion). Full article

The life quality of populations, especially in large agglomerations, is significantly reduced due to air pollution. Major sources of pollution include motor vehicles, industrial facilities and the burning of fossil fuels. A particularly significant source of pollution is thermal power plants and coal-fired power plants, which are widely used in developing countries. The Astana city in the Republic of Kazakhstan is a fast-growing agglomeration where air pollution is compounded by intensive construction and the use of coal for heating. The research is important for the development of urbanism in terms of ensuring the sustainable development of urban agglomerations, which are growing rapidly. Long memory in time series of concentrations of air pollutants (particulate matter PM10, PM2.5) from four stations in Astana using the fractal R/S analysis method was studied. The Hurst exponents for the studied stations are 0.723; 0.548; 0.442 and 0.462. In addition, the behavior of the Hurst exponent in dynamics is studied by the flow window method based on R/S analysis. As a result, it was found that the pollution indicators of one of the stations are characterized by the presence of long-term memory and the time series is persistent. According to the analysis of recordings from the second station, the series is defined as close to random, and for stations 3 and 4, anti-persistence is characteristic. The calculated Hurst exponent values explain the sharp increase in pollution levels in October 2021. The reason for the increase in polluting substances concentration in the air is the close location of thermal power plants to the city. The method of time series fractal analysis can be the ecological state indicator in the corresponding region. Persistent pollution time series can be used to predict the occurrence of a critical pollution level. One of the reasons for anti-persistence or the occurrence of a temporary contamination level may be the close location of the observation station to the source of contamination. The obtained results indicate that the fractal time series analysis method can be an indicator of the ecological state in the relevant region. Full article

Detection of abnormal situations in mobile systems not only provides predictions about risky situations but also has the potential to increase energy efficiency. In this study, two real-world drives of a battery electric vehicle and unsupervised hybrid anomaly detection approaches were developed. The anomaly detection performances of hybrid models created with the combination of Long Short-Term Memory (LSTM)-Autoencoder, the Local Outlier Factor (LOF), and the Mahalanobis distance were evaluated with the silhouette score, Davies–Bouldin index, and Calinski–Harabasz index, and the potential energy recovery rates were also determined. Two driving datasets were evaluated in terms of chaotic aspects using the Lyapunov exponent, Kolmogorov–Sinai entropy, and fractal dimension metrics. The developed hybrid models are superior to the sub-methods in anomaly detection. Hybrid Model-2 had 2.92% more successful results in anomaly detection compared to Hybrid Model-1. In terms of potential energy saving, Hybrid Model-1 provided 31.26% superiority, while Hybrid Model-2 provided 31.48%. It was also observed that there is a close relationship between anomaly and chaoticity. In the literature where cyber security and visual sources dominate in anomaly detection, a strategy was developed that provides energy efficiency-based anomaly detection and chaotic analysis from data obtained without additional sensor data. Full article

Corrosion negatively impacts the functionality of civil structures. This paper introduces a new methodology that combines the fractality of vibration signals with a data processing stage utilizing autoencoders to detect corrosion damage in a truss-type bridge. Firstly, the acquired vibration signals are analyzed using six fractal dimension (FD) algorithms (Katz, Higuchi, Petrosian, Sevcik, Castiglioni, and Box dimension). The obtained FD values are then used to generate a gray-scale image. Then, autoencoders analyze these images to generate a damage indicator based on the reconstruction error between input and output images. These indicators estimate the damage probability in specific locations within the structure. The methodology was tested on a truss-type bridge model placed at the Vibrations Laboratory from the Autonomous University of Queretaro, Mexico, where three damage corrosion levels were evaluated, namely incipient, moderate, and severe, as well as healthy conditions. The results demonstrate that the proposal is a reliable tool to evaluate the condition of truss-type bridges, achieving an accuracy of 99.8% in detecting various levels of corrosion, including incipient stages, within the elements of truss-type structures regardless of their location. Full article