Fractals最新文献

Every fluid problem is greatly affected by its boundary conditions, especially the near-shore seabed could produce an irrevocable harm when a tsunami wave is approaching, and a real-life mathematical model could stave off the worst effect. This paper assumes that the unsmooth seabed is a fractal surface, and fractal-fractional governing equations are established according to physical laws in the fractal space. The geometrical potential theory is used to explain the force produced by the wave surface, and Kong-He friction law is applied to further figuring out the local and memory properties of the friction along the fractal boundary. This paper aims at studying tsunami waves in a fractal space, rendering a reliable mathematical model for both prediction of the tsunami motion and the coastal protection.

In this paper, we have extended the concept of advanced Julia function for the discovery of new type of trajectories existing inside outer and inner wings. A dynamical system based on four rotors, referred to as quadrotor unmanned aerial vehicle (QUAV), is considered for the first time to seek the generation of extra wings using fractal theory. Moreover, we have used Julia and advanced Julia function techniques for the creation of outer and inner wings, respectively. Furthermore, the trajectories achieved using both techniques are analyzed and compared with the aid of basin of attraction and bifurcation diagram. This work covers a gap in the literature, that what is the impact of global dynamics on the hidden fractals. Therefore to fill this gap, sets of initial points are plotted from each basin constructed for fractals-based QUAV systems. Finally, numerical simulations are carried out using MATLAB, whereas the data generated for the corresponding basins are attached for the convenience of readers to reproduce each figure.

In this paper, we consider the networks modeled by several self-affine sets based on the Bedford–Mcmullen carpet. We calculate three properties of the networks, including the cumulative degree distribution, the average clustering coefficient and the average path length. We show that such networks have scale-free and small-world effects.

Motivated by previous authors’ work, where Shannon entropy, box covering and information dimension were applied to quantify pulmonary lesions, this paper extends such a contribution in two fashions: (i) Following the approach to quantify pulmonary lesions with Deng entropy and Deng information dimension obtained through box covering method; (ii) exploiting the Shannon and Deng lesion quantification for pulmonary illnesses classification with a bidirectional Long Short Term Memory (bLSTM). The referred pulmonary illnesses are Common Pneumonia (CP) and COVID-19. Shannon entropy and information dimension are performed here and called the Shannon sequence. Then, Deng entropy and Deng information dimension are computed for chest Computed Tomography (CT) images to obtain and combine two data sequences to quantify the pulmonary lesions. The data sequence resulting from the data combination is called the Deng sequence. Both Shannon and Deng sequences are independently used as input for the bLSTM. CT lung scans of 531 healthy subjects, 497 confirmed COVID-19 diagnoses and 516 with CP were analyzed to obtain the Shannon and Deng sequences. The results demonstrate that Deng entropy and Deng information dimension of CT images can differentiate similar lung lesions between COVID-19 and CP. Besides, a statistical analysis shows that: (a) Classification by the bLSTM is better when using the Deng sequence than the Shannon sequence; (b) Deng sequences plus bLSTM significantly outperform DenseNet-201, GoogLeNet and MobileNet-v2 in classifying COVID-19, CP and Normal CT (healthy subjects) in time and accuracy. Hence, the Deng sequence and bLSTM are fast and accurate tools for helping in diagnosing CP and COVID-19.

Since the discovery of momentum effect, people have started the journey of using the momentum effect to construct momentum strategies. As a result of coupling cross-sectional and time-series momentum strategy, dual momentum strategy (DM strategy) has been widely used in practice and closely followed by academics. To address the shortcoming of the classical DM strategy that has not considered the multi-fractal characteristics of the stock market, we construct the fractal dual momentum strategy (FDM strategy) from the two aspects of optimizing the ranking index of the cross-sectional momentum strategy by using fractal statistical measures and improving the timing selection of the time-series momentum strategy by using the trend entropy dimension. The empirical results show that the FDM strategy outperforms the DM strategy. Both in terms of the size and stability of the strategy returns, the FDM strategy shows an optimization effect compared with the DM strategy, which is beneficial to provide investors with better decision-making references.

The permeability of shale controls gas transport in shale gas reservoirs. The shale has a complex pore structure at the nanoscale and its permeability is affected by multiple transport and action mechanisms. In this study, a 3D fractal model for predicting the apparent gas permeability of shale matrix is presented, accounting for the effects of the transport mechanisms (bulk gas transport and adsorption gas diffusion) and action mechanisms (gas adsorption, real gas properties, water film, stress dependence, and total organic carbon (TOC) content). The proposed model is validated with the published experimental data. A series of sensitivity analyses are performed to investigate the influence of fractal characteristics and action mechanisms on the apparent permeability caused by each transport mechanism. The results show that the real gas properties, water film, and stress dependence cause different effects on the total apparent permeability of shale under different fractal characteristics. The maximum pore diameter is inversely proportional to the effects of these action mechanisms, and the porosity is positively proportional to the effects of real gas properties and water film but inversely proportional to the effects of stress dependence. An increase in TOC content can cause an improvement in the total apparent permeability. Furthermore, the effects of action mechanisms on the apparent permeability caused by different transport mechanisms are differently affected by the fractal characteristics. Changes in fractal characteristics mainly affect the apparent permeability caused by slip flow in the real gas effect, slip flow and Knudsen diffusion in the water film effect, and all transport mechanisms in the stress dependence effect.

Underground coal mining in China has gradually moved into deeper seams in recent years, which results in a higher ambient temperature in the mining space and significantly affects the mechanical behavior of coal. In this study, dehydrated coal samples were obtained at different temperatures ranging from 30${}^{\circ }$ to 70${}^{\circ }$, and the mechanical behavior of the dehydrated coal was investigated through compressive loading tests. The digital image correlation (DIC) method was used to acquire the strain field of coal, and a multifractal analysis was conducted to characterize the strain evolution of coal. The findings suggest that the increasing temperatures result in higher moisture desorption rates and greater volumetric contraction strain in coal. Furthermore, coal with higher moisture desorption exhibits higher peak stress and peak strains when subjected to compressive loading. The multifractal analysis of the inhomogeneous strain evolution indicates a gradual decrease in the parameter $\mathrm{\Delta }\alpha$ under compressive loading, followed by a sudden increase before reaching the failure point due to strain localization. The multifractal mechanism was further investigated, revealing that the inhomogeneous strain field of coal is inherently affected by the microstructure of coal. In addition, a mathematical model was proposed to elucidate the relationship between the inhomogeneous coal strain and the microstructure of coal. The result indicates that the inhomogeneity of the coal strain is directly associated with the multifractal singularity of the coal microstructure. Finally, the feasibility of using the multifractal parameter $\mathrm{\Delta }\alpha$ to identify coal strain localization has been demonstrated, indicating its potential value in aiding engineers to determine the SLZ in deep coal mines.

In this paper, the fractional Schrödinger equation is described with beta derivative, which is used to elucidate the dynamic interaction of ultra-short pulses with quantum properties in optical fibers. This work is to study the solitary wave and periodic solutions of the fractional Schrödinger equation by employing three powerful and simple mathematical approaches like fractional Kudryashov method, fractional cosine–sine method and fractional tanh function method. The acquired outcomes illustrate that the proposed three computational approaches are simple, efficient, concise and can be adopted to study more complex phenomena. Finally, the dynamical behavior of these acquired solitary wave solutions is illustrated by sketching some 3D figures with proper parameters.

Several new network information dimension definitions have been proposed in recent decades, expanding the scope of applicability of this seminal tool. This paper proposes a new definition based on Deng entropy and d-summability (a concept from geometric measure theory). We will prove to what extent the new formulation will be useful in the theoretical and applied points of view.

The fracture network in fractured coal is the main channel of coal seam gas flow. Not only the geometric topology properties (such as fractal characteristics) of a single fracture but also the connection topology properties (interconnection characteristics between fractures) of the fracture network have an important impact on the fluid flow in fracture networks. In this study, the connection topology properties of the fracture network in the fractured coal are explored based on the complex network theory for the first time. The property parameters such as the fracture node degree, the clustering coefficient, and the average path length are analyzed. It shows that the average clustering coefficient of the fracture network in fractured coal is larger, and the average path length is smaller. The connection property of the fracture network in the fractured coal presents a typical “small-world” clustering model. Further, by considering the fractal characteristics of the single fracture and the clustering characteristics of the fracture network, an improved clustered fractal discrete fracture network (DFN) model is developed. Then, based on the lattice Boltzmann method, the permeability properties of the generated clustered fractal DFNs are analyzed. The results show that the permeability of DFNs is positively correlated with the average clustering coefficient of fracture network, and negatively correlated with the fractal dimension of fracture. Therefore, the topological clustering characteristics of fracture networks and the fractal characteristics of fractures cannot be ignored in describing the fluid flow in the fracture network, and our clustered fractal DFN model provides a new idea for guiding the optimization design in DFN engineering.