Search Results (145)

In this article, we present an efficient and highly accurate numerical scheme that achieves exponential convergence for solving nonlinear Riesz distributed-order fractional differential equations (RDFDEs) in one- and two-dimensional initial–boundary value problems. The proposed method is based on a two-stage collocation framework. In the first stage, spatial discretization is performed using the shifted Legendre–Gauss–Lobatto (SL-G-L) collocation method, where the approximate solutions and spatial derivatives are expressed in terms of shifted Legendre polynomial expansions. This reduces the original problem to a system of fractional differential equations (FDEs) for the expansion coefficients. Then, the temporal discretization is achieved in the second stage via Romanovski–Gauss–Radau collocation approach, which converts the system into a system of algebraic equations that can be solved efficiently. The method is applied to one- and two-dimensional nonlinear RDFDEs, and numerical experiments confirm its spectral accuracy, computational efficiency, and reliability. Existing numerical approaches to distributed-order fractional models often suffer from poor accuracy, instability in nonlinear settings, and high computational costs. By combining the efficiency of Legendre polynomials for bounded spatial domains with the stability of Romanovski polynomials for temporal discretization, the proposed two-stage framework effectively overcomes these limitations and achieves superior accuracy and stability. Full article

In this study, we introduced and analyzed the Slash–Log–Logistic (SlaLL) distribution, a novel statistical model developed by applying the slash methodology to log–logistic and beta distributions. The SlaLL distribution is particularly suited for modeling datasets characterized by heavy tails and extreme values, frequently encountered in survival time analyses. We derived the mathematical representation of the distribution involving Gauss hypergeometric and beta functions, explicitly established the probability density function, cumulative distribution function, hazard rate function, and reliability function, and provided clear definitions of its moments. Through comprehensive simulation studies, the accuracy and robustness of maximum likelihood and Bayesian methods for parameter estimation were validated. Comparative empirical analyses demonstrated the SlaLL distribution’s superior fitting performance over well-known slash-based models, emphasizing its practical utility in accurately capturing the complexities of real-world survival time data. Full article

To address the difficulty in obtaining analytical solutions for the torsional vibration response of pile foundations in orthotropic layered soil foundations subjected to torsional excitation at the pile top, this study investigates a layered recursive algorithm based on the Hankel transform. An integral transformation method is employed to reduce the dimensionality of the coupled pile–soil torsional vibration equations, converting the three-dimensional system of partial differential equations into a set of ordinary differential equations. Combining the constitutive properties of transversely anisotropic strata with interlayer contact conditions, a transfer matrix model is established. Employing inverse transformation coupled with the Gauss–Kronrod integration method, an explicit frequency-domain solution for the torsional dynamic impedance at the pile top is derived. The research findings indicate that the anisotropy coefficient of the foundation significantly influences both the real and imaginary parts of the impedance magnitude. The sequence of soil layer distribution and the bonding state at interfaces jointly affect the nonlinear transmission characteristics of torque along the pile shaft. Full article

Beta regression models are a class of models used frequently to model response variables in the interval $(0, 1)$. Although there are articles in which these models are used to model clustered and longitudinal data, the prediction of random effects is limited, and residual analysis has not been implemented. In this paper, a random intercept beta regression model is proposed for the complete analysis of this type of data structure. We proposed some types of residuals and formulate a methodology to obtain the best prediction of random effects. This model is developed through the parameterisation of beta distribution in terms of the mean and dispersion parameters. A log-likelihood function is approximated by the Gauss–Hermite quadrature to numerically integrate the distribution of random intercepts. A simulation study is used to investigate the performance of the estimation process and the sampling distributions of the residuals. Full article

Just as Gauss’s interpretation of complex numbers as points in a number plane in the form of a suitably formulated axiom found its way into the vector representation of Fourier transforms, this is the case with Euler’s formula and Riemann’s Zeta function considered here. The description of the connection between variables through complex numbers as it is given in Euler’s formula and emphasized by Riemann is reflected here with great flexibility in the introduction of non-classically generalized complex numbers and the vector representation of the generalized Zeta function based on them. For describing such dependencies of two variables with the help of generalized complex numbers, we introduce manifolds underlying certain Lie groups as level sets of norms, antinorms or semi-antinorms. No undefined or “imaginary” quantities are used for this. In contrast to the approach of Hamilton and his numerous successors, the vector-valued vector product of non-classically generalized complex numbers is commutative, and the whole number system satisfies a weak distributivity property as considered by Hankel, but not the strong one. Full article

Many studies have examined how species are shifting their ranges poleward in response to climate change, using statistical approaches such as graphical analyses, t-tests, correlation analyses, and circular data methods. However, these methods are often constrained by assumptions of linearity or reliance on a single explanatory variable, which limits their ecological applicability. This study introduces a new statistical methodology to evaluate the significance of poleward range expansion, aiming to overcome these limitations and improve the robustness of ecological inference. We developed four parameterized nonlinear models—simple, multivariable, fixed, and transformed—to characterize the relationship between latitude and species richness across 1253 islands. Model parameters were estimated using the Gauss–Newton algorithm, and residuals were calculated as the difference between observed and predicted values. To test for distributional shifts, likelihood ratio tests were applied to the residuals, with statistical significance assessed using chi-square statistics and p-values derived from the −2 log-likelihood ratio. Finally, an intuitive indicator based on the fitted models was introduced to evaluate the direction of range shifts, thereby providing a direct means of identifying northward expansion trends under climate change. Applying this framework revealed significant poleward shifts of warm temperate and subtropical species (χ² = 52.4–61.3; p < 0.001). Among the four models, the multivariable model incorporating island area provided the best fit (AIC, BIC), reflecting its ability to account for collinearity. Taken together, these results underscore the robustness and ecological relevance of the methodology, demonstrating its utility for detecting species-specific range shifts and comparing alternative models under climate change. Full article

We investigate the influence of the higher-order curvature corrections on a static configuration with constant density in Einstein–Gauss–Bonnet (EGB) gravity. This analysis is applied to both neutral and charged fluid distributions in arbitrary spacetime dimensions. The EGB field equations are generated, and the condition of pressure isotropy is shown to generalise the general relativity equation. The gravitational potentials are unique in all spacetime dimensions for neutral gravitating spheres. Charged gravitating spheres are not unique and depend on the form of the electric field. Our treatment is extended to the particular case of a charged fluid distribution with a constant energy density and constant electric field intensity. The charged EGB field equations are integrated to give exact models in terms of hypergeometric functions which can also be written as a series. Full article

This paper introduces and explores a novel class of Brown and Levy steady-state motions. These motions generalize, respectively, the Ornstein-Uhlenbeck process (OUP) and the Levy-driven OUP. As the OUP and the Levy-driven OUP: the motions are Markov; their dynamics are Langevin; and their steady-state distributions are, respectively, Gauss and Levy. As the Levy-driven OUP: the motions can display the Noah effect (heavy-tailed amplitudal fluctuations); and their memory structure is tunable. And, as Gaussian-stationary processes: the motions can display the Joseph effect (long-ranged temporal dependencies); and their correlation structure is tunable. The motions have two parameters: a critical exponent which determines the Noah effect and the memory structure; and a clock function which determines the Joseph effect and the correlation structure. The novel class is a compelling stochastic model due to the following combination of facts: on the one hand the motions are tractable and amenable to analysis and use; on the other hand the model is versatile and the motions display a host of both regular and anomalous features. Full article

Lewis’ theory of multiple scattering has been modeled as a random walk on a unit sphere for calculating the multiple scattering angular distribution of charged particles, which is more intuitive and mathematically simpler. This formalism can lead to the Goudsmit–Saunderson theory and the Lewis theory of multiple scattering angular distribution, thus providing an easier-to-understand framework to unify both the Goudsmit–Saunderson and the Lewis theories. This new random walk method eliminates the need for integro-differential expansions in Lewis theory and is faster at calculating multiple scattering angular distributions, reducing the required Legendre series terms by 80% at small step (path) length (<20) and providing much greater calculation efficiency. Crucially, the random walk formalism explicitly preserves spherical symmetry by treating angular deflections as steps on a unit sphere, enabling the efficient sampling of scattering events while maintaining accuracy. Further, a robust algorithm for numerically calculating multiple scattering angular distributions of electrons based on the Goudsmit–Saunderson and Lewis theories has been developed. Partial wave elastic scattering differential cross-sections, generated with the program ELSEPA, have been used in the calculations. A two-point Gauss–Legendre quadrature method is used to calculate the Legendre coefficients (multiple scattering moments). Full article

This paper investigates Abelian theorems for operators with complex Gaussian kernels over distributions of compact support. Furthermore, our investigation encompasses an examination of the Gauss–Weierstrass semigroup, the linear canonical transform, and the Ornstein–Uhlenbeck semigroup as particular cases within the scope of our study. Full article

This paper analyzes the impact of a single soft error on the performance of a synchronous and self-timed pipeline. A nuclear particle running through the integrated circuit body is considered the most probable soft error source. The existing estimates show that self-timed circuits offer an advantage in terms of single soft error tolerance. The paper proves these estimates on the basis of a comparative probability analysis of a critical fault in two types of pipelines. The mathematical models derived in the paper describe the probability of a critical fault depending on the circuit’s characteristics, its operating discipline, and the soft error parameters. The self-timed pipeline operates in accordance with a two-phase discipline, based on the request–acknowledge interaction within the pipeline’s stages, which provides it with increased immunity to soft errors. Quantitative calculations performed on the basis of the derived mathematical models show that the self-timed pipeline has about 6.1 times better tolerance to a single soft error in comparison to its synchronous counterpart. The obtained results are in good agreement with empirical estimates of the soft error tolerance level of synchronous and self-timed circuits. Full article

This study introduces the Lindley Stochastic Frontier Analysis—LSFA model, a novel approach that incorporates the Lindley distribution to enhance the flexibility and accuracy of efficiency estimation. The LSFA model is compared against traditional SFA models, including the half-normal, exponential, and gamma models, using advanced numerical methods such as the Gauss–Hermite Quadrature, Monte Carlo Integration, and Simulated Maximum Likelihood Estimation for parameter estimation. Simulation studies revealed that the LSFA model outperforms in scenarios involving small sample sizes and complex, skewed distributions, particularly those characterized by gamma distributions. In contrast, traditional models such as the half-normal model perform better in larger samples and simpler settings, while the gamma model is particularly effective under exponential inefficiency distributions. Among the numerical techniques, the Gauss–Hermite Quadrature demonstrates a strong performance for half-normal distributions, the Monte Carlo Integration offers consistent results across models, and the Simulated Maximum Likelihood Estimation shows robustness in handling gamma and Lindley distributions despite higher errors in simpler cases. The application to a banking dataset assessed the performance of 12 commercial banks pre-COVID-19 and during COVID-19, demonstrating LSFA’s superior ability to handle skewed and intricate data structures. LSFA achieved the best overall reliability in terms of the root mean square error and bias, while the gamma model emerged as the most accurate for minimizing absolute and percentage errors. These results highlight LSFA’s potential for evaluating efficiency during economic shocks, such as the COVID-19 pandemic, where data patterns may deviate from standard assumptions. This study highlights the advantages of the Lindley distribution in capturing non-standard inefficiency patterns, offering a valuable alternative to simpler distributions like the exponential and half-normal models. However, the LSFA model’s increased computational complexity highlights the need for advanced numerical techniques. Future research may explore the integration of generalized Lindley distributions to enhance model adaptability with enriched numerical optimization to establish its effectiveness across diverse datasets. Full article

The shortage of fresh water resources and soil salinization restrict the sustainable development of oasis agriculture in Xinjiang, China. Magnetically treated brackish water can physically improve the quality of water used for irrigation, and this technology is being gradually applied in agricultural production. However, the infiltration characteristics of magnetized brackish water and its response to the distribution of soil water and salt are still unknown. We conducted infiltration tests using a one-dimensional soil column system, employing magnetized water at concentrations of 0.2, 1, 3, and 5 g·L⁻¹, with a magnetization strength of 3000 gauss (GS), and explored the effects of salinity of magnetized water on water–salt transport and infiltration characteristics of soil under drip irrigation. The migration rate of the wetting front of magnetized water infiltration slowed, and the cumulative infiltration content increased. Specifically, compared to the unmagnetized control, the infiltration time at a depth of 40 cm for magnetized water concentrations of 0.2, 1, 3, and 5 g·L⁻¹ increased by 17.42%, 42.16%, 47.02%, and 39.19%, respectively. Correspondingly, the cumulative infiltration volume increased by 7.88%, 8.09%, 10.60%, and 5.38%. Further, the infiltration of magnetized brackish water increased the water retention capacity of soil, effectively reduced the salt content of soil layers, and had a remarkable desalting effect. Salinity of the soil profile showed an L-shaped trend of salt accumulation in the lower layer and desalting in the upper layer. For water salinity of 3 g·L⁻¹, soil desalting intensity was greatest. In addition, $K_S{h}_f$, suction rate, empirical coefficient a, initial infiltration rate, and stable infiltration rate all decreased under magnetization treatment with the same salinity. Thus, this study provides a new way to alleviate the shortage of fresh water resources in arid areas, a guideline for safely using brackish water and also increasing productivity of saline–alkali land. Full article

In this paper, we have established a numerical method for a class of time-fractional and Riesz space distributed-order advection–diffusion equation with time-delay. Firstly, we transform the Riesz space distributed-order derivative term of the diffusion equation into multi-term fractional derivatives by using the Gauss quadrature formula. Secondly, we discretize time by using second-order finite differences, discretize space by using second kind Chebyshev polynomials, and convert the multi-term fractional equation to a system of algebraic equations. Finally, we solve the algebraic equations by the iterative method, and prove the stability and convergence. Moreover, relevant examples are shown to verify the validity of our algorithm. Full article

The symmetric Kullback–Leibler centroid, also called the Jeffreys centroid, of a set of mutually absolutely continuous probability distributions on a measure space provides a notion of centrality which has proven useful in many tasks, including information retrieval, information fusion, and clustering. However, the Jeffreys centroid is not available in closed form for sets of categorical or multivariate normal distributions, two widely used statistical models, and thus needs to be approximated numerically in practice. In this paper, we first propose the new Jeffreys–Fisher–Rao center defined as the Fisher–Rao midpoint of the sided Kullback–Leibler centroids as a plug-in replacement of the Jeffreys centroid. This Jeffreys–Fisher–Rao center admits a generic formula for uni-parameter exponential family distributions and a closed-form formula for categorical and multivariate normal distributions; it matches exactly the Jeffreys centroid for same-mean normal distributions and is experimentally observed in practice to be close to the Jeffreys centroid. Second, we define a new type of inductive center generalizing the principle of the Gauss arithmetic–geometric double sequence mean for pairs of densities of any given exponential family. This new Gauss–Bregman center is shown experimentally to approximate very well the Jeffreys centroid and is suggested to be used as a replacement for the Jeffreys centroid when the Jeffreys–Fisher–Rao center is not available in closed form. Furthermore, this inductive center always converges and matches the Jeffreys centroid for sets of same-mean normal distributions. We report on our experiments, which first demonstrate how well the closed-form formula of the Jeffreys–Fisher–Rao center for categorical distributions approximates the costly numerical Jeffreys centroid, which relies on the Lambert W function, and second show the fast convergence of the Gauss–Bregman double sequences, which can approximate closely the Jeffreys centroid when truncated to a first few iterations. Finally, we conclude this work by reinterpreting these fast proxy Jeffreys–Fisher–Rao and Gauss–Bregman centers of Jeffreys centroids under the lens of dually flat spaces in information geometry. Full article