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Due to the numerous applications of the Nizhnik-Novikov-Veselov system (NNVS) in fluid mechanics, thus, the current investigation is focused on studying the fractional form of this model to reveal the ambiguity around many nonlinear phenomena that arise in different fluid medias. Accordingly, we aim to derive several families of symmetric solitons and traveling wave solutions to the (2 + 1)-dimensional fractional asymmetric NNVS (FANNVS), defined in conformable fractional derivatives' sense. For this purpose, a groundbreaking analytical technique known as the modified extended direct algebraic method (mEDAM) is utilized to solve and analyze the FANNVS. According to this method, four cases with several families of soliton-like solutions are derived. Our research uncovers various soliton solutions, including solitary waves, periodic waves, shocks, dual shock waves (lump waves), and anti-shock waves. These solutions are graphically discussed to understand their dynamical proprieties against the fractional parameters. This broad range of soliton-like solutions supports the relevance of our findings and demonstrates the effectiveness of our methodology. These findings significantly advance the field by deepening our understanding of solitonic behavior in FANNVS and demonstrating the effectiveness of the medium approach in solving challenging nonlinear systems. [ABSTRACT FROM AUTHOR]

The current study introduces the generalised New Extended Direct Algebraic Method (gNEDAM) for producing and examining propagation of kink soliton solutions within the framework of the Conformable Kolmogorov–Petrovskii–Piskunov Equation (CKPPE), which entails conformable fractional derivatives into account. The primary justification around employing conformable derivatives in this study is their special ability to comply with the chain rule, allowing for in the solution of aimed nonlinear model. The CKPPE is a crucial model for a number of disciplines, such as mathematical biology, reaction-diffusion mechanisms, and population increase. CKPPE is transformed into a Nonlinear Ordinary Differential Equation by the proposed gNEDAM, and many kink soliton solutions are found by applying the series form solution. These kink soliton solutions shed light on propagation mechanisms within the framework of the CKPPE model. Furthermore, our research offers multiple graphical depictions that facilitate the examination and analysis of the propagation patterns of the identified kink soliton solutions. Through the integration of mathematical biology and reaction-diffusion principles, our research broadens our comprehension of intricate occurrences in various academic domains. [ABSTRACT FROM AUTHOR]

In this paper, we design and analyze a space-time spectral method for the subdiffusion equation. Here, we are facing two difficulties. The first is that the solutions of this equation are usually singular near the initial time. Consequently, traditional high-order numerical methods in time are inefficient. The second obstacle is that the resulting system of the space-time spectral approach is usually large and time consuming to solve. We aim at overcoming the first difficulty by proposing a novel approach in time, which is based on variable transformation techniques. Suitable ψ-fractional Sobolev spaces and the new variational framework are introduced to establish the well-posedness of the associated variational problem. This allows to construct our space-time spectral method using a combination of temporal generalized Jacobi polynomials (GJPs) and spatial Legendre polynomials. For the second difficulty, we propose a fast algorithm to effectively solve the resulting linear system. The fast algorithm makes use of a matrix diagonalization in space and QZ decomposition in time. Our analysis and numerical experiments show that the proposed method is exponentially convergent with respect to the polynomial degrees in both space and time directions, even though the exact solution has very limited regularity. [ABSTRACT FROM AUTHOR]

Linguistic variables (LVs) provide a reliable expression of cognitive information. By inheriting the advantages of LVs, we can express uncertain and incomplete cognitive information in multiple attribute decision-making (MADM), and they do so better than existing methods. In the decision-making process, we can consider decision experts’ (DEs’) bounded rationality, such as cognition toward loss caused by the DEs’ cognitive limitations during the decision process. Therefore, it is necessary to propose a novel cognitive decision approach to handle MADM problems in which the cognitive information is expressed by LVs. In this paper, we employ LVs to represent uncertain and hesitant cognitive information. Then, we propose a mathematical programming approach to solve the MADM problems where attributes or cognitive preferences are not independent. Moreover, the validity and superiority of the presented approach are verified by dealing with a practical problem.

The study of long-term GNSS time series provides valuable insights for researchers in the field of earth sciences. Understanding the trends in these time series is particularly important for geodynamic researchers focused on earth crust movements. Functional and stochastic models play a crucial role in estimating trend values within time series data. Various methods are available to estimate variance components in GNSS time series. The least squares variance component estimation (LS-VCE) method stands out as one of the most effective approaches for this purpose. We introduce an innovative method, which streamlines calculations and simplifies equations, and therefore significantly boosting the processing speed for diagonal(ized) cofactor matrices. The method can be applied to the GNSS time series of linear stochastic models consisting of white noise, flicker noise and random walk noise. Moreover, unlike the conventional approaches, our method experiences high computational efficiency even with an increase in the number of colored noise components in time series data. For GNSS time series, this variable transformation has been applied to both univariate and multivariate modes, preserving the optimal properties of LS-VCE. We conducted simulations on daily time series spanning 5, 10, 15, and 20 years, employing two general and fast modes with one and two colored noise components plus white noise. The computation time for estimating variance components was compared between the two modes, revealing a notable decrease in processing time with the fast mode. [ABSTRACT FROM AUTHOR]

Statistical regression models are used for predicting outcomes based on the values of some predictor variables or for describing the association of an outcome with predictors. With a data set at hand, a regression model can be easily fit with standard software packages. This bears the risk that data analysts may rush to perform sophisticated analyses without sufficient knowledge of basic properties, associations in and errors of their data, leading to wrong interpretation and presentation of the modeling results that lacks clarity. Ignorance about special features of the data such as redundancies or particular distributions may even invalidate the chosen analysis strategy. Initial data analysis (IDA) is prerequisite to regression analyses as it provides knowledge about the data needed to confirm the appropriateness of or to refine a chosen model building strategy, to interpret the modeling results correctly, and to guide the presentation of modeling results. In order to facilitate reproducibility, IDA needs to be preplanned, an IDA plan should be included in the general statistical analysis plan of a research project, and results should be well documented. Biased statistical inference of the final regression model can be minimized if IDA abstains from evaluating associations of outcome and predictors, a key principle of IDA. We give advice on which aspects to consider in an IDA plan for data screening in the context of regression modeling to supplement the statistical analysis plan. We illustrate this IDA plan for data screening in an example of a typical diagnostic modeling project and give recommendations for data visualizations. [ABSTRACT FROM AUTHOR]

AbstractComputer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors. [ABSTRACT FROM AUTHOR]

The transformation technique can assist in improving the efficiency of the estimators in terms of bias and mean square error reduction. A class of ratio estimators for estimating population mean utilizing the transformation of an auxiliary variable has been proposed under stratified random sampling. The bias and mean square error of the proposed estimators up to the first order of approximation are obtained. Simulation studies and an application to rubber data in Thailand are investigated to compare the performance of the proposed class of estimators with existing estimators, the results show that the proposed class of estimators performs much better than other existing estimators under given conditions. [ABSTRACT FROM AUTHOR]

Due to the numerous applications of the Nizhnik-Novikov-Veselov system (NNVS) in fluid mechanics, thus, the current investigation is focused on studying the fractional form of this model to reveal the ambiguity around many nonlinear phenomena that arise in different fluid medias. Accordingly, we aim to derive several families of symmetric solitons and traveling wave solutions to the (2 + 1)-dimensional fractional asymmetric NNVS (FANNVS), defined in conformable fractional derivatives’ sense. For this purpose, a groundbreaking analytical technique known as the modified extended direct algebraic method (mEDAM) is utilized to solve and analyze the FANNVS. According to this method, four cases with several families of soliton-like solutions are derived. Our research uncovers various soliton solutions, including solitary waves, periodic waves, shocks, dual shock waves (lump waves), and anti-shock waves. These solutions are graphically discussed to understand their dynamical proprieties against the fractional parameters. This broad range of soliton-like solutions supports the relevance of our findings and demonstrates the effectiveness of our methodology. These findings significantly advance the field by deepening our understanding of solitonic behavior in FANNVS and demonstrating the effectiveness of the medium approach in solving challenging nonlinear systems.

This study introduces the generalized Khater Method (gKM) as a tool for investigating the existence and dynamics of soliton solutions within the realm of the Fractional Coupled Higgs System (FCHS) with conformable fractional derivatives. The methodology involves obtaining Nonlinear Ordinary Differential Equations (NODEs) from FCHS via a fractional complex transformation. The reduced NODEs are further used to uncover various families of soliton solutions, elucidating their intricate interrelationships and dynamics. Using some 3D graphics, we visually depict the behavior of some soliton solutions, including shock, lump-type shock, periodic, multiple periodic, rogue, and kink solitons. These findings contribute to a better understanding of the dynamics inherent in the FCHS and have potential applications in domains dealing with nonlinear fractional partial differential equations. Arguably, the study also highlights the importance of incorporating the conformable fractional derivative into the FCHS to provide it with more degrees of freedom, which allows for a deeper exploration of its behaviors and could have significant implications for particle physics, particularly for phenomena such as mass origin and electroweak violation of symmetry. [ABSTRACT FROM AUTHOR]

Interval linear fractional programming problem (ILFPP) approaches uncertainties in real-world systems such as business, manufacturing, finance, and economics. In this study, we propose solving the interval linear fractional programming (ILFP) problem using interval arithmetic. Further, to construct the problem, a suitable variable transformation is used to form an equivalent ILP problem, and a new algorithm is depicted to obtain the optimal solution without converting the problem into its conventional form. This paper compares the range, solutions, and approaches of ILFP with fuzzy linear fractional programming (FLFP) in solving real-world optimization problems. The illustrated numerical examples show a better range of interval solutions on practical applications of ILFPs and uncertain parameters. [ABSTRACT FROM AUTHOR]

The study of soliton solutions for Nonlinear Fractional Partial Differential Equations (NFPDEs) has gained prominence recently because of its ability to realistically recreate complex physical processes. Numerous mathematical techniques have been devised to handle the problem of NFPDEs where soliton solutions are difficult to obtain. Due to their accuracy in reproducing complex physical phenomena, soliton solutions for Nonlinear Fractional Partial Differential Equations (NFPDEs) have recently attracted interest. Several mathematical techniques have been devised to tackle the difficult task of solving non-finite partial differential equations (NFPDEs) soliton. Studies of soliton solutions for Nonlinear Fractional Partial Differential Equations (NFPDEs) have garnered increased attention recently due to its capacity to accurately represent complex physical processes. Due to the difficulty of obtaining soliton solutions, NFPDEs can be solved using a wide variety of mathematical methods. In this way, it facilitates the extraction of the recently found abundance of optical soliton solutions. To further understanding of the results, the study also includes contour and three-dimensional images that visually depict particular optical soliton solutions for particular parameter selections, suggesting the existence of different soliton structures in the nonlinear fractional Kundu–Eckhaus equation (NFKEE) region. It is shown that the proposed technique is quite powerful and effective in solving several nonlinear FDEs.

The present research applies an improved version of the modified Extended Direct Algebraic Method (mEDAM) called +mEDAM to examine soliton phenomena in a notable mathematical model, namely the (2+1)-dimensional Nizhnik-Novikov-Veselov Model (NNVM), which possesses potential applications in exponentially localized structure interactions. The generalized hyperbolic and trigonometric functions are used to disclose a variety of soliton solutions, including kinks, anti-kink, bell-shaped and periodic soliton. Some 3D graphs are plotted for visual representations of these solutions which highlight their adaptability. The results provide a basis for practical usage and expansions to related mathematical models or physical systems. They also expand our understanding of the NNVM's dynamics, providing insights into its behavior and prospective applications.

In this research work, we investigate the complex structure of soliton in the Fractional Kudryashov–Sinelshchikov Equation (FKSE) using conformable fractional derivatives. Our study involves the development of soliton solutions using the modified Extended Direct Algebraic Method (mEDAM). This approach involves a key variable transformation, which successfully transforms the model into a Nonlinear Ordinary Differential Equation (NODE). Following that, by using a series form solution, the NODE is turned into a system of algebraic equations, allowing us to construct soliton solutions methodically. The FKSE is the governing equation, allowing for heat transmission and viscosity effects while capturing the behaviour of pressure waves in liquid–gas bubble mixtures. The solutions we discover include generalised trigonometric, hyperbolic, and rational functions with kinks, singular kinks, multi-kinks, lumps, shocks, and periodic waves. We depict two-dimensional, three-dimensional, and contour graphs to aid comprehension. These newly created soliton solutions have far-reaching ramifications not just in mathematical physics, but also in a wide range of subjects such as optical fibre research, plasma physics, and a variety of applied sciences. [ABSTRACT FROM AUTHOR]

The present research applies an improved version of the modified Extended Direct Algebraic Method (mEDAM) called r+mEDAM to examine soliton phenomena in a notable mathematical model, namely the (2+1)-dimensional Nizhnik-Novikov-Veselov Model (NNVM), which possesses potential applications in exponentially localized structure interactions. The generalized hyperbolic and trigonometric functions are used to disclose a variety of soliton solutions, including kinks, anti-kink, bell-shaped and periodic soliton. Some 3D graphs are plotted for visual representations of these solutions which highlight their adaptability. The results provide a basis for practical usage and expansions to related mathematical models or physical systems. They also expand our understanding of the NNVM's dynamics, providing insights into its behavior and prospective applications. [ABSTRACT FROM AUTHOR]

The purpose of this article was to introduce the basic concepts, calculation methods, two examples and SAS implementation related to the quantitative data correspondence analysis. The basic concepts included variable and sample, explicit variable and latent variable, factor analysis, R-type analysis and Q-type analysis, and correspondence analysis. The calculation methods involved the basic principles, variable transformation, construction of R-type and Q-type covariance matrices, and factor analysis. The data in the two examples were "per capita consumption expenditure data of rural households in 10 provinces in China in a certain year" and "frequency of occurrence of various genes of different ethnic groups". With the help of SAS software, the quantitative data in the two examples were analyzed by quantitative data correspondence analysis, and the SAS output results were given an explanation. [ABSTRACT FROM AUTHOR]

In this paper, we suggest the modified Extended Direct Algebraic Method (mEDAM) to examine the existence and dynamics of solitary wave solutions in the context of the fractional coupled Higgs system, with Caputo's fractional derivatives. The method begins with the formulation of nonlinear differential equations using a fractional complex transformation, followed by the derivation of solitary wave solutions. Two-dimensional, Three-dimensional and contour graphs are used to investigate the behavior of traveling wave solutions. The research reveals many families of solitary wave solutions as well as their deep interrelationships and dynamics. These discoveries add to a better understanding of the dynamics of the fractionally coupled Higgs system and have potential applications in areas that use nonlinear Fractional Partial Differential Equations (FPDEs). [ABSTRACT FROM AUTHOR]

In this investigation, we utilize advanced versions of the Extended Direct Algebraic Method (EDAM), namely the modified EDAM (mEDAM) and r+mEDAM, to explore families of optical soliton solutions in the Fractional Perturbed Radhakrishnan–Kundu–Lakshmanan Model (FPRKLM). Our study stands out due to its in-depth investigation and the identification of multiple localized and stable soliton families, illuminating their complex behavior. We offer visual validation via carefully designed 3D graphics that capture the complex behaviors of these solitons. The implications of our research extend to fiber optics, communication systems, and nonlinear optics, with the potential for driving developments in optical devices and information processing technologies. This study conveys an important contribution to the field of nonlinear optics, paving the way for future advancements and a greater comprehension of optical solitons and their applications. [ABSTRACT FROM AUTHOR]

This research uses a novel analytical method known as the modified Extended Direct Algebraic Method (mEDAM) to explore families of soliton solutions for the complex structured Coupled Fractional Biswas–Arshed Model (CFBAM) in Birefringent Fibers. The Direct Algebraic Method (DAM) is extended by the mEDAM's methodology to compute more analytical solutions that would otherwise be difficult to acquire. We use this method to derive several families of soliton solutions and examine their characteristics. We also look at how different model parameters, such as amplitude, width, and propagation speed, affect the dynamics of soliton. Our use of 2D and 3D graphics to illustrate the soliton solutions also makes it possible to see the soliton dynamics more clearly. The outcomes also demonstrate that the method suggested has proven successful in producing soliton solutions for intricate structures such as the CFBAM. [ABSTRACT FROM AUTHOR]

The article considers non-stationary heat transfer in a plate under conditions of high-temperature aerosol spraying during the formation of thermal protection on this plate. To solve such a problem in a domain with a moving boundary, which increases the computational domain, a substitution is used that reduces the domain with a moving boundary to a domain with fixed boundaries. The finite cosine Fourier transform was applied to the obtained problem, for which the problem for eigenvalues and eigenfunctions with boundary conditions of the third kind was previously solved. A new analytical solution and results on the thermal state of the entire system with a complex temperature pattern at the moving boundary are obtained. [ABSTRACT FROM AUTHOR]

In this research work, we investigate the complex structure of soliton in the Fractional Kudryashov–Sinelshchikov Equation (FKSE) using conformable fractional derivatives. Our study involves the development of soliton solutions using the modified Extended Direct Algebraic Method (mEDAM). This approach involves a key variable transformation, which successfully transforms the model into a Nonlinear Ordinary Differential Equation (NODE). Following that, by using a series form solution, the NODE is turned into a system of algebraic equations, allowing us to construct soliton solutions methodically. The FKSE is the governing equation, allowing for heat transmission and viscosity effects while capturing the behaviour of pressure waves in liquid–gas bubble mixtures. The solutions we discover include generalised trigonometric, hyperbolic, and rational functions with kinks, singular kinks, multi-kinks, lumps, shocks, and periodic waves. We depict two-dimensional, three-dimensional, and contour graphs to aid comprehension. These newly created soliton solutions have far-reaching ramifications not just in mathematical physics, but also in a wide range of subjects such as optical fibre research, plasma physics, and a variety of applied sciences.

The purpose of the paper was to introduce the test for homoscedasticity and the SAS implementation. The test of homogeneity of variance could be divided into the following three categories， ①analysis of variance directly based on comparison of variances， ②analysis of variance based on mean comparison was adopted for the new data from the variable transformation of the original data， ③the method of the χ2 test was used to analyze the quantitative raw data which followed the normal distribution. In the first category， a test statistic that followed the F distribution was constructed directly based on the variance ratio of the two samples. In the second category， there were a variety of different variable transformation approaches for the original data， and the new data after the transformation， which was viewed as the univariate quantitative data collected from a single-factor with multi-level design， was analyzed by using one-way ANOVA. In the third category， the χ2 test statistic was constructed for quantitative data that followed the normal distribution. The paper was based on SAS software to test the homogeneity of variances of three examples， and explained the output results.

A novel non-iterative (explicit) formulation of generalized wall functions that applies to equilibrium and non-equilibrium boundary layer flows was proposed. The proposed formulation uses a set of variables that are more useful for computational fluid dynamics codes as they allow for calculating wall shear stress without an iterative procedure. In addition, an explicit form of the formulation was provided that applies to wall models with and without pressure gradients. The new variable transformation casts the generalizedwall function into a new form that simplifies the implementation and evaluation of wall-shear stress in computational codes.

In this paper, we suggest the modified Extended Direct Algebraic Method (mEDAM) to examine the existence and dynamics of solitary wave solutions in the context of the fractional coupled Higgs system, with Caputo’s fractional derivatives. The method begins with the formulation of nonlinear differential equations using a fractional complex transformation, followed by the derivation of solitary wave solutions. Two-dimensional, Three-dimensional and contour graphs are used to investigate the behavior of traveling wave solutions. The research reveals many families of solitary wave solutions as well as their deep interrelationships and dynamics. These discoveries add to a better understanding of the dynamics of the fractionally coupled Higgs system and have potential applications in areas that use nonlinear Fractional Partial Differential Equations (FPDEs).

This research uses a novel analytical method known as the modified Extended Direct Algebraic Method (mEDAM) to explore families of soliton solutions for the complex structured Coupled Fractional Biswas–Arshed Model (CFBAM) in Birefringent Fibers. The Direct Algebraic Method (DAM) is extended by the mEDAM’s methodology to compute more analytical solutions that would otherwise be difficult to acquire. We use this method to derive several families of soliton solutions and examine their characteristics. We also look at how different model parameters, such as amplitude, width, and propagation speed, affect the dynamics of soliton. Our use of 2D and 3D graphics to illustrate the soliton solutions also makes it possible to see the soliton dynamics more clearly. The outcomes also demonstrate that the method suggested has proven successful in producing soliton solutions for intricate structures such as the CFBAM.

In this investigation, we utilize advanced versions of the Extended Direct Algebraic Method (EDAM), namely the modified EDAM (mEDAM) and r+mEDAM, to explore families of optical soliton solutions in the Fractional Perturbed Radhakrishnan–Kundu–Lakshmanan Model (FPRKLM). Our study stands out due to its in-depth investigation and the identification of multiple localized and stable soliton families, illuminating their complex behavior. We offer visual validation via carefully designed 3D graphics that capture the complex behaviors of these solitons. The implications of our research extend to fiber optics, communication systems, and nonlinear optics, with the potential for driving developments in optical devices and information processing technologies. This study conveys an important contribution to the field of nonlinear optics, paving the way for future advancements and a greater comprehension of optical solitons and their applications.

Complex matrices such as soil have a range of measurable characteristics, and thus data to describe them can be considered multidimensional. These characteristics can be strongly influenced by factors that introduce confounding effects that hinder analyses. Traditional statistical approaches lack the flexibility and granularity required to adequately evaluate such matrices, particularly those with large dataset of varying data types (i.e. quantitative non-compositional, quantitative compositional). We present a statistical workflow designed to effectively analyse complex, multidimensional systems, even in the presence of confounding variables. The developed methodology involves exploratory analysis to identify the presence of confounding variables, followed by data decomposition (including strategies for both compositional and non-compositional quantitative data) to minimise the influence of these confounding factors such as sampling site/location. These data processing methods then allow for common patterns to be highlighted in the data, including the identification of biomarkers and determination of non-trivial associations between variables. We demonstrate the utility of this statistical workflow by jointly analysing the chemical composition and fungal biodiversity of New Zealand vineyard soils that have been managed with either organic low-input or conventional input approaches. By applying this pipeline, we were able to identify biomarkers that distinguish viticultural soil from both approaches and also unearth links and associations between the chemical and metagenomic profiles. While soil is an example of a system that can require this type of statistical methodology, there are a range of biological and ecological systems that are challenging to analyse due to the complex interplay of global and local effects. Utilising our developed pipeline will greatly enhance the way that these systems can be studied and the quality and impact of insight gained from their analysis. [Display omitted] • Novel statistical workflow to effectively analyse complex, multidimensional systems. • Methods allow identification of biomarker and non-trivial variable associations. • Workflow demonstrated through analysing New Zealand vineyard soils. • Utility shown by jointly analysing chemical composition and fungal biodiversity. • Developed pipeline will enhance analysis of other complex multidimensional systems. [ABSTRACT FROM AUTHOR]

We consider the numerical computation of I [ f ] = ∫ = a b f (x) d x , the Hadamard finite part of the finite-range singular integral ∫ a b f (x) d x , f (x) = g (x) / (x - t) m with a < t < b and m ∈ { 1 , 2 , ... } , assuming that (i) g ∈ C ∞ (a , b) and (ii) g(x) is allowed to have arbitrary integrable singularities at the endpoints x = a and x = b . We first prove that ∫ = a b f (x) d x is invariant under any legitimate variable transformation x = ψ (ξ) , ψ : [ α , β ] → [ a , b ] , hence there holds ∫ = α β F (ξ) d ξ = ∫ = a b f (x) d x , where F (ξ) = f (ψ (ξ)) ψ ′ (ξ) . Based on this result, we next choose ψ (ξ) such that F (ξ) , the T -periodic extension of F (ξ) , T = β - α , is sufficiently smooth, and prove, with the help of some recent extension/generalization of the Euler–Maclaurin expansion, that we can apply to ∫ = α β F (ξ) d ξ the quadrature formulas derived for periodic singular integrals developed in an earlier work of the author: [A. Sidi, "Unified compact numerical quadrature formulas for Hadamard finite parts of singular integrals of periodic functions." Calcolo, 58, 2021. Article number 22]. We give a whole family of numerical quadrature formulas for ∫ = α β F (ξ) d ξ for each m, which we denote T ^ m , n (s) [ F ] . Letting G (ξ) = (ξ - τ) m F (ξ) , with τ ∈ (α , β) determined from t = ψ (τ) , and letting h = T / n , for m = 3 , for example, we have the three formulas T ^ 3 , n (0) [ F ] = h ∑ j = 1 n - 1 F (τ + j h) - π 2 3 G ′ (τ) h - 1 + 1 6 G ′ ′ ′ (τ) h , T ^ 3 , n (1) [ F ] = h ∑ j = 1 n F (τ + j h - h / 2) - π 2 G ′ (τ) h - 1 , T ^ 3 , n (2) [ F ] = 2 h ∑ j = 1 n F (τ + j h - h / 2) - h 2 ∑ j = 1 2 n F (τ + j h / 2 - h / 4). We show that all of the formulas T ^ m , n (s) [ F ] converge to I[f] as n → ∞ ; indeed, if ψ (ξ) is chosen such that F (i) (α) = F (i) (β) = 0 , i = 0 , 1 , ... , q - 1 , and F (q) (ξ) is absolutely integrable in every closed interval not containing ξ = τ , then T ^ m , n (s) [ F ] - I [ f ] = O (n - q) as n → ∞ , where q is a positive integer determined by the behavior of g(x) at x = a and x = b and also by ψ (ξ) . As such, q can be increased arbitrarily (even to q = ∞ , thus inducing spectral convergence) by choosing ψ (ξ) suitably. We provide several numerical examples involving nonperiodic integrands and confirm our theoretical results. [ABSTRACT FROM AUTHOR]

The following equation is considered in this work: x'' + (−α + β cos (ρ t) + ƒ(t))x = 0, where the parameters α and β are real numbers, the frequency ρ is a positive real number, and ƒ: [0,∞) → R is a continuous bounded function, i.e., there exists a positive constant ƒ* such that |ƒ(t)| ≤ ƒ* for t ≥ 0. This equation is generally referred to as the Mathieu equation when ƒ* = 0. This work proposes a non-oscillation theorem that can be applied even if ƒ* ≠ 0. The required conditions are expressed by the parameters α, β, ρ, and a positive constant ƒ*. The results obtained herein include those by Sugie and Ishibashi [Appl. Math. Comput. 346 (2019), pp. 491–499]. Further, the result can be proved using the phase plane analysis proposed by Sugie [Monatsh. Math. 186 (2018), pp. 721–743]. Finally, the simple non-oscillation and oscillation conditions of the generalized Mathieu equation are summarized. [ABSTRACT FROM AUTHOR]

In recent years, many supplementary designs have been developed incorporating the assumption that data follow the particular non-normal distribution. The $V_{R}$ -control chart is one such design proposed to monitor the parameter $\sigma^{2}$ of the single parameter Rayleigh distributed data. Commonly, in authentic situations, practitioners need to estimate the scale parameter $\sigma $ in the observed processes instead of $\sigma ^{2}$ . However, the positive square root of the $V_{R}$ -statistic used in the existing design of $V_{R}$ -control chart is not an unbiased estimator of $\sigma $ and thus could not be practiced to monitor the scale parameter $\sigma $ of the Rayleigh distributed process. A new structure of the $V_{R}$ -control chart namely $V_{SQR}$ for monitoring the scale parameter of the Rayleigh distributed data has been originally developed in this study. The statistical basis of this newly $V_{SQR}$ design in terms of average run length $\left ({ARL }\right)$ , characteristic function and power curve have been derived. The analytical results are utilized further to determine the parameters of $V_{SQR}$ -chart and in comparing the performance of the proposed control chart with existing competitors. Comparative results illustrate the effectiveness of the proposed design in view of statistical power. Finally, the computational procedure of this newly $V_{SQR}$ -chart has been demonstrated using simulated data and real data on the breaking strength of carbon fibers.

In the solution of frequency- and time-domain integral equations, the singularity cancellation transformations are well-known for the treatments of the singular integrals involving Green’s functions and their gradients. The cancellation transformations for strongly near-singular integrals become inaccurate and inefficient for an extremely deformed triangular patch, and this phenomenon is referred to as shape-dependent problem in this article. As the degree of the singularity increases, the shape-dependent problem of strong near-singularity is more severe than that of the weakly near-singular integrals. Furthermore, if the source triangle is deformed, the accuracy of the cancellation methods decreases when no near singularity exists. In this work, we first investigate the reason for these problems via the theoretical analysis with numerical verification. Second, an updated framework of singularity cancellation methods for strongly near-singular integrals is proposed, which has a fast and consistent convergence rate for both regular and irregular triangles. Third, some numerical experiments are presented to illustrate the effectiveness of the theoretical framework and the proposed transformations. [ABSTRACT FROM AUTHOR]

Estimation of statistical moments remains one of the main topics of stochastic analysis whose accuracy greatly affects reliability analysis results. In this work, conjugate unscented transformation (CUT) methods, which can balance accuracy and efficiency, are introduced for the statistical moment estimation of responses. Because of the drawbacks of existing CUT methods, a family of enhanced conjugate unscented transformation (ECUT) methods, including ECUT-4, ECUT-6, and ECUT-8 methods, is proposed for statistical moment estimation by combining the original CUT, variable transformation, and exact dimension reduction method. Then the probability density function of a performance function is reconstructed by the improved maximum entropy method (IMEM) with the available statistical moments as constraints. To demonstrate the accuracy and effectiveness of the proposed methods, five numerical examples, including linear and nonlinear, low-dimensional and high-dimensional, and explicit and implicit performance functions, are investigated, in which the results obtained from the proposed methods are compared with other existing moment methods and a Monte Carlo simulation (MCS) method. The results of the examples show that the proposed ECUT-6 and ECUT-8 methods have fairly high accuracy and efficiency for structural reliability analysis. [ABSTRACT FROM AUTHOR]

Invoking predictive approach to estimation and making use of variable transformation, we have, in this paper, proposed a class of ratio-type and product-type estimators for estimating the finite population mean. The proposed ratiotype and product-type estimators of order k are found to be more efficient than the competing estimators when is chosen optimally. The efficacy of the suggested estimators has been demonstrated through numerical examples. [ABSTRACT FROM AUTHOR]

The singularity cancellation transformations are well-known for calculating weakly singular and near-singular integrals in integral equation solutions. However, some singularity cancellation methods suffer from the shape-dependent problem of inaccuracy and inefficiency for deformed triangles. By the theoretical analysis and numerical verification in this article, the relation between the near-singularity and shape-dependence of the singularity cancellation schemes is discussed. Moreover, a novel framework for devising cancellation transformations of weakly near-singular integrals is presented, which results in fast convergence for both regular and irregular triangular domains. Some numerical results are given to illustrate the validity of the theoretical framework and the efficiency of the proposed transformations. [ABSTRACT FROM AUTHOR]

Abstract The widely used “Maxent” software for modeling species distributions from presence‐only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high‐predictive performance but low‐ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models—that is, models which are more complex but not necessarily predictively better—than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence–environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence‐only data and logistic regression (GLM) for presence–absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.

This paper develops a new method for the design of a recursive allpass phase-compensating system (PCS) with an arbitrarily prescribed stability margin (SM). The design methodology comes from the generalised stability-triangle (GST) that defines a closed stability region for the second-order system, and the closed stability region is described by a pair of parameterised inequalities. Since the GST is developed for the second-order recursive system, to design a high-order PCS, we first construct a high-order PCS using the cascade of a set of second-order systems (sections), and then apply the GST to each of the second-order sections. The two coefficients of each second-order section are first transformed into two new variables by employing a coefficient-transformation technique, and then all the new variables are optimised in such a way that a given phase specification is best approximated. Thanks to both the coefficient transformations and the general stability condition from the GST, the resulting high-order PCS is not only stable, but also satisfies a prescribed SM design specification. This paper includes two examples to illustrate the usefulness of the GST-based PCS design. [ABSTRACT FROM AUTHOR]

Standard QTL mapping procedures seek to identify genetic loci affecting the phenotypic mean while assuming that all individuals have the same residual variance. But when the residual variance differs systematically between groups, perhaps due to a genetic or environmental factor, such standard procedures can falter: in testing for QTL associations, they attribute too much weight to observations that are noisy and too little to those that are precise, resulting in reduced power and and increased susceptibility to false positives. The negative effects of such “background variance heterogeneity” (BVH) on standard QTL mapping have received little attention until now, although the subject is closely related to work on the detection of variance-controlling genes. Here we use simulation to examine how BVH affects power and false positive rate for detecting QTL affecting the mean (mQTL), the variance (vQTL), or both (mvQTL). We compare linear regression for mQTL and Levene’s test for vQTL, with tests more recently developed, including tests based on the double generalized linear model (DGLM), which can model BVH explicitly. We show that, when used in conjunction with a suitable permutation procedure, the DGLM-based tests accurately control false positive rate and are more powerful than the other tests. We also find that some adverse effects of BVH can be mitigated by applying a rank inverse normal transform. We apply our novel approach, which we term “mean-variance QTL mapping”, to publicly available data on a mouse backcross and, after accommodating BVH driven by sire, detect a new mQTL for bodyweight.

It is well-known that the trapezoidal formula is an accurate quadrature formula for the infinite integral of exponentially decaying functions. Even for the semi-infinite integral of exponentially decaying functions, the trapezoidal formula works accurately combined with a conformal map proposed by Lund. Another conformal map for this case was introduced by Muhammad and Mori, and their map worked better in all the numerical comparisons conducted so far. Therefore, the conformal map by Muhammad and Mori is expected to give better results in all instances than that by Lund. This paper proves the conjecture. [ABSTRACT FROM AUTHOR]

This paper discusses joint rectangular chance or probabilistic constrained geometric programs. We present a new reformulation of the joint rectangular chance constrained geometric programs where the random parameters are elliptically distributed and pairwise independent. As this reformulation is not convex, we propose new convex approximations based on the variable transformation together with piecewise linear approximation methods. For the latter, we provide a theoretical bound for the number of segments in the worst case. Our numerical results show that our approximations are asymptotically tight. [ABSTRACT FROM AUTHOR]

The widely used "Maxent" software for modeling species distributions from presence‐only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high‐predictive performance but low‐ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models—that is, models which are more complex but not necessarily predictively better—than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence–environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence‐only data and logistic regression (GLM) for presence–absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem. [ABSTRACT FROM AUTHOR]

In this paper, we propose a variable transformation way and obtain the optimal stop-loss reinsurance under value at risk (VaR) and conditional tail expectation (CTE) criteria, respectively. Let X be the initial loss of an insurer with cumulative distribution function FX(x)=P(X≤x) and survival function SX(x)=1-FX(x). Denote a transformation variable Y=-ln(SX(X)). Firstly, we analyze properties of the variables X and Y. Then, under VaR- and CTE-optimization criteria, we provide the necessary and sufficient conditions for the optimal retention existence of Y, respectively. Further, the optimal retention of X is obtained. Some examples are given to illustrate these results. [ABSTRACT FROM AUTHOR]

We discuss a few principles to guide the design of efficient Metropolis- Hastings proposals for well-behaved target distributions without deeply divided modes. We illustrate them by developing and evaluating novel proposal kernels using a variety of target distributions. Here, efficiency is measured by the variance ratio relative to the independent sampler. The first principle is to introduce negative correlation in the MCMC sample or to reduce positive correlation: to propose something new, propose something different. This explains why singlemoded proposals such as the Gaussian random-walk is poorer than the uniform random walk, which is in turn poorer than the bimodal proposals that avoid values very close to the current value. We evaluate three new bimodal proposals called Box, Airplane and StrawHat, and find that they have similar performance to the earlier Bactrian kernels, suggesting that the general shape of the proposal matters, but not the specific distributional form. We propose the "Mirror" kernel, which generates new values around the mirror image of the current value on the other side of the target distribution (effectively the "opposite" of the current value). This introduces negative correlations, leading in many cases to efficiency of > 100%. The second principle, applicable to multidimensional targets, is that a sequence of well-designed one-dimensional proposals can be more efficient than a single d-dimensional proposal. Thirdly, we suggest that variable transformation be explored as a general strategy for designing efficient MCMC kernels. We apply these principles to a high-dimensional Gaussian target with strong correlations, a logistic regression problem and a molecular clock dating problem to illustrate their practical utility. [ABSTRACT FROM AUTHOR]

In this paper, the two-dimensional steady-state anisotropic field problems are transformed into the Laplace equation by direct domain mapping, and then the Sidi-Israeli quadrature method is applied to solve the weakly singular boundary integral equation of the Laplace equation. Especially, the kress's variable transformation is used for the polygon case in order to improve the accuracy by smoothing the singularities of the exact solution at the corner points of the boundary. The convergence and error analysis of numerical solutions are given by use of collective compact theory. At last, numerical examples are tested and results verify the theoretical analysis. [ABSTRACT FROM AUTHOR]