Explore the vast range of titles available.

This paper presents a compact and efficient 88-line MATLAB code for the parameterized level set method based topology optimization using radial basis functions (RBFs), which is applied to minimize the compliance of a two-dimensional linear elastic structure. This parameterized level set method using radial basis functions can maintain a relatively smooth level set function with an approximate re-initialization scheme during the optimization process. It also has less dependency on initial designs due to its capability in nucleation of new holes inside the material domain. The MATLAB code and simple modifications are explained in detail with numerical examples. The 88-line code included in the appendix is intended for educational purposes.

To improve the accuracy and reliability of coal quality assessment via near-infrared spectroscopy, this study proposes a multi-method analysis framework for robust spectral feature selection. A core challenge is reconciling the trade-offs between different analytical approaches: statistical methods often yield smooth but diffuse results, while machine learning models can identify sharp, localized features that may lack stability. Our framework addresses this by integrating diverse analytical perspectives, including statistical correlations, SHAP-interpreted machine learning models, and latent-variable regression. We then introduce a novel fusion strategy that synthesizes the importance profiles from these methods based on inter-method consistency, curve smoothness, and local concentration. Experimental results demonstrate this fusion yields more interpretable and physicochemically coherent wavelength importance profiles for both Moisture (Mad) and Volatile Matter (Vad). The selected features consistently achieve superior prediction performance across various regression models, showing particular robustness with limited training data. This work offers a structured methodology for identifying compact and informative spectral features, facilitating the development of efficient models for online monitoring and contributing to improved process control.

We prove that the sequence spaces ℓ p ⊕ ℓ q and the spaces of infinite matrices ℓ p ( ℓ q ) , ℓ q ( ℓ p ) and ( ⨁ n = 1 ∞ ℓ p n ) ℓ q , which are isomorphic to certain Besov spaces, have an almost greedy basis whenever 0 < p < 1 < q < ∞ . More precisely, we custom-build almost greedy bases in such a way that the Lebesgue parameters grow in a prescribed manner. Our arguments critically depend on the extension of the Dilworth–Kalton–Kutzarova method from Dilworth et al. (Stud Math 159(1):67–101, 2003), which was originally designed for constructing almost greedy bases in Banach spaces, to make it valid for direct sums of mixed-normed spaces with nonlocally convex components. Additionally, we prove that the fundamental functions of all almost greedy bases of these spaces grow as ( m 1 / q ) m = 1 ∞ .

Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD), the Radial Basis Function Neural Network (RBF) and the Least Square Support Vector Machine (SVM), an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM) is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM), the EMDRBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.

In this paper, we consider the quadratic eigenvalue problem (QEP) expressed in various commonly used bases, including Taylor, Newton, and Lagrange bases. We propose to investigate the backward errors of the computed eigenpairs and condition numbers of eigenvalues for QEP solved by a class of block Kronecker linearizations. To improve the backward error and condition number of the QEP expressed in a non-monomial basis, we combine the tropical scaling with the block Kronecker linearization. We then establish upper bounds for the backward error of an approximate eigenpair of the QEP relative to the backward error of an approximate eigenpair of the block Kronecker linearization with and without tropical scaling. Moreover, we get bounds for the normwise condition number of an eigenvalue of the QEP relative to that of the block Kronecker linearization. Our investigation is accompanied by adequate numerical experiments to justify our theoretical findings.

Stock market is considered chaotic, complex, volatile and dynamic. Undoubtedly, its prediction is one of the most challenging tasks in time series forecasting. Moreover existing Artificial Neural Network (ANN) approaches fail to provide encouraging results. Meanwhile advances in machine learning have presented favourable results for speech recognition, image classification and language processing. Methods applied in digital signal processing can be applied to stock data as both are time series. Similarly, learning outcome of this paper can be applied to speech time series data. Deep learning for stock prediction has been introduced in this paper and its performance is evaluated on Google stock price multimedia data (chart) from NASDAQ. The objective of this paper is to demonstrate that deep learning can improve stock market forecasting accuracy. For this, (2D) 2 PCA + Deep Neural Network (DNN) method is compared with state of the art method 2-Directional 2-Dimensional Principal Component Analysis (2D) 2 PCA + Radial Basis Function Neural Network (RBFNN). It is found that the proposed method is performing better than the existing method RBFNN with an improved accuracy of 4.8% for Hit Rate with a window size of 20. Also the results of the proposed model are compared with the Recurrent Neural Network (RNN) and it is found that the accuracy for Hit Rate is improved by 15.6%. The correlation coefficient between the actual and predicted return for DNN is 17.1% more than RBFNN and it is 43.4% better than RNN.

Objective: To identify existing gaps in the adoption of full accrual accounting in the public sector, with a focus on analyzing the financial statements of the Ministry of Defense (MD), and to investigate the application of accrual accounting in the public sector, considering its complex regulations.   Theoretical Framework: For the analyses, the theoretical foundation is based on the theory of regulation applicable to accounting regimes in the public sector.   Methodology: The research, using the Ministry of Defense (MD) as a case study, adopted a multifaceted methodological approach, including a case study, literature review, and documentary analysis. Data sources included books, dissertations, scientific articles, and legislation. Documentary analysis focused on the MD's Financial Statements, accessed through the government portal, covering the years 2018 to 2022. The research also employed content analysis techniques, following the methodology suggested by Sampaio and Lycarião (2021), to evaluate the content of the information in relation to the established objectives.   Results and Conclusions: The research identified that there is already partial adherence of the public sector to international standards, as the cash basis is still observed for revenues, and areas for improvement were noted, such as the adoption of the Statement of Comprehensive Income in the context of the public sector.   Research Implications: It was observed that regulatory changes are important instruments for influencing the accountability processes of the public sector.   Originality/Value: The research emphasizes the partial adoption of international public accounting standards and suggests further discussions and analysis to achieve complete adherence, confirming a gap between the implementation of standards and their practical application.

In this article, the authors simulate and study dark and bright soliton solutions of 1D and 2D regularized long wave (RLW) models. The RLW model occurred in various fields such as shallow-water waves, plasma drift waves, longitudinal dispersive waves in elastic rods, rotating flow down a tube, and the anharmonic lattice and pressure waves in liquid–gas bubble mixtures. First of all, the tanh–coth method is applied to obtain the soliton solutions of RLW equations, and thereafter, the approximation of finite domain interval is done by truncating the infinite domain interval. For computational modeling of the problems, a meshfree method based on local radial basis functions and differential quadrature technique is developed. The meshfree method converts the RLW model into a system of nonlinear ordinary differential equations (ODEs), then the obtained system of ODEs is simulated by the Runge–Kutta method. Further, the stability of the proposed method is discussed by the matrix technique. Finally, in numerical experiments, some problems are considered to check the competence and chastity of the developed method.

Bayesian inversion offers a valuable means of estimating uncertainty, allowing us to evaluate the impact of inversion. However, tackling Bayesian inversion in high-dimensional spaces remains a crucial area of research. Building upon Hawkins’ work, we have developed a tree-based Bayesian inversion scheme specifically designed to address the challenges posed by the magnetotellurics inversion problem. By employing the cdf9/7 wavelet as our basis function, we conducted a numerical simulation of a low-resistance abnormal body, yielding highly accurate inversion results.

The formation of suitable cycle bases corresponding to sparse flexibility matrices for the force method of frame analysis has always been an interesting problem in structural mechanics. These cycle bases are needed for the formation of static bases for efficient force method of structural analysis. Similarly, such bases are required in the mesh analysis of other networks. This paper reviews methods for the cycle basis selection by utilizing different embeddings on higher dimensional topological spaces, and using the ideas and concept from this study, graph theory algorithms are developed for efficient computational algorithms for the formation of subminimal, and minimal cycle bases.