A simple and easy to implement method for improving the convergence of a power series is presented. We observe that the most obvious or analytically convenient point about which to make a series expansion is not always the most computationally efficient. Series convergence can be dramatically improved by choosing the center of the series expansion to be at or near the average value at which the series is to be evaluated. For illustration, we apply this method to the well-known simple pendulum and to the Mexican hat type of potential. Large performance gains are demonstrated. While the method is not always the most computationally efficient on its own, it is effective, straightforward, quite general, and can be used in combination with other methods.
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http://dx.doi.org/10.1088/1361-6404/aad876 | DOI Listing |
Sci Rep
January 2025
Research and Development, Aesculap AG, Tuttlingen, Germany.
In clinical movement biomechanics, kinematic measurements are collected to characterise the motion of articulating joints and investigate how different factors influence movement patterns. Representative time-series signals are calculated to encapsulate (complex and multidimensional) kinematic datasets succinctly. Exacerbated by numerous difficulties to consistently define joint coordinate frames, the influence of local frame orientation and position on the characteristics of the resultant kinematic signals has been previously proven to be a major limitation.
View Article and Find Full Text PDFMethodsX
December 2024
Institute of Computer Science, University of Silesia, Bedzinska 39, Sosnowiec, 41-200, Poland.
This study introduces a family of root-solvers for systems of nonlinear equations, leveraging the Daftardar-Gejji and Jafari Decomposition Technique coupled with the midpoint quadrature rule. Despite the existing application of these root solvers to single-variable equations, their extension to systems of nonlinear equations marks a pioneering advancement. Through meticulous derivation, this work not only expands the utility of these root solvers but also presents a comprehensive analysis of their stability and semilocal convergence; two areas of study missing in the existing literature.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Respiratory and Critical Care Medicine, The First Medical Centre of Chinese PLA General Hospital, Haidian District, Beijing, China.
Moth Flame Optimization (MFO) is a swarm intelligence algorithm inspired by the nocturnal flight mode of moths, and it has been widely used in various fields due to its simple structure and high optimization efficiency. Nonetheless, a notable limitation is its susceptibility to local optimality because of the absence of a well-balanced exploitation and exploration phase. Hence, this paper introduces a novel enhanced MFO algorithm (BWEMFO) designed to improve algorithmic performance.
View Article and Find Full Text PDFPLoS One
January 2025
School of Economics and Trade, Guangzhou Xinhua University, Dongguan, China.
Stock price prediction is a challenging research domain. The long short-term memory neural network (LSTM) widely employed in stock price prediction due to its ability to address long-term dependence and transmission of historical time signals in time series data. However, manual tuning of LSTM parameters significantly impacts model performance.
View Article and Find Full Text PDFJ Korean Med Sci
January 2025
Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Korea.
Background: The coronavirus disease 2019 (COVID-19) pandemic has altered daily behavioral patterns based on government healthcare policies, including consumption and movement patterns. We aimed to examine the extent to which changes in the government's healthcare policy have affected people's lives, primarily focusing on changes in consumption and population movements.
Methods: We collected consumption data using weekly credit card transaction data from the Hana Card Corporation and population mobility data using mobile phone data from SK Telecom in Seoul, South Korea.
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