A recently developed FORTRAN program computing far-field optical observables for spherical particles in an absorbing medium has exhibited numerical instability arising when the product of the particle vacuum size parameter and the imaginary part of the refractive index of the host becomes sufficiently large. We offer a simple analytical explanation of this instability and propose a compact numerical algorithm for the stable computation of Lorenz-Mie coefficients based on an upward recursion formula for spherical Hankel functions of a complex argument. Extensive tests confirm an excellent accuracy of this algorithm approaching machine precision. The improved public-domain FORTRAN program is available at https://www.giss.nasa.gov/staff/mmishchenko/Lorenz-Mie.html.
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http://dx.doi.org/10.1016/j.jqsrt.2018.05.034 | DOI Listing |
PLoS One
January 2025
School of Business Management, Zhejiang Financial College, Hangzhou, Zhejiang, China.
This paper investigates optimal ordering strategies in supply chains under two-level price fluctuations and initial profit allocation. By utilizing Copula functions to model the complex relationship between fluctuating prices and uncertain demand, the study develops both continuous and discrete decision models for practical applications. A discrete algorithm is proposed to approximate the optimal solution, with its convergence rigorously proven.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Mathematics and Engineering Physics, Faculty of Engineering, Mansoura University, Mansoura, Egypt.
This paper focuses on modeling Resistor-Inductor (RL) electric circuits using a fractional Riccati initial value problem (IVP) framework. Conventional models frequently neglect the complex dynamics and memory effects intrinsic to actual RL circuits. This study aims to develop a more precise representation using a fractional-order Riccati model.
View Article and Find Full Text PDFJ Med Syst
January 2025
Department of Computing, University of North Florida, 1 UNF Dr., Jacksonville, 32246, FL, USA.
The "no-show" problem in healthcare refers to the prevalent phenomenon where patients schedule appointments with healthcare providers but fail to attend them without prior cancellation or rescheduling. In addressing this issue, our study delves into a multivariate analysis over a five-year period involving 21,969 patients. Our study introduces a predictive model framework that offers a holistic approach to managing the no-show problem in healthcare, incorporating elements into the objective function that address not only the accurate prediction of no-shows but also the management of service capacity, overbooking, and idle resource allocation resulting from mispredictions.
View Article and Find Full Text PDFAging Clin Exp Res
January 2025
Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, 710054, Shaanxi, China.
Objective: This study aims to analyze adverse drug events (ADE) related to romosozumab from the second quarter of 2019 to the third quarter of 2023 from FAERS database.
Methods: The ADE data related to romosozumab from 2019 Q2 to 2023 Q3 were collected. After data normalization, four signal strength quantification algorithms were used: ROR (Reporting Odds Ratios), PRR (Proportional Reporting Ratios), BCPNN (Bayesian Confidence Propagation Neural Network), and EBGM (Empirical Bayesian Geometric Mean).
iScience
January 2025
Department of Artificial Intelligence, Hanyang University, Seoul 04763, South Korea.
We present a Fourier neural operator (FNO)-based surrogate solver for the efficient optimization of wavefronts in tunable metasurface controls. Existing methods, including the Gerchberg-Saxton algorithm and the adjoint optimization, are often computationally demanding due to their iterative processes, which require numerical simulations at each step. Our surrogate solver overcomes this limitation by providing highly accurate gradient estimations with respect to changes in tunable meta-atoms without the need for direct simulations.
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