Results 41 to 50 of about 2,080 (215)
Machine learning for the classification of serial electron diffraction patterns: synthetic data
Acta Crystallographica Section A, EarlyView.Machine learning based sorting of synthetic serial electron diffraction patterns into 2D zonal patterns and patterns representing intersections of multiple Laue zones is demonstrated. The extracted zonal patterns can be used for the determination of unit‐cell parameters.Serial electron crystallography faces a fundamental challenge due to the flat Ewald
Tatiana E. Gorelik, Evgeny Gorelik
wiley +1 more source
Incomplete Bivariate Fibonacci and Lucas 𝑝-Polynomials
Discrete Dynamics in Nature and Society, 2012 We define the incomplete bivariate Fibonacci and Lucas 𝑝-polynomials. In the case 𝑥=1, 𝑦=1, we obtain the incomplete Fibonacci and Lucas 𝑝-numbers. If 𝑥=2, 𝑦=1, we have the incomplete Pell and Pell-Lucas 𝑝-numbers.
Dursun Tasci +2 more
doaj +1 more source
Electronic Research Archive, 2022 Let $ \alpha $ be the golden ratio, $ m\in \mathbb N $, and $ B(\alpha^m) $ the Beatty sequence (or Beatty set) generated by $ \alpha^m $. In this article, we give some combinatorial structures of $ B(\alpha^m) $ and use them in the study of associated ...
Prapanpong Pongsriiam
doaj +1 more source
On Mixed Concatenations of Fibonacci and Lucas Numbers Which are Fibonacci Numbers
, 2022 Let $(F_n)_{n\geq 0}$ and $(L_n)_{n\geq 0}$ be the Fibonacci and Lucas sequences, respectively. In this paper we determine all Fibonacci numbers which are mixed concatenations of a Fibonacci and a Lucas numbers. By mixed concatenations of $ a $ and $ b $, we mean the both concatenations $\overline{ab}$ and $\overline{ba}$ together, where $ a $ and $ b $
Altassan, Alaa, Alan, Murat
openaire +2 more sources
Intelligent Systems in Accounting, Finance and Management, Volume 32, Issue 3, September 2025.ABSTRACT The development of an in‐house accounting bot—an artificial intelligence (AI) assistant capable of generating internally structured bookkeeping double‐entry posting schemes—is explored in this paper. The processes of curating a suitable dataset, selecting, and fine‐tuning a seven‐billion‐parameter language model, categorized as a small ...
Mario Zupan
wiley +1 more source
Complete k-ary trees and generalized meta-Fibonacci sequences [PDF]
Discrete Mathematics & Theoretical Computer Science, 2006 We show that a family of generalized meta-Fibonacci sequences arise when counting the number of leaves at the largest level in certain infinite sequences of k-ary trees and restricted compositions of an integer.
Chris Deugau, Frank Ruskey
doaj +1 more source
Restricted Permutations, Fibonacci Numbers, and k-generalized Fibonacci Numbers
, 2002 A permutation $ \in S_n$ is said to {\it avoid} a permutation $ \in S_k$ whenever $ $ contains no subsequence with all of the same pairwise comparisons as $ $. For any set $R$ of permutations, we write $S_n(R)$ to denote the set of permutations in $S_n$ which avoid every permutation in $R$. In 1985 Simion and Schmidt showed that $|S_n(132, 213, 123)
Egge, Eric S., Mansour, Toufik
openaire +3 more sources
Mathematical Methods in the Applied Sciences, Volume 48, Issue 13, Page 12654-12674, September 2025.ABSTRACT We have studied possible applications of a particular pseudodifferential algebra in singular analysis for the construction of fundamental solutions and Green's functions of a certain class of elliptic partial differential operators. The pseudodifferential algebra considered in the present work, comprises degenerate partial differential ...
Heinz‐Jürgen Flad +1 more
wiley +1 more source
Fibonacci factoriangular numbers
Indagationes Mathematicae, 2017 Abstract Let ( F m ) m ≥ 0 be the Fibonacci sequence given by F 0 = 0 , F 1 = 1 and F m + 2 = F m + 1 + F m , for all m ≥ 0 . In Castillo (2015), it is conjectured that 2 , 5 and 34 are the only Fibonacci numbers of the form n ! + n
Florian Luca +2 more
openaire +3 more sources
AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice
Annals of Neurology, Volume 98, Issue 2, Page 211-230, August 2025.Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This review, part 1 of a 3‐part series, provides neurologists and neuroscientists with a foundational understanding of AI's key concepts, terminology, and applications.
Matthew Rizzo, Jeffrey D. Dawson
wiley +1 more source