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Academic Articles & Research Papers
Showing 20 of 131 academic articles
Yeon-Moon Choo, Ji-Min Kim, Ik-Tae An • Entropy
(2021)
Since the 1960s, many rivers have been destroyed as a consequence of the process of rapid urbanization. As accurate figures are important to repair rivers, there have been many research reports on methods to obtain the exact river slope and...
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Open Access (DOAJ)
DOI: 10.3390/e23080965
Luis A. Anchordoqui, Ignatios Antoniadis, Dieter Lüst • Physics Letters B
(2024)
Over the last few years, low- and high-redshift observations set off tensions in the measurement of the present-day expansion rate H0 and in the determination of the amplitude of the matter clustering in the late Universe (parameterized by ...
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Open Access (DOAJ)
DOI: 10.1016/j.physletb.2024.138775
Marcin Wieśniak • Entropy
(2025)
The measurement problem in quantum mechanics (QM) is related to the inability to include learning about the properties of a quantum system by an agent in the formalism of quantum theory. It includes questions about the physical processes be...
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Open Access (DOAJ)
DOI: 10.3390/e27030248
Jesús Poza, María García, Javier Gomez-Pilar • Entropy
(2021)
A thorough and comprehensive understanding of the human brain ultimately depends on knowledge of large-scale brain organization[...]...
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Open Access (DOAJ)
DOI: 10.3390/e23091157
Raúl Toral • Entropy
(2009)
The volume of the body enclosed by the n-dimensional Lamé curve defined by Ʃni=1 xbi = E is computed....
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Open Access (DOAJ)
DOI: 10.3390/e11041121
J. Javier Brey, Vicente Buzón, Maria Isabel García de Soria, Pablo Maynar • Entropy
(2017)
The dynamics of a system of hard spheres enclosed between two parallel plates separated a distance smaller than two particle diameters is described at the level of kinetic theory. The interest focuses on the behavior of the quasi-two-dimens...
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Open Access (DOAJ)
DOI: 10.3390/e19020068
Andrea Murari, Michele Lungaroni, Emmanuele Peluso, Pasquale Gaudio et al. • Entropy
(2018)
Understanding the details of the correlation between time series is an essential step on the route to assessing the causal relation between systems. Traditional statistical indicators, such as the Pearson correlation coefficient and the mut...
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Open Access (DOAJ)
DOI: 10.3390/e20090627
Matteo Fraschini, Simone Maurizio La Cava, Luca Didaci, Luigi Barberini • Entropy
(2020)
The idea of estimating the statistical interdependence among (interacting) brain regions has motivated numerous researchers to investigate how the resulting connectivity patterns and networks may organize themselves under any conceivable sc...
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Open Access (DOAJ)
DOI: 10.3390/e23010005
Vikram S. Vijayaraghavan, Ryan G. James, James P. Crutchfield • Entropy
(2017)
Collective organization in matter plays a significant role in its expressed physical properties. Typically, it is detected via an order parameter, appropriately defined for each given system’s observed emergent patterns. Recent developments...
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Open Access (DOAJ)
DOI: 10.3390/e19050214
Yijie Lv, Jiguang He, Weikai Xu, Lin Wang • Entropy
(2023)
In this article, a graph-theoretic method (taking advantage of constraints among sets associated with the corresponding parity-check matrices) is applied for the construction of a double low-density parity-check (D-LDPC) code (also known as...
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Open Access (DOAJ)
DOI: 10.3390/e25081189
Manik Awale, Shrirang Pund, Hassan S. Bakouch, Aishwarya Ghodake et al. • Entropy
(2026)
In this article, we propose a first-order integer-valued autoregressive (INAR(1)) model based on binomial thinning, in which the innovation sequence follows a zero–one–two-modified Poisson (ZOTMPo) distribution. The proposed model accommoda...
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Open Access (DOAJ)
DOI: 10.3390/e28080889
Kaiwen Shi, Haiyan Su, Xinlong Feng • Entropy
(2022)
In this article, we mainly consider a first order penalty finite element method (PFEM) for the 2D/3D unsteady incompressible magnetohydrodynamic (MHD) equations. The penalty method applies a penalty term to relax the constraint “∇·u=0”, whi...
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Open Access (DOAJ)
DOI: 10.3390/e24101395
Bob Rehder • Entropy
(2025)
A desirable property of any theory of causal reasoning is to explain not only why people make causal reasoning errors but also when they make them. The mutation sampler is a rational process model of human causal reasoning that yields norma...
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Open Access (DOAJ)
DOI: 10.3390/e27060548
Ronald Stevens, Trysha Galloway, Donald Halpin, Ann Willemsen-Dunlap • Entropy
(2016)
Research on the microscale neural dynamics of social interactions has yet to be translated into improvements in the assembly, training and evaluation of teams. This is partially due to the scale of neural involvements in team activities, sp...
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Open Access (DOAJ)
DOI: 10.3390/e18120427
Andrea Scarciglia, Vincenzo Catrambone, Martina Bianco, Claudio Bonanno et al. • NeuroImage
(2024)
Functional magnetic resonance imaging (fMRI) is a powerful non-invasive method for studying brain function by analyzing blood oxygenation level-dependent (BOLD) signals. These signals arise from intricate interplays of deterministic and sto...
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Open Access (DOAJ)
DOI: 10.1016/j.neuroimage.2024.120562
Krai Cheamsawat, Thiparat Chotibut • Entropy
(2025)
Quantum reservoir computing (QRC) has emerged as a promising paradigm for harnessing near-term quantum devices to tackle temporal machine learning tasks. Yet, identifying the mechanisms that underlie enhanced performance remains challenging...
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Open Access (DOAJ)
DOI: 10.3390/e27010088
Jonathan Schrock, Alex J. McCaskey, Kathleen E. Hamilton, Travis S. Humble et al. • Entropy
(2017)
A content-addressable memory (CAM) stores key-value associations such that the key is recalled by providing its associated value. While CAM recall is traditionally performed using recurrent neural network models, we show how to solve this p...
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Open Access (DOAJ)
DOI: 10.3390/e19090500
Yukio-Pegio Gunji, Kyoko Nakamura, Kazuto Sasai, Iori Tani et al. • Entropy
(2026)
Artificial intelligence is typically formulated as an information-processing system composed of artificial neurons, where computation is understood as recursive operations connecting inputs and outputs. However, real neural systems are mate...
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Open Access (DOAJ)
DOI: 10.3390/e28050498
Jiawei Yong, Sihai Tang • Entropy
(2026)
Solving Bayesian inverse problems efficiently stands as a major bottleneck in scientific computing. Although Bayesian Physics-Informed Neural Networks (B-PINNs) have introduced a robust way to quantify uncertainty, the high-dimensional para...
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Open Access (DOAJ)
DOI: 10.3390/e28020156
Wenhui Luo, Olivia Chen, Nobuyuki Yoshikawa, Naoki Takeuchi • Scientific Reports
(2022)
Abstract Alternative computing such as stochastic computing and bio-inspired computing holds promise for overcoming the limitations of von Neumann computers. However, one difficulty in the implementation of such alternative computing is the...
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Open Access (DOAJ)
DOI: 10.1038/s41598-022-24230-5