Many changes occur in general and specific cognitive abilities in children between 5 and 7 years of age, the period coinciding with entrance into formal schooling. The current study focused on the relative contributions of approximate number system (ANS) acuity, mapping precision between numeral symbols and their corresponding magnitude (mapping precision) and working memory (WM) capacity to mathematics achievement in 5- and 7-year-olds. Children's performance was examined in different tasks: nonsymbolic number comparison, number line estimation, working memory, mathematics achievement, and vocabulary. This latter task was used to determine whether predictors were general or specific to mathematics achievement. The results showed that ANS acuity was a significant specific predictor of mathematics achievement only in 5-year-olds, mapping precision was a significant specific predictor at the two ages, and WM was a significant general predictor only in 7-year-olds. These findings suggest that a general cognitive ability, especially WM, becomes a stronger predictor of mathematics achievement after entrance into formal schooling, whereas ANS acuity, a specific cognitive ability, loses predictive power. Moreover, mediation analyses showed that mapping precision was a partial mediator of the relation between ANS acuity and mathematics achievement in 5-year-olds but not in 7-year-olds. Conversely, in 7-year-olds but not in 5-year-olds, WM fully mediated the relation between ANS acuity and mathematics achievement. These results showed that between 5 and 7 years of age, the period of transition into formal mathematical learning, important changes occurred in the relative weights of different predictors of mathematics achievement.
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http://dx.doi.org/10.1016/j.jecp.2018.09.013 | DOI Listing |
Electromagn Biol Med
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Department of Mathematics, University of Gour Banga, Malda, India.
In cardiovascular research, electromagnetic fields generated by Riga plates are utilized to study or manipulate blood flow dynamics, which is particularly crucial in developing treatments for conditions such as arterial plaque deposition and understanding blood behavior under varied flow conditions. This research predicts the flow patterns of blood enhanced with gold and maghemite nanoparticles (gold-maghemite/blood) in an electromagnetic microchannel influenced by Riga plates with a temperature gradient that decays exponentially, under sudden changes in pressure gradient. The flow modeling includes key physical influences like radiation heat emission and Darcy drag forces in porous media, with the flow mathematically represented through unsteady partial differential equations solved using the Laplace transform (LT) method.
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Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
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June 2025
Department of Biology, Faculty of Mathematics and Natural Sciences, Mulawarman University, Samarinda, 75242, Indonesia.
The use of eggshells as a primary source for developing value-added materials has garnered significant attention in recent years due to their effectiveness as an excellent adsorbent and support. In this study, the Solid-State Dispersion (SSD) method was utilized to prepare composite photocatalysts of eggshells (ES)/TiO₂ in various ratios. TiO₂ and eggshell photocatalysts were also employed as control samples.
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Engineering Laboratory, University of Cambridge, Cambridge, UK.
Molecular dynamics simulation is an important tool in computational materials science and chemistry, and in the past decade it has been revolutionized by machine learning. This rapid progress in machine learning interatomic potentials has produced a number of new architectures in just the past few years. Particularly notable among these are the atomic cluster expansion, which unified many of the earlier ideas around atom-density-based descriptors, and Neural Equivariant Interatomic Potentials (NequIP), a message-passing neural network with equivariant features that exhibited state-of-the-art accuracy at the time.
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Department of Surface and Plasma Science, Faculty of Mathematics and Physics, Charles University, V Holešovičkách 2, Prague 180 00, Czechia.
This work investigates the surface chemistry of the Ru/CeO catalyst under varying pretreatment conditions and during the oxidation of propane, focusing on both dry and humid environments. Our results show that the Ru/CeO catalyst calcined in O at 500 °C initiates propane oxidation at 200 °C, achieves high conversion rates above 400 °C, and demonstrates almost no change in activity in the presence of water vapor across the entire studied temperature range of 200-500 °C. Prereduction of the oxidized Ru/CeO catalyst in H significantly enhances its activity, though this enhancement diminishes at higher temperatures.
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