Although it is increasingly recognized that evaluation is a key phase for a two-fold creativity model, the neural model is not yet well understood. To this end, we constructed a theoretical model of creative evaluation and supported it with neural evidence through event-related potentials (ERPs) technology during a creative advertising task. Participants were required to evaluate the relationship between target words and advertising that systematically varied in novelty and usefulness. The ERPs results showed that (a) the novelty-usefulness and novelty-only conditions evoked a larger N1-P2 amplitude, reflecting an automatic attentional bias to novelty, and (b) these two novelty conditions elicited a larger N200-500 amplitude, reflecting an effort to process the novel content; (c) the novelty-usefulness and usefulness-only conditions induced a larger LPC amplitude, reflecting that valuable associations were formed through retrieval of relevant memories. These results propose a neural model of creative evaluation in advertising: the N1-P2, N200-500, and LPC should be the key indices to define three sub-processes of novelty perception, conception expansion, and value selection, respectively.
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http://dx.doi.org/10.1038/s41598-020-79044-0 | DOI Listing |
Sci Rep
January 2025
University of Ghana, P.O. Box 134, Legon-Accra, Ghana.
Sentiment analysis has become a difficult and important task in the current world. Because of several features of data, including abbreviations, length of tweet, and spelling error, there should be some other non-conventional methods to achieve the accurate results and overcome the current issue. In other words, because of those issues, conventional approaches cannot perform well and accomplish results with high efficiency.
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January 2025
Department of Mechanical Engineering, College of Engineering and Computer Sciences, Jazan University, P.O Box 45124, Jazan, Saudi Arabia.
Fluid flow across a Riga Plate is a specialized phenomenon studied in boundary layer flow and magnetohydrodynamic (MHD) applications. The Riga Plate is a magnetized surface used to manipulate boundary layer characteristics and control fluid flow properties. Understanding the behavior of fluid flow over a Riga Plate is critical in many applications, including aerodynamics, industrial, and heat transfer operations.
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January 2025
Cardiovascular Research Center, Rajaie Cardiovascular, Medical, and Research Center, University of Medical Sciences, Tehran, Iran.
Assessing myocardial viability is crucial for managing ischemic heart disease. While late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) is the gold standard for viability evaluation, it has limitations, including contraindications in patients with renal dysfunction and lengthy scan times. This study investigates the potential of non-contrast CMR techniques-feature tracking strain analysis and T1/T2 mapping-combined with machine learning (ML) models, as an alternative to LGE-CMR for myocardial viability assessment.
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January 2025
Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.
Objectives: To use finite element (FE) modeling and in vivo optical coherence tomography (OCT) imaging to explore the effect of ciliary muscle traction on optic nerve head (ONH) deformation during accommodation.
Methods: We developed a FE model to mimic the ciliary muscle traction during accommodation, and varied the stiffness of the sclera, choroid, Bruch's membrane (BM), prelaminar neural tissue and lamina cribrosa (LC) to assess their effects on accommodation-induced ONH strains. To validate the FE model, OCT images of the right eyes' ONHs from 20 subjects (25 ± 1.
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
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