Working memory processes are important for analytic problem-solving; however, their role in multiply-constrained problem-solving is currently debated. This study explored individual differences in working memory and successful completion of analytic and multiply-constrained problem-solving by having participants solve algebra and compound remote associate (CRAT) problems of varying difficulty under low and high memory demand conditions. Working memory was predictive of both algebra and multiply-constrained problem-solving. Specifically, participants with high working memory solved more problems than those with low working. Memory load did not differentially affect performance for low and high working memory participants. However, for multiply-constrained problem-solving the effect of item difficulty was more detrimental for high-span participants than low-span participants. Together, these findings suggest that working memory processes are important for both types of problem-solving and that participants with low working memory capacity may need to offload internal memory demands onto the environment to efficiently solve problems.
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http://dx.doi.org/10.1177/1747021820909703 | DOI Listing |
Pilot Feasibility Stud
January 2025
Department of Health Service & Population Research, David Goldberg Centre, King's College London, Denmark Hill, London, UK.
Background: Mental health disorders are one of the leading causes of illness globally. The importance of psychosocial skills acquired in early childhood, such as executive functions, inhibitory control, emotional regulation, and social problem-solving, in preventing mental disorders has been reported. Furthermore, mental health care delivery is evolving, and mobile technology is becoming the medium for assessment and intervention.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Software, Faculty of Artificial Intelligence and Software, Gachon University, Seongnam-si, 13120, Republic of Korea.
Network security is crucial in today's digital world, since there are multiple ongoing threats to sensitive data and vital infrastructure. The aim of this study to improve network security by combining methods for instruction detection from machine learning (ML) and deep learning (DL). Attackers have tried to breach security systems by accessing networks and obtaining sensitive information.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Neurology, St. Josef-Hospital, Ruhr-University Bochum, Gudrunstr. 56, 44791, Bochum, Germany.
A substantial proportion of patients suffer from Post-COVID Syndrome (PCS) with fatigue and impairment of memory and concentration being the most important symptoms. We here set out to perform in-depth neuropsychological assessment of PCS patients referred to the Neurologic PCS clinic compared to patients without sequelae after COVID-19 (non-PCS) and healthy controls (HC) to decipher the most prevalent cognitive deficits. We included n = 60 PCS patients with neurologic symptoms, n = 15 non-PCS patients and n = 15 healthy controls.
View Article and Find Full Text PDFComput Biol Med
January 2025
Thai Nguyen University of Information and Communication Technology, Thai Nguyen City, Viet Nam. Electronic address:
Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regulatory role in cellular processes. Dysregulated succinylation has been implicated in the onset and progression of various diseases, including liver, cardiac, pulmonary, and neurological disorders. However, identifying succinylation sites through experimental methods is often labor-intensive, costly, and technically challenging.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of Management, Xi'an University of Architecture and Technology, Xi'an, 710055, China.
Accurately predicting carbon prices is crucial for effective government decision-making and maintenance the stable operation of carbon markets. However, the instability and nonlinearity of carbon prices, driven by the complex interaction between economic, environmental, and political factors, often result in inaccurate predictions. To confront this challenge, this paper proposed a carbon price prediction model that integrates dual decomposition integration and error correction.
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