Objectives: With the rapid growth of the older population worldwide, understanding how older adults with mild cognitive impairment (MCI) use memory strategies to mitigate cognitive decline is important. This study investigates differences between amnestic and nonamnestic MCI subtypes in memory strategy use in daily life, and how factors associated with cognition, general health, and psychological well-being might relate to strategy use.
Methods: One hundred forty-eight participants with MCI (mean age = 67.9 years, SD = 8.9) completed comprehensive neuropsychological, medical, and psychological assessments, and the self-report 'Memory Compensation Questionnaire'. Correlational and linear regression analyses were used to explore relationships between memory strategy use and cognition, general health, and psychological well-being.
Results: Memory strategy use does not differ between MCI subtypes (p > .007) despite higher subjective everyday memory complaints in those with amnestic MCI (p = .03). The most marked finding showed that increased reliance-type strategy use was significantly correlated with more subjective memory complaints and poorer verbal learning and memory (p < .01) in individuals with MCI. Moreover, fewer subjective memory complaints and better working memory significantly predicted (p < .05) less reliance strategy use, respectively, accounting for 10.6% and 5.3% of the variance in the model.
Conclusions: In general, the type of strategy use in older adults with MCI is related to cognitive functioning. By examining an individual's profile of cognitive dysfunction, a clinician can provide more personalized clinical recommendations regarding strategy use to individuals with MCI, with the aim of maintaining their day-to-day functioning and self-efficacy in daily life.
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http://dx.doi.org/10.1017/S1355617719000912 | DOI Listing |
Sci Rep
December 2024
College of Sciences, National University of Defense Technology, 410073, Changsha, China.
Deep Convolutional Neural Networks (DCNNs), due to their high computational and memory requirements, face significant challenges in deployment on resource-constrained devices. Network Pruning, an essential model compression technique, contributes to enabling the efficient deployment of DCNNs on such devices. Compared to traditional rule-based pruning methods, Reinforcement Learning(RL)-based automatic pruning often yields more effective pruning strategies through its ability to learn and adapt.
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December 2024
Civil and Environmental Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, 31261, Saudi Arabia.
Air pollution monitoring and modeling are the most important focus of climate and environment decision-making organizations. The development of new methods for air quality prediction is one of the best strategies for understanding weather contamination. In this research, different air quality parameters were forecasted, including Carbon Monoxide (CO), Nitrogen Monoxide (NO), Nitrogen Dioxide (NO), Ozone (O), Sulphur Dioxide (SO), Fine Particles Matter (PM), Coarse Particles Matter (PM), and Ammonia (NH).
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October 2024
Dr. Van Hoof: Associate Professor, University of Connecticut School of Nursing, Storrs, and Department of Community Medicine and Health Care, University of Connecticut School of Medicine, Farmington, CT.
The science of learning (learning science) is an interprofessional field that concerns itself with how the brain learns and remembers important information. Learning science has compiled a set of evidence-based strategies, such as distributed practice, retrieval practice, and interleaving, which are quite relevant to continuing professional development. Spreading out study and practice separated by cognitive breaks (distributed practice), testing oneself to check mastery and memory of previously learned information (retrieval practice), and mixing the learning of separate but associated information (interleaving) represent strategies that are underutilized in continuing professional development.
View Article and Find Full Text PDFNeural Netw
December 2024
Communications and Signal Processing Group, Department of Electrical and Electronic Engineering, Imperial College London, London, SW7 2AZ, United Kingdom.
First spike timings are crucial for decision-making in spiking neural networks (SNNs). A recently introduced first-spike (FS) coding method demonstrates comparable accuracy to firing-rate (FR) coding in processing complex temporal information through supervised learning. However, its performance still falls behind advanced approaches.
View Article and Find Full Text PDFJ Funct Biomater
December 2024
Department of Surgery, Division of Ophthalmology, University of Sherbrooke, Sherbrooke, QC J1G 2E8, Canada.
Intraocular lenses (IOLs) play a pivotal role in restoring vision following cataract surgery. The evolution of polymeric biomaterials has been central to addressing challenges such as biocompatibility, optical clarity, mechanical stability, and resistance to opacification. This review explores essential requirements for IOL biomaterials, emphasizing their ability to mitigate complications like posterior capsule opacification (PCO) and dysphotopsias while maintaining long-term durability and visual quality.
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