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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7447341 | PMC |
| http://dx.doi.org/10.22603/ssrr.2019-0077 | DOI Listing |
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Comparative analysis of Q-learning, SARSA, and deep Q-network for microgrid energy management.
Sci Rep
January 2025
Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Bengaluru, India.
The growing integration of renewable energy sources within microgrids necessitates innovative approaches to optimize energy management. While microgrids offer advantages in energy distribution, reliability, efficiency, and sustainability, the variable nature of renewable energy generation and fluctuating demand pose significant challenges for optimizing energy flow. This research presents a novel application of Reinforcement Learning (RL) algorithms-specifically Q-Learning, SARSA, and Deep Q-Network (DQN)-for optimal energy management in microgrids.
View Article and Find Full Text PDFUntapped Resources? Engaging Junior Faculty with Advanced Career Training in Near-Peer Mentoring to Build Professional Identity.
South Med J
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From the Department of Internal Medicine, Division of General Internal Medicine, Emory University School of Medicine, and Grady Memorial Hospital, Atlanta, Georgia.
Effective early mentoring is essential for propelling academic careers. There is a growing cohort of junior faculty with advanced skills in medical education and research; these individuals need both mentoring and the opportunity to take on mentorship roles. Generalists who pursue advanced training in medical education or clinical research often enter junior faculty positions alongside recent residency graduates, leaving their skillset untapped.
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PCM Consulting, Pathways Connectivity Maps Inc., Mountain View, CA, USA.
Background: High-throughput assays have attracted significant attention in Alzheimer's Disease (AD) research, especially for enabling rapid diagnostics screening for factors at the molecular level contributing to the disease recurrence. With advances in laboratory automation, there is a growing need for quality pre-clinical data. Assays such as Microarrays, Proteomics, or AI are all dependent on high-quality input data that serve as a starting point.
View Article and Find Full Text PDFClinical Manifestations.
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Imperial College London, London, United Kingdom.
Background: Cortical atrophy in Alzheimer's disease (AD) is typically thought to commence in the medial temporal lobe. However, growing evidence suggests that this may not be true for all patients. Here, we explored atrophy patterns in patients with mild cognitive impairment (MCI) and AD and whether atrophy patterns are associated with specific clinical and neuropathological outcomes.
View Article and Find Full Text PDFClinical Manifestations.
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Univ of PA TRL Room 2115, philadelphia, PA, USA.
Epidemiologic studies have revealed a relationship between chronic persistent short sleep and the risk of dementia, yet whether short sleep is an early symptom of dementia or a contributor to dementia in humans has not been resolved. A rapidly growing body of research in animal models, however, strongly supports the concept that chronic disruption of sleep can impart significant injury in the brain consistent with some of the important pathology evident in Alzheimer's disease (AD), the most common form of age-related dementia. Transgenic models of AD pathology develop an increased density of amyloid plaques in response to chronic sleep loss, and this response is independent of significant stress.
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