Forgetting curves plot skill decay over time. After exposure to a simulation-based radiograph interpretation learning system, we determined the rate of learning decay and how this was impacted by testing (with and without feedback). Further, we examined the association of initial learning parameters on the forgetting curve. This was a multicenter, four-arm randomized control trial. Medical trainees completed 80 elbow radiographs and a 20-case post-test. Group 1 had no testing until 12 months; Groups 2-4 had testing every 2 months until 12 months. At 6 months, Group 3 testing was feedback-enhanced, while Group 4 had feedback-enhanced testing at 2, 6, and 10 months. There were 106 participants ( = 42 Group 1; = 22 Groups 2 and 3; = 20 Group 4). Group 1 showed an -8.1% learning decay at 12-months relative to other groups. In Groups 2, 3, and 4, there was no significant learning decay (+0.8%), and there were no differences in skill decay between these groups. Initial score and learning curve slope were predictive of retained skill. Learning decay was mitigated by exposure to 20 test cases (with and without feedback) every two months. Initial learning parameters predicted learning retention and may inform refresher education scheduling.
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http://dx.doi.org/10.1080/0142159X.2019.1570098 | DOI Listing |
PLoS One
January 2025
Department of Finance, Zhejiang University of Finance and Economics, Hangzhou, China.
This study explores the intricate dynamics of volatility within high-frequency financial markets, focusing on 225 of Chinese listed companies from 2016 to 2023. Utilizing 5-minute high-frequency data, we analyze the realized volatility of individual stocks across six distinct time scales: 5-minute, 10-minute, 30-minute, 1-hour, 2-hour, and 4-hour intervals. Our investigation reveals a consistent power law decay in the auto-correlation function of realized volatility across all time scales.
View Article and Find Full Text PDFSensors (Basel)
December 2024
Center for Precision Neutrino Research, Department of Physics, Chonnam National University, Gwangju 61186, Republic of Korea.
Reactor-emitted electron antineutrinos can be detected via the inverse beta decay reaction, which produces a characteristic signal: a two-fold coincidence between a prompt positron event and a delayed neutron capture event within a specific time frame. While liquid scintillators are widely used for detecting neutrinos reacting with matter, detection is difficult because of the low interaction of neutrinos. In particular, it is important to distinguish between neutron (n) and gamma (γ) signals.
View Article and Find Full Text PDFEnviron Pollut
January 2025
Department of Population Health Sciences, Duke University, Durham, NC 27708, United States; Duke Cancer Institute, Duke University, Durham, NC 27708, United States.
Radon is a naturally occurring radioactive gas derived from the decay of uranium in the Earth's crust. Radon exposure is the leading cause of lung cancer among non-smokers in the US. Radon infiltrates homes through soil and building foundations.
View Article and Find Full Text PDFHeliyon
December 2024
Xinxiang Medical University, Xinxiang, 453000, China.
This study proposes a public opinion monitoring model that combines the K-means clustering algorithm with Particle Swarm Optimization (PSO) to enhance the accuracy and effectiveness of public opinion monitoring on social media. The model's performance across various dissemination indicators is studied in detail. Through experiments conducted on social media datasets, the study comprehensively evaluates the model from four dimensions: dissemination speed, scope, depth, and sentiment dissemination effectiveness.
View Article and Find Full Text PDFCurr Med Imaging
January 2025
Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
Background: Pneumonia is an acute respiratory infection that has emerged as the predominant catalyst for escalating mortality rates worldwide. In the pursuit of the prevention and prediction of pneumonia, this work employs the development of an advanced deep-learning model by using a federated learning framework. The deep learning models rely on the utilization of a centralized system for disease prediction on the medical imaging data and pose risks of data breaches and exploitation; however, federated learning is a decentralized architecture which significantly reduces data privacy concerns.
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