Objective: Educational offerings to fill the bioinformatics knowledge gap are a key component to enhancing access and use of health data from the All of Us Research Program. We developed a Train the Trainer-based, innovative training series including project-based learning, modular on-demand demonstrations, and unstructured tutorial time as a model for educational engagement in the All of Us community.
Materials And Methods: We highlight our training modules and content, with training survey data informing cycles of development in the creation of a 6-module training series with modular demonstrations.
Results: We have conducted 2 public iterations of the Train the Trainer (Tx3) Series based on survey feedback while training over 300 registered researchers to access and analyze data on the All of Us Researcher Workbench.
Discussion And Conclusion: Future directions of the Tx3 Series include enhanced focus on project-based learning and learner requests for modularity and asynchronous materials access.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11631107 | PMC |
http://dx.doi.org/10.1093/jamia/ocae226 | DOI Listing |
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January 2025
State Key Laboratory of Rice Biology and Breeding, China National Center for Rice Improvement, China National Rice Research Institute, Hangzhou 311400, China.
Rice ( L.) is a staple crop for nearly half of the global population and one of China's most extensively cultivated cereals. Heading date, a critical agronomic trait, determines the regional and seasonal adaptability of rice varieties.
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January 2025
Department of Architectural Engineering, Dankook University, 152 Jukjeon-ro, Yongin-si 16890, Republic of Korea.
In the construction industry, ensuring the proper installation, retention, and dismantling of temporary structures, such as jack supports, is critical to maintaining safety and project timelines. However, inconsistencies between on-site data and construction documentation remain a significant challenge. To address this, this study proposes an integrated monitoring framework that combines computer vision-based object detection and document recognition techniques.
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January 2025
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. The model consists of multiple parallel temporal correlation perceptual attention (TCPA) modules, enabling us to extract features at different timescales and identify dependencies among features across various timescales.
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January 2025
Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China.
Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. Using deep learning algorithms to establish prediction models for sensor data is an effective approach; however, the performance of these models is significantly influenced by the quantity and quality of the training data. In real production environments, the amount of time series data that can be collected during the manufacturing process is limited, which can lead to a decline in model performance.
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January 2025
College of Energy and Power Engineering, Xihua University, Chengdu 610039, China.
Artificial intelligence (AI) technologies have been widely applied to the automated detection of pipeline leaks. However, traditional AI methods still face significant challenges in effectively detecting the complete leak process. Furthermore, the deployment cost of such models has increased substantially due to the use of GPU-trained neural networks in recent years.
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