An electrical power grid, is an interconnected network for delivering electricity from producers to consumers. Electrical grids vary in size from covering a single building through national grids (which cover whole countries) to transnational grids (which can cross continents). As the rollout of smart meters continues worldwide, there are use-cases where common solutions fail and the network availability of certain meters is very low due to poor communication conditions. This paper proposes a data slicing model for large data files which have to travel securely and reliably throughout the Smart Grid. The manuscript addresses improvements for PRIME PLC network availability by using correct data slicing at the application level along a tuned transmission rate in accordance with the noise levels of the power grid. Successful communications, even at low rates, mean that no manual interaction from energy supplier operators is needed reducing the maintenance costs for both the energy companies as well as for the end user. Experiments on a low power electrical grid setup have been performed in order to evaluate availability improvements through the proposed method as well as the feasibility of remote firmware upgrades. The results have shown that the current approach has similar upgrade time results with a manual firmware upgrade performed through an optical probe. Moreover, the results show that the presented remote firmware upgrade method is reliable and practical.
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http://dx.doi.org/10.3390/s20247256 | DOI Listing |
Nat Commun
January 2025
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan.
Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects.
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January 2025
Divisions of Cardiac Surgery (H.T., A.Q., R.E., R.V., M.M., J.H.C., S.V.), Li Ka Shing Knowledge Institute, St. Michael's Hospital of Unity Health Toronto, Ontario, Canada.
The idea of self-organized signal processing in the cerebral cortex has become a focus of research since Beggs and Plentz reported avalanches in local field potential recordings from organotypic cultures and acute slices of rat somatosensory cortex. How the cortex intrinsically organizes signals remains unknown. A current hypothesis was proposed by the condensed matter physicists Bak, Tang, and Wiesenfeld when they conjectured that if neuronal avalanche activity followed inverse power law distributions, then brain activity may be set around phase transitions within self-organized signals.
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December 2024
Interventional Endoscopy Unit, Fondazione IRCCS San Gerardo dei Tintori, 20900 Monza, Italy.
Background And Aims: Despite technical advances, endoscopic retrograde cholangiopancreatography (ERCP) is associated with complications and potentially lethal outcomes. Sarcopenia, a complex syndrome mainly associated with aging, has been recognized as a predictor of poor surgical outcomes. Thus far, the impact of sarcopenia on ERCP remains unknown.
View Article and Find Full Text PDFCancers (Basel)
January 2025
Department of Neurosurgery, Río Hortega University Hospital, 47014 Valladolid, Spain.
Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset.
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