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Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., conserving structural controllability. Our "effective augmentation" algorithm takes advantage of clusters intrinsic to the network topology, and permits rapidly and efficient augmentation of a large number of nodes in one time-step. "Effective augmentation" is shown to work successfully on a wide range of model and real networks. The method has numerous applications (e.g. study of biological, social, power and technological networks) and potentially of significant practical and economic value.
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http://dx.doi.org/10.1038/srep25627 | DOI Listing |
Plant Physiol
August 2025
The BioActives Lab, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia.
Carotenoids and their derivative apocarotenoids are diverse isoprenoid metabolites vital to plants and critical to humans. Recent discoveries have expanded our understanding of the intricate mechanisms modulating their metabolism and revealed their new functions in plants. Many new regulators and regulatory modules that potentially link carotenoid metabolism with developmental, hormonal, and environmental cues have been unraveled.
View Article and Find Full Text PDFComput Biol Med
September 2025
School of Biomedical Engineering, Shanghai Jiao Tong University, China. Electronic address:
Automatic seizure detection using machine learning can reduce the workload of clinicians in epilepsy diagnosis. However, the class imbalance between seizure and non-seizure data limits model performance. Data augmentation offers a solution, yet few studies have systematically compared different augmentation strategies for seizure classification.
View Article and Find Full Text PDFPLoS One
May 2025
School of Computing and Informatics, Universiti Teknologi Brunei, Jalan Tungku Link Gadong, Brunei-Muara, Brunei Darussalam.
As challenging as it is to use face recognition with a Single Sample Per Person, it becomes even more difficult when face recognition based on a single sample is performed in an unconstrained environment. The unconstrained environment is normally considered irregular in facial expressions, pose, occlusion, and illumination. This degree of difficulty increases as a result of the single sample and in the presence of occlusion.
View Article and Find Full Text PDFHealth Technol Assess
May 2025
Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
Background: Lithium and several atypical antipsychotics are the recommended first-line augmentation options for treatment-resistant depression; however, few studies have compared them directly, and none for longer than 8 weeks. Consequently, there is little evidence-based guidance for clinicians when choosing an augmentation option for patients with treatment-resistant depression.
Objectives: This trial examined whether it is more clinically and cost-effective to prescribe lithium or quetiapine augmentation therapy for patients with treatment-resistant depression over 12 months.
Environ Monit Assess
March 2025
Photonics Engineering Group, Universidad de Cantabria, 39005, Santander, Spain.
This study presents an innovative approach to high-resolution land cover classification using deep learning, tackling the challenge of working with an exceptionally small dataset. Manual annotation of land cover data is both time-consuming and labor-intensive, making data augmentation crucial for enhancing model performance. While data augmentation is a well-established technique, there has not been a comprehensive and comparative evaluation of a wide range of data augmentation methods specifically applied to land cover classification until now.
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