Active Distribution Networks
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Related Articles from SNS
Hosting Capacity Assessment and Enhancement for Edge Data Centers in Active Distribution Networks
new Abstract: With the increasing demand for edge computing and AI-driven workloads, integrating small and medium-sized edge data centers into distribution networks has become increasingly important. This paper investigates the hosting capacity of distribution networks for data center integration and identifies the key physical mechanisms that limit the maximum allowable data center load. The baseline analysis shows that data center hosting capacity varies significantly across candidate...
LdT: An indicator of ionospheric activity based on statistical distributions in GNSS-derived TEC rates of change
arXiv:2504.06056v3 Announce Type: replace Abstract: Many aspects of our societies now depend upon satellite telecommunications, such as those requiring Global Navigation Satellite Systems (GNSS). GNSS is based on radio waves that propagate through the ionosphere and experience complicated propagation effects caused by inhomogeneities in its electron density. The Earth's ionosphere forms part of the solar-terrestrial environment, and its state is determined by the spatial distribution and...
A prognostic human brain network for diffuse midline glioma
Abstract Diffuse midline gliomas (DMGs) are near-universally lethal tumours of the childhood central nervous system1,2. In animal models, DMGs form brain-wide integrated networks through neuron-to-glioma synapses3,4,5,6 and glioma-to-glioma gap junctional coupling3. This extensive connectivity robustly promotes the growth and invasion of DMG3,4,5,6,7,8,9 and other glial malignancies10,11,12 through paracrine mechanisms and direct neuron-to-glioma synapses.
Stimulus-response correlation analysis dissociates spatiotemporal cortical networks supporting speech production
Introduction: Understanding the spatiotemporal distribution of cortical activation during language production is a central question in cognitive neuroscience with broad clinical applications. High spatial/temporal resolution recording over multiple brain regions and specific psycholinguistic manipulations with testable behavioral predictions are necessary to separate neural variance attributable to processing stages. Objective: We combine a delayed naming paradigm with intracranial...
Theta phase and theta-gamma coupling organise the spoken language network
Speech production requires rapid coordination of conceptual and lexical processes across distributed cortical networks, yet the neurophysiological mechanisms enabling this coordination remain poorly understood. Oscillatory coupling has emerged as a candidate mechanism for coordinating neural activity across spatial scales. Here, we used whole-head magnetoencephalography during overt picture naming to test how phase and phase-amplitude coupling organise neural dynamics preceding articulation.
Robust Frequency-Calibrated Virtual EEG Channel Generation from Four Frontal Electrodes for Wearable EEG Augmentation
Announce Type: replace Abstract: Low-channel wearable electroencephalography (EEG) is attractive for long-term monitoring, but four frontal electrodes provide only a sparse and spatially biased view of distributed scalp activity. We present FAVC-Net, a compact frequency-calibrated virtual-channel network that generates 13 unmeasured EEG channels from Fp1, Fp2, F7, and F8. The model combines shared multi-scale source encoding, source-state embeddings, target-conditioned signed source-block...
Neurophysiological Functional Connectivity Changes during Difficult Listening in Older and Younger Adults
Older adults often report increased difficulty understanding speech in noisy listening environments. These difficulties are thought to arise from neurophysiological changes associated with aging, including at the level of cortex. Speech comprehension is believed to rely on coordinated activity across distributed cortical regions, mediated by the directional flow of neural signals -- their functional connectivity.
Aligned Training: A Parameter-Free Method to Improve Feature Quality and Stability of Sparse Autoencoders (SAE)
Announce Type: replace Abstract: Sparse autoencoders (SAEs) are one of the main methods to interpret the inner workings of deep neural networks (DNNs), decomposing activations into higher-dimensional features. However, they exhibit critical shortcomings where a large fraction of features are never activated and are unstable. Despite variants of SAEs that attempt to mitigate these issues, they require additional data, resampling, or training.
Slow Oscillations Gate Interictal Spikes Across the Human Thalamocortical-Epileptogenic Network
Background: Slow oscillations (SOs; 0.5-1.5 Hz), a hallmark of non-rapid eye movement (NREM) sleep, are associated with a marked amplification of interictal epileptiform spike (IIS) activity in focal epilepsy. However, the network-level organization of this effect across the thalamocortical-epileptogenic system, and whether IIS-permissive SOs can be predicted from pre-onset brain states, remain unclear. Methods: We analyzed simultaneous scalp EEG and stereo-EEG (SEEG) recordings from 6...
Paradoxical noise preference in RNNs
Announce Type: replace Abstract: In recurrent neural networks (RNNs) used to model biological neural networks, noise is typically introduced during training to emulate biological variability and regularize learning. The expectation is that removing the noise at test time should preserve or improve performance. Contrary to this intuition, we find that continuous-time RNNs (CTRNNs) often perform best at or near the training noise level.