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Earlier sources: Science
Science investigates the natural world through observation, experiments, and mathematical models. Explore papers and developments in biology, medicine, materials, neuroscience, physics, climate, and mathematics; a reported finding is not necessarily established consensus.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗1687 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- arXiv · Published · First archived
Precursors, Pathways, and State-Dependent Reliability of Long-range ENSO Prediction in the CESM2 Seasonal-to-Multiyear Large Ensemble ↗
Understanding the sources of ENSO prediction skill and error is essential for improving long-range climate prediction. Here we examine ENSO precursors in CESM2 Seasonal-to-Multiyear Large Ensemble (SMYLE) hindcasts initialized in February and August during 1970-2019, using ensemble sensitivity analysis and an extended nonlinear recharge oscillator (XRO) benchmark. SMYLE exhibits meaningful long-lead skill, with February-initialized predictions remaining skillful through the following winter, and August-initialized predictions retaining useful skill at the December target sixteen months ahead.…
- arXiv · Published · First archived
Hydrological Constraints on Temperature Sensitivities of Precipitation Frequency and Intensity ↗
To improve understanding of how precipitation frequency and intensity change with warming, we combine the coupled land--atmosphere water balance with a stochastic hydrological model that retains explicit dependence on precipitation frequency and event depth. We find a linear relationship between co-variations of rainfall frequency and intensity, where the slope is a hydrological sensitivity determined by the dryness and storage indices and the intercept is a forcing term associated with changes in moisture convergence, potential evapotranspiration, and effective water-storage capacity. While a…
- arXiv · Published · First archived
West-WRF AI 2-km: High-Resolution Prediction of Integrated Vapor Transport and Precipitation ↗
We introduce a stretched-grid artificial intelligence (AI) weather forecasting model with 2-km resolution over the western United States and part of the Northeast Pacific and approximately 31-km resolution elsewhere globally. Forecasting over the western U.S. is challenging because complex topography and atmospheric rivers (ARs) strongly influence orographic precipitation. West-WRF AI 2-km builds on a global model pretrained with a 40-year European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset and is fine-tuned with the Center for Western Weather and Water Extremes (CW…
- arXiv · Published · First archived
FAST-ML: A Hybrid Physics-Machine Learning Framework for Tropical Cyclone Intensity Forecasting ↗
Rapid intensification (RI) remains one of the most consequential and difficult aspects of tropical cyclone (TC) forecasting. Although full-physics numerical weather prediction models can represent the processes governing RI, resolving storm-environment interactions remains computationally expensive, while purely data-driven approaches often lack physical interpretability. We present FAST-ML, a hybrid framework that bridges data-driven efficiency with physical constraints. A physically informed dual-stream neural parameterization ingests 3D ERA5 fields to diagnose ventilation controls---environ…
- OpenAlex · Published · First archived
A Physics-Data Dual-Channel Collaborative Cross-Domain Transfer Learning Fault Diagnosis Method for Main Circulation Pump in Converter Valve Cooling Systems ↗
To address the three major challenges in fault diagnosis of main circulating pumps in converter station valve cooling systems—small-sample scenarios, cross-domain distribution shift, and lack of physical interpretability—a physics-data dual-channel collaborative cross-domain transfer learning framework is proposed. The physics channel designs a physics-constrained conditional generative adversarial network (PC-cGAN), which explicitly embeds the pump's Euler equation into the loss function to constrain the hydraulic characteristics of generated samples. The data channel constructs a multi-sourc…
- OpenAlex · Published · First archived
Chemistry-transferable physics-informed neural network surrogate for lithium-ion cells with online continual learning ↗
This paper presents a physics-informed neural network (PINN) surrogate of the single particle model (SPM) for predicting lithium-ion concentrations and terminal voltage under varying operating and aging conditions. The proposed approach employs a two-stage learning framework. In the offline stage, the network learns the nominal electrochemical dynamics by enforcing the governing partial differential equations along with their associated initial and boundary conditions. During online operation, the physics-informed hidden layers are held fixed, and only the parameters of the output layer are ad…
- OpenAlex · Published · First archived
Modeling multicycle anodic degradation in hard carbon-based sodium-ion batteries ↗
Due to abundant raw materials and ecological friendliness, sodium-ion batteries (SIBs) are considered a promising alternative to lithium-ion batteries (LIBs) for energy storage applications. This assertion is based on the recent advances in technology and the proven capabilities of SIBs. Physics-based modeling (PBM) is a cost-effective approach for understanding the electrochemical kinetics and degradation mechanisms of batteries. In this paper, the authors developed a single-particle model with electrolyte interactions (SPMe) coupled with anodic degradation in a sodium vanadium phosphate fluo…
- OpenAlex · Published · First archived
Physics-Guided Deep Learning for Short-Term Probabilistic Offshore Wind Power Forecasting ↗
To address the nonlinear amplification of numerical weather prediction (NWP) errors and the difficult trade-off between coverage and sharpness in short-term offshore wind power forecasting, this paper proposes PRWind, a physics-guided framework for short-term probabilistic forecasting. Built upon a Transformer encoder, the framework integrates horizon-adaptively weighted power-curve, persistence, and wind-speed cubic-law baselines; propagates wind-speed uncertainty into a power distribution through a probabilistic wind-speed distribution with Monte Carlo integration; and employs spatial graph…
- OpenAlex · Published · First archived
RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction ↗
Protein-ligand binding affinity (PLA) prediction is essential for computational drug discovery, yet existing deep learning methods typically employ a single globally trained model that fails to adapt to individual query samples. In this paper, we propose a novel end-to-end framework that retrieves hard protein-ligand pairs-samples whose nearest neighbors have conflicting labels under manifold smoothness constraints-and integrates them via a semi-supervised graph convolutional network (GCN). For each query, we automatically construct a graph where nodes represent protein-ligand pairs and edges…
- OpenAlex · Published · First archived
Miniaturized biosensor platforms for early detection of neurodegenerative diseases and their potential for clinical translation ↗
Neurodegenerative disorders (NDs), such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and prion diseases, are defined by the gradual deterioration of neurons that frequently occurs prior to the onset of clinically observable symptoms. The early diagnosis of these conditions relies on analytical systems that are capable in detecting disease-related biomarkers at low concentrations within complex biological fluids. This analysis extensively evaluates miniaturized biosensor systems aimed at disease-specific biomarkers, such as amyloid-β and tau for Alzheimer’s disease, α-synuclein (α-syn…
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How to assess evidence about Science
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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