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Earlier sources: Artificial Intelligence
Artificial intelligence covers systems that learn from data, generate content, perceive their surroundings, or act toward a goal. Follow model research, agent tooling, evaluation, and the infrastructure used to build and run these systems.
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 ↗5380 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.
- OpenAlex · Published · First archived
Lightweight, interpretable acoustic emission diagnostics for multi-mode damage in bonded CFRP joints using consensus-guided machine learning ↗
Adhesively bonded carbon fibre-reinforced polymer (CFRP) joints exhibit complex, multimode damage involving concurrent matrix cracking, debonding, delamination and fibre breakage, which challenges acoustic-emission (AE)-based diagnosis under industrial hit-triggered acquisition. This study is framed as a focused methodological investigation on a single-lap bonded CFRP joint system, rather than as a claim of universal damage-frequency thresholds. The proposed workflow uses an offline consensus procedure to derive structure-specific operational passbands from standard AE descriptors, then uses t…
- OpenAlex · Published · First archived
AI-Generated Text Detection via Structural and Semantic Entropy Optimization with Adversarial Learning ↗
AI-generated text detectors often achieve high in-domain accuracy but remain sensitive to changes in domain, generator, and writing style. This limitation partly arises because conventional detectors rely predominantly on latent language-model representations and fixed decision boundaries, without explicitly characterizing sample-level structural and semantic variation. We introduce S2E-AL, an end-to-end score-aware structure–semantic entropy adversarial learning framework for AI-generated text detection. S2E-AL encodes local dependency, global contextual, and semantic entropy features into a…
- OpenAlex · Published · First archived
AI-Empowered Governance of University Data: Value Dimensions, Risk Challenges and Governance Responses ↗
The purpose of this study is to explore a sustainable paradigm for data governance in universities empowered by artificial intelligence (AI), creating a collaborative governance system that integrates technological, institutional, organizational, and ethical dimensions to combine intelligence, resilience, and humanistic care. This study adopts an inductive–deductive qualitative content analysis to propose an integrative “Value–Risk–Pathway” analytical framework. The study first identifies the sustainable benefits of AI in university data governance. These benefits include enhanced data integra…
- OpenAlex · Published · First archived
From Prediction to Generation: Artificial Intelligence Reconstructing the Paradigm of Protein Glycosylation Research ↗
Among post-translational modifications, protein glycosylation is distinguished by its abundance and structural intricacy. Unlike protein biosynthesis, glycan assembly follows no template; it reflects glycosyltransferase/glycosidase competition, sugar-donor availability, and organelle-enforced compartmentalization, yielding heterogeneous glycoforms that confer functional versatility but complicate analysis. Three bottlenecks persist: stereochemical control in chemical glycosylation, ambiguous assignment of occupancy sites, and reproducing native conformations of fully glycosylated proteins. Ado…
- OpenAlex · Published · First archived
Measuring Versioned Information Releases and Observable Complementor Links in Generative AI Platform Ecosystems ↗
Generative artificial intelligence (GenAI) model repositories expose versioned information artifacts, but public platform records rarely provide complete adoption histories. This study develops a reproducible architecture for measuring temporal relations between repository documentation (README) revision candidates and observable public complementor links. A task-stratified frame of 3577 models, fixed before downstream outcomes were measured, yielded 365 eligible candidates and 311 matched pairs. Exact model identifiers in public Spaces were traced to repository versions, producing 8137 curren…
- OpenAlex · Published · First archived
From Linear Regression to Temporal Fusion Transformers: Benchmarking Electricity Price Forecasting Models in Markets with Different Renewable Energy Shares ↗
Rising renewable penetration increases wholesale price volatility and challenges traditional forecasting approaches. This paper benchmarks three methods—adaptive linear regression, Random Forest (RF), and the Temporal Fusion Transformer (TFT)—on hourly day-ahead prices in four European markets with contrasting generation mixes: Slovakia (nuclear-dominated), Spain (solar-rich), Denmark (wind-dominated), and Germany (mixed). Using Energy-Charts data for 2015–2025, all the models are evaluated under one day-ahead protocol on a common 12-month hold-out against naive 24-h persistence. With price-hi…
- OpenAlex · Published · First archived
Interpretable AI-guided discovery of degradable ionizable lipids with enhanced hepatic mRNA delivery in vivo ↗
Abstract Lipid nanoparticles (LNPs) enable RNA medicines, yet ionizable-lipid discovery remains largely empirical, particularly when in vitro activity must be translated into functional hepatic delivery in vivo. Here we establish an interpretable discovery framework that combines modular degradable lipid chemistry, high-throughput phenotyping, machine learning, virtual library expansion, and independent in vivo validation to define a learnable ionizable-lipid design space. Starting from a 520-member library, we train an XGBoost model on firefly luciferase (Fluc) expression in HeLa cells and us…
- OpenAlex · Published · First archived
Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High BMI: A Secondary Analysis of the IDEA Trial ↗
OBJECTIVE: ) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and characterize the shared and individual variation between vGRF profiles and clinical features in individuals with KOA and high BMI. METHODS: ) were analyzed. vGRF waveforms (n=2,394) were represented using three functional data analysis methods: time- and bodyweight-normalization; elastic…
- OpenAlex · Published · First archived
Opening ecological data to reduce bias and strengthen AI in ecology ↗
In 1985, Richard Stallman introduced copyleft, a licensing principle that allows source code to be used, modified, and redistributed provided that the same freedoms are preserved in derivative works (Stallman, 1985). Although developed for software, copyleft offers a useful analogy for ecological research, where data, metadata, and analytical code should be accessible and reusable under clear licenses. Similarly, our ability to understand and address ecological challenges such as biodiversity loss, species range shifts, land degradation, and climate change depends on ecological knowledge being…
- OpenAlex · Published · First archived
Transformer-Driven Machine-Learning Framework for Low-Resource Sentiment Classification ↗
Sentiment classification in under-resourced languages remains challenging because of scarce annotated corpora, linguistic complexity, and computational constraints. Indian regional language Malayalam further intensifies these challenges through its agglutinative morphology and limited labeled resources. This study systematically evaluates a Transformer-assisted machine-learning framework for Malayalam sentiment analysis. Across two datasets, five multilingual Transformer encoders were paired with eight classical classifiers, yielding 80 Transformer–ML configurations and 10 end-to-end baselines…
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How to assess evidence about Artificial Intelligence
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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