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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 ↗5927 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
Enhanced Neural Network Filtering for High-Temperature H2O Concentration Detection Based on U-Net ↗
To address severe noise interference in high-temperature H2O absorption spectra in metallurgical environments and the limited performance of traditional filtering algorithms, this study proposes an enhanced U-Net neural-network filtering method based on residual connections and smoothness constraints, termed Residual & Smooth U-Net (RSU-Net). Based on the classical U-Net architecture, the proposed model incorporates dilated convolutions, residual connections, a smoothness constraint in the loss function, and endpoint smoothing to suppress boundary fluctuations while preserving spectral line pr…
- OpenAlex · Published · First archived
Operationalizing the Precautionary Approach in Marine Pollution Control: A Machine Learning Framework to Examine Score Sensitivity in Potentially Polluting Shipwreck Risk Assessment ↗
Potentially Polluting Wrecks (PPWs) represent a critical global threat to marine ecosystems, requiring proactive risk management aligned with the Precautionary Approach of international environmental law. However, standard multi-criteria risk assessment frameworks frequently suffer from sensitivity to criterion weights, where maximum point allocations determine the relative contributions of criteria to final risk scores. The consequences of these allocations for management classifications require explicit examination. To examine this sensitivity, this study presents a data-driven diagnostic fr…
- OpenAlex · Published · First archived
Towards Sustainable Vocational Education: An Explainable Green AI Ecosystem for Workforce Readiness ↗
Background Vocational education increasingly requires the integration of artificial intelligence-supported learning and sustainability competencies to prepare students for technology-intensive and environmentally responsible workplaces. However, limited empirical evidence exists on learning ecosystems that combine explainable artificial intelligence, sustainability oriented competency development, and workforce readiness. This study developed and examined an Explainable Green Artificial Intelligence Ecosystem for sustainable vocational education. Methods A sequential explanatory mixed-methods…
- OpenAlex · Published · First archived
Social and technical drivers of traditional craftspeople’s continuance intention to use AI-generated content systems: a mixed SEM–fsQCA study ↗
This study investigates the interplay between social and technological factors affecting traditional craftspeople’s ongoing willingness to utilize generative artificial intelligence (AIGC) tools, specifically text-based models and image generators, aiming to elucidate the cognitive and affective mechanisms underlying their behavior, grounded in social influence theory and the stimulus–organism–response (SOR) model. This study uses a mixed-methods approach combining partial least squares structural equation modeling (PLS-SEM) with fuzzy-set qualitative comparative analysis (fsQCA) to analyze da…
- OpenAlex · Published · First archived
Experimental and Machine Learning Based Evaluation of Sustainable Self Compacting Concrete Reinforced with Recycled Steel Wire and Crumb Rubber from End-of-Life Tyres ↗
This study investigates sustainable self-compacting concrete (SCC) incorporating untreated and heat-treated crumb rubber (CR) from waste tyres and tyre-derived steel wire (SW). CR replaced fine aggregate at 5–15%, while SW was added at 1% by volume. Untreated CR reduced workability and strength due to its hydrophobic nature, weak rubber–cement interfacial transition zone (ITZ), and increased porosity. Consequently, 28-day compressive strength decreased from 35.36 MPa to 28.75 MPa, while slump flow declined from 690 mm to 520 mm at 15% CR. Heat treatment improved rubber surface roughness and ru…
- OpenAlex · Published · First archived
LLM-H2G: biomedical semantic-enhanced hypergraph contrastive learning for herb–disease association prediction ↗
Abstract Background Herb–disease association prediction is central to computational traditional medicine, but existing graph and hypergraph methods mainly rely on observed topology and underuse biomedical textual semantics, especially in heterogeneous or sparse association networks. Results We propose LLM-H2G, a biomedical semantic-enhanced hypergraph contrastive learning framework that integrates herbs, compounds, proteins and diseases through an enhanced heterogeneous hypergraph. Entity names are encoded as large language model-derived semantic representations, injected into compound and pro…
- OpenAlex · Published · First archived
Multi-domain feature fusion and machine learning-based SFRA diagnosis of transformer winding faults using a novel fault deviation index ↗
Abstract Sweep Frequency Response Analysis (SFRA) is one of the commonly used techniques for evaluating transformer winding integrity. However, the interpretation of the SFRA results remains qualitative and depends on expert evaluation. In this study, a quantitative, measurement-oriented diagnostic framework based on the advanced normalized Multi-Domain Fault Deviation Index (MFDI) is proposed. Within this method, magnitude, phase, impedance, and resonance-based descriptors are integrated in a unified formulation. The proposed method combines frequency band decomposition with multi-domain feat…
- OpenAlex · Published · First archived
From capability to assertability: epistemic regulation of LLM-mediated claim presentation ↗
Abstract Improvements in large language model (LLM) capability can increase accuracy, reasoning, and access to relevant information while leaving unassessed whether a particular output is appropriately presented for epistemic uptake. This creates an evaluative gap: first-order capability metrics can register genuine gains while leaving claim-presentation conditions underdetermined. LLM outputs increasingly occupy assertion-like roles without thereby acquiring the standing of accountable asserters. We develop assertability as a relational normative status of an output-in-practice, determined by…
- OpenAlex · Published · First archived
Artificial Intelligence for environmental sustainability: a systematic review of applications, impact pathways, and governance implications ↗
The escalating urgency of climate change and environmental degradation has intensified demand for scalable solutions that strengthen system resilience and support progress toward the Sustainable Development Goals. Artificial intelligence offers substantial potential to enhance environmental monitoring and decision support, yet the evidence remains dispersed across domains, methods, and industry contexts. This study provides a systematic review of AI for environmental sustainability, synthesizing evidence from 493 identified journal articles. The review examines how AI is applied to environment…
- OpenAlex · Published · First archived
Toward Agentic Runtime Healing ↗
Self-healing systems have long been a focus of research, aiming to enable software to recover from unexpected runtime errors without human intervention. Traditional approaches rely on predefined heuristic rules, such as reusing error handlers or rolling back to checkpoints, but these methods struggle to adapt to the diverse range of runtime errors. The emergence of large language models offers a new opportunity to address this challenge. Leveraging their ability to understand and generate code and natural language, we propose using LLMs to dynamically generate error-handling strategies in real…
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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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