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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 ↗5979 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
Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity ↗
ABSTRACT Calcium silicate hydrate (C–S–H), the primary hydration product of cement, governs the strength and durability of concrete but remains difficult to model due to its chemical heterogeneity and disordered nanostructure. Machine learning potentials (MLPs) offer a route to extend quantum accuracy to large‐scale simulations, yet progress has been limited by the absence of representative datasets. Here, we present a stoichiometry‐generalizable MLP, CSH‐MLP, trained on a broad and representative dataset containing over 50 000 DFT‐labeled configurations, which is derived from nearly 700 disti…
- OpenAlex · Published · First archived
Computational and machine learning analysis of bio-convective magnetized Ree–Eyring nanofluid flow on Riga plate with thermal radiation and activation energy ↗
This work examines bio-convective magnetized Ree-Eyring nanofluid flow on a convectively heated Riga surface. Thermal diffusion is controlled through use of Cattaneo-Christov thermal flux and Brownian motion effects along with thermal radiations. The flow is also affected by Darcy-Forchheimer model, activation energy and presence of microorganisms. The leading equations have valuated initially using bvp4c technique. The obtained set from this numerical technique is then used as a dataset to implement ANN technique in current problem. It has deduced in this work that, optimal Mean Squared Error…
- OpenAlex · Published · First archived
Artificial intelligence for forecasting, optimization, and energy storage in energy communities: a review ↗
Abstract Energy communities (ECs) are emerging as a key paradigm for decentralized, consumer-centric energy systems, yet their operation is challenged by uncertainty, asset heterogeneity, and the need for coordinated decision-making. Artificial intelligence (AI) has been widely applied to forecasting, optimization, and management of energy storage systems (ESSs) in ECs, but existing studies remain fragmented across decision layers. This paper presents a comprehensive and systematic review of 130 peer-reviewed studies on AI-based forecasting, optimization, and ESS management in ECs. By jointly…
- OpenAlex · Published · First archived
Auditable Symbolic Candidate Generation for Biomedical Ontology Matching in Medical AI Systems ↗
Biomedical ontology matching is a key step for semantic interoperability in medical AI systems, supporting clinical terminology mapping, cross-resource search, biomedical knowledge graph construction, and phenotype-aware data integration. This paper presents ARB, an auditable symbolic candidate-generation module designed for settings where external biomedical resources, learned embeddings, or reference-alignment calibration are unavailable or undesirable. ARB starts from a token inverted-index blocker, rescues missed candidates through hierarchy-pooled and neighbour-label evidence under a puri…
- OpenAlex · Published · First archived
A Novel Stable-Inverse Fractional-Order Predictive PID Control with Machine Learning-Based Performance Prediction for Non-Minimum Phase Systems ↗
Non-minimum-phase behavior introduces an additional limitation in predictive process control because directly inverting a right-half-plane zero can result in an unstable controller. This study develops a Stable-Inverse Fractional-Order Predictive PID (SIFOPPID) controller for the non-minimum-phase operating configuration of a laboratory-scale quadruple-tank system. The proposed technique combines fractional-order PID dynamics with predictive dead-time compensation and a stable approximate inverse implemented through a fractional low-pass weighting function. This work evaluates three SIFOPPID v…
- OpenAlex · Published · First archived
The shifting landscape of assessment in STEM education in the age of generative AI ↗
Abstract This article focuses on the impact of generative AI (GenAI) on assessment in STEM education. Guided by the assessment triangle as a conceptual framework, we take an ecosystems perspective to review the extant STEM education literature and identify how GenAI affects what is assessed (cognition), how evidence of learning is elicited (observation), and the inferences drawn from that evidence (interpretation). Our review of recent relevant studies identifies three emerging themes that scholars may further explore in this article collection: (1) how AI has become entangled in assessment sy…
- OpenAlex · Published · First archived
Model-Based Scenario Engineering Using Large Language Models for Vehicle Concept Development ↗
Future-oriented requirements are difficult to capture during the early phases of vehicle concept development because conventional requirements engineering primarily considers current stakeholder needs and system environments. This paper proposes an evidence-based Model-Based Scenario Engineering approach that integrates Large Language Models (LLMs) into Model-Based Systems Engineering (MBSE) methodology to systematically incorporate future developments into early requirements engineering. The proposed method combines three complementary models: a stakeholder model, a system model, and a future…
- OpenAlex · Published · First archived
From Digital Twin to AI-Integrated Control: A Review and Research Agenda for Large-Scale PEM Electrolyzer Plant Management ↗
Large-scale green hydrogen production via PEM electrolysis demands control strategies that surpass the limitations of traditional distributed control systems (DCSs). Digital twin (DT) technology has been introduced as a structured design framework for predictive maintenance and operational optimization across hydrogen production pathways, from steam methane reforming and green ammonia to next-generation PEM electrolyzer. A critical constraint exists; passive DT architectures cannot autonomously close the control loop. The resulting prediction–action latency gap introduces delays of 28–120 min…
- OpenAlex · Published · First archived
Personalized risk assessment of musculoskeletal disorders using artificial intelligence enabled predictive imaging models ↗
Abstract Musculoskeletal disorders (MSDs) impose a substantial burden on mobility, quality of life, and healthcare systems, while early individualized risk stratification remains difficult because structural, clinical, and biomechanical determinants interact nonlinearly. This study presents a reproducible AI-enabled multimodal framework for personalized MSD risk estimation using MRI-like, CT-like, and X-ray-like synthetic imaging surrogates together with synthetic clinical and biomechanical variables. A modality-specific residual CNN architecture extracts imaging representations, structured en…
- OpenAlex · Published · First archived
Hybrid RLHF-AIF: Uncertainty-Guided Routing of Human and AI Feedback for Cost-Efficient Safety Alignment of Large Language Models ↗
Safety alignment of large language models faces a feedback dilemma: human judgment is reliable but costly to scale; automated judgment is cheap but less trustworthy. Existing methods commit to one source per run, so misjudged responses are never corrected. This paper proposes Hybrid RLHF-AIF, which picks the feedback source for each sample online. First, a multi-label DistilBERT discriminator scores every response across 19 harm categories, reporting confidence through Monte Carlo dropout. Second, an uncertainty-aware scheduler sends only ambiguous or low-confidence responses to the reliable r…
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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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