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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 ↗5971 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-Based Prediction of the Dry Sliding Wear Behaviour of Al2O3-Al6061 Metal Matrix Composites ↗
This study investigated the dry sliding wear behaviour of Al2O3 particulates (2–6 wt.%) in Al6061 metal matrix composites produced by ultrasonic stir casting, examining mechanical and tribological behaviour. We modelled wear rate using machine learning. Optical studies confirmed uniform Al2O3 particle dispersion, minimal agglomeration, and strong interfacial bonding between the Al6061 and Al2O3 phases. With an increase in Al2O3 content, density and hardness increased by 1.3% and 33%, respectively. The Al6061–6 wt.% Al2O3 composite exhibited 40% higher wear resistance than the base alloy in dry…
- OpenAlex · Published · First archived
Statistical optimization and neural network-based characterization using a copper-core eddy current probe ↗
Abstract This study presents the design and performance evaluation of a novel eddy-current probe incorporating copper cores to enhance defect detection and characterization in low-conductivity materials used in nuclear applications. The design combines the simplicity of an absolute probe with the directional sensitivity of advanced configurations. The probe generates a highly symmetric magnetic field and a nearly uniform eddy current distribution within the inspected material, thereby improving sensitivity to surface and subsurface defects. Parametric analyses were conducted to investigate the…
- OpenAlex · Published · First archived
Multimodal Hyperspectral Imaging Unveils Pigment Rearrangement in the Diatom Cyclotella meneghiniana under Changing Physicochemical Conditions ↗
Abstract Revealing the spatial organization and stress-induced rearrangements of the photosynthetic machinery in thylakoids of diatoms is crucial for understanding their metabolic responses and/or adaptation. Using a multimodal framework that combines coherent anti-Stokes Raman scattering (CARS), two-photon excited fluorescence, and confocal fluorescence microscopy with machine learning, we investigated the spatial pigment distribution in thylakoids of Cyclotella meneghiniana. Under physiological conditions, we observed dense central packing of fucoxanthins (Fx’s). In contrast, the peripheral…
- OpenAlex · Published · First archived
Two pathways at once: An investigation of how reactance and ethicality shape student learning with AI ↗
Generative AI tools promise to expand student agency in management education, yet the same tool can help and hurt learning inside a single classroom. We argue this contradiction is the signature of two psychological pathways running in parallel. Drawing on the cognitive-affective-conative framework, we develop a dual-pathway serial mediation model in which a cognitive pathway runs from psychological reactance through autonomy perceptions to effectiveness judgments, while an affective pathway runs from ethical perceptions through emotional engagement to effectiveness judgments. Ninety-seven und…
- OpenAlex · Published · First archived
Fault recognition approach applied to a rotating machine supported by hydrodynamic bearings using explainable artificial intelligence ↗
Abstract Rotating machines are widely employed in modern industry. The growing demand for efficiency and operational durability drives the development of intelligent rotors equipped with Artificial Intelligence (AI) methods that promote the extension of equipment lifespan through proactive maintenance actions and reducing costs associated with unscheduled downtime. Some common failures in rotating machines, such as unbalance, misalignment, and cracks, have similar symptoms, making it difficult to diagnose accurately. The absence of labeled historical data and explainable models that are unders…
- OpenAlex · Published · First archived
Towards rapid and reliable GNSS ambiguity resolution using residual-based machine learning in challenging environments ↗
Abstract High-precision Global Navigation Satellite System (GNSS) positioning relies on the successful resolution of carrier-phase ambiguities. However, in challenging environments characterized by severe Non-Line-of-Sight (NLOS) reception and multipath effects, such as urban canyons with dense vegetation and high-rise buildings, conventional model-driven validation methods often suffer from model–reality discrepancies, leading to degraded ambiguity fixing rate and reduced reliability of ambiguity resolution. Machine Learning (ML) has recently attracted increasing interest in GNSS because of i…
- OpenAlex · Published · First archived
Language of Toxicity: An eXplainable Artificial Intelligence Approach ↗
Abstract Toxicity prediction in small molecules represents a fundamental challenge in drug development and chemical safety assessment. Traditional approaches heavily rely on predefined molecular descriptors or fingerprints, potentially limiting the ability to capture complex and nonlinear structure–activity relationships. Here, we present a descriptor-free, language-inspired framework that can be applied to different toxicity prediction tasks within a unified architecture. The model proposed combines a multiscale Convolutional Neural Network (CNN) layer to capture chemical patterns at differen…
- OpenAlex · Published · First archived
DICD-IE: an expert-annotated benchmark for evaluating pretrained biomedical language models in dental implant literature and clinical knowledge extraction ↗
Dental implant literature contains clinically important information on risk factors, treatments, outcomes, and, in rare cases, co-occurrences with oral cancer have been reported. However, this information is typically embedded in concise narrative statements, which complicates automated extraction and limits large-scale evidence synthesis and structured clinical information extraction. Because existing biomedical datasets and pretrained models are insufficient for this domain due to a lack of implant-specific terminology, procedural context, and clinically relevant relationships reported in im…
- OpenAlex · Published · First archived
A framework for institutional change in the age of AI ↗
Generative AI is rapidly reshaping STEM higher education. Not only are our educational practices changing, but how we think about educational transformation must also adapt. Existing models of institutional change in STEM, aimed at interactive engagement, have largely followed an adoption logic: relatively stable, well-researched educational practices are evaluated and then scaled. These assumptions do not hold for generative AI, which is an arrival technology – entering classrooms before a sufficient pedagogical evidence base could form, and requiring institutions to act under uncertainty. Bu…
- OpenAlex · Published · First archived
Domain-Wall-Mediated Ultralow-Barrier Sliding and Pinning in Ferroelectric Moire Superlattices Revealed by Machine Learning ↗
Abstract Sliding ferroelectrics built from stacked nonpolar monolayers enable out-of-plane polarization and unconventional switching via interlayer sliding, yet the microscopic sliding dynamics remain unclear. Using machine-learning molecular dynamics, we reveal spontaneous, thermally driven interlayer sliding in ferroelectric MoS2 moiré superlattices, with relative velocities on the order of 1 m/s at 300 K. Instead of rigid translation of the entire bilayer, the motion appears as a global drift of the moiré pattern. Such thermally driven sliding is inconsistent with a meV/atom-scale rigid-s…
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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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