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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 ↗5944 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
Explainable and Uncertainty-Aware Machine Learning for EV Energy Consumption Prediction ↗
Electric vehicle consumption prediction is essential for improving route planning, charging management, and battery utilization. Although machine learning approaches have shown promising capability in prediction, the majority of the existing research mostly emphasizes point prediction, providing limited model interpretability and little or no quantification of prediction uncertainty. As a result, their practical application in actual EV energy management is still difficult, where confidence estimation and prediction transparency are crucial. This paper proposes an explainable and uncertainty-a…
- OpenAlex · Published · First archived
A BIM-Based AI Agent for Early-Stage Seismic Eccentricity Assessment and Design Decision Support: EccenAI ↗
Architectural decisions made during the early design stage can substantially influence the mass–stiffness distribution of a building, yet eccentricity-related seismic performance is typically assessed only after major design decisions have been established. This study presents EccenAI, a Building Information Modeling (BIM)-based artificial intelligence (AI) agent developed as an Autodesk Revit plug-in to support early-stage eccentricity assessment and design decision-making. The framework integrates deterministic rule-based calculations with large language model (LLM)-assisted interpretation a…
- OpenAlex · Published · First archived
AI-supported instructional design and preservice teachers’ perceived metacreativity: a mixed-methods study ↗
This study examined the role of an artificial intelligence (AI)-supported instructional program in relation to preservice teachers’ attitudes toward AI, perceived metacreativity skills, and academic achievement in an undergraduate Philosophy of Education course. A convergent parallel mixed-methods design was employed. Quantitative data were collected using pretest-posttest measures from an experimental group ( n = 22) and a control group ( n = 28), while qualitative data were obtained through reflective journals and written responses to open-ended questions. In the experimental group, positive…
- OpenAlex · Published · First archived
Portable urine analysis device based on optical spectroscopy and machine learning ↗
Optical spectroscopy combined with machine learning offers a promising route to affordable, portable urinalysis. Here, we report a low-cost, noninvasive device that leverages a machine-learning model and visible and near-infrared transmission spectroscopy. The handheld system records spectra of LED light transmitted through a standard urine collection container using an 18-channel spectrometer covering 410–940 nm. From the optical spectra, we trained 19 parameter-specific binary classifiers for specific gravity, pH, protein, glucose, ketones, leukocyte esterase, nitrite, urobilinogen, blood, r…
- OpenAlex · Published · First archived
New Presentation of the Twisted Yangian of Type AI ↗
We construct a new presentation of the twisted Yangian of type AI with a finite number of generators. By using this presentation, we propose a new definition of the twisted affine Yangian of type AI .
- OpenAlex · Published · First archived
Nonlinear associations between the built environment and urban vitality in Anyang using multi-source data and explainable machine learning ↗
As China’s urban development shifts from spatial expansion toward quality-oriented renewal, scientific planning has become increasingly important for small and medium-sized cities. Taking the main urban area of Anyang, China, as a case study, this study developed an integrated framework to assess urban vitality and explore its associations with the built environment. A four-dimensional vitality index covering economic, social, cultural, and ecological dimensions was constructed. Four regression models were compared, and SHAP was employed to interpret feature importance, nonlinear relationships…
- OpenAlex · Published · First archived
Graded identification of air leakage in coal-mine gas drainage boreholes using a physics-augmented transformer–LSTM framework ↗
Air leakage in gas drainage boreholes in coal mines reduces methane concentration and extraction efficiency, yet reliable grading remains difficult because weak leakage signatures can be obscured by noise and changes in operating conditions. This study proposes a physics-augmented Global–Local Transformer–LSTM (GL-TransLSTM) framework for classifying normal, slight, moderate, and severe leakage. The four grades were defined in controlled underground tests by setting the valve opening of a drainage branch exposed to ambient mine air to 0%, 10%, 50%, and 100%, respectively. Complete ensemble emp…
- OpenAlex · Published · First archived
Investigating nozzle-based additive manufacturing dynamics with high-speed imaging and machine learning ↗
Abstract Additive manufacturing (AM) offers great design flexibility but remains constrained by the complexity of tuning processing parameters from a vast parameter search space either by traditional trial-and-error experimental approach or expensive computational fluid dynamics simulations. Further, AM processes involve critical transient phenomena at very small timescales ranging from microsecond to millisecond, which are difficult to observe and characterize experimentally. This work presents a novel approach that combines high-speed photography with machine learning (ML) to model and predi…
- OpenAlex · Published · First archived
Generative AI Disclosure in Advertising: From Intent- to Production-Based Disclosure ↗
Generative artificial intelligence (GenAI) is transforming advertising production and introducing new forms of disclosure at the point of exposure. Existing research treats disclosure primarily as a cue signaling persuasive intent, yet GenAI disclosures increasingly communicate how content was produced rather than why it was created. This paper reconceptualizes disclosure in AI-mediated advertising by positioning GenAI disclosure as a signal of production conditions. Integrating signaling theory, source credibility theory, the persuasion knowledge model, and the heuristic–systematic model, the…
- OpenAlex · Published · First archived
Quantum neural network-based modeling of radiative magneto-ternary nanofluid flow over a porous stretching surface with enhanced thermal transport analysis ↗
The research studied how nonlinear fluid mechanics affects motion and heat transfer combined phase mix on porous surfaces, with A l 2 O 3 − C u − T i O 2 / H 2 O nanofluids moving at various rates to simulate the presence of a stretched porous surface. It will be important to include nonlinear radiation, viscous dissipation, and suction/injection effects in this investigation. To model transport processes in the examined material systems a novel framework based on quantum intelligent models (inspired by quantum computing and machine learning using Bayesian regularization) was developed and…
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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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