THE INTERNET IS THINKING
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 ↗5549 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
Transformer-Based Semantic Segmentation for UAV Imagery: Application to Cyanobacterial Bloom Monitoring in Taihu Lake ↗
Cyanobacterial blooms in eutrophic lakes pose severe threats to aquatic ecosystem health and drinking water safety. Accurate, timely, and spatially explicit monitoring of cyanobacterial distribution is therefore critical for effective water quality management and risk mitigation. This study proposes a transformer-based semantic segmentation framework—built upon Mask2Former—for automated detection of cyanobacterial blooms in Taihu Lake using high-resolution unmanned aerial vehicle (UAV) imagery. To support robust model training and evaluation, we constructed a manually annotated semantic segmen…
- OpenAlex · Published · First archived
Single-Ended Transmission Line Fault Location Based on Multi-Wavelet Time–Frequency Feature Fusion and a Residual Neural Network ↗
Accurate fault location on transmission lines is essential for rapid fault isolation and reliable power-system operation. This paper presents a single-ended transmission line fault location method based on multi-wavelet time–frequency feature fusion and a residual neural network. First, a complex-domain transient analytical model accounting for the frequency-dependent characteristics of transmission lines is developed to efficiently generate fault-induced traveling-wave samples. The continuous wavelet transform (CWT) is then applied to these samples using multiple mother wavelets, and the resu…
- OpenAlex · Published · First archived
Temperature-Dependent Electric Field Distribution in Current Transformers with Advanced Paper–Liquid Insulation Systems ↗
The dielectric performance of current transformer insulation systems is influenced by the temperature-dependent properties of solid and liquid insulating materials. This study analyzes the dielectric and electric field behavior of advanced paper–liquid insulation systems by characterizing Kraft paper impregnated with mineral oil, natural ester, and synthetic ester to assess the influence of the liquid and mineral oil-impregnated Kraft paper, Thermally Upgraded Kraft, and Diamond Printed Enhanced papers to assess the influence of the paper. Frequency Domain Spectroscopy measurements were perfor…
- OpenAlex · Published · First archived
AI-Driven Smart Haulage Technologies for Decarbonization and Sustainable Mine Transportation: A Comprehensive Review ↗
Haul trucks generate more than half of direct greenhouse-gas emissions in typical mining operations, making haulage decarbonization central to the industry’s net zero commitments. Artificial intelligence (AI) is increasingly applied to haulage, but existing reviews examine individual technologies rather than linking them to decarbonization and ESG outcomes. This comprehensive review synthesizes 73 peer-reviewed sources from 2017 to 2026 across route optimization, electric and hybrid trucks, predictive maintenance, fleet monitoring, carbon footprint reduction, and ESG performance, grades report…
- OpenAlex · Published · First archived
Large Language Models for Information Extraction from Neurosurgical Clinical Notes: A Benchmark Against Human Auditors ↗
Retrospective clinical research depends on reliable extraction of structured variables from unstructured medical documentation, but manual chart abstraction is slow and vulnerable to inter-rater variability. We compared zero-shot extraction of clinically relevant variables from German neurosurgical records by large language models (LLMs) with extraction by two human auditors. We analyzed 881 de-identified clinical notes from 211 patients undergoing microvascular decompression and extracted 15 predefined binary variables across three domains: preoperative symptoms, postoperative complications,…
- OpenAlex · Published · First archived
“It Works, but I Don’t Know Why”: Uncritical Use, Verification Behavior and Perceived Understanding in Non-CS Students’ Generative AI-Assisted Programming Projects ↗
Generative artificial intelligence (GenAI) enables students with little programming experience to produce working software, raising the concern that learners may build artifacts they cannot explain. This convergent mixed-methods study examined how uncritical GenAI use and verification behavior (VB) relate to novices’ self-reported understanding of their own AI-assisted projects. Participants were undergraduates from predominantly non-computing majors in two offerings of a general-education Python course; 64 provided complete survey data and 68 written reflections were thematically analyzed. Un…
- OpenAlex · Published · First archived
Multiphysics Modelling of Tri-Hybrid Nanofluid Transport over a Quadratically Radiative Riga Surface with Electroosmotic Effects and Pollutant Transport: Numerical Simulation, AI-Based Surrogate Prediction, and Industrial Applications ↗
In this study, an integrated finite-difference and deep neural network analysis of the simultaneous multiphysics of coupled momentum, heat, nanoparticle, and pollutant transport in a Sutterby tri-hybrid nanofluid over a quadratically radiative Riga surface is developed. The Fe3O4, Al2O3, and TiO2 nanoparticles are considered, and a mixture of propylene glycol and water is used, along with electroosmotic forcing, electromagnetic actuation, nonlinear thermal radiation, viscous dissipation, Brownian motion, thermophoresis, and pollutant transport. The key novelty is the ability to formally connec…
- OpenAlex · Published · First archived
Data Quality and Predictive Reliability in AI-Enabled Aviation Energy Management: Physics-Informed Sensitivity Analysis with Open-Data Validation ↗
The objective of this study is to quantify how independently degraded operational-data quality affects predictive accuracy, predictive uncertainty, and prediction-level eligibility for decision support in AI-enabled aviation energy management. The evidence base combines a 24,000-flight physics-informed synthetic experiment, an independent distribution-shift experiment, a 17,520 h synthetic airport-energy time series, and external validation using 686 observed fuel-burn intervals from 79 flights and 10 aircraft types in the EUROCONTROL PRC 2025/OpenSky dataset. In the primary flight experiment,…
- OpenAlex · Published · First archived
Human–AI collaborative patterns in GenAI-supported instructional design among pre-service teachers: an analysis of interaction sequences and cognitive load ↗
Abstract The rapid advancement of generative artificial intelligence is profoundly reshaping the paradigm of instructional design. However, the micro-level process patterns of human-AI collaboration in generative AI-enabled instructional design for pre-service teachers still require in-depth analysis. This study aims to uncover the behavioral patterns and cognitive load characteristics of pre-service teachers when collaborating with generative AI to complete instructional design tasks and to explore the patterns of efficient human-AI collaboration. A total of 67 pre-service teachers completed…
- OpenAlex · Published · First archived
Understanding Artists’ Perceptions of Culturally Sensitive AI in Thai Buddhist Digital Art: Evidence from Principal Component and Regression Analyses ↗
Artificial intelligence (AI) is increasingly applied to digital art creation; however, its adoption in religious cultural heritage remains insufficiently understood because artistic production involves cultural symbolism, spiritual meaning, and human creativity. This study investigates artists’ perceptions of culturally sensitive AI adoption in Thai Buddhist digital art using survey data from 182 experienced digital Buddhist art designers in Thailand. An exploratory quantitative approach combining Principal Component Analysis (PCA) with Varimax rotation and multiple linear regression analysis…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.