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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 ↗5186 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
Integrating Well Logs, Drilling Parameters, and Machine Learning for Subsurface Characterization of Volcanic Geothermal Rocks ↗
Abstract Characterizing subsurface rock properties in high-temperature geothermal systems is challenging because extreme conditions often limit the availability and reliability of wireline, cuttings, and core data. This study applies a data-driven workflow to well THG13 in northeastern Iceland, combining objective-driven preprocessing of wireline and drilling data with unsupervised machine learning to derive electrofacies associated with rock composition and relative porosity. These electrofacies are then integrated with rate of penetration and weight on bit to investigate the mechanical respo…
- OpenAlex · Published · First archived
EEG-biometric authentication system using multi-paradigm neurophysiological measurements and artificial intelligence ↗
Electroencephalography (EEG) provides a promising physiological modality for biometric authentication due to its inherent neural uniqueness and resistance to spoofing attacks. However, reliable EEG-based authentication remains challenging because brain signals are highly non-stationary and vary across cognitive states. To address this limitation, this study proposes a multi-paradigm neurophysiological measurement protocol designed to capture stable subject-specific EEG signatures across diverse brain conditions. EEG signals were recorded from participants during six complementary paradigms, in…
- OpenAlex · Published · First archived
From Answer Generator to Questioning Coach: Designing AI-Assisted Coaching for Self-Regulated Learning in Higher Education ↗
Generative artificial intelligence (GenAI) can support self-regulated learning (SRL), but its pedagogical value depends on whether interaction elicits learner cognition or substitutes for it. This study examined how students’ reported use of GenAI corresponded to an intended question-first coaching design, the perceived processes and tensions associated with SRL, and conditions relevant to potential scale. A qualitatively driven embedded case study utilized 31 reflective portfolios as the primary dataset, complemented by 53 survey responses and 68 weekly logs from 12 maximum-variation cases. A…
- OpenAlex · Published · First archived
Adaptive stewardship of understanding: Artificial intelligence and intergenerational governance in mining ↗
Mining stewardship has traditionally focused on the governance of physical assets, operational performance, environmental impacts, and organizational decision-making. While these responsibilities remain essential, they assume that the understanding supporting stewardship is comparatively stable. In reality, understanding continually evolves as new observations emerge, assumptions are revised, stakeholders change, institutions adapt, and stewardship extends across increasingly long temporal horizons. Artificial intelligence further transforms this process by participating in how observations ar…
- OpenAlex · Published · First archived
A RAG-enhanced multi-agent LLM/VLM framework for auditable and region-aware structural health monitoring of a historic masonry structure ↗
Abstract Historic masonry structures require monitoring workflows that are non-destructive, interpretable, and robust to field variability. This paper presents a proof-of-concept, RAG-enhanced structural health monitoring (SHM) workflow demonstrated on a single historic masonry minaret case study, in which a manager–orchestrator coordinates signal analysis, visual analysis, and cross-modal fusion. Vibration data from four sensors installed on this minaret are processed through a reproducible pipeline that includes data cleaning, PSD-based baseline characterisation, adaptive windowing, robust n…
- OpenAlex · Published · First archived
Data-driven insight into CO2 electroreduction: Ensemble machine learning for CO Faraday efficiency prediction in single-atom catalysts ↗
This study presents an ensemble machine-learning (ML) framework to predict the Faraday efficiency of CO formation (FE CO ) for electrocatalytic CO 2 reduction (CO 2 RR) on single-atom catalysts (SACs). A literature-derived dataset of 795 experimental entries from 42 primary studies was compiled, representing 24 carrier classes, 12 dopant atoms, and H-cell, flow-cell, and membrane-electrode-assembly (MEA) configurations. Eighteen regressors from nine algorithm families were systematically evaluated. A stacking ensemble combining gradient boosting decision tree, random forest, extremely randomiz…
- OpenAlex · Published · First archived
Machine Learning-Guided Virtual Screening Identifies a Novel MELK-Targeted Antiproliferative Lead Compound with G2/M Arrest Activity in Triple-Negative Breast Cancer and Lung Adenocarcinoma Cells ↗
Maternal embryonic leucine zipper kinase (MELK) is a cell cycle-regulated serine/threonine kinase frequently overexpressed in human malignancies that has been controversially pursued as an anticancer target. Here we applied an integrated computational workflow combining four machine learning regressors (Random Forest, Gradient Boosting, XGBoost, LightGBM), hierarchical Glide docking (HTVS to SP to XP), MM/GBSA rescoring, ADMET prediction, and 100 ns molecular dynamics simulation to a ~15.58 million-compound commercial library, yielding 12 prioritized candidates. Three commercially available co…
- OpenAlex · Published · First archived
Predicting the Water Retention Behavior of Biochar-Amended Soils: A Comparative Study of Six Machine Learning Approaches ↗
The soil-water retention curve (SWRC) is a key indicator characterizing soil water retention capacity. Biochar incorporation can affect the SWRC by altering soil pore structure and physicochemical properties. The SWRC of biochar-amended soils is influenced by both soil- and biochar-related factors. To address the time-consuming and labor-intensive nature of conventional SWRC measurement methods, this study employed six machine learning models, Decision Tree (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), Light Gradient Boosting (LightGBM…
- OpenAlex · Published · First archived
AR: An Artificial Intelligence‐Driven Fluorescent Reporter System for Gene Editing Evaluation ↗
ABSTRACT Accurate and scalable evaluation of gene‐editing efficiency is crucial for the development of novel editing tools. However, conventional methods are laborious, costly, and low‐throughput. Here, we developed an artificial intelligence (AI)‐driven fluorescent reporter (AR) platform, an integrated system that automates and streamlines the entire evaluation workflow. The AR platform combines an enhanced fluorescent reporter with an AI‐driven analytical pipeline. The dual‐fluorescence reporter system, in which mCherry carrying a PEST domain is paired with GFP, supports Cas variants with di…
- OpenAlex · Published · First archived
TrajOnco: a multi-agent framework for temporal reasoning over longitudinal EHR for multi-cancer early detection ↗
Abstract Accurate estimation of cancer risk from longitudinal electronic health records (EHRs) could support earlier detection and improved care, but modeling such complex patient trajectories remains challenging. We present TrajOnco, a training-free, multi-agent large language model (LLM) framework designed for scalable multi-cancer early detection. Using a chain-of-agents architecture with long-term memory, TrajOnco performs temporal reasoning over sequential clinical events to generate patient-level summaries, evidence-linked rationales, and predicted risk scores. We evaluated TrajOnco on d…
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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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