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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 ↗5488 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
Artificial intelligence for geothermal energy systems: a systematic evidence map of validation, transferability and deployment readiness ↗
Global installed geothermal power capacity is approximately 18 GWe, and geothermal plants generally provide dispatchable electricity at high capacity factors. Expansion depends on reducing the capital costs and operational risks of reservoir management, plant control and equipment maintenance, and artificial intelligence has been applied to all three. Whether that work has produced evidence sufficient for deployment has not been assessed at the level of individual studies. This review reports a PRISMA-guided systematic evidence map. Searches of Scopus and Web of Science returned 8,113 records;…
- OpenAlex · Published · First archived
MCI-DETR: A Multi-Level Context Interaction Transformer for Small-Object Detection in UAV Imagery ↗
Small objects in UAV imagery often exhibit weak visual representations, suffer from background clutter, and receive insufficient cross-scale feature interaction. To address these challenges, we develop MCI-DETR, a multi-level contextual interaction detector built upon RT-DETR. First, a Local-Context Modulated Interaction Module (LCMI) is designed to selectively modulate contextual features using local structural responses, thereby suppressing irrelevant background responses while expanding the effective receptive field. Second, CATM is incorporated into the high-level feature interaction stage…
- OpenAlex · Published · First archived
Aero‐ VLM : Vision‐Language Guided Oriented Aircraft Detection for Remote Sensing Imagery ↗
ABSTRACT Fine‐grained aircraft detection in remote sensing (RS) imagery is challenged by limited labelled data, domain shifts, high intra‐class similarity and complex environmental conditions. Conventional detectors rely heavily on dense supervision, whereas vision–language models (VLMs) often lack the geometric precision required for accurate localization in aerial scenes. To address these challenges, we propose Aero‐VLM, a hybrid CNN–Transformer framework that combines self‐supervised visual representation learning with pretrained vision–language semantic embeddings for data‐efficient fine‐g…
- OpenAlex · Published · First archived
PACS-Integrated Artificial Intelligence-Based Software Tool for Rapid and Fully Automatic Bone Fracture Assessment of the Tibial Plateau ↗
Background/Objectives: Tibial plateau fractures (TPF) are complex intra-articular injuries requiring accurate reduction and stable fixation; inadequate planning can prolong recovery and increase osteoarthritis risk. Although CT-based 3D reconstructions are routine, automated 3D identification and separation of individual fracture fragments is uncommon. We evaluated a PACS-integrated AI workflow for automated 3D segmentation of intact bones, fracture detection, and fracture-fragment delineation. Methods: This single-center retrospective study analyzed preoperative TPF CT scans acquired on 64- a…
- OpenAlex · Published · First archived
Explainable AI-integrated multi-objective optimisation for circular material routing in modular construction ↗
The rising digitisation of the construction sector has increased the demand for automated, data-driven decision-making in BIM-enabled operations. However, material routing decisions in modular construction are still primarily manual or based on opaque optimisation models, which limits their integration with construction automation systems. To solve this, the Explainable AI-Integrated Multi-Objective Optimisation (XAIOM) framework is presented, which allows for automated material routing straight from BIM-derived data. The system combines machine learning (ML), SHAP and LIME explainability, non…
- OpenAlex · Published · First archived
CCD-MAF: A Cognitive Conceptualization Diagram-driven Multi-Agent Framework for structured CBT dialogue generation ↗
While Large Language Models (LLMs) have shown potential in mental health support, existing approaches often provide unstructured emotional responses and struggle to deliver structured, evidence-based interventions such as Cognitive Behavioral Therapy (CBT). Existing CBT dialogue datasets also lack sufficient clinical depth and phase-specific patient resistance. To address these limitations, we construct the CCD-CBT Dataset, a multi-phase CBT dialogue corpus integrating expert textbook knowledge with real-world doctor–patient interactions sourced from Haodf Online and other medical consultation…
- OpenAlex · Published · First archived
Explainable Emergency Response Recommendation for Foundation Pit Collapse Accidents: An LLM-Enabled E/B/R Ontology-Driven Hybrid Reasoning Framework ↗
Foundation pit collapse accidents require rapid identification of accident conditions and appropriate emergency measures. Accident investigation reports contain valuable response knowledge, but their unstructured format limits direct reuse. This study develops an LLM-enabled E/B/R ontology-driven hybrid reasoning framework for explainable emergency response recommendations. Accident knowledge is organized as Event (E), Bearing (B), and Response (R). The resulting ontology contains 51 subclasses, and 69 foundation pit collapse reports were retained from 1110 collected construction collapse repo…
- OpenAlex · Published · First archived
Automating Injury Severity Score Calculation From Free-Text Discharge Diagnoses Using Domain-Specific Retrieval-Augmented Large Language Models: Retrospective Validation Study ↗
BACKGROUND: Trauma registries are crucial for evaluating the prognosis of injuries and the quality of trauma care. However, the calculation of Injury Severity Score (ISS) remains a predominantly manual process, which is labor-intensive, potentially error-prone, and difficult to scale. Automating this process is a critical step toward enhancing the efficiency and quality of trauma registry data. OBJECTIVE: This study conducts a feasibility test on the use of large language models (LLMs) to automatically calculate ISS from free-text discharge diagnoses. We specifically investigate the efficacy o…
- OpenAlex · Published · First archived
Performance evaluation and interpretability of a Qwen LangGraph agent for AI written and AI rewritten text detection ↗
Abstract The widespread adoption of large language models has increased the need to distinguish human-authored text from AI-generated content, including cases in which generated text is subsequently rewritten or paraphrased. In practice, many detectors output only a single AI-likelihood score or percentage, offering limited transparency and little indication of whether a passage appears directly AI-written (AIW) or AI-rewritten (AIR). This study presents an agentic detection framework for two connected tasks: binary provenance detection (Human versus AI) and conditional subtype detection (AIW…
- OpenAlex · Published · First archived
Characteristics and Applications of Chemosensors for Biometric and Microclimate Measurements in AI Astronaut Suits: A Systematic Review ↗
Background: Astronauts operating in extravehicular activity (EVA) and intravehicular activity (IVA) suits are exposed to a closed, thermally demanding microenvironment in which sweat accumulation, hydration status, skin-barrier disturbance, optical-signal interference, and changes in oxygen-carrying capacity can affect health, comfort, and task performance. This review therefore evaluates five sensing modalities—humidity, pH, sebum, melanin, and hemoglobin—not as general cosmetic sensors, but according to their potential roles in AI-enabled astronaut-suit monitoring. Methods: A systematic lite…
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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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