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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 ↗5546 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
Prediction of Carbon Amount Based on Formation Rate in Catalyzed Methane Pyrolysis via Induction Heating: An Application of a Physics-Informed Neural Network ↗
Carbon deposition during catalytic methane pyrolysis under induction heating governs reactor operability and carbon recovery; however, the available datasets are sparse. In this study, literature-derived experimental data were curated and split into training, validation and test sets (70%, 15%, 15%) to develop physics-informed neural networks (PINNs) that predict the deposited carbon mass (MC) from the operating variables. Two models were proposed: PINN-M1, which embeds an Arrhenius-type carbon formation rate in the loss function, and PINN-M2, which constrains the time derivative of the MC to…
- OpenAlex · Published · First archived
An urban patrol case urgency classification method based on self-supervised clustering of a graph neural network ↗
Accurate assessment of urban patrol cases plays an important role in maintaining urban safety and improving the efficiency of urban governance. The hazard level of urban patrol cases can be reflected by the urgency level and prioritizing high-urgency cases can help reduce potential risks to public safety. However, it remains challenging to assess the urgency level of urban patrol cases when reliable urgency labels are limited or unavailable. To address this problem, this paper proposes an urban patrol case urgency classification method based on self-supervised clustering of a graph neural netw…
- OpenAlex · Published · First archived
Predicting the CERCHAR Abrasiveness Index (CAI) from Geotechnical and Geophysical Rock Properties Using Machine Learning (ML) Regression Models ↗
The CERCHAR Abrasiveness Index (CAI) is widely used to quantify rock abrasiveness, yet its contact-based scratch procedure is highly sensitive to operational factors such as scratch length, surface preparation, stylus hardness, confining pressure, scratch velocity, and observer variability. The requirement for a prepared, polished surface and styli of specific Rockwell hardness further complicates testing, making CAI difficult to standardize across laboratories and challenging to compile into consistent databases. This study evaluates whether CAI can be reliably predicted from geotechnical and…
- OpenAlex · Published · First archived
AI-Based Prognostic Risk Stratification of Adult-Type Diffuse Glioma Patients from H&E-Stained Slides ↗
Background/Objectives: Adult-type diffuse gliomas (ADGs) are aggressive brain tumors with highly variable clinical outcomes. Prognostic assessment directly from routine hematoxylin and eosin (H&E)-stained whole-slide images (WSIs) could support personalized decision-making. Methods: To our knowledge, this is the first systematic benchmarking of pathology foundation models (FMs) and multiple instance learning (MIL) methods for overall survival (OS) risk stratification in ADGs under the 2021 World Health Organization (WHO) classification. A retrospective, multi-institutional cohort of 817 subjec…
- OpenAlex · Published · First archived
LLM fine-tuning for international news style generation and cross-cultural dissemination quality assessment ↗
The rapid globalization of news media has heightened the demand for high-quality, culturally adapted international news content. However, generating news articles that faithfully conform to the stylistic conventions of diverse national media outlets while maintaining cross-cultural semantic fidelity remains a formidable challenge. This paper presents a novel framework that leverages fine-tuned large language models (LLMs) for international news style generation and introduces a comprehensive cross-cultural dissemination quality assessment (CDQA) methodology. Specifically, we construct a multil…
- OpenAlex · Published · First archived
Constructing a graduate employability knowledge graph with multi-source data fusion and graph neural network-based multi-task prediction ↗
Addressing the limitations of existing employability prediction frameworks in cross-domain data integration and heterogeneous knowledge modelling, this study proposes a graduate employability knowledge graph construction framework grounded in multi-source heterogeneous data fusion and graph neural networks. A bidirectional confidence-scored alignment strategy is employed to integrate learning behaviour, market demand, and knowledge mastery data across three cross-domain sources, constructing a dynamic knowledge graph encompassing student, skill, and occupation entities across three hierarchica…
- OpenAlex · Published · First archived
From Traditional Passive Design to Artificial Intelligence-Based Optimization: A Systematic Review of Passive Design in Residential Buildings ↗
Buildings account for a significant share of global energy consumption and greenhouse gas emissions, highlighting the importance of passive design strategies for improving residential energy efficiency and thermal comfort. This systematic review, conducted and reported in accordance with PRISMA 2020, examines the evolution of passive residential design from bioclimatic principles toward building energy simulation, computational optimization, and artificial intelligence (AI). A total of 191 studies were included in the systematic evidence synthesis. The reviewed evidence identifies orientation,…
- OpenAlex · Published · First archived
The Moderating Effect of Gender on the Relationship Between Artificial Intelligence Usage, Behavioral Intentions, and Academic Engagement in Mathematics Education ↗
In the age of technology, where the educational sphere and strategic advancements drive development, it is essential to understand the complex nature of gender and implement innovative tools to foster an inclusive learning environment and empower every learner. This study examines the moderating effect of gender on the relationships among Artificial Intelligence (AI) use, behavioral intentions, and academic engagement in mathematics education. Using a cross-sectional design, the study sampled 328 mathematics undergraduates from public universities in southwestern Nigeria. A structured online q…
- OpenAlex · Published · First archived
Introduction to using symbolic regression for interpretable machine learning in healthcare ↗
Abstract The past decade has seen machine learning play an increasingly important role in medicine. However, many of the state-of-the-art models being developed and deployed suffer from a lack of transparency, limiting integration into the clinical decision-making process and raising concerns about patient safety. Here we call attention to symbolic regression, a largely overlooked machine learning technique that aims to be both accurate and interpretable by learning readable mathematical models from data. In recent years, it has undergone rapid advancements with successful application to probl…
- OpenAlex · Published · First archived
Making Healthcare AI Explainable: A Structured Review of Methods and Clinical Practice ↗
Artificial intelligence is rapidly transforming modern medicine, providing powerful tools to assist clinicians. However, the most accurate models are opaque, and their limited interpretability hinders clinical adoption and raises regulatory concerns. Explainable Artificial Intelligence (XAI) aims to make these models’ decisions comprehensible. Despite its rapid growth and the many reviews already published, aspects such as the rigor of explanation evaluation, clinician involvement, and the use of large language models to generate explanations appear to have received limited attention. This art…
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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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