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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 ↗5938 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
A Frozen Plant DNA Language Model for Computational Prioritization and Functional Annotation of Melon (Cucumis melo) Variants ↗
Background/Objectives: Fruit quality in melon is determined by complex traits including sugar accumulation, aroma, flesh color, ripening behavior and disease resistance. Conventional genomic selection provides limited mechanistic interpretation, especially for regulatory and de novo variants. We adapted the EVEE interpretable embedding–probing framework to melon using PlantCAD2 as a frozen DNA language model backbone. Methods: The model was not fine-tuned or further pre-trained. Instead, lightweight probes were trained on fixed embeddings derived from sequence windows surrounding genetic varia…
- OpenAlex · Published · First archived
An explainable machine learning framework for early student dropout risk prediction and stratification ↗
Student dropout remains a persistent challenge in higher education institutions, affecting academic continuity, institutional performance, and long-term socioeconomic outcomes. Early identification of at-risk students enables timely intervention and improved retention strategies. This study proposes an explainable machine learning framework for identifying and stratifying students according to a constructed dropout-risk label using academic, behavioural, financial, health-related, and personal factors. A dataset of 253 student records was collected through structured Google Form surveys and tr…
- OpenAlex · Published · First archived
"Hi Alex" or "Dear Dr. Morgan"?: Exploring How AI-suggested Politeness Strategies Influence Email Writing and Social Perception Among Native and Non-native Speakers ↗
As AI writing assistants are increasingly used for interpersonal communication, they may have profound impacts on interpersonal relationships. Politeness is one important aspect of social communication that is grounded in people’s perceptions of relational dynamics and significantly shapes social interactions. We investigate how politeness strategies in AI-generated suggestions affect people’s email writing and alter their perception of the social situation. Through a within-subject online study (N = 52), we found that human writers tend to mirror the type of politeness strategies used in the…
- OpenAlex · Published · First archived
Reframing the architect’s role in AI and digital design tools: Case studies, field study, and ethical reflections ↗
This study investigates how AI-assisted and digital/parametric tools transform architectural design processes and the architect’s evolving professional role. Despite rapid technological advancement, the integration of these tools within everyday workflows remains ethically and practically complex. Adopting a mixed-methods approach, the research combines a comparative analysis of three pioneering case studies (Autodesk Toronto Office, The Edge in Amsterdam, and Google Bay View Campus), with a field study involving 30 practicing architects. The results demonstrate that AI-assisted systems, inclu…
- OpenAlex · Published · First archived
Artificial Intelligence and Multi-Agent Approaches for Decision-Making and Business Process Optimization in Industrial Systems ↗
This study develops a conceptual and simulation-based framework for intelligent decision-making and process optimization in mineral processing systems. The framework integrates artificial intelligence, multi-agent coordination, adaptive control, and digital twins to support real-time optimization under uncertain and variable operating conditions. Systems analysis, mathematical modeling, simulation, and industrial data-processing methods are combined, while two-sample t-statistics assess differences between baseline and proposed scenarios across 50 independent simulation runs. The proposed arch…
- OpenAlex · Published · First archived
A hybrid physics and machine learning framework for trajectory prediction and uncertainty quantification of undrained behaviour in gassy clay ↗
The undrained response of gassy clay is strongly path-dependent and uncertain, making continuous trajectory prediction difficult when only limited experimental data are available. This study develops a lightweight physics-guided machine learning framework for path-level prediction and uncertainty quantification under undrained shearing. The framework combines MCC-based physical-reference trajectories, which provide stress-history-dependent mechanical information, with experimental gassy-clay trajectories, which provide target-domain response information. Both data sources are converted into a…
- OpenAlex · Published · First archived
Integration of a large language model as an agent of the ChronobioticsDB pharmacological database ↗
Objective. To develop a methodologically rigorous approach for employing large language models as an interface for accessing data from pharmacological repositories (exemplified by ChronobioticsDB). Material and methods. A method for integrating a large language model as an intelligent access agent for the ChronobioticsDB pharmacological database was proposed. The approach is based on two-stage meta-prompting and does not require additional model training. Relational database data are pre-transformed into the structured text cards, which are used as a sole source of information for the model. A…
- OpenAlex · Published · First archived
Federated hybrid quantum convolutional neural network for Alzheimer’s disease classification using magnetic resonance imaging ↗
Alzheimer’s disease (AD) is a progressive neurological disorder and one of the most common types of dementia, causing impaired memory and quality of life. Therefore, an early and accurate diagnosis is essential for intervention and patient care. Convolutional Neural Networks (CNNs) and other deep learning models have demonstrated promise in medical imaging but may need centralized collection of data, which raises privacy concerns. In this study, we propose a Federated Hybrid Quantum Convolutional Neural Network (FH-QCNN) that integrates quantum computing with federated learning for privacy-pre…
- OpenAlex · Published · First archived
Nondestructive firmness prediction for apples using force-aware through-transmission ultrasound with machine learning ↗
Firmness is a critical postharvest quality attribute for apples, yet conventional destructive testing precludes continuous grading. While through-transmission (TT) ultrasound offers a promising nondestructive alternative, prediction accuracy is often compromised by inconsistent transducer–fruit coupling, which has not been systematically addressed across varying contact conditions. This study developed a force-monitored TT platform integrating motorized transducer positioning, a dual-axis curvature wear plate, and real-time load-cell feedback to ensure repeatable coupling at high (20 N), low (…
- OpenAlex · Published · First archived
Machine learning enabled cross-stage prediction of cathode performance using manufacturing parameters and measured electrode descriptors ↗
Optimisation of lithium-ion battery manufacturing has traditionally relied on time-consuming and resource-intensive experimental trial-and-error. Here, we fabricated LiNi 0.8 Mn 0.1 Co 0.1 O 2 (NMC811) cathodes using varied manufacturing parameters, generating an experimentally measured laboratory dataset comprising 160 electrodes and 99 coin cells across varied manufacturing conditions. Group A and Group B cathodes were matched within manufacturing batches. Group A was uncalendered, whereas Group B was calendered. We built gradient boosting regressor (GBR) models to predict the cathode proper…
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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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