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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 ↗6056 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
Governance-Specific Use of AI, Ownership Structure, Financing, and Operational Agility: Evidence from Indian SMEs ↗
BAs firms begin integrating AI into governance functions, research on how ownership structure and financing relate to this shift remains early-stage, especially among SMEs. To address this gap, this study draws on an unweighted analytical sample of 1215 Indian SMEs from the World Bank Enterprise Surveys India 2025 round linked to the 2026 AI Follow-up Survey. The follow-up sample overrepresents digitally intensive sectors, testing ten hypotheses via logistic regression and bootstrapped decomposition. The analysis showed that owner-managed firms were less likely to use AI for strategic decision…
- OpenAlex · Published · First archived
Machine learning-driven single-cell and bulk RNA sequencing integration unveils key genes modulating radiosensitivity and tumor progression in esophageal squamous cell carcinoma ↗
Abstract Background Radiotherapy resistance remains a significant clinical challenge in oesophageal squamous cell carcinoma (ESCC), often leading to treatment failure and tumour recurrence. Understanding the molecular mechanisms underlying radiotherapy resistance is crucial for improving patient outcomes. In this study, we explored the roles of MAP1LC3B and HSD17B10 in radiotherapy resistance and their potential as therapeutic targets. Methods We employed single-cell RNA sequencing (scRNA-seq), bulk RNA-seq and machine learning to identify key genes and cellular mechanisms associated with resp…
- OpenAlex · Published · First archived
A Scalable AI-Driven Digital Twin Framework for Hydroelectric Power Plants ↗
The growing U.S. energy demand, combined with the cost reduction and reliability enhancement needs of existing hydropower plants, necessitates smart modernization. Digital twin (DT) frameworks can enhance hydropower operations by leveraging advanced data-driven models for energy production optimization, degradation detection, anomaly prediction, and early-warning alarms. DTs can not only improve operational efficiency and asset longevity but also strengthen energy dominance in critical infrastructure by minimizing costly downtimes. Here, we present a new, scalable DT framework developed to sup…
- OpenAlex · Published · First archived
SSRESF-OPT: A Sensitivity-Aware Framework for Single-Particle Radiation Effects Simulation and Optimization in SoC Platforms Using Machine Learning ↗
The increasing scale of System-on-Chip (SoC) architectures exacerbates the reliability risks of single-particle radiation effects, rendering traditional exhaustive gate-level fault injection prohibitively slow. To address this, we propose SSRESF-OPT, an end-to-end simulation and optimization framework combining accelerated dynamic fault injection with machine learning-driven prediction. The framework first introduces a cross-layer static binding mechanism to eliminate runtime string resolution, reducing per-injection simulation overhead to \(\mathcal {O}(1) \) complexity. Building on this opti…
- OpenAlex · Published · First archived
Machine Learning-Assisted Predictive Formulation Modeling for Segmented Combination Vaginal Rings: Construction and Validation ↗
The vaginal ring represents an innovative sustained-release drug delivery system in the field of women’s reproductive health, demonstrating significant clinical value. However, its formulation development remains constrained by a traditional “trial-and-error” empirical approach, which often requires substantial cost investment for studies involving multiple factors and levels. To address evolving patient needs and diverse drug properties, this research investigates the application of machine learning algorithms in pharmaceutical development, with a specific focus on the development and optimiz…
- OpenAlex · Published · First archived
Explainable machine learning and hybrid transformer-based proteochemometric modelling for breast cancer molecular subtyping and multi-target natural product discovery ↗
Abstract Breast cancer comprises five intrinsic molecular subtypes with distinct transcriptomic programs, prognoses, and therapeutic vulnerabilities. Yet, accurate subtype discrimination remains limited by boundary-overlap and poor cross-cohort generalisability, while single-target agents often fail against pathway redundancy and acquired resistance. We developed an integrated computational framework linking transcriptomic-classification, explainable target prioritisation, and multi-target natural-product discovery. Three transcriptomic cohorts (METABRIC-BRCA-2016, TCGA-BRCA-2012, and TCGA-BRC…
- OpenAlex · Published · First archived
A Trustworthy Large Language Model-Based Method for Bearing Predictive Maintenance ↗
Large language models (LLMs) can integrate multisource information and generate structured maintenance recommendations. However, their outputs may underestimate risk and fluctuate over time, reducing the practical executability of decisions. To address these issues, this study proposes a trustworthy LLM-based method for bearing predictive maintenance. First, gradient boosting is combined with quantile regression to predict remaining useful life (RUL), followed by operating-condition shrinkage bias correction and width-scaled conformal calibration. The calibrated RUL information and leakage-fre…
- OpenAlex · Published · First archived
Integrating Machine Learning for Predictive Modeling of Carbon Sequestration in Mixed Microalgal Systems ↗
Abstract Microalgal carbon sequestration offers an energy-efficient solution to global carbon emissions. Mixed microalgae outperform pure cultures in adaptability and carbon fixation. This study carried out mixed microalgae cultivation experiments under different experimental conditions, focusing on examining the effects of five key factors: inoculation concentration, initial inoculation ratio, nitrate concentration, CO2 gas flow rate, and CO2 concentration. In the single-variable experiment where the CO2 gas flow rate was adjusted, the maximum carbon sequestration of 5.35 g/L was achieved. An…
- OpenAlex · Published · First archived
Sustainability intelligence: Integrating Artificial Intelligence, the Internet of Things, and Digital Twins for resource-efficient and climate-resilient systems ↗
Sustainability has become one of the defining scientific and societal challenges of the twenty-first century. Climate change, rapid urbanization, population growth, increasing energy demand, and declining natural resources require decision-making that is continuous, adaptive, and supported by reliable environmental intelligence. Conventional monitoring systems, which often rely on periodic measurements and isolated analytical tools, are no longer sufficient for managing highly dynamic environmental systems. Recent advances in Artificial Intelligence (AI), the Internet of Things (IoT), edge com…
- OpenAlex · Published · First archived
Global insights into machine learning-enabled smart grids: Challenges and solutions ↗
This paper reviews machine learning (ML)-enabled smart grids (SGs), with emphasis on practical power-system implementation, current learning paradigms, global practices, challenges, and deployment-oriented solutions. A two-route structured review-and-synthesis methodology identified 167 records, removed 20 duplicates, screened 147 records, and retained 113 sources for the primary thematic evidence synthesis. The final evidence base comprised 67 core technical studies and 46 supporting standards, policy documents, technical reports, books, and foundational sources; its field-oriented subset com…
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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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