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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 ↗5934 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
From digital libraries to future libraries: Reframing academic libraries as knowledge infrastructures in the age of artificial intelligence ↗
Abstract Generative artificial intelligence (AI) is reshaping the production, organization, dissemination, and use of knowledge. In this context, the traditional positioning of academic libraries as knowledge repositories is increasingly challenged by commercial AI platforms, while growing disintermediation raises questions about their role in contemporary knowledge ecosystems. Drawing on Infrastructure Studies and socio-technical systems theory, this study explores the conceptual transition of libraries from knowledge repositories to knowledge infrastructures. To support this argument, it pro…
- OpenAlex · Published · First archived
AI-guided optimization of nanocomposite-based phytoremediation for enhanced soil and crop health in contaminated land ↗
Contamination of soil by multiple toxic metals requires remediation strategies that combine high removal efficiency, ecological compatibility, and rational materials optimization. Here, an explainable artificial intelligence (XAI)-assisted framework was developed to optimize multifunctional GO-biochar-PEI-metal oxide hybrid nanocomposites for coupled contaminant removal and phytoremediation. Random forest (RF) and artificial neural network (ANN) models were trained using independent physicochemical and operational descriptors to predict an Overall Performance Index integrating metal removal, p…
- OpenAlex · Published · First archived
A Hybrid Deep Learning Approach for Early Detection of Wildfires Using Transformer Models and Feature Fusion Techniques ↗
Natural disasters, particularly wildfires, pose a serious threat to ecosystems, human lives, and infrastructure, making rapid and accurate fire detection essential for effective disaster management. Despite recent advances in deep learning-based wildfire detection, conventional CNN-based approaches are inherently limited in modeling long-range spatial dependencies, while relying on a single deep architecture may provide an incomplete representation of complex and multi-scale fire patterns. Furthermore, high-dimensional deep features may contain redundant information, increasing computational c…
- OpenAlex · Published · First archived
Machine Learning-Based Fault Classification for Intelligent Condition Monitoring in Industrial Production Systems ↗
Industrial production systems require intelligent maintenance solutions to minimize unplanned downtime, improve reliability, and support data-driven decision making. This study presents a machine learning framework for condition monitoring and fault classification in industrial production systems. The framework is evaluated using an AI4I-derived synthetic dataset comprising 10,000 production events characterized by operational sensor measurements and machine failure indicators. An exploratory analysis is first conducted to examine data distributions, failure patterns, and relationships among o…
- OpenAlex · Published · First archived
An imputation method for single‐cell RNA sequencing data based on a parallel gated recurrent unit neural network ↗
Abstract Single‐cell RNA sequencing (scRNA‐seq) generates high‐dimensional transcriptomic data but is severely affected by dropout events, which obscure true gene–gene relationships. These missing values introduce bias into downstream analyses, complicate data processing, and reduce analytical efficiency. Therefore, accurate imputation of dropout events is essential for recovering genuine expression signals and improving the reliability of scRNA‐seq studies. In this work, we propose a novel scRNA‐seq data imputation method, imputation with a parallel gated recurrent unit neural network (IPGRU)…
- OpenAlex · Published · First archived
Knowledge-grounded multimodal cognitive decision-making for aerospace machining: A physics-aware reasoning framework for generative process planning of special-shaped features ↗
Complex aerospace products encompass numerous special-shaped features (SSFs), whose machining processes rely on highly customized process planning. Traditional data-driven discriminative models predominantly operate as physics-agnostic "black boxes." Moreover, applying general-purpose Multimodal Large Language Models (MLLMs) to manufacturing often causes "physical hallucinations", generating linguistically plausible but mechanically infeasible process plans. To bridge this gap, this paper proposes a Knowledge-Grounded Multimodal Cognitive Framework (KGMCF), which extends discriminative feature…
- OpenAlex · Published · First archived
Beyond the efficiency trap: Regional AI exposure, AI knowledge, and the asymmetric governance of data-driven value propositions ↗
Artificial intelligence (AI) is reshaping corporate strategy, yet how regional AI exposure (RAE), the intensity of local industry peers' AI inventive activities, shapes firms' data-driven value propositions (DDVPs) remains unclear. Using panel data on Chinese listed firms from 2013 to 2023, we examine the mechanism linking RAE to DDVPs. We find that higher RAE is associated with greater subsequent AI knowledge generation, which is more strongly related to efficiency-oriented than product-oriented DDVPs, reflecting an efficiency trap. Dynamic capabilities govern this conversion asymmetrically:…
- OpenAlex · Published · First archived
Segmentation-free hybrid CNN–transformer CAD framework with handcrafted feature fusion for explainable Leukaemia diagnosis ↗
Diagnosing leukaemia from microscopic blood smear images is nevertheless challenging for traditional Computer-Aided Diagnosis (CAD) systems. Many current methods depend on segmentation and basic feature representations. This makes it hard to tell apart morphologically similar subtypes. This work presents an extended hybrid CAD framework to address these limitations by fusing handcrafted texture descriptors, deep CNN representations, Transformer-based contextual modelling and explainable AI. Multi-resolution handcrafted features using the Discrete Wavelet Transform (DWT), Grey-Level Co-occurren…
- OpenAlex · Published · First archived
Negotiating AI integration in higher education curricula: Coping behaviour or career opportunity? ↗
This study examines how higher education educators perceive artificial intelligence (AI) as both a coping resource and a catalyst for career development. Grounded in the Stimulus-Organism-Response (S-O-R) framework and integrating Social Cognitive Career Theory (SCCT) with Lazarus and Folkman's Stress Theory (L&FST), the study explores how psychological and motivational factors shape career adaptability. Data were collected from 155 educators using structured surveys. Reliability and validity were established, and relationships among variables were tested using Partial Least Squares Structural…
- OpenAlex · Published · First archived
Machine learning prediction of UHPC creep coefficient using optimized extremely randomized trees ↗
The increased mechanics, durability, and affordability of ultra-high-performance concrete (UHPC) with regular Coarse Aggregate (CA) make it promising. machine learning (ML) was used to study UHPC Creep. Using feature significance analysis, the Aggregate-To-Cement Ratio (AG/C), compressive strength at loading age (CSLA), Water-To-Binder Ratio (W/B), relative humidity ( $$RH$$ ), Steel Fiber Volume Content (SF), Elastic Modulus at Loading Age (EMLA), and Curing Temperature (Tc) were the most essential characteristics. To estimate the Creep Coefficient (CC), this study attempts to demonstrate the…
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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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