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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 ↗6073 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
Distributed Artificial Intelligence for Agentic Intent-Based Networking in Mobile Networks ↗
Through intent-based networking (IBN), network objectives are specified declaratively, yet in mobile networks their realisation spans administrative domains that compete for coupled radio, transport and compute resources but cannot share local models and telemetry. A distributed artificial intelligence (DAI) controller with a Belief-Desire-Intention-eXecution (BDIX) cycle is presented, centred on shared-space coordination. The local decision of each domain agent is mapped through Gi into a common p-dimensional resource space, so that only resource contributions and a common price vector cross…
- OpenAlex · Published · First archived
How Does Artificial Intelligence Shape Green Innovation Ambidexterity? The Moderating Role of Managerial Characteristics ↗
Amid the rapid advancement of artificial intelligence (AI) technology and growing environmental pressures, this study empirically examines the impact of AI adoption intensity on green innovation ambidexterity (GIA), drawing on organizational ambidexterity theory and dynamic capabilities theory. We also examine the complementary and moderating roles of CEO characteristics in the context of AI technology. Using data from Chinese A-share listed firms during 2011–2025, we test these hypotheses. The results show that AI adoption intensity exhibits an inverted U-shaped relationship with GIA. Mechani…
- OpenAlex · Published · First archived
Rock Strength Classification in a Brazilian Iron Ore Mine Using Operational Drilling Variables and Machine Learning ↗
Rock strength is a key parameter for mine planning and operational optimization, but conventional laboratory testing is costly and provides limited spatial coverage. This study develops a methodology for classifying operational rock-strength classes in a Brazilian iron ore mine using reverse circulation (RC) drilling data and machine learning. Approximately 700 m of drilling data from ten boreholes were analyzed using operational variables acquired by onboard sensors. Data consistency was assessed through twin-hole analysis using Principal Component Analysis (PCA) and LSTM-based autoencoders,…
- OpenAlex · Published · First archived
Application of Artificial Intelligence and Surface Electromyography in the Assessment of Quadriceps Neuromuscular Fatigue and Functional Risk of Knee Injury in Professional Basketball Players: A Pilot Study ↗
Background: Neuromuscular fatigue reduces a muscle’s capacity to generate force and is a key determinant of performance and injury risk. In basketball, the quadriceps femoris (QF) is central to jumping, acceleration, and changes in direction, so objective detection of QF fatigue is clinically valuable. Objective: To develop and validate machine-learning models for detecting QF neuromuscular fatigue in professional basketball players from surface electromyography (sEMG), and to relate fatigue-induced changes to knee biomechanics. Methods: In this pilot study, eight male athletes (n = 8; basketb…
- OpenAlex · Published · First archived
QUINN: Quantized Inference and Dataflow Characterization for Neural Networks on RISC-V Near-Memory Architectures ↗
Machine learning inference is moving to the edge due to latency, energy, and privacy constraints. The growing complexity of edge workloads places increasing pressure on memory hierarchies, making data movement a major limitation for both performance and energy efficiency. Fixed-function accelerators inherit the memory wall of the von Neumann model and lack the flexibility to follow evolving models. Programmable Vector Processing Unit (VPU)-based near-memory architectures alleviate this bottleneck using standard Complementary Metal-Oxide Semiconductor (CMOS) while remaining programmable, but st…
- OpenAlex · Published · First archived
Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-Based Code Generation ↗
LLM-based systems increasingly incorporate long-term memory to improve cross-session continuity. However, once insecure coding preferences are stored, they may silently influence security-critical decisions in subsequent generations. In this study, we conduct the first systematic empirical study on the impact of insecure coding preferences stored in long-term memory on the security of LLM-based code generation. We evaluate four LLMs (ChatGPT, Gemini, Qwen, and Grok) across five programming languages (Python, C, C++, Go, and JavaScript). Our results show that insecure memories significantly inc…
- OpenAlex · Published · First archived
Integrative Machine Learning and Immunoinformatics-Guided Design and In Silico Validation of a Multiepitope DNA Vaccine Against Avian Metapneumovirus ↗
Avian metapneumovirus (aMPV) is an emerging viral pathogen causing many outbreaks in chickens all over the world. There are several subtypes of the MPV circulating in chickens in the US. The current circulating subtypes of the virus in the US chicken are A, B, and C. Despite the availability of some aMPV vaccines in the US, most of these vaccines are based on foreign strains, particularly European aMPV-A/B strains. Their protective efficacy against contemporary U.S. aMPV isolates has not been fully established. The main goal of this study is to integrate the most recent aMPV genome sequencing…
- OpenAlex · Published · First archived
Integrated Thermal and Energy Optimization of a Solar-Powered 500 MW AI Data Center in Central North Texas ↗
AI data centers raise concerns about electricity consumption and costs, water shortages, and air pollution, as AI, machine learning, cloud computing, and high-performance computing rapidly increase electricity demand and thermal loads. This study develops an integrated framework for a large-scale, solar-assisted AI data center in Granbury, Texas, combining high-efficiency liquid cooling, heat-exchanger optimization, solar photovoltaic (PV) generation, and grid electricity. A MATLAB-based thermal and energy model was developed to evaluate GPU workloads, IT power consumption, cooling demand, coo…
- OpenAlex · Published · First archived
Predictive Value of the Albumin-Bilirubin Score for All-Cause Mortality in Individuals with Stroke: Machine Learning Prediction Models with SHAP-Based Interpretation ↗
Background The albumin-bilirubin (ALBI) score, an objective liver function and prognostic indicator, has shown predictive value in various diseases, but its long-term prognostic significance in stroke survivors remains unclear. Objective To evaluate the ALBI score's predictive value for all-cause mortality (ACM) in individuals with stroke. Methods Data from the National Health and Nutrition Examination Survey (1999–2018) were analyzed. The Boruta algorithm selected key variables associated with ACM. Kaplan–Meier curves, Cox regression, and restricted cubic splines examined associations and non…
- OpenAlex · Published · First archived
Integrated machine learning and structure-based discovery of noncovalent natural product inhibitors targeting SARS-CoV-2 papain-like protease ↗
Papain-like protease is an essential severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) enzyme involved in viral polyprotein processing and modulation of host innate immune responses, supporting its consideration as a viable target in antiviral research. Here, we present an integrated computational and experimental strategy to identify noncovalent inhibitors of SARS-CoV-2 papain-like protease from a natural product chemical space. A curated dataset of 893 reported papain-like protease inhibitors was used to train machine learning classification models, among which a light gradient bo…
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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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