THE INTERNET IS THINKING
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 ↗5927 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
Thermodynamic characteristics evaluation of a biomimetic printed circuit heat exchanger inspired by spine morphology integrating machine learning prediction and interpretability insights ↗
This study introduces a novel Spine-Inspired Printed Circuit Heat Exchanger (SI-PCHE), leveraging the physiological curvature and spinous processes of the human spine to optimize thermodynamic characteristics. While the inherent mechanical resilience and flexural rigidity of the vertebral structure provide a foundational guarantee for equipment integrity, its potential for thermodynamic characteristics intensification through bending features and spinous process inducing geometric perturbations remains to be systematically elucidated. Utilizing three-dimensional numerical simulations, the rese…
- OpenAlex · Published · First archived
A physics-guided machine learning approach to screening spectral induced polarization measurements ↗
Spectral induced polarization (SIP) measurements are increasingly used in time-lapse monitoring studies and require consistent quality control before interpretation. SIP spectra are information-rich but susceptible to acquisition artifacts, noise, and nonphysical behavior, making reliable screening essential before spectra are used to infer material or process changes. Existing approaches rely largely on manual expert review or automated classifiers that do not explicitly evaluate physical plausibility. Here, we develop a physics-guided machine-learning workflow for raw SIP quality control tha…
- OpenAlex · Published · First archived
Hybrid application of response surface methodology modelling and machine learning exploratory prediction for reducing sugar recovery from potato peel waste for bioethanol production ↗
Global interest in bioethanol production from waste lignocellulosic biomasses (LCB) has heighten, however, current efforts are often constrained by lack of predictability of outcome in terms of sugar recovery towards bioethanol produced. Thus, focusing on reducing sugar (RS) recovery, this study models reduced sugar release from nano-based PPW pretreatment. RSM was initially employed to design 15 experimental runs for each nanoparticle treatment and to optimize RS recovery. Subsequently, four ML algorithms: Random Forest Regression (RF), Gradient Boosting Regression (GBR), Support Vector Regre…
- OpenAlex · Published · First archived
Multimodal Evaluation of Exoskeleton Assistance Using Optimized Machine Learning ↗
Repetitive manual material handling (MMH) remains a leading occupational risk factor for work-related musculoskeletal disorders (WMSD), and passive shoulder-support exoskeletons have emerged as promising assistive technologies. Most evaluations rely on group-level statistics from single sensor modalities, leaving open whether their effects can be detected at the individual level. Thirty-five adults performed low-risk (4.54 kg, Lift Index (LI) < 1.0) lifting tasks, derived from the Revised NIOSH Lifting Equation, with and without the passive shoulder exoskeleton support. Surface electromyograph…
- OpenAlex · Published · First archived
Nutritional Accuracy and Clinical Applicability of Large Language Model-Generated Therapeutic Diet Plans for Phenylketonuria: A Comparative Simulation Study ↗
Background: Large language models (LLMs) are increasingly used to generate nutrition-related recommendations, but their reliability for therapeutic diets requiring precise nutrient control remains uncertain. This study evaluated AI-generated one-day diet plans for standardized simulated cases of classical phenylketonuria (PKU). Methods: Eight standardized simulated cases representing infancy, adolescence, adulthood, and maternal PKU were evaluated. Between 8 and 10 February 2026, ChatGPT-5.2, Claude 4.5, and Gemini 3 were each used through their standard consumer-facing chat interfaces to gene…
- OpenAlex · Published · First archived
Representing Search State to a Large Language Model: A Structured Telemetry Interface for Runtime Control of Adaptive Large Neighbourhood Search ↗
Adaptive large neighbourhood search is steered by high-level control decisions, yet its standard adaptation reacts to a narrow reward signal that conveys little about the state of the search. Recent work has strengthened this control with machine-learning and reinforcement-learning policies, but these generally require task-specific training. This paper proposes a framework in which a large language model acts as a bounded, training-free runtime supervisor of the search. The central contribution is a structured telemetry interface that compresses the evolving search into a compact, reproducibl…
- OpenAlex · Published · First archived
Benchmarking descriptor-based AI approaches for predicting the BBB permeability of drug-like xenobiotics ↗
Accurate prediction of blood-brain barrier (BBB) permeability is widely recognized as important for drug discovery, neurotoxicity assessment, and evaluating xenobiotic exposure linked to neurodegenerative diseases. In silico models, particularly those based on artificial intelligence (AI), are increasingly used to predict small-molecule BBB permeability. However, challenges remain due to heterogeneous datasets, limited sample sizes, and the incomplete capture of relevant molecular features, which affect model reliability and interpretability. Feature selection plays a key role in enhancing the…
- OpenAlex · Published · First archived
Long-Term Wind Power Forecasting via a Hybrid Statistical–Stochastic–Machine Learning Approach ↗
Accurate long-term wind-power forecasting benefits from probabilistic scenario generation that reflects both the temporal dependence of wind conditions and the uncertainty associated with distributional parameters. This study develops a hybrid Statistical–Stochastic–Machine Learning framework for month-ahead wind-power scenario generation. Monthly wind-speed observations are represented using Weibull distributions, whose shape and scale parameters are estimated from historical data and modelled through a bivariate vector autoregressive process defined on the original parameter scale. A heteros…
- OpenAlex · Published · First archived
INTEGRATIVE TRANSCRIPTOMIC AND MACHINE LEARNING ANALYSIS REVEALS CANDIDATE HUB GENES IN DUCHENNE MUSCULAR DYSTROPHY ↗
Duchenne muscular dystrophy (DMD) is a fatal X-linked disorder characterized by progressive muscle degeneration and cardiomyopathy, for which reliable early biomarkers remain limited. This study aimed to identify key molecular biomarkers and regulatory networks in DMD cardiac fibroblasts using integrative transcriptomic and machine learning approaches. RNA-sequencing data (GSE237014) from induced pluripotent stem cell–derived cardiac fibroblasts of four DMD patients and four healthy controls were analyzed to identify differentially expressed genes (DEGs). Protein–protein interaction networks w…
- OpenAlex · Published · First archived
ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: The Evolution of Large Language Models and Their Impact on Animal Sciences ↗
Abstract The rapid rise of large language models (LLM) is reshaping the scientific landscape, transitioning from early statistical language models to advanced transformer-based architectures capable of synthesizing knowledge across disciplines. While their predictive capacity and scalability have opened new avenues in data analysis, hypothesis generation, and decision support, concerns remain regarding bias, hallucination, reproducibility, and ethical governance. In animal sciences, LLM are gradually applied to challenges in nutrition modeling, animal health, genetic selection, and sustainabil…
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
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.
See the collection and counting methodology ↗ · Read the weekly synthesis ↗
Follow a thread
Explore related regions
These navigation suggestions are part of the map’s editorial structure, rather than measured evidence of a connection.