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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 ↗5236 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
Data-driven static position modeling of a parallel tendon-actuated soft continuum robot using artificial neural networks ↗
Abstract Parallel soft continuum robots combine the compliance and adaptability of soft structures with the structural advantages of parallel mechanisms, offering promising capabilities for complex robotic applications. However, their highly nonlinear behavior presents significant challenges for accurate and computationally efficient modeling. This study presents a data-driven approach for static end-effector position modeling of a parallel tendon-actuated soft continuum robot using an artificial neural network (ANN). A physics-based model of the robot is developed in the SoRoSim MATLAB framew…
- OpenAlex · Published · First archived
The Impact of AI on Education and Careers: US Students' Perceptions ↗
The growing use of Artificial Intelligence (AI) tools in higher education and the workplace has sparked both optimism and concern among educators, policymakers, and employers. These changes directly affect students, but their voices receive less attention than those of faculty members and institutions. This study examines key aspects of student-AI interaction, including familiarity, usage patterns, and perceived career impact. The study used a qualitative design based on semi-structured interviews. Respondents included six undergraduate students in the business administration department of a m…
- OpenAlex · Published · First archived
Product Design Analysis Based on Advanced Neural Network Models: A Review of Recent Findings and Directions for Further Development ↗
Recently, neural network (NN) modeling and analysis approaches have been successfully applied across a wide range of scientific and technological fields, including product design. To build more accurate and advanced NN models, researchers have expanded upon existing findings by incorporating memory effects, reaction–diffusion structures, impulsive perturbations, uncertainties, and fractional-order dynamics. Also, the effective design and real-world application of NN models rely on their qualitative properties. In fact, control and stability are fundamental to the development and practical rele…
- OpenAlex · Published · First archived
A Two-Stage Neural Network Framework for Swimming Direction Control of an Underwater Walking Robot: Kinematic Validation via Simulation and In-Air Experiments ↗
Each year, hundreds of thousands of tons of derelict fishing gear become entangled in ship propellers, and resolving such incidents currently requires divers to manually cut the entangled material underwater, exposing them to the risk of secondary accidents. To address this problem, we developed a radial quadruped underwater robot that walks along the hull of a ship using electromagnetic feet and swims freely using propellers mounted on the distal link of each leg. However, because each leg has only four degrees of freedom (DOFs), the platform is an underactuated system that cannot independent…
- OpenAlex · Published · First archived
From AI Consumers to AI Value Creators: Assessing Urban Indian University Students' Readiness for Viksit Bharat 2047 ↗
This study aims to explore the technological sovereignty of urban Indian university students with respect to the use of AI towards Viksit Bharat 2047. It demonstrates the current state of AI usage orientation among the younger generation as AI consumers or AI value creators and strives to assess the path that we are journeying in towards a developed nation. With the current digital era, AI tools seek to help in research, improve problem-solving skills, and encourage innovative thinking. Generative AI and prompt templates help in comprehending the cognitive abilities of university students usin…
- OpenAlex · Published · First archived
Machine-Learning-Assisted DFOS Framework for Piled-Raft Foundation Monitoring ↗
Reliable interpretation of distributed fiber optic sensing (DFOS) data from large building foundations requires accounting for thermal effects, temporal response, and structural context while preserving distributed spatial resolution. This study develops a machine-learning-assisted framework for pointwise screening and engineering interpretation of DFOS measurements from a reinforced-concrete piled-raft foundation. The workflow integrates temperature compensation, reference-profile comparison, deterministic engineering screening, structural-zone mapping, and Isolation Forest analysis using ana…
- OpenAlex · Published · First archived
Interpretable AI Accelerates the Discovery of High‐Light‐Yield Inorganic Scintillators for X‐ray Imaging ↗
ABSTRACT Inorganic scintillators are essential for X‐ray detection and imaging applications, but their rational design remains challenging due to limited experimental datasets and the chemical heterogeneity across oxides, fluorides, chlorides, and iodides, each with distinct luminescence mechanisms. Herein, we develop a machine learning framework based on a Mixture‐of‐Experts Kolmogorov–Arnold Network (MoE‐KAN) to accelerate the discovery of inorganic scintillators with high light yield (LY). By integrating chemistry‐aware gating with KAN‐based expert regressors, MoE‐KAN effectively captures n…
- OpenAlex · Published · First archived
Single-channel wearable EEG for ADL recognition in Healthy and Epileptic cohorts using MODWT-MRA and machine learning ↗
Wearable single-channel EEG is increasingly used to monitor epileptic patients outside the clinic, but the physical activity of daily living (ADL) continuously shapes the recorded signal. This activity-coupled variability is typically discarded as noise, even though it holds recoverable contextual information. Until now, EEG-based human activity recognition (HAR) has been studied almost exclusively in healthy participants under controlled lab conditions, leaving ADL recognition in clinical epileptic populations unaddressed. This study introduces a single-channel frontal EEG framework for ADL r…
- OpenAlex · Published · First archived
Current status and future outlook of artificial intelligence and automation in intravenous infusion systems ↗
Abstract The combination of the Internet of Things (IoT), artificial intelligence (AI), and wearable biosensors is transforming healthcare by allowing continuous, real-time monitoring and more intelligent clinical decision-making. This review explores AI-driven monitoring, smart intravenous (IV) infusion automation, and wearable biosensors, and proposes Intelligent IV Automation and Smart Patient Synchronization (INFUSYNC), a conceptual framework synchronizing monitoring, automated fluid delivery, and predictive analytics. The literature shows meaningful progress: wearables increasingly suppor…
- OpenAlex · Published · First archived
Entrepreneurial theorizing with generative artificial intelligence ↗
Abstract Research Summary Whether large language model (LLM) assistance changes how entrepreneurs construct theories remains unclear. On the one hand, LLMs may help entrepreneurs build more developed causal structures and surface diverse information that supports the formulation of distinctive theories. On the other hand, LLMs may steer causal structures toward conventional forms and discourage the counterintuitive reasoning needed to develop distinctive theories. In a within‐subject experiment with 199 participants theorizing both with and without LLM assistance, we find no significant averag…
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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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