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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 ↗5969 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
AI algorithms empowered terahertz functional devices design and perception applications ↗
Artificial intelligence (AI) algorithms have profoundly reshaped scientific research paradigms, providing data-driven, automated, and intelligent solutions to problems traditionally constrained by complexity and inefficiency. Recently, the integration of AI with terahertz (THz) technology has emerged as a highly significant research area. This is because AI algorithms not only facilitate the design of metasurface devices but also assist in the processing of data from THz-related research. We reviewed recent research progress from two major perspectives. 1) Device structural design: AI has been…
- OpenAlex · Published · First archived
Machine learning-based prediction of compressive strength of wastewater-exposed concrete using explainable and optimized models ↗
Reinforced concrete structures are extensively used in wastewater collection systems; however, their structural performance can progressively deteriorate under aggressive sewer environments associated with gas generation and chemical attack. Conventional empirical approaches for estimating residual compressive strength may be time-consuming and may not adequately capture complex nonlinear relationships among influencing parameters. The compressive strength of concrete subjected to wastewater was therefore predicted using optimized and interpretable machine learning algorithms. Four nonlinear m…
- OpenAlex · Published · First archived
Crisis-induced hybrid learning, cognitive offloading, and generative AI reliance among Pakistani CS undergraduates ↗
Purpose In spring 2026, geopolitical tensions prompted the Pakistani government to mandate full online instruction (10 March–3 April 2026), followed by a hybrid schedule for the rest of the semester. This shift is treated here as an externally imposed crisis context for AI adoption, not as a natural experiment. No pre-crisis baseline or control group was available. The study characterises generative AI (GenAI) adoption patterns and the psychological antecedents of AI dependency among undergraduate computer-science (CS) students during this window. Three hypotheses, grounded in the reviewed lit…
- OpenAlex · Published · First archived
Physics-informed explainable machine learning and thermodynamic optimisation of CNT-based nanolubricants for enhanced vapour compression refrigeration system performance ↗
The significance of refrigeration lies in decreasing electricity consumption. However, carbon nanotube (CNT) nanolubricants have nonlinear effects that are challenging to predict through traditional analysis. This research develops a comprehensive experimental, thermodynamic, explainable machine learning (ML), and physics-based optimisation framework for the refrigeration system with R134a working fluid and CNT/polyolester (POE) nanolubricant. Experimental data for different concentrations of CNT ranging from 0 to 1 wt% are analysed without data pre-processing, whereas states, refrigeration ef…
- OpenAlex · Published · First archived
Interpretable machine learning for predicting cumulative plastic strain of marine soils under multi-factor coupling effects ↗
Accurate prediction of cumulative plastic strain (CPS) is essential for assessing the deformation stability of marine strata subjected to artificial ground freezing (AGF). This study aims to clarify how variations in clay content and pore-water salinity regulate cyclic responses after freeze–thaw (FT) disturbance. Cyclic triaxial tests were conducted to investigate CPS under coupled effects of clay content, salinity, FT disturbance, and cyclic stress ratio (CSR). The resulting observations were used to develop Bayesian-optimized support vector regression (SVR), random forest (RF), extreme grad…
- OpenAlex · Published · First archived
Vulnerability-aware reinforcement learning scheduling of AI data centers under typhoon-induced thermal coupling ↗
The rapid growth of AI data-center loads, amplified by typhoon-induced thermal coupling between workloads and cooling systems, poses new challenges to transmission grid vulnerability assessment and scheduling. This paper develops a vulnerability-aware reinforcement-learning (RL) scheduling framework that integrates a measured-data-driven AI load model, a typhoon-day temperature–PUE–cooling model, a seven-indicator vulnerability assessment covering physical, structural, and AI-specific dimensions, and RL scheduling via PPO, SAC, and TD3 with a PTDF-based top- K branch-selection mechanism that c…
- OpenAlex · Published · First archived
CALT-GNN: a graph neural network with cross-attention and long-tail experts for multi-omics cancer subtype classification ↗
Abstract Cancer subtype classification based on molecular features is important for characterizing tumor heterogeneity, evaluating patient prognosis, and supporting precision treatment. Multi-omics integration provides complementary molecular information across genomic, epigenomic, transcriptomic, and proteomic levels. However, the high dimensionality of multi-omics data, complex cross-omics relationships, and imbalanced subtype distributions remain major challenges. To address these issues, we propose CALT-GNN, a graph neural network framework that integrates patient-similarity graph learning…
- OpenAlex · Published · First archived
The influence of artificial intelligence on students’ academic performance through career aspiration emotional intelligence and psychological well being among secondary school students in Awi Zone ↗
This study aimed to examine the Influences of Artificial Intelligence on students’ academic performance through Career Aspiration, Emotional Intelligence and Psychological Well-Being. A quantitative cross-sectional research design was employed, and data were collected from 200 students in secondary schools that have integrated Artificial Intelligence technologies into their tasks. A structured questionnaire comprising standardized measurement scales was used to assess Artificial Intelligence usage, emotional intelligence, career aspiration, psychological well-being, and students’ academic perf…
- OpenAlex · Published · First archived
The Algorithmic Well‐Being Paradox: When AI Decision Delegation Helps and Hurts Consumer Well‐Being ↗
ABSTRACT As generative AI increasingly shapes consumer decision‐making, its implications for consumer well‐being remain unclear. Existing research largely focuses on the trade‐off between algorithmic efficiency and autonomy loss. This research advances understanding by proposing the algorithmic well‐being paradox, whereby the same AI delegation can enhance or diminish well‐being depending on the consumption context. Integrating Savoring Theory and Self‐Determination Theory, we develop a framework that explains when and why these divergent outcomes occur. To test the proposed relationships, fou…
- OpenAlex · Published · First archived
Transformer based FinBERT framework with explainable artificial intelligence for money laundering detection ↗
The increasing complexity of suspicious financial activities has reduced the effectiveness of conventional rule-based monitoring for modern anti-money laundering (AML) applications. This study proposes FinBERT-AML, a transformer-based framework fine-tuned on transaction feature tokens to learn complex dependencies across heterogeneous financial attributes. Through self-attention-based representation learning, the framework jointly models categorical and continuous transaction characteristics while overcoming limitations associated with conventional machine learning and sequential architectures…
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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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