Results 81 to 90 of about 8,644,059 (236)

Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks

open access: yesAdvanced Science, EarlyView.
Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley   +1 more source

Gambling on the Internet: motivating and inhibiting factors [PDF]

open access: yes, 2010
Gambling operators can certainly benefit from knowing who their customers are, and why they choose to gamble. Professor Mark Griffiths and Abby McCormack, of Nottingham Trent University, discuss different socio-cultural player profiles, and assess how ...
Griffiths, MD, McCormack, A
core  

Compound Abrus cantoniensis Extract Alleviates AFB1‐Induced Liver Injury by Regulating PI3K‐AKT Pathway and Gut Microbiota/Butyrate Metabolism

open access: yesAdvanced Science, EarlyView.
ACCE has a good alleviating effect on AFB1‐induced liver injury, and this alleviating effect is related to the PI3K‐AKT pathway regulated by ACCE and the butyrate metabolism of gut microbiota. ABSTRACT Aflatoxin B1 (AFB1) is a potent hepatotoxin commonly ingested through contaminated food. Abrus cantoniensis Hance, a traditional Chinese medicinal herb,
Hongjie Hu   +12 more
wiley   +1 more source

What Really Drives Agri‐Environment‐Climate Measures' Adoption? Mandatory Requirements, Behavioral Traits, and Structural Factors in the CAP Green Architecture

open access: yesApplied Economic Perspectives and Policy, EarlyView.
ABSTRACT To increase farmer adoption of green practices, the EU Common Agricultural Policy includes both mandatory (conditionality) and voluntary instruments (eco‐schemes–ECS– and more demanding, multi‐annual agri‐environment‐climate measures –AECM–). Building on the experiment of Barreiro‐Hurle et al.
L. Sanchez‐Mata   +4 more
wiley   +1 more source

Gambling Behaviour Quarterly Telephone Survey, 2021

open access: yes, 2023
copyright UK Data Service and data collection copyright owner.The Gambling Behaviour Quarterly Telephone Survey produces overall official statistics estimates of gambling participation, prevalence of problem gambling and perceptions of gambling based on ...
Gambling Commission
core   +2 more sources

Internet gambling: issues, concerns, and recommendations. [PDF]

open access: yes, 2003
The influence of technology in the field of gambling innovation continues to grow at a rapid pace. After a brief overview of gambling technologies and deregulation issues, this review examines the impact of technology on gambling by highlighting salient ...
Griffiths, MD
core  

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

open access: yesAdvanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang   +3 more
wiley   +1 more source

Online Gambling Patterns and Predictors of Problem Gambling Among Korean Adolescents During the COVID-19 Pandemic: A Cross-sectional Study

open access: yesAsian Nursing Research
S U M M A R Y: Purpose: This study examined online gambling patterns among Korean adolescents during the COVID-19 pandemic and identified predictors of problem gambling based on a socio-ecological model.
Young-Sil Sohn, Hyunmi Son
doaj   +1 more source

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai   +3 more
wiley   +1 more source

Integrating Reinforcement Learning With Explainable Artificial Intelligence for Real‐Time Clinical Decision Support in Dynamic Healthcare Environments

open access: yesAdvanced Intelligent Systems, EarlyView.
A hybrid Reinforcement Learning–Explainable AI framework integrates SHAP and LIME explanations directly into a Deep Q‐Network inference loop for real‐time ICU decision support. Trained on 18 142 mechanically ventilated stays from the eICU database, the system attains 93.0% decision accuracy, 20% fewer errors than RL alone, and a 91% clinician trust ...
Jannatul Ferdaus Disha   +2 more
wiley   +1 more source

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