Results 141 to 150 of about 67,574 (313)

Adolescent gambling and gambling-type games on social networking sites: issues, concerns, and recommendations

open access: yes, 2015
Research indicates that compared to the general population, teenagers and students make the most use of social networking sites (SNSs). Although SNSs were originally developed to foster online communication between individuals, they now have the ...
Griffiths, MD
core  

Evolution of Physical Intelligence Across Scales

open access: yesAdvanced Intelligent Discovery, EarlyView.
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu   +7 more
wiley   +1 more source

A Multimodal Intelligent System for Human Digital Twin Simulation with Continuous Kinematic Data Tracking, Biometric Prognosis, and Cognitive State Feedback in Industrial Environments

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article implements a unified human digital twin framework that integrates cutting edge actuation, sensing, simulation, and bidirectional feedback capability. The approach includes integrating multimodal sensing, AI, and biomechanical simulation into one compact system.
Tajbeed Ahmed Chowdhury   +4 more
wiley   +1 more source

Adolescent gambling in Great Britain

open access: yes, 2008
This article briefly overviews some of the latest research into adolescent gambling and looks at the recent 2005 Gambling Act in relation to adolescent gambling measures. The new Gambling Act came into force on 1 September 2007, and replaces existing law
Griffiths, MD
core  

An Autonomous Large Language Model‐Agent Framework for Transparent and Local Time Series Forecasting

open access: yesAdvanced Intelligent Discovery, EarlyView.
Architecture of the proposed large language model (LLM)‐based agent framework for autonomous time series forecasting in thermal power generation systems. The framework operates through a vertical pipeline initiated by natural language queries from users, which are processed by the LLM Agent Core powered by Llama.cpp and a ReAct loop with persistent ...
William Gouvêa Buratto   +5 more
wiley   +1 more source

Pathways to understanding problem gambling among adolescents

open access: yesBMC Public Health
Background Gambling among youth is attracting the attention of health experts worldwide. A need has arisen for more research on the pathways to the development of adolescent problem gambling behavior and related factors.
Hyun Jung Lee, Gyungjoo Lee
doaj   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

open access: yesAdvanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Counting the cost: inquiry into the costs of problem gambling [PDF]

open access: yes, 2013
This report presents the findings of an inquiry commissioned to inform policy makers and the community about the true costs of problem gambling and where they fall.

core  

House edge: hold percentage and the cost of EGM gambling

open access: yes, 2013
Price in commercial gambling is effectively the house edge of the game. For electronic gaming machines (EGMs), house edge is the hold percentage. The paper tracks changes in hold percentage for club and hotel EGM gambling in Australia.
Harrigan, Kevin   +7 more
core   +1 more source

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

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