Results 191 to 200 of about 5,337,624 (364)

Galled Tree-Child Networks [PDF]

open access: yesarXiv
We propose the class of galled tree-child networks which is obtained as intersection of the classes of galled networks and tree-child networks. For the latter two classes, (asymptotic) counting results and stochastic results have been proved with very different methods.
arxiv  

MICG-AI: A multidimensional index of child growth based on digital phenotyping with Bayesian artificial intelligence [PDF]

open access: yesarXiv
This document proposes an algorithm for a mobile application designed to monitor multidimensional child growth through digital phenotyping. Digital phenotyping offers a unique opportunity to collect and analyze high-frequency data in real time, capturing behavioral, psychological, and physiological states of children in naturalistic settings ...
arxiv  

Childhood mental health: promotion, prevention and early intervention [PDF]

open access: yes
Good mental health is essential for children\u27s learning, social development, self-esteem and resilience to stress throughout the life-course. Over half a million Australian children have significant mental health problems.
Centre for Community Child Health
core  

Figame: A Family Digital Game Based on JME for Shaping Parent-Child Healthy Gaming Relationship [PDF]

open access: yesarXiv
With the development of technology, digital games have permeated into family and parent-child relationships, leading to cognitive deficiencies and inter-generational conflicts that have yet to be effectively addressed. Building on previous research on digital games and parent-child relationships, we have developed Figame, a Joint Media Engagement (JME)
arxiv  

Innovative Tokyo [PDF]

open access: yes
This paper compares and contrasts Tokyo's innovation structure with the industrial districts model and the international hub model in the literature on urban and regional development.
Child Hill, Richard, Fujita, Kumiko
core  

Enhanced dose prediction for head and neck cancer artificial intelligence‐driven radiotherapy based on transfer learning with limited training data

open access: yesJournal of Applied Clinical Medical Physics, EarlyView.
Abstract Purpose Training deep learning dose prediction models for the latest cutting‐edge radiotherapy techniques, such as AI‐based nodal radiotherapy (AINRT) and Daily Adaptive AI‐based nodal radiotherapy (DA‐AINRT), is challenging due to limited data.
Hui‐Ju Wang   +5 more
wiley   +1 more source

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