Results 41 to 50 of about 561,088 (291)

‘They Need to Hear You Say It’: Healthcare Professionals’ Perspectives on Barriers and Enablers to End‐of‐Life Discussions With Adolescents and Young Adults With Cancer

Pediatric Blood &Cancer, EarlyView.
ABSTRACT End‐of‐life conversations with adolescents and young adults (AYAs) with cancer rarely occur without the guidance of healthcare professionals. As a part of the ‘Difficult Discussions’ study, focused on palliative care and advance care planning discussions with AYAs with cancer, we investigated the factors that healthcare professionals identify ...
Justine Lee, Holly Evans, Claire E. Wakefield, Antoinette Anazodo, Richard J. Cohn, Brittany C. McGill, Elizabeth A. Lobb, Lori Wiener, Madeleine L. Juhrmann, Ursula M. Sansom‐Daly   +9 more
wiley   +1 more source

Kernel Methods for Surrogate Modeling

, 2019
This chapter deals with kernel methods as a special class of techniques for surrogate modeling. Kernel methods have proven to be efficient in machine learning, pattern recognition and signal analysis due to their flexibility, excellent experimental performance and elegant functional analytic background.
Santin G., Haasdonk B.
openaire   +2 more sources

Defining Roles in Pediatric Palliative Care: Perspectives From Oncology and Palliative Care Teams

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Early integration of pediatric palliative care (PPC) is associated with improved symptom management, quality of life, and healthcare utilization for children with cancer. Despite this, variation persists in how PPC is understood, operationalized, and integrated within pediatric oncology programs. In particular, ambiguity surrounding
Leeat Granek, Lori Wiener, Stephanie Veldhuijzen van Zanten, Donna Johnston, Fyeza Hasan, Adam Rapoport, Nathasha Datoo, Kimberley Widger, Sumit Gupta, Karen Fergus, Anthony K. C. Chan, Alisha Kassam, Emily McCullogh, Dave Lysecki   +13 more
wiley   +1 more source

Behavioral Study of Bayesian Neural Networks Under a Typical Surrogate Model-Assisted Evolutionary Search Framework

IEEE Access
The machine learning method for surrogate modeling is a keystone in surrogate model-assisted evolutionary algorithms (SAEAs). The current arguably most widely used surrogate modeling methods in SAEAs are Gaussian process and radial basis function.
Yushi Liu, Muhammad Ali Imran, Masood Ur-Rehman, Bo Liu   +3 more
doaj   +1 more source

Application of Surrogate Optimization Routine with Clustering Technique for Optimal Design of an Induction Motor

Energies, 2021
This paper proposes a new surrogate optimization routine for optimal design of a direct on line (DOL) squirrel cage induction motor. The geometry of the motor is optimized to maximize its electromagnetic efficiency while respecting the constraints, such ...
Aswin Balasubramanian, Floran Martin, Md Masum Billah, Osaruyi Osemwinyen, Anouar Belahcen   +4 more
doaj   +1 more source

Particle filter-based Gaussian process optimisation for parameter inference [PDF]

, 2014
We propose a novel method for maximum likelihood-based parameter inference in nonlinear and/or non-Gaussian state space models. The method is an iterative procedure with three steps.
Dahlin, Johan, Lindsten, Fredrik
core   +2 more sources

Feasibility and Preliminary Efficacy of Integrative Neuromuscular Training for Childhood Cancer Survivors: A Pilot Study

Pediatric Blood &Cancer, EarlyView.
ABSTRACT Background Survivors of childhood acute lymphoblastic leukemia (ALL) often exhibit early deficits in muscle and movement competence, which can compromise long‐term health. Integrative neuromuscular training (INT), a multifaceted approach combining fundamental movement activities with strength exercises, may help address these deficits during ...
Anna Maria Markarian, Robert U. Newton, Dennis R. Taaffe, Nicholas G. Gottardo, Santosh Valvi, Carolyn J. Peddle‐McIntyre, Jodie Cochrane Wilkie, Daniel A. Galvão   +7 more
wiley   +1 more source

Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms

Array
Surrogate-assisted evolutionary algorithms (SAEAs) are widely used to solve expensive optimization problems where evaluating candidate solutions is computationally intensive. To reduce this cost, SAEAs employ surrogate models—machine learning models that
Yuki Hanawa, Tomohiro Harada, Yukiya Miura   +2 more
doaj   +1 more source

Reduced-Dimension Surrogate Modeling to Characterize the Damage Tolerance of Composite/Metal Structures

Modelling, 2023
Complex engineering models are typically computationally demanding and defined by a high-dimensional parameter space challenging the comprehensive exploration of parameter effects and design optimization.
Corey Arndt, Cody Crusenberry, Bozhi Heng, Rochelle Butler, Stephanie TerMaath   +4 more
doaj   +1 more source

Design Optimization Utilizing Dynamic Substructuring and Artificial Intelligence Techniques [PDF]

, 2008
In mechanical and structural systems, resonance may cause large strains and stresses which can lead to the failure of the system. Since it is often not possible to change the frequency content of the external load excitation, the phenomenon can only be ...
Akcay Perdahcioglu, D., Boer, A. de, Ellenbroek, M.H.M., Hoogt, P.J.M. van der   +3 more
core   +1 more source